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Diffstat (limited to 'gnqa/paper2_eval/data/dataset')
246 files changed, 18192 insertions, 1158 deletions
diff --git a/gnqa/paper2_eval/data/dataset/domain_expert_aging_1 b/gnqa/paper2_eval/data/dataset/domain_expert_aging_1 deleted file mode 100644 index 1a249db..0000000 --- a/gnqa/paper2_eval/data/dataset/domain_expert_aging_1 +++ /dev/null @@ -1,193 +0,0 @@ -{ - "titles": [ - "2017 - Regular exercise participation improves genomic stability in diabetic patients an exploratory study to analyse telomere length and DNA damage.pdf", - "2020 - Clinical Genetics and Genomics of Aging.pdf", - "2008 - Telomeres and Aging.pdf", - "2006 - Sex-specific telomere length profiles.pdf", - "2018 - Sex Differences in Aging Genomic Instability.pdf", - "2002 - Mitochondrial dysfunction leads to telomere attrition.pdf", - "2006 - Sex-specific telomere length profiles.pdf", - "2017 - The Aging Cardiovascular System.pdf", - "2020 - Clinical Genetics and Genomics of Aging.pdf", - "2018 - Repetitive Fragile Sites Centromere Satellite DNA.pdf" - ], - "extraction_id": [ - "0e53122e-a308-55f7-8ee8-a0857ac9c52f", - "efd18101-9cf2-56b5-8f86-c2aba6caa0bc", - "13990eb4-bef2-58ce-bf3e-0e3bc294caab", - "6d3bfe47-f26e-50dc-8d77-19f3797e53a0", - "396708f1-aa0a-571e-a8d3-7cb8404e9502", - "b92ede07-74a7-524a-8d2c-54b2559e8425", - "eb8d8e40-a484-57cb-8125-3fd5eb3f6389", - "6949970f-7bc7-5585-a57a-96de1b5ba6ec", - "d4afa45a-5efa-577b-822e-7a82c2f6508d", - "3b0cb0ab-421d-54d7-9816-c6a2e6f1ac68" - ], - "document_id": [ - "dcaf7b09-2d54-5cbf-b061-e3c4e6c6c518", - "62b635c3-040e-512a-b016-6ef295308a1e", - "61d9c326-d36e-55c1-a891-335dc943e70f", - "09c78a17-4a1f-52c1-be4d-994fd9fd71d0", - "8cfb5529-7f0c-58fc-b6e4-b3ee800fb72f", - "d8bc729b-7513-58b7-b12e-0db1fb6d3b7d", - "09c78a17-4a1f-52c1-be4d-994fd9fd71d0", - "d3ff8471-986b-5fa0-b9c4-96eaaa8fce7c", - "62b635c3-040e-512a-b016-6ef295308a1e", - "262df0d6-ad68-544a-88ed-b4568f305858" - ], - "id": [ - "chatcmpl-ABLwBBugt6fTuTWqXb74qvoPVubbX", - "bb069c10-45f1-5a83-95e3-4b7655874ba7", - "28e98b7e-f273-5bdd-9979-185133f311af", - "5f940245-af1d-5eee-84dc-942017c523d0", - "607cbd31-d430-5517-8212-208b25af32bf", - "53508a9e-d064-58a3-a4f9-0785470a1462", - "7fad29bd-12bf-53d0-af89-aadd38b974ff", - "64ef9964-1831-5a7a-8a69-5e8d0c332d37", - "1b453e12-a0c4-59db-a978-bbebd689e7dc", - "65fb74aa-f3c3-5c80-919f-329169db982f", - "f181e6da-58b6-5f26-87a2-355e25388673" - ], - "contexts": [ - "repetitive nucleotide sequences at the end of each eukaryotic chromosome, which protects them from attrition and damage. Although the relationship between leukocyte telomere length (LTL) and diabetes is still questioned 8, different studies have shown that T2D individuals have shorter leukocyte telomeres than non-T2D individuals9, 10 that may be associated with disease progression11. Indeed, the decreased antioxidant capacity described in patients", - "Telomeres are arrays of linked nucleotide hexamer repeats that are found at the ends of chromosomes in a vast clade of organisms [14]. While the sequence of these telomeric repeats can vary between organisms, their biological function is highly conserved, which is to limit damage inflicted on genes during the replica- tion of chromosomes. Telomere length is progressively shortened with each round of genomic replication, unless it is restored through the action of a ribonucleo-", - "telomere length,a phenomenon attributed to higher levels of oxidativestress at the cellular level (70). More recent studies havelinked telomere length in smooth muscle cells with senes-cence and disease severity in patients with atherosclero-sis (141, 150). Leukocyte telomere length was also short ina cohort of similar patients and associated with a higherrisk of developing occult cardiovascular disease (71).More data are needed to understand and validate the useof leukocyte telomere length as a biomarker", - "age telomere length through accumulation of several short telo- meres (Londono-Vallejo et al., 2001; Martens et al., 2000) is responsible for senescence or whether a speci c chromosome arm limits the replication potential of human cells (Hemann et al., 2001). Individual chromosome arms were shown to have large variations in their length (Lansdorp et al., 1996; Benn, 1997; Londono-Vallejo et al., 2001), and chromosome 17p seemed to be equipped with especially short telomeres in hu-", - "Telomeres are specialized structures that protect the ends of linear chromosomes. They shorten during aging due to the unidirectional activity of DNA polymerase, which leaves a section of DNA unrepli-cated on the lagging strand. Telomeres also are subject to shortening by genotoxic stress, such as oxidative damage (33). Among many eukaryotes, the enzyme telomerase maintains telomere length; but telomerase activity varies over the lifespan and between cell types, tissues, and species (34). In most human", - "TTAGGG sequence that cap the ends of chromosomes, protect-ing them from degradation and fusion. The length of telomererepeats is primarily maintained by active telomerase, which iscomposed of Telomerase RNA (TR) and a catalytic subunitTelomerase Reverse Transcriptase (TERT) (Blackburn, 2001).Extensive evidence has shown that telomere shortening anderosion lead to chromosome end-to-end fusions and genomicinstability (Blasco et al ., 1997; Hande et al ., 1999), causing", - "a pivotal role in maintenance of genomic integrity and func-tion (Moyzis et al., 1988; McElligott and Wellinger, 1997; van Steensel and de Lange, 1997). It is generally accepted that telomeres shorten during DNA replication both in vitro and in vivo. In individuals, short telomeres are considered to be a sign of advanced age. Cawthon and coworkers (2003) showed that telomere shortening in hu-", - "Each cell division shortens telomeric DNA until, at a critical length, the cells lose capping function at thechromosomal ends, activating DNA damage check-points, cell senescence, and eventually apoptosis.Telomere shortening has particular relevance in thesetting of CVD. Leukocyte telomere length (LTL) associates signi cantly with vascular cell senescence,", - "nization may directly affect telomere attrition, resulting in accelerated replicative senescence and progeroid phenotypes [180]. Telomeres are regions constituted by tandem repeats of non-coding DNA sequences 5-(TTAGGG)n-3 and a protein complex called shelterin, bound to them. This structure ensures the stability of the genome and protects the chromosomes from a wrong action of the DNA repair machinery [184] by allowing the formation of a chromatin loop called T-Loop [185].", - "telomeres, the repetitive sequence at the end of linear chromosomes, has garnered much attention for its relation to aging. Telomere repeats serve as an internal clock for cycling cells because each round of replication results in the loss of telomeric DNA in the absence of active telomerase (reviewed in [66]). Eventually, this loss over cellular generations culminates in telomere crisis and a permanent state of" - ], - "answer": "The length of telomeres is significant for several reasons: 1. **Protection of Chromosomes**: Telomeres are repetitive nucleotide sequences at the ends of eukaryotic chromosomes that protect them from attrition and damage during replication [1], [2], [6]. They prevent the loss of important genetic information and maintain genomic stability [9]. 2. **Aging and Cellular Senescence**: Telomere length is progressively shortened with each round of genomic replication unless restored by the enzyme telomerase [2], [5]. Short telomeres are considered a sign of advanced age and are associated with cellular senescence, where cells lose their ability to divide and function properly [4], [7], [8]. 3. **Disease Association**: Shorter telomeres have been linked to various diseases. For instance, individuals with Type 2 Diabetes (T2D) have been shown to have shorter leukocyte telomeres, which may be associated with disease progression [1]. Similarly, telomere length in smooth muscle cells has been linked to disease severity in atherosclerosis, and shorter leukocyte telomeres are associated with a higher risk of developing cardiovascular disease [3], [8]. 4. **Genomic Integrity**: Telomeres play a crucial role in maintaining genomic integrity and function. Shortening and erosion of telomeres can lead to chromosome end-to-end fusions and genomic instability, which can cause various cellular dysfunctions and diseases [6], [7]. 5. **Biomarker Potential**: Due to their association with aging and disease, telomere length is being studied as a potential biomarker for various health conditions, including cardiovascular diseases and diabetes [3]. In summary, the length of telomeres is crucial for protecting chromosomes, regulating cellular aging, and maintaining genomic stability. It also has significant implications for understanding and potentially diagnosing age-related diseases and conditions.", - "question": "What is the significance of the length of telomeres?" -}{ - "titles": [ - "2017 - Regular exercise participation improves genomic stability in diabetic patients an exploratory study to analyse telomere length and DNA damage.pdf", - "2020 - Clinical Genetics and Genomics of Aging.pdf", - "2008 - Telomeres and Aging.pdf", - "2006 - Sex-specific telomere length profiles.pdf", - "2018 - Sex Differences in Aging Genomic Instability.pdf", - "2002 - Mitochondrial dysfunction leads to telomere attrition.pdf", - "2006 - Sex-specific telomere length profiles.pdf", - "2017 - The Aging Cardiovascular System.pdf", - "2020 - Clinical Genetics and Genomics of Aging.pdf", - "2018 - Repetitive Fragile Sites Centromere Satellite DNA.pdf" - ], - "extraction_id": [ - "0e53122e-a308-55f7-8ee8-a0857ac9c52f", - "efd18101-9cf2-56b5-8f86-c2aba6caa0bc", - "13990eb4-bef2-58ce-bf3e-0e3bc294caab", - "6d3bfe47-f26e-50dc-8d77-19f3797e53a0", - "396708f1-aa0a-571e-a8d3-7cb8404e9502", - "b92ede07-74a7-524a-8d2c-54b2559e8425", - "eb8d8e40-a484-57cb-8125-3fd5eb3f6389", - "6949970f-7bc7-5585-a57a-96de1b5ba6ec", - "d4afa45a-5efa-577b-822e-7a82c2f6508d", - "3b0cb0ab-421d-54d7-9816-c6a2e6f1ac68" - ], - "document_id": [ - "dcaf7b09-2d54-5cbf-b061-e3c4e6c6c518", - "62b635c3-040e-512a-b016-6ef295308a1e", - "61d9c326-d36e-55c1-a891-335dc943e70f", - "09c78a17-4a1f-52c1-be4d-994fd9fd71d0", - "8cfb5529-7f0c-58fc-b6e4-b3ee800fb72f", - "d8bc729b-7513-58b7-b12e-0db1fb6d3b7d", - "09c78a17-4a1f-52c1-be4d-994fd9fd71d0", - "d3ff8471-986b-5fa0-b9c4-96eaaa8fce7c", - "62b635c3-040e-512a-b016-6ef295308a1e", - "262df0d6-ad68-544a-88ed-b4568f305858" - ], - "id": [ - "chatcmpl-ABLwBBugt6fTuTWqXb74qvoPVubbX", - "bb069c10-45f1-5a83-95e3-4b7655874ba7", - "28e98b7e-f273-5bdd-9979-185133f311af", - "5f940245-af1d-5eee-84dc-942017c523d0", - "607cbd31-d430-5517-8212-208b25af32bf", - "53508a9e-d064-58a3-a4f9-0785470a1462", - "7fad29bd-12bf-53d0-af89-aadd38b974ff", - "64ef9964-1831-5a7a-8a69-5e8d0c332d37", - "1b453e12-a0c4-59db-a978-bbebd689e7dc", - "65fb74aa-f3c3-5c80-919f-329169db982f", - "f181e6da-58b6-5f26-87a2-355e25388673" - ], - "contexts": [ - "repetitive nucleotide sequences at the end of each eukaryotic chromosome, which protects them from attrition and damage. Although the relationship between leukocyte telomere length (LTL) and diabetes is still questioned 8, different studies have shown that T2D individuals have shorter leukocyte telomeres than non-T2D individuals9, 10 that may be associated with disease progression11. Indeed, the decreased antioxidant capacity described in patients", - "Telomeres are arrays of linked nucleotide hexamer repeats that are found at the ends of chromosomes in a vast clade of organisms [14]. While the sequence of these telomeric repeats can vary between organisms, their biological function is highly conserved, which is to limit damage inflicted on genes during the replica- tion of chromosomes. Telomere length is progressively shortened with each round of genomic replication, unless it is restored through the action of a ribonucleo-", - "telomere length,a phenomenon attributed to higher levels of oxidativestress at the cellular level (70). More recent studies havelinked telomere length in smooth muscle cells with senes-cence and disease severity in patients with atherosclero-sis (141, 150). Leukocyte telomere length was also short ina cohort of similar patients and associated with a higherrisk of developing occult cardiovascular disease (71).More data are needed to understand and validate the useof leukocyte telomere length as a biomarker", - "age telomere length through accumulation of several short telo- meres (Londono-Vallejo et al., 2001; Martens et al., 2000) is responsible for senescence or whether a speci c chromosome arm limits the replication potential of human cells (Hemann et al., 2001). Individual chromosome arms were shown to have large variations in their length (Lansdorp et al., 1996; Benn, 1997; Londono-Vallejo et al., 2001), and chromosome 17p seemed to be equipped with especially short telomeres in hu-", - "Telomeres are specialized structures that protect the ends of linear chromosomes. They shorten during aging due to the unidirectional activity of DNA polymerase, which leaves a section of DNA unrepli-cated on the lagging strand. Telomeres also are subject to shortening by genotoxic stress, such as oxidative damage (33). Among many eukaryotes, the enzyme telomerase maintains telomere length; but telomerase activity varies over the lifespan and between cell types, tissues, and species (34). In most human", - "TTAGGG sequence that cap the ends of chromosomes, protect-ing them from degradation and fusion. The length of telomererepeats is primarily maintained by active telomerase, which iscomposed of Telomerase RNA (TR) and a catalytic subunitTelomerase Reverse Transcriptase (TERT) (Blackburn, 2001).Extensive evidence has shown that telomere shortening anderosion lead to chromosome end-to-end fusions and genomicinstability (Blasco et al ., 1997; Hande et al ., 1999), causing", - "a pivotal role in maintenance of genomic integrity and func-tion (Moyzis et al., 1988; McElligott and Wellinger, 1997; van Steensel and de Lange, 1997). It is generally accepted that telomeres shorten during DNA replication both in vitro and in vivo. In individuals, short telomeres are considered to be a sign of advanced age. Cawthon and coworkers (2003) showed that telomere shortening in hu-", - "Each cell division shortens telomeric DNA until, at a critical length, the cells lose capping function at thechromosomal ends, activating DNA damage check-points, cell senescence, and eventually apoptosis.Telomere shortening has particular relevance in thesetting of CVD. Leukocyte telomere length (LTL) associates signi cantly with vascular cell senescence,", - "nization may directly affect telomere attrition, resulting in accelerated replicative senescence and progeroid phenotypes [180]. Telomeres are regions constituted by tandem repeats of non-coding DNA sequences 5-(TTAGGG)n-3 and a protein complex called shelterin, bound to them. This structure ensures the stability of the genome and protects the chromosomes from a wrong action of the DNA repair machinery [184] by allowing the formation of a chromatin loop called T-Loop [185].", - "telomeres, the repetitive sequence at the end of linear chromosomes, has garnered much attention for its relation to aging. Telomere repeats serve as an internal clock for cycling cells because each round of replication results in the loss of telomeric DNA in the absence of active telomerase (reviewed in [66]). Eventually, this loss over cellular generations culminates in telomere crisis and a permanent state of" - ], - "answer": "The length of telomeres is significant for several reasons: 1. **Protection of Chromosomes**: Telomeres are repetitive nucleotide sequences at the ends of eukaryotic chromosomes that protect them from attrition and damage during replication [1], [2], [6]. They prevent the loss of important genetic information and maintain genomic stability [9]. 2. **Aging and Cellular Senescence**: Telomere length is progressively shortened with each round of genomic replication unless restored by the enzyme telomerase [2], [5]. Short telomeres are considered a sign of advanced age and are associated with cellular senescence, where cells lose their ability to divide and function properly [4], [7], [8]. 3. **Disease Association**: Shorter telomeres have been linked to various diseases. For instance, individuals with Type 2 Diabetes (T2D) have been shown to have shorter leukocyte telomeres, which may be associated with disease progression [1]. Similarly, telomere length in smooth muscle cells has been linked to disease severity in atherosclerosis, and shorter leukocyte telomeres are associated with a higher risk of developing cardiovascular disease [3], [8]. 4. **Genomic Integrity**: Telomeres play a crucial role in maintaining genomic integrity and function. Shortening and erosion of telomeres can lead to chromosome end-to-end fusions and genomic instability, which can cause various cellular dysfunctions and diseases [6], [7]. 5. **Biomarker Potential**: Due to their association with aging and disease, telomere length is being studied as a potential biomarker for various health conditions, including cardiovascular diseases and diabetes [3]. In summary, the length of telomeres is crucial for protecting chromosomes, regulating cellular aging, and maintaining genomic stability. It also has significant implications for understanding and potentially diagnosing age-related diseases and conditions.", - "question": "What is the significance of the length of telomeres?" -}{ - "titles": [ - "2017 - Regular exercise participation improves genomic stability in diabetic patients an exploratory study to analyse telomere length and DNA damage.pdf", - "2020 - Clinical Genetics and Genomics of Aging.pdf", - "2008 - Telomeres and Aging.pdf", - "2006 - Sex-specific telomere length profiles.pdf", - "2018 - Sex Differences in Aging Genomic Instability.pdf", - "2002 - Mitochondrial dysfunction leads to telomere attrition.pdf", - "2006 - Sex-specific telomere length profiles.pdf", - "2017 - The Aging Cardiovascular System.pdf", - "2020 - Clinical Genetics and Genomics of Aging.pdf", - "2018 - Repetitive Fragile Sites Centromere Satellite DNA.pdf" - ], - "extraction_id": [ - "0e53122e-a308-55f7-8ee8-a0857ac9c52f", - "efd18101-9cf2-56b5-8f86-c2aba6caa0bc", - "13990eb4-bef2-58ce-bf3e-0e3bc294caab", - "6d3bfe47-f26e-50dc-8d77-19f3797e53a0", - "396708f1-aa0a-571e-a8d3-7cb8404e9502", - "b92ede07-74a7-524a-8d2c-54b2559e8425", - "eb8d8e40-a484-57cb-8125-3fd5eb3f6389", - "6949970f-7bc7-5585-a57a-96de1b5ba6ec", - "d4afa45a-5efa-577b-822e-7a82c2f6508d", - "3b0cb0ab-421d-54d7-9816-c6a2e6f1ac68" - ], - "document_id": [ - "dcaf7b09-2d54-5cbf-b061-e3c4e6c6c518", - "62b635c3-040e-512a-b016-6ef295308a1e", - "61d9c326-d36e-55c1-a891-335dc943e70f", - "09c78a17-4a1f-52c1-be4d-994fd9fd71d0", - "8cfb5529-7f0c-58fc-b6e4-b3ee800fb72f", - "d8bc729b-7513-58b7-b12e-0db1fb6d3b7d", - "09c78a17-4a1f-52c1-be4d-994fd9fd71d0", - "d3ff8471-986b-5fa0-b9c4-96eaaa8fce7c", - "62b635c3-040e-512a-b016-6ef295308a1e", - "262df0d6-ad68-544a-88ed-b4568f305858" - ], - "id": [ - "chatcmpl-ABLwBBugt6fTuTWqXb74qvoPVubbX", - "bb069c10-45f1-5a83-95e3-4b7655874ba7", - "28e98b7e-f273-5bdd-9979-185133f311af", - "5f940245-af1d-5eee-84dc-942017c523d0", - "607cbd31-d430-5517-8212-208b25af32bf", - "53508a9e-d064-58a3-a4f9-0785470a1462", - "7fad29bd-12bf-53d0-af89-aadd38b974ff", - "64ef9964-1831-5a7a-8a69-5e8d0c332d37", - "1b453e12-a0c4-59db-a978-bbebd689e7dc", - "65fb74aa-f3c3-5c80-919f-329169db982f", - "f181e6da-58b6-5f26-87a2-355e25388673" - ], - "contexts": [ - "repetitive nucleotide sequences at the end of each eukaryotic chromosome, which protects them from attrition and damage. Although the relationship between leukocyte telomere length (LTL) and diabetes is still questioned 8, different studies have shown that T2D individuals have shorter leukocyte telomeres than non-T2D individuals9, 10 that may be associated with disease progression11. Indeed, the decreased antioxidant capacity described in patients", - "Telomeres are arrays of linked nucleotide hexamer repeats that are found at the ends of chromosomes in a vast clade of organisms [14]. While the sequence of these telomeric repeats can vary between organisms, their biological function is highly conserved, which is to limit damage inflicted on genes during the replica- tion of chromosomes. Telomere length is progressively shortened with each round of genomic replication, unless it is restored through the action of a ribonucleo-", - "telomere length,a phenomenon attributed to higher levels of oxidativestress at the cellular level (70). More recent studies havelinked telomere length in smooth muscle cells with senes-cence and disease severity in patients with atherosclero-sis (141, 150). Leukocyte telomere length was also short ina cohort of similar patients and associated with a higherrisk of developing occult cardiovascular disease (71).More data are needed to understand and validate the useof leukocyte telomere length as a biomarker", - "age telomere length through accumulation of several short telo- meres (Londono-Vallejo et al., 2001; Martens et al., 2000) is responsible for senescence or whether a speci c chromosome arm limits the replication potential of human cells (Hemann et al., 2001). Individual chromosome arms were shown to have large variations in their length (Lansdorp et al., 1996; Benn, 1997; Londono-Vallejo et al., 2001), and chromosome 17p seemed to be equipped with especially short telomeres in hu-", - "Telomeres are specialized structures that protect the ends of linear chromosomes. They shorten during aging due to the unidirectional activity of DNA polymerase, which leaves a section of DNA unrepli-cated on the lagging strand. Telomeres also are subject to shortening by genotoxic stress, such as oxidative damage (33). Among many eukaryotes, the enzyme telomerase maintains telomere length; but telomerase activity varies over the lifespan and between cell types, tissues, and species (34). In most human", - "TTAGGG sequence that cap the ends of chromosomes, protect-ing them from degradation and fusion. The length of telomererepeats is primarily maintained by active telomerase, which iscomposed of Telomerase RNA (TR) and a catalytic subunitTelomerase Reverse Transcriptase (TERT) (Blackburn, 2001).Extensive evidence has shown that telomere shortening anderosion lead to chromosome end-to-end fusions and genomicinstability (Blasco et al ., 1997; Hande et al ., 1999), causing", - "a pivotal role in maintenance of genomic integrity and func-tion (Moyzis et al., 1988; McElligott and Wellinger, 1997; van Steensel and de Lange, 1997). It is generally accepted that telomeres shorten during DNA replication both in vitro and in vivo. In individuals, short telomeres are considered to be a sign of advanced age. Cawthon and coworkers (2003) showed that telomere shortening in hu-", - "Each cell division shortens telomeric DNA until, at a critical length, the cells lose capping function at thechromosomal ends, activating DNA damage check-points, cell senescence, and eventually apoptosis.Telomere shortening has particular relevance in thesetting of CVD. Leukocyte telomere length (LTL) associates signi cantly with vascular cell senescence,", - "nization may directly affect telomere attrition, resulting in accelerated replicative senescence and progeroid phenotypes [180]. Telomeres are regions constituted by tandem repeats of non-coding DNA sequences 5-(TTAGGG)n-3 and a protein complex called shelterin, bound to them. This structure ensures the stability of the genome and protects the chromosomes from a wrong action of the DNA repair machinery [184] by allowing the formation of a chromatin loop called T-Loop [185].", - "telomeres, the repetitive sequence at the end of linear chromosomes, has garnered much attention for its relation to aging. Telomere repeats serve as an internal clock for cycling cells because each round of replication results in the loss of telomeric DNA in the absence of active telomerase (reviewed in [66]). Eventually, this loss over cellular generations culminates in telomere crisis and a permanent state of" - ], - "answer": "The length of telomeres is significant for several reasons: 1. **Protection of Chromosomes**: Telomeres are repetitive nucleotide sequences at the ends of eukaryotic chromosomes that protect them from attrition and damage during replication [1], [2], [6]. They prevent the loss of important genetic information and maintain genomic stability [9]. 2. **Aging and Cellular Senescence**: Telomere length is progressively shortened with each round of genomic replication unless restored by the enzyme telomerase [2], [5]. Short telomeres are considered a sign of advanced age and are associated with cellular senescence, where cells lose their ability to divide and function properly [4], [7], [8]. 3. **Disease Association**: Shorter telomeres have been linked to various diseases. For instance, individuals with Type 2 Diabetes (T2D) have been shown to have shorter leukocyte telomeres, which may be associated with disease progression [1]. Similarly, telomere length in smooth muscle cells has been linked to disease severity in atherosclerosis, and shorter leukocyte telomeres are associated with a higher risk of developing cardiovascular disease [3], [8]. 4. **Genomic Integrity**: Telomeres play a crucial role in maintaining genomic integrity and function. Shortening and erosion of telomeres can lead to chromosome end-to-end fusions and genomic instability, which can cause various cellular dysfunctions and diseases [6], [7]. 5. **Biomarker Potential**: Due to their association with aging and disease, telomere length is being studied as a potential biomarker for various health conditions, including cardiovascular diseases and diabetes [3]. In summary, the length of telomeres is crucial for protecting chromosomes, regulating cellular aging, and maintaining genomic stability. It also has significant implications for understanding and potentially diagnosing age-related diseases and conditions.", - "question": "What is the significance of the length of telomeres?" -}
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Gelman R, Watson A, Bronson R et al (1988) Murine chromo- somal regions correlated with longevity. Genetics 118(4):693704 12. Jackson AU, Galecki AT, Burke DT et al (2002) Mouse loci associated with life span exhibit sex-specic and epistatic effects. J Gerontol A Biol Sci Med Sci 57(1):B9B15 13. Foreman JE, Lionikas A, Lang DH et al (2009) Genetic archi- tecture for hole-board behaviors across substantial time intervalsin young, middle-aged and old mice. Genes Brain Behav", - "Long-lived rodents reveal signatures of positive selection in genes associated with lifespan. PLoS Genet. 14:e1007272. doi: 10.1371/journal.pgen.100 7272 Schchter, F., Faure-Delanef, L., Gunot, F., Rouger, H., Froguel, P., Lesueur-Ginot, L., et al. (1994). Genetic associations with human longevity at the APOE and ACE loci. Nat. Genet. 6, 2932. doi: 10.1038/ng0194-29 Schinaman, J. M., Rana, A., Ja, W. W., Clark, R. I., and Walker, D. W. (2019).", - "of the mouse growth hormone receptor results in severely decreased body weights, insulin, and insulin- like growth factor I levels and increased life span. Endocrinology 144:37993810. DOI: https://doi.org/10.1210/en. 2003-0374, PMID: 12933651 de Haan G, Williams RW. 2005. A genetic and genomic approach to identify longevity genes in mice. Mechanisms of Ageing and Development 126:133138. DOI: https://doi.org/10.1016/j.mad.2004.09.012, PMID: 15610771", - "of the mouse growth hormone receptor results in severely decreased body weights, insulin, and insulin- like growth factor I levels and increased life span. Endocrinology 144:37993810. DOI: https://doi.org/10.1210/en. 2003-0374, PMID: 12933651 de Haan G, Williams RW. 2005. A genetic and genomic approach to identify longevity genes in mice. Mechanisms of Ageing and Development 126:133138. DOI: https://doi.org/10.1016/j.mad.2004.09.012, PMID: 15610771", - "Mulvey L, Sinclair A, Selman C (2014) Lifespan modulation in mice and the confounding effects of genetic background. J Genet Genomics 41:497503. doi: 10.1016/j.jgg.2014.06.002 OConnor TP, Lee A, Jarvis JUM, Buffenstein R (2002) Prolonged longevity in naked mole-rats: age-related changes in metabolism, body composition and gastrointestinal function. Comp Biochem Physiol A 133:835842. doi: 10.1016/S1095-6433(02)00198-8 Opazo JC, Palma RE, Melo F, Lessa EP (2005) Adaptive evolution of", - "/ mice by Lmna heterozy- gosity ameliorates progeroid phenotypes and extends lifespan [143, 174, 175].", - "References 1. Hook Met al.Genetic cartography of longevity in humans and mice: Current landscape and horizons. Biochim. Biophys. Acta1864, 27182732 (2018). 2. Kuningas Met al.Genes encoding longevity: from model organisms to humans. Aging Cell7, 270 280 (2008). [PubMed: 18208581] 3. de Magalhes JP, Wuttke D, Wood SH, Plank M & V ora C Genome-environment interactions that modulate aging: Powerful targets for drug discovery. Pharmacol. Rev. 64, 88101 (2012). [PubMed: 22090473]", - "\"Murine chromosomal regions correlated with longevity.\" Genetics 118: 693-704.", - "expression of alpha-1,2-mannosidase I extends lifespan in Drosophila melanogaster and Caenorhabditis elegans . Aging Cell, 2009 , 8(4), 370-9. [73] Wang, H.D.; Kazemi-Esfarjani, P.; Benzer, S. Multiple-stress analysis for isolation of Drosophila longevity genes . Proc Natl Acad Sci U S A , 2004 , 101(34), 12610-5. [74] Lin, Y.J.; Seroude, L.; Benzer, S. Extended life-span and stress resistance in the Drosophila mutant methuselah . Science , 1998 , 282(5390), 943-6.", - "sion analysis of mouse liver genes: effect of age and of thelongevity mutant Prop1df. J Gerontol A Biol Sci Med Sci 56: B72B80, 2001. 12.Fabrizio P, Pozza F, Pletcher SD, Gendron CM, and Longo VD. Regulation of longevity and stress resistance by Sch9 in Yeast. Science 292: 288 290, 2001. 13.Haase D, Lehmann MH, Korner MM, Korfer R, Sigusch HH, and Figulla HR. Identi cation and validation of selective" - ], - "answer": "The mouse genes associated with longevity include: 1. **Mouse loci associated with life span**: These loci exhibit sex-specific and epistatic effects [1]. 2. **Mouse growth hormone receptor**: Alterations in this gene result in increased lifespan [3], [4]. 3. **Lmna heterozygosity**: This genetic modification ameliorates progeroid phenotypes and extends lifespan [6]. These references provide evidence of specific genes and loci in mice that have been linked to increased longevity.", - "question": "Which mouse genes have been associated with longevity?" -}{ - "titles": [ - "2015 - A Chromosome 13 locus is associated with male-specific mortality in mice.pdf", - "2021 - Footprints in the Sand Deep Taxonomic Comparisons in Vertebrate Genomics to Unveil the Genetic Programs of Human Longevity.pdf", - "2021 - Genetic loci and metabolic states associated with murine epigenetic aging.pdf", - "2021 -Mozhui- Epigenetic aging.pdf", - "2016 - Unraveling the message insights into comparative genomics.pdf", - "2012 - Chromatin Remodeling, DNA Damage Repair and Aging.pdf", - "2021 - Gene-by-environment modulation of lifespan and weight gain in the murine BXD family.pdf", - "2006 - THE GENETIC REGULATION OF THE RESPONSE OF HEMATOPOIETIC STEM_PROG.pdf", - "2012 - Genome-Scale Studies of Aging Challenges and Opportunities.pdf", - "2003 - Lifelong voluntary exercise in the mouse prevents.pdf" - ], - "extraction_id": [ - "5cc56e3b-53ab-5299-814d-014e2ed31d2f", - "11ca91fa-a13f-5cc5-90c8-53d1ebe76836", - "a9ebf1d8-5ef8-5c52-962e-110873476823", - "e662d80d-b529-5749-856c-ed734c6e3eaa", - "c6f50e80-1bc5-5b0a-b57b-4c2bfe524d96", - "d9a12bd9-c65e-547a-89aa-4e0231558ddc", - "30ba3324-6e19-58c2-9e32-508f827af3e5", - "c04cac81-a0b0-5d0a-b21e-2f94494bb302", - "9669b6fe-e9d7-55e8-a91a-c015df633daa", - "6a2cdf66-f3c9-5be9-b6b0-f203be169103" - ], - "document_id": [ - "ad8f2626-87fb-520e-8cef-ee9a9cc3ab0b", - "0dc45abe-ab02-5b07-9916-7093b53323c0", - "b82bd9e1-2373-577b-a942-164565eaca6b", - "d23daa43-4176-54e6-b3c3-b889843e92f1", - "0deba7bb-c27a-5d9e-b1b2-e48a5574882c", - "594e5dbe-b92a-5b0c-9f65-2a10670f9517", - "4d082da4-fa48-5170-8147-c4fea47a5d4b", - "b84914bc-195d-5c48-8e89-0db719675c1f", - "b77aace0-fa36-5fd4-8e2a-c8932198acd1", - "24d4f270-f45b-5830-84f9-b1e5bcd3c070" - ], - "id": [ - "chatcmpl-ABLwRFLcOLGvXJuXhHs6NCge9tY7Z", - "09da6f9e-b996-5438-91be-41d9438cb930", - "14bf5e8a-4095-536f-b98b-00c8cdae3a31", - "f8fdd2ee-710c-5d2c-8a70-bf48f4927653", - "e613d3df-adb0-56b0-abfd-8828020c23c3", - "02296a91-f1a4-5b35-a5d1-e1851797404b", - "90214d4d-4068-5490-9049-5604b5dcf3e2", - "56e03e38-0ae5-5b29-b929-662fa091e0ac", - "ebc5b444-a63f-5819-9d3a-ffbf96b3d367", - "80d01818-7573-5321-b33d-c7e291f3fe74", - "11af155f-85c6-5f8b-8943-5391ad678f7e" - ], - "contexts": [ - "11. 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(2019).", - "of the mouse growth hormone receptor results in severely decreased body weights, insulin, and insulin- like growth factor I levels and increased life span. Endocrinology 144:37993810. DOI: https://doi.org/10.1210/en. 2003-0374, PMID: 12933651 de Haan G, Williams RW. 2005. A genetic and genomic approach to identify longevity genes in mice. Mechanisms of Ageing and Development 126:133138. DOI: https://doi.org/10.1016/j.mad.2004.09.012, PMID: 15610771", - "of the mouse growth hormone receptor results in severely decreased body weights, insulin, and insulin- like growth factor I levels and increased life span. Endocrinology 144:37993810. DOI: https://doi.org/10.1210/en. 2003-0374, PMID: 12933651 de Haan G, Williams RW. 2005. A genetic and genomic approach to identify longevity genes in mice. Mechanisms of Ageing and Development 126:133138. DOI: https://doi.org/10.1016/j.mad.2004.09.012, PMID: 15610771", - "Mulvey L, Sinclair A, Selman C (2014) Lifespan modulation in mice and the confounding effects of genetic background. J Genet Genomics 41:497503. doi: 10.1016/j.jgg.2014.06.002 OConnor TP, Lee A, Jarvis JUM, Buffenstein R (2002) Prolonged longevity in naked mole-rats: age-related changes in metabolism, body composition and gastrointestinal function. Comp Biochem Physiol A 133:835842. doi: 10.1016/S1095-6433(02)00198-8 Opazo JC, Palma RE, Melo F, Lessa EP (2005) Adaptive evolution of", - "/ mice by Lmna heterozy- gosity ameliorates progeroid phenotypes and extends lifespan [143, 174, 175].", - "References 1. Hook Met al.Genetic cartography of longevity in humans and mice: Current landscape and horizons. Biochim. Biophys. Acta1864, 27182732 (2018). 2. Kuningas Met al.Genes encoding longevity: from model organisms to humans. Aging Cell7, 270 280 (2008). [PubMed: 18208581] 3. de Magalhes JP, Wuttke D, Wood SH, Plank M & V ora C Genome-environment interactions that modulate aging: Powerful targets for drug discovery. Pharmacol. Rev. 64, 88101 (2012). [PubMed: 22090473]", - "\"Murine chromosomal regions correlated with longevity.\" Genetics 118: 693-704.", - "expression of alpha-1,2-mannosidase I extends lifespan in Drosophila melanogaster and Caenorhabditis elegans . Aging Cell, 2009 , 8(4), 370-9. [73] Wang, H.D.; Kazemi-Esfarjani, P.; Benzer, S. Multiple-stress analysis for isolation of Drosophila longevity genes . Proc Natl Acad Sci U S A , 2004 , 101(34), 12610-5. [74] Lin, Y.J.; Seroude, L.; Benzer, S. Extended life-span and stress resistance in the Drosophila mutant methuselah . Science , 1998 , 282(5390), 943-6.", - "sion analysis of mouse liver genes: effect of age and of thelongevity mutant Prop1df. J Gerontol A Biol Sci Med Sci 56: B72B80, 2001. 12.Fabrizio P, Pozza F, Pletcher SD, Gendron CM, and Longo VD. Regulation of longevity and stress resistance by Sch9 in Yeast. Science 292: 288 290, 2001. 13.Haase D, Lehmann MH, Korner MM, Korfer R, Sigusch HH, and Figulla HR. Identi cation and validation of selective" - ], - "answer": "The mouse genes associated with longevity include: 1. **Mouse loci associated with life span**: These loci exhibit sex-specific and epistatic effects [1]. 2. **Mouse growth hormone receptor**: Alterations in this gene result in increased lifespan [3], [4]. 3. **Lmna heterozygosity**: This genetic modification ameliorates progeroid phenotypes and extends lifespan [6]. These references provide evidence of specific genes and loci in mice that have been linked to increased longevity.", - "question": "Which mouse genes have been associated with longevity?" -}{ - "titles": [ - "2015 - A Chromosome 13 locus is associated with male-specific mortality in mice.pdf", - "2021 - Footprints in the Sand Deep Taxonomic Comparisons in Vertebrate Genomics to Unveil the Genetic Programs of Human Longevity.pdf", - "2021 - Genetic loci and metabolic states associated with murine epigenetic aging.pdf", - "2021 -Mozhui- Epigenetic aging.pdf", - "2016 - Unraveling the message insights into comparative genomics.pdf", - "2012 - Chromatin Remodeling, DNA Damage Repair and Aging.pdf", - "2021 - Gene-by-environment modulation of lifespan and weight gain in the murine BXD family.pdf", - "2006 - THE GENETIC REGULATION OF THE RESPONSE OF HEMATOPOIETIC STEM_PROG.pdf", - "2012 - Genome-Scale Studies of Aging Challenges and Opportunities.pdf", - "2003 - Lifelong voluntary exercise in the mouse prevents.pdf" - ], - "extraction_id": [ - "5cc56e3b-53ab-5299-814d-014e2ed31d2f", - "11ca91fa-a13f-5cc5-90c8-53d1ebe76836", - "a9ebf1d8-5ef8-5c52-962e-110873476823", - "e662d80d-b529-5749-856c-ed734c6e3eaa", - "c6f50e80-1bc5-5b0a-b57b-4c2bfe524d96", - "d9a12bd9-c65e-547a-89aa-4e0231558ddc", - "30ba3324-6e19-58c2-9e32-508f827af3e5", - "c04cac81-a0b0-5d0a-b21e-2f94494bb302", - "9669b6fe-e9d7-55e8-a91a-c015df633daa", - "6a2cdf66-f3c9-5be9-b6b0-f203be169103" - ], - "document_id": [ - "ad8f2626-87fb-520e-8cef-ee9a9cc3ab0b", - "0dc45abe-ab02-5b07-9916-7093b53323c0", - "b82bd9e1-2373-577b-a942-164565eaca6b", - "d23daa43-4176-54e6-b3c3-b889843e92f1", - "0deba7bb-c27a-5d9e-b1b2-e48a5574882c", - "594e5dbe-b92a-5b0c-9f65-2a10670f9517", - "4d082da4-fa48-5170-8147-c4fea47a5d4b", - "b84914bc-195d-5c48-8e89-0db719675c1f", - "b77aace0-fa36-5fd4-8e2a-c8932198acd1", - "24d4f270-f45b-5830-84f9-b1e5bcd3c070" - ], - "id": [ - "chatcmpl-ABLwRFLcOLGvXJuXhHs6NCge9tY7Z", - "09da6f9e-b996-5438-91be-41d9438cb930", - "14bf5e8a-4095-536f-b98b-00c8cdae3a31", - "f8fdd2ee-710c-5d2c-8a70-bf48f4927653", - "e613d3df-adb0-56b0-abfd-8828020c23c3", - "02296a91-f1a4-5b35-a5d1-e1851797404b", - "90214d4d-4068-5490-9049-5604b5dcf3e2", - "56e03e38-0ae5-5b29-b929-662fa091e0ac", - "ebc5b444-a63f-5819-9d3a-ffbf96b3d367", - "80d01818-7573-5321-b33d-c7e291f3fe74", - "11af155f-85c6-5f8b-8943-5391ad678f7e" - ], - "contexts": [ - "11. Gelman R, Watson A, Bronson R et al (1988) Murine chromo- somal regions correlated with longevity. Genetics 118(4):693704 12. Jackson AU, Galecki AT, Burke DT et al (2002) Mouse loci associated with life span exhibit sex-specic and epistatic effects. J Gerontol A Biol Sci Med Sci 57(1):B9B15 13. Foreman JE, Lionikas A, Lang DH et al (2009) Genetic archi- tecture for hole-board behaviors across substantial time intervalsin young, middle-aged and old mice. Genes Brain Behav", - "Long-lived rodents reveal signatures of positive selection in genes associated with lifespan. PLoS Genet. 14:e1007272. doi: 10.1371/journal.pgen.100 7272 Schchter, F., Faure-Delanef, L., Gunot, F., Rouger, H., Froguel, P., Lesueur-Ginot, L., et al. (1994). Genetic associations with human longevity at the APOE and ACE loci. Nat. Genet. 6, 2932. doi: 10.1038/ng0194-29 Schinaman, J. M., Rana, A., Ja, W. W., Clark, R. I., and Walker, D. W. (2019).", - "of the mouse growth hormone receptor results in severely decreased body weights, insulin, and insulin- like growth factor I levels and increased life span. Endocrinology 144:37993810. DOI: https://doi.org/10.1210/en. 2003-0374, PMID: 12933651 de Haan G, Williams RW. 2005. A genetic and genomic approach to identify longevity genes in mice. Mechanisms of Ageing and Development 126:133138. DOI: https://doi.org/10.1016/j.mad.2004.09.012, PMID: 15610771", - "of the mouse growth hormone receptor results in severely decreased body weights, insulin, and insulin- like growth factor I levels and increased life span. Endocrinology 144:37993810. DOI: https://doi.org/10.1210/en. 2003-0374, PMID: 12933651 de Haan G, Williams RW. 2005. A genetic and genomic approach to identify longevity genes in mice. Mechanisms of Ageing and Development 126:133138. DOI: https://doi.org/10.1016/j.mad.2004.09.012, PMID: 15610771", - "Mulvey L, Sinclair A, Selman C (2014) Lifespan modulation in mice and the confounding effects of genetic background. J Genet Genomics 41:497503. doi: 10.1016/j.jgg.2014.06.002 OConnor TP, Lee A, Jarvis JUM, Buffenstein R (2002) Prolonged longevity in naked mole-rats: age-related changes in metabolism, body composition and gastrointestinal function. Comp Biochem Physiol A 133:835842. doi: 10.1016/S1095-6433(02)00198-8 Opazo JC, Palma RE, Melo F, Lessa EP (2005) Adaptive evolution of", - "/ mice by Lmna heterozy- gosity ameliorates progeroid phenotypes and extends lifespan [143, 174, 175].", - "References 1. Hook Met al.Genetic cartography of longevity in humans and mice: Current landscape and horizons. Biochim. Biophys. Acta1864, 27182732 (2018). 2. Kuningas Met al.Genes encoding longevity: from model organisms to humans. Aging Cell7, 270 280 (2008). [PubMed: 18208581] 3. de Magalhes JP, Wuttke D, Wood SH, Plank M & V ora C Genome-environment interactions that modulate aging: Powerful targets for drug discovery. Pharmacol. Rev. 64, 88101 (2012). [PubMed: 22090473]", - "\"Murine chromosomal regions correlated with longevity.\" Genetics 118: 693-704.", - "expression of alpha-1,2-mannosidase I extends lifespan in Drosophila melanogaster and Caenorhabditis elegans . Aging Cell, 2009 , 8(4), 370-9. [73] Wang, H.D.; Kazemi-Esfarjani, P.; Benzer, S. Multiple-stress analysis for isolation of Drosophila longevity genes . Proc Natl Acad Sci U S A , 2004 , 101(34), 12610-5. [74] Lin, Y.J.; Seroude, L.; Benzer, S. Extended life-span and stress resistance in the Drosophila mutant methuselah . Science , 1998 , 282(5390), 943-6.", - "sion analysis of mouse liver genes: effect of age and of thelongevity mutant Prop1df. J Gerontol A Biol Sci Med Sci 56: B72B80, 2001. 12.Fabrizio P, Pozza F, Pletcher SD, Gendron CM, and Longo VD. Regulation of longevity and stress resistance by Sch9 in Yeast. Science 292: 288 290, 2001. 13.Haase D, Lehmann MH, Korner MM, Korfer R, Sigusch HH, and Figulla HR. Identi cation and validation of selective" - ], - "answer": "The mouse genes associated with longevity include: 1. **Mouse loci associated with life span**: These loci exhibit sex-specific and epistatic effects [1]. 2. **Mouse growth hormone receptor**: Alterations in this gene result in increased lifespan [3], [4]. 3. **Lmna heterozygosity**: This genetic modification ameliorates progeroid phenotypes and extends lifespan [6]. These references provide evidence of specific genes and loci in mice that have been linked to increased longevity.", - "question": "Which mouse genes have been associated with longevity?" -}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/domain_expert_aging_3 b/gnqa/paper2_eval/data/dataset/domain_expert_aging_3 deleted file mode 100644 index d89d819..0000000 --- a/gnqa/paper2_eval/data/dataset/domain_expert_aging_3 +++ /dev/null @@ -1,193 +0,0 @@ -{ - "titles": [ - "2009 - The Human Ageing Genomic Resources online.pdf", - "2011 - How pleiotropic genetics of the musculoskeletal system.pdf", - "2021 - Genome-wide association studies identify.pdf", - "2012 - Genome-Environment Interactions That Modulate.pdf", - "2020 - Clinical Genetics and Genomics of Aging.pdf", - "2001 - A genome-wide scan for linkage to human.pdf", - "2007 - Temporal and spatial transcriptional profiles.pdf", - "2012 - Genome-Environment Interactions That Modulate.pdf", - "2020 - Role of Helicobacter pylori infection in the manifestation of old age-related diseases.pdf", - "2012 - Genome-Environment Interactions That Modulate.pdf" - ], - "extraction_id": [ - "7ada6b55-99c2-5e20-bf96-d153f927256c", - "b5b3c74a-90de-5b1e-9580-8031b10be7ec", - "cd7730b6-22dc-5256-9310-79fc348b3226", - "d59d7882-333d-5576-86ab-3cfa6354b946", - "4ea8424f-1cd8-569c-a1df-3f0f54206e70", - "17246c43-2e44-579b-867d-3dc7150ceedd", - "2e42619b-d0b2-5d33-aab8-6f04002ee807", - "d59d7882-333d-5576-86ab-3cfa6354b946", - "e6916baa-9f9d-57aa-b44d-95fb614610a8", - "a01ca925-4ccf-5863-a162-7bd4c754fe89" - ], - "document_id": [ - "e43cd3b6-ad8e-5422-ba7c-ceb6e66cc529", - "ed31486c-a651-5894-bd96-21fbd78f2646", - "60c2e869-1fee-53ea-b332-26d9c2abc747", - "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", - "62b635c3-040e-512a-b016-6ef295308a1e", - "1431984a-82d9-51d4-a23c-5f76a02ab554", - "38f27ec7-08bf-5397-b2b8-bde95e0dc3f8", - "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", - "e99c68d2-4f35-5591-8072-cfdb31966e68", - "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec" - ], - "id": [ - "chatcmpl-ABLwW9HA9VG184zgOmenEBU2eMIMc", - "3117c019-7311-53ae-8ab1-927ca822c709", - "0ad664d2-6756-5123-b192-8a56cf6887a5", - "9fa00091-9661-57bd-91c7-f0bf436805a7", - "786d2756-4c4d-5ac0-8d3d-63f914d51664", - "a0672677-71ad-5603-8427-a0648eec407f", - "e0cce1c5-8709-5218-99b6-48a6ba242931", - "bf2cd208-273f-5848-b243-df8b95ea7833", - "413f8f54-b5cc-5089-9f5c-d9e3b8bcf594", - "50581d4f-396c-5d12-aec6-5f42e2ab88ef", - "3c369292-4b9c-5156-a80f-4b3301026f30" - ], - "contexts": [ - "It is undisputed that genetic factors influence aging. In a remarkable", - "perform a study of the genetic sources of biological aging. However, to be successful, the genetic study of acomplex condition requires a heritable phenotype to be developed and validated. Genome-wide association studies offer an unbiased approach to identify newcandidate genes for human diseases. It is hypothesized that convergent results from multiple aging-related traits will point out the genes responsible for the general agingof the organism. This perspective focuses on the", - "population dynamics on the genetic architecture of human longevity. Aging (Albany NY). 2018;10(8):1947 63. 68. Bellenguez C, Kucukali F, Jansen I, Andrade V, Morenau-Grau S, Amin N, et al. Large meta-analysis of genome-wide association studies expands knowledge of the genetic etiology of Alzheimer disease and highlights potential translational opportunities. medRxiv. 2020. 69. Kojima T, Shimazui T, Hinotsu S, Joraku A, Oikawa T, Kawai K, et al. Decreased expression of CXXC4 promotes a", - "In addition to aging- and CR-related genes, another source of candidate genes and pathways for drug designare human longevity-associated genes (Barzilai andShuldiner, 2001; Browner et al., 2004; Kenyon, 2010).Dozens of genes have now been associated with humanlongevity (de Magalha es et al., 2009a), although only ahandful of genes have been shown to have consistenteffects across populations. Many longevity-associated genes are related to spe-", - "Clinical Genetics and Genomics of Aging", - "effect fundamental mechanisms of aging (14, 16). The drawbacksof such studies include the improbability of picking the right geneto study the myriad of known and unknown genes affecting theprocess of interest (17). The linkage study described heremarkedly improves the efficiency of such association studies bydefining a region likely to contain polymorphism(s) with signif-icant influence on life span. Additional association studies with these families and repli-", - "The multifactorial and temporal features of aging can beanalyzed efficiently by genome-wide transcriptional profiling,which has been conducted in various model organisms and hu-mans (Melov and Hubbard 2004). Aging is associated with alter-ations in transcript levels of many genes, including those in-volved in evolutionarily conserved mitochondrial and protea-somal functions (McCarroll et al. 2004), some of which havebeen shown to be directly involved in regulating lifespan in C.", - "overexpressed with age seem to be a response to aging,in that they have been previously found to have protec-tive functions (de Magalha es et al., 2009b). As such,these genes may help organisms manage aging andcould be targets for manipulation. Likewise, gene ex-pression analysis of CR has been conducted to identifyassociated genes (Lee et al., 1999, 2000). A number ofmolecular signatures have emerged from such studiesthat could be useful to identify candidate processes andpathways that affect aging,", - "Mol Genet Genomic Med. 2020;00:e1157. | 1 of 11 https://doi.org/10.1002/mgg3.1157 wileyonlinelibrary.com/journal/mgg3 1 | INTRODUCTION Aging is one of the inevitably dominant risk associated with many diseases. Several biological factors contribute to this etiology which", - "al., 2009; Stanfel et al., 2009). Many of these genesmodulate the response to environmental signals, such asfood availability, and act in signaling pathways that ifunderstood can be targeted (Fig. 1). The genetic regula-tion of aging is therefore an emerging field with multipleapplications in the human nutrition, cosmetic, and phar-maceutical industries. AGING GENES AS TARGETS FOR DRUG DISCOVERY 91" - ], - "answer": "Genetic factors associated with aging include: 1. **Genome-wide association studies (GWAS)**: These studies offer an unbiased approach to identify new candidate genes for human diseases, including those related to aging. Convergent results from multiple aging-related traits can point out the genes responsible for the general aging of the organism [2]. 2. **Longevity-associated genes**: Dozens of genes have been associated with human longevity, although only a handful have shown consistent effects across populations. These genes are often related to specific biological pathways and processes [4]. 3. **Genes involved in mitochondrial and proteasomal functions**: Aging is associated with alterations in the transcript levels of many genes, including those involved in evolutionarily conserved mitochondrial and proteasomal functions. Some of these genes have been shown to be directly involved in regulating lifespan [7]. 4. **Genes with protective functions**: Certain genes that are overexpressed with age seem to have protective functions, helping organisms manage aging. These genes could be targets for manipulation to potentially influence the aging process [8]. These genetic factors highlight the complex interplay of various genes and pathways in the aging process.", - "question": "what genetic factor are associated with aging" -}{ - "titles": [ - "2009 - The Human Ageing Genomic Resources online.pdf", - "2011 - How pleiotropic genetics of the musculoskeletal system.pdf", - "2021 - Genome-wide association studies identify.pdf", - "2012 - Genome-Environment Interactions That Modulate.pdf", - "2020 - Clinical Genetics and Genomics of Aging.pdf", - "2001 - A genome-wide scan for linkage to human.pdf", - "2007 - Temporal and spatial transcriptional profiles.pdf", - "2012 - Genome-Environment Interactions That Modulate.pdf", - "2020 - Role of Helicobacter pylori infection in the manifestation of old age-related diseases.pdf", - "2012 - Genome-Environment Interactions That Modulate.pdf" - ], - "extraction_id": [ - "7ada6b55-99c2-5e20-bf96-d153f927256c", - "b5b3c74a-90de-5b1e-9580-8031b10be7ec", - "cd7730b6-22dc-5256-9310-79fc348b3226", - "d59d7882-333d-5576-86ab-3cfa6354b946", - "4ea8424f-1cd8-569c-a1df-3f0f54206e70", - "17246c43-2e44-579b-867d-3dc7150ceedd", - "2e42619b-d0b2-5d33-aab8-6f04002ee807", - "d59d7882-333d-5576-86ab-3cfa6354b946", - "e6916baa-9f9d-57aa-b44d-95fb614610a8", - "a01ca925-4ccf-5863-a162-7bd4c754fe89" - ], - "document_id": [ - "e43cd3b6-ad8e-5422-ba7c-ceb6e66cc529", - "ed31486c-a651-5894-bd96-21fbd78f2646", - "60c2e869-1fee-53ea-b332-26d9c2abc747", - "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", - "62b635c3-040e-512a-b016-6ef295308a1e", - "1431984a-82d9-51d4-a23c-5f76a02ab554", - "38f27ec7-08bf-5397-b2b8-bde95e0dc3f8", - "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", - "e99c68d2-4f35-5591-8072-cfdb31966e68", - "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec" - ], - "id": [ - "chatcmpl-ABLwW9HA9VG184zgOmenEBU2eMIMc", - "3117c019-7311-53ae-8ab1-927ca822c709", - "0ad664d2-6756-5123-b192-8a56cf6887a5", - "9fa00091-9661-57bd-91c7-f0bf436805a7", - "786d2756-4c4d-5ac0-8d3d-63f914d51664", - "a0672677-71ad-5603-8427-a0648eec407f", - "e0cce1c5-8709-5218-99b6-48a6ba242931", - "bf2cd208-273f-5848-b243-df8b95ea7833", - "413f8f54-b5cc-5089-9f5c-d9e3b8bcf594", - "50581d4f-396c-5d12-aec6-5f42e2ab88ef", - "3c369292-4b9c-5156-a80f-4b3301026f30" - ], - "contexts": [ - "It is undisputed that genetic factors influence aging. In a remarkable", - "perform a study of the genetic sources of biological aging. However, to be successful, the genetic study of acomplex condition requires a heritable phenotype to be developed and validated. Genome-wide association studies offer an unbiased approach to identify newcandidate genes for human diseases. It is hypothesized that convergent results from multiple aging-related traits will point out the genes responsible for the general agingof the organism. This perspective focuses on the", - "population dynamics on the genetic architecture of human longevity. Aging (Albany NY). 2018;10(8):1947 63. 68. Bellenguez C, Kucukali F, Jansen I, Andrade V, Morenau-Grau S, Amin N, et al. Large meta-analysis of genome-wide association studies expands knowledge of the genetic etiology of Alzheimer disease and highlights potential translational opportunities. medRxiv. 2020. 69. Kojima T, Shimazui T, Hinotsu S, Joraku A, Oikawa T, Kawai K, et al. Decreased expression of CXXC4 promotes a", - "In addition to aging- and CR-related genes, another source of candidate genes and pathways for drug designare human longevity-associated genes (Barzilai andShuldiner, 2001; Browner et al., 2004; Kenyon, 2010).Dozens of genes have now been associated with humanlongevity (de Magalha es et al., 2009a), although only ahandful of genes have been shown to have consistenteffects across populations. Many longevity-associated genes are related to spe-", - "Clinical Genetics and Genomics of Aging", - "effect fundamental mechanisms of aging (14, 16). The drawbacksof such studies include the improbability of picking the right geneto study the myriad of known and unknown genes affecting theprocess of interest (17). The linkage study described heremarkedly improves the efficiency of such association studies bydefining a region likely to contain polymorphism(s) with signif-icant influence on life span. Additional association studies with these families and repli-", - "The multifactorial and temporal features of aging can beanalyzed efficiently by genome-wide transcriptional profiling,which has been conducted in various model organisms and hu-mans (Melov and Hubbard 2004). Aging is associated with alter-ations in transcript levels of many genes, including those in-volved in evolutionarily conserved mitochondrial and protea-somal functions (McCarroll et al. 2004), some of which havebeen shown to be directly involved in regulating lifespan in C.", - "overexpressed with age seem to be a response to aging,in that they have been previously found to have protec-tive functions (de Magalha es et al., 2009b). As such,these genes may help organisms manage aging andcould be targets for manipulation. Likewise, gene ex-pression analysis of CR has been conducted to identifyassociated genes (Lee et al., 1999, 2000). A number ofmolecular signatures have emerged from such studiesthat could be useful to identify candidate processes andpathways that affect aging,", - "Mol Genet Genomic Med. 2020;00:e1157. | 1 of 11 https://doi.org/10.1002/mgg3.1157 wileyonlinelibrary.com/journal/mgg3 1 | INTRODUCTION Aging is one of the inevitably dominant risk associated with many diseases. Several biological factors contribute to this etiology which", - "al., 2009; Stanfel et al., 2009). Many of these genesmodulate the response to environmental signals, such asfood availability, and act in signaling pathways that ifunderstood can be targeted (Fig. 1). The genetic regula-tion of aging is therefore an emerging field with multipleapplications in the human nutrition, cosmetic, and phar-maceutical industries. AGING GENES AS TARGETS FOR DRUG DISCOVERY 91" - ], - "answer": "Genetic factors associated with aging include: 1. **Genome-wide association studies (GWAS)**: These studies offer an unbiased approach to identify new candidate genes for human diseases, including those related to aging. Convergent results from multiple aging-related traits can point out the genes responsible for the general aging of the organism [2]. 2. **Longevity-associated genes**: Dozens of genes have been associated with human longevity, although only a handful have shown consistent effects across populations. These genes are often related to specific biological pathways and processes [4]. 3. **Genes involved in mitochondrial and proteasomal functions**: Aging is associated with alterations in the transcript levels of many genes, including those involved in evolutionarily conserved mitochondrial and proteasomal functions. Some of these genes have been shown to be directly involved in regulating lifespan [7]. 4. **Genes with protective functions**: Certain genes that are overexpressed with age seem to have protective functions, helping organisms manage aging. These genes could be targets for manipulation to potentially influence the aging process [8]. These genetic factors highlight the complex interplay of various genes and pathways in the aging process.", - "question": "what genetic factor are associated with aging" -}{ - "titles": [ - "2009 - The Human Ageing Genomic Resources online.pdf", - "2011 - How pleiotropic genetics of the musculoskeletal system.pdf", - "2021 - Genome-wide association studies identify.pdf", - "2012 - Genome-Environment Interactions That Modulate.pdf", - "2020 - Clinical Genetics and Genomics of Aging.pdf", - "2001 - A genome-wide scan for linkage to human.pdf", - "2007 - Temporal and spatial transcriptional profiles.pdf", - "2012 - Genome-Environment Interactions That Modulate.pdf", - "2020 - Role of Helicobacter pylori infection in the manifestation of old age-related diseases.pdf", - "2012 - Genome-Environment Interactions That Modulate.pdf" - ], - "extraction_id": [ - "7ada6b55-99c2-5e20-bf96-d153f927256c", - "b5b3c74a-90de-5b1e-9580-8031b10be7ec", - "cd7730b6-22dc-5256-9310-79fc348b3226", - "d59d7882-333d-5576-86ab-3cfa6354b946", - "4ea8424f-1cd8-569c-a1df-3f0f54206e70", - "17246c43-2e44-579b-867d-3dc7150ceedd", - "2e42619b-d0b2-5d33-aab8-6f04002ee807", - "d59d7882-333d-5576-86ab-3cfa6354b946", - "e6916baa-9f9d-57aa-b44d-95fb614610a8", - "a01ca925-4ccf-5863-a162-7bd4c754fe89" - ], - "document_id": [ - "e43cd3b6-ad8e-5422-ba7c-ceb6e66cc529", - "ed31486c-a651-5894-bd96-21fbd78f2646", - "60c2e869-1fee-53ea-b332-26d9c2abc747", - "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", - "62b635c3-040e-512a-b016-6ef295308a1e", - "1431984a-82d9-51d4-a23c-5f76a02ab554", - "38f27ec7-08bf-5397-b2b8-bde95e0dc3f8", - "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", - "e99c68d2-4f35-5591-8072-cfdb31966e68", - "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec" - ], - "id": [ - "chatcmpl-ABLwW9HA9VG184zgOmenEBU2eMIMc", - "3117c019-7311-53ae-8ab1-927ca822c709", - "0ad664d2-6756-5123-b192-8a56cf6887a5", - "9fa00091-9661-57bd-91c7-f0bf436805a7", - "786d2756-4c4d-5ac0-8d3d-63f914d51664", - "a0672677-71ad-5603-8427-a0648eec407f", - "e0cce1c5-8709-5218-99b6-48a6ba242931", - "bf2cd208-273f-5848-b243-df8b95ea7833", - "413f8f54-b5cc-5089-9f5c-d9e3b8bcf594", - "50581d4f-396c-5d12-aec6-5f42e2ab88ef", - "3c369292-4b9c-5156-a80f-4b3301026f30" - ], - "contexts": [ - "It is undisputed that genetic factors influence aging. In a remarkable", - "perform a study of the genetic sources of biological aging. However, to be successful, the genetic study of acomplex condition requires a heritable phenotype to be developed and validated. Genome-wide association studies offer an unbiased approach to identify newcandidate genes for human diseases. It is hypothesized that convergent results from multiple aging-related traits will point out the genes responsible for the general agingof the organism. This perspective focuses on the", - "population dynamics on the genetic architecture of human longevity. Aging (Albany NY). 2018;10(8):1947 63. 68. Bellenguez C, Kucukali F, Jansen I, Andrade V, Morenau-Grau S, Amin N, et al. Large meta-analysis of genome-wide association studies expands knowledge of the genetic etiology of Alzheimer disease and highlights potential translational opportunities. medRxiv. 2020. 69. Kojima T, Shimazui T, Hinotsu S, Joraku A, Oikawa T, Kawai K, et al. Decreased expression of CXXC4 promotes a", - "In addition to aging- and CR-related genes, another source of candidate genes and pathways for drug designare human longevity-associated genes (Barzilai andShuldiner, 2001; Browner et al., 2004; Kenyon, 2010).Dozens of genes have now been associated with humanlongevity (de Magalha es et al., 2009a), although only ahandful of genes have been shown to have consistenteffects across populations. Many longevity-associated genes are related to spe-", - "Clinical Genetics and Genomics of Aging", - "effect fundamental mechanisms of aging (14, 16). The drawbacksof such studies include the improbability of picking the right geneto study the myriad of known and unknown genes affecting theprocess of interest (17). The linkage study described heremarkedly improves the efficiency of such association studies bydefining a region likely to contain polymorphism(s) with signif-icant influence on life span. Additional association studies with these families and repli-", - "The multifactorial and temporal features of aging can beanalyzed efficiently by genome-wide transcriptional profiling,which has been conducted in various model organisms and hu-mans (Melov and Hubbard 2004). Aging is associated with alter-ations in transcript levels of many genes, including those in-volved in evolutionarily conserved mitochondrial and protea-somal functions (McCarroll et al. 2004), some of which havebeen shown to be directly involved in regulating lifespan in C.", - "overexpressed with age seem to be a response to aging,in that they have been previously found to have protec-tive functions (de Magalha es et al., 2009b). As such,these genes may help organisms manage aging andcould be targets for manipulation. Likewise, gene ex-pression analysis of CR has been conducted to identifyassociated genes (Lee et al., 1999, 2000). A number ofmolecular signatures have emerged from such studiesthat could be useful to identify candidate processes andpathways that affect aging,", - "Mol Genet Genomic Med. 2020;00:e1157. | 1 of 11 https://doi.org/10.1002/mgg3.1157 wileyonlinelibrary.com/journal/mgg3 1 | INTRODUCTION Aging is one of the inevitably dominant risk associated with many diseases. Several biological factors contribute to this etiology which", - "al., 2009; Stanfel et al., 2009). Many of these genesmodulate the response to environmental signals, such asfood availability, and act in signaling pathways that ifunderstood can be targeted (Fig. 1). The genetic regula-tion of aging is therefore an emerging field with multipleapplications in the human nutrition, cosmetic, and phar-maceutical industries. AGING GENES AS TARGETS FOR DRUG DISCOVERY 91" - ], - "answer": "Genetic factors associated with aging include: 1. **Genome-wide association studies (GWAS)**: These studies offer an unbiased approach to identify new candidate genes for human diseases, including those related to aging. Convergent results from multiple aging-related traits can point out the genes responsible for the general aging of the organism [2]. 2. **Longevity-associated genes**: Dozens of genes have been associated with human longevity, although only a handful have shown consistent effects across populations. These genes are often related to specific biological pathways and processes [4]. 3. **Genes involved in mitochondrial and proteasomal functions**: Aging is associated with alterations in the transcript levels of many genes, including those involved in evolutionarily conserved mitochondrial and proteasomal functions. Some of these genes have been shown to be directly involved in regulating lifespan [7]. 4. **Genes with protective functions**: Certain genes that are overexpressed with age seem to have protective functions, helping organisms manage aging. These genes could be targets for manipulation to potentially influence the aging process [8]. These genetic factors highlight the complex interplay of various genes and pathways in the aging process.", - "question": "what genetic factor are associated with aging" -}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/domain_expert_aging_4 b/gnqa/paper2_eval/data/dataset/domain_expert_aging_4 deleted file mode 100644 index accd8c0..0000000 --- a/gnqa/paper2_eval/data/dataset/domain_expert_aging_4 +++ /dev/null @@ -1,193 +0,0 @@ -{ - "titles": [ - "2012 - Genome-Environment Interactions That Modulate.pdf", - "2009 - The Human Ageing Genomic Resources online.pdf", - "2012 - Genome-Environment Interactions That Modulate.pdf", - "2011 - Clinical aspects and molecular diagnostics of skin aging.pdf", - "2015 - Transcriptomic profiles of aging in purified.pdf", - "2004 - A Transcriptional Profile of Aging.pdf", - "2020 - A multidimensional systems biology.pdf", - "2011 - Metabolism, Genomics, and DNA Repair in the Mouse Aging Liver.pdf", - "2010 - MicroRNA, mRNA, and protein expression link.pdf", - "2012 - GeneFriends An online co-expression analysis.pdf" - ], - "extraction_id": [ - "d59d7882-333d-5576-86ab-3cfa6354b946", - "52c67b46-63f2-54ae-a78e-e9d54a55f6e4", - "d59d7882-333d-5576-86ab-3cfa6354b946", - "674e1da7-73d5-5101-b5a5-4981e483123c", - "e5fd1ff0-8df5-577f-9f2d-31b0941d5ce5", - "8b47c304-ee91-5c52-8324-79fd0bd32b27", - "9d1656aa-32d2-5094-8232-4817655b1cbd", - "a6a6b5ba-3a72-55c5-91bb-abe747624348", - "fc9974c9-2e48-5a08-9112-0109df9ce096", - "1839dfa6-7080-5de4-96cb-3493ca2056d3" - ], - "document_id": [ - "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", - "e43cd3b6-ad8e-5422-ba7c-ceb6e66cc529", - "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", - "e32f8f2c-d3ad-5dae-a393-9bd87c370ebe", - "50f357a3-e0f2-5e32-a6b2-771fb4b1e1c6", - "4ab656a7-9656-526b-94e1-422875409b44", - "d040bfe3-e409-5b5c-b8f8-f3dd4fc060e3", - "a94fd15d-373e-51c5-ad74-a17e4260d32a", - "c3ae47b0-42dd-5ab0-8fec-a41831d1bbfa", - "be20af52-c782-5098-893a-9a92000bf5a0" - ], - "id": [ - "chatcmpl-ABLwhCwS1z9hZBn1zWSbHm5JcD7CF", - "b719fbc0-94e4-5df0-abb7-0d13fc36214c", - "8fd5ab85-67ed-55e6-bbfa-09436c4fdbfb", - "413f8f54-b5cc-5089-9f5c-d9e3b8bcf594", - "896169ed-4b9b-5ebc-9c9d-1cd2e6f3437c", - "6b4fb407-fd3f-52a3-9cfd-07dc5c891dd5", - "17ce11f7-55df-59bd-a801-a6f38ae9a9ef", - "61baeaa5-d65a-54b5-bfee-9bab8bbf1985", - "3414ff44-7d60-5492-9956-353ab9a94a43", - "b2d47567-09dc-5c77-be72-9448aa954e6b", - "1c3f7772-e1fa-5063-bda7-04b2f7e7b0e3" - ], - "contexts": [ - "lar signatures of mammalian aging. Some of the genes", - "www.ncbi.nlm.nih.gov/homologene) of genes strongly asso-ciated with aging in model organisms. Also included are genesin which mutations result in segmental progeroid syndromes,such as the Werners syndrome gene, as well as genes criticalin pathways previously related to aging, such as the insulin/insulin-like signalling pathway (de Magalhes et al ., 2005a). The", - "overexpressed with age seem to be a response to aging,in that they have been previously found to have protec-tive functions (de Magalha es et al., 2009b). As such,these genes may help organisms manage aging andcould be targets for manipulation. Likewise, gene ex-pression analysis of CR has been conducted to identifyassociated genes (Lee et al., 1999, 2000). A number ofmolecular signatures have emerged from such studiesthat could be useful to identify candidate processes andpathways that affect aging,", - "expression profile of aging in human muscle. Physiol Genomics 2003;14:149-59. 142. Rodwell GE, Sonu R, Zahn JM. A transcriptional profile of aging inthe human kidney. PLoS Biol 2004;e427:2. 143. Hasty P, Campisi J, Hoeijmakers J, van Steeg H, Vijg J. Aging and genome maintenance: lessons from the mouse? Science 2003;299:1355-9. 144. Kyng KJ, May A, Klvraa S, Bohr VA. Gene expression profiling in Werner syndrome closely resembles that of normal aging. Proc Natl Acad Sci U S A 2003;100:12259-64.", - "neurodegenerative diseases. Nature. 2006;443:787 95. 50. de Magalhes JP, Curado J, Church GM. Meta-analysis of age-related gene expression profiles identifies common signatures of aging. Bioinformatics. 2009;25:875 81. 51. Zahn JM, Poosala S, Owen AB, Ingram DK, Lustig A, Carter A, et al. AGEMAP: a gene expression database for aging in mice. PLoS Genet. 2007;3:e201. 52. Liu LF, Shen WJ, Ueno M, Patel S, Kraemer FB. Characterization of age- related gene expression profiling in bone marrow and epididymal", - "Ly DH, Lockhart DJ, Lerner RA, Schultz PG (2000) Mitotic misregulation and human aging. Science 287: 24862492. McCarroll SA, Murphy CT, Zou S, Pletcher SD, Chin CS, et al. (2004) Comparing genomic expression patterns across species identies shared transcriptional prole in aging. Nat Genet 36: 197204. Murphy CT, McCarroll SA, Bargmann CI, Fraser A, Kamath RS, et al. (2003) Genes that act downstream of DAF-16 to inuence the lifespan of Caenorhabditis elegans Nature 424: 277283.", - "genes driving cellular senescence, and perform various integrative analyses. Genes inducing cellular senescence tend to be overexpressed with age in human tissues and are significantly overrepresented in anti-longevity and tumor-suppressor genes, while genes inhibiting cellular senescence overlap with pro-longevity and oncogenes. Furthermore, cellular senescence genes are strongly conserved in mammals but not in invertebrates. We also build", - "exhibits important alterations in global gene expressionproles with age. In mice, aging is accompanied by changesin expression of genes associated with increased inamma-tion, cellular stress, brosis, altered capacity for apoptosis,xenobiotic metabolism, normal cell-cycle control, and DNAreplication [ 5]. Lifelong calorie restriction reversed the", - "stance, genes associated with energy production, which decrease their expression during aging across various tissues and species (Zahn et al. 2006, 2007; de Magalha es et al. 2009), start decreasing at this transition point in our data (group 5; Fig. 2A). Hence, 25 yr of age in humans may mark the beginning of systemic change associated with certain senescence processes. Conservation of expression changes with age We observe that both developmental and aging expression pro-", - "p <10 -6; Table 1 shows the top 25 genes. Many of these genes have been associated with age-related diseases.Several other genes that have been shown to play a role in aging such as lysosomal-associated membrane protein-2 Lamp2 [19] (p = 5.68 -30), Fas [20] (p = 2.70-31) and growth hormone receptor Ghr [21] (p = 1.34-19) also showed a significant co-expression. Anxa2, Anxa3 and Anxa4 also show a low p-value (p < 10-25) as well as several S100 calcium binding proteins which have been" - ], - "answer": "Genes typically associated with early aging include those involved in segmental progeroid syndromes, such as the Werner syndrome gene [2]. Additionally, genes critical in pathways previously related to aging, such as the insulin/insulin-like signaling pathway, are also associated with early aging [2].", - "question": "which genes are typically associated with early aging?" -}{ - "titles": [ - "2012 - Genome-Environment Interactions That Modulate.pdf", - "2009 - The Human Ageing Genomic Resources online.pdf", - "2012 - Genome-Environment Interactions That Modulate.pdf", - "2011 - Clinical aspects and molecular diagnostics of skin aging.pdf", - "2015 - Transcriptomic profiles of aging in purified.pdf", - "2004 - A Transcriptional Profile of Aging.pdf", - "2020 - A multidimensional systems biology.pdf", - "2011 - Metabolism, Genomics, and DNA Repair in the Mouse Aging Liver.pdf", - "2010 - MicroRNA, mRNA, and protein expression link.pdf", - "2012 - GeneFriends An online co-expression analysis.pdf" - ], - "extraction_id": [ - "d59d7882-333d-5576-86ab-3cfa6354b946", - "52c67b46-63f2-54ae-a78e-e9d54a55f6e4", - "d59d7882-333d-5576-86ab-3cfa6354b946", - "674e1da7-73d5-5101-b5a5-4981e483123c", - "e5fd1ff0-8df5-577f-9f2d-31b0941d5ce5", - "8b47c304-ee91-5c52-8324-79fd0bd32b27", - "9d1656aa-32d2-5094-8232-4817655b1cbd", - "a6a6b5ba-3a72-55c5-91bb-abe747624348", - "fc9974c9-2e48-5a08-9112-0109df9ce096", - "1839dfa6-7080-5de4-96cb-3493ca2056d3" - ], - "document_id": [ - "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", - "e43cd3b6-ad8e-5422-ba7c-ceb6e66cc529", - "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", - "e32f8f2c-d3ad-5dae-a393-9bd87c370ebe", - "50f357a3-e0f2-5e32-a6b2-771fb4b1e1c6", - "4ab656a7-9656-526b-94e1-422875409b44", - "d040bfe3-e409-5b5c-b8f8-f3dd4fc060e3", - "a94fd15d-373e-51c5-ad74-a17e4260d32a", - "c3ae47b0-42dd-5ab0-8fec-a41831d1bbfa", - "be20af52-c782-5098-893a-9a92000bf5a0" - ], - "id": [ - "chatcmpl-ABLwhCwS1z9hZBn1zWSbHm5JcD7CF", - "b719fbc0-94e4-5df0-abb7-0d13fc36214c", - "8fd5ab85-67ed-55e6-bbfa-09436c4fdbfb", - "413f8f54-b5cc-5089-9f5c-d9e3b8bcf594", - "896169ed-4b9b-5ebc-9c9d-1cd2e6f3437c", - "6b4fb407-fd3f-52a3-9cfd-07dc5c891dd5", - "17ce11f7-55df-59bd-a801-a6f38ae9a9ef", - "61baeaa5-d65a-54b5-bfee-9bab8bbf1985", - "3414ff44-7d60-5492-9956-353ab9a94a43", - "b2d47567-09dc-5c77-be72-9448aa954e6b", - "1c3f7772-e1fa-5063-bda7-04b2f7e7b0e3" - ], - "contexts": [ - "lar signatures of mammalian aging. Some of the genes", - "www.ncbi.nlm.nih.gov/homologene) of genes strongly asso-ciated with aging in model organisms. Also included are genesin which mutations result in segmental progeroid syndromes,such as the Werners syndrome gene, as well as genes criticalin pathways previously related to aging, such as the insulin/insulin-like signalling pathway (de Magalhes et al ., 2005a). The", - "overexpressed with age seem to be a response to aging,in that they have been previously found to have protec-tive functions (de Magalha es et al., 2009b). As such,these genes may help organisms manage aging andcould be targets for manipulation. Likewise, gene ex-pression analysis of CR has been conducted to identifyassociated genes (Lee et al., 1999, 2000). A number ofmolecular signatures have emerged from such studiesthat could be useful to identify candidate processes andpathways that affect aging,", - "expression profile of aging in human muscle. Physiol Genomics 2003;14:149-59. 142. Rodwell GE, Sonu R, Zahn JM. A transcriptional profile of aging inthe human kidney. PLoS Biol 2004;e427:2. 143. Hasty P, Campisi J, Hoeijmakers J, van Steeg H, Vijg J. Aging and genome maintenance: lessons from the mouse? Science 2003;299:1355-9. 144. Kyng KJ, May A, Klvraa S, Bohr VA. Gene expression profiling in Werner syndrome closely resembles that of normal aging. Proc Natl Acad Sci U S A 2003;100:12259-64.", - "neurodegenerative diseases. Nature. 2006;443:787 95. 50. de Magalhes JP, Curado J, Church GM. Meta-analysis of age-related gene expression profiles identifies common signatures of aging. Bioinformatics. 2009;25:875 81. 51. Zahn JM, Poosala S, Owen AB, Ingram DK, Lustig A, Carter A, et al. AGEMAP: a gene expression database for aging in mice. PLoS Genet. 2007;3:e201. 52. Liu LF, Shen WJ, Ueno M, Patel S, Kraemer FB. Characterization of age- related gene expression profiling in bone marrow and epididymal", - "Ly DH, Lockhart DJ, Lerner RA, Schultz PG (2000) Mitotic misregulation and human aging. Science 287: 24862492. McCarroll SA, Murphy CT, Zou S, Pletcher SD, Chin CS, et al. (2004) Comparing genomic expression patterns across species identies shared transcriptional prole in aging. Nat Genet 36: 197204. Murphy CT, McCarroll SA, Bargmann CI, Fraser A, Kamath RS, et al. (2003) Genes that act downstream of DAF-16 to inuence the lifespan of Caenorhabditis elegans Nature 424: 277283.", - "genes driving cellular senescence, and perform various integrative analyses. Genes inducing cellular senescence tend to be overexpressed with age in human tissues and are significantly overrepresented in anti-longevity and tumor-suppressor genes, while genes inhibiting cellular senescence overlap with pro-longevity and oncogenes. Furthermore, cellular senescence genes are strongly conserved in mammals but not in invertebrates. We also build", - "exhibits important alterations in global gene expressionproles with age. In mice, aging is accompanied by changesin expression of genes associated with increased inamma-tion, cellular stress, brosis, altered capacity for apoptosis,xenobiotic metabolism, normal cell-cycle control, and DNAreplication [ 5]. Lifelong calorie restriction reversed the", - "stance, genes associated with energy production, which decrease their expression during aging across various tissues and species (Zahn et al. 2006, 2007; de Magalha es et al. 2009), start decreasing at this transition point in our data (group 5; Fig. 2A). Hence, 25 yr of age in humans may mark the beginning of systemic change associated with certain senescence processes. Conservation of expression changes with age We observe that both developmental and aging expression pro-", - "p <10 -6; Table 1 shows the top 25 genes. Many of these genes have been associated with age-related diseases.Several other genes that have been shown to play a role in aging such as lysosomal-associated membrane protein-2 Lamp2 [19] (p = 5.68 -30), Fas [20] (p = 2.70-31) and growth hormone receptor Ghr [21] (p = 1.34-19) also showed a significant co-expression. Anxa2, Anxa3 and Anxa4 also show a low p-value (p < 10-25) as well as several S100 calcium binding proteins which have been" - ], - "answer": "Genes typically associated with early aging include those involved in segmental progeroid syndromes, such as the Werner syndrome gene [2]. Additionally, genes critical in pathways previously related to aging, such as the insulin/insulin-like signaling pathway, are also associated with early aging [2].", - "question": "which genes are typically associated with early aging?" -}{ - "titles": [ - "2012 - Genome-Environment Interactions That Modulate.pdf", - "2009 - The Human Ageing Genomic Resources online.pdf", - "2012 - Genome-Environment Interactions That Modulate.pdf", - "2011 - Clinical aspects and molecular diagnostics of skin aging.pdf", - "2015 - Transcriptomic profiles of aging in purified.pdf", - "2004 - A Transcriptional Profile of Aging.pdf", - "2020 - A multidimensional systems biology.pdf", - "2011 - Metabolism, Genomics, and DNA Repair in the Mouse Aging Liver.pdf", - "2010 - MicroRNA, mRNA, and protein expression link.pdf", - "2012 - GeneFriends An online co-expression analysis.pdf" - ], - "extraction_id": [ - "d59d7882-333d-5576-86ab-3cfa6354b946", - "52c67b46-63f2-54ae-a78e-e9d54a55f6e4", - "d59d7882-333d-5576-86ab-3cfa6354b946", - "674e1da7-73d5-5101-b5a5-4981e483123c", - "e5fd1ff0-8df5-577f-9f2d-31b0941d5ce5", - "8b47c304-ee91-5c52-8324-79fd0bd32b27", - "9d1656aa-32d2-5094-8232-4817655b1cbd", - "a6a6b5ba-3a72-55c5-91bb-abe747624348", - "fc9974c9-2e48-5a08-9112-0109df9ce096", - "1839dfa6-7080-5de4-96cb-3493ca2056d3" - ], - "document_id": [ - "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", - "e43cd3b6-ad8e-5422-ba7c-ceb6e66cc529", - "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", - "e32f8f2c-d3ad-5dae-a393-9bd87c370ebe", - "50f357a3-e0f2-5e32-a6b2-771fb4b1e1c6", - "4ab656a7-9656-526b-94e1-422875409b44", - "d040bfe3-e409-5b5c-b8f8-f3dd4fc060e3", - "a94fd15d-373e-51c5-ad74-a17e4260d32a", - "c3ae47b0-42dd-5ab0-8fec-a41831d1bbfa", - "be20af52-c782-5098-893a-9a92000bf5a0" - ], - "id": [ - "chatcmpl-ABLwhCwS1z9hZBn1zWSbHm5JcD7CF", - "b719fbc0-94e4-5df0-abb7-0d13fc36214c", - "8fd5ab85-67ed-55e6-bbfa-09436c4fdbfb", - "413f8f54-b5cc-5089-9f5c-d9e3b8bcf594", - "896169ed-4b9b-5ebc-9c9d-1cd2e6f3437c", - "6b4fb407-fd3f-52a3-9cfd-07dc5c891dd5", - "17ce11f7-55df-59bd-a801-a6f38ae9a9ef", - "61baeaa5-d65a-54b5-bfee-9bab8bbf1985", - "3414ff44-7d60-5492-9956-353ab9a94a43", - "b2d47567-09dc-5c77-be72-9448aa954e6b", - "1c3f7772-e1fa-5063-bda7-04b2f7e7b0e3" - ], - "contexts": [ - "lar signatures of mammalian aging. Some of the genes", - "www.ncbi.nlm.nih.gov/homologene) of genes strongly asso-ciated with aging in model organisms. Also included are genesin which mutations result in segmental progeroid syndromes,such as the Werners syndrome gene, as well as genes criticalin pathways previously related to aging, such as the insulin/insulin-like signalling pathway (de Magalhes et al ., 2005a). The", - "overexpressed with age seem to be a response to aging,in that they have been previously found to have protec-tive functions (de Magalha es et al., 2009b). As such,these genes may help organisms manage aging andcould be targets for manipulation. Likewise, gene ex-pression analysis of CR has been conducted to identifyassociated genes (Lee et al., 1999, 2000). A number ofmolecular signatures have emerged from such studiesthat could be useful to identify candidate processes andpathways that affect aging,", - "expression profile of aging in human muscle. Physiol Genomics 2003;14:149-59. 142. Rodwell GE, Sonu R, Zahn JM. A transcriptional profile of aging inthe human kidney. PLoS Biol 2004;e427:2. 143. Hasty P, Campisi J, Hoeijmakers J, van Steeg H, Vijg J. Aging and genome maintenance: lessons from the mouse? Science 2003;299:1355-9. 144. Kyng KJ, May A, Klvraa S, Bohr VA. Gene expression profiling in Werner syndrome closely resembles that of normal aging. Proc Natl Acad Sci U S A 2003;100:12259-64.", - "neurodegenerative diseases. Nature. 2006;443:787 95. 50. de Magalhes JP, Curado J, Church GM. Meta-analysis of age-related gene expression profiles identifies common signatures of aging. Bioinformatics. 2009;25:875 81. 51. Zahn JM, Poosala S, Owen AB, Ingram DK, Lustig A, Carter A, et al. AGEMAP: a gene expression database for aging in mice. PLoS Genet. 2007;3:e201. 52. Liu LF, Shen WJ, Ueno M, Patel S, Kraemer FB. Characterization of age- related gene expression profiling in bone marrow and epididymal", - "Ly DH, Lockhart DJ, Lerner RA, Schultz PG (2000) Mitotic misregulation and human aging. Science 287: 24862492. McCarroll SA, Murphy CT, Zou S, Pletcher SD, Chin CS, et al. (2004) Comparing genomic expression patterns across species identies shared transcriptional prole in aging. Nat Genet 36: 197204. Murphy CT, McCarroll SA, Bargmann CI, Fraser A, Kamath RS, et al. (2003) Genes that act downstream of DAF-16 to inuence the lifespan of Caenorhabditis elegans Nature 424: 277283.", - "genes driving cellular senescence, and perform various integrative analyses. Genes inducing cellular senescence tend to be overexpressed with age in human tissues and are significantly overrepresented in anti-longevity and tumor-suppressor genes, while genes inhibiting cellular senescence overlap with pro-longevity and oncogenes. Furthermore, cellular senescence genes are strongly conserved in mammals but not in invertebrates. We also build", - "exhibits important alterations in global gene expressionproles with age. In mice, aging is accompanied by changesin expression of genes associated with increased inamma-tion, cellular stress, brosis, altered capacity for apoptosis,xenobiotic metabolism, normal cell-cycle control, and DNAreplication [ 5]. Lifelong calorie restriction reversed the", - "stance, genes associated with energy production, which decrease their expression during aging across various tissues and species (Zahn et al. 2006, 2007; de Magalha es et al. 2009), start decreasing at this transition point in our data (group 5; Fig. 2A). Hence, 25 yr of age in humans may mark the beginning of systemic change associated with certain senescence processes. Conservation of expression changes with age We observe that both developmental and aging expression pro-", - "p <10 -6; Table 1 shows the top 25 genes. Many of these genes have been associated with age-related diseases.Several other genes that have been shown to play a role in aging such as lysosomal-associated membrane protein-2 Lamp2 [19] (p = 5.68 -30), Fas [20] (p = 2.70-31) and growth hormone receptor Ghr [21] (p = 1.34-19) also showed a significant co-expression. Anxa2, Anxa3 and Anxa4 also show a low p-value (p < 10-25) as well as several S100 calcium binding proteins which have been" - ], - "answer": "Genes typically associated with early aging include those involved in segmental progeroid syndromes, such as the Werner syndrome gene [2]. Additionally, genes critical in pathways previously related to aging, such as the insulin/insulin-like signaling pathway, are also associated with early aging [2].", - "question": "which genes are typically associated with early aging?" -}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/domain_expert_aging_5 b/gnqa/paper2_eval/data/dataset/domain_expert_aging_5 deleted file mode 100644 index c1c597d..0000000 --- a/gnqa/paper2_eval/data/dataset/domain_expert_aging_5 +++ /dev/null @@ -1,193 +0,0 @@ -{ - "titles": [ - "2001 - A genome-wide scan for linkage to human.pdf", - "2023 - A transcriptome-based single-cell biological age model.pdf", - "2015 - A Chromosome 13 locus is associated with male-specific mortality in mice.pdf", - "2007 - Longevity Genomics Across Species.pdf", - "2020 - Clinical Genetics and Genomics of Aging.pdf", - "2017 - Systems genetic analysis in GeneNetwork.org.pdf", - "2021 - Gene-by-environment modulation of lifespan and weight gain in the murine BXD family.pdf", - "2016 - Systems genetics identifies Hp1bp3 as a novel modulator of cognitive aging.pdf", - "2015 - A Chromosome 13 locus is associated with male-specific mortality in mice.pdf", - "2009 - Meta-analysis of age-related gene expression profiles identifies.pdf" - ], - "extraction_id": [ - "17246c43-2e44-579b-867d-3dc7150ceedd", - "0fd46f00-d3e1-54f4-9395-6c3e8294ed51", - "5cc56e3b-53ab-5299-814d-014e2ed31d2f", - "522e2616-daa1-5bf3-8673-a717dfb9b13f", - "5c3840bd-45a5-5928-84ab-a1f2d8536691", - "59121146-02b9-5479-96e2-9fb45cffc81b", - "396683f9-b2e3-5942-bec8-f96fa798c341", - "382122b9-6922-5d85-9e8c-acfa86aff085", - "df0b4be9-3393-5642-a722-ccafffb60df8", - "4d95f551-34bd-5e7a-8702-eb59de73a480" - ], - "document_id": [ - "1431984a-82d9-51d4-a23c-5f76a02ab554", - "9be234b7-f37d-5cd5-8895-bfe676441b2f", - "ad8f2626-87fb-520e-8cef-ee9a9cc3ab0b", - "1ab0b63f-d97c-5f5c-98ee-0bde785fa630", - "62b635c3-040e-512a-b016-6ef295308a1e", - "41be0f9f-a5af-5586-b6cd-16e56fd89cdc", - "4d082da4-fa48-5170-8147-c4fea47a5d4b", - "8cde78ac-cb0e-5983-86ee-91074b2fe1e3", - "ad8f2626-87fb-520e-8cef-ee9a9cc3ab0b", - "5c2cf97f-a57a-5284-85a3-b8d9c5943113" - ], - "id": [ - "chatcmpl-ABLwlxjoJ15UXMdKPBfDnYfvZNLDD", - "e0cce1c5-8709-5218-99b6-48a6ba242931", - "9f9fef49-0bda-5948-93bd-0f8f43bbefdf", - "09da6f9e-b996-5438-91be-41d9438cb930", - "ab0845d4-b4db-53db-927e-b96a52cf7667", - "c2299f0f-9e0b-5279-90e5-37c6bd664976", - "3004d1fd-c5ce-5587-bfab-471e7141952c", - "9082d164-59f8-58a0-ace7-8b3aa9d884e2", - "7abf14d2-cdfe-5c37-8217-6b63bd8fb255", - "380ca35e-b42b-59b4-aef7-aaf2ba3bb59d", - "eea576fd-d766-5ae7-9e63-045869a3f8f7" - ], - "contexts": [ - "effect fundamental mechanisms of aging (14, 16). The drawbacksof such studies include the improbability of picking the right geneto study the myriad of known and unknown genes affecting theprocess of interest (17). The linkage study described heremarkedly improves the efficiency of such association studies bydefining a region likely to contain polymorphism(s) with signif-icant influence on life span. Additional association studies with these families and repli-", - "Map contains 1119 and 1459 curated human and mouse aginggenes, respectively, covering almost all scales of aging, rangingfrom molecular damage to genetic predisposition. Cross-speciescomparison revealed a modest overlap between known humanand mouse aging genes, suggesting both conservation of core sen- escence pathways and fundamental differences in aging between mice and humans (Fig. 2E). Aging-associated genes can alternatively be identified in a", - "11. Gelman R, Watson A, Bronson R et al (1988) Murine chromo- somal regions correlated with longevity. Genetics 118(4):693704 12. Jackson AU, Galecki AT, Burke DT et al (2002) Mouse loci associated with life span exhibit sex-specic and epistatic effects. J Gerontol A Biol Sci Med Sci 57(1):B9B15 13. Foreman JE, Lionikas A, Lang DH et al (2009) Genetic archi- tecture for hole-board behaviors across substantial time intervalsin young, middle-aged and old mice. Genes Brain Behav", - "Along with longevity, a select group of potential aging-related biomarkers will be assayed for each of these mouse models. In addition, it should be possible to assay several of these mouse lines for resistance to specific age-associated diseases, such as diabetes and neurological disorders, by crossing them into the appropriate transgenic disease back- ground. CONCLUSION Our understanding of the basic mechanisms of aging have benefited greatly from the use of simple model systems", - "198 the study of age-related diseases for various reasons: (a) mice are closely related to humans, with nearly 99% of human orthologous in mice; (b) their relatively short lifespan and small size allow surveillance of the aging process within a pertinent time frame and make their housing less expensive; (c) the feasibility of performing genetic manipulations facilitates the engineering of transgenic strains (gain- and loss-of function mice) that model premature aging disorders. In this section, we", - "Hsu HC, Lu L, Yi N, Van Zant G, Williams RW, Mountz JD. Quantitative trait locus (QTL) mapping in aging systems. Methods in Molecular Biology (Clifton, NJ ). 2007; 371:321348. Hunter KW, Crawford NPS. The future of mouse QTL mapping to diagnose disease in mice in the age of whole-genome association studies. Annual Review of Genetics. 2008; 42:131141. Ito R, Robbins TW, Everitt BJ. Differential control over cocaine-seeking behavior by nucleus", - "multiscalar integration of traits. Cell150, 12871299 (2012). [PubMed: 22939713] 33. De Haan G & Van Zant G Genetic analysis of hemopoietic cell cycling in mice suggests its involvement in organismal life span. FASEB J. Off. Publ. Fed. Am. Soc. Exp. Biol. 13, 707713 (1999). 34. Gelman R, Watson A, Bronson R & Yunis E Murine chromosomal regions correlated with longevity. Genetics 118, 693704 (1988). [PubMed: 3163317] 35. Houtkooper RHet al.The metabolic footprint of aging in mice. Sci. Rep1, (2011).", - "mice to identify genetic factors involved in the regulation of cognitive aging that may have gone undetected in either complex human studies or murine studies utilizing only a single genetic background. Aging is a leading risk factor for age-associated de- mentias such as AD, and our work and others suggest that geneticfactors and mechanisms underlying biological processes during midlife play a key role in determining an individual s susceptibility", - "span and have yielded insights into potential biological pathways and processes related to aging. Despite these successes, several problems are inherent in human longevity studies including potentially high degrees ofenvironmental heterogeneity, genetic diversity, and lack of birth matched controls, among others [ 8]. Inbred mouse strains represent a powerful alternative for identifying genes underlying complex trait genes such as longevity [ 9]. Initial mapping approaches include quanti-", - "Recently, the Atlas of Gene Expression in Mouse Aging Project (AGEMAP) reported gene expression proles with age for 8932genes in 16 mouse tissues (Zahn et al ., 2007). We chose not to" - ], - "answer": "To generate a linkage or association mapping study in mice to understand aging, you can follow these steps: 1. **Select Appropriate Mouse Strains**: Choose inbred mouse strains that are known to have variations in lifespan and aging-related traits. Inbred strains are powerful for identifying genes underlying complex traits such as longevity due to their genetic uniformity [9]. 2. **Identify Aging-Related Traits**: Determine the specific aging-related traits you want to study. This could include longevity, resistance to age-associated diseases, or other biomarkers of aging [4]. 3. **Cross Mouse Strains**: Perform crosses between different inbred strains to generate F1 and F2 generations. This helps in creating genetic diversity necessary for mapping studies [5]. 4. **Phenotyping**: Assay the F2 generation (or other relevant generations) for the aging-related traits of interest. This could involve measuring lifespan, resistance to diseases, or other biomarkers [4]. 5. **Genotyping**: Genotype the mice using molecular markers such as SNPs (Single Nucleotide Polymorphisms) to identify genetic variations across the genome [6]. 6. **Quantitative Trait Locus (QTL) Mapping**: Use QTL mapping techniques to identify chromosomal regions associated with the aging-related traits. This involves statistical analysis to correlate genetic markers with phenotypic traits [6]. 7. **Association Studies**: Conduct association studies to further refine the regions identified by QTL mapping. This involves looking for specific polymorphisms within the identified regions that are significantly associated with the traits [1]. 8. **Replication and Validation**: Validate the findings by replicating the study in different mouse populations or using additional genetic crosses. This helps to confirm the association between the identified genetic regions and aging traits [1]. By following these steps, you can generate a linkage or association mapping study in mice to understand the genetic factors influencing aging.", - "question": "How do I generate a linkage or association mapping study in mice to understand aging?" -}{ - "titles": [ - "2001 - A genome-wide scan for linkage to human.pdf", - "2023 - A transcriptome-based single-cell biological age model.pdf", - "2015 - A Chromosome 13 locus is associated with male-specific mortality in mice.pdf", - "2007 - Longevity Genomics Across Species.pdf", - "2020 - Clinical Genetics and Genomics of Aging.pdf", - "2017 - Systems genetic analysis in GeneNetwork.org.pdf", - "2021 - Gene-by-environment modulation of lifespan and weight gain in the murine BXD family.pdf", - "2016 - Systems genetics identifies Hp1bp3 as a novel modulator of cognitive aging.pdf", - "2015 - A Chromosome 13 locus is associated with male-specific mortality in mice.pdf", - "2009 - Meta-analysis of age-related gene expression profiles identifies.pdf" - ], - "extraction_id": [ - "17246c43-2e44-579b-867d-3dc7150ceedd", - "0fd46f00-d3e1-54f4-9395-6c3e8294ed51", - "5cc56e3b-53ab-5299-814d-014e2ed31d2f", - "522e2616-daa1-5bf3-8673-a717dfb9b13f", - "5c3840bd-45a5-5928-84ab-a1f2d8536691", - "59121146-02b9-5479-96e2-9fb45cffc81b", - "396683f9-b2e3-5942-bec8-f96fa798c341", - "382122b9-6922-5d85-9e8c-acfa86aff085", - "df0b4be9-3393-5642-a722-ccafffb60df8", - "4d95f551-34bd-5e7a-8702-eb59de73a480" - ], - "document_id": [ - "1431984a-82d9-51d4-a23c-5f76a02ab554", - "9be234b7-f37d-5cd5-8895-bfe676441b2f", - "ad8f2626-87fb-520e-8cef-ee9a9cc3ab0b", - "1ab0b63f-d97c-5f5c-98ee-0bde785fa630", - "62b635c3-040e-512a-b016-6ef295308a1e", - "41be0f9f-a5af-5586-b6cd-16e56fd89cdc", - "4d082da4-fa48-5170-8147-c4fea47a5d4b", - "8cde78ac-cb0e-5983-86ee-91074b2fe1e3", - "ad8f2626-87fb-520e-8cef-ee9a9cc3ab0b", - "5c2cf97f-a57a-5284-85a3-b8d9c5943113" - ], - "id": [ - "chatcmpl-ABLwlxjoJ15UXMdKPBfDnYfvZNLDD", - "e0cce1c5-8709-5218-99b6-48a6ba242931", - "9f9fef49-0bda-5948-93bd-0f8f43bbefdf", - "09da6f9e-b996-5438-91be-41d9438cb930", - "ab0845d4-b4db-53db-927e-b96a52cf7667", - "c2299f0f-9e0b-5279-90e5-37c6bd664976", - "3004d1fd-c5ce-5587-bfab-471e7141952c", - "9082d164-59f8-58a0-ace7-8b3aa9d884e2", - "7abf14d2-cdfe-5c37-8217-6b63bd8fb255", - "380ca35e-b42b-59b4-aef7-aaf2ba3bb59d", - "eea576fd-d766-5ae7-9e63-045869a3f8f7" - ], - "contexts": [ - "effect fundamental mechanisms of aging (14, 16). The drawbacksof such studies include the improbability of picking the right geneto study the myriad of known and unknown genes affecting theprocess of interest (17). The linkage study described heremarkedly improves the efficiency of such association studies bydefining a region likely to contain polymorphism(s) with signif-icant influence on life span. Additional association studies with these families and repli-", - "Map contains 1119 and 1459 curated human and mouse aginggenes, respectively, covering almost all scales of aging, rangingfrom molecular damage to genetic predisposition. Cross-speciescomparison revealed a modest overlap between known humanand mouse aging genes, suggesting both conservation of core sen- escence pathways and fundamental differences in aging between mice and humans (Fig. 2E). Aging-associated genes can alternatively be identified in a", - "11. Gelman R, Watson A, Bronson R et al (1988) Murine chromo- somal regions correlated with longevity. Genetics 118(4):693704 12. Jackson AU, Galecki AT, Burke DT et al (2002) Mouse loci associated with life span exhibit sex-specic and epistatic effects. J Gerontol A Biol Sci Med Sci 57(1):B9B15 13. Foreman JE, Lionikas A, Lang DH et al (2009) Genetic archi- tecture for hole-board behaviors across substantial time intervalsin young, middle-aged and old mice. Genes Brain Behav", - "Along with longevity, a select group of potential aging-related biomarkers will be assayed for each of these mouse models. In addition, it should be possible to assay several of these mouse lines for resistance to specific age-associated diseases, such as diabetes and neurological disorders, by crossing them into the appropriate transgenic disease back- ground. CONCLUSION Our understanding of the basic mechanisms of aging have benefited greatly from the use of simple model systems", - "198 the study of age-related diseases for various reasons: (a) mice are closely related to humans, with nearly 99% of human orthologous in mice; (b) their relatively short lifespan and small size allow surveillance of the aging process within a pertinent time frame and make their housing less expensive; (c) the feasibility of performing genetic manipulations facilitates the engineering of transgenic strains (gain- and loss-of function mice) that model premature aging disorders. In this section, we", - "Hsu HC, Lu L, Yi N, Van Zant G, Williams RW, Mountz JD. Quantitative trait locus (QTL) mapping in aging systems. Methods in Molecular Biology (Clifton, NJ ). 2007; 371:321348. Hunter KW, Crawford NPS. The future of mouse QTL mapping to diagnose disease in mice in the age of whole-genome association studies. Annual Review of Genetics. 2008; 42:131141. Ito R, Robbins TW, Everitt BJ. Differential control over cocaine-seeking behavior by nucleus", - "multiscalar integration of traits. Cell150, 12871299 (2012). [PubMed: 22939713] 33. De Haan G & Van Zant G Genetic analysis of hemopoietic cell cycling in mice suggests its involvement in organismal life span. FASEB J. Off. Publ. Fed. Am. Soc. Exp. Biol. 13, 707713 (1999). 34. Gelman R, Watson A, Bronson R & Yunis E Murine chromosomal regions correlated with longevity. Genetics 118, 693704 (1988). [PubMed: 3163317] 35. Houtkooper RHet al.The metabolic footprint of aging in mice. Sci. Rep1, (2011).", - "mice to identify genetic factors involved in the regulation of cognitive aging that may have gone undetected in either complex human studies or murine studies utilizing only a single genetic background. Aging is a leading risk factor for age-associated de- mentias such as AD, and our work and others suggest that geneticfactors and mechanisms underlying biological processes during midlife play a key role in determining an individual s susceptibility", - "span and have yielded insights into potential biological pathways and processes related to aging. Despite these successes, several problems are inherent in human longevity studies including potentially high degrees ofenvironmental heterogeneity, genetic diversity, and lack of birth matched controls, among others [ 8]. Inbred mouse strains represent a powerful alternative for identifying genes underlying complex trait genes such as longevity [ 9]. Initial mapping approaches include quanti-", - "Recently, the Atlas of Gene Expression in Mouse Aging Project (AGEMAP) reported gene expression proles with age for 8932genes in 16 mouse tissues (Zahn et al ., 2007). We chose not to" - ], - "answer": "To generate a linkage or association mapping study in mice to understand aging, you can follow these steps: 1. **Select Appropriate Mouse Strains**: Choose inbred mouse strains that are known to have variations in lifespan and aging-related traits. Inbred strains are powerful for identifying genes underlying complex traits such as longevity due to their genetic uniformity [9]. 2. **Identify Aging-Related Traits**: Determine the specific aging-related traits you want to study. This could include longevity, resistance to age-associated diseases, or other biomarkers of aging [4]. 3. **Cross Mouse Strains**: Perform crosses between different inbred strains to generate F1 and F2 generations. This helps in creating genetic diversity necessary for mapping studies [5]. 4. **Phenotyping**: Assay the F2 generation (or other relevant generations) for the aging-related traits of interest. This could involve measuring lifespan, resistance to diseases, or other biomarkers [4]. 5. **Genotyping**: Genotype the mice using molecular markers such as SNPs (Single Nucleotide Polymorphisms) to identify genetic variations across the genome [6]. 6. **Quantitative Trait Locus (QTL) Mapping**: Use QTL mapping techniques to identify chromosomal regions associated with the aging-related traits. This involves statistical analysis to correlate genetic markers with phenotypic traits [6]. 7. **Association Studies**: Conduct association studies to further refine the regions identified by QTL mapping. This involves looking for specific polymorphisms within the identified regions that are significantly associated with the traits [1]. 8. **Replication and Validation**: Validate the findings by replicating the study in different mouse populations or using additional genetic crosses. This helps to confirm the association between the identified genetic regions and aging traits [1]. By following these steps, you can generate a linkage or association mapping study in mice to understand the genetic factors influencing aging.", - "question": "How do I generate a linkage or association mapping study in mice to understand aging?" -}{ - "titles": [ - "2001 - A genome-wide scan for linkage to human.pdf", - "2023 - A transcriptome-based single-cell biological age model.pdf", - "2015 - A Chromosome 13 locus is associated with male-specific mortality in mice.pdf", - "2007 - Longevity Genomics Across Species.pdf", - "2020 - Clinical Genetics and Genomics of Aging.pdf", - "2017 - Systems genetic analysis in GeneNetwork.org.pdf", - "2021 - Gene-by-environment modulation of lifespan and weight gain in the murine BXD family.pdf", - "2016 - Systems genetics identifies Hp1bp3 as a novel modulator of cognitive aging.pdf", - "2015 - A Chromosome 13 locus is associated with male-specific mortality in mice.pdf", - "2009 - Meta-analysis of age-related gene expression profiles identifies.pdf" - ], - "extraction_id": [ - "17246c43-2e44-579b-867d-3dc7150ceedd", - "0fd46f00-d3e1-54f4-9395-6c3e8294ed51", - "5cc56e3b-53ab-5299-814d-014e2ed31d2f", - "522e2616-daa1-5bf3-8673-a717dfb9b13f", - "5c3840bd-45a5-5928-84ab-a1f2d8536691", - "59121146-02b9-5479-96e2-9fb45cffc81b", - "396683f9-b2e3-5942-bec8-f96fa798c341", - "382122b9-6922-5d85-9e8c-acfa86aff085", - "df0b4be9-3393-5642-a722-ccafffb60df8", - "4d95f551-34bd-5e7a-8702-eb59de73a480" - ], - "document_id": [ - "1431984a-82d9-51d4-a23c-5f76a02ab554", - "9be234b7-f37d-5cd5-8895-bfe676441b2f", - "ad8f2626-87fb-520e-8cef-ee9a9cc3ab0b", - "1ab0b63f-d97c-5f5c-98ee-0bde785fa630", - "62b635c3-040e-512a-b016-6ef295308a1e", - "41be0f9f-a5af-5586-b6cd-16e56fd89cdc", - "4d082da4-fa48-5170-8147-c4fea47a5d4b", - "8cde78ac-cb0e-5983-86ee-91074b2fe1e3", - "ad8f2626-87fb-520e-8cef-ee9a9cc3ab0b", - "5c2cf97f-a57a-5284-85a3-b8d9c5943113" - ], - "id": [ - "chatcmpl-ABLwlxjoJ15UXMdKPBfDnYfvZNLDD", - "e0cce1c5-8709-5218-99b6-48a6ba242931", - "9f9fef49-0bda-5948-93bd-0f8f43bbefdf", - "09da6f9e-b996-5438-91be-41d9438cb930", - "ab0845d4-b4db-53db-927e-b96a52cf7667", - "c2299f0f-9e0b-5279-90e5-37c6bd664976", - "3004d1fd-c5ce-5587-bfab-471e7141952c", - "9082d164-59f8-58a0-ace7-8b3aa9d884e2", - "7abf14d2-cdfe-5c37-8217-6b63bd8fb255", - "380ca35e-b42b-59b4-aef7-aaf2ba3bb59d", - "eea576fd-d766-5ae7-9e63-045869a3f8f7" - ], - "contexts": [ - "effect fundamental mechanisms of aging (14, 16). The drawbacksof such studies include the improbability of picking the right geneto study the myriad of known and unknown genes affecting theprocess of interest (17). The linkage study described heremarkedly improves the efficiency of such association studies bydefining a region likely to contain polymorphism(s) with signif-icant influence on life span. Additional association studies with these families and repli-", - "Map contains 1119 and 1459 curated human and mouse aginggenes, respectively, covering almost all scales of aging, rangingfrom molecular damage to genetic predisposition. Cross-speciescomparison revealed a modest overlap between known humanand mouse aging genes, suggesting both conservation of core sen- escence pathways and fundamental differences in aging between mice and humans (Fig. 2E). Aging-associated genes can alternatively be identified in a", - "11. Gelman R, Watson A, Bronson R et al (1988) Murine chromo- somal regions correlated with longevity. Genetics 118(4):693704 12. Jackson AU, Galecki AT, Burke DT et al (2002) Mouse loci associated with life span exhibit sex-specic and epistatic effects. J Gerontol A Biol Sci Med Sci 57(1):B9B15 13. Foreman JE, Lionikas A, Lang DH et al (2009) Genetic archi- tecture for hole-board behaviors across substantial time intervalsin young, middle-aged and old mice. Genes Brain Behav", - "Along with longevity, a select group of potential aging-related biomarkers will be assayed for each of these mouse models. In addition, it should be possible to assay several of these mouse lines for resistance to specific age-associated diseases, such as diabetes and neurological disorders, by crossing them into the appropriate transgenic disease back- ground. CONCLUSION Our understanding of the basic mechanisms of aging have benefited greatly from the use of simple model systems", - "198 the study of age-related diseases for various reasons: (a) mice are closely related to humans, with nearly 99% of human orthologous in mice; (b) their relatively short lifespan and small size allow surveillance of the aging process within a pertinent time frame and make their housing less expensive; (c) the feasibility of performing genetic manipulations facilitates the engineering of transgenic strains (gain- and loss-of function mice) that model premature aging disorders. In this section, we", - "Hsu HC, Lu L, Yi N, Van Zant G, Williams RW, Mountz JD. Quantitative trait locus (QTL) mapping in aging systems. Methods in Molecular Biology (Clifton, NJ ). 2007; 371:321348. Hunter KW, Crawford NPS. The future of mouse QTL mapping to diagnose disease in mice in the age of whole-genome association studies. Annual Review of Genetics. 2008; 42:131141. Ito R, Robbins TW, Everitt BJ. Differential control over cocaine-seeking behavior by nucleus", - "multiscalar integration of traits. Cell150, 12871299 (2012). [PubMed: 22939713] 33. De Haan G & Van Zant G Genetic analysis of hemopoietic cell cycling in mice suggests its involvement in organismal life span. FASEB J. Off. Publ. Fed. Am. Soc. Exp. Biol. 13, 707713 (1999). 34. Gelman R, Watson A, Bronson R & Yunis E Murine chromosomal regions correlated with longevity. Genetics 118, 693704 (1988). [PubMed: 3163317] 35. Houtkooper RHet al.The metabolic footprint of aging in mice. Sci. Rep1, (2011).", - "mice to identify genetic factors involved in the regulation of cognitive aging that may have gone undetected in either complex human studies or murine studies utilizing only a single genetic background. Aging is a leading risk factor for age-associated de- mentias such as AD, and our work and others suggest that geneticfactors and mechanisms underlying biological processes during midlife play a key role in determining an individual s susceptibility", - "span and have yielded insights into potential biological pathways and processes related to aging. Despite these successes, several problems are inherent in human longevity studies including potentially high degrees ofenvironmental heterogeneity, genetic diversity, and lack of birth matched controls, among others [ 8]. Inbred mouse strains represent a powerful alternative for identifying genes underlying complex trait genes such as longevity [ 9]. Initial mapping approaches include quanti-", - "Recently, the Atlas of Gene Expression in Mouse Aging Project (AGEMAP) reported gene expression proles with age for 8932genes in 16 mouse tissues (Zahn et al ., 2007). We chose not to" - ], - "answer": "To generate a linkage or association mapping study in mice to understand aging, you can follow these steps: 1. **Select Appropriate Mouse Strains**: Choose inbred mouse strains that are known to have variations in lifespan and aging-related traits. Inbred strains are powerful for identifying genes underlying complex traits such as longevity due to their genetic uniformity [9]. 2. **Identify Aging-Related Traits**: Determine the specific aging-related traits you want to study. This could include longevity, resistance to age-associated diseases, or other biomarkers of aging [4]. 3. **Cross Mouse Strains**: Perform crosses between different inbred strains to generate F1 and F2 generations. This helps in creating genetic diversity necessary for mapping studies [5]. 4. **Phenotyping**: Assay the F2 generation (or other relevant generations) for the aging-related traits of interest. This could involve measuring lifespan, resistance to diseases, or other biomarkers [4]. 5. **Genotyping**: Genotype the mice using molecular markers such as SNPs (Single Nucleotide Polymorphisms) to identify genetic variations across the genome [6]. 6. **Quantitative Trait Locus (QTL) Mapping**: Use QTL mapping techniques to identify chromosomal regions associated with the aging-related traits. This involves statistical analysis to correlate genetic markers with phenotypic traits [6]. 7. **Association Studies**: Conduct association studies to further refine the regions identified by QTL mapping. This involves looking for specific polymorphisms within the identified regions that are significantly associated with the traits [1]. 8. **Replication and Validation**: Validate the findings by replicating the study in different mouse populations or using additional genetic crosses. This helps to confirm the association between the identified genetic regions and aging traits [1]. By following these steps, you can generate a linkage or association mapping study in mice to understand the genetic factors influencing aging.", - "question": "How do I generate a linkage or association mapping study in mice to understand aging?" -}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/domain_expert_aging_6 b/gnqa/paper2_eval/data/dataset/domain_expert_aging_6 deleted file mode 100644 index 455da0d..0000000 --- a/gnqa/paper2_eval/data/dataset/domain_expert_aging_6 +++ /dev/null @@ -1,193 +0,0 @@ -{ - "titles": [ - "2010 - A Meta-analysis of Four Genome-Wide Association Studies.pdf", - "2014 - Whole-Genome Sequencing of the World?s Oldest People.pdf", - "2011 - Genome-wide association study identifies a single major locus contributing to survival into old age the APOE locus revisited.pdf", - "2017 - Four Genome-Wide Association Studies Identify New.pdf", - "2012 - Whole genome sequences of a male and female supercentenarian, ages greater than 114 years.pdf", - "2013 - Genome Instability and Aging.pdf", - "2012 - Genome-wide miRNA signatures of human longevity.pdf", - "2012 - Whole genome sequences of a male and female supercentenarian, ages greater than 114 years.pdf", - "2011 - Genome-wide association study identifies a single major locus contributing to survival into old age the APOE locus revisited.pdf", - "2017 - Genome-wide meta-analysis associates HLA.pdf" - ], - "extraction_id": [ - "8bc54e5b-f45f-54f9-9591-1e26dd80b50d", - "c918522d-c0bf-5b7a-9ced-a69d485b2cb6", - "a4aa5d3a-81e8-582c-aee6-3ebdd329de86", - "b539194c-50bb-55e5-83b2-e779f63ed363", - "402ab5b5-e6fa-58fe-8f32-7c235be7a746", - "f33756b1-7d64-5ab9-bcd6-717deaf05339", - "e79b0811-a0f3-5f44-8004-89fe59aa8a3e", - "402ab5b5-e6fa-58fe-8f32-7c235be7a746", - "a4aa5d3a-81e8-582c-aee6-3ebdd329de86", - "9c6a9e93-5dc5-571d-b3c2-b600ed95e102" - ], - "document_id": [ - "8e452186-a71c-5b62-81b2-7681c87c8e1d", - "d2a5ec28-873a-5ff3-9cf4-dbec3b52dd21", - "05208abc-5ac0-5d4d-b600-2caf59ce75b7", - "c10653f6-b3d7-5b92-9271-ab8fcc7905a7", - "408cdcd5-ab70-520a-b2c4-d9028b0a8d6f", - "71e08916-8cc8-5d96-8c06-4461b972b54d", - "18407659-c241-5f37-8ad2-ab59f6a7e288", - "408cdcd5-ab70-520a-b2c4-d9028b0a8d6f", - "05208abc-5ac0-5d4d-b600-2caf59ce75b7", - "3a565ba9-ee5b-5596-b870-ce8c055cb1f1" - ], - "id": [ - "chatcmpl-ABLwzkPUEqxCEqW5L5wugbbowvYPv", - "c2234f77-2268-57d0-a227-e931fc4802c1", - "fb0af8f1-5b2a-5ba1-8a53-ee543a9267bf", - "754929a6-af78-569a-969c-e750d174b952", - "4a6d2b9b-9496-5d90-a24a-43c643c4916b", - "1f4437a7-cee1-5dc2-80e1-9924248857d0", - "91010ff1-43a7-53f6-966d-601913e3b26b", - "63ebd662-9aca-5b8a-b3e3-89860a45da42", - "53a8e33f-da6f-5550-bf18-e45f2779f7a9", - "57227bee-d562-52c9-86dc-f9e2fcea1792", - "b1b9f731-236c-5b4b-8cc6-fcf1e06d866a" - ], - "contexts": [ - "GENOME-WIDE ASSOCIATION STUDY OF LONGEVITY 479 INCREASES in longevity of the general population world - wide are an unprecedented phenomenon with significant health and social impact. Although environmental factors have led to an increase in life span, there is ample evidence that genetic factors are involved in extreme longevity both in humans (17) and in other organisms (8). The protective genetic factors that lead to longevity are likely to involve", - "that any genetic variant that contributes strongly to extremelongevity would also be rare. One possibility is that a specificmutation could alter the protein-coding region in a gene andconfer a significant increase in longevity. Such a mutation couldact in a dominant or recessive fashion, and might be shared by asignificant fraction of the supercentenarian genomes but not bycontrol genomes. We created a computational pipeline todetermine whether our supercentenarian genomes are enrichedfor such a variant", - "ever, natural human and animal longevity is presumed to be acomplex trait (Finch & Tanzi, 1997). In humans, both candidategene and genome-wide genetic association approaches havebeen applied in an attempt to identify longevity loci. The fre-quency of genetic variants has been typically compared between nonagenarian cases and young controls, revealing", - "genetic makeup of extreme longevity is based on a combination of common and rare variants, with common vari-ants that create the background to survive to relatively common old ages, and specific combinations of uncommon and rare variants that add an additional survival advantage to even older ages. Our analy-sis showed that LAVs discovered through a casecontrol study are not necessarily the variants that make someone live to extreme old age, and additional survival analysis is needed to characterize and", - "genetic determination of human exceptional longevity, they arethe rst step toward the generation of a comprehensive referencepanel of exceptionally long-lived individuals. The data also provideinteresting insights into genetic backgrounds that are conduciveto exceptional longevity and allow us to test different models of exceptional longevity. www.frontiersin.org January 2012 | Volume 2 | Article 90 | 1", - "tremely long lived individuals. Longevity has a genetic component, with an estimated heritability of average life expectancy of approximately 25% (105, 106). Family studies of centenarians, thosewho live to 100 years or more, suggest that the relationship between genetics and longevity is stronger in the oldest-old adults (107, 108), supporting the utility of long-lived individuals as a model system for studying genetic variations that predispose people to longevity.", - "because of genetic variation that becomes particularly important for sur- vival at advanced age (Hjelmborg et al. , 2006). Epidemiological studies have revealed that long-lived individuals (LLI), that is, people surviving to the 95th percentile of the respective birth cohort-specic age distribu- tions (Gudmundsson et al. , 2000), frequently show a favorable (healthy) course of the aging process, with the absence or a delayed onset of age-", - "Studies of centenarians have provided strong evidence to sup-port the hypothesis that a genetic contribution to human excep-tional longevity is decisive, although only a small number ofgenetic variants with modest effects have been irrefutably linkedto this phenotype ( Schachter et al., 1994; Barzilai et al., 2003 ; Christensen et al., 2006 ;Wheeler and Kim, 2011 ). The tech- nology of next generation sequencing provides a tool to gen-erate data that may eventually provide an answer ( Metzker, 2009).", - "genetic contribution to human lifespan variation was estimated at 2530% in twin studies (Gudmundsson et al. , 2000; Skytthe et al. , 2003; Hjelmborg et al. , 2006). The most prominent genetic inuence is observed in families in which the capacity toattain a long lifespan clusters (Perls et al. , 2000; Schoenmaker et al. , 2006). Exceptional longevity can be reached with a low degree of age-related disability (Christensen et al. , 2008; Terry et al. , 2008), raising the question whether protective mecha-", - "age, usually de ned by a threshold, such as 90 years). Up to 25% of the variability in human lifespan has been estimated to be genetic1, but genetic variation at only three loci (near APOE , FOXO3A and CHRNA3/5 )25have so far been demonstrated to be robustly associated with lifespan. Prospective genomic studies of lifespan have been hampered by the fact that subject participation is often only recent, allowing insuf cient follow-up time for a well-powered analysis of" - ], - "answer": "Yes, there are specific genetic variations that can contribute to someone living longer. Research has shown that certain genes and their variations can influence longevity. For example, variations near the genes APOE, FOXO3A, and CHRNA3/5 have been robustly associated with lifespan [10]. These genetic variations can affect how our bodies age and how well we can avoid or delay age-related diseases. In simpler terms, think of these genes as parts of a car. Just like how certain parts can make a car run more efficiently and last longer, these genetic variations can help our bodies function better and live longer. However, it's important to note that living a long life is usually a combination of both genetic factors and lifestyle choices, such as diet, exercise, and avoiding harmful habits.", - "question": "is there a specific genetic variation that can cause someone to live longer? please make your answer accessible to a non-expert" -}{ - "titles": [ - "2010 - A Meta-analysis of Four Genome-Wide Association Studies.pdf", - "2014 - Whole-Genome Sequencing of the World?s Oldest People.pdf", - "2011 - Genome-wide association study identifies a single major locus contributing to survival into old age the APOE locus revisited.pdf", - "2017 - Four Genome-Wide Association Studies Identify New.pdf", - "2012 - Whole genome sequences of a male and female supercentenarian, ages greater than 114 years.pdf", - "2013 - Genome Instability and Aging.pdf", - "2012 - Genome-wide miRNA signatures of human longevity.pdf", - "2012 - Whole genome sequences of a male and female supercentenarian, ages greater than 114 years.pdf", - "2011 - Genome-wide association study identifies a single major locus contributing to survival into old age the APOE locus revisited.pdf", - "2017 - Genome-wide meta-analysis associates HLA.pdf" - ], - "extraction_id": [ - "8bc54e5b-f45f-54f9-9591-1e26dd80b50d", - "c918522d-c0bf-5b7a-9ced-a69d485b2cb6", - "a4aa5d3a-81e8-582c-aee6-3ebdd329de86", - "b539194c-50bb-55e5-83b2-e779f63ed363", - "402ab5b5-e6fa-58fe-8f32-7c235be7a746", - "f33756b1-7d64-5ab9-bcd6-717deaf05339", - "e79b0811-a0f3-5f44-8004-89fe59aa8a3e", - "402ab5b5-e6fa-58fe-8f32-7c235be7a746", - "a4aa5d3a-81e8-582c-aee6-3ebdd329de86", - "9c6a9e93-5dc5-571d-b3c2-b600ed95e102" - ], - "document_id": [ - "8e452186-a71c-5b62-81b2-7681c87c8e1d", - "d2a5ec28-873a-5ff3-9cf4-dbec3b52dd21", - "05208abc-5ac0-5d4d-b600-2caf59ce75b7", - "c10653f6-b3d7-5b92-9271-ab8fcc7905a7", - "408cdcd5-ab70-520a-b2c4-d9028b0a8d6f", - "71e08916-8cc8-5d96-8c06-4461b972b54d", - "18407659-c241-5f37-8ad2-ab59f6a7e288", - "408cdcd5-ab70-520a-b2c4-d9028b0a8d6f", - "05208abc-5ac0-5d4d-b600-2caf59ce75b7", - "3a565ba9-ee5b-5596-b870-ce8c055cb1f1" - ], - "id": [ - "chatcmpl-ABLwzkPUEqxCEqW5L5wugbbowvYPv", - "c2234f77-2268-57d0-a227-e931fc4802c1", - "fb0af8f1-5b2a-5ba1-8a53-ee543a9267bf", - "754929a6-af78-569a-969c-e750d174b952", - "4a6d2b9b-9496-5d90-a24a-43c643c4916b", - "1f4437a7-cee1-5dc2-80e1-9924248857d0", - "91010ff1-43a7-53f6-966d-601913e3b26b", - "63ebd662-9aca-5b8a-b3e3-89860a45da42", - "53a8e33f-da6f-5550-bf18-e45f2779f7a9", - "57227bee-d562-52c9-86dc-f9e2fcea1792", - "b1b9f731-236c-5b4b-8cc6-fcf1e06d866a" - ], - "contexts": [ - "GENOME-WIDE ASSOCIATION STUDY OF LONGEVITY 479 INCREASES in longevity of the general population world - wide are an unprecedented phenomenon with significant health and social impact. Although environmental factors have led to an increase in life span, there is ample evidence that genetic factors are involved in extreme longevity both in humans (17) and in other organisms (8). The protective genetic factors that lead to longevity are likely to involve", - "that any genetic variant that contributes strongly to extremelongevity would also be rare. One possibility is that a specificmutation could alter the protein-coding region in a gene andconfer a significant increase in longevity. Such a mutation couldact in a dominant or recessive fashion, and might be shared by asignificant fraction of the supercentenarian genomes but not bycontrol genomes. We created a computational pipeline todetermine whether our supercentenarian genomes are enrichedfor such a variant", - "ever, natural human and animal longevity is presumed to be acomplex trait (Finch & Tanzi, 1997). In humans, both candidategene and genome-wide genetic association approaches havebeen applied in an attempt to identify longevity loci. The fre-quency of genetic variants has been typically compared between nonagenarian cases and young controls, revealing", - "genetic makeup of extreme longevity is based on a combination of common and rare variants, with common vari-ants that create the background to survive to relatively common old ages, and specific combinations of uncommon and rare variants that add an additional survival advantage to even older ages. Our analy-sis showed that LAVs discovered through a casecontrol study are not necessarily the variants that make someone live to extreme old age, and additional survival analysis is needed to characterize and", - "genetic determination of human exceptional longevity, they arethe rst step toward the generation of a comprehensive referencepanel of exceptionally long-lived individuals. The data also provideinteresting insights into genetic backgrounds that are conduciveto exceptional longevity and allow us to test different models of exceptional longevity. www.frontiersin.org January 2012 | Volume 2 | Article 90 | 1", - "tremely long lived individuals. Longevity has a genetic component, with an estimated heritability of average life expectancy of approximately 25% (105, 106). Family studies of centenarians, thosewho live to 100 years or more, suggest that the relationship between genetics and longevity is stronger in the oldest-old adults (107, 108), supporting the utility of long-lived individuals as a model system for studying genetic variations that predispose people to longevity.", - "because of genetic variation that becomes particularly important for sur- vival at advanced age (Hjelmborg et al. , 2006). Epidemiological studies have revealed that long-lived individuals (LLI), that is, people surviving to the 95th percentile of the respective birth cohort-specic age distribu- tions (Gudmundsson et al. , 2000), frequently show a favorable (healthy) course of the aging process, with the absence or a delayed onset of age-", - "Studies of centenarians have provided strong evidence to sup-port the hypothesis that a genetic contribution to human excep-tional longevity is decisive, although only a small number ofgenetic variants with modest effects have been irrefutably linkedto this phenotype ( Schachter et al., 1994; Barzilai et al., 2003 ; Christensen et al., 2006 ;Wheeler and Kim, 2011 ). The tech- nology of next generation sequencing provides a tool to gen-erate data that may eventually provide an answer ( Metzker, 2009).", - "genetic contribution to human lifespan variation was estimated at 2530% in twin studies (Gudmundsson et al. , 2000; Skytthe et al. , 2003; Hjelmborg et al. , 2006). The most prominent genetic inuence is observed in families in which the capacity toattain a long lifespan clusters (Perls et al. , 2000; Schoenmaker et al. , 2006). Exceptional longevity can be reached with a low degree of age-related disability (Christensen et al. , 2008; Terry et al. , 2008), raising the question whether protective mecha-", - "age, usually de ned by a threshold, such as 90 years). Up to 25% of the variability in human lifespan has been estimated to be genetic1, but genetic variation at only three loci (near APOE , FOXO3A and CHRNA3/5 )25have so far been demonstrated to be robustly associated with lifespan. Prospective genomic studies of lifespan have been hampered by the fact that subject participation is often only recent, allowing insuf cient follow-up time for a well-powered analysis of" - ], - "answer": "Yes, there are specific genetic variations that can contribute to someone living longer. Research has shown that certain genes and their variations can influence longevity. For example, variations near the genes APOE, FOXO3A, and CHRNA3/5 have been robustly associated with lifespan [10]. These genetic variations can affect how our bodies age and how well we can avoid or delay age-related diseases. In simpler terms, think of these genes as parts of a car. Just like how certain parts can make a car run more efficiently and last longer, these genetic variations can help our bodies function better and live longer. However, it's important to note that living a long life is usually a combination of both genetic factors and lifestyle choices, such as diet, exercise, and avoiding harmful habits.", - "question": "is there a specific genetic variation that can cause someone to live longer? please make your answer accessible to a non-expert" -}{ - "titles": [ - "2010 - A Meta-analysis of Four Genome-Wide Association Studies.pdf", - "2014 - Whole-Genome Sequencing of the World?s Oldest People.pdf", - "2011 - Genome-wide association study identifies a single major locus contributing to survival into old age the APOE locus revisited.pdf", - "2017 - Four Genome-Wide Association Studies Identify New.pdf", - "2012 - Whole genome sequences of a male and female supercentenarian, ages greater than 114 years.pdf", - "2013 - Genome Instability and Aging.pdf", - "2012 - Genome-wide miRNA signatures of human longevity.pdf", - "2012 - Whole genome sequences of a male and female supercentenarian, ages greater than 114 years.pdf", - "2011 - Genome-wide association study identifies a single major locus contributing to survival into old age the APOE locus revisited.pdf", - "2017 - Genome-wide meta-analysis associates HLA.pdf" - ], - "extraction_id": [ - "8bc54e5b-f45f-54f9-9591-1e26dd80b50d", - "c918522d-c0bf-5b7a-9ced-a69d485b2cb6", - "a4aa5d3a-81e8-582c-aee6-3ebdd329de86", - "b539194c-50bb-55e5-83b2-e779f63ed363", - "402ab5b5-e6fa-58fe-8f32-7c235be7a746", - "f33756b1-7d64-5ab9-bcd6-717deaf05339", - "e79b0811-a0f3-5f44-8004-89fe59aa8a3e", - "402ab5b5-e6fa-58fe-8f32-7c235be7a746", - "a4aa5d3a-81e8-582c-aee6-3ebdd329de86", - "9c6a9e93-5dc5-571d-b3c2-b600ed95e102" - ], - "document_id": [ - "8e452186-a71c-5b62-81b2-7681c87c8e1d", - "d2a5ec28-873a-5ff3-9cf4-dbec3b52dd21", - "05208abc-5ac0-5d4d-b600-2caf59ce75b7", - "c10653f6-b3d7-5b92-9271-ab8fcc7905a7", - "408cdcd5-ab70-520a-b2c4-d9028b0a8d6f", - "71e08916-8cc8-5d96-8c06-4461b972b54d", - "18407659-c241-5f37-8ad2-ab59f6a7e288", - "408cdcd5-ab70-520a-b2c4-d9028b0a8d6f", - "05208abc-5ac0-5d4d-b600-2caf59ce75b7", - "3a565ba9-ee5b-5596-b870-ce8c055cb1f1" - ], - "id": [ - "chatcmpl-ABLwzkPUEqxCEqW5L5wugbbowvYPv", - "c2234f77-2268-57d0-a227-e931fc4802c1", - "fb0af8f1-5b2a-5ba1-8a53-ee543a9267bf", - "754929a6-af78-569a-969c-e750d174b952", - "4a6d2b9b-9496-5d90-a24a-43c643c4916b", - "1f4437a7-cee1-5dc2-80e1-9924248857d0", - "91010ff1-43a7-53f6-966d-601913e3b26b", - "63ebd662-9aca-5b8a-b3e3-89860a45da42", - "53a8e33f-da6f-5550-bf18-e45f2779f7a9", - "57227bee-d562-52c9-86dc-f9e2fcea1792", - "b1b9f731-236c-5b4b-8cc6-fcf1e06d866a" - ], - "contexts": [ - "GENOME-WIDE ASSOCIATION STUDY OF LONGEVITY 479 INCREASES in longevity of the general population world - wide are an unprecedented phenomenon with significant health and social impact. Although environmental factors have led to an increase in life span, there is ample evidence that genetic factors are involved in extreme longevity both in humans (17) and in other organisms (8). The protective genetic factors that lead to longevity are likely to involve", - "that any genetic variant that contributes strongly to extremelongevity would also be rare. One possibility is that a specificmutation could alter the protein-coding region in a gene andconfer a significant increase in longevity. Such a mutation couldact in a dominant or recessive fashion, and might be shared by asignificant fraction of the supercentenarian genomes but not bycontrol genomes. We created a computational pipeline todetermine whether our supercentenarian genomes are enrichedfor such a variant", - "ever, natural human and animal longevity is presumed to be acomplex trait (Finch & Tanzi, 1997). In humans, both candidategene and genome-wide genetic association approaches havebeen applied in an attempt to identify longevity loci. The fre-quency of genetic variants has been typically compared between nonagenarian cases and young controls, revealing", - "genetic makeup of extreme longevity is based on a combination of common and rare variants, with common vari-ants that create the background to survive to relatively common old ages, and specific combinations of uncommon and rare variants that add an additional survival advantage to even older ages. Our analy-sis showed that LAVs discovered through a casecontrol study are not necessarily the variants that make someone live to extreme old age, and additional survival analysis is needed to characterize and", - "genetic determination of human exceptional longevity, they arethe rst step toward the generation of a comprehensive referencepanel of exceptionally long-lived individuals. The data also provideinteresting insights into genetic backgrounds that are conduciveto exceptional longevity and allow us to test different models of exceptional longevity. www.frontiersin.org January 2012 | Volume 2 | Article 90 | 1", - "tremely long lived individuals. Longevity has a genetic component, with an estimated heritability of average life expectancy of approximately 25% (105, 106). Family studies of centenarians, thosewho live to 100 years or more, suggest that the relationship between genetics and longevity is stronger in the oldest-old adults (107, 108), supporting the utility of long-lived individuals as a model system for studying genetic variations that predispose people to longevity.", - "because of genetic variation that becomes particularly important for sur- vival at advanced age (Hjelmborg et al. , 2006). Epidemiological studies have revealed that long-lived individuals (LLI), that is, people surviving to the 95th percentile of the respective birth cohort-specic age distribu- tions (Gudmundsson et al. , 2000), frequently show a favorable (healthy) course of the aging process, with the absence or a delayed onset of age-", - "Studies of centenarians have provided strong evidence to sup-port the hypothesis that a genetic contribution to human excep-tional longevity is decisive, although only a small number ofgenetic variants with modest effects have been irrefutably linkedto this phenotype ( Schachter et al., 1994; Barzilai et al., 2003 ; Christensen et al., 2006 ;Wheeler and Kim, 2011 ). The tech- nology of next generation sequencing provides a tool to gen-erate data that may eventually provide an answer ( Metzker, 2009).", - "genetic contribution to human lifespan variation was estimated at 2530% in twin studies (Gudmundsson et al. , 2000; Skytthe et al. , 2003; Hjelmborg et al. , 2006). The most prominent genetic inuence is observed in families in which the capacity toattain a long lifespan clusters (Perls et al. , 2000; Schoenmaker et al. , 2006). Exceptional longevity can be reached with a low degree of age-related disability (Christensen et al. , 2008; Terry et al. , 2008), raising the question whether protective mecha-", - "age, usually de ned by a threshold, such as 90 years). Up to 25% of the variability in human lifespan has been estimated to be genetic1, but genetic variation at only three loci (near APOE , FOXO3A and CHRNA3/5 )25have so far been demonstrated to be robustly associated with lifespan. Prospective genomic studies of lifespan have been hampered by the fact that subject participation is often only recent, allowing insuf cient follow-up time for a well-powered analysis of" - ], - "answer": "Yes, there are specific genetic variations that can contribute to someone living longer. Research has shown that certain genes and their variations can influence longevity. For example, variations near the genes APOE, FOXO3A, and CHRNA3/5 have been robustly associated with lifespan [10]. These genetic variations can affect how our bodies age and how well we can avoid or delay age-related diseases. In simpler terms, think of these genes as parts of a car. Just like how certain parts can make a car run more efficiently and last longer, these genetic variations can help our bodies function better and live longer. However, it's important to note that living a long life is usually a combination of both genetic factors and lifestyle choices, such as diet, exercise, and avoiding harmful habits.", - "question": "is there a specific genetic variation that can cause someone to live longer? please make your answer accessible to a non-expert" -}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/gpt4o_cs_aging.json b/gnqa/paper2_eval/data/dataset/gpt4o/gpt4o_cs_aging.json new file mode 100644 index 0000000..50bedda --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/gpt4o_cs_aging.json @@ -0,0 +1,289 @@ +{ + "question": [ + "What are the main genetic factors that influence aging?", + "How do genes affect the aging process in humans?", + "What lifestyle choices can help slow down genetic aging?", + "How do scientists study the genetics of aging in animals?", + "Are there specific genes that have been linked to longer lifespans?", + "How do telomeres affect the aging process?", + "What role does DNA repair play in aging?", + "Can genetic research lead to treatments that slow down aging?", + "How does mitochondrial DNA influence aging?", + "Are there any known genetic mutations that cause premature aging?", + "What recent discoveries have been made about the genetics of aging?", + "How do epigenetic changes affect aging?", + "What is the role of the gene FOXO3 in longevity?", + "How does the environment interact with genes to influence aging?", + "What are senescent cells and how do they contribute to aging?", + "Are there any known lifestyle interventions that can positively impact genes related to aging?", + "What is the 'epigenetic clock,' and how is it used in aging research?", + "How do researchers use model organisms like yeast or worms to study human aging?", + "Are there any promising anti-aging therapies being developed based on genetic research?", + "How do caloric restriction and diet impact the genetics of aging?" + ], + "answer": [ + "The main genetic factors that influence aging include: 1. **Genomic Instability**: Aging is associated with the accumulation of DNA damage and the dysregulation of repair mechanisms, leading to genomic instability [9]. 2. **Telomere Attrition**: The shortening of telomeres, which are protective caps at the ends of chromosomes, is a significant factor in aging [9], [10]. 3. **Epigenetic Alterations**: Changes in epigenetic marks, which regulate gene expression without altering the DNA sequence, play a crucial role in aging [2], [4], [9], [10]. 4. **Deregulated Nutrient Sensing**: The pathways that sense and respond to nutrients become deregulated with age, affecting longevity [10]. 5. **Mitochondrial Dysfunction**: Mitochondria, the energy-producing organelles in cells, become less efficient with age, contributing to the aging process [10]. 6. **Cellular Senescence**: The process by which cells lose the ability to divide and function properly is a hallmark of aging [9], [10]. 7. **Loss of Proteostasis**: The ability of cells to maintain protein homeostasis declines with age, leading to the accumulation of damaged proteins [9], [10]. 8. **Stem Cell Exhaustion**: The decline in the regenerative capacity of stem cells contributes to aging [10]. These factors collectively shape the complex genetic landscape of aging, influencing the expression of aging phenotypes and lifespan [7].", + "Genes affect the aging process in humans through complex interactions and pathways. Research has shown that single genes can regulate aging in model organisms, indicating that aging can be genetically manipulated [2]. Hundreds of genes have been identified that modulate longevity in these organisms, and some of these genes and their associated pathways, such as the insulin/IGF1/GH pathway, have been shown to affect longevity across different species, suggesting that some mechanisms of aging are evolutionarily conserved [3]. In humans, it has been more challenging to identify specific longevity candidate genes, but studies have shown that certain genes associated with aging in model organisms are evolutionarily conserved and may be relevant to human aging [5]. Additionally, approximately 4% of genes analyzed in a study of postmortem human brain tissue showed significant age-related expression changes, indicating that these genes play central roles in processes like synaptic plasticity, vesicular transport, and mitochondrial function [8]. Overall, while aging is a complex process involving multiple genes and their interactions with the environment, genetic studies in model organisms provide insights that may be applicable to understanding human aging [9].", + "To slow down genetic aging, several lifestyle choices can be beneficial: 1. **Dietary Restriction (DR)**: Reducing food intake without causing malnutrition has been shown to extend lifespan in various organisms. This approach improves healthspan by influencing nutrient-sensing signaling networks and metabolism [1]. 2. **Healthy Diet and Physical Exercise**: Engaging in regular physical activity and maintaining a healthy diet can slow down aging and prevent or modify many chronic diseases prevalent in older adults. This contributes to successful aging, allowing individuals to achieve physical, social, and mental well-being [4]. 3. **Caloric Restriction**: This is considered a reasonable anti-aging intervention, as it has been shown to slow aging in multiple species, including yeast, nematodes, fruit flies, and rodents [6]. 4. **Exercise**: While exercise may not retard aging in all tissues, it can contribute to molecular-level aging retardation, which is beneficial for overall health and longevity [10]. These lifestyle choices, when combined, can help mitigate the effects of genetic aging and promote a longer, healthier life.", + "Scientists study the genetics of aging in animals using a variety of approaches and model organisms. Here are some key methods and models mentioned in the context: 1. **Animal Model Systems**: Rodents, such as rats and mice, are indispensable for molecular biological studies on aging due to their practical advantages and similarities in aging processes to humans [1]. 2. **Tissue-Specific Studies**: Recent genomic studies have been performed on specialized mammalian tissues, both post-mitotic (e.g., heart, nervous system) and mitotic (e.g., liver), to understand the tissue-specific effects of aging. These studies also explore how caloric restriction affects age-related transcriptional changes, which are tissue- or species-specific [2]. 3. **Laboratory Models**: Common laboratory models for studying aging include Caenorhabditis elegans (nematode worm) and Mus musculus (mice), which have shorter lifespans than humans, allowing for more rapid observation of aging processes [5]. 4. **Transgenic Mouse Strains and Interventional Studies**: These studies have identified evolutionarily conserved pathways involved in lifespan regulation and common denominators of aging across different organisms [6]. These methods and models help scientists uncover the genetic and molecular mechanisms underlying aging, providing insights that can be applied to understanding human aging and developing potential interventions.", + "Yes, specific genes have been linked to longer lifespans. Research has identified more than 700 genes that regulate lifespan in model organisms, with many of these genes and their associated pathways, such as the insulin/IGF1/GH pathway, shown to affect longevity across different model organisms [2]. Additionally, variants near genes such as CHRNA3/5 and APOE have been found to have age- and sex-related effects on human lifespan [7]. Genome-wide association studies have also identified loci associated with exceptional human longevity [10].", + "Telomeres affect the aging process in several ways: 1. **Telomere Shortening**: Telomeres are specialized structures that protect the ends of linear chromosomes. They shorten during aging due to the unidirectional activity of DNA polymerase, which leaves a section of DNA unreplicated on the lagging strand [1]. This shortening is a natural part of the aging process and is exacerbated by genotoxic stress, such as oxidative damage [1]. 2. **Cellular Senescence and Apoptosis**: The shortening of telomeres can induce processes such as apoptosis (programmed cell death) and cellular senescence (a state where cells stop dividing), which affect the health and lifespan of an individual [2]. When telomeres become critically short, they trigger a DNA damage response, leading to cellular senescence or apoptosis [4]. 3. **Genome Stability**: Telomeres ensure the stability of the genome and protect chromosomes from incorrect actions by the DNA repair machinery [3]. When telomeres are too short, they can no longer form protective structures, leading to genome instability and potentially contributing to aging [8]. 4. **Cancer Prevention**: Short telomeres limit the number of cell cycles, which is important for preventing the onset of cancer. However, this also contributes to the aging process as cells enter a state of permanent cell cycle arrest (senescence) [7]. 5. **Telomerase Activity**: The enzyme telomerase can maintain telomere length, but its activity varies over the lifespan and between cell types, tissues, and species [1]. In most human somatic cells, telomerase activity is limited, which contributes to telomere shortening and aging [4]. Overall, telomere shortening acts as a biological clock that limits cellular replication, contributing to aging and age-related diseases [6].", + "DNA repair plays a significant role in aging by maintaining the integrity and stability of the nuclear genome. Impairment of DNA repair mechanisms can result in accelerated aging and/or cancer [2]. As organisms age, endogenous sources of genotoxins increase, DNA repair capacity declines, and levels of DNA damage and mutations increase [2]. This accumulation of DNA damage is associated with aging phenotypes, as DNA damage can activate cellular responses that contribute to aging [6]. The DNA damage theory of aging suggests that genomic instability, caused by accumulated DNA damage, plays a causal role in aging [5]. Additionally, the burden of DNA lesions is greater in older mammals compared to younger ones, indicating that DNA repair is crucial for mitigating the effects of aging [5].", + "Yes, genetic research can potentially lead to treatments that slow down aging. Several pieces of evidence from the context support this possibility: 1. The discovery of genetic markers for slow aging in humans suggests that understanding these genes could pave the way for therapeutic interventions for age-related maladies, including cancers, neurodegeneration, and metabolic syndrome [4]. 2. Research indicates that manipulating aging-related genes through various means, such as diet, lifestyle, and pharmaceuticals, could dramatically improve human health and lead to the development of drugs against age-related diseases [7]. 3. Advances in molecular biology, such as CRISPR/Cas9, are expected to clarify aging processes and identify new potential therapeutic targets, which could be crucial for developing treatments that slow aging [6]. 4. The use of senolytic drugs, which target senescent cells, has shown promise in halting biological aging in mice, and trials are underway to test their effectiveness in humans [3]. 5. There is a suggestion that interventions targeting DNA methylation and other genetic modifications could prevent age-related diseases and promote longevity, highlighting the potential of genetic research in developing therapeutic strategies against aging [10]. Overall, while the research is still ongoing and some findings are speculative, there is significant potential for genetic research to contribute to treatments that slow down the aging process.", + "Mitochondrial DNA (mtDNA) influences aging through several mechanisms: 1. **Oxidative Damage**: Mitochondria are crucial for energy production and are highly susceptible to oxidative damage. The accumulation of oxidative lesions in mtDNA is a significant source of age-related damage [1]. 2. **Mutations and Lifespan**: Mutations in mtDNA can reduce lifespan. These mutations can aggravate aging and impair brain development, indicating a direct link between mtDNA mutations and the aging process [2]. 3. **Mitochondrial Dysfunction**: Aging is associated with mtDNA mutations, which contribute to mitochondrial dysfunction. This dysfunction is linked to age-related diseases and metabolic disorders, further influencing lifespan [4]. 4. **Genetic Instability**: The mutation rate for mtDNA is significantly higher than for nuclear DNA. These mutations can compromise mitochondrial functions, such as electron transport and oxidative phosphorylation, leading to declines in ATP levels and increased production of reactive oxygen species, which further damage both nuclear and mitochondrial DNA [9]. Overall, mtDNA influences aging by accumulating mutations and oxidative damage, leading to mitochondrial dysfunction and contributing to age-related physiological decline.", + "Yes, there are known genetic mutations that cause premature aging. Specifically, mutations in the LMNA gene are associated with Hutchinson-Gilford Progeria Syndrome, a disease characterized by premature aging [4]. Additionally, mutations in the RECQL4 gene are linked to Rothmund-Thomson syndrome, which also involves premature aging [4]. These genetic disorders highlight the connection between genome integrity and premature aging [7].", + "Recent discoveries in the genetics of aging include the identification of a number of genes capable of altering the aging process significantly in animal models and even in some humans [2]. Additionally, recent efforts have focused on isolating aging mutants through mutagenesis experiments to determine the mechanistic basis for unusual life spans, leading to the discovery of genes that can either enhance or reduce life span [4]. These findings contribute to a growing understanding of the genetic factors influencing aging and longevity.", + "Epigenetic changes affect aging through several mechanisms: 1. **Alterations in Chromatin Structure**: During aging, there are various epigenetic alterations such as the accumulation of histone variants, changes in chromatin accessibility, loss of histones and heterochromatin, and imbalances in activating/repressing histone modifications. These changes can affect transcription and translation processes, impacting cellular function [1]. 2. **Epigenetic Drift**: As individuals age, epigenetic changes accumulate, leading to a phenomenon known as epigenetic drift. This drift results in the cumulative loss of gene regulation over time, impairing cellular and tissue function. It is suggested that this disruption may also play a role in the development of age-related diseases, such as cancer [3]. 3. **DNA Damage and Chromatin Remodeling**: It is suggested that epigenetic alterations during aging are largely triggered by DNA damage. This damage leads to chromatin remodeling and redistribution of chromatin modifiers, which are recruited away from their normal sites to engage in DNA repair [4]. 4. **Increased Expression of Pro-aging Genes**: Epigenetic dysregulation can lead to increased expression of pro-aging genes, such as the cell-cycle inhibitor p16, which drives cell senescence. This contributes to increased transcriptional noise and decreased coordination of gene expression, further contributing to organismal aging [10]. 5. **Potential for Reversibility**: Despite these changes, there is potential reversibility in epigenetic modifications, offering opportunities to alter the trajectory of age-related diseases. This highlights the plasticity of aging and the potential for interventions that could slow down the aging process [7]. Overall, epigenetic changes play a crucial role in the aging process by affecting gene expression, cellular function, and the development of age-related diseases.", + "The gene FOXO3 plays a significant role in human longevity. Multiple studies have shown a strong association between variations in the FOXO3 gene and increased lifespan. For instance, the FOXO3A genotype has been strongly linked with human longevity, as demonstrated in studies by Willcox et al. (2008) and confirmed in various populations, including German and Southern Italian centenarians [1], [2], [3]. The FOXO3 locus is associated with extreme longevity in humans, particularly among centenarians [5]. Additionally, specific variants at the FOXO3 locus have been identified that respond to cellular stress, which may contribute to their role in promoting longevity [8].", + "The environment interacts with genes to influence aging through several mechanisms: 1. **Signaling Pathways and Gene Expression**: Environmental factors can trigger signaling pathways and modulate gene expression, impacting aging. For example, certain genes have varying effects on lifespan depending on environmental factors like diet [1]. 2. **Epigenetic Modifications**: Environmental factors can lead to changes in DNA methylation, acetylation, or deacetylation of histones, which are epigenetic modifications that influence gene expression. These changes can result in cellular damage and accelerated aging, such as the shortening of telomeres [2], [6]. 3. **Epigenetics as a Link**: Epigenetic modifications provide a potential link between the environment, disease, and aging. This suggests that specific environmental factors might directly induce specific epigenetic changes, which could be targeted for interventions aimed at improving healthspan or promoting healthy aging [3]. 4. **Physiological Capacity and Longevity**: Environmental factors, along with protective genetic alleles, contribute to an individual's physiological capacity, indirectly determining healthy lifespan and longevity. For instance, caloric restriction and smoking have opposite effects on the rate of aging [4]. 5. **Plasticity of Aging**: Aging is a plastic process that can be manipulated by both genetic and environmental factors. Understanding these interactions can help identify targets for anti-aging therapies, potentially through diet, lifestyle, and pharmacological interventions [5], [10]. Overall, the interaction between the environment and genes is complex and involves multiple pathways and mechanisms that collectively influence the aging process.", + "Senescent cells are cells that have stopped dividing and have entered a state of permanent growth arrest. They are characterized by an altered metabolism and the secretion of pro-inflammatory factors, a phenomenon known as the senescence-associated secretory phenotype (SASP) [1], [3]. These cells accumulate in tissues over time and are resistant to apoptosis, meaning they are not easily cleared from the body [9]. Senescent cells contribute to aging and age-related diseases in several ways. They impact the tissue environment by secreting inflammatory cytokines, proteases, and growth factors, which can lead to chronic inflammation and tissue dysfunction [3], [4]. This chronic inflammation is a significant factor in the development of age-related degenerative diseases [1], [4]. Additionally, senescent cells can alter the tissue microenvironment, promoting the degeneration of organs and stem cell niches, and potentially stimulating cancer cell growth [6]. The accumulation of senescent cells is associated with various age-related pathologies, such as atherosclerosis, osteoarthritis, and Alzheimer's disease [5], [9]. Recent studies have shown that clearing senescent cells can prevent or delay tissue dysfunction and extend health span, highlighting their causative role in aging [5].", + "Yes, there are known lifestyle interventions that can positively impact genes related to aging. Dietary interventions, such as dietary restriction (DR) and calorie restriction, have been shown to alter patterns of DNA methylation and induce long-lasting changes in gene expression that improve health during aging and extend lifespan [1], [8]. These interventions can modify the epigenome, which is linked to the biology of aging [5]. Additionally, glucose restriction has been shown to extend human cellular lifespan through SIRT1-mediated epigenetic and genetic mechanisms [7].", + "The 'epigenetic clock' is a molecular biomarker of aging that is based on the DNA methylation levels of specific CpG sites. These methylation patterns are highly correlated with an individual's chronological age, with a robust correlation coefficient of approximately 0.9 for individuals aged between 20 and 100 years [1]. The epigenetic clock serves as a reliable predictor of biological age, which refers to how well a person's body functions compared to their chronological age [2]. In aging research, the epigenetic clock is used to estimate the biological age of cells, tissues, or organs by analyzing the methylation levels of select CpGs, often referred to as clock CpGs [8]. This estimated age, known as the epigenetic age, can indicate different aging rates between individuals with the same chronological age, providing insights into the biological basis of aging [9]. The epigenetic clock has been applied in various studies to understand the relationship between epigenetic aging and factors such as metabolism, and it is considered one of the most promising molecular estimators of biological age [6], [8].", + "Researchers use model organisms like yeast and worms to study human aging due to their simpler genomes, short lifespans, and the ease with which they can be genetically and environmentally manipulated. These characteristics make them ideal for identifying and characterizing genes and signaling pathways involved in aging [3]. Yeast, specifically Saccharomyces cerevisiae, is a highly informative model for aging studies because of its genetic tools and the ability to measure aging through replicative or chronological lifespan assays [2], [5]. Yeast has been extensively used to identify genes and interventions responsible for lifespan extension, providing insights into the aging processes of all eukaryotic organisms [10]. Similarly, the nematode Caenorhabditis elegans is another widely used model organism in biogerontology. Researchers study these organisms to understand whether the aging process is evolutionarily conserved and to what degree mechanisms in these simpler organisms can be indicative of aging mechanisms in humans [1], [6]. These model organisms help explore both genetic and environmental determinants of lifespan, contributing to hypotheses surrounding extended lifespan and healthspan [7].", + "Yes, there are promising anti-aging therapies being developed based on genetic research. Several approaches are being explored: 1. **Senolytic Drugs**: Research has shown that abolishing senescent cells through genetic manipulation or senolytic drugs can significantly halt biological aging in mice. Trials are underway to test the ability of senolytics to postpone age-associated pathologies in humans [3]. 2. **Genetic Discoveries in Aging**: A number of genes capable of altering the aging process have been identified in animal models and even in humans. This area of research is promising as it explores the association of multiple alleles with human exceptional longevity [6]. 3. **Manipulation of Aging-Related Genes**: There is potential in manipulating aging-related genes through diet, lifestyle, and pharmaceuticals to improve human health and develop drugs against age-related diseases such as cancer, heart disease, type 2 diabetes, obesity, and neurodegenerative diseases [8]. These developments indicate that genetic research is paving the way for potential anti-aging therapies.", + "Caloric restriction and diet have significant impacts on the genetics of aging through various mechanisms: 1. **Gene Expression and Lifespan Extension**: Caloric restriction (CR) has been shown to delay age-related gene-expression changes in mice and, to some extent, in flies. This suggests that CR may influence the genetic pathways associated with aging, potentially contributing to lifespan extension [4]. 2. **Epigenetic and Post-Translational Mechanisms**: In calorie-restricted rats, transcriptome analysis indicates that CR involves epigenetic and post-translational mechanisms, which are implicated in neuroprotection and aging. These mechanisms may alter genome function to promote increased health and lifespan [3], [5]. 3. **mTOR Pathway**: Caloric restriction is associated with decelerating mTOR-driven aging, which is a significant pathway involved in cellular growth and metabolism. By modulating this pathway, CR may influence the genetic regulation of aging processes [5]. 4. **Genomic and Epigenetic Approaches**: Nutritional modulation, including caloric restriction, can impact aging through genomic and epigenetic approaches. This suggests that diet can influence the genetic and epigenetic landscape, potentially affecting the aging process [6]. Overall, caloric restriction and diet can modulate genetic pathways and mechanisms that are crucial for aging, potentially leading to increased lifespan and improved health during aging." + ], + "contexts": [ + [ + "It is undisputed that genetic factors influence aging. In a remarkable", + "males: what are the molecular and evolutionary causes? Aging Cell. 2007;6:225233. doi:10.1111/j.1474-9726.2007.00279.x 63. Benayoun BA, Pollina EA, Brunet A. Epigenetic regulation of ageing: link- ing environmental inputs to genomic stability. Nat Rev Mol Cell Biol. 2015;16:593610. doi:10.1038/nrm4048 64. Sen P, Shah PP, Nativio R, Berger SL. Epigenetic mechanisms of longevity and aging. Cell. 2016;166:822839. doi:10.1016/j.cell.2016.07.050", + "Clinical Genetics and Genomics of Aging", + "standing the cause and mechanisms of aging is imperative in assisting to suppress age-related diseases and promote healthylongevity. It is well-known that aging is influenced by a combin- ation of genetic and environmental factors. Previous twin stud- ies have shown that the genetic contribution to general human longevity is about 2030% [ 4,5], whereas environmental factors in human aging and longevity still account for the largest effect. Epigenetic factors influence the regulation of gene expres-", + "Recent developments on the genetics of aging can be seen as several streams of effort. In general, humans show a relatively modest ( <50%) heritability of", + "effect genetic variants on human longevity. Aging 2, 612620. Yu, C.E., Seltman, H., Peskind, E.R., Galloway, N., Zhou, P.X., Rosenthal, E., Wijsman, E.M., Tsuang, D.W., Devlin, B., Schellenberg, G.D., 2007. Comprehensive analysis of APOE and selected proximate markers for late-onset Alzheimers disease: patterns of linkage disequilibrium and disease/marker association. Genomics", + "factors shape a complex scenario for which clear answers of the regulation of longevity have been dicult to distill. With the discovery of genetic factors underlying aging in experimental laboratory models, forays into the genetic regulation of these properties have rapidly expanded, uncovering conserved mechanisms across diverse metazoa that inuence expression of aging phenotypes and lifespan. Yet, the story gets muddled in that these factors are often", + "In addition to aging- and CR-related genes, another source of candidate genes and pathways for drug designare human longevity-associated genes (Barzilai andShuldiner, 2001; Browner et al., 2004; Kenyon, 2010).Dozens of genes have now been associated with humanlongevity (de Magalha es et al., 2009a), although only ahandful of genes have been shown to have consistenteffects across populations. Many longevity-associated genes are related to spe-", + "tion for decades, the underlying molecular genetic causes of and responses to aging remain an area of active study. Research from model systems hascharacterized a range of physiological and molecular phenotypes associated with aging. These include genomic instability caused by accumulation of DNA damage, dysregulation of repair mechanisms, and telomere attri- tion; epigenetic alterations; dysregulation of transcription; loss of proteostasis; cellular senescence; and deregulated", + "143 The molecular bases of ageing are multi factorial, but there are nine distinctive features related to this process, which include genomic instability, telomere shorten- ing, de-regulated nutrient sensing, mitochondrial dysfunction, cellular senescence, stem cell exhaustion, altered cellular senescence, loss of proteostasis and a change in the patterns of epigenetic modifications [4, 5]. Epigenetics andAgeing Epigenetics is considered as a dynamic interface between the genome and the envi-" + ], + [ + "potentially associated with human ageing. For eachgene, a description compiled from the studies that linkthe gene to ageing is provided. It should be noted thatour focus is on genes that might affect the ageingprocess, rather than individual age-related pathologies; genes affecting multiple, even if not all, age-related", + "showing that single genes can regulate aging in modelorganisms demonstrate that aging can be geneticallymanipulated (Finch and Ruvkun, 2001; Kenyon, 2010).Hundreds of genes that modulate longevity have nowbeen identified in model organisms (de Magalha es et al.,2009a). In some cases (e.g., in worms), mutations insingle genes can extend lifespan by almost 10-fold (Ayy-adevara et al., 2008). Nonetheless, aging is a complexprocess that derives not from single genes but from theinteractions of multiple genes", + "genes (http://genomics.senescence.info/genes/), more than700 genes have been identified that regulate lifespan inmodel organisms (de Magalha es et al., 2009a). Many ofthese genes and their associated pathwayssuch as theinsulin/IGF1/GH pathwayhave been shown to affect lon-gevity across different model organisms (Kenyon, 2010).Therefore, at least some mechanisms of aging are evolu-tionarily conserved and may have potential therapeuticapplications (Baur et al., 2006). For example, evidencesuggests the use of", + "key genes and pathways important in aging; geneticstudies of heritable diseases that cause the appearanceof premature aging in affected people; physiological ex-Introductionperiments that relate the pace of aging to caloric intake;Is aging the final act in the script of developmental biol-and advances in human genetics, as well as cell andogy? The characteristic changes that are part and parcelmolecular biology leading to an understanding of theof aging appear similar to developmentally regulatedbasis of", + "shown that genes associated with aging and/or longevity inmodel organisms are evolutionary conserved in terms of havingmore homologues than predicted by chance (Budovsky et al .,2007, 2008) and exhibiting slower molecular evolution rates (de Magalhes & Church, 2007). Therefore, it is now clear that atleast some genes identified in model organisms may be relevantto human aging. To allow researchers to focus specifically on human aging,", + "expression of certain genes have an effect upon longevity. Although similar aging processes are likely to operateacross multiple species [30], it has been much more diffi-cult to identify longevity candidate genes in human studies[30]. A key question in human aging is to what extent asignature of aging may be detectable across tissues. Until now there has been a lack of large transcriptional profiles from the same human individuals in multiple tissues. TheMuTHER study provides ins ight into the human aging", + "complex.108,109Studies on models such as the yeast Sac- charomyces cerevisiae110the nematode Caenorhabditis elegans,111the fly Drosophila melanogaster,112-114the mouse Mus musculus,115and humans116show that single gene mutations can contribute to the initiation of aging andinduce premature aging syndromes. There are, however, nospecial genes that can cause aging-associated damages. Themanifestation of aging is mostly due to the failure of main-tenance and repair mechanisms. 117,118", + "on model organisms [3] or have been confined to specificaging-associated disorders such as progeria syndromes [4]. A study of postmortem human brain tissue from 30 individuals aged 26 to 106 years [5] showed that approxi- mately 4% of approximately 11,000 genes analyzed show a significant age-related expression change (1.5-fold or more) in individuals aged >40 years. These genes were reported to play central roles in synaptic plasticity, vesi- cular transport, and mitoch ondrial function. Another", + "of multiple genes with each other and withthe environment. Evidence from animal systems showsa major impact of the environment on aging, yet envi-ronmental manipulations of aging act through genesand proteins, usually by triggering signaling pathwaysand modulating gene expression. In fact, some geneshave been shown in model organisms to have varyingeffects on lifespan depending on diet (Heikkinen et al.,2009). Genes that can regulate aging in model organ-isms cannot be directly applied to humans through", + "[2] L. Partridge, D. Gems, Mechanisms of ageing: public or private? Nat. Rev. Genet. 3 (2002) 165 175. [3] A.M. Leroi, et al., What evidence is there for the existence of individual genes with antagonistic pleiotropic effects? Mech. Ageing Dev. 126 (2005)421429. [4] S.N. Austad, Is aging programmed? Aging Cells 3 (2004) 249 251. [5] V.D. Longo, J. Mitteldorf, V.P. Skulachev, Opinion: programmed and altruistic ageing, Nat. Rev. Genet. 6 (2005) 866 872." + ], + [ + "as diabetes, cancer and neurodegenerative disorders [1, 2]. Environmental and genetic interventions can ameliorate the effects of aging, with nutrition, nutrient-sensing signaling networks and metabolism playing evolutionarily conserved roles [1, 3 5]. Diet- ary restriction (DR), in which food intake is reducedwhile avoiding malnutrition, extends lifespan in di- verse model and non-model organisms [3, 6]. DR induces a remarkably broad-spectrum improvement in", + "limiting exposure to exogenous genotoxins and by suppressing metabolism thereby producing fewer reactive species. However, DNA damage, like caloric restriction, can also elicit a protective survival response that promotes longevity and healthy aging. Recently, the use of sirolimus in mice was found to extend their life span and de - lay the development of conditions associated with aging, including cancer. 1 Sirolimus is one of pre -", + "Longev. Heal. 2, 10 (2013). 7. Kreienkamp Ret al.Doubled lifespan and patient-like pathologies in progeria mice fed high-fat diet. Aging Cell18, e12852 (2019). [PubMed: 30548460] 8. Heilbronn LK & Ravussin E Calorie restriction and aging: review of the literature and implications for studies in humans. Am. J. Clin. Nutr. 78, 361369 (2003). [PubMed: 12936916] 9. Liang Yet al.Calorie restriction is the most reasonable anti-ageing intervention: a meta-analysis of", + "can be slowed down to some extent by eating a healthy diet and taking physical exercise, and many of the chronic diseases prevalent in older adults are either preventable or modi able with healthy lifestyle habits. Thus, older adults can experience successful aging that allows them to achieve physical, social and mental well - being over the life course and to participate in society. Much research has been conducted in recent years to", + "During the past century, remarkable progress has been made in unveiling the mechanisms of aging. Genetic and molecular pathways that regulate healthspan and lifespan have been identified in various model organisms, provid-ing a rich knowledge base (Longo etal. 2015; Lopez-Otin etal. 2013, 2016; Singh etal. 2019). However, the focus on", + "13,14 Prior studies have identified dozens of genetic and environ - mental modifiers of chronological or replicative longevity, some of which are now known to function similarly to modulate life span in multicellular eukaryotes. 15-17 One example of such a con - served longevity intervention is dietary restriction, which has been shown to slow aging in many different species including yeast, nematodes, fruit flies and rodents, 18,19 and most recently", + "Genetic studies have shown that aging can be slowed in mutants that are defective in a wide range of cellularprocesses (such as mitochondrial function, chromatin regu- lation, insulin signaling, tran scriptional regulation, and genome stability). This indicates that aging is a complex process driven by diverse molecular pathways and biochem- ical events. As such, a powerful approach to study aging is touse systems biology, which allows a multitude of factors", + "Dietary interventions, including starvation and protein deprivation, can also alter patterns of DNA methyla- tion, potentially in a long-lasting manner [42, 43], including transgenerationally [26, 44]. Dietary, genetic and pharmacological interventions that improve health during aging and extend lifespan induce long-lasting changes in gene expression that mediate their effects. Here we have asked if and how age-related DNA methylation, transcription and lipid", + "in yeast , Drosophila, and C. elegans is able to slow aging and increase lifespan [252-255]. Follow -up stud ies out of Richard Millers laboratory reproduced these findings in mice fed a diet with rapamycin incorporated [256, 257]. These studies suggested that inhibiting mTOR via rapamycin could delay age-associated diseases and extend lifespan in mammals. A subsequent study replicated these findings by genetically manipulating a", + "appears to retard aging at the molecular level as indi-cated by the gene expression analysis? Most likely,aging retardation at the molecular level by exercise isnot observed in all tissues, including some that maylimit lifespan. For example, if exercise does not reduceaging rates in replicative tissues, then it will not retardage-related tumor onset, which tends to limit maxi-mum lifespan. Another possibility relates to the obser-vation that wheel running decreased to an average 680m/day at 33 mo of age" + ], + [ + "for molecular biological studies on aging. Although material from humans should be employed where possible, for prac- tical reasons animal model systems like rats and mice are indispensible. There is evidence that, provided their health sta- tus and husbandry is optimal, rodents age much in the same way as humans do (Burek 1978). For studying certain funda- mental processes, such as the occurrence of various types of DNA rearrangement, lower organisms and cell lines can also", + "Until now most of the genomic studies of invertebrate models have been performed on whole animals. Several studies, however, recently performed on specialized mammalian tissues, either post-mitotic (heart or nervous system) or mitotic (liver), show that the effects of aging are tissue-specific [19-25]. In addition, effects of caloric restriction on age related transcriptional changes are also tissue- or species-specific [19]. To better understand the aging process in invertebrate", + "opportunities for assessing the efcacy of interventions onaging. When considering the advantages and disadvantages of dogs as a model for geroscience research, it is useful tonote that the vast majority of mammalian studies on thebasic biology of aging are performed in a relatively small number of inbred mouse strains. Typical average lifespan for most of these mouse strains is approximately 23 years,", + "[14] Gerstbrein, B., Stamatas, G., Kollias, N., Driscoll, M. In vivo spec- trofluorimetry reveals endogenous biomarkers that report health- span and dietary restriction in Caenorhabditis elegans . Aging Cell 2005 , 4: 127-137. [15] Kennedy, B.K. The genetics of ageing: insight from genome-wide approaches in invertebrate model organisms. J. Intern. Med. 2008 , 263: 142-152. [16] Kenyon, C., Chang, J., Gensch, E., Rudner, A., Tabtiang, R. A C.", + "the DNA level leads to changes in gross phenotype, we must now look downstream at changes in gene expression associ - ated with genetic variation, aging, and ARD. Comparison With Laboratory Models of Aging Laboratory models typically used to study aging, such as Caenorhabditis elegans (nematode worm) and Mus musculus (mice), have drastically shorter life spans than our own (~3 wk [ 51] and ~3 y [ 52], respectively, vs a 122 y maxi - mum for humans thus far; [ 53]). In some respects, these", + "ing studies on invertebrate models of aging, long-lived mam-mals, transgenic mouse strains, and interventional studies, have led to the identification of evolutionarily conserved path- ways involved in life span regulation, as well as common de- nominators of aging in different organisms. 4 In this review, the pathophysiological roles of these aging mechanisms, including oxidative stress, mitochondrial dysfunction, impaired resis-", + "chain triglyceride oil on life span of genetically heterogeneous mice. J. Gerontol. A. Biol. Sci. Med. Sci. 68, 616 (2013). [PubMed: 22451473] 24. Yuan R, Peters LL & Paigen B Mice as a mammalian model for research on the genetics of aging. ILAR J. Natl. Res. Counc. Inst. Lab. Anim. Resour. 52, 415 (2011). 25. Saul MC, Philip VM, Reinholdt LG & Chesler EJ High-diversity mouse populations for complex traits. Trends Genet. 35, 501514 (2019). [PubMed: 31133439]", + "lowing the discovery of genes and pathways involved inanimal lifespan extension, human research has focusedon the corresponding candidate human genes withgenetic, genomic and epigenetic studies into ageingand longevity. The designs of these studies differwith respect to the selection of naturally occurringphenotypes and the study populations, which includepopulation-based, patient-based, family-based andexposure-based cohorts. Studies into human age-related disease phenotypes", + "Animal studies as stalking horses for human biogerontology. For the most part, studies on the biology of aging are as difficult and imprac-tical in humans as are studies of health insurance in rodents. It is fairlyCopyright National Academy of Sciences. All rights reserved.Cells and Surveys: Should Biological Measures Be Included in Social Science Research? http://www.nap.edu/catalog/9995.html", + "review of the evidence for genotype-dependent eects on lifespan. Ageing Res. Rev. 11, 254270. doi: 10.1016/j.arr.2011.12.006 Turturro, A., Witt, W. W., Lewis, S., Hass, B. S., Lipman, R. D., and Hart, R. W. (1999). Growth curves and survival characteristics of the animals used in the biomarkers of aging program. J. Gerontol. Ser. Biol. Sci. Med. Sci 54, B492B501. doi: 10.1093/gerona/54.11.b492 Vertti-Quintero, N., Berger, S., Solvas, X. C. I, Statzer, C., Annis, J., Ruppen," + ], + [ + "genes analyzed for their possible association with human lon-gevity (http://genomics.senescence.info/genes/longevity.html).All longevity association studies in humans we could find by thetime of the latest update were added to this list. These includestudies reporting negative results, which we see as essentialsince many genes display population-specific associations withlongevity. Fig. 1 From the main page of the Human Ageing", + "genes (http://genomics.senescence.info/genes/), more than700 genes have been identified that regulate lifespan inmodel organisms (de Magalha es et al., 2009a). Many ofthese genes and their associated pathwayssuch as theinsulin/IGF1/GH pathwayhave been shown to affect lon-gevity across different model organisms (Kenyon, 2010).Therefore, at least some mechanisms of aging are evolu-tionarily conserved and may have potential therapeuticapplications (Baur et al., 2006). For example, evidencesuggests the use of", + "Exceptional Longevity One approach to identifying genes associated with low mortality is to examine the genes of those who survive to the oldest ages. Several studieshave examined gene frequencies among centenarians or nonagenariansand compared them with frequencies at younger ages. Since changes ingene frequencies are more rapid when mortality rates are high, cross-sectional comparisons must be adjusted for differences in mortality amongcohorts.", + "informed by age-related disease identifies loci for exceptional human longevity. Li H, editor. PLoS Genet. 2015. https://doi.org/10.1371/journal.pgen. 15. Polderman TJC, Benyamin B, de Leeuw CA, Sullivan PF, van Bochoven A, Visscher PM, etal. Meta-analysis of the heritability of human traits based on fifty years of twin studies. Nat Genet. 2015;47:7029. 16. Cellerino A, Ori A.What have we learned on aging from omics studies? Semin Cell Dev Biol. 2017;70:17789.", + "GENOME-WIDE ASSOCIATION STUDY OF LONGEVITY 479 INCREASES in longevity of the general population world - wide are an unprecedented phenomenon with significant health and social impact. Although environmental factors have led to an increase in life span, there is ample evidence that genetic factors are involved in extreme longevity both in humans (17) and in other organisms (8). The protective genetic factors that lead to longevity are likely to involve", + "expression of certain genes have an effect upon longevity. Although similar aging processes are likely to operateacross multiple species [30], it has been much more diffi-cult to identify longevity candidate genes in human studies[30]. A key question in human aging is to what extent asignature of aging may be detectable across tissues. Until now there has been a lack of large transcriptional profiles from the same human individuals in multiple tissues. TheMuTHER study provides ins ight into the human aging", + "4. Joshi, P. K. et al. Variants near CHRNA3/5 and APOE have age- and sex- related effects on human lifespan. Nat. Commun. 7, 11174 (2016). 5. Pilling, L. C. et al. Human longevity is in uenced by many genetic variants: evidence from 75,000 UK Biobank participants. Aging 8, 547560 (2016). 6. Deelen, J. et al. Genome-wide association meta-analysis of human longevity identi es a novel locus conferring survival beyond 90 years of age. Hum. Mol. Genet. 23, 4420 4432 (2014).", + "79-91. [97] Smith, E.D.; Kennedy, B.K.; Kaeberlein, M. Genome-wide identification of conserved longevity genes in yeast and worms . Mech. Ageing Dev. , 2007 , 128(1), 106-11. [98] Chen, D.; Pan, K.Z.; Palter, J.E.; Kapahi, P. Longevity determined by developmental arrest genes in Caenorhabditis elegans. Aging Cell, 2007 , 6(4), 525-33. [99] Curran, S.P.; Ruvkun, G. Lifespan regulation by evolutionarily conserved genes essential for viability . PLoS Genet. , 2007 , 3(4), e56.", + "9. vB Hjelmborg J, Iachine I, Skytthe A, Vaupel JW, McGue M, et al. (2006) Genetic influence on human lifespan and longevity. Hum Genet 119: 312321.doi:10.1007/s00439-006-0144-y. 10. Sebastiani P, Perls TT (2012) The genetics of extreme longevity: lessons from the new England centenarian study. Front Genet 3: 277. doi:10.3389/fgene.2012.00277.11. Perls TT, Wilmoth J, Levenson R, Drinkwater M, Cohen M, et al. (2002) Life-", + "39. Fortney K, Dobriban E, Garagnani P, etal. Genome-wide scan informed by age-related disease identifies loci for exceptional human longevity. PLoS Genet. 2015;11:e1005728. doi:10.1371/journal.pgen.1005728 40. Beekman M, Nederstigt C, Suchiman HE, et al. Genome-wide asso- ciation study (GWAS)-identified disease risk alleles do not compromise human longevity. Proc Natl Acad Sci U S A. 2010;107:1804618049. doi:10.1073/pnas.1003540107" + ], + [ + "Telomeres are specialized structures that protect the ends of linear chromosomes. They shorten during aging due to the unidirectional activity of DNA polymerase, which leaves a section of DNA unrepli-cated on the lagging strand. Telomeres also are subject to shortening by genotoxic stress, such as oxidative damage (33). Among many eukaryotes, the enzyme telomerase maintains telomere length; but telomerase activity varies over the lifespan and between cell types, tissues, and species (34). In most human", + "that shorten their length with progressing age. This shortening of telomeres is the result of the absence of the activity of an enzyme called telomerase, and in turn it induces several processes, such as apoptosis, senescence, or oncogenic transforma- tion of somatic cells, affecting the health and lifespan of an individual [42]. Human telomere shortening has been mostly studied in leukocytes and linked not only to ageing and life expectancy [43] but also to age-related diseases, including cardio-", + "nization may directly affect telomere attrition, resulting in accelerated replicative senescence and progeroid phenotypes [180]. Telomeres are regions constituted by tandem repeats of non-coding DNA sequences 5-(TTAGGG)n-3 and a protein complex called shelterin, bound to them. This structure ensures the stability of the genome and protects the chromosomes from a wrong action of the DNA repair machinery [184] by allowing the formation of a chromatin loop called T-Loop [185].", + "Telomeres play a central role in cell fate and aging by adjusting the cellular response to stress and growth stimulation on thebasis of previous cell divisions and DNA damage. At least a few hundred nucleotides of telomere repeats must cap eachchromosome end to avoid activation of DNA repair pathways. Repair of critically short or uncapped telomeres by telomeraseor recombination is limited in most somatic cells and apoptosis or cellular senescence is triggered when too many uncappedtelomeres accumulate.", + "ing (84). This process is believed to be the trigger for the aging process, according to the telomere theory (11, 85, 86). It is further supported by Bodnar etal. who proved that telomere elongation caused by ectopic expression of telomerase avoids the senescence phenotype (87). His work relied on one of the earliest studies linking telomere shortening to aging which was performed", + "telomeres, the repetitive sequence at the end of linear chromosomes, has garnered much attention for its relation to aging. Telomere repeats serve as an internal clock for cycling cells because each round of replication results in the loss of telomeric DNA in the absence of active telomerase (reviewed in [66]). Eventually, this loss over cellular generations culminates in telomere crisis and a permanent state of", + "and consequently lose telomeric sequences, thereby limiting the number of cell cycles, which is important for preventing the onset of cancer. Cells perceive critically short telomeres as persistentDNA damage. This activates the DNA damage responses, including cell cycle checkpoints, which ultimately leads to a permanent cell cycle arrest (cellular senescence). Senescence protects from cancer but contributes to the aging process (37).", + "When the telomeres shorten, this loop is no longer able to form and in turn, the epigenetic regulation is changed to activation of the TPE-OLD genes. This happens before the telomeres reach the critical length that causes activation of DDR, thus leading to another earlier possible effect of telomere shortening on aging (138, 139). Interestingly, a following study by Kim etal. showed that one of the TPE-OLD sensitive genes is hTERT, the core reverse transcriptase component of telomerase (140). This is", + "to maintain proliferation potential (94). Cells with mutated telomerase exhibited irregular morphology and short telomeres, but these changes did not cause deadly damage and determinate senescence (95). One hypothesis connects aging to telomere erosion through the transcription of subtelomeric genes. Genes located in subtelomeric regions are affected by transcriptional silencing which was found to change in an age-related manner. Kim et al. (96) found that silencing of genes in subtelomeric", + "evidence implicates telomere shortening in cellularsenescence. Telomeres consist of repetitive nucleotides e q u e n c e s( T T A G G G )a tt h ee n d so fm a m m a l i a nc h r o -mosomes, that preserve chromosome stability andintegrity by preventing deterioration or fusion withneighboring chromosomes (76) (Central Illustration ).JACC VOL. 69, NO. 15, 2017 Paneni et al . APRIL 18, 2017:1952 67 The Aging Cardiovascular System1957" + ], + [ + "Effect of age on DNA repair Research over the past decades suggest that many steps in DNA metabolism are altered with age in a variety of tissues and animal models (56,57). The relation of DNArepair to aging has been studied by measuring the ability of cells from organisms of various life spans to repair DNA damage and by experiments that have comparedthe ability of cells from young and old organisms to repair DNA damage. Interest was peaked by the original", + "BI87CH14_Niedernhofer ARI 18 May 2018 15:1 SUMMARY POINTS 1. Evolutionarily conserved DNA repair pathways maintain the integrity and stability of the nuclear genome. Impairment of DNA repair mechanisms results in accelerated agingand/or cancer. 2. Evidence in humans and model organisms supports the conclusions that with age (a) endogenous sources of genotoxins increase, ( b) DNA repair capacity declines, and (c) levels of DNA damage and mutations increase.", + "Several lines of evidence suggest that DNA repair capacity might decrease with age. However,it should be noted that measuring DNA repair in tissues is challenging and that the validity ofsurrogate markers of repair capacity is not well established. For example, a reduction in expression of DNA repair genes/proteins is not proven to impact DNA repair. Frequently, the reduction in", + "improved DNA repair. Finally, there should be a plausible mechanism by which DNA damage can drive aging. Here, we review the evidence currently supporting each of these predictions. EVIDENCE THAT DNA DAMAGE INCREASES WITH AGE Sources of Damage Increase with Age The free radical theory of aging posits that aging is caused primarily by oxidative damage in- curred by ROS that chemically modify critical cellular biomolecules (13). This theory has evolved", + "All rights reservedKeywords DNA damage, aging, mutations, senescence, DNA damage response, DNA repair Abstract The nuclear genome decays as organisms age. Numerous studies demon- strate that the burden of several classes of DNA lesions is greater in older mammals than in young mammals. More challenging is proving this is acause rather than a consequence of aging. The DNA damage theory of aging, which argues that genomic instability plays a causal role in aging,", + "repaired; otherwise the genome would soon become saturated with damage and life would cease. There is some evidence that DNA damage accumulates with age in some tissues ( Maslov et al., 2013 ), but the exact nature of the damage remains unclear. Indeed, even these low levels of spontaneous DNA damage may represent a steady state due to continu- ous repair and induction of new damage. However, DNA damage can cause certain aging phenotypes by activating cellular responses, such", + "36:1049-1062. 66. Hasty P, Vijg J: Accelerating aging by mouse reverse genetics: a rational approach to understanding longevity. Aging Cell 2004, 3:55-65. 67. Bohr VA: Deficient DNA repair in the human progeroid dis- order, Werner syndrome. Mutat Res 2005, 577:252-259. 68. Nouspikel T, Hanawalt PC: DNA repair in term inally differenti- ated cells. DNA Repair 2002, 1:59-75. 69. Nouspikel T, Hanawalt PC: When parsimony backfires: neglect- ing DNA repair may doom neurons in Alzheimer's disease.", + "DNA repair. In the latterdifficult to arrive at a strict, experimentally useful defini-context, most premature aging syndromes are causedtion of aging. Factors implicated in organismal declineby mutations in genes encoding proteins involved inin genetic models might not play a role in the normalDNA repair ( Karanjawala and Lieber, 2004 ). Accumula-aging processes. A related difficulty is that prematuretion of mutations in critical genes may be one generalaging models fail to recapitulate all aspects of", + "escape the repair process and accumulate in the genome, impacting several processes and aging [67,145147]. There is little evidence of association between DNA repair improvement and life- time expansion [ 148,149], thus, indicating that such mechanism seems to have evolved to maintain DNA stabilityand therefore healthonly until reproductive age, without any regard for the fate of the individual in old age, both in terms of quality and length of", + "with age, and DNA repairtween different tissues. These differences likely reflectdefects can cause phenotypes resembling prematurefunctional characteristics of those tissues, such as mi-aging. We discuss how cellular DNA damage re-totic rate, transcriptional activity, metabolism, and thesponses may contribute to manifestations of aging.action of specific DNA repair systems.We review Sir2, a factor linking genomic stability, me-Reactive Oxygen Species: An Important Sourcetabolism, and aging. We conclude" + ], + [ + "raises the possibility of therapies to slow aging. Therefore the discoveryof a gerontogene with even very rare mutations that increased longevitywould cause speculation about future trends in mortality. However, thediscovery of such a gene would be relevant only to long-term (and, there-fore, very speculative) projections. Prospective Epidemiologic Surveys that Include Genetic Information Some epidemiologic cohort studies of populations have collected", + "need to develop approaches and therapies targeting theaging process and age-related diseases (Butler et al.,2008). Delaying the process of aging, even slightly,would have profound social, medical and economic ben-efits (Olshansky et al., 2006; Butler et al., 2008). Forexample, slowing aging by a mere 7 years would cutmortality of age-related diseases by half at every age.Therefore, the potential benefits from research on thebasic biology and genetics of aging are unparalleled interms of improving quality", + "Interestingly, when senescent cells are abolished either through genetic manipulation or via senolytic drugs, biological aging is signicantly halted in mice [ 53,54]. Therefore, trials are now under way to test the ability of senolytics to postpone age-associated pathologies in humans [ 55]. Notably, multi- ple drugs are being pursued that either directly or indirectly impact DNA repair or the consequenceof DNA damage. Future Prospects: Developing Interventions through DNA Repair", + "and potentially important genetic markers for slow aging have been found in humans (Suh et al. 2008). Elucidating the function of such genes is believed to enable decipher- ing the core of the aging process, answer to what extentthe process is conserved, and pave the way for therapeutic interventions of age-related maladies, including cancers, neurodegeneration, and metabolic syndrome (Guarente 2011). The identity of the virtual gerontogenes so far discov-", + "discover specific genes that directly influence how quickly people age, beyond diseases. If such genes exist, their effects were too small to be detected in this study. The next step will be to expand the study to include more participants, which will hopefully pinpoint further genomic regions and help disentangle the biology of ageing and disease. DOI: https://doi.org/10.7554/eLife.39856.002", + "using bulk mRNA or even analyzing single cells (scRNA-seq). In addition, advances in molecular biology and cell culture approaches (for instance Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/Cas9) will be benecial in clarifying aging-processes across species. An improved understanding of epigenetic mechanisms affecting longevity will be deciding crucial step towards the identication of new potential therapeutic targets. In", + "century. Manipulation of aging-related genes by diet,lifestyle, and pharmaceuticals could dramatically im-prove human health and could be used to develop drugsagainst age-related diseases such as cancer, heart dis-ease, type 2 diabetes, obesity, and neurodegenerativediseases. The hundreds of aging-related genes and genesrelated to CR already identified offer enormous oppor-tunities for target discovery (Fig. 2). Although aging-related genes cannot be modified in humans, under-standing how these can be", + "5. Goldman DP, etal. Substantial health and economic returns from delayed aging may warrant a new focus for medical research. Health Aff (Millwood). 2013;32(10):1698705. 6. Esplin ED, Oei L, Snyder MP.Personalized sequencing and the future of medicine: discov- ery, diagnosis and defeat of disease. Pharmacogenomics. 2014;15(14):177190. 7. Marian AJ.Clinical applications of molecular genetic discoveries. Transl Res. 2016;168:614.", + "a medical intervention), without changing the fundamental rateof organismal aging. Nevertheless, it does seem that manyso-called longevity genes, as well as dietary restriction, appear to extend not only life span, but also health span (Kauffman et al., 2010; Luo et al., 2010 ). In that regard, it does appear that it is possible to experimentally slow the rate of aging. Still, in each case, aging does continue on as if there is some", + "genetic modification. Currently, emerging evidence suggeststhat certain interventions (e.g. CR, dietary supplementation andchemical drugs) can prevent age-related diseases and promote longevity, at least in part, through reversing the aberrant age- associated changes in DNA methylation, suggesting the greatpotential of DNA methylation in therapeutic strategies againstage-related diseases ( Figure 1B ).However, to further understand the roles of DNA methyla-" + ], + [ + "In addition to nuclear DNA, mitochondrial DNA (mtDNA) also is affected by aging. Alterations in mitochondrial function and mito-chondrial-nuclear signaling occur during aging and have been linked to sex biases in aging and age-related diseases (28). Due to their role in energy production, mitochondria are at high risk of oxida-tive damage. Not surprisingly, accumulation of oxidative lesions is an important source of age-related mtDNA damage (29). In aged Wistar rats brains, DNA oxidation, as measured by", + "mitochondrial DNA mutations can reduce lifespan. Sci Rep. 2014;4:6569. 20. Ross JM, Stewart JB, Hagstrm E, Bren S, Mourier A, Coppotelli G, Freyer C, Lagouge M, Hoffer BJ, Olson L. Germline mitochondrial DNA mutations aggravate ageing and can impair brain development. Nature. 2013;501(7467):412 5. 21. Sondheimer N, Glatz CE, Tirone JE, Deardorff MA, Krieger AM, Hakonarson H. Neutral mitochondrial heteroplasmy and the influence of aging. Hum Mol Genet. 2011;20(8):1653 9.", + "102. Zhang R, Wang Y , Ye K, Picard M, Gu Z.Independent impacts of aging on mitochondrial DNA quantity and quality in humans. BMC Genomics. 2017;18:890. https://doi.org/10.1186/ s12864-017-4287-0. 103. Norddahl GL, et al. Accumulating mitochondrial DNA mutations drive premature hema- topoietic aging phenotypes distinct from physiological stem cell aging. Cell Stem Cell. 2011;8:499510. https://doi.org/10.1016/j.stem.2011.03.009.", + "other studies, the risk for metabolic disorders is highly associated with age-related diseases that affect lifespan, and interestingly these conditions exhibit mitochon- drial dysfunction [73]. Aging is a complex process as a time-dependent progressive loss of physiologi- cal integrity, leading to impaired function and increased vulnerability to death [74], and as we described above, aging is highly associated with mtDNA mutations; in", + "mt, and overall mitonuclear genomic compatibility. Given the uncertainty of mtDNA mutation accumulation in driving the natural aging process, it is plausible that mito - chondrial communication may be a significant evolutionarily conserved force that influences lifespan and/or healthspan. Acknowledgements Funding was provided by the American Federa- tion for Aging Research (AFAR), the National Institute on Aging (T32", + "abolic regulation through mitochondrial signaling. Am J Physiol Endocrinol Metab. 2014;306:E58191. 74. Zhang R, Wang Y , Ye K, Picard M, Gu Z.Independent impacts of aging on mitochondrial DNA quantity and quality in humans. BMC Genomics. 2017;18:890. 75. Hebert SL, Lanza IR, Nair KS.Mitochondrial DNA alterations and reduced mitochondrial function in aging. Mech Ageing Dev. 2010;131:45162. 76. Liu D, Li H, Lu J, Bai Y .Tissue-specific implications of mitochondrial alterations in aging.", + "Sun., N, Youle, R. J. and Finkel, T. (2016). The mitochondrial basis of aging. Mol. Cell 61, 654-666. doi:10.1016/j.molcel.2016.01.028 Symer, D. E., Connelly, C., Szak, S. T., Caputo, E. M., Cost, G. J., Parmigiani, G. and Boeke, J. D. (2002). Human L1 retrotransposition is associated with genetic instability in vivo. Cell110, 327-338. doi:10.1016/S0092-8674(02)00839-5 Szabo, L., Morey, R., Palpant, N. J., Wang, P. L., Afari, N., Jiang, C., Parast,", + "than ones that affect mitochondrial DNA12,57,58,71.So,this is an important reason for favouring nuclear DNA as the ultimate damage target in natural ageing. Nevertheless, it is conceivable that when mutations occur in the mitochondrial genome, mutant-protein production could increase the inefficiency of the mitochondrial respiratory chain, thereby resulting in more reactive oxygenspecies, which would then damage nuclear and mitochondrial DNA further.", + "generation animals as they grow older.Mitochondrial DNAGenetic instability outside of the nuclear genome mightalso contribute to aging (reviewed in Lee et al., 1997;Wallace et al., 1998). The mutation rate for mitochondrialDNA (mtDNA) is 10- to 20-fold greater than for nuclearDNA, and it is believed that mtDNA mutations may com-promise mitochondrial functions in different ways (Fig-ure 4). First, defects in electron transport and oxidativephosphorylation could lead to declines in ATP levelsand the NAD:NADH", + "of the human aging process(Corral-Debrinski et al., 1992; Soong et al., 1992;Wei etal., 1996b), and it has been demonstrated that certain pointmutations of mitochondrial DNA accumulate in the aginghuman brain (Zhang et al., 1993; Liu et al., 1997). However,thefunctionalimplicationsofthesendingsarecontroversial(Hayashietal.,1994).Tocomplicatethematterfurther,Takaiand co-workers discuss the possibility that the commonage-associated changes in human and mouse" + ], + [ + "logical phenomena is often facilitated by the study of genetic mutants, and, in the case of humans, genetic disorders. Accordingly, a search was made, over the years, for genetic disorders characterized by premature aging. If DNA dam- age and repair has anything to do with aging it should be evidenced in such individuals. Martin (1978) listed 162 genetic syndromes in humans with some or many signs of premature aging. About 21 feahares are considered as markers for", + "[315] Szilard, L. On the nature of the aging process. Proc. Natl. Acad. Sci. USA 45:3545; 1959. [316] Vijg, J.; Dolle, M. E. Large genome rearrangements as a primary cause of aging. Mech. Ageing Dev. 123:907915; 2002. [317] Vijg, J. Somatic mutations and aging: a re-evaluation. Mutat. Res. 447:117135; 2000. [318] Martin, G. M. Genetic syndromes in Man with potential relevance to the pathobiology of aging. Birth Defects Orig. Artic. Ser. 14:539; 1978.", + "19 6. Milholland B, Suh Y , Vijg J.Mutation and catastrophe in the aging genome. Exp Gerontol. 2017;94:3440. 7. Maslov AY , Ganapathi S, Westerhof M, Quispe-Tintaya W, White RR, Van Houten B, etal. DNA damage in normally and prematurely aged mice. Aging Cell. 2013;12:46777. 8. Blokzijl F, de Ligt J, Jager M, Sasselli V , Roerink S, Sasaki N, etal. Tissue-specific mutation accumulation in human adult stem cells during life. Nature. 2016;538:2604.", + "143 Gonzalo S, Kreienkamp R & Askjaer P (2017) Hutchinson -Gilford Progeria Syndrome: A premature aging disease caused by LMNA gene mutations. Ageing Res. Rev. 33, 1829. 144 Lu L, Jin W & Wang LL (2017) Aging in Ro thmund -Thomson syndrome and related RECQL4 genetic disorders. Ageing Res. Rev. 33, 3035. 145 de Renty C & Ellis NA (2017) Blooms syndrome: Why not premature aging? Ageing Res. Rev. 33, 3651. 146 Shiloh Y & Lederman HM (2017) Ataxia -telangiectasia (A -T): An emerging", + "genetic disease model of premature aging, In: Harrison,D.E., eds, Genetic Effects on Aging II (Telford Press, Caldwell,NJ), pp. 521542. [2] Djawdan, M., Sugiyama, T., Schlaeger, L., Bradley, T.J. and Rose, M.R. (1996) Metabolic aspects of the trade-off between fecundity and longevity in Drosophila melanogaster ,Physiol. Zool. 69, 11751195. [3] Fleming, J.E., Spicer, G.S., Garrison, R.C. and Rose, M.R.", + "genes of a whole chromosome ineffective, couldbe a main causal factor in aging (Szilard, 1959).According to Maynard Smith, such types of mu-tations do not seem likely to be common enoughto be the main cause of aging. However, at thetime quantitative information on the possible age-related accumulation of different types of muta-tions in various tissues of mammals wascompletely lacking. The question, therefore,whether somatic mutations are a cause of aging,has not been resolved, more than four decadesafter", + "features of premature aging (16, 17). Subsequent experiments conrmed that mitochondrial DNA mutations and deletions were the driving force behind the observed accelerated aging phenotypes(18). THE LINK BETWEEN NUCLEAR GENOME INTEGRITY AND PREMATURE AGING The notion that the majority of currently identied progeria syndromes originate from defects in genome maintenance highlights the importance of the condition of DNA in the process of", + "Tryggvason K,ZhouZ.Genomicinstability inlaminopathy based premature aging,NatMed. 2005;11:780 785. 13.MisteliT,ScaffidiP.Genomeinstability inprogeria:when repairgetsold,NatMed. 2005;11:718 719. 14.PereiraS,Bourgeois P,NavarroC,EstevesVieiraV,CauP,De SandreGiovannoli A,LvyN.HGPSandrelatedpremature aging disorders: Fromgenomicidentification tothefirsttherapeutic approaches, MechAgeingDev.2008;129:449 459. 15.SmithED,Kudlow BA,FrockRL,KennedyBK.Atypenuclear", + "Nature Genetics | Volume 55 | February 2023 | 268279 278 Article https://doi.org/10.1038/s41588-022-01279-621. Tiwari, V. & Wilson, D. M. 3rd. DNA damage and associated DNA repair defects in disease and premature aging. Am. J. Hum. Genet. 105, 237257 (2019). 22. Tamae, D., Lim, P., Wuenschell, G. E. & Termini, J. Mutagenesis and repair induced by the DNA advanced glycation end product N2-1-(carboxyethyl)-2-deoxyguanosine in human cells. Biochemistry 50, 23212329 (2011).", + "[36] J. de Boer, J.O. Andressoo, J. de Wit, J. Huijmans, R.B. Beems, H. van Steeg, et al., Premature aging in mice decient in DNA repair and transcription, Science 296 (2002) 12761279. [37] S.M. Schuh-Huerta, N.A. Johnson, M.P. Rosen, B. Sternfeld, M.I. Cedars, R.A. Reijo Pera, Genetic markers of ovarian follicle number and menopause in women of multiple ethnicities, Hum. Genet. 131 (2012) 17091724." + ], + [ + "During the past century, remarkable progress has been made in unveiling the mechanisms of aging. Genetic and molecular pathways that regulate healthspan and lifespan have been identified in various model organisms, provid-ing a rich knowledge base (Longo etal. 2015; Lopez-Otin etal. 2013, 2016; Singh etal. 2019). However, the focus on", + "series of recent breakthroughs, a number of genes capable ofaltering the aging process as a whole or at least to a largedegree have been identified in animal models and even a fewin humans (Finch & Ruvkun, 2001; de Magalhes, 2005; Kenyon,2005). Furthermore, multiple alleles have been examined fortheir association with human exceptional longevity (Vijg & Suh,2005). This is a fascinating and important area of research, yetthere are now so many genes being associated with aging andlongevity that keeping", + "Recent developments on the genetics of aging can be seen as several streams of effort. In general, humans show a relatively modest ( <50%) heritability of", + "One approach that has become increasingly common in the characterization of the ge-netics of aging is to isolate aging mutants, usually from mutagenesis experiments, andthen to determine the mechanistic basis for the unusual life span in the mutants. Thisapproach has led to the discovery of genes that can enhance (e.g., Maynard Smith 1958;Lin et al. 1988; reviewed in Guarente and Kenyon 2000, Kim 2007) or reduce life span(e.g., Pearl and Parker 1922). Most of the large-effect mutants affecting aging", + "One approach that has become increasingly common in the characterization of the ge-netics of aging is to isolate aging mutants, usually from mutagenesis experiments, andthen to determine the mechanistic basis for the unusual life span in the mutants. Thisapproach has led to the discovery of genes that can enhance (e.g., Maynard Smith 1958;Lin et al. 1988; reviewed in Guarente and Kenyon 2000, Kim 2007) or reduce life span(e.g., Pearl and Parker 1922). Most of the large-effect mutants affecting aging", + "genetics of aging I. What is aging? Frontiers in Genetics. doi:10.3389/fgene.2012.00134. r ose, Michael r ., Anthony D. Long, Laurence D. Mueller, Cristina L. r izza, Kennedy C. Matsagas, LeeF. Greer, and Bryant villeponteau. 2009. e volutionary nutrigenomics. In The future of aging, eds. G. M. Fahy, M. D. West, L. S. Coles, and S. B. h arris. Berlin: Springer. r ushton, J. p hillippe. 1995. Race, evolution, and behavior: A life history approach. New Brunswick, NJ: Transaction p ublishers.", + "informed by age-related disease identifies loci for exceptional human longevity. Li H, editor. PLoS Genet. 2015. https://doi.org/10.1371/journal.pgen. 15. Polderman TJC, Benyamin B, de Leeuw CA, Sullivan PF, van Bochoven A, Visscher PM, etal. Meta-analysis of the heritability of human traits based on fifty years of twin studies. Nat Genet. 2015;47:7029. 16. Cellerino A, Ori A.What have we learned on aging from omics studies? Semin Cell Dev Biol. 2017;70:17789.", + "eries that have inspired thousands of researchers across the world to study aging, and we acknowledge the wider significance of the creation of a field that has the potential to transform human health. Genetics Aging is influenced by genetic factors. It may be surprising to know that as recently as the 1970s and 1980s, the concept of modulating Downloaded from https://academic.oup.com/biomedgerontology/article/76/7/e85/6145792 by guest on 15 October 2023", + "discover specific genes that directly influence how quickly people age, beyond diseases. If such genes exist, their effects were too small to be detected in this study. The next step will be to expand the study to include more participants, which will hopefully pinpoint further genomic regions and help disentangle the biology of ageing and disease. DOI: https://doi.org/10.7554/eLife.39856.002", + "males: what are the molecular and evolutionary causes? Aging Cell. 2007;6:225233. doi:10.1111/j.1474-9726.2007.00279.x 63. Benayoun BA, Pollina EA, Brunet A. Epigenetic regulation of ageing: link- ing environmental inputs to genomic stability. Nat Rev Mol Cell Biol. 2015;16:593610. doi:10.1038/nrm4048 64. Sen P, Shah PP, Nativio R, Berger SL. Epigenetic mechanisms of longevity and aging. Cell. 2016;166:822839. doi:10.1016/j.cell.2016.07.050" + ], + [ + "Figure 1. Epigenetics of aging and aging-relate d diseases. During aging, various ep igenetic alterations occur including accumulation of histone variants, change s in chromatin accessibility mediated by chromatin remodeling complexes, loss of histones and heterochroma tin, imbalance of activating /repressing histone modifications and aberrant expres- sion/activity of miRNAs. These deregulations can affect transcrip tion and, subsequently, transl ation, as well as the stabi-", + "ment of 5 years corresponded to a 21% increased risk of mortality overall [7]. Thus, predictions of epigenetic agemay be an indication of an individual s biological state of aging. Beyond these examples of advanced epigenetic aging, a complementary but unanswered question is whether epigenetic clocks can also be slowed. Epigenetic aging studies in humans have not thus far been well suited to address questions of slowed aging, given the lack of well-documented interventions that enhance health or", + "al., 2005 ). The epigenetic changes that accumulated with age had a dramatic effect on gene expression, thus the authors propos e that a so-called epigenetic drift accompanies the aging process. Epigenetic modifications can result in the cumulative loss of gene regulation over time, ultimately impairing cellular and tissue function. Further, recent data sugge st that epigenetic disruption of tissue specific stem and progenitor cells may play a role in cancer development (Feinberg et al., 2006 ). The", + "epigenetic changes during aging are currentlyunknown (Fig. 3). It has been suggested thatthe epigenetic alterations are largely triggered by DNA damage (reviewed in Oberdoerffer and Sinclair 2007). In this scenario, randomlyoccurring DNA damage leads to chromatin remodeling and to redistribution of chromatin modiers within the genome with modiersbeing recruited away from their normal sites so that they can engage in the repair of the", + "Epigenetic Dysregulation with Age", + "Epigenetic Dysregulation with Age", + "Recently, studying the direct relationship between epigeneticmechanisms and the aging process itself is gaining increasing attention. The potential reversibility of these epigenetic changes that occur as a hallmark of aging offers excitingopportunities to alter the trajectory of age-related diseases. 8 This is especially important given the remarkable plasticityof aging. 9,10In the literature, age-associated epigenetic alter- ations have been identified by epigenome-wide association", + "in gene transcription and, as a consequence, translation as well as the stabilization or degradation of molecular factors. While mechanisms underlying aging-related pathologies remain to be elucidated in detail, various studies demonstrate an epigenetic component. In fact, the aforementioned epigenetic modications were shown to play essential roles in diseases including inammation, cancer, osteoporosis, neurodegenerative diseases, and diabetes.", + "PLoS Biology | www.plosbiology.org August 2007 | Volume 5 | Issue 8 | e201 1759 Epigenetic Dysregulation with Age", + "and increased expression of proaging genes such as the cell-cycle inhibitor p16, which drives cell senescence. Additional consequences of epigenetic dys-regulation include increased transcriptional noise and decreased coordination of gene expression that contributes to organismal aging. Cell148, January 20, 2012 2012 Elsevier Inc. 53" + ], + [ + "27 Willcox, B. J. et al. 2008 FOXO3A genotype is strongly associated with human longevity. Proc. Natl Acad. Sci. USA 105, 13 98713 992. ( doi:10.1073/ pnas.0801030105 ) 28 Flachsbart, F., Caliebe, A., Kleindorp, R., Blanche, H., von Eller-Eberstein, H., Nikolaus, S., Schreiber, S. & Nebela, A. 2009 Association of FOXO3A variationwith human longevity conrmed in GermanGenomics of human longevity P . E. Slagboom et al. 41", + "3. Willcox BJ, Donlon TA, He Q et al (2008) FOXO3A genotype is strongly associated with human longevity. Proc Natl Acad Sci USA 105(37):1398713992. doi: 10.1073/pnas.0801030105 4. Anselmi CV, Malovini A, Roncarati R et al (2009) Association of the FOXO3A locus with extreme longevity in a southern Italian centenarian study. Rejuvenation Res 12(2):95104. doi: 10.1089/ rej.2008.0827 5. Flachsbart F, Caliebe A, Kleindorp R et al (2009) Association of FOXO3A variation with human longevity conrmed in German", + "are, in fact, part of the same insulin/IGF1/GH pathway(Fig. 1) that modulates lifespan across organisms (Ke-nyon, 2010). A strong association between FOXO3 and human longevity has been reported (Willcox et al., 2008)and subsequently validated in other populations (forreview, see Kenyon, 2010). FOXO3 was also associatedAGING GENES AS TARGETS FOR DRUG DISCOVERY 95", + "Biogerontology 11:28797 117. Willcox BJ, Donlon TA, He Q, Chen R, Grove JS, et al. 2008. FOXO3A genotype is strongly associated with human longevity. Proc. Natl. Acad. Sci. USA 105:1398792 118. Soerensen M, Dato S, Christensen K, McGue M, Stevnsner T, et al. 2010. Replication of an association of variation in the FOXO3A gene with human longevity using both case-control and longitudinal data. Aging Cell 9:101017 119. Mardis ER. 2011. A decades perspective on DNA sequencing technology. Nature 470:198203", + "FOXO3 locus is associated with extreme longevity in humans (centenarians) [2, 58, 59]. NRF/SKN-1 activates the expression of genes involved in protecting the cell in response to ROS, toxins, and metabolic changes through mTOR and insulin/IGF signaling, and it is also dysregulated later in life [60, 61]. Increasing the levels of L. Garca-Velzquez and C. Arias", + "A. 2003;100:406671. https://doi.org/10.1073/pnas.2628028100. 24. van den Akker EB, Deelen J, Slagboom PE, Beekman M. Exome and whole genome sequencing in aging and longevity. Adv Exp Med Biol. 2015;847:12739. https://doi. org/10.1007/978-1-4939-2404-2_6. 25. Flachsbart F, etal. Association of FOXO3A variation with human longevity confirmed in German centenarians. Proc Natl Acad Sci U S A. 2009;106:27005. https://doi.org/10.1073/ pnas.0809594106. A. Garca-Venzor and E. A. Mandujano-Tinoco", + "X.L., 2009. Genetic association of FOXO1A and FOXO3A with longevity trait in Han Chinese populations. Hum. Mol. Genet. 18, 48974904. Lunetta, K.L., DAgostino Sr., R.B., Karasik, D., Benjamin, E.J., Guo, C.Y., Govindaraju, R., Kiel, D.P., Kelly-Hayes, M., Massaro, J.M., Pencina, M.J., Seshadri, S., Murabito, J.M., 2007. Genetic correlates of longevity and selected age-related phenotypes:", + "theFOXO3 locus is not surprising, since this locus was previously reported in the longevity GWA study from the CHARGE con- sortium 7, from which many cohorts are included in these meta- analyses. So far, three functional longevity-associated variants have been identi ed at the FOXO3 locus (rs2802292, rs12206094, and rs4946935). For all of them, an allele-speci c response to cellular stress was observed. Consistently, the longevity-associated alleles of all three variants were shown to induce FOXO3", + "exceptional longevity with no significant genetic contribution. Interestingly, the authors found that FOXO3A, a longevity allele, may not be related to healthy aging phenotype [29]. Aging is a complex process usually accompanied by the onset of different dis- eases like neurodegenerative disorders (Alzheimers disease and Parkinsons dis- ease), cardiovascular illnesses, and cancer. The study of the genetic basis of these aging-related diseases is another approach in the study of the genomic basis of", + "centenarians. Proc Natl Acad Sci USA 106(8):27002705. doi: 10. 1073/pnas.0809594106 6. Li Y, Wang WJ, Cao H et al (2009) Genetic association of FOXO1A and FOXO3A with longevity trait in Han Chinese populations. Hum Mol Genet 18(24):48974904. doi: 10.1093/ hmg/ddp459 7. Soerensen M, Dato S, Christensen K et al (2010) Replication of an association of variation in the FOXO3A gene with human longevity using both case-control and longitudinal data. AgingCell 9(6):10101017. doi: 10.1111/j.1474-9726.2010.00627.x" + ], + [ + "of multiple genes with each other and withthe environment. Evidence from animal systems showsa major impact of the environment on aging, yet envi-ronmental manipulations of aging act through genesand proteins, usually by triggering signaling pathwaysand modulating gene expression. In fact, some geneshave been shown in model organisms to have varyingeffects on lifespan depending on diet (Heikkinen et al.,2009). Genes that can regulate aging in model organ-isms cannot be directly applied to humans through", + "Several studies show the influence of the environment on the ageing process [24]. Environmental factors may affect homeostasis and lead to the development of dis- eases, thus affecting the quality of life in older age [25]. They also produce cellular damage, which causes an accelerated shortening of the telomeres at the genetic level, accompanied by changes in DNA methylation, acetylation or deacetylation of histones, among others. Altogether, these changes induce an aberrant gene", + "changes are generated during the aging process. For a long time it has been believed that epigenetic modications occurring during aging may depend on environmental factors. This idea is attractive because, if true, epigenetics could provide a link between the environment, disease and aging. It also opens the possibility of targeted intervention aimed, for example, at improving healthspan or healthy aging. Thus, the rst question is whether specic environmental factors can directly induce specic epigenetic", + "In addition, environmental factors influence the organism s ability to withstand the increase in entropy with aging: for example, caloric restriction and smoking can exert opposite effects on the rate ofaging (Colman et al. 2009 ; Fraser and Shavlik 2001 ). Both protective alleles and a benevolent environment contribute to excess physiological capacity, which in turn indirectly determines an individual s healthy life span and longevity (Martin et al. 2007 ). The well-", + "to humans through ge-netic manipulations for numerous legal, ethical, andtechnical reasons. If we could understand how the envi-ronment modulates these aging-related genes, we mightbe able to create antiaging therapies applicable to hu-mans, potentially through diet, lifestyle, and even phar-macological interventions. Therefore, understanding ge-nome-environment interactions in the context of agingcan be a powerful approach to identify attractive targetsfor drug design.", + "ing human life span have been identified [2,3]. At the same time, there is a growing realization that environ- mental factors are major contributors to aging and age- associated illness. Epigenetics is the study of chemical modifications of the genome, heritable by cell progeny, and it has been an attractive target for studies of aging and environmentally influenced disease. Several groups have shown differences in DNA methylation - a covalent", + "al., 2009; Stanfel et al., 2009). Many of these genesmodulate the response to environmental signals, such asfood availability, and act in signaling pathways that ifunderstood can be targeted (Fig. 1). The genetic regula-tion of aging is therefore an emerging field with multipleapplications in the human nutrition, cosmetic, and phar-maceutical industries. AGING GENES AS TARGETS FOR DRUG DISCOVERY 91", + "standing the cause and mechanisms of aging is imperative in assisting to suppress age-related diseases and promote healthylongevity. It is well-known that aging is influenced by a combin- ation of genetic and environmental factors. Previous twin stud- ies have shown that the genetic contribution to general human longevity is about 2030% [ 4,5], whereas environmental factors in human aging and longevity still account for the largest effect. Epigenetic factors influence the regulation of gene expres-", + "known to affect the function of epigenetic regulators, this may be an example of how aging interacts with our genome to inuence AD development.", + "consequently the incidence of age-related diseasessuch as heart disease, cancer, and neurodegenerativediseases, is projected to increase considerably in thecoming decades. Findings from model organisms haverevealed that aging is a surprisingly plastic processthat can be manipulated by both genetic and environ-mental factors. Here we review a broad range of find-ings in model organisms, from environmental to ge-netic manipulations of aging, with a focus on thosewith underlying gene-environment interactions" + ], + [ + "senescence, exhausting the ability for a tissue to regenerate after injury, impacting mitochondrial function,and inducing protein aggregation. Senescent cells have altered metabolism, and they can secreteproinammatory factors and alter the local tissue environment, thereby contributing to aging andage-related degenerative diseases. In addition, stem cell function can be impacted by DNA damage by bothcell autonomous and nonautonomous mechanisms. Proper function of mitochondria is dependent upongenome", + "[87] and the accumulation of senescent cells in human tissues with age has been implicated as a driver of aging- related diseases. Indeed, pharmacological approaches targeting senescent cells, like senolytics, are a major and timely area of research that could result in human clin- ical applications [ 5,88]. It is imperative that we fully understand and deconstruct cellular senescence in order to target aging-related diseases. We hope that CellAge will help researchers understand the role that CS plays", + "An important source of inflammatory signals in aged organ- isms is thought to be the accumulation of senescent cells across tissues [ 5,7]. Indeed, accumulating evidence has shown that senescent cells are characterized by a senescence-associatedsecretory phenotype [ 810], which includes a panoply of pro-inflammatory cytokines, proteases, growth factors and metabolites [ 10,11]. The impact of senescent cells on age-related inflammation, and their potential role as a target for pro-", + "senescent cells [150]. SASP factors exert their functions in either an autocrine or a paracrine manner and are responsible for the induction of the chronic inflammation and cell proliferation that contributes to cell dysfunction and cancer. Thus, the accu- mulation of senescent cells in tissue is closely associated with aging-related dis- eases. Recently, it was determined that senescent fibroblasts significantly increase the expression of HLA-E, which inhibits the receptor NKG2A in killer cells, and", + "atherosclerosis, osteoarthritis, sarcopenia, ulcer formation, cancer, and Alzheimer disease, which is suggestive of a causative role. However, the most convincing evidence that senescent cells causeaging comes from recent genetic (85) and pharmacologic studies (86) revealing that clearance of senescent cells can prevent or delay tissue dysfunction and extend health span. Senescent cells induce autocrine, as well as paracrine, signaling by secretion of proinamma-", + "senescence can deplete both stem (5153) and stromal (10,11) cell pools. Moreover, because senescent cellspersist, they have the ability to alter the tissue micro-environment, and can therefore also promote the degen-eration of organs and stem cell niches (14,46). Finally, senescent cells secrete factors such as matrix metallopro- teinase-3 (MMP-3), which favors extra-cellular matrixremodeling, promotes defects in epithelial cell dierentia-tion and stimulates cancer cell growth (46,54,55).", + "potential role of senescence in in vivo aging and disease has been difficult to assess and somewhat controversial [146]. However, recent studies have shown that senescent cells accumulate in normal arterial tissue over the lifespan of humans [147, 148]. Likewise, the accumulation of senescent cells has been reported in diseased tissues, such as atherosclerotic plaques [149] and abdominal aortic aneurysms [150]. Baker et al. showed that", + "51. Jeyapalan JC, Ferreira M, Sedivy JM, Herbig U. 2007. Accumulation of senescent cells in mitotic tissue of aging primates. Mech. Ageing Dev. 128:3644 52. Boyle J, Kill IR, Parris CN. 2005. Heterogeneity of dimer excision in young and senescent human dermal broblasts. Aging Cell 4:24755 53. Seluanov A, Mittelman D, Pereira-Smith OM, Wilson JH, Gorbunova V. 2004. DNA end joining becomes less efcient and more error-prone during cellular senescence. PNAS 101:762429", + "in many accelerated-aging mouse models and in a plethora of human age-associated pathologies, including osteoporosis, atherosclerosis, glomerular disease, diabetic venous ulcers, chronic ob-structive pulmonary disease and emphysema, osteoarthritis, herniated intervertebral discs, and vascular calcication (112). Senescent cells are resistant to apoptosis and accumulate exponen- tially with age as a consequence of inefcient clearance. Unlike apoptotic tissues, senescent tissues 436 VermeijHoeijmakersPothof", + "wound healing [ 8], and immune clearance [ 9,10]. By contrast, the gradual accumulation and chronic persistence of senescent cells with time promotes dele- terious effects that are considered to accelerate deterior- ation and hyperplasia in aging [ 11]. Senescent cells secrete a cocktail of inflammatory and stromal regula- torsdenoted as the senescence-associated secretory phenotype, or SASP which adversely impact neighbor- ing cells, the surrounding extracellular matrix, and other" + ], + [ + "Dietary interventions, including starvation and protein deprivation, can also alter patterns of DNA methyla- tion, potentially in a long-lasting manner [42, 43], including transgenerationally [26, 44]. Dietary, genetic and pharmacological interventions that improve health during aging and extend lifespan induce long-lasting changes in gene expression that mediate their effects. Here we have asked if and how age-related DNA methylation, transcription and lipid", + "Longev. Heal. 2, 10 (2013). 7. Kreienkamp Ret al.Doubled lifespan and patient-like pathologies in progeria mice fed high-fat diet. Aging Cell18, e12852 (2019). [PubMed: 30548460] 8. Heilbronn LK & Ravussin E Calorie restriction and aging: review of the literature and implications for studies in humans. Am. J. Clin. Nutr. 78, 361369 (2003). [PubMed: 12936916] 9. Liang Yet al.Calorie restriction is the most reasonable anti-ageing intervention: a meta-analysis of", + "a medical intervention), without changing the fundamental rateof organismal aging. Nevertheless, it does seem that manyso-called longevity genes, as well as dietary restriction, appear to extend not only life span, but also health span (Kauffman et al., 2010; Luo et al., 2010 ). In that regard, it does appear that it is possible to experimentally slow the rate of aging. Still, in each case, aging does continue on as if there is some", + "As we describe above, a small but growing number ofinterventions has been shown to reproducibly increase lifespan in laboratory animals and, in a few cases, to also delay or reverse age-related declines in multiple organsystems. These healthy aging interventions could, in prin- ciple, be tested to determine whether they also increase lifespan and promote healthspan in dogs (Table 1). There are several questions that immediately present themselves when considering the design of a healthy aging interven-", + "be linked to the biology of stem cell quiescence and self-renewal. Although genetic and environmental interventions have clearly proven to be effective in prolonging life span, we postulate thatthose interventions, as well as the rejuvenating interventions described above, are, in fact, acting primarily to modify theepigenome. Consistent with this, genetic interventions directlytargeting the epigenome can extend life span ( Greer et al., 2010 ). Studying aging and rejuvenation through the lens of", + "During the past century, remarkable progress has been made in unveiling the mechanisms of aging. Genetic and molecular pathways that regulate healthspan and lifespan have been identified in various model organisms, provid-ing a rich knowledge base (Longo etal. 2015; Lopez-Otin etal. 2013, 2016; Singh etal. 2019). However, the focus on", + "205. Li, Y.; Tollefsbol, T.O. p16INK4a Suppression by Glucose Restriction Contributes to Human Cellular Lifespan Extension through SIRT1-Mediated Epigenetic and Genetic Mechanisms. PLoS ONE 2011 ,6, e17421. [CrossRef] 206. Daniel, M.; Tollefsbol, T.O. Epigenetic linkage of aging, cancer and nutrition. J. Exp. Biol. 2015 ,218, 5970. [CrossRef] 207. Kapahi, P .; Kaeberlein, M.; Hansen, M. Dietary restriction and lifespan: Lessons from invertebrate models. Ageing Res. Rev. 2017 , 39, 314. [CrossRef]", + "as diabetes, cancer and neurodegenerative disorders [1, 2]. Environmental and genetic interventions can ameliorate the effects of aging, with nutrition, nutrient-sensing signaling networks and metabolism playing evolutionarily conserved roles [1, 3 5]. Diet- ary restriction (DR), in which food intake is reducedwhile avoiding malnutrition, extends lifespan in di- verse model and non-model organisms [3, 6]. DR induces a remarkably broad-spectrum improvement in", + "53. Mair W & Dillin A Aging and survival: the genetics of life span extension by dietary restriction. Annu. Rev. Biochem. 77, 727754 (2008). [PubMed: 18373439] 54. Masoro EJCaloric restriction-induced life extension of rats and mice: a critique of proposed mechanisms. Biochim. Biophys. Acta1790, 10401048 (2009). [PubMed: 19250959] 55. Weindruch R, Walford RL, Fligiel S & Guthrie D The retardation of aging in mice by dietary", + "In addition to genes associated with aging, research has focused on identifying genes associated with the life- extending effects of CR. One method is to identify genesthat decrease or cancel out the life-extending effects of CRwhen mutated (Gems et al., 2002; Bishop and Guarente,2007). More than 100 such genes have been identified inmodel organisms (D. Wuttke, C. Vora, J. P. de Magalhes,unpublished observations). The growth hormone receptor(GHR) is the only gene so far identified in mammals that" + ], + [ + "vided one of the most reliable aging biomarkers. An epigenetic clock is a group of CpG sites with particular methylation patterns that are highly related to the chrono- logical age of an individual. This correlation is very robust (r=0.9) for individuals between 20 and 100years. The epigenetic clock is a breakthrough discovery that will allow novel experimental approaches to understand the biological basis of aging [113]. For example, by using the epigenetic clock as a measure of cellular", + "Epigenetic Clock Chronological age is the number of years a person has lived, and biological or phys- iological age refers to a measure of how well your body functions compared to your chronological age. Biological age is influenced by multiple factors (genes, lifestyle, behavior, environment, among others) and correlates with mortality and health sta- tus. The epigenetic clock is one potentially reliable predictor of biological age.", + "Background Epigenetic clocks are sets of CpG dinucleotides whose DNA methylation (DNAm) can be used to accurately predict a person s chronological age [ 1]. In recent years, various epigenetic clocks have been developed [ 25]. Well-known examples are the clocks de- veloped by Hannum et al., trained on blood samples and containing 71 CpGs [ 2], and Horvath, a multi-tissue predictor consisting of 353 CpGs [ 3]. A popular application of", + "An EpigeneticClock The aging transcriptome could be used to gauge the physiological age of worms, and in that way serve as an epigenetic clock revealing how much of life span has been spent and how much remains (23). Middle-aged worms show an aging transcriptome half-way between the aging expression profiles of young and old worms. This provides an independent way to assess the age of an animal independent of its life span. This is important as there are at least 2 explanations to", + "The epigenetic aging clock measures the sum of all the age-related pathways affecting cellular physiology in old age. The aging epigen- etic clock is heavily enriched for germline- and intestinal-expressed genes, but lack muscle- and neuronal-expressed genes (23, 25). Expression changes in the germline and intestine were expected as there are massive changes in the morphology of gonad at the end of fertility and the intestine in old age. The aging transcriptome pro-", + "etic mouse aging and may be used to inform future studies in other model organisms and humans focused on studying the relationship between epigenetic aging and metabolism. Introduction Epigenetic clocks are widely used molecular biomarkers of aging (Horvath and Raj, 2018). These DNA methylation (DNAm) age predictors are based on the methylation levels of select CpGs that are RESEARCH ARTICLE *For correspondence: kmozhui@uthsc.edu Competing interest: See page 22 Funding: See page 22", + "etic mouse aging and may be used to inform future studies in other model organisms and humans focused on studying the relationship between epigenetic aging and metabolism. Introduction Epigenetic clocks are widely used molecular biomarkers of aging (Horvath and Raj, 2018). These DNA methylation (DNAm) age predictors are based on the methylation levels of select CpGs that are RESEARCH ARTICLE *For correspondence: kmozhui@uthsc.edu Competing interest: See page 22 Funding: See page 22", + "estimators epigenetic clocks; telomere length; transcriptomic-, proteomic-, and metabolomic-based estimators; and composite biomarkers concluded that the epi- genetic clock is the most promising molecular estimator of biological age [26]. Epigenetic age estimators are sets of CpGs (also known as clock CpGs) that are coupled with a mathematical algorithm to estimate the age of a DNA source, such as cells, tissues, or organs. This estimated age, also referred to as epigenetic age or", + "proved epigenetic clock. It should be noted that building a biological age predictor is difficult since there is no clear definition of biological age. Nevertheless, one of the essential features of biological age is its ability to in- dicate the different ageing rates between individuals with the same chronological age. A previous study has re- ported a number of CpG sites that show variation in the longitudinal changing rates between individuals [ 40].", + "ranging from 0.15 to 0.19 [ 8,9]. Individuals with epigenetic clock estimates greater than their chronological age display age acceleration and have been shown to be at a greater risk of all-cause mortality and multiple adverse health outcomes [ 10]. Conse- quently, identification of genetic and environmental contributors to the variation in these measures in populations has become a major goal in the field [ 11]. The first generation of epigenetic aging clocks used penalized regression models to" + ], + [ + "the nematode Caenorhabditis elegans , and the budding yeast Saccharomyces cerevisiae , have emerged as the most widely used and, hence, best characterized, model organisms in bio- gerontology. When considering the use of simple eukaryotes to study aging and age-related disease, it is pertinent to ask whether, and to what degree, the aging process is evolutionarily con- served. Does a yeast cell age by the same mechanism(s) as a", + "Studies on the aging of mammals are rather limited by the long life span of the commonly used model organisms. Thus, both nonverte-brate and invertebrate organisms, with their shorter life span and ease of genetic and environmental manipulations, gained popularity amongresearchers in the aging field as experimental models for aging studies. Among them, budding yeast or Saccharomyces cerevisiae is a highly in- formative organismal model for aging studies with its genetic tools,", + "Abstract Cellular models such as yeasts are a driving force in biogerontology studies. Their simpler genome, short lifespans and vast genetic and genomics resources make them ideal to characterise pro-ageing and anti-ageing genes and signalling pathways.Over the last three decades, yeasts have contributed to the understanding of fundamental aspects of lifespan regulation including the roles of nutrient response, global protein translation rates and quality, DNA damage, oxidative stress,", + "usually chosen for convenience rather than for specific features applicable to human aging. Hence, choosing the suitable animal model to answer the specific question we aim to understand is of high importance in these types of studies. Among the most prevalent aging model organisms are Saccharomyces cerevisiae , Caenorhabditis elegans, Drosophila melanogaster, and Mus mus - culus . As a single-celled organism, S. cerevisiae is easily grown,", + "mammalian genes that affect aging than any other model organism. Aging in yeast is assayed primarily by measurement of replicative or chronological life span. Here, we review the genes and mechanisms implicated in these two aging model systems and key remaining issues that need to be addressed for their optimization.", + "be more exaggerated in more distantly related species (such as the worm and mouse models). There are, however, simi - larities between aged humans and aged model organisms; they all tend to have decreasing overall fitness, and there - fore, studies using model organisms continue as they may be at least indicative of some aging mechanisms in humans. Extensions to life span in model organisms are mostly associated with disruption to fundamental metabolic path -", + "eukaryote model organisms, namely yeast, worms, ies,and sh, as well as mice and rats, to explore both genetic and environmental determinants of lifespan. While these short-lived models have each yielded a number of fasci- nating ndings and insights into hypotheses surrounding extended lifespan and healthspan, they may also haveconstrained this complex, multifactorial eld to areas in which they are best suited, most notably short-term inter-", + "et al., 2010 ). These effects require an intact germline, andTable 2. Repositories and Tools for Aging Research Models Description Link/Reference Yeast Saccharomyces genome database http://www.yeastgenome.org/ published lifespan data http://lifespandb.sageweb.org/ (McCormick et al., 2015 ) Wilcoxon rank sum test to test signicance of lifespan differenceshttp://data.kaeberleinlab.org/scripts/ranksum.php yeast outgrowth data analyzer (YODA) for chronological lifespan assayshttp://yoda.sageweb.org/", + "for molecular biological studies on aging. Although material from humans should be employed where possible, for prac- tical reasons animal model systems like rats and mice are indispensible. There is evidence that, provided their health sta- tus and husbandry is optimal, rodents age much in the same way as humans do (Burek 1978). For studying certain funda- mental processes, such as the occurrence of various types of DNA rearrangement, lower organisms and cell lines can also", + "short life span, and fully sequenced genome (20 ,21). Despite being uni- cellular, yeast has been an excellent model to identify and characterize conserved basic biological processes, including aging. Yeast has beenextensively used to identify genes and interventions responsible for lifespan extension and to gain insights into the aging processes of all eu- karyotic organisms. In parallel, over the years, studies on invertebrate organisms, such as Drosophila melanogaster (flies) and Caenorhabditis" + ], + [ + "need to develop approaches and therapies targeting theaging process and age-related diseases (Butler et al.,2008). Delaying the process of aging, even slightly,would have profound social, medical and economic ben-efits (Olshansky et al., 2006; Butler et al., 2008). Forexample, slowing aging by a mere 7 years would cutmortality of age-related diseases by half at every age.Therefore, the potential benefits from research on thebasic biology and genetics of aging are unparalleled interms of improving quality", + "raises the possibility of therapies to slow aging. Therefore the discoveryof a gerontogene with even very rare mutations that increased longevitywould cause speculation about future trends in mortality. However, thediscovery of such a gene would be relevant only to long-term (and, there-fore, very speculative) projections. Prospective Epidemiologic Surveys that Include Genetic Information Some epidemiologic cohort studies of populations have collected", + "Interestingly, when senescent cells are abolished either through genetic manipulation or via senolytic drugs, biological aging is signicantly halted in mice [ 53,54]. Therefore, trials are now under way to test the ability of senolytics to postpone age-associated pathologies in humans [ 55]. Notably, multi- ple drugs are being pursued that either directly or indirectly impact DNA repair or the consequenceof DNA damage. Future Prospects: Developing Interventions through DNA Repair", + "5. Goldman DP, etal. Substantial health and economic returns from delayed aging may warrant a new focus for medical research. Health Aff (Millwood). 2013;32(10):1698705. 6. Esplin ED, Oei L, Snyder MP.Personalized sequencing and the future of medicine: discov- ery, diagnosis and defeat of disease. Pharmacogenomics. 2014;15(14):177190. 7. Marian AJ.Clinical applications of molecular genetic discoveries. Transl Res. 2016;168:614.", + "J.L. Kirkland, Barriers to the Preclinical Development of Therapeutics that Target Aging Mechanisms, J. Gerontol. A Biol. Sci. Med Sci. 71 (11) (2016) 1388 1394 . [2]D.J. Baker, B.G. Childs, M. Durik, M.E. Wijers, C.J. Sieben, J. Zhong, R.A. Saltness, K.B. Jeganathan, G.C. Verzosa, A. Pezeshki, K. Khazaie, J.D. Miller, J.M. van Deursen, Naturally occurringp16(Ink4a)-positive cells shorten healthy lifespan, Nature 530 (7589) (2016) 184 189.", + "series of recent breakthroughs, a number of genes capable ofaltering the aging process as a whole or at least to a largedegree have been identified in animal models and even a fewin humans (Finch & Ruvkun, 2001; de Magalhes, 2005; Kenyon,2005). Furthermore, multiple alleles have been examined fortheir association with human exceptional longevity (Vijg & Suh,2005). This is a fascinating and important area of research, yetthere are now so many genes being associated with aging andlongevity that keeping", + "pharmaceutical and other interventions for human aging based on research that starts with the genomic information required to sustain adaptation, and thus health, in older fruit flies [36-39]. Naturally, any such genomic short-cut to reverse-engineering the evolution of slowed aging from fruit flies to humans is fraught with potential for error. Such evolutionarily deep orthologies are sure to supply", + "century. Manipulation of aging-related genes by diet,lifestyle, and pharmaceuticals could dramatically im-prove human health and could be used to develop drugsagainst age-related diseases such as cancer, heart dis-ease, type 2 diabetes, obesity, and neurodegenerativediseases. The hundreds of aging-related genes and genesrelated to CR already identified offer enormous oppor-tunities for target discovery (Fig. 2). Although aging-related genes cannot be modified in humans, under-standing how these can be", + "[7] Hughes, S.E., Evason, K., Xiong, C., Kornfeld, K. Genetic and pharmacological factors that influence reproductive aging in nema- todes. PLoS Genet. 2007 , 3: e25. [8] Vijg, J., Campisi, J. Puzzles, promises and a cure for ageing. Na- ture 2008 , 454: 1065-1071. [9] Rolland, Y., Czerwinski, S., Abellan Van Kan, G., Morley, J.E., Cesari, M., Onder, G., Woo, J., Baumgartner, R., Pillard, F., Boirie, Y., Chumlea, W.M., Vellas, B. Sarcopenia: its assessment, etiol-", + "for the aging process during the 20th Century. Thissituation poses a fundamental challenge to anti-aging medicine: how to develop effective therapies for a genomically complex pathology. We propose such astrategy. As a rst step, we recommend the use of modelsystems in which signicant genetic intervention is not proscribed or impractical. Second, we propose that work" + ], + [ + "caloric restriction. Physiol. Genom. 17, 307 315.Van Remmen, H., Ward, W.F., Sabia, R.V ., Richardson, A., 1995. Gene expression and protein degradation. In: Masoro, E.J. (Ed.), Handbook ofPhysiology. Section 11: Aging. Oxford University Press, New York, pp. 171234. Weindruch, R., Walford, R.L., 1982. Dietary restriction in mice beginning at 1 year of age: effect on life-span and spontaneous cancer incidence.Science 215, 1415 1418.S.R. Spindler / Mechanisms of Ageing and Development 126 (2005) 960 966 966", + "extension by dietary restriction. Annu Rev Biochem 2008, 77:727-54. 8. Harper JM, Leathers CW, Austad SN: Does caloric restriction extend life iin wild mice? Aging Cell 2006, 5:441-9. 9. Forster MJ, Morris P, Sohal RS: Genotype and age influence the effect of caloric intake on mortality in mice. FASEB J 2003, 17:690-2. 10. Spindler SR, Mote PL: Screening candidate longevity therapeu- tics using gene-e xpression arrays. Gerontology 2007, 53:306-21.", + "analysis in calorie-restricted rats implicates epigenetic and post-translational mechanisms in neuroprotection and aging. Genome Biol. 2015;16:285. 21. Gillespie ZE, Pickering J, Eskiw CH. Better living through chemistry: caloric restriction (CR) and CR mimetics alter genome function to promote increased health and lifespan. Front Genet. 2016;7:142. 22. Jiang T, Liebman SE, Lucia MS, Phillips CL, Levi M. Calorie restriction modulates renal expression of sterol regulatory element binding proteins, lipid", + "Calorie restriction, a dietary regimen that extends the lifespan of numerous organisms, also delays the majority of age-related gene-expression changes in mice and, to a certain extent, in flies45,50. It is currently unclear whether the effect of calorie restriction on gene expression underlies its beneficial effect on lifespan or is merely a consequence thereof. Findings in yeast suggest that there may be a causal link: Sir2 not only facilitates heterochromatin and promotes DNA stability, but is", + "Transcriptome analysis in calorie-restricted rats implicates epigenetic and post- translational mechanisms in neuroprotection and aging. Genome Biol. 16,2 8 (2015). 204. M. V. Blagosklonny, Calorie restriction: Decelerating mTOR-driven aging from cells to or- ganisms (including humans). Cell Cycle 9, 683 688 (2010). 205. D. K. Ingram, G. S. Roth, Calorie restriction mimetics: Can you have your cake and eat it, too? Ageing Res. Rev. 20,4 662 (2015).", + "life-span extension by calorie restriction in Saccharomyces cerevisiae. Science 289:21262128. Mair W, Goymer P, Pletcher SD, and Partridge L (2003) Demography of dietary restriction and death in Drosophila. Science 301:17311733. Masoro EJ (2005) Overview of caloric restriction and ageing. Mech Ageing Dev 126:913922. Mathers JC (2006) Nutritional modulation of ageing: genomic and epigenetic ap- proaches. Mech Ageing Dev 127:584589. Meric-Bernstam F and Gonzalez-Angulo AM (2009) Targeting the mTOR signaling", + "Keywords: Caloric restriction; Short-term; Longevity; Cancer; Microarray; Affymetrix Aging is widely assumed to result from the gradual age- related accumulation of essentially irreversible moleculardamage. In this context, CR is often viewed as preventing orslowing the accumulation of such damage, thereby slowingthe process of aging ( Bokov et al., 2004 ). This view is intuitively appealing, as it provides a straightforwardexplanation for the stochastic nature of aging and the onset", + "of short- and long-term caloric restriction effects in the liver of agingmice. Proc. Natl. Acad. Sci. U.S.A. 98, 10630 10635.Capstick, F., Brooks, B.A., Burns, C.M., Zilkens, R.R., Steinbeck, K.S., Yue, D.K., 1997. Very low calorie diet (VLCD): a useful alternative inthe treatment of the obese NIDDM patient. Diab. Res. Clin. Pract. 36, 105111. Chen, H., 2004. Gene expression by the anterior pituitary gland: effects of age and caloric restriction. Mol. Cell. Endocrinol. 222, 21 31.", + "genomic effects of caloric restriction. Mech. Ageing Dev. 126 : 960 966 . Sun , H. , R.J. Bennett , and N. Maizels . 1999 . The Saccharomyces cerevisiae Sgs1 helicase effi ciently unwinds G-G paired DNAs. Nucleic Acids Res. 27 : 1978 1984 . Thompson , L.H. , and D. Schild . 2002 . Recombinational DNA repair and human disease. Mutat. Res. 509 : 49 78 .", + "L. & Spindler, S. R. Genomic profiling of short- and long-term caloric restriction effects in the liver of aging mice. Proc. Natl Acad. Sci. USA 98, 1063010635 (2001). 62. Harman, D. The aging process. Proc. Natl Acad. Sci. USA 78, 71247128 (1981). 63. van der Pluijm I, G. G.et.al. Impaired genome maintenance suppresses the growth hormoneinsulin-like growth factor 1 axis in mice with cockayne syndrome. PLoS Biol. 5, 2338 (2006). 64. Yang, H., Baur, J." + ] + ], + "task_id": [1,2,3,4,5,6,7,8,9,10,1,2,3,4,5,6,7,8,9,10] +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/gpt4o_cs_diabetes.json b/gnqa/paper2_eval/data/dataset/gpt4o/gpt4o_cs_diabetes.json new file mode 100644 index 0000000..a750195 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/gpt4o_cs_diabetes.json @@ -0,0 +1,289 @@ +{ + "question": [ + "How do genetic mutations in the insulin gene affect glucose metabolism?", + "What are the most common genetic loci associated with an increased risk of Type 2 diabetes?", + "How does genome-wide association studies (GWAS) help in identifying diabetes-related genes?", + "What is the role of the HLA region in the genetic predisposition to Type 1 diabetes?", + "How do genetic differences contribute to variations in diabetes prevalence among different populations?", + "What is the function of the PPAR-gamma gene in diabetes, and how do its variants impact the disease?", + "How can CRISPR/Cas9 technology be used to study or treat genetic forms of diabetes?", + "What is the significance of genetic polymorphisms in the GLUT4 gene for Type 2 diabetes?", + "How do microRNAs regulate gene expression related to diabetes?", + "What insights have been gained from studying the genetic basis of MODY (Maturity Onset Diabetes of the Young)?", + "What genes are most commonly associated with an increased risk of developing diabetes?", + "How can genetic testing help predict a person's risk for diabetes?", + "What role do family genetics play in the likelihood of getting diabetes?", + "Can lifestyle changes affect genetic risk factors for diabetes?", + "What recent breakthroughs have been made in understanding the genetic causes of diabetes?", + "How do genes influence how our bodies respond to sugar and insulin?", + "Are there specific genetic markers that can indicate a higher risk for Type 1 versus Type 2 diabetes?", + "How can new gene therapies potentially cure or treat diabetes?", + "What is the difference between monogenic and polygenic diabetes?", + "How does studying the DNA of people with diabetes help scientists find better treatments or cures?" + ], + "answer": [ + "Genetic mutations in the insulin gene can significantly affect glucose metabolism in several ways: 1. Mutations that result in mutant insulin or the inability to convert proinsulin to insulin can lead to glucose intolerance. This is because the proper conversion and function of insulin are crucial for maintaining normal glucose levels in the blood [1]. 2. Insulin gene mutations can cause early-onset diabetes, with marked differences in clinical presentation and metabolic status. These mutations can lead to the retention of insulin in the endoplasmic reticulum, causing stress and potentially leading to beta-cell apoptosis, which reduces insulin production and secretion [2], [10]. 3. Insulin-deficient conditions, such as those caused by certain mutations, can lead to severe hyperglycemia, as insulin is a central hormone for maintaining glucose homeostasis. Without sufficient insulin, glucose cannot be effectively taken up by cells, leading to elevated blood glucose levels [5]. 4. Some mutations may result in abnormal proinsulin folding, leading to an accumulation of misfolded proteins in the endoplasmic reticulum. This accumulation can cause endoplasmic reticulum stress and beta-cell apoptosis, further impairing insulin production and secretion [10]. Overall, genetic mutations in the insulin gene can disrupt normal insulin synthesis and function, leading to impaired glucose metabolism and conditions such as diabetes.", + "The most common genetic loci associated with an increased risk of Type 2 diabetes include: 1. **TCF7L2** [2], [5], [7] 2. **KCNJ11** [2], [5] 3. **PPARG** [2], [5] 4. **CDKAL1** [2], [5], [7] 5. **CDKN2A/CDKN2B** [2], [5], [7] 6. **IGF2BP2** [2], [5], [7] 7. **SLC30A8** [2], [5], [7] 8. **FTO** [2], [5] 9. **HHEX** [2], [5], [7] These loci have been identified and replicated in multiple studies as being associated with an increased susceptibility to Type 2 diabetes.", + "Genome-wide association studies (GWAS) help in identifying diabetes-related genes by examining associations between a large number of single-nucleotide polymorphisms (SNPs) and the disease, such as type 2 diabetes mellitus (T2DM) [1]. GWAS have successfully identified genetic loci associated with T2DM, providing insights into the genetic risk factors and biological pathways underlying the disease [2], [4]. These studies have identified hundreds of SNPs associated with T2DM, which can uncover previously unexpected disease pathways and contribute significantly to our understanding of the genetics of diabetes [5], [9]. Additionally, meta-analyses of GWAS data have improved the ability to detect new loci by combining findings from multiple studies, thereby increasing statistical power [2], [10]. Despite challenges such as the modest effect sizes of individual variants and stringent significance thresholds, GWAS remain a powerful tool for identifying genetic loci for complex diseases like diabetes [7], [10].", + "The HLA region plays a significant role in the genetic predisposition to Type 1 diabetes. The major genetic susceptibility determinants for Type 1 diabetes have been mapped to the MHC class II genes, specifically HLA-DQB1 and HLA-DRB1 [7]. These genes are part of the highly polymorphic HLA class II genes, which play the most important single role in susceptibility to Type 1 diabetes [3]. The class II genes encoding HLA-DR and HLA-DQ, along with one or more additional genes within the HLA region, confer most of the genetic risk for Type 1 diabetes [4]. The contribution of HLA genes to Type 1 diabetes susceptibility accounts for about 44% of the genetic risk [6]. However, variation at these loci alone cannot explain all of the genetic association and linkage of the MHC with Type 1 diabetes, indicating that other genes within the MHC region may also affect Type 1 diabetes risk [3].", + "Genetic differences contribute to variations in diabetes prevalence among different populations in several ways: 1. **Genetic Heterogeneity**: Different genes may be responsible for the development of type 2 diabetes mellitus (T2DM) in different populations, indicating genetic heterogeneity. Even within the same ethnic group, different genes might be responsible for different subtypes of diabetes, such as those with predominating failure in insulin secretion or insulin resistance [3]. 2. **Ethnicity-Specific Genetic Variants**: The pathogenesis of T2DM involves genetic variants in candidate genes, and these variants can show ethnicity-specific differences. The interactions between genes involved in insulin signaling and secretory pathways play a crucial role in determining an individual's susceptibility to T2DM [4]. 3. **Single Nucleotide Polymorphisms (SNPs) and Haplotypes**: Minute genetic differences, such as SNPs and haplotypes, are used to explain patterns of disease between populations. These genetic markers are understood as the basis for biological differences between populations, which can affect diabetes prevalence [5]. 4. **Risk Allele Frequencies**: Human populations exhibit substantial variation in risk allele frequencies, which contributes to different susceptibilities to diseases, including diabetes, among populations [7]. 5. **Insulin Secretion Capacity**: In some populations, such as the Asian population, genetic influences on the prevalence of type 2 diabetes are mainly related to insulin secretion capacity. This suggests that genes associated with insulin secretion capacity are particularly relevant in contributing to ethnic differences in diabetes prevalence [10]. Overall, genetic differences, including variations in specific genes, SNPs, and risk allele frequencies, contribute to the observed variations in diabetes prevalence among different populations. These genetic factors interact with environmental and lifestyle factors to influence diabetes risk.", + "The PPAR-gamma gene encodes a transcription factor that plays a crucial role in regulating insulin sensitivity and lipid metabolism, which are important in the context of diabetes [4]. PPAR-gamma influences insulin sensitivity partly by modulating circulating adiponectin levels, with PPAR-gamma agonists known to increase these levels [1]. This gene is a target for the thiazolidinedione class of oral antidiabetic agents, which are used to improve insulin sensitivity in patients with type 2 diabetes [4]. Variants of the PPAR-gamma gene can significantly impact the risk and progression of type 2 diabetes. The Pro12Ala variant, for example, is a well-established genetic variant that modulates insulin sensitivity and increases the risk of type 2 diabetes [1], [4]. Other pathogenic PPARG variants, such as R194Q, A417V, R212W, P387S, M203I, and T356R, have been identified in patients with partial lipodystrophy and are associated with an increased risk of type 2 diabetes [7]. These variants can affect the gene's function in adipocyte differentiation and metabolism, thereby influencing the development of insulin resistance and type 2 diabetes [10].", + "CRISPR/Cas9 technology can be used to study or treat genetic forms of diabetes in several ways: 1. **Genome and Epigenome Editing**: CRISPR/Cas9 has revolutionized the ability to modify genomes and epigenomes, allowing researchers to target specific genes associated with Type 2 Diabetes (T2D) and manipulate cis-regulatory elements related to beta cell function and glucose homeostasis [1]. 2. **Gene Knockout Studies**: CRISPR has been used to knock out genes by introducing frameshift mutations, which result in protein depletion. This approach has been adopted to study several genes in beta cell lines and human embryonic stem cell-derived beta cells, providing insights into the genetic basis of diabetes [4]. 3. **Correction of Point Mutations**: CRISPR technology has been used to correct point mutations in patient-derived induced pluripotent stem cells (iPSCs) targeting diabetes-related gene defects. The most efficient method for this is CRISPR/Cas9-based homology-directed repair (HDR), which involves generating a Cas9-mediated cut near the mutation site and using a homologous donor template to introduce the intended nucleotide change [5]. 4. **Disease Modeling**: CRISPR/Cas9 can be employed in human pluripotent stem cells (hPSCs) to model diabetes and metabolic syndromes in vitro. This allows researchers to study the disease mechanisms and test potential treatments in a controlled environment [3]. 5. **Animal Models**: CRISPR/Cas9 has been used to create knockout and knock-in mouse models, such as leptin and leptin receptor knockout mice, which serve as tools for diabetes and obesity research. These models help in understanding the genetic factors contributing to diabetes and testing therapeutic interventions [7]. Overall, CRISPR/Cas9 technology provides a powerful platform for both studying the genetic underpinnings of diabetes and developing potential gene therapies to treat the disease.", + "Genetic polymorphisms in the GLUT4 gene have been studied to determine their significance in relation to Type 2 diabetes. However, according to the provided context, none of the identified polymorphisms in the GLUT4 gene have been linked to or found to be associated with Type 2 diabetes across various populations [1]. This suggests that, despite investigations, there is no established connection between GLUT4 gene polymorphisms and the susceptibility to Type 2 diabetes.", + "MicroRNAs (miRNAs) regulate gene expression related to diabetes by acting at the post-transcriptional level to control their target genes. They are involved in several crucial pathways associated with diabetes, including insulin secretion, cholesterol biosynthesis, fat metabolism, and adipogenesis [2]. miRNAs also play significant roles in pancreatic islet development, beta-cell dysfunction, insulin synthesis and secretion, and insulin resistance, which are key factors in the pathology of both Type 1 and Type 2 Diabetes Mellitus (T1DM and T2DM) [6]. Additionally, specific miRNAs have been implicated in the pathogenesis of diabetic complications, such as diabetic nephropathy, where miRNAs like miR-192, miR-216a, miR-217, and miR-377 are up-regulated [2]. These miRNAs can modulate the actions of growth factors and inflammatory factors, further influencing diabetic complications [5].", + "Studying the genetic basis of MODY (Maturity Onset Diabetes of the Young) has provided several important insights: 1. **Genetic Heterogeneity**: MODY is caused by mutations in multiple genes, with at least 13 known genes implicated. The most prevalent mutations occur in the genes HNF1A, GCK, and HNF4A [3]. This genetic diversity leads to different subtypes of MODY, each with distinct clinical characteristics such as age of onset, pattern of hyperglycemia, response to treatment, and associated extrapancreatic manifestations [3]. 2. **Inheritance Pattern**: MODY is inherited in an autosomal dominant manner, which means that it can be passed down through families. This inheritance pattern allows for the collection of multigenerational pedigrees, making MODY an attractive model for genetic studies [2]. 3. **Clinical Presentation**: MODY typically presents in young adults, often before the age of 25, and is characterized by primary insulin secretion defects. It is not related to obesity or autoimmune processes, distinguishing it from other forms of diabetes like type 1 and type 2 diabetes [5]. 4. **Pathogenic Mechanisms**: Despite advances in understanding the molecular pathogenesis of MODY, there remain unknown genetic determinants in many patients with a MODY-like phenotype, suggesting additional locus heterogeneity and new pathogenic mechanisms yet to be discovered [4]. 5. **Impact on Treatment and Diagnosis**: Genetic testing for MODY can lead to more accurate diagnoses and tailored treatment plans. Many patients with MODY are currently undiagnosed or misdiagnosed with type 1 or type 2 diabetes, highlighting the importance of genetic testing in identifying this condition [7]. These insights underscore the complexity and variability of MODY, as well as the importance of genetic research in improving diagnosis and treatment strategies for this form of diabetes.", + "The genes most commonly associated with an increased risk of developing diabetes, particularly type 2 diabetes, include: 1. **CDKAL1, CDKN2A, CDKN2B** - These genes are linked to reduced insulin secretion via reduced beta-cell mass [1]. 2. **MTNR1B, TCF7L2, KCNJ11** - These genes are associated with beta-cell dysfunction [1]. 3. **FTO** - This gene is related to increased insulin resistance associated with obesity [1]. 4. **IRS1, PPARG** - These genes are related to increased insulin resistance unrelated to obesity [1]. 5. **IGF2BP2, HHEX, SLC30A8, WFS1** - These genes have been shown to increase susceptibility to type 2 diabetes in reproducible studies [3]. 6. **JAZF1, CDC123/CAMK1D, TSPAN8/LGR5, THADA, ADAMTS9, NOTCH2** - These are additional variants identified in a recent meta-analysis as being associated with type 2 diabetes [3]. 7. **KCNQ1** - This gene is associated with susceptibility to type 2 diabetes in East Asian and European populations [6]. These genes have been identified through various genome-wide association studies (GWAS) and other genetic research efforts.", + "Genetic testing can help predict a person's risk for diabetes in several ways: 1. **Tailored Interventions**: Knowing an individual's genotype can allow for the development of personalized lifestyle intervention programs aimed at preventing or significantly delaying the onset of type 2 diabetes [1]. 2. **Role of Genetic Factors**: Genetic factors play a role in determining an individual's risk of developing diabetes, suggesting that genetic testing can help identify those at higher risk [2]. 3. **Genetic Risk Scores**: A genotype risk score can predict type 2 diabetes from a young age, as demonstrated in studies like the CARDIA study [6]. This score can help identify individuals who are at increased risk due to their genetic makeup. 4. **Heritability and Risk Assessment**: Type 2 diabetes is heritable, and genetic testing can help identify individuals with a familial risk, which is increased by a factor of 2 to 6 compared to those without familial diabetes [7]. 5. **Improved Prediction and Stratification**: Genetic testing offers the potential for improved prediction and stratification of patients according to their risk, which can aid in selecting possible therapeutic targets [8]. 6. **Identification of Genetic Variants**: By genotyping specific single nucleotide polymorphisms (SNPs) associated with diabetes, genetic testing can improve the ability to detect who will ultimately develop the disease [9]. Overall, genetic testing provides valuable insights into an individual's risk for diabetes, enabling more targeted prevention and management strategies.", + "Family genetics play a significant role in the likelihood of developing diabetes. Several studies and observations highlight this connection: 1. Genetic factors are important in determining an individual's risk of developing diabetes [1]. 2. A family history of diabetes, particularly in first-degree relatives such as parents or siblings, is associated with a two- to fourfold increased risk of developing diabetes [3]. 3. Type 2 diabetes is strongly influenced by genetics, as evidenced by high concordance rates in identical twins, with studies showing a 58-75% concordance rate [5]. In some studies, the concordance rate is reported to be nearly 100% [6]. 4. The risk of developing type 2 diabetes is approximately 70% if both parents have the disease and about 40% if one parent is affected [7], [9]. 5. Type 2 diabetes clusters in families, and having a first-degree relative with the disease increases the lifetime risk significantly, up to 40% or more [9]. Overall, these findings underscore the strong genetic component in the susceptibility to diabetes, particularly type 2 diabetes.", + "Yes, lifestyle changes can affect genetic risk factors for diabetes. Evidence from both observational studies and clinical trials suggests that lifestyle factors can modify the genetic effects on the risk of developing type 2 diabetes mellitus (T2DM) [1]. Lifestyle modifications, such as improvements in dietary quality and increased physical activity, have been shown to be particularly effective in preventing and managing type 2 diabetes, even in individuals with a high genetic risk [6]. Furthermore, behavioral changes can substantially mitigate the diabetogenic effects of individual or multiple risk alleles, indicating that the adverse effects of some established T2D-associated loci may be greatly attenuated by appropriate lifestyle changes [9].", + "Recent breakthroughs in understanding the genetic causes of diabetes include several key advancements: 1. The number of genetic regions causally implicated in both type 1 and type 2 diabetes has increased to 11 each, marking significant progress in identifying genetic factors associated with these conditions [1]. 2. Genome-wide association studies have been particularly successful in uncovering genetic determinants of complex diseases like diabetes. These studies have identified a number of novel disease-associated variants, which have been largely replicated by independent research groups, highlighting their reliability and significance [2]. 3. Advances in technology, such as the use of single nucleotide polymorphism (SNP) chips, have enabled the analysis of up to a million genetic variations. This technological leap has greatly enhanced our understanding of the genetic basis of complex, multifactorial forms of diabetes, bringing it closer to the understanding of rarer, single-gene disorders [5]. 4. Recent studies have yielded six new replicating gene regions associated with type 2 diabetes, providing new insights into the genetic architecture of the disease and suggesting potential directions for future research [8]. These breakthroughs represent a significant leap forward in the genetic understanding of diabetes, driven by technological advancements and collaborative research efforts.", + "Genes influence how our bodies respond to sugar and insulin in several ways: 1. **Insulin Secretion and Resistance**: Genes are directly related to insulin secretion and indirectly influence insulin resistance by interacting with other genes. This interaction supports the idea that environmentally triggered insulin resistance can interact with genetically programmed beta-cell dysfunction, leading to diabetes [1]. 2. **Beta-Cell Function**: Many genetic variants primarily affect beta-cell function, development, or survival, rather than insulin sensitivity. This suggests that genetic factors play a significant role in how beta cells respond to sugar and insulin [2]. 3. **Expression in Metabolically Relevant Tissues**: Genes affecting insulin sensitivity are often expressed in peripheral insulin-sensitive tissues, such as the liver and adipose tissue, and their expression can be responsive to metabolic status. For instance, a high-fat diet can decrease the expression of several of these genes, indicating a genetic influence on how the body responds to dietary changes [3]. 4. **Regulation by Metabolic Conditions**: The expression of certain genes can be altered by conditions such as fasting and feeding, which are known to affect peripheral insulin sensitivity. This suggests that genetic regulation can influence how the body responds to changes in sugar and insulin levels [4]. 5. **Genetic Variation and Insulin Response**: Specific genetic variations, such as those in the GIPR gene, can influence glucose and insulin responses to an oral glucose challenge, highlighting the role of genetic differences in individual responses to sugar intake [9]. Overall, genetic factors can influence both the secretion of insulin and the body's sensitivity to it, affecting how we metabolize sugar and respond to dietary changes.", + "Yes, there are specific genetic markers that can indicate a higher risk for Type 1 versus Type 2 diabetes. For Type 1 diabetes, genome-wide association studies have identified over 50 loci associated with an increased genetic risk. Several candidate genes within these regions have been suggested or identified, although the exact molecular mechanisms by which they contribute to islet cell inflammation and beta cell destruction are not fully understood [1]. For Type 2 diabetes, specific genetic markers have also been identified. Some of the loci associated with an increased risk include TCF7L2, PPARG, FTO, KCNJ11, NOTCH2, WFS1, CDKAL1, IGF2BP2, SLC30A8, JAZF1, and HHEX [9]. Additionally, markers such as TCF7L2 and CAPN10 have been strongly associated with the risk of developing Type 2 diabetes [8]. These findings indicate that while both types of diabetes have genetic components, the specific markers and loci associated with each type differ, reflecting their distinct pathophysiological mechanisms.", + "New gene therapies have the potential to cure or treat diabetes through several innovative approaches: 1. **Gene-Based Therapies**: Advances in understanding the biological mechanisms that maintain glucose homeostasis and the molecular defects leading to chronic hyperglycemia could lead to the development of gene-based therapies. These therapies aim to target specific genetic factors involved in diabetes, potentially offering more precise treatment options [3]. 2. **In Vivo Gene Therapy**: This approach involves directly inserting a vector containing the desired gene into the patient. It is considered a promising therapeutic strategy for type 1 diabetes, although challenges remain in developing safe and effective vectors [9]. 3. **Inducing Islet Neogenesis**: Gene therapy techniques, such as betacellulin gene therapy, have been shown to induce islet neogenesis in the liver and reverse diabetes in mice. This suggests that gene therapy can stimulate the body to produce insulin-producing cells, offering a potential cure for diabetes [10]. These strategies highlight the potential of gene therapies to address the underlying genetic causes of diabetes and restore normal insulin production and glucose regulation.", + "Monogenic and polygenic diabetes are distinct forms of diabetes with different genetic underpinnings: 1. **Monogenic Diabetes**: This form of diabetes results from a mutation in a single gene that is highly penetrant, meaning it has a strong effect on the individual who carries it [1], [6]. Monogenic diabetes is often associated with defects in beta-cell function, leading to a decrease in the number or function of these cells [2]. It is typically characterized by early onset, often before the age of 25, and can include conditions like Maturity-Onset Diabetes of the Young (MODY) [5]. Monogenic diabetes is relatively rare, representing about 2%-5% of diabetes cases [2]. 2. **Polygenic Diabetes**: In contrast, polygenic diabetes results from the combined effect of multiple genetic variants, each contributing a small effect, along with environmental and lifestyle factors [1], [6]. This form of diabetes is more common and is the predominant mode of inheritance in type 2 diabetes [7]. The genetic variants involved in polygenic diabetes do not have as strong an effect individually as those in monogenic diabetes, but together they contribute to the disease risk in the presence of other factors like obesity and sedentary lifestyle [3]. In summary, monogenic diabetes is caused by a single gene mutation with a strong effect, while polygenic diabetes involves multiple genes with smaller effects combined with environmental influences.", + "Studying the DNA of people with diabetes helps scientists find better treatments or cures in several ways: 1. **Identification of Genetic Determinants**: By performing genetic profiling on diabetic patients, scientists can identify genetic determinants that define the targets of current and future therapies. This leads to the development of therapies that are more specific to the genetic makeup of individuals with diabetes [1]. 2. **Understanding Disease Mechanisms**: Genetic studies improve our understanding of the biological mechanisms that maintain glucose homeostasis and reveal molecular defects leading to chronic hyperglycemia. This knowledge can lead to the development of more specifically targeted antidiabetic drugs or even gene-based therapies [4]. 3. **Pharmacogenetics**: Pharmacogenetic testing can be used to predict therapeutic responses to different classes of drugs for each patient, allowing for more personalized treatment plans [4]. 4. **Discovery of New Therapeutic Targets**: A greater understanding of the genetic and epigenetic basis of diabetes can enable the discovery of new therapeutic targets, potentially leading to novel treatments for diabetes and its complications [3]. 5. **Stratification of Diabetes Subclasses**: By analyzing DNA variations and their interactions with environmental factors, scientists can stratify type 2 diabetes into subclasses. This stratification allows for more effective treatment strategies tailored to specific genetic and lifestyle interactions [8]. 6. **Identification of Key Genetic Elements**: Genetic studies can identify key genetic elements that determine susceptibility to diabetes, disease progression, and responsiveness to specific therapies. This information helps in identifying novel targets for future interventions [9]. Overall, studying the DNA of people with diabetes provides critical insights that drive the development of more effective and personalized treatments." + ], + "contexts": [ + [ + "Mutations that result in mutant insulin or the inability to convert proinsulin to insulin result in gl ucose intolerance in some of these cases. Genetic defects in the insulin receptor or in the signal transduction pathway of insulin have been demonstrated to result in hyperinsulinemia and modest hyperglycemia to severe diabetes[1]. Disease of the exocrine pancreas Damage of the cells of the pancreas due to diffused injury of the pancreas can cause diabetes. This damage", + "A, et al. Insulin gene mutations resulting in early-onset diabetes: marked differences in clinical presentation, metabolic status, and pathogenic effect through endoplasmic reticulum retention. Diabetes. 2010;59:653 61. 21. Steele AM, Shields BM, Wensley KJ, Colclough K, Ellard S, Hattersley AT. Prevalence of vascular complications among pa- tients with glucokinase mutations and prolonged, mild hyperglyce- mia. JAMA. 2014;311:279 86.22. Chakera AJ, Spyer G, Vincent N, Ellard S, Hattersley AT, Dunne FP.", + "presumed glucose toxicity (34). The finding that a mutation of a single nucleotide in the gene encoding the glucokinase enzyme can result in NIDDM lends credibility to the hypoth- esis that inherited defects in insulin production contribute to NIDDM (6). Increased insulin demand of obesity and insulin resistance is accompanied by enhanced insulin biosynthesis,", + "insulin synthesis and function while mutations in the insulin gene ( INS) obviously affect the key hormone made by pancreatic beta cells [62]. ATP synthesis defect (mitochondrial diabetes) and mutations in ATP- sensitive potassium channel subunits (channel-building Kir6.2 [po- tassium inwardly-rectifying channel, subfamily J, member 11;KCNJ11 ] and regulatory SUR1 [ATP-binding cassette transporter subfamily C member 8], ABCC8 ) all affect insulin secretion [63].", + "Insulin gene mutations Insulin is synthesized in 13-cells of the islets of Langerhans and is a central honnone that maintains glucose homeostasis. Insulin-deficient mice die shortly after birth due to severe hyperglycemia.53 All cell types of the endocrine pancreas are present in insulin deficient mice suggesting that insulin is not required for development and differentiation of the endocrine pancreas. 53 Naturally occurring mutations in the insulin gene that result in the", + "Theprevalenceofgeneticmutationsaffectingthestructure oftheinsulinmoleculeinthegeneralpopulationisunknown. Uptothepresent,onlythosepatientsmanifestingthemutant insulinsyndrome(5-8,36)withunusualorfamilialTypeII diabeteshavebeenscreenedanddiscovered.Thus,mutantin- sulinspecieswithnormalorrelativelywell-preservedbinding andbiologicalactivitycharacteristics,andthereforenormal metabolicclearances,areunlikelytobediscoveredbythisap- proachsincehyperinsulinemiawillbeabsentorsubtle.Future", + "at various steps, resulting in an impaired insulin action and potential development of extreme insulin resistant clinical conditions. Many mutations have been identified in the insulin receptor gene. These mutations may lead to: Decreased insulin receptor biosynthesis Premature chain termination in extracellular or intracellular domain Accelerated receptor degradation Defect in the receptor transport to plasma membranes Decreased insulin binding affinity Impaired tyrosine kinase activity", + "15. Steiner DF, Tager HS, Chan SJ, et al . Lessons learned from molecular biology of insulin-gene mutations. Diabetes Care 1990; 13: 600609. 16. Vionnet N, Stoffel M, Takeda J, et al . Nonsense mutation in the glucokinase gene causes early-onset non-insulin-dependent diabetes mellitus. Nature 1992; 356 : 721722. 17. Sakagashira S, Sanke T, Hanabusa T, et al . Missense mutation of amylin gene (S20G) in Japanese NIDDM patients. Diabetes 1996; 45: 12791281.", + "vating mutations in the gene encoding Kir6.2 alter fetal and postnatal growthand also cause neonatal diabetes. J Clin Endocrinol Metab 2006; 91(7): 27822788. 93. Stoy J, Edghill EL, Flanagan SE, et al. Insulin gene mutations as a cause of permanent neonatal diabetes. Proc Natl Acad Sci U S A 2007; 104(38): 1504015044. 94. Pulizzi N, Lyssenko V, Jonsson A, et al. Interaction between prenatal growth and high-risk genotypes in the devel-opment of type 2 diabetes. Diabetolo- gia2009; 52(5): 825829.", + "(Edghill et al., 2008; Garin et al., 2010; Stoy et al., 2007). Hyperglycemia occurs due to decreased insulin biosynthe-sis, in which most of the reported missense heterozygous mutations are expected to cause an abnormal proinsulin folding. An accumulation of the misfolded protein in the en-doplasmic reticulum (ER) consequently occurs, resulting in ER stress and betacell apoptosis (Liu, Hodish, Rhodes, & Arvan, 2007). Our identified de novo novel variant in INS is expected to result in aberrant proinsulin" + ], + [ + "novel risk loci for type 2 diabetes. Nature 2007, 445(7130) :881-885.5. Gaulton KJ, Willer CJ, Li Y, Scott LJ, Conneely KN, Jackson AU, Duren WL, Chines PS, Narisu N, Bonnycastle LL, et al:Comprehensive association study of type 2 diabetes and related quantitative traits with 222 candidate genes. Diabetes 2008, 57(11) :3136-3144. 6. Hu C, Zhang R, Wang C, Wang J, Ma X, Lu J, Qin W, Hou X, Bao Y, Xiang K, et al:PPARG, KCNJ11, CDKAL1, CDKN2A-CDKN2B, IDE-KIF11-HHEX,", + "ly associated with type 2 diabetes: TCF7L2, KCNJ11, and PPARG . 5-7 However, in 2007, a number of novel genetic variants ( CDKAL1, IGF2BP2, the locus on chromosome 9 close to CDKN2A/CDKN2B, FTO, HHEX, SLC30A8, and WFS1)8-14 were shown to in - crease susceptibility to type 2 diabetes in repro - ducible studies. Furthermore, a recent meta-analy - sis identified six novel variants ( JAZF1, CDC123/ CAMK1D, TSPAN8/LGR5, THADA, ADAMTS9, and NOTCH2 ) that are associated with type 2 dia - betes. 15", + "2009. There are now at least 19 loci containing genes that increase risk of T2D, including PPARG [27], KCNJ11 [27], KCNQ1 [28,29], PLoS Genetics | www.plosgenetics.org 1 February 2010 | Volume 6 | Issue 2 | e1000847", + "et al. Association between type 2 diabetes loci and measures of fatness. PLoS One 5, e8541 (2010). 22 Ng, M. C., Park, K. S., Oh, B., Tam, C. H., Cho, Y. M., Shin, H. D. et al. Implication of genetic variants near TCF7L2, SLC30A8, HHEX, CDKAL1, CDKN2A/B, IGF2BP2, and FTO in type 2 diabetes and obesity in 6,719 Asians. Diabetes 57,22262233 (2008). 23 Thorsby, P. M., Midthjell, K., Gjerlaugsen, N., Holmen, J., Hanssen, K. F., Birkeland, K. I.", + "Genome-wide association studies validated these old culprits of T2D and expanded them to include hundreds of single-nucleotide variants (SNVs) that represent more than 150 genomic loci that are associated with T2D, insulin secretion, and insulin resistance [ 11]. Besides TCF7L2 ,PP ARG , and KCNJ11 loci, the most replicated T2D susceptibility variants identied in GWASs were found in and around CDKN2A/2B ,IGF2BP2 ,SLC30A8 ,CDKAL1 and FTO genes [ 1215]. The variants that are most", + "Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes. Nat Genet 2008;40:638-45. 20. Dupuis J, Langenberg C, Prokopenko I, et al. New genetic loci implicated in fasting glucose homeostasis and their im - pact on type 2 diabetes risk. Nat Genet 2010;42:105-16. 21. Qi L, Cornelis MC, Kraft P, et al. Ge - netic variants at 2q24 are associated with susceptibility to type 2 diabetes. Hum Mol Genet 2010;19:2706-15.", + "multiple loci associated with susceptibility to type 2 diabetes, includ- ingTCF7L2 (transcription factor 7-like 2), which had been originally identied by a large-scale association mapping prompted by prior evidence of linkage in that area2,SLC30A8 (solute carrier family 30 member 8), HHEX (haematopoietically expressed homeobox), CDKAL1 (CDK5 regulatory subunit associated protein 1-like 1), CDKN2A/B (cyclin-dependent kinase inhibitor 2A/B) and IGF2BP2 (insulin-like growth factor 2 mRNA-binding protein 2)37.", + "associated with susceptibility to type 2 diabetes mellitus. Nat Genet 2008; 40: 109297 . 74 Unoki H, Takahashi A, Kawaguchi T, et al. SNPs in KCNQ1 are associated with susceptibility to type 2 diabetes in East Asian and European populations. Nat Genet 2008; 40: 1098102. 75 Lyssenko V, Lupi R, Marchetti P, et al. Mechanisms by which common variants in the TCF7L2 gene increase risk of type 2 diabetes. J Clin Invest 2007; 117: 215563. 76 Lyssenko V, Jonsson A, Almgren P, et al. Clinical risk factors, DNA", + "type 2 diabetes or the inability to replicate linkage withdened loci. However, at least one susceptibility gene, namelyCAPN10, was found using a genome-wide scan approach [3]. Obesity is the greatest risk factor for type 2 diabetes mellitus, as it is known to induce insulin resistance via variousmechanisms ( TNF release, free fatty acids, etc.). Both", + "Clinical Risk Factors, DNA Variants, and the Development of Type 2 Diabetes n engl j med 359;21 www.nejm.org november 20, 2008 2231MPP subjects (P = 0.001) and from 0.79 to 0.83 in the Botnia subjects (P = 0.006). Of the 16 loci that have been associated with type 2 diabetes previously,8-15 we showed that 11 TCF7L2, PPARG, FTO, KCNJ11, NOTCH2, WFS1, CDKAL1, IGF2BP2, SLC30A8, JAZF1, and HHEX were associated with an enhanced risk of future" + ], + [ + "BMC Medical Genomics 2009, 2:72 http://www.biomedcentral.com/1755-8794/2/72 Page 2 of 8 (page number not for citation purposes)Background Genome-wide association study (GWAS) offers unbiased ways to examine association of more than a million singlenucleotide polymorphisms (SNPs) with disease [1]. Sev-eral GWAS have indentified novel genomic regions influ-encing risk for type 2 diabetes mellitus (T2DM) [2-6].However, the challenge remains to prioritize SNPs from", + "GWAS have successfully identified genetic loci associ- ated with a variety of conditions such as type 2 diabetes2 and coronary disease.35The large number of statistical tests required in GWAS poses a special challenge because few studies that have DNA and high-quality phenotypedata are sufficiently large to provide adequate statisticalpower for detecting small to modest effect sizes. 6Meta- analyses combining previously published findings have im-proved the ability to detect new loci.", + "diabetes mellitus6,7. However, the traditional GWAS ignored a large number of loci with moderate effects, because of the strin-gent signi cance thresholds used. Gene-based analysis takes a gene as a basic unit for association analysis. As this method can combine genetic information given by all the SNPs in a gene to obtain moreinformative results 8, it is being used as a novel method com- plementing SNP-based GWAS to identify disease susceptibilitygenes. Notably, this method can increase our chance of nd-", + "1. Genome-wide association studies (GW AS) have made considerable progress in identifying genetic risk factors and in providing evidence for more in-depth understanding of the biological and pathological pathways underlying T2D. A recent study performed a meta-analysis of T2D across 32 GW AS of European ancestry par - ticipants and identified 243 genome-wide significant loci (403 distinct genetic variants) associated with T2D risk", + "that a genome-wide approach could uncover previously unexpected disease pathways. In early 2007, GW AS provided by far the biggest increment to date in our knowledge of the genetics of this common health problem. Six new gene regions identified Together, the six recent GW AS papers provide convincing evidence for six new gene regions involved in type 2 diabetes1621; a seventh publication describes how one of these variants alters BMI and represents by far the best example of an association", + "Abstract Genome-wide association studies (GWASs) have discovered association of several loci with Type 2 diabetes (T2D), a common complex disease characterized by impaired insulin secretion by pancreatic bcells and insulin signaling in target tissues. However, effect of genetic risk variants on continuous glycemic measures in nondiabetic subjects mainly elucidatesperturbation of insulin secretion. Also, the disease associated genes do not clearly converge on functional categories", + "mechanisms of DR remain poorly understood. A genome-wide association study (GWAS) is a powerful tool to identify genetic loci for complex diseases, and a large number of genetic loci for the susceptibility to various diseases, such astype 2 diabetes, have been successfully identified through GWAS (69). GWAS for DR have been performed, but most of the studies only reported suggestive signals with no replication ( 5)b e c a u s e of their limited sample sizes. Recently, several loci with genome-", + "kidney disease, several loci have been identi ed and validated, but the results were quite heterogenic across different popula- tions and depended on the type of diabetes and stage of disease. The major bene t of GWAS results is to be found in the in- creased understanding of disease mechanism and identi ca- tion of novel pathways and possibly new therapeutic targets.Follow-up studies are important in order to identify variants with speci c biological effect and may provide important", + "Abstract Genome-wide association studies (GWASs) have identified hundreds of single nucleotide polymorphisms (SNPs) associated with type 2 diabetes (T2D) and coronary artery disease (CAD), respectively. Nevertheless, these studies were generally per -", + "linkage or association data. But, none of these studies include in the analysis existing data from GWAs. Finally, a recent study identied additional susceptibility loci for type 2 diabetes by performing a meta-analysis of three published GWAs.21As acknowledged by the authors, GWAs are limited by the modest effect sizes of individual common variants and the need for stringent statistical thresholds. Thus, by combining data involving 10,128 samples, the authors found" + ], + [ + "conferred by specic alleles, genotypes, and haplotypes ofthe HLA class II (and class I) genes. There are currentlyabout 50 non-HLA region loci that also affect the type 1diabetes risk. Many of the assumed functions of thenon-HLA genes of interest suggest that variants at theseloci act in concert on the adaptive and innate immunesystems to initiate, magnify, and perpetuate /H9252-cell destruc-", + "II HLA gene associated with type 1 diabetes maps to the 240-kbregion near HLA-B. Diabetes 49: 22172221, 2000. 303. Nejentsev S, Howson JM, Walker NM, Szeszko J, Field SF. Localization of type 1 diabetes susceptibility to the MHC class Igenes HLA-B and HLA-A. Nature 450: 887892, 2007. 304. Nejentsev S, Walker N, Riches D, Egholm M, Todd JA. Rare variants of IFIH1, a gene implicated in antiviral responses, protectagainst type 1 diabetes. Science 324: 387389, 2009.", + "Although the highly polymorphic HLA class II genesclearly play the most important single role in susceptibilityto type 1 diabetes, variation at these loci alone cannotexplain all of the evidence of genetic association andlinkage of the MHC with type 1 diabetes. To better denegenes within the MHC that may affect type 1 diabetes riskand would therefore merit further studies, the T1DGCundertook a comprehensive study of the genetics of theclassic 4-Mb MHC region. More than 3,000 SNPs and 66microsatellite", + "age to type 1 diabetes in the HLA region and suggestive evidence at a small number of other regions in the genome. In general, the emerging picture from linkage studies is that the class II genes encoding HLA-DR and HLA-DQ, as well as one or more additional genes within the HLA re - gion, confer most of the genetic risk for type 1 dia - betes. Genes outside the HLA region also con - tribute to the risk of type 1 diabetes, but their individual contributions are much smaller than that of HLA.", + "Benkalha and Polychronakos, 2008 ). Other genetic loci ( Table 1) are believed to in uence population-level risk for T1D, although it is poorly understood how these non-HLA loci contribute to disease susceptibility (Ram et al., 2016a ). 2.1. Human leukocyte antigen (HLA) The association between T1D and the HLA complex was rst de- monstrated in 1973 following observation of an increased frequency ofHL-W15 (HLA antigen) in T1D patients compared to controls ( Singal", + "cyte Antigen (HLA) gene region in immune regulation, and ready availability of serologic markers, led investigators to discover the association between certainHLAalleles and T1D in the early 1970s (33,130,158). The global importance of theHLAonT1Dhassincebeenconrmedingenome-widescansforlinkage:All suchscansperformedtodateshowamajorlocusatthe HLA(28,32,36,78,119). Thefractionofallgeneticrisk,whichcanbeattributedtothecontributionof HLA genes to T1D susceptibility, is about 44%, with a Sof3.4 (160).", + "The major histocompatibility complex (MHC) on chromosome 6 is associated with susceptibility to more common diseases than any other region of the human genome, including almost all dis- orders classified as autoimmune. In type 1 diabetes the major genetic susceptibility determinants have been mapped to the MHC class II genes HLA-DQB1 andHLA-DRB1 (refs 13), but these genes cannot completely explain the association between type 1 diabetes and the MHC region411.Owing to the regions", + "The HLA class I A locus a ects susceptibility to type 1 diabetes. Hum. Immunol. 63, 657 664. pii). https://doi.org/S0198885902004214 . Noble, J.A., Valdes, A.M., Cook, M., Klitz, W., Thomson, G., Erlich, H.A., 1996. The role of HLA class II genes in insulin-dependent diabetes mellitus: molecular analysis of 180 Caucasian, multiplex families. Am. J. Hum. Genet. 59, 1134 1148 . Noble, J.A., Valdes, A.M., Thomson, G., Erlich, H.A., 2000. The HLA class II locus DPB1", + "to type 1diabetes susceptibility, including within the MHC itself.Currently, there are over 50 non-HLA regions that signi-cantly affect the risk for type 1 diabetes (http://www.t1dbase.org). Many of these regions contain interesting,but previously unrecognized, candidate genes. A few re-gions contain genes of unknown function or no knownannotated genes, suggesting roles for long-distance generegulatory effects, noncoding RNAs, or unknown mecha-nisms. Against a background of ever-improving knowledgeof the", + "the 240-kb region near HLA-B. Diabetes 49,22172221 (2000). 6. Lie, B. A. et al. The predisposition to type 1 diabetes linked to the human leukocyte antigen complex includes at least one non-class II gene. Am. J. Hum. Genet. 64, 793800 (1999). 7. Valdes, A. M. et al. Extended DR3 D6S273-HLA-B haplotypes are associated with increased susceptibility to type 1 diabetes in US Caucasians. Tissue Antigens 65,115119 (2005). 8. Valdes, A. M., Erlich, H. A. & Noble, J. A. Human leukocyte antigen class I B and C" + ], + [ + "of diabetes when compared to the native population while not necessar-ily different from populations where they origi-nate from. Risk factors for diabetes appear to be similar between populations, mostly insulin resistance, obesity, and sedentary lifestyle with possible genetic differences contributing to the increased susceptibility. Some data suggest a greater prevalence of microvascular complica-", + "nants of type 2 diabetes between immigrant and native populations. Some studies in South Asian (Indian) populations suggest that genetic differ-ences may exist [ 17 , 30 ], but larger studies are needed to get better insight into this issue. Prevalence Estimates The prevalence of diabetes in minorities is affected by ethnicity and country of residence. In one study in the UK [ 59 ], standardized preva-", + "majority of cases it is difficult to replicate the findingsin other populations. One of the major problems in thesearch for genes responsible for common forms ofdiabetes is the genetic heterogeneity of the diseasewith different genes responsible for the developmentof T2DM in different populations. Furthermore, evenwithin the same ethnic group, different genes may beresponsible for different subtypes of diabetes (for in-stance with predominating failure in insulin secretionor insulin resistance). This is", + "across different races or populations but show ethnicity- specific differences. The pathogenesis of T2D involves genetic variants in the candidate genes. The interactions between the genes involved in insulin signaling and secre - tory pathways are believed to play an important role in determining an individuals susceptibility towards T2D. Therefore, the present study was initiated to examine the differences, if any, in the contribution of polymorphisms", + "That is, the minute genetic differences discernable with SNPs, patterns of single nu-cleotides (A,G,T ,C), and other mutation analysis technologies are now used to explainpatterns of disease between populations, which are in turn understood as the basisfor biological differences between the populations themselves. The case of diabetesgenetics research affords a more nuanced look at what is labeled genetic determinism.It is evident in diabetes research that SNPs and haplotypes, (an inherited pattern of 99", + "- tion for disease classification. This genetic component may be specifically important when understanding the pathogenesis of diabetes in ethnic groups, when BMI [14, 15] and HbA1c [16] show distinct differences between ethnicities. Though applying patient-matched, genomic information is currently unrealistic for disease diagnosis, it may hold the key for revealing commonalities across ethnic and demographic groups when classifying diabetic onset, progression, and severity.", + "particularly useful for understanding differences in dis-ease prevalence and drug response among differentpopulations. There is ample evidence that human popu-lations have different susceptibility to diseases, exhibit-ing substantial variation in risk allele frequencies [1].For example, genetic predisposition to asthma differsamong the differentially-admixed Hispanic populations of the United States, with the highest prevalence observed in Puerto Ricans. Ge netic variants responsible", + "populations and across countries. World-wide differences in prevalence of theforms of diabetes necessitates inclusion of currently understudied populationsfor the development of precision diag-nostics and therapeutics. As a result, theprecise subtype of diabetes a particularindividual is diagnosed with may vary indifferent populations based on subtypefrequency or genetic or dietary or life-style differences. The communication strategy used by the interventionalist and the patient s", + "were positively associated with country level income [49]. However, the drivers for the observed pattern with geographi- cal differences and varying time trends are still unclear. Susceptibility to type 1 diabetes denitely has a strong genetic component (HLA genotype) [50], but the heterogeneity of type 1 diabetes cannot be explained solely by the prevalence of susceptibility genes [5153] . Thus, the reasons for changes in", + "twice higher than that of 2010 [3] . The genetic influences on the prevalence of type 2 diabetes i n the Asian population are mainly related to insulin secretion capacity [4] ; other genes involved in the risk of type 2 diabetes are not substantially different in other ethnic groups [5] . The most relevant genes contributing to ethnic differences are associated with insulin secretion capacity, and they are" + ], + [ + "The transcription factor peroxisome-proliferator- activated receptor gamma (PPAR g) is known to inuence insulin sensitivity, and acts partly via amodulation of the circulating adiponectin level (PPAR gagonists increase the adiponectin level) (Ref. 38). The PPAR gP12A SNP is a well- established genetic variant that modulates insulin sensitivity and the risk of type 2 diabetes (Ref. 39). In a Chinese family study, Yang et al.demonstrated a genetic interaction between the", + "intricate regulation of PPAR signaling to pave the way to tailored therapies in patients with insulin resistance and T2D. Keywords PPARG genetic variants .Dominant-negative isoforms .Post-tranlational modifications .Adipose tissue dysfunctions .Drug responsiveness .Type 2 diabetes Introduction Peroxisome proliferator activated receptor gamma (PPAR ) is a ligand-activated transcription factor belonging to the nu-", + "2 . A widespread Gly482Ser polymorphism of PGC1 - (known as PPARGC1 ), a transcriptional coactivator of a series of nuclear receptors includ-ing PPARG , has been associated with a 1.34 genotype relative risk of T2DM [93] . In this study, a test for interaction with the Pro12Ala variant in PPARG gave no indication for additive effects on diabetes status. Other genes have been shown to be implicated in the genetic", + "PPARG Peroxisome proliferator-activated receptor- gene. This gene is located on chromosome 3p25, and has been studied as a candidate genefor type 2 diabetes based on its role in adipocyte and lipid metabolism. The Pro12Ala variant in particular has been associated with adecrease in insulin sensitivity and a several-fold increased risk of type 2 diabetes. PPAR is a target for the thiazolidinedione class of oralantidiabetic agents", + "Genetic variation in the peroxisome proliferator-activated receptor (PPAR) and peroxisome proliferator-activated receptor gamma co-activator 1 (PGC1) gene families and type 2 diabetes. Ann Hum Genet 78:2332 Vimaleswaran KS, Radha V, Ghosh S, Majumder PP, Deepa R, Babu HN etal (2005) Peroxisome proliferator-activated receptor-gamma co-activator-1alpha (PGC-1alpha) gene polymorphisms and their relationship to type 2 diabetes in Asian Indians. Diabetic Med 22:15161521", + "Dali-Youcef N, et al. The Pro12Ala PPARgamma2 variant deter- mines metabolism at the gene-environment interface. Cell Metab. 2009;9:88 98. 53. Agostini M, Schoenmakers E, Mitchell C, Szatmari I, Savage D, Smith A, et al. Non-DNA binding, dominant-negative, human PPARgamma mutations cause lipodystrophic insulin resistance. Cell Metab. 2006;4:303 11. 54. Agostini M, Gurnell M, Savage DB, Wood EM, Smith AG, Rajanayagam O, et al. Tyrosine agonists reverse the molecular", + "associated with a marked increase in T2D risk in the general population, schematized in Fig. 1. The latter systematically tested all the possible PPAR protein variants by using a large-scale pooled functional assay based on a human macro- phage cell line. Using these in vitro data to train a classifier by supervised machine learning, they identified six pathogenic PPARG variants (R194Q, A417V, R212W, P387S, M203I, and T356R) in patients with partial lipodystrophy [ 109].", + "lipid metabolism, as well as insulin sensitivity and inflammatory pathways. These pleiotropic functions confer great relevance to PPAR in physiological regulation of whole-body metabolism, as well as in the etiology of metabolic disorders. Accordingly, PPARG gene mutations, nucleotide variations, and post-translational modifications have been associated with adipose tissue disorders and the related risk of insulin resistance and type 2 diabetes (T2D). Moreover, PPAR alternative splicing isoforms", + "the PPARgamma locus. Diabetes 2001;50:686 689 12. Kahara T, Takamura T, Hayakawa T, et al. PPARgamma gene polymorphism is as-sociated with exercise-mediated changes of insulin resistance in healthy men. Me- tabolism 2003;52:209 212 13. Franks PW, Luan J, Browne PO, et al. Does peroxisome proliferator-activated receptor gamma genotype (Pro12ala) modify the association of physical activityand dietary fat with fasting insulin level? Metabolism 2004;53:11 16 14. Memisoglu A, Hu FB, Hankinson SE, et al.", + "30. Majithia, A. R. et al. Rare variants in PPARG with decreased activity in adipocyte differentiation are associated with increased risk of type 2 diabetes. Proc Natl Acad Sci USA 111, 1312713132 (2014). 31. Majithia, A. R. et al. Prospective functional classification of all possible missense variants in PPARG . Nat. Genet. 48, 15701575 (2016). 32. Claussnitzer, M. et al. Leveraging cross-species transcription factor binding" + ], + [ + "A variety of cellular and animal models have been developed and applied over the past few years to experimentally manipulate cis-regulatory elements and their target gene function as it related to beta cell/isletfunction, glucose homeostasis, and T2D pathogenesis. CRISPR/Cas9 hasrevolutionized our ability to modify genomes and epigenomes almost at will. Unsurprisingly, CRISPR (epi)genome editing tools can and have been used to target putative T2D target genes [54] orcis-REs[55] in beta", + "to how CRISPR/Cas9 technology may nd clinical application in patients with diabetes. Keywords: genome editing, beta cell, genome-wide association studies, maturity onset of diabetes of the young, stem cells, mouse models INTRODUCTION Type 2 diabetes (T2D) affects an estimated 425 million people worldwide, a number predicted to rise to 629 million by 2045 ( 1). The disease usually involves insulin resistance but is ultimately the result", + "hPSCs [48,49] for correcting the COL7A1 [50] anda1-antitrypsin genes [51]. Given the superior cutting ef ciency, CRISPR/Cas9 is increasingly becoming the favored choice for genome editing inhPSCs [16,52] . 3.2. Employing hPSCs and genome editing tools to study diabetes and metabolic syndromes In general, the strategy to carry out in vitro disease modeling of dia-", + "Due to its simplicity and adaptability, CRISPR has rapidly become the most popular genome editing tool available for the mammalian genome ( 50,63). Because NHEJ DNA repair often introduces unwanted indels at the Cas9 cutting site, CRISPR hasbeen used to knock-out genes by introducing frameshiftmutations, resulting in protein depletion ( 156,157). In the diabetes eld, CRISPR has also been adopted to study several genes in bcell lines and in human ES-derived bcells ( 21,151,", + "samples ( 236). CRISPR technology has been used recently to correct point mutations in patient-derived iPSCs to target diabetes-relatedgene defects. To date, the most ef cient method used in iPSC is CRISPR/Cas9-based homology-directed repair (HDR). Here, a Cas9-mediated cut is generated adjacent to the site of interest. A homologous donor template with the intended nucleotidechange containing silent mutations in the gRNA sequence(167) can then be recombined by HDR. This approach has", + "in response to various stimuli including glucose aftertransplantation in an immunocompromised mouse model (230,231). However, the use of iPSC is controversial and there are some concerns over genetic and epigenetic variations iniPSCs which might affect cell function after differentiation ( 275). Manipulation of hESC/iPSC cells via CRISPR-Cas9 technology provides a platform for the correction of genomic mutations not only in diabetes but in other disease elds as well", + "RNP and single strand edDNA (ssDNA) donor which carriesdesired changes such as insertion of loxP site ( 255,259265). Using CRISPR-Cas9, leptin and leptin receptor knockout mice have been established as tools in diabetes and obesity research ( 160,255,256). Knock-in mouse models have also been established via HDR to achieve cell-speci c deletion of the gene ( 266). Genome Editing: Clinical Application in Diabetes An important goal in genetic research is to identify the genetic", + "CRISPR-Cas9 epigenome editing enables high-throughput screening for functionalregulatory elements in the human genome. Nature Biotechnology 35(6):561 e568. [58] Hodson, D.J., Mitchell, R.K., Marselli, L., Pullen, T.J., Gimeno Brias, S., Semplici, F., et al., 2014. ADCY5 couples glucose to insulin secretion in humanislets. Diabetes 63(9):3009 e3021 . [59] Zhou, Y., Park, S.-Y., Su, J., Bailey, K., Ottosson-Laakso, E., Shcherbina, L.,", + "free IPSCs from Human Pancreatic Cells Using the CRISPR-Cas9 System. J Vis Exp JoVE (2017). doi: 10.3791/56260 277. Millette K, Georgia S. Gene Editing and Human Pluripotent Stem Cells: Tools for Advancing Diabetes Disease Modeling and Beta-Cell Development. Curr Diabetes Rep (2017) 17:116. doi: 10.1007/s11892-017-0947-3Hu et al. Genome Editing of Pancreatic Beta Cells Frontiers in Endocrinology | www.frontiersin.org October 2020 | Volume 11 | Article 576632 19", + "DNA donors as templates, it is possible the nCas9-RT will beable to convert all variants at once. This new technique, however,is still in early development, and its editing ef ciency and side- effects remain to be seen.FUTURE PROSPECTIVES Recent technological developments around CRISPR-Cas9 and itsderivative technologies, combined with advances in humancellular models, should accelerate our understanding of theinterplay between diabetes risk-associated genetic variants and" + ], + [ + "Effectors Glucose transporters. A number of polymorphisms have been identified in the GLUT4 gene. None of them have been linked to or found to be associated with type 2 diabetes in a variety of populations. 5960 Interestingly, an association was found between a polymorphism in the human GLUT! gene and type 2 diabetes60 that was significant for obese women. Regulation of GLUT4 protein expression in diabetes occurs in a strongly tissue-specific", + "M,XiangKS,etal.1996.Geneticcontri-bution of polymorphism of the GLUT1and GLUT4 genes to the susceptibilityto type 2 (non-insulin-dependent) dia-betes mellitus in different populations.Acta Diabetologica 33:19397 141. Poulsen P, Kyvik KO, Vaag A, Beck- Nielsen H. 1999. Heritability of type II(non-insulin-dependent) diabetes melli-tus and abnormal glucose toleranceapopulation-basedtwinstudy. Diabetolo- gia42:13945 142. Pugliese A, Zeller M, Fernandez AJ,", + "A mutation in the Glut2 glucose transporter gene of a diabetic patientabolishes transport activity. J Biol Chem 269: 1776517767, 1994. 36.Patel P, Bell GI, Cook JT, Turner RC, Wainscoat JS. Multiple restriction fragment length polymorphisms at the GLUT2 locus: GLUT2haplotypes for genetic analysis of type 2 (non-insulin-dependent) diabetesmellitus. Diabetologia 34: 817821, 1991. 37.Pereira MA, FitzerGerald SJ, Gregg EW, Joswiak ML, Ryan WJ, Suminski RR, Utter AC, Zmuda JM. A collection of Physical Activity", + "NootherrecentassociationsofpolymorphismswithT2Dhavebeenreplicated to date (Table 5). However, a recent meta-analysis (106) identied some earlyreproducibilityofanassociationbetweenvariationin GLUT1andT2D,originally reportedin1988(104).Itislikelythatthisassociationhasnotbeenpursuedfurtherfor several reasons, but one possibility is a study that reported the rejection oflinkageto GLUT1athighlevelsofsignicance(46).However,linkagehaslimited", + "mechanism by which type 2 diabetes is influenced remains to be identified. There have been several attempts to clarify the role of the polymorphism in SLC30A8 in the development of type 2 diabetes and the focus has been set on insulin secretion dueto the importance of ZnT-8 for insulin storage in the granulaof pancreatic cells. The results are controversial, but there appears to be an association between the risk variant of rs13266634 and reduced insulin secretion. Interestingly, decreased insulin", + "glucose tolerance, suggesting a r ole for this polymorphism in the onset of GDM as well as type 2 diabetes mellitus ( 17). The switch on IRS-1 of the amino acid GLY972 Arg (rs1801278) impairs insulinsecretion, and a study on 1306 GDM patients and 1973 pregnantwomen without GDM found a signi cant association between the presence of this polymorphism and the risk of GDM ( 18). Intriguing results were generated by a study on the genetic", + "tients the EUGENE2 study. Diabetologia 2008;51:816 820 32. Kirchhoff K, Machicao F, Haupt A, et al. Polymorphisms in the TCF7L2, CDKAL1 and SLC30A8 genes are associated with impaired proinsulinconversion. Diabetologia 2008;51:597 601 33. Nicolson TJ, Bellomo EA, Wijesekara N, et al. Insulin storage and glucose homeostasis in mice null for the granule zinc transporter ZnT8 and studies of the type 2 diabetes-associated variants. Diabetes 2009;58:2070 2083", + "is markedly reduced in glucose-unresponsive islets from ani-mal models of type 2 diabetes (51). In a previous study in PimaIndians, we found that ~5% of this population carries a mis-sense polymorphism in exon 3 of the GLUT2 gene (52), but this polymorphism was not associated with the residual fast-ing plasma insulin concentration in the present study.Despite the fact that GLUT2 is an attractive candidate, it", + "polymorphisms in 24 DNA samples. Common variants were thengenotyped in 760 type 2 diabetic patients and 641 nondiabetic sub-jects. Genetic associations with diabetes-related phenotypes werealso analyzed. Results: Nine polymorphisms were identified, and four common poly- morphisms [g. /H110021500C /H11022G, g./H110021062G /H11022C, g./H11002994C/H11022T, g./H11001408C/H11022A (Leu72Met)] were genotyped in a larger study. The genotype distri-butions of these four common polymorphisms in type 2 diabetes pa-", + "in turn, result in a defective or poorly expressed glucagonprotein and lead to decreased insulin secretion and conse- quently hyperglycaemia [ 48]. The current study identified, for the first time, several type 2 diabetes-associated risk alleles associated with a higher riskof GDM, namely rs7957197 ( HNF1A ), rs10814916 ( GLIS3 ), rs3802177 ( SLC30A8 ) and rs7041847 ( GLIS3 ). These SNPs" + ], + [ + "MicroRNAs (miRNA) ar e single -stranded, small RNA molecules that act at the post - transcriptional standard to regulate their target or source genes. Many biological processes are regulated by this Micro RNA. Since its discovery about two decades ago. It is correlated with a com prehensive set of diseases and described by numerous miRNAs, including T2DM and cardiovascular diseases. Specifically, with respect to T2DM, micro RNA plays a", + "they can act as oncogenes or tumor suppressors (8, 29, 72). miRs are associated with the 341 regulation of genes relevant to insulin secre tion, cholesterol biosynthesis, fat metabolism and 342 adipogenesis, crucial pathways in the pathogene sis of diabetes (53, 114, 115). miRs have also 343 been implicated in TGF- signaling related to th e pathogenesis of diabetic nephropathy with key 344 miRs such as miR-192, miR-216a, miR-217 and miR-377 being up-regula ted in glomerular 345", + "Lim LP, Lau NC, Garrett-Engele P, Grimson A, Schelter JM et al (2005) Microarray analysis shows that some microRNAs down-regulate large numbers of target mRNAs. Nature 433:769773 Lovis P, Roggli E, Laybutt DR, Gattesco S, Yang JY et al (2008) Alterations in microRNA expression contribute to fatty acid-induced pancreatic beta-cell dysfunction. Diabetes 57:27282736 Nadler ST, Stoehr JP, Schueler KL, Tanimoto G, Yandell BS et al", + "Abstract Recent advances in the understanding of the genetics of type 2 diabetes (T2D) susceptibility have focused attention on the regulation of transcriptional activity within the pancreatic beta-cell. MicroRNAs (miRNAs) represent an important component of regulatory control, and have proven roles in the development of human disease and control of glucose", + "evidence demonstrates that miRNAs and lncRNAs can alsoregulate the expression of genes and modulate the actions of growth factors and inflammatory factors related to diabetic complications [ 8]. These reports have been described in sev- eral reviews [ 8,8791] and are only briefly discussed here. Numerous recent reports have demonstrated abnormal ex- pression of various miRNAs in renal, vascular and retinal cellsunder diabetic conditions, and in vivo models of related", + "In addition, miRNAs have been shown to be involved in T2DM. For example, miRNAs play major roles in pancreatic islet development, cell dysfunction, insulin synthesis and secretion and insulin resistance [148] . Studies based on miRNA microarray analysis have identified many different miRNAs involved in the pathology of both T1DM and T2DM; these miRNAs include mi R-375, miR -29, miR -9, miR-124a, miR -195, miR -222, miR -126, miR -133a, miR -296, miR -96, miR -34a, miR -146b, miR -657,", + "26. He Y , Ding Y , Liang B, Lin J, Kim TK, Yu H, Hang H, Wang K. A Systematic Study of Dysregulated MicroRNA in Type 2 Diabetes Mellitus. Int J Mol Sci. 2017:18. 27. Dias S, Hemmings S, Muller C, Louw J, Pheiffer C. MicroRNA Expression Varies according to Glucose Tolerance, Measurement Platform, and Biological Source. Biomed Res Int. 2017;2017:1080157. 28. El Ouaamari A, Baroukh N, Martens GA, Lebrun P, Pipeleers D, van Obberghen E. miR-375 targets 3'-phosphoinositide-dependent protein kinase-1 and", + "nucleotide RNA molecules that potentially regulate the expression of thousands of genes. To understand therelationship between miRNA regulation and obesity- induced diabetes, we quantitatively proled approximately220 miRNAs in pancreatic islets, adipose tissue, and liver from diabetes-resistant (B6) and diabetes-susceptible (BTBR) mice. More than half of the miRNAs proled wereexpressed in all three tissues, with many miRNAs in each tissue showing signicant changes in response to genetic", + "11. Bartel DP. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell. 2004;116(2):281 97. 12. Pirola L, Balcerczyk A, Tothill RW, et al. Genome-wide analysis distinguishes hyperglycemia regulated epigenetic signatures of pri- mary vascular cells. Genome Res. 2011;21(10):1601 15. 13.Cooper ME, El-Osta A. Epigenetics: mechanisms and implications for diabetic complications. Circ Res. 2010;107(12):1403 13.Thispaper also provides a review of evidence pertaining to the role", + "128. Diao X, Shen E, Wang X, Hu B. Differentially expressed microRNAs and their target genes in the hearts of streptozotocin-induced diabetic mice. Mol Med Rep (2011) 4:63340. doi:10.3892/mmr.2011.489 129. La Sala L, Cattaneo M, De Nigris V , Pujadas G, Testa R, Bonfigli AR, et al. Oscillating glucose induces microRNA-185 and impairs an efficient antioxidant response in human endothelial cells. Cardiovasc Diabetol (2016) 15:71. doi:10.1186/s12933-016-0390-9" + ], + [ + "studying the highly familial MODY form of young - onset diabetes or other rare forms of monogenic diabetes. Table 12.2 The different subtypes of maturity - onset diabetes of the young ( MODY ). MODY type Gene locus Gene name Year of discovery Distribution Onset of diabetes Primary defect Severity of diabetes Complications OMIM MODY1 20q HNF4A ( TCF14 ) 1996 Rare (2 3%) Adolescence/", + "penetrance and early - onset diabetes, allows the collection of multigenerational pedigrees, making MODY an attractive model for genetic studies. MODY usually develops in thin young adults (usually before 25 years of age; in childhood, adolescence or young adulthood), and is associated with primary insulin - secretion defects [4,5] . The prevalence of MODY is estimated to be less than 1 2% of patients with T2DM, although it could represent as many as 5% of European cases of diabetes [4,25] . MODY is not", + "[2] . Mutations in 13 genes are known to cause MODY; the most prevalent are HNF1A , GCK and HNF4A [3, 4] . The MODY subtypes differ in age of onset of diabetes, the pattern of hyperglycemia, response to treatment, and associated extrapancreatic manifesta-tions [5] . As compared to type 2 diabetes, the clinical Key Words Best practice Genetic testing Healthcare providers Interview study Maturity onset diabetes of the young Abstract", + "causal for MODY , although genetic or functional evidence of obvious pathogenicity is not fully compelling (Table 1). Despite these important advances in understanding the mo- lecular pathogenesis of MODY , the genetic determinants in many patients with young-onset diabetes resembling a MODY-like phenotype remain unknown, suggesting addi- tional locus heterogeneity and new pathogenic mechanismsto be yet discovered. This has particularly been observed in", + "MODY Maturity Onset Diabetes of the Young. This is an uncommon form of diabetes, inherited as an autosomal dominant condition, and displaysa slow onset of symptoms. It generally presents before 25 years of age, is not related to obesity, and appears to have no autoi mmune basis. Multiple forms of MODY have been characterised based on mutations affecting different genes involved in the control of -cellfunction, and display different degrees of disease severity Continued over page", + "Genetic Testing for MODY Public Health Genomics 2015;18:5259 DOI: 10.1159/00036796359 1 Singh R, Pearson ER: The importance of mak- ing a genetic diagnosis of diabetes. Can J Dia-betes 2006; 30: 183190. 2 Ledermann HM: Is maturity onset diabetes at young age (MODY) more common in Europe than previously assumed? Lancet 1995; 345: 648.", + "Genetic Testing for MODY Public Health Genomics 2015;18:5259 DOI: 10.1159/00036796353symptoms present often at a relatively young age in pa- tients without overweight, who have a positive family his-tory. As compared to type 1 diabetes, progression may be less severe, and the required dosage of insulin low. Many patients with MODY are currently undiagnosed or misdiagnosed with type 1 or 2 diabetes mellitus [4] . In", + "in 1992, through familial linkage analysis of French pedigreeswith early-onset, non-auto-immune, non-obese diabetes thatwas also called maturity-onset diabetes of the young (MODY) (Froguel et al., 1992 ). Mutations in GCK (encoding glucokinase) were shown to cause a relatively benign form of MODY. Inciden-tally, it was the rst time that the direct causative effect of rela- tive insulin deciency was demonstrated in T2D, when insulin", + "gene studies were under powered. However, studies of monogenic forms of diabetes, specifically maturity onset diabetes of the young 2 (MODY2), provided some of the first insights into the contribution of genetic variation to hyperglycemia observed during pregnancy and fetal outcomes. MODY2 is an autosomal dominant form of MODY due to mutations in glucokinase ( GCK ) [2527]. Table 1. Characteristics and treatment modalities of different forms of diabetes mellitus Characteristics Treatment modalities", + "is variable, underlining that this disorder is genetically heterogeneous. Table 1. Definition of MODY Impaired glucose tolerance Age of onset <25 years Autosomal-dominant inheritance Using genetic linkage and candidate gene approaches, mutations in genes on chromosomes 2, 7, 12, 13, 19, and 20 have been linked to MODY and collectively may represent up to 3% of all patients with type 2 diabetes (Table 2). The gene on chromosome 7 (MODY2) encodes the glycolytic" + ], + [ + "of Diabetes Results of several genome-wide association stud- ies (GWAS) have linked the following common gene variants with a 1520% increased risk of diabetes: reduced insulin secretion via reduce beta-cell mass (CDKAL1, CDKN2A, CDKN2B) and beta-cell dysfunction (MTNR1B, TCF7L2, KCNJ11) and increased insulin resistance related to obesity (FTO) and unrelated to obesity (IRS1, PPARG) [ 11 ]. While most of the early studies", + "gene are associated with NIDDM in Caucasians. Diabetes 1996 , 45, 825-831. 46. Tarasov, A.I.; Nicolson, T.J. ; Riveline, J.P.; Taneja, T.K. ; Baldwin, S.A.; Baldwin, J.M.; Charpentier, G.; Gautier, J.F. ; Froguel, P.; Vaxillaire, M.; et al. A rare mutation in ABCC8/SUR1 leading to altered ATP-sensitive K+ channel activ ity and beta-cell glucose sensing is associated with type 2 diabetes in adults. Diabetes 2008 , 57, 1595-1604.", + "ly associated with type 2 diabetes: TCF7L2, KCNJ11, and PPARG . 5-7 However, in 2007, a number of novel genetic variants ( CDKAL1, IGF2BP2, the locus on chromosome 9 close to CDKN2A/CDKN2B, FTO, HHEX, SLC30A8, and WFS1)8-14 were shown to in - crease susceptibility to type 2 diabetes in repro - ducible studies. Furthermore, a recent meta-analy - sis identified six novel variants ( JAZF1, CDC123/ CAMK1D, TSPAN8/LGR5, THADA, ADAMTS9, and NOTCH2 ) that are associated with type 2 dia - betes. 15", + "CDKAL1 in uences insulin response and risk of type 2 diabetes. Nat Genet 2007; 39: 77075. 69 Wu Y , Li H, Loos RJ, et al. Common variants in CDKAL1, CDKN2A/ B, IGF2BP2, SLC30A8, and HHEX/IDE genes are associated with type 2 diabetes and impaired fasting glucose in a Chinese Han population. Diabetes 2008; 57: 283442. 70 Sandhu MS, Weedon MN, Fawcett KA, et al. Common variants in WFS1 confer risk of type 2 diabetes. Nat Genet 2007; 39: 95153.", + "Genes signifying increased risk for both type 1 and type 2 dia-betes have been identified. Genomewide association studies have identified over 50 loci associated with an increased genetic risk of type 1 diabetes. Several T1D candidate genes for increased risk of developing type 1 diabetes have been sug-gested or identified within these regions, but the molecular basis by which they contribute to islet cell inflammation and beta cell destruction is not fully understood. 12 Also, several", + "associated with susceptibility to type 2 diabetes mellitus. Nat Genet 2008; 40: 109297 . 74 Unoki H, Takahashi A, Kawaguchi T, et al. SNPs in KCNQ1 are associated with susceptibility to type 2 diabetes in East Asian and European populations. Nat Genet 2008; 40: 1098102. 75 Lyssenko V, Lupi R, Marchetti P, et al. Mechanisms by which common variants in the TCF7L2 gene increase risk of type 2 diabetes. J Clin Invest 2007; 117: 215563. 76 Lyssenko V, Jonsson A, Almgren P, et al. Clinical risk factors, DNA", + "type 2 diabetes or the inability to replicate linkage withdened loci. However, at least one susceptibility gene, namelyCAPN10, was found using a genome-wide scan approach [3]. Obesity is the greatest risk factor for type 2 diabetes mellitus, as it is known to induce insulin resistance via variousmechanisms ( TNF release, free fatty acids, etc.). Both", + "50 most cases of type 2 diabetes are thought to be due to genetic variations that are more common but exert less e ect. In early studies, genetic variants in the peroxisome proliferator-activated receptor- gene (PPARG) 51 and the ATP-sensitive potassium channel Kir62 (KCNJ11) were reproducibly associated with type 2 diabetes. 52 In Asian populations, the protective e ect of the PPARG*A12Ala allele on insulin resistance and risk of type 2 diabetes was not consistently seen. 53", + "49. Cornelis MC, Qi L, Zhang C, et al. Joint e ects of common genetic variants on the risk for type 2 diabetes in U.S. men and women ofEuropean ancestry. Ann Intern Med . 2009;150:541 550(in eng). 50. Hu C, Zhang R, Wang C, et al. PPARG, KCNJ11, CDKAL1, CDKN2A-CDKN2B, IDE-KIF11-HHEX, IGF2BP2 and SLC30A8are associated with type 2 diabetes in a Chinese population. PLoS One. 2009;4:e7643 (in eng). 51. Lin X, Song K, Lim N, et al. Risk prediction of prevalent diabetes in", + "46. Sladek R, Rocheleau G, Rung J et al (2007) A genome-wide asso- ciation study identifies novel risk loci for type 2 diabetes. Nature 445:881 885 47. Lauenborg J, Grarup N, Damm P et al (2009) Common type 2 diabetes risk gene variants associate with gestational diabetes. J Clin Endocrinol Metab 94:145 150 48. Florez JC, Jablonski KA, Bayley N et al (2006) TCF7L2 polymor- phisms and progression to diabetes in the Diabetes Prevention Program. N Engl J Med 355:241 250" + ], + [ + "genetic knowledge beyond its use for predic-tion of the individuals type 2 diabetes risk?One major advantage of knowing an at-riskpersons genotype could be to offer an individ-ually tailored lifestyle intervention program to prevent or, at least, to significantly retard the", + "Genetic factors appear to play a role in determining an individuals risk of developing diabetes. It is hoped", + "(35). If genetic tests are not helpful in the prediction and prevention of diabetes,they could have a role in discriminatingbetween type 1 and type 2 diabetes. Theepidemic of obesity (36) has made it moredifcult to distinguish diabetes type be- cause many children and young adultswith type 1 diabetes are also obese (37).Misclassi cation poses signi cant risks; an incorrect diagnosis of type 2 diabetes", + "geted at specific genetic mutations, it is likely that accompa-nying diagnostic tests for biomarkers will also become available to confirm whether the target biomarker is present. Genomic Analyses for Diabetes Risk", + "genes improves prediction of type 1 diabetes[published correction appears in Diabetologia. 2015; 58(1):206]. Diabetologia . 2014; 57(12):2521 2529. 57. Oram RA, Patel K, Hill A, Shields B, McDonald TJ, Jones A, Hattersley AT, Weedon MN. A type 1 diabetes genetic risk score can aid discrimination between type 1 and type 2 diabetes in young adults.Diabetes Care . 2016; 39(3):337 344. 58. Redondo MJ, Oram RA, Steck AK. Genetic risk", + "10.2337/db13-1663. 20. Vassy JL, et al. A genotype risk score predicts type 2 diabetes from young adulthood: the CARDIA study. Diabetologia. 2012;55:26042612. doi: 10.1007/s00125-012-2637-7. 21. Vassy JL, et al. Is genetic testing useful to predict type 2 diabe-tes? Best Pract Res Clin Endocrinol Metab. 2012;26:189201. doi: 10.1016/j.beem.2011.09.002. 22. Khera AV, et al. Genome-wide polygenic score to identify a monogenic risk-equivalent for coronary disease. bioRxiv. 2017. doi: 10.1101/218388.", + "Genotype Score for Prediction of Type 2 Diabetes n engl j med 359;21 www.nejm.org november 20, 2008 2209Type 2 diabetes mellitus is a m ajor health problem worldwide.1 Fortunately, its development can be prevented in many instances,2 and persons at risk can be readily identified with the measurement of a few com - mon risk factors.3-5 Type 2 diabetes is heritable, with a risk for people with familial diabetes as compared with those without familial diabetes that is increased by a factor of 2 to 6.", + "risk of type 1 diabetes offers the potential for improved prediction, stratification of patients according to risk, and selection of possible therapeutic targets. As germ-line factors, genetic risk variants are present and amenable to study at all times be -", + "offers the opportunity to test whetherknowledge of these genetic loci canimprove our ability to detect who willultimately develop diabetes. To answerthis question, we genotyped 18 well-validated single nucleotide polymorph-isms that had previously been associat- ed with diabetes in large genetics", + "Comprehension of Genomic Risk for Diabetes Public Health Genomics 2014;17:95104 DOI: 10.1159/000358413101their results in-person from a genetic counselor were able to correctly indicate their genomic or lifetime risk score for T2DM and interpret their genomic risk, compared to 50% of participants receiving their results online. This finding aligns with reports that suggest genetic counsel-ing (though limited to reporting of test results in this study) improves patients accuracy of risk perception" + ], + [ + "Genetic factors appear to play a role in determining an individuals risk of developing diabetes. It is hoped", + "Metabolic Syndrome and Family History of Diabetes Public Health Genomics 2010;13:353359 357able difference in the odds between these 2 risk levels. This table indicates that, compared with the average fa-milial risk, a moderate or high familial risk of diabetes increases the odds for each single component of the met-a b o l i c s y n d r o m e . T h e s e o d d s v a r y f r o m 1 . 1 9 ( 9 5 % C I : 0.881.61) to 1.53 (95% CI: 1.301.81). C o n c l u s i o n", + "For type 2 diabetes, there have been a few studies utilising a candidate-gene approach as well as genome-wide association studies, although some argue that genetic factors play only a minor role among Caribbean populations [ 90 ]. A family history of diabetes in any rst- degree relative (parent, sibling) or in a grandpar-ent is associated with a two- to fourfold increased risk of diabetes [ 10 , 91 ]. A family history of dia-", + "evidenced by a very high positive rate of family history of diabetes, and drastically different prevalence in various ethnic groups. Therefore, there is no doubt that type 2 diabetes is a disease with a strong genetic influence. However, the prediction of the relative contribution of genetic influence and number of genes involved in the pathogenesis of the disease has changed in the past few years. Initially, enthusiastic searches of diabetes genes were", + "can decrease risk of diabetes.22 Diet may also play a role. High calorie diets, including those high in fat, and especially saturated fat, have been implicated in the development of type 2 diabetes?4-26 Family history is a very strong risk factor for type 2 diabetes. A strong genetic component is suggested by the 58-75% concordance rates for type 2 diabetes observed in identical twins (Table 3).3 Table 3. Estimated risk of developing type 2 diabetes by family history One parent with type 2 diabetes", + "The fact that type 2 diabetes is a genetic disease is well known to clinicians by how it occurs in families, and by there being ethnic populations who are particularly high risk. The genetic link was clearly shown more than two decades ago by a famous study of identical twins in the U.K. that found essentially a 100% concordance rate for this disease if one twin developed type 2 diabetes, then the other one invariably developed it (9). However, this kind of study", + "genetic factors play an important role in the susceptibility to T2D. The risk of the disease developing at some point of life is ~70% when both parents are diabetic and ~40% when one parent has T2D [ 4]. Furthermore, latest data show that more than 400 genetic risk variants at 250 loci for T2D have been Genes 2018 ,9, 374; doi:10.3390/genes9080374 www.mdpi.com/journal/genes", + "36 Herder C, Roden M. Genetics of type 2 diabetes: pathophysiologic and clinical relevance. Eur J Clin Invest 2011; 41: 67992. 37 Dabelea D, Hanson RL, Lindsay RS, et al. Intrauterine exposure to diabetes conveys risks for type 2 diabetes and obesity: a study of discordant sibships. Diabetes 2000; 49: 220811. 38 Voight BF, Scott LJ, Steinthorsdottir V, et al. Twelve type 2 diabetes susceptibility loci identi ed through large-scale association analysis. Nat Genet 2010; 42: 57989.", + "long follow-up. Type 2 diabetes and impaired glucose tolerance (IGT) cluster in families. Thus, most patients have a positive family history, and the lifetime risk for developing type 2 diabetes is increased up to 40% (more than five times the background rate) by having a first degree relative with the disease. If both parents have type 2 diabetes the risk to the offspring may be as high as 70%. Available evidence supports a polygenic mode of inheritance with a considerable environmental input. 1", + "Genetic factors Type 2 diabetes has a strong genetic component and most Asian patients have a rst-degree relative with diabetes. 48,49 Much progress has been made in our understanding of the genetics of this disease. Importantly, most of the loci originally associated with diabetes in European populations have been replicated in Asian populations. Whereas monogenic forms of diabetes result from rare genetic mutations with large e ects, such as those seen in maturity-onset diabetes of young people," + ], + [ + "of a given genetic variant is modified by the environ - mental milieu (and vice versa). Evidence that lifestyle factors modify the genetic effects on T2DM risk has been generated from both observational studies and clinical trials82. However, genetic background might also affect the individuals response to lifestyle interventions83. In addition, replication data are sparse, and comprehensive, large-scale studies have failed to provide a compelling", + "genetic risk for diabetes may not moti-vate improvements in lifestyle behaviors.Indeed, knowledge of increased geneticrisk for diabetes may decrease motiva-tion to modify behavior in genetic fatal-ists (83). Diet recommendations optimized to the individual have been shown to re-duce postprandial glycemic excursionsto a greater extent than standard approaches in healthy individuals (84).Meal compositions that induce the most favorable glycemic pro les have been", + "diabetes regardless of the underlying genetic risk. This contrasts with theextensive epidemiological evidence sug-gesting that the relationship of lifestylewith obesity is dependent on genetic risk(7881); however, with few exceptions (e.g., [74]), analyses in large randomizedcontrolled trials have failed to show thatthese same genetic variants modifyweight loss in response to lifestyle in-tervention (82). It is also important to recognize that knowledge of increased", + "Genetic factors appear to play a role in determining an individuals risk of developing diabetes. It is hoped", + "suggested to attenuate its negative e ect on metabolic pro le, body weight, and diabetes risk ( Franks et al., 2007 ; Kilpelainen et al., 2008 ; Lindi et al., 2002 ; Ruchat et al., 2010 ) ( Table 1 ). The notion that lifestyle modi cation can eliminate the increased risk for development of T2DM in subjects with genetic suscepti-bility is also supported by ndings of Barwell et al. (2008) who", + "proven particularly effective for preven-tion and management of type 2 diabetes.For example, improvement in dietaryquality, in conjunction with other lifestylemodications like increased physical ac-tivity, was shown to be more effectivethan pharmacological treatment in pre-vention of diabetes in individuals at highrisk (1). Further, lifestyle modicationmay mitigate the risk associated with thestrongest known diabetes risk loci (2).While the existence of environmental in-uences on genetic risk (and vice", + "who is lean, genetic risk factors are more likely to be present than in someone who is obese and develops the disease or that weight loss enhances the genetic risk ofdiabetes. Genetic analyses performed in clinical trials involving intensive lifestyle modi - cation provide an important adjunct to the epidemiological literature on gene- lifestyle interactions in type 2 diabetes.On one hand, a major advantage of ran- domized controlled trials is that interac-", + "Lifestyle behaviors and genetic loci have clear and distinguishable effects on T2D risk; however, the pattern of disease occurrence within and between popula-tions that differ in their genetic and environmental underpinnings suggests T2D is caused in part by the interaction between adverse lifestyle behaviors and the genetic profile of an individual. For many, this seems a reasonable assumption, but there is little robust empirical evidence supporting the presence of such interactions.", + "this occurs. Findings to date, however, indicate that behavioral changes can substantially mitigate diabetogenic and obesogenic effects of individual or multiple risk alleles, which has much broader clinical and public health implications.We have seen considerable progress in our understanding of the role that both environ- ment and genetics play in the development of T2D. Recent work suggests that the adverse effect of some established T2D-associated loci may be greatly attenuated by appropriate", + "Susceptibility to obesity and diabetes is deter- mined by both genetic and lifestyle factors.Suggestive evidence of genelifestyle interac- tion (Box 33.3) in the development of common diseases such as obesity and type 2 diabetes wasrst provided by descriptive epidemiological studies such as migration studies that compare the disease risk between genetically related pop-ulations who live different lifestyles. A classicalexample is the comparison of the risk of obesity" + ], + [ + "understanding of the genetic basis of diabetes, and the advances of recent months are arguably the most important made since the role of the HLA region was recognised in type1 diabetes. The number of genetic regions causally implicated is now 11 each for type 1 and type 2 diabetes [ 19], and is set to rise further. The bewildering pace of new discovery standsin stark contrast to the slow progress that characterised the previous two decades, with a total combined output of three", + "It has proven to be challenging to isolate the genes underlying the genetic components conferring susceptibility to type 1 and type 2 diabetes. Unlike previous approaches, genome-wide association studies have extensively delivered on the promise of uncovering genetic determinants of complexdiseases, with a number of novel disease-associated variants being largelyreplicated by independent groups. This review provides an overview of these recent breakthroughs in the context of type 1 and type 2 diabetes, and", + "The history of diabetes genetics traces human genetic research more broadly.Initially, only a few polymorphic genetic markers were known, and these werestudiedinpopulation-basedassociationstudies.Withthedevelopmentofgenome-wide maps for family-based linkage analysis and of positional cloning, attentionturned to monogenic forms of disease. The application of family-based linkagemethods to common forms of diabetes, however, met with less clear success.More recently, with progress in genome sequencing and", + "the elucidation of the wide spectrum of genes that played a role in the molecular mechanism of diabetes development[142-144]. However , despite the vast flow of genetic information including the identification of many gene mutations and a large array of single nucleotide polymorphisms (SNPs) in many genes involved in the metabolic pathways that affect blood glucose levels, the exact genetic mechanism of diabetes remains elusive[145,146]. Evidently, a major complication is the", + "confirmed genes for type 2 diabetes and six for type 1(Fig. 1). At last, it seems, our understanding of the genetic basis of complex, multifactorial forms of diabetes is catching up with that of rarer, single-gene disorders. This leap in knowledge is the result of major advances in technology plus an improved understanding of patterns of human genetic variation. Using single nucleotide polymor- phism (SNP) chips it is now possible to analyse up to a million", + "make dissection of the black box of genetics of diabetespossible in the near future, but at this point, apart fromthe pro les that distinguish between type 1 and type 2 diabetes and a limited number of speci c variants that identify small subgroups of patients (MODY), genetics has not been successful in further differentiating subclasses ofdiabetes. Research Gaps After consideration of the known genetic associations with diabetes risk, consensus developed that the eld is", + "studies provide new insights into type 2diabetes aetiology. Nat Rev Genet 2007;8:657662 11. Grant RW, Moore AF, Florez JC. Genetic architecture of type 2 diabetes: recentprogress and clinical implications. Diabe-tes Care 2009;32:11071114 12. Dupuis J, Langenberg C, Prokopenko I,", + "early results have been excellent, yielding six new replicating gene regions. Here I discuss the insights into type 2 diabetes genetics that have been provided by these new findings. I consider where diabe - tes genetic studies might go from here, and present a perspective that may be applicable to other common traits. I also briefly discuss the wider implications that surround the identification of a common gene that predis - poses to type", + "that genetic studies will ultimately identify key genetic elements that help determine susceptibility to diabetes,disease progression, and responsiveness to specific therapies, as well as help identify novel targets for futureintervention. A substantial number of genetic loci, gene polymorphisms, and mutations have already beenreported as having variable degrees of association with one or other type of diabetes (type 1, type 2, maturityonset diabetes of the young [MODY]), while others appear to be involved", + "24. Varshney, A. et al. Genetic regulatory signatures underlying islet gene expression and type 2 diabetes. Proc. Natl. Acad. Sci. USA 114, 23012306 (2017). 25. Thurner, M. et al. Integration of human pancreatic islet genomic data refines regulatory mechanisms at Type 2 diabetes susceptibility loci. eLife 7, e31977 (2018). 26. Gaulton, K. J. et al. Genetic fine mapping and genomic annotation defines causal mechanisms at type 2 diabetes susceptibility loci. Nat. Genet. 47, 14151425 (2015)." + ], + [ + "genes relate directly to insulin secretion and indirectly, through collaborating with other genes, to insulin resistance. Thisseems to support the epidemiological evidence that environmentally triggered insulin resistance interacts with geneticallyprogrammed bcell dysfunction to precipitate diabetes. Citation: Jain P, Vig S, Datta M, Jindel D, Mathur AK, et al. (2013) Systems Biology Approach Reveals Genome to Phenome Correlation in Type 2 Diabetes. PLoS ONE 8(1): e53522. doi:10.1371/journal.pone.0053522", + "have been the subject of most follow-up studies to date.Specifically, we examined acute changes in expression of these genes in response to feeding and fasting and longer term changes in the expression of these genes inresponse to a diet high in fat and sugar, recognized as a critical environmental risk factor for type 2 diabetes. It has been hypothesized that most of the new genetic variants affect -cell function, development or survival but not insulin sensitivity [6]. Consistent with this,", + "or survival. However, we also found evidence that most of the genes could have potential roles in other metabolically-relevant tissues. Genes affecting insulinsensitivity may be expected to be expressed in peripheralinsulin sensitive tissues, such as liver and adipose tissue, and be responsive to metabolic status. Consumption of a high fat diet was associated with a tendency for the ex- pression of several of these genes to be decreased. Simi-larly, many of the genes were regulated by feeding and", + "secretion versus insulin sensitivity). We also sought todetermine whether any of these genes are regulated by conditions known to alter the expression of metabolic- ally relevant genes. We examined the expression of thesegenes under fasting and non-fasting conditions (e.g. in response to insulin), which might be altered if they affect peripheral insulin sensitivity. Consumption of diets high in fats and sugars is associated with risk of developing type 2 diabetes [34] and many genes that are critical for", + "regulating sugar metabolism. Moreover, genes that were", + "Figure 2: The role of type 2 diabetes genes in insulin secretion Pancreatic -cell genes associated with type 2 diabetes are in italics. G6P=glucose-6-phosphate. Adapted from Florez JC. Newly identi ed loci highlight beta cell dysfunction as a key cause of type 2 diabetes: where are the insulin resistance genes? Diabetologia 2008; 51: 110010, by kind permission of the author and Springer Science + Business Media. Positive calorie balance Cycle A++ Cycle B Liver fat Insulin suppression of", + "tive Glis3 expression, which in turn drive increased levels of beta cell apoptosis and senescence. Genetic susceptibility could be replicated by elevated levels of dietary fat. Transcriptional analysis of human islets identified the same genetic networks at play. Together, these findings demonstrate both the important role of genetic variation in beta cells for diabetes susceptibility and a mechanism by which the Western diet may contribute to the growing diabetes epidemic. RESULTS", + "associated with fasting proinsulin levels and provides new insights into the pathophysiology of type 2 diabetes. Diabetes 60, 26242634 (2011). 65. Saxena, R. etal. Genetic variation in GIPR influences the glucose and insulin responses to an oral glucose challenge. Nat. Genet. 42, 142148 (2010). 66. Tobacco and Genetics Consortium. Genome-wide meta-analyses identify multiple loci associated with smoking behavior. Nat. Genet. 42, 441447 (2010).", + "38. Saxena R, Hivert M, Langenberg C, Tanaka T, Pankow JS, et al. (2010) Genetic variation in GIPR influences the glucose and insulin responses to an oral glucose challenge. Nat Genet 42: 142148. doi:10.1038/ng.521. 39. Neale BM, Sham PC (2004) The future of association studies: gene-based analysis and replication. Am J Hum Genet 75: 353362. doi:10.1086/423901. 40. Saccone SF, Hinrichs AL, Saccone NL, Chase GA, Konvicka K, et al. (2007)", + "Nature Reviews | EndocrinologyFactors that aect insulin secretion and action Body weight Level of physical activity Smoking Heavy alcohol consumption Genetic predisposition Geneenvironment interaction Positive risk prole Negative risk prole Normoglycaemia/uni03B2-cell dysfunction and insulin resistanceAdipose tissue Skeletal muscle LiverInsulin-mediated glucose production /uni2191Insulin-mediated glucose uptake /uni2193 Insulin-mediated glucose uptake /uni2193 Hyperglycaemia Epigenetics" + ], + [ + "Genes signifying increased risk for both type 1 and type 2 dia-betes have been identified. Genomewide association studies have identified over 50 loci associated with an increased genetic risk of type 1 diabetes. Several T1D candidate genes for increased risk of developing type 1 diabetes have been sug-gested or identified within these regions, but the molecular basis by which they contribute to islet cell inflammation and beta cell destruction is not fully understood. 12 Also, several", + "Genetics of Type 2 Diabetes Chapter 12 197400 multiallelic markers (short tandem repeats or microsatellites, with a density of 1 marker/10 cmol) allows identi cation of polymorphic markers showing strong allele identity by descent in diabetic family members (i.e. allele sharing in sibships is signi - cantly higher than 50%). Once identi ed, such susceptibility genes for diabetes may then be positionally cloned in the intervals of linkage.", + "3. Katsarou, A. etal. Type 1 diabetes mellitus. Nat. Rev. Dis. Primers 3, 17016 (2017). 4. Onengut-Gumuscu, S. etal. Fine mapping of type 1 diabetes susceptibility loci and evidence for colocalization of causal variants with lymphoid gene enhancers. Nat. Genet. 47, 381386 (2015). 5. Barrett, J. C. etal. Genome-wide association study and meta-analysis find that over 40 loci affect risk of type 1 diabetes. Nat. Genet. 41, 703707 (2009).", + "Clinical Risk Factors, DNA Variants, and the Development of Type 2 Diabetes n engl j med 359;21 www.nejm.org november 20, 2008 2229(Fig. 3). An increase in the BMI and a concomi - tant decrease in insulin sensitivity during the 8-year period were consistent findings, with no differences between subjects at high and low genetic risk (Fig. 3A and 3B). However, subjects with a high genetic risk did not increase their insulin secretion (disposition index) to compen -", + "and genetic markers to improve the prediction of type 2 diabetes: theEPIC-Potsdam Study. Diabetes Care . 2009;32:2116 2119 (in eng). 56. Cauchi S, Meyre D, Durand E, et al. Post genome-wide association studies of novel genes associated with type 2 diabetes show gene-gene interaction and high predictive value. PLoS One . 2008;3(5): e2031 . 57. Lyssenko V, Jonsson A, Almgren P, et al. Clinical risk factors, DNA variants, and the development of type 2 diabetes. N Engl J Med . 2008;359:2220 2232 (in eng).", + "etically expressed homeobox variant (rs1111875) on type 2 diabetes risk. Molecular Genetics and Metabolism , 102 (2), 194199. Watanabe, R. M., Black, M. H., Xiang, A. H., Allayee, H., Lawrence, J. M., & Buchanan, T. A. (2007). Genetics of gestational diabetes mellitus and type 2 diabetes. Diabetes Care , 30 (Suppl. 2), S134S140. Williams, M. A., Qiu, C., Dempsey , J. C., & Luthy , D. A. (2003). Familial aggregation of type 2", + "markers, genetic markers do not change with disease progression.Dimas and collaborators examined the association of 37 establishedT2D susceptibility loci and indices of proinsulin processing, insulin secretion, and insulin sensitivity in 58,614 nondiabetic subjects [6]. Cluster analysis classi ed the risk loci into ve major categories on the basis of their association with glycemic phenotypes. The rst cluster was characterized by the effects of the risk alleles of PPARG ,KLF14 ,", + "recently, meta-analysis of GWAS data involving African American type 2 diabetes patients identified similar loci to the previous studies with the addition of two novel loci, HLA-B and INS-IGF[157]. These results provide strong evidence of common genetic determinants including common specific genes that are linked to diabetes. A small list of specific genetic markers seem strongly associated with the risk of developing type 2 diabetes including the TCF7L2[158] and CAPN10[159,160]", + "Clinical Risk Factors, DNA Variants, and the Development of Type 2 Diabetes n engl j med 359;21 www.nejm.org november 20, 2008 2231MPP subjects (P = 0.001) and from 0.79 to 0.83 in the Botnia subjects (P = 0.006). Of the 16 loci that have been associated with type 2 diabetes previously,8-15 we showed that 11 TCF7L2, PPARG, FTO, KCNJ11, NOTCH2, WFS1, CDKAL1, IGF2BP2, SLC30A8, JAZF1, and HHEX were associated with an enhanced risk of future", + "Clinical Risk Factors, DNA Variants, and the Development of Type 2 Diabetes n engl j med 359;21 www.nejm.org november 20, 2008 2227(Fig. 1B), whereas impaired fasting glucose or impaired glucose tolerance developed in 313 of 2039 subjects (15.4%). Clinical Factors Predicting Incidence of Diabetes In both the MPP and Botnia studies, a family his - tory of diabetes, an increased BMI, and increased levels of blood pressure and serum levels of tri -" + ], + [ + "unraveling the pathophysiological mechanisms of this disease, identifying candidate diabetic genes, and discovering and testing new therapeutic agents. The classical rodent models of diabetes allow unbiased discovery, while the new models made by genetic manipulation allow testing of the role of specific genes and tissues. Experimental animal models are an irreplaceable resource for diabetes research and are hastening the progress towards the goals of better treatment, prevention, and cure.", + "is absence of reliable methods for generating specific celltypes,immunologicalrejectionofthetransplantedcells,anddifficulty in purification of specific lineages [55]. Furtherconcernsincludetheuncontrolledproliferationofthetrans-planted embryonic stem cells into a specific type, once theyaretransplanted[56].Still,despiteofitsmanifoldlimitationsboth scientific and ethical, the application of stem cell tech-nologyholdsimmenseprospectsintreatmentofdiabetes. 6. Gene Therapy in Diabetes", + "T ogether, these discoveries will continue to improve our understanding of the biologic mechanisms that maintain glucose homeostasis, and of still hidden molecular defects leading to chronic hyperglycemia, and could also lead to the development of more speci cally targeted antidiabetic drugs or even gene - based therapies. Moreover, pharmacogenetic testing might then be used to predict, for each patient, the therapeutic response to different classes of drugs. The identi cation of T2DM genes will", + "Greatstrideshavebeenmadeclinicallyintheprevention, development,andtreatmentofthediseasebutnotherapeuticmethod have been completely successful till date. With newtechnologies revolutionizing the treatment possibilities, thesearch for an effective medication is not far ahead. Theextensive research leading to the discovery of the pathwaygenes contributing to the development of the disease andthe sequencing of complete genomes have revolutionized the diabetes research. The development of the techniques", + "into different genetic levels of disease categories, from which pre- vention or treatment methods could be provided accordingly [ 4]. For example, some forms of diabetes are directly related to a change in a single gene [ 34]. Some patients who are diagnosed with type 1 diabetes can now be tested for one of monogenic diabetes. The appropriate treatment for these patients is not injecting insulin, but giving oral sulfonylureas [ 34]. Moreover, it is now well understood", + "pp .430435,2003. [58] M. Zalzman, S. Gupta, R. K. Giri et al., Reversal of hyperglycemia in mice by using human expandable insulin- producing cells differentiated from fetal liver progenitor cells,Proceedings of the National Academy of Sciences of the United StatesofAmerica ,vol.100,no .12,pp .72537258,2003. [59] H.-S. Jun and J.-W. Yoon, Approaches for the cure of type 1 diabetes by cellular and gene therapy, Current Gene Therapy , vol.5,no.2,pp.249262,2005.", + "transgenics. It is likely that animal models will play an importantrole in the eventual cure of human diabetes mellitus. Competing interests None declared. References 1Sima AAF, Shafrir E, eds. Animal Models of Diabetes: A Primer. Amsterdam: Harwood Academic Publishers, 2000. 2British Union for the Abolition of Vivisection. Home page. Available from: http://www.buav.org. 3Patterson C. Eternal Treblinka. Our Treatment of Animals and the Holocaust . New York: Lantern Books, 2002. 4Regan T.", + "Third, this view of diabetes pathogenesis is consistent with the growing portfolio of available therapies. We have agents and interventions that can prevent or ameliorate diabetesthrough, for example, beneficial effects on islet function (e.g. sulfonylureas), obesity (weight loss), insulin resistance (e.g. exercise), fuel partitioning (e.g. thiazolidinediones) andmicrobiome content (metformin, possibly). Just as diabetes risk alleles influence metabolic phenotype through pushing", + "aprospectivetherapeuticapproachfortype1diabetes[59]. Thein vivogene therapy is the method of choice as a therapeutic strategy because it is simpler and the vectorcontaining the desired gene is directly inserted into thepatient, but the development of safe (not toxic to host)and effective vectors remains as a challenging task for genetherapist. Presently, the strategies for in vivotherapy involve", + "betacellulin gene therapy induces islet neogenesis in the liver a n dr e v e r s e sd i a b e t e si nm i c e , Nature Medicine ,v o l .9 ,n o .5 , pp.596603,2003. [73] S. Ferber, A. Halkin, H. Cohen et al., Pancreatic and duode- nal homeobox gene 1 induces expression of insulin genes inliver and ameliorates streptozotocin-induced hyperglycemia, Nature Medicine ,vol.6,no .5,pp .568572,2000. [74] P.A.Halban,S.E.Kahn, A.Lernmark,andC.J.Rhodes,Gene andcell-replacementtherapyinthetreatmentoftype1diabetes." + ], + [ + "to improve diagnosis. Monogenic vs. polygenic diabetes Monogenic and polygenic diabetes are traditionally considered distinct, with monogenic diabetes resulting from one highly penetrant variant in one gene in a given individual, and polygenic diabetes resulting from the contribution of several variants with smaller effects in the context of environmental/lifestyle factors. In T1D, autoimmune dysfunction is the prominent mechanism, with variation in the major histocompatibility", + "represent about 2%-5% of diabetes patients. Mono - genic diabetes results primarily from gene defects that lead to a decrease in beta cell number or function. Monogenic diabetes genes were identified using linkage studies or code for proteins that directly affected glucose homeostasis. The majority of genes responsible for monogenetic diabetes code for either transcription factors that participate in the control of nuclear gene expression or proteins that are located on the cell", + "diabetic patients inwhom rare, highly penetrant mutations ofasingle gene cause their diabetes (13). While com - mon variants ofthese genes that make a small contribution topolygenic diabetes may also exist (13), thevariants causing monogenic diabetes have limited util- ityinpharmacogenetics duetotheir low allele frequency. Thevast majority oftype 2diabetes patients have polygenetic forms ofthedisease that typically also require a permissive environment (e.g., obesity, sed-", + "diabetes exist along more of a continuum than previously appre - ciated. Therefore, knowledge about monogenic diabetes not only provides opportunities for etiology-based treatment of the minori- ty of individuals with highly penetrant variants, but also informs broader understanding of diabetes etiology. Types of monogenic diabetes Maturity-onset diabetes of the young MODY comprises most monogenic diabetes cases, with classical characteristics of young diagnosis age, family history of diabe -", + "Monogenic Diabetes Monogenic diabetes is a class of diabetes associated with genetic defects in beta - cell function. They are frequently associated with early onset of hyperglycemia (typically before 25 years of age). Three common forms of mono-genic diabetes include maturity - onset diabetes of the", + "HNF4A-MODY and requires genetic testing to diagnose. Here we will describe monogenic diabetes types, etiologies, diagnosis, management, and strategies to improve diagnosis. Monogenic versus polygenic diabetes Monogenic and polygenic diabetes are traditionally considered distinct, with monogenic diabetes resulting from one highly pene - trant variant in one gene in a given individual and polygenic diabe - tes resulting from the contribution of several variants with smaller", + "Monogenic inheritance is caused by mutation of a single gene. There are some well-defined monogenic rodent models. In humans, monogenic obesity and diabetes exist as well, but are extremely rare. Polygenic inheritance is the result of multiple contributing genes and is the predominant mode of inheritance in human type 2 diabetes. Multiple polygenic animal models are also available. However, even in monogenic animal models, genetic background plays an important influence. For", + "(Mendelian) that may also cause type 2 diabetes (Yang & Chan, 2016). More than twenty genes highly expressed in pancreatic cells have been identified within these mono-genic subtypes (AlkortaAranburu et al., 2014). Recently, two national surveys revealed that most patients with mono-genic diabetes are likely to be unrecognized and misdiag-nosed as type 1 or type 2 diabetes (Delvecchio et al., 2017; Johansson et al., 2017). Genetic diagnosis leads to improved treatment, better prediction of disease", + "Key words: diabetes, gene, polygenic, monogenic Introduction Diabetes is one of the most common metabolic disor - ders. It is estimated that the number of diabetes pa - tients worldwide has already exceeded 200 million [92]. This creates a need to understand the etiology ofthe disease, genetic and enviromental factors influ - encing development of diabetes. Diabetes is a group of metabolic diseases that are characterized by ele - vated glucose level. Poorly controlled or undiagnosed", + "2 1.1.2 Introduction Monogenic diabetes is caused by a single defect in one of over 40 genes1,2. Since MODY (maturity onset diabetes of the young) was named by Fajans for the T2D -like presentation in young people with an autosomal dominant pattern of inheritance3,4, our understanding of phenotypic and genetic heterogeneity in monogenic diabetes has increased. The major monogenic diabetes categories are MODY, neon atal diabetes" + ], + [ + "by performing a genetic profile on diabetic patients (pharmacogenetics). Furthermore, identification of genetic determinants of diabetic patients will better define the targets of current and future therapies, and will lead to therapies that are more specific for their genetic constitutes. SUMMARY With the advancement of the Human Genome Project, we enter the era of a sequence-based biology. Some progress has been made in the", + "Todate,studiesofdiabeteshaveplayedamajorroleinshapingthinkingabout thegeneticanalysisofcomplexdiseases.Basedontrendsingenomicinformationandtechnology,combinedwiththegrowingpublichealthimportanceofdiabetes,diabetes will likely continue to be an important arena in which methods will bepioneeredandlessonslearned.Itiswithgreatenthusiasmthatwelookforwardtothis effort, and with avid curiosity we await to see whether the lessons of todaywill be supported by the data of tomorrow.", + "DNA code. Therefore, greater unders tanding of the epigenetic basis of disease could enable the 576 discovery new therapeutic targets for the treat ment of numerous human diseases including 577 diabetes and its complications. 578 579 580", + "T ogether, these discoveries will continue to improve our understanding of the biologic mechanisms that maintain glucose homeostasis, and of still hidden molecular defects leading to chronic hyperglycemia, and could also lead to the development of more speci cally targeted antidiabetic drugs or even gene - based therapies. Moreover, pharmacogenetic testing might then be used to predict, for each patient, the therapeutic response to different classes of drugs. The identi cation of T2DM genes will", + "research will contribute positive ly to the life of people living with T1D . Being able pinpoint mutations, and then discover how they contribute to the genetic cause of a condition, can help to open up path s for pharmaceutical treatments. Currently, m ost treatment strategies for genetic disorders do not alter the underlying genetic mutation; but are designed to improve particular signs and symptoms associated with the disorder. For instance, T1D is managed by", + "Epigenomic approaches: applications in diabetic complications research Epigenetic studies in human disease have been greatly accel- erated as a result of advances in whole-genome and epige- nome profiling technologies as well as bioinformatics andgenomic data analysis platforms [ 99,100]. DNAme is analysed using bisulfite conversion of genomic DNA, immu- noprecipitation of methylated DNA, followed byhybridisation to arrays or next-generation sequencing to ob-", + "new therapeutic targets and identify potential diabetic neuropathy biomarkers. The genes identied in the current study conrm datagathered from experimental models of diabetes and provide a comprehensive picture of the expression of multiple targets in asingle human tissue sample. Our initial analyses of this data set classied the patient samples based on myelinated bre density and found that two large groups emerged; those with a loss of myelinated bre density 5500 bres/mm", + "DNA variation with disease processes in a range of settings, from cell lines to human populations, and major advances have been made in coupling these complex datasets with information about extrinsic environmental exposures including drug prescription in ways that allowthe logical interrogation of gene-drug and gene-lifestyle interactions. Doing so may teach us about disease etiology and help stratify type 2 diabetes (T2D) into subclasses that can be treated more effectively, with", + "that genetic studies will ultimately identify key genetic elements that help determine susceptibility to diabetes,disease progression, and responsiveness to specific therapies, as well as help identify novel targets for futureintervention. A substantial number of genetic loci, gene polymorphisms, and mutations have already beenreported as having variable degrees of association with one or other type of diabetes (type 1, type 2, maturityonset diabetes of the young [MODY]), while others appear to be involved", + "the onset and progression of diabetic neuropathy is of primeimportance. The current study takes an important rst step towards this goal by identifying specic sets of genes whose expression accurately classies patient samples with regard to diabetic neuropathy pro- gression and by analysing their interactions within known cellularpathways. Identifying common elements in these complex net-works will yield novel insights into disease pathogenesis, provide" + ] + ], + "task_id": [1,2,3,4,5,6,7,8,9,10,1,2,3,4,5,6,7,8,9,10] +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/gpt4o_cs_gn.json b/gnqa/paper2_eval/data/dataset/gpt4o/gpt4o_cs_gn.json new file mode 100644 index 0000000..7c5bec1 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/gpt4o_cs_gn.json @@ -0,0 +1,289 @@ +{ + "question": [ + "What is GeneNetwork.org, and how does it help scientists understand genetics?", + "How do researchers use GeneNetwork.org to study diseases?", + "What can GeneNetwork.org tell us about how genes interact with each other?", + "How does GeneNetwork.org help in finding the genetic causes of common diseases?", + "Can GeneNetwork.org predict my risk of developing certain health conditions based on my genes?", + "How does GeneNetwork.org make use of data from different populations around the world?", + "What kinds of genetic data are available on GeneNetwork.org?", + "How do scientists use GeneNetwork.org to study differences in gene expression?", + "Can GeneNetwork.org be used to learn about genetic influences on behavior?", + "What role does GeneNetwork.org play in personalized medicine?", + "How does the information on GeneNetwork.org help in developing new treatments for diseases?", + "What is a gene network, and why is it important for understanding genetics?", + "How do researchers identify which genes are important for certain traits using GeneNetwork.org?", + "How can GeneNetwork.org help in understanding complex traits like height or intelligence?", + "Are there any known genetic mutations that cause premature aging?", + "What are the practical applications of the research done through GeneNetwork.org?", + "How can I access and use the data available on GeneNetwork.org?", + "What are some recent discoveries made using GeneNetwork.org?", + "How do scientists ensure the accuracy of the data on GeneNetwork.org?", + "What\u2019s the difference between looking at one gene and studying a whole gene network?" + ], + "answer": [ + "GeneNetwork.org is a web service for systems genetics that started in 2001 as WebQTL, an online version of Ken Manly's Map Manager QT program [3]. It serves as a depository of datasets and tools for use in complex systems biology approaches to generate or predict higher-order gene function [8]. GeneNetwork.org is an open-access database that collates genomic information from diverse experimental crosses and reference panels, as well as phenotypic data from various research groups [9]. GeneNetwork.org helps scientists understand genetics by providing a platform for systems genetics, which involves the study of complex traits through the integration of networks of genes, transcripts, and traits such as toxicity, cancer susceptibility, and behavior across several species [10]. It offers tools for correlation and mapping strategies to assess associations among multiple genes and quantitative trait loci (QTLs), making the study of complex traits widely available to the scientific community [2]. Additionally, it supports predictive medicine and systems genetics by constantly being maintained and improved with data from multiple species and multi-omics analysis [1].", + "Researchers use GeneNetwork.org to study diseases by leveraging its capabilities as a bioinformatics tool for systems genetics analysis. This platform allows researchers to explore large phenotype and genome datasets from multiple species, which are essential for understanding complex biological networks and predicting molecular interactions [4], [5]. GeneNetwork.org supports a systems genetics approach, which examines how diverse sets of genetic and molecular markers contribute to phenotypes and diseases, rather than focusing on single gene mutations [2]. This approach is facilitated by the extensive data available on the platform, including gene expression patterns and drug response data, which can be compared and analyzed statistically [4]. The platform also enables correlation and network analysis, allowing researchers to compare associations between tissues and across different species, such as rodents and humans [6]. By studying networks of genes, proteins, metabolites, and other biomarkers, researchers can model genuine biological pathways, which helps in uncovering disease genes and understanding complex diseases [9]. Overall, GeneNetwork.org provides a comprehensive resource for predictive medicine and systems genetics, aiding researchers in constructing biological networks that are predictive of disease outcomes [1], [5].", + "GeneNetwork.org provides several tools and resources to explore how genes interact with each other. It is a bioinformatics tool that allows users to explore systems genetics data, which is crucial for defining biological networks and predicting molecular interactions [1]. GeneNetwork offers correlation and mapping strategies to assess associations among multiple genes and quantitative trait loci (QTLs), facilitating the study of complex traits [3]. Additionally, it provides access to a wide variety of data, including genotypes and phenotypes, which can illuminate relationships between different levels of a biological system, such as the genome, transcriptome, and phenome [6]. GeneNetwork also computes gene-gene co-expression correlations, which are essential for understanding gene interactions [9].", + "GeneNetwork.org aids in finding the genetic causes of common diseases by utilizing a systems genetics approach. This approach allows researchers to explore which phenotypes and diseases result from diverse sets of genetic and molecular markers, rather than focusing on single gene mutations [1]. GeneNetwork provides access to a wide variety of data, including genotypes (e.g., SNPs) and phenotypes, which can be used to study complex traits [7]. The platform integrates data on gene expression, protein interactions, and DNA-protein binding to construct biological networks that are predictive of disease [4]. Additionally, GeneNetwork offers correlation and mapping strategies for assessing associations among multiple genes and quantitative trait loci (QTLs), facilitating the study of complex traits [5]. This makes GeneNetwork a powerful tool for predictive medicine and systems genetics, helping researchers understand the genetic underpinnings of common diseases [6].", + "The context provided does not specifically mention GeneNetwork.org or its capabilities in predicting health conditions based on genetic information. However, the context does discuss the general potential of genetic information to predict disease risk. For example, it mentions the ability to identify individuals at higher genetic risk for common diseases [1], and the potential for genomic profiling to measure susceptibility to diseases [10]. While these references indicate that genetic information can be used to assess disease risk, there is no direct mention of GeneNetwork.org's specific capabilities in this area. Therefore, based on the provided context, it is unclear if GeneNetwork.org itself offers such predictive services.", + "GeneNetwork.org utilizes data from different populations around the world by integrating diverse genomic information and phenotypic data from various experimental crosses and reference panels. This allows for comprehensive exploratory and statistical analysis of large published phenotype and genome datasets [3], [4]. The platform includes data from multiple species, which facilitates the comparison of gene expression patterns with drug responses and other phenotypic traits [3]. Additionally, GeneNetwork.org provides analytical tools that enable users to compare traits across datasets from different experimenters, further enhancing the ability to study correlations and perform data mining in genomic regions [5], [9]. This integration of diverse datasets supports the construction of predictive biological networks by interfacing DNA variation data with gene expression, protein interactions, and DNA-protein binding information [6].", + "GeneNetwork.org provides a variety of genetic data, including: 1. Genomic information from diverse experimental crosses and reference panels, as well as phenotypic data from various research groups [3]. 2. Genetic variants such as SNPs (single nucleotide polymorphisms), insertions, deletions, and duplications [4]. 3. Extensive phenotype data extracted from the literature and submitted by users, which allows for comparisons of drug responses with gene expression patterns [5]. 4. Microarray data of gene expression in the brain and data of other phenotypes [8]. 5. Genotypes, including SNPs, and phenotypes obtained from various studies [10]. These datasets are designed to support systems genetics research and include data from multiple species [2], [5].", + "Scientists use GeneNetwork.org to study differences in gene expression by leveraging a variety of analytical tools and datasets available on the platform. GeneNetwork provides access to large published phenotype and genome datasets from several species, allowing for exploratory and statistical analysis [2]. The platform includes microarray data of gene expression in the brain and other phenotypes, which can be used to compare traits across different datasets [1]. GeneNetwork also facilitates the comparison of gene expression patterns with drug responses and other phenotypic data, making it practical for identifying candidate genes for complex traits through QTL analyses [2], [4]. The platform supports correlation and network analysis to compare associations between tissues and across rodent or human datasets, which is useful for systems genetics mapping [5]. Additionally, bioinformatic analyses on GeneNetwork.org include tools for gene ontology, presence of cis-regulation or polymorphisms, phenotype correlations, and principal component analyses, which help in evaluating differentially expressed genes and understanding distinct biological processes [10].", + "Yes, GeneNetwork.org can be used to learn about genetic influences on behavior. It is a comprehensive resource equipped with tools and features for studying genetic correlates to neurobehavioral phenotypes [5]. The platform includes a phenotype database with data on behavioral traits, among others, which can be used for correlation and network analyses to identify relationships with genetic data [4]. Additionally, GeneNetwork focuses on correlations of behavioral phenotypes with gene expression levels in recombinant inbred and inbred panels of mice and rats, which helps in identifying candidate genes for complex traits [6]. The resource is designed for the multivariate genetic analysis of complex traits, including behavior, in genetic reference populations [9].", + "GeneNetwork.org plays a significant role in personalized medicine by serving as an open-access, online data analysis resource for systems biology and systems genetics [1]. It is a tool for systems genetics and predictive medicine, which aims to predict and potentially avoid phenotypic outcomes such as diseases [2]. The platform supports the integration of networks of genes, transcripts, and traits, which is crucial for understanding complex genetic interactions and their implications for personalized medicine [10]. Additionally, GeneNetwork.org facilitates the comparison of data on drug responses with gene expression patterns, which is essential for tailoring therapeutic strategies to individual genetic profiles [9].", + "The information on GeneNetwork.org aids in developing new treatments for diseases in several ways: 1. **Insight into Gene Function**: GeneNetwork.org provides insights into gene function and how altered gene function can lead to disease. This understanding is crucial for translating genetic discoveries into new therapeutics, as it helps elucidate the mechanisms of action for newly identified disease genes, which is a major bottleneck in drug development [1]. 2. **Predictive Medicine and Systems Genetics**: The platform is an exciting resource for predictive medicine and systems genetics. It integrates data from multiple species and omics analyses, which can be used to predict phenotypic outcomes such as disease, potentially allowing for the development of treatments that can prevent these outcomes [2], [4]. 3. **Identification of Drug Targets**: Genetic information from GeneNetwork.org can be used to identify new targets for pharmaceutical intervention. This includes providing information about the long-term safety of pathway interventions, which is crucial for developing effective and safe treatments [5]. 4. **Exploratory and Statistical Analysis**: GeneNetwork.org is designed for exploratory and statistical analysis of large phenotype and genome datasets. This makes it practical to compare data on drug responses with gene expression patterns, facilitating the identification of potential therapeutic targets [8]. 5. **Studying Gene Networks**: By studying networks of genes, proteins, metabolites, and other biomarkers, GeneNetwork.org helps uncover disease genes. This network-based approach combines the effects of multiple genes, producing stronger signals and reducing the complexity of statistical analyses, which can accelerate the discovery of new treatments [10]. Overall, GeneNetwork.org serves as a comprehensive tool for researchers to explore genetic data and develop insights that are critical for the creation of new therapeutic strategies.", + "A gene network is a graphical model comprised of nodes and edges, where the nodes typically represent genes, gene products, or other biological entities [1]. These networks illustrate how genes do not function in isolation but operate in complex networks that define the behavior of biological systems [2]. Understanding gene networks is crucial for interpreting the roles of individual genes within the broader context of these networks, which can provide insights into complex system behaviors, including diseases [1], [2]. By considering genes within their networks, researchers can better understand the interrelationships and regulatory mechanisms that contribute to phenotypic traits and disease processes [4].", + "Researchers identify important genes for certain traits using GeneNetwork.org through a series of steps and tools provided by the platform: 1. **Data Selection and Trait Mining**: Researchers begin by selecting a data set and mining it for traits of interest based on user search queries [1]. This involves using the main search page to query specific data sets and identify traits that are relevant to their study. 2. **Trait Collection and Analysis**: Once traits are identified, they are selected and placed in a collection for further inspection and quantitative analysis [1]. This allows researchers to organize and focus on specific traits for deeper investigation. 3. **Advanced Search Options**: GeneNetwork offers advanced search options that enable researchers to query data sets for specific genomic intervals and locate traits with the highest likelihood ratio statistic (LRS) values, which are indicative of strong genetic associations [4]. 4. **Correlation and Genetic Linkage Mapping**: Researchers can establish associations between transcript abundance, phenotypic traits, and genotype using correlation or genetic linkage mapping functions [5]. This helps in identifying candidate genes linked to specific traits. 5. **QTL Analysis and Network Graphs**: The platform allows for the generation of quantitative trait loci (QTL) analyses, network graphs, and correlation matrices, which are essential for understanding the genetic architecture of complex traits [3]. By utilizing these tools and processes, researchers can effectively identify and analyze genes that are important for specific traits using GeneNetwork.org.", + "GeneNetwork.org can assist in understanding complex traits like height or intelligence through several key features: 1. **Analytical Tools and Data Sets**: GeneNetwork provides a variety of analytical tools that allow users to compare traits with numerous datasets available from other researchers. This includes microarray data of gene expression in the brain and other phenotypic data, which can be crucial for studying complex traits [1]. 2. **Systems Genetics Approach**: The platform offers a systems genetics approach, which helps illuminate the relationships between different biological system levels, such as the genome, transcriptome, and phenome. This comprehensive view can provide insights into the roles of individual genes and developmental pathways involved in complex traits [2]. 3. **Correlation and Genetic Linkage Mapping**: GeneNetwork allows for the establishment of associations between transcript abundance, phenotypic traits, and genotype using correlation or genetic linkage mapping functions. This can help identify genetic factors contributing to complex traits like height or intelligence [6]. 4. **Data Mining and Trait Correlations**: The platform can be used to study correlations between traits and perform data mining in genomic regions containing candidates for quantitative trait genes. This feature is particularly useful for identifying genetic components of complex traits [4]. 5. **Multi-Omics Analysis**: GeneNetwork has been updated to include multi-omics analysis, which integrates various types of biological data. This holistic approach can enhance the understanding of complex traits by considering multiple layers of biological information [7]. Overall, GeneNetwork.org provides a comprehensive suite of tools and data that can facilitate the exploration and understanding of complex traits like height and intelligence through a systems genetics framework.", + "Yes, there are known genetic mutations that cause premature aging. Some specific genetic syndromes associated with premature aging include: 1. Hutchinson-Gilford Progeria Syndrome, which is caused by mutations in the LMNA gene [4]. 2. Rothmund-Thomson syndrome and related disorders, which are associated with mutations in the RECQL4 gene [4]. 3. Ataxia-telangiectasia, which is another genetic disorder linked to premature aging [4]. Additionally, Martin (1978) listed 162 genetic syndromes in humans that exhibit some or many signs of premature aging [1]. These conditions highlight the connection between genetic mutations and premature aging.", + "The research done through GeneNetwork.org has several practical applications: 1. **Predictive Medicine and Systems Genetics**: GeneNetwork is a valuable resource for predictive medicine and systems genetics, providing tools and data for multi-omics analysis across multiple species [1]. 2. **Teaching Tool**: It serves as a teaching tool in neuroscience and genetics, allowing educators to use it for dry-lab teaching and helping students explore gene-to-phenotype relationships [2]. 3. **Exploration of Systems Genetics Data**: GeneNetwork is used to explore systems genetics data, which is crucial for defining biological networks and predicting molecular interactions [4]. 4. **Complex Systems Biology Approaches**: It provides datasets and tools for complex systems biology approaches, aiding in the generation or prediction of higher-order gene functions [5]. 5. **Virtual Laboratory for Hypothesis Testing**: GeneNetwork can be used as a virtual laboratory to test specific biological hypotheses or to generate new ideas from scratch [8]. 6. **Identification of Regulatory Genes**: The platform can identify novel potential master regulatory genes for further investigation, enhancing the understanding of genetic regulation [9]. 7. **User-Friendly Systems Genetics Analyses**: It allows researchers without advanced bioinformatics skills to perform systems genetics analyses, making it accessible to a broader range of scientists [10].", + "To access and use the data available on GeneNetwork.org, you can follow these steps: 1. **Navigating to the Website**: Start by visiting the GeneNetwork website at www.genenetwork.org [8]. 2. **Searching for Data**: There are two primary ways to search for data on GeneNetwork: - Use the global search bar located at the top of the page. This feature allows you to search for genes, mRNAs, or proteins across all datasets, providing data across various species, groups, and types of data [5]. - Alternatively, you can follow the main search workflow, which involves selecting a dataset, mining it for traits of interest based on user search queries, selecting traits from the search, and placing them in a collection for further inspection and quantitative analysis [3]. 3. **Analyzing Data**: Once you have selected the data, GeneNetwork provides an analytical environment where you can perform correlation analysis and linkage mapping. This environment helps identify and substantiate gene targets for further research [7]. 4. **Accessing Genotype Files**: If you need genotype files, they can be accessed directly via a specific URL: http://www.genenetwork.org/webqtl/main.py?FormID=sharinginfo&GN_AccessionId=600 [2]. 5. **Using Additional Resources**: The banner menu on the main search page contains additional search options and helpful resources to assist with your analyses [3]. By following these steps, you can effectively access and utilize the data available on GeneNetwork.org for your research needs.", + "The provided context does not explicitly mention any specific recent discoveries made using GeneNetwork.org. However, it highlights the platform's capabilities and improvements, such as the addition of data from 10 species, multi-omics analysis, updated code, and new tools, which make it a valuable resource for predictive medicine and systems genetics [1]. Additionally, GeneNetwork.org is described as a powerful statistical platform for online network analyses and mapping, which could enable numerous breakthroughs in neuroscience and other fields [7]. While these improvements and capabilities suggest potential for discoveries, specific recent discoveries are not detailed in the context provided.", + "Scientists ensure the accuracy of the data on GeneNetwork.org through several methods: 1. **Quality Control and Normalization**: Data are entered into GeneNetwork after being processed through systems like PhenoGen, which have extensive capabilities for normalization and quality control [3]. 2. **Quality Checking and Preprocessing**: Phenotypic data undergo quality checks and preprocessing before being uploaded to GeneNetwork. This includes normalization, removal of outliers, and transformation of data to achieve a normal distribution [8]. 3. **Data Curation and Informatics Support**: The GeneNetwork.org team provides excellent data curation and informatics support to maintain data accuracy [4]. These steps help ensure that the data on GeneNetwork.org is accurate and reliable for scientific research.", + "The difference between looking at one gene and studying a whole gene network lies in the scope and context of the analysis. When examining a single gene, the focus is on understanding the role and function of that specific gene, often in isolation. This approach can be limited because it does not consider the interactions and relationships that gene may have with others. In contrast, studying a whole gene network involves analyzing a system of interconnected genes, which provides a broader context. A gene network is a graphical model where nodes represent genes or gene products, and edges represent interactions between them [1]. This approach allows researchers to explore how multiple genes interact within biological pathways, potentially uncovering complex relationships and combined effects that a single-gene analysis might miss [2]. By studying gene networks, researchers can gain insights into the collective behavior of genes, which can be crucial for understanding complex diseases and biological functions [2]. Additionally, gene networks can help identify highly connected subgraphs that correspond to biologically relevant networks, aiding in the identification of causative genes and their regulatory roles [5]." + ], + "contexts": [ + [ + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "of links to external resources for tracing the interrelationships of a gene among multiple Web-based resources. GeneNetwork also offers a number of correlation and mapping strategies for assessing associations among multiple genes and QTLs. GeneNetwork aims to make the study of complex traits through the use of systems genetics widely available to the scientific community. A powerful tool that can be integrated with GeneNetwork or used on", + "inbred strain; Reverse genetics; dbSNP; GeneWeaver; BioGPS; NCBI; GeneRIF; UCSC Genome Browser; Gemma; GEO; Allen Brain Atlas; GWAS Catalog; GTEx; WebGestalt; PLINK; Manhattan plot; eQTL analysis; R/qtl; WGCNA; Proteomics; Metabolomics; Metagenomics 1 Introduction GeneNetwork ( www.genenetwork.org , GN) is a web service for systems genetics. It started in 2001 as WebQTL an online version of Ken Manlys Map Manager QT program [ 1]", + "inbred strain; Reverse genetics; dbSNP; GeneWeaver; BioGPS; NCBI; GeneRIF; UCSC Genome Browser; Gemma; GEO; Allen Brain Atlas; GWAS Catalog; GTEx; WebGestalt; PLINK; Manhattan plot; eQTL analysis; R/qtl; WGCNA; Proteomics; Metabolomics; Metagenomics 1 Introduction GeneNetwork ( www.genenetwork.org , GN) is a web service for systems genetics. It started in 2001 as WebQTL an online version of Ken Manlys Map Manager QT program [ 1]", + "GeneNetwork http://www.genenetwork.org is anexample of a bioinformatics tool that can be used to explore systems genetics data. The importance of defining biological networks and predicting molecular interactions has been emphasized by several reports [1,2]. Such studies emphasize that when knowledge about DNA variation within popula- tions is interfaced with data on gene expression, protein interactions and DNA-protein binding, biological networks can be constructed that are predictive of the", + "GeneNetwork.org is also a valuable teaching tool. While mainly designed for researchers interested in testing gene-to- phenotype relationships, GeneNetwork. orghas been adapted for dry-lab teaching in neuroscience and genetics ( Grisham et al., 2017 ). A useful approach is to assign sets of vetted questions, such as the exam- ples discussed above, and to help students work toward answers, solutions, or novelquestions. Several examples relating to the", + "GeneNetwork.org is also a valuable teaching tool. While mainly designed for researchers interested in testing gene-to- phenotype relationships, GeneNetwork. orghas been adapted for dry-lab teaching in neuroscience and genetics ( Grisham et al., 2017 ). A useful approach is to assign sets of vetted questions, such as the exam- ples discussed above, and to help students work toward answers, solutions, or novelquestions. Several examples relating to the", + "subnetworks GeneNetwork (www.genenetwork.org) is a depository of data- sets and tools for use in complex systems biology approaches in order to generate or predict higher order gene function ( 23, 24 ).", + "GeneNetwork is an open-access database that collates genomic information of diverse experimental crosses and reference panels as well as phenotypic data from miscellaneous research groups [26]. Statistics Data generation, statistical analysis and graph creation were performed with SPSS Statistics 21 (IBM, Ehningen, Germany). As appropriate, mean and median values were further used for QTLanalysis. Phenotypic robustness for each strain was assessed by the", + "deposited in the GeneNetwork website (http://www.genenetwork.org) so that other investigators can look for correlations between gene expression patterns and phenotypic traits. The GeneNetwork is an open resource and consists of a set of linked resources for systems genetics. It has been designed for integration of networks of genes, transcripts, and traits such as toxicity, cancer susceptibility, and behavior for several species. Phenotypic QTLs using the" + ], + [ + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "users can take advantage of a systems genetics approach (Rosen et al., 2003, 2007). While the candidate gene approach asks which one gene mutation causes a particular disease, the systems genetics approach explores which phenotypes and diseases result from diverse sets of genetic and molecular markers (Rosen et al., 2003, 2007). The majority of data sets in GeneNetwork are collected from GRPs consisting of hundreds of diverse, inbred strains of", + "Based on this, Goh et al. created networks using data from the Online Mendelian Inheritance in Man (OMIM) [18]database that houses lists of disease gene links. Two networks emerged: the human disease network inwhich disease nodes were connected if they were caused by mutations in the same gene, and the disease gene network where gene nodes were", + "al., 2005). GeneNetwork is designed primarily as a web service for exploratory and statistical analysis of large published phenotype and genome datasets, and includes data from several species (see Supplementary Discussion). GeneNetwork includes extensive phenotype data extracted from the literature and submitted by users, which makes it practical to compare data on drug responses with gene expression patterns. Gene expression", + "GeneNetwork http://www.genenetwork.org is anexample of a bioinformatics tool that can be used to explore systems genetics data. The importance of defining biological networks and predicting molecular interactions has been emphasized by several reports [1,2]. Such studies emphasize that when knowledge about DNA variation within popula- tions is interfaced with data on gene expression, protein interactions and DNA-protein binding, biological networks can be constructed that are predictive of the", + "including correlation and network analysis to compare associations between tissues and between other rodent or human data sets[32] Many of the Data Sets are amenable to systems genetics mapping and other methods and are accessible at GeneNetwork. The Description and Usage column provides details about the data set and potential", + "including correlation and network analysis to compare associations between tissues and between other rodent or human data sets[32] Many of the Data Sets are amenable to systems genetics mapping and other methods and are accessible at GeneNetwork. The Description and Usage column provides details about the data set and potential", + "atic way. Users begin by selecting one or more human diseases and clicking on Compare. The genes associated with the selected disease are tested for enrichment against all sets of known associat ed genes for worm phenotypes. The result reveals functionally coherent , evolution- arily conserved gene networks. Alternatively, users can also start by selecting worm pheno types, which are tested against human diseases. In addition to cross -species", + "is tackling this immense challenge bystudying networks of genes, proteins,metabolites, and other biomarkers thatrepresent models of genuine biologicalpathways. Studying complex diseasesin terms of gene networks rather thanindividual genes or genomic loci shouldaid in uncovering disease genes. Withthis approach, the effects of multiplegenes in the network are combined,producing a stronger signal and reducingthe number of statistical tests of associ-ation that must be performed.", + "subnetworks GeneNetwork (www.genenetwork.org) is a depository of data- sets and tools for use in complex systems biology approaches in order to generate or predict higher order gene function ( 23, 24 )." + ], + [ + "GeneNetwork http://www.genenetwork.org is anexample of a bioinformatics tool that can be used to explore systems genetics data. The importance of defining biological networks and predicting molecular interactions has been emphasized by several reports [1,2]. Such studies emphasize that when knowledge about DNA variation within popula- tions is interfaced with data on gene expression, protein interactions and DNA-protein binding, biological networks can be constructed that are predictive of the", + "Molecular Genetics and Genomics 1 3 as overexpression, knockdown, knockout and mutation (Online Resource 1). Gene network construction Genegene interaction data were extracted from the STRING database (http://strin g-db.org/) (Christian etal. 2003), a web resource that includes comprehensively predicted and known interaction information. Then, the genegene interaction pairs were imported into Cytoscape software (Version 3.5.1) (http://cytos cape.org/ ) (Smoot etal. 2011 ) to construct a", + "of links to external resources for tracing the interrelationships of a gene among multiple Web-based resources. GeneNetwork also offers a number of correlation and mapping strategies for assessing associations among multiple genes and QTLs. GeneNetwork aims to make the study of complex traits through the use of systems genetics widely available to the scientific community. A powerful tool that can be integrated with GeneNetwork or used on", + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "is shown in Figure 1A. Associations between transcript abundance, phenotypic traits and genotype can be estab- lished either using correlation or genetic linkage mapping functions [29,30]. The main page of GeneNetwork at http://www.genenetwork.org provides access to subsets of data through pull-down menus that allow specific data sets to be queried. The datasets can be further restricted using a single text box for specific database entries to query probe set or trait ID, or annotations associated with", + "genetics approaches can not only provide insights into the roles of individual genes or developmental pathways but also illuminate relationships between different levels of a biologic system, such as the genome, transcriptome, and phenome [ 10]. One such resource of systems genetics is the GeneNetwork website and resource (www.genenetwork.org ) that provides access to a wide variety of data such as genotypes (e.g., SNPs), phenotypes that are obtained", + "occurrence; GN, gene neighbor; GT, genetic interaction; LC, literature-curated protein interactions; MS, affinity purification/mass spectrome try; PG, phy- logenetic profiles; PI, fly protein interactions; TS, tertiary structure; and YH, yeast two-hybrid). Detailed descriptions are listed in Suppleme ntal Table S1. ( B) Essential genes were highly interconnected in HumanNet, and thus predictable from the network, as shown by ROC analysis. Genes were ranked by their sum", + "from co-regulation patterns found within tens of thousands of samples for which gene expression was measured. GeneNetwork provid es un- precedented resolution and predictive power across multip le cell types and tissues. Analogous to discovering patterns in expressi on data, the network of protein-protein interactions can also be comput ationally pre- dicted using various methods[381]. The combined current knowledge of how cells control functio ns", + "(http://string-db.org/ ). STRING creates networks representing the best available knowledge of gene interconnections. Each protein-protein interaction is annotated with scores indicating how likely an interaction should be true. Scores rank from 0 to 1, with one being the highest confidence. A score of 0.5 indicates roughly every second interaction might be erroneous. Gene-gene co-expression cor- relations were computed as Pearson product-moment correlations (r) in Genenetwork.org after removing outliers.", + "addition to this, GeneNetwork can be used to study correlations between traits and to perform data mining in genomic regions containing candidates for quantitative trait genes (Hoffman et al., 2011). All datasets in GeneNetwork are linked to a materials and methods information page that summarizes experimental details relating to the dataset. Databases within GeneNetwork include the transcriptome database, the BXD published" + ], + [ + "users can take advantage of a systems genetics approach (Rosen et al., 2003, 2007). While the candidate gene approach asks which one gene mutation causes a particular disease, the systems genetics approach explores which phenotypes and diseases result from diverse sets of genetic and molecular markers (Rosen et al., 2003, 2007). The majority of data sets in GeneNetwork are collected from GRPs consisting of hundreds of diverse, inbred strains of", + "Based on this, Goh et al. created networks using data from the Online Mendelian Inheritance in Man (OMIM) [18]database that houses lists of disease gene links. Two networks emerged: the human disease network inwhich disease nodes were connected if they were caused by mutations in the same gene, and the disease gene network where gene nodes were", + "Genetics Home Reference - Genetics Home Reference provides consumer-friendly information about the effects of genetic variations on human health. http://ghr.nlm.nih.gov/ Gene Reviews Features expert-authored, peer-reviewed, current disease descriptions that apply genetic testing to the diagnosis, management, and genetic counseling of patients and families with specific inherited conditions. www.genetests.org/servlet/access?", + "GeneNetwork http://www.genenetwork.org is anexample of a bioinformatics tool that can be used to explore systems genetics data. The importance of defining biological networks and predicting molecular interactions has been emphasized by several reports [1,2]. Such studies emphasize that when knowledge about DNA variation within popula- tions is interfaced with data on gene expression, protein interactions and DNA-protein binding, biological networks can be constructed that are predictive of the", + "of links to external resources for tracing the interrelationships of a gene among multiple Web-based resources. GeneNetwork also offers a number of correlation and mapping strategies for assessing associations among multiple genes and QTLs. GeneNetwork aims to make the study of complex traits through the use of systems genetics widely available to the scientific community. A powerful tool that can be integrated with GeneNetwork or used on", + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "genetics approaches can not only provide insights into the roles of individual genes or developmental pathways but also illuminate relationships between different levels of a biologic system, such as the genome, transcriptome, and phenome [ 10]. One such resource of systems genetics is the GeneNetwork website and resource (www.genenetwork.org ) that provides access to a wide variety of data such as genotypes (e.g., SNPs), phenotypes that are obtained", + "eron Genetics Center ( https://www.regeneron.com/ge - netics-center ), and aims to identify rare loss-of-function mutations in founder populations to delineate further the genetic factors that underpin health and disease. This ini - tiative is also addressed at developing countries and those in resource-limiting environments, under the coordina - tion of the Genomic Medicine Alliance ( http://www.ge - nomicmedicinealliance.org ), a founding partner of the", + "to understand the genetics of a variety of diseases andbiological systems including aging, the immune system and ironregulation [26,27,28,29,30]. Much of this work has been madeavailable through GeneNetwork (formerly WebQTL ) an on-line", + "GeneNetwork.org is also a valuable teaching tool. While mainly designed for researchers interested in testing gene-to- phenotype relationships, GeneNetwork. orghas been adapted for dry-lab teaching in neuroscience and genetics ( Grisham et al., 2017 ). A useful approach is to assign sets of vetted questions, such as the exam- ples discussed above, and to help students work toward answers, solutions, or novelquestions. Several examples relating to the" + ], + [ + "Letters NATure GeNeTicsIn our testing dataset, 19.8% of participants were at threefold increased risk for at least 1 of the 5 diseases studied (Table 2). The potential to identify individuals at significantly higher genetic risk, across a wide range of common diseases and at any age, poses a number of opportunities and challenges for clinical medicine. Where effective prevention or early detection strategies are available, key issues will include the allocation of attention and", + "genetic risks of disease on risk-reducing health behaviour: Systematic review with meta-analysis. BMJ. 2016;352:i1102. 57. Vernarelli JA. Impact of genetic risk assessment on nutrition-related life- style behaviours. Proc Nutr Soc . 2013;72(1):153159. 58. Marteau TM, French DP , Griffin SJ, et al. Effects of communicating DNA- based disease risk estimates on risk-reducing behaviours. Cochrane Database Syst Rev . 2010;(10). 59. National Human Genome Research Institute. All about The Human", + "personalized screening based on age and polygenic risk profile. 12 Pashayan N, Pharoah P. Translating genomics into improved population screening: hype or hope? Hum. Genet. 130(1), 1921 (2011). 13 Pharoah PD, Antoniou A, Bobrow M, Zimmern RL, Easton DF, Ponder BA. Polygenic susceptibility to breast cancer and implications for prevention. Nat. Genet. 31(1), 3336 (2002). nn\t Examines the potential for prediction of risk based on common genetic variation and compares this with the prediction that", + "Eur J Hum Genet. 12. Janssens AC, van Duijn CM (2008) Genome-based prediction of common diseases: advances and prospects. Hum Mol Genet 17: R166173. 13. Wray NR, Goddard ME, Visscher PM (2007) Prediction of individual genetic risk to disease from genome-wide association studies. Genome Res 17:15201528. 14. Wray NR, Goddard ME, Visscher PM (2008) Prediction of individual genetic risk of complex disease. Curr Opin Genet Dev 18: 257263. 15. Jakobsdottir J, Gorin MB, Conley YP, Ferrell RE, Weeks DE (2009)", + "within the general population and toutedfor its potential contribution to personal-ized medicine (1315), although the un-derlying clinical utility has yet to bedemonstrated (16,17). Given the poten-tial for individual genetic risk to beempirically quantied and rapidly com-municated, it is of interest to both clini-cians and the general public to discover ifmodiable characteristics like diet canmitigate risk in individuals empiricallydened as high risk on the basis ofgenotype.", + "Comprehension of Genomic Risk for Diabetes Public Health Genomics 2014;17:95104 DOI: 10.1159/000358413103 9 Green MJ, Peterson SK, Baker MW, Harper GR, Friedman LC, Rubinstein WS, Mauger DT: Effect of a computer-based decision aid on knowledge, perceptions, and intentions about genetic testing for breast cancer suscep-tibility: a randomized controlled trial. JAMA 2004; 292: 442452. 10 Bernhardt JM, McClain J, Parrott RL: Online", + "Comparison of family history and SNPs for predicting risk of complex disease. PLoS Ge-net 2012; 8:e1002973. Downloaded from http://karger.com/phg/article-pdf/17/2/95/3426597/000358413.pdf by guest on 03 July 2023", + "Genetics Home Reference - Genetics Home Reference provides consumer-friendly information about the effects of genetic variations on human health. http://ghr.nlm.nih.gov/ Gene Reviews Features expert-authored, peer-reviewed, current disease descriptions that apply genetic testing to the diagnosis, management, and genetic counseling of patients and families with specific inherited conditions. www.genetests.org/servlet/access?", + "Khoury, M. J. (2006). Family history of type 2 diabetes: apopulation-based screening tool for prevention? Genetics in Medicine, 8 (2), 102 108. Hunter, D. J., Khoury, M. J., & Drazen, J. M. (2008). Letting the genome out of the bottle will we get our wish? The New England Journal of Medicine, 358 (2), 105 107. Ioannidis, J. P. A. (2009). Personalized genetic prediction: too limited, too expensive, or too soon? Annals of Internal Medicine, 150 (2), 139141.", + "genomic profiling for measuring susceptibility to common diseasesand targeting interventions. Genet Med 2004; 6:3847. 42Vineis P, Christiani DC. Genetic testing for sale. Epidemiology 2004; 15:35. 43Haga SB, Khoury MJ, Burke W. Genomic profiling to promote ahealthy lifestyle: not ready for prime time. Nat Genet 2003; 34:34750. 44Yang Q, Khoury MJ, Botto L et al. Improving the prediction of complex diseases by testing for multiple disease-susceptibility genes.Am J Hum Genet 2003; 72:63649." + ], + [ + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "GeneNetwork (www.genenetwork.org). The web -based software further allows extraction of sets of", + "al., 2005). GeneNetwork is designed primarily as a web service for exploratory and statistical analysis of large published phenotype and genome datasets, and includes data from several species (see Supplementary Discussion). GeneNetwork includes extensive phenotype data extracted from the literature and submitted by users, which makes it practical to compare data on drug responses with gene expression patterns. Gene expression", + "GeneNetwork is an open-access database that collates genomic information of diverse experimental crosses and reference panels as well as phenotypic data from miscellaneous research groups [26]. Statistics Data generation, statistical analysis and graph creation were performed with SPSS Statistics 21 (IBM, Ehningen, Germany). As appropriate, mean and median values were further used for QTLanalysis. Phenotypic robustness for each strain was assessed by the", + "GeneNetwork provides users with an array of analyticaltools to compare a given trait with a number of data setsavailable from other experimenters. Microarray data ofgene expression in the brain and data of other phenotypes are two such examples of possible tools. For this study, we", + "GeneNetwork http://www.genenetwork.org is anexample of a bioinformatics tool that can be used to explore systems genetics data. The importance of defining biological networks and predicting molecular interactions has been emphasized by several reports [1,2]. Such studies emphasize that when knowledge about DNA variation within popula- tions is interfaced with data on gene expression, protein interactions and DNA-protein binding, biological networks can be constructed that are predictive of the", + "distributed neuroscience data sharing with ever expanding prospects for future breakthroughs. GeneNetwork.org : genetic analysis for all neuroscientists Originally named webqtl, GeneNetwork.org is the oldest contin- uously operating website in biomedical research ( Williams, 1994). This massive database contains ;40 million datasets. GeneNetwork.org also offers a powerful statistical platform for online network analyses and mapping, enabling numerous mo-", + "distributed neuroscience data sharing with ever expanding prospects for future breakthroughs. GeneNetwork.org : genetic analysis for all neuroscientists Originally named webqtl, GeneNetwork.org is the oldest contin- uously operating website in biomedical research ( Williams, 1994). This massive database contains ;40 million datasets. GeneNetwork.org also offers a powerful statistical platform for online network analyses and mapping, enabling numerous mo-", + "addition to this, GeneNetwork can be used to study correlations between traits and to perform data mining in genomic regions containing candidates for quantitative trait genes (Hoffman et al., 2011). All datasets in GeneNetwork are linked to a materials and methods information page that summarizes experimental details relating to the dataset. Databases within GeneNetwork include the transcriptome database, the BXD published", + "abundance data sets directly within GeneNetwork's ana- lytical environment we provide simple web access to the data for the research community. In this environment, a combination of correlation analysis and linkage mapping provides the potential to identify and substantiate gene targets for saturation mapping and positional cloning. By integrating datasets from an unsequenced crop plant (bar- ley) in a database that has been designed for an animal model species (mouse) with well established genome" + ], + [ + "This paper analyzes existing, publicly available data. These data sets accession numbers are provided in the Key Resource Table , and throughout the manuscript. Genotype les can be found at http://www.genenetwork.org/webqtl/main.py?FormID= sharinginfo&GN_AccessionId=600 . GeneNetwork.org original code is publicly available at https://github.com/genenetwork/genenetwork2 and https://github.com/ genenetwork/genenetwork1 .", + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "GeneNetwork is an open-access database that collates genomic information of diverse experimental crosses and reference panels as well as phenotypic data from miscellaneous research groups [26]. Statistics Data generation, statistical analysis and graph creation were performed with SPSS Statistics 21 (IBM, Ehningen, Germany). As appropriate, mean and median values were further used for QTLanalysis. Phenotypic robustness for each strain was assessed by the", + "genetic variants (SNPs, insertions, deletions, duplications, etc.) that segregate in the family [ 13]. The strains are appropriate for systems genetics /systems biology analysis [ 14], genetic mapping and genetic correlations of parameter means, and thus constitute an ideal platform for toxicogenomic research [ 15]. All data are available at www.genenetwork.org. GeneNetwork exists in two forms, GN1 and GN2 [ 16]. GN2 is an expansion and renement of the features of GN1. A tutorial of how to use GN1 may be", + "al., 2005). GeneNetwork is designed primarily as a web service for exploratory and statistical analysis of large published phenotype and genome datasets, and includes data from several species (see Supplementary Discussion). GeneNetwork includes extensive phenotype data extracted from the literature and submitted by users, which makes it practical to compare data on drug responses with gene expression patterns. Gene expression", + "GeneNetwork (www.genenetwork.org). The web -based software further allows extraction of sets of", + "addition to this, GeneNetwork can be used to study correlations between traits and to perform data mining in genomic regions containing candidates for quantitative trait genes (Hoffman et al., 2011). All datasets in GeneNetwork are linked to a materials and methods information page that summarizes experimental details relating to the dataset. Databases within GeneNetwork include the transcriptome database, the BXD published", + "GeneNetwork provides users with an array of analyticaltools to compare a given trait with a number of data setsavailable from other experimenters. Microarray data ofgene expression in the brain and data of other phenotypes are two such examples of possible tools. For this study, we", + "deposited in the GeneNetwork website (http://www.genenetwork.org) so that other investigators can look for correlations between gene expression patterns and phenotypic traits. The GeneNetwork is an open resource and consists of a set of linked resources for systems genetics. It has been designed for integration of networks of genes, transcripts, and traits such as toxicity, cancer susceptibility, and behavior for several species. Phenotypic QTLs using the", + "genetics approaches can not only provide insights into the roles of individual genes or developmental pathways but also illuminate relationships between different levels of a biologic system, such as the genome, transcriptome, and phenome [ 10]. One such resource of systems genetics is the GeneNetwork website and resource (www.genenetwork.org ) that provides access to a wide variety of data such as genotypes (e.g., SNPs), phenotypes that are obtained" + ], + [ + "GeneNetwork provides users with an array of analyticaltools to compare a given trait with a number of data setsavailable from other experimenters. Microarray data ofgene expression in the brain and data of other phenotypes are two such examples of possible tools. For this study, we", + "al., 2005). GeneNetwork is designed primarily as a web service for exploratory and statistical analysis of large published phenotype and genome datasets, and includes data from several species (see Supplementary Discussion). GeneNetwork includes extensive phenotype data extracted from the literature and submitted by users, which makes it practical to compare data on drug responses with gene expression patterns. Gene expression", + "data are entered into GeneNetwork after they have been shepherded through a system like PhenoGen that has extensive capabilities for normalization and quality control. A comparison of the brain gene expression datasets and some of the tools for data analysis available on PhenoGen and GeneNetwork is shown in Table 3, and more detailed information on features provided by each site is outlined in the Supplementary DiscussionHoffman et al. Page 5 Addict Biol . Author manuscript; available in PMC 2012 July 1.", + "(description of GeneNetwork provided by Dr. Robert W. Williams). Both of these websites focus to a large extent on correlations of behavioral phenotype with gene expression levels in recombinant inbred and inbred panels of mice and rats, and on QTL analyses, as a means to identify candidate genes for complex traits. What distinguishes PhenoGen, in addition to the tools for raw expression data analysis described above, is that the user can not only", + "including correlation and network analysis to compare associations between tissues and between other rodent or human data sets[32] Many of the Data Sets are amenable to systems genetics mapping and other methods and are accessible at GeneNetwork. The Description and Usage column provides details about the data set and potential", + "including correlation and network analysis to compare associations between tissues and between other rodent or human data sets[32] Many of the Data Sets are amenable to systems genetics mapping and other methods and are accessible at GeneNetwork. The Description and Usage column provides details about the data set and potential", + "by example in the Supplementary Methods, and in the Users Manual that can be downloaded from the website. There are a number of databases that investigators can use to assist in various aspects of gene expression data storage and mining (e.g., (Chesler et al., 2005; Galperin and Cochrane, 2009; Gentleman et al., 2004; Mailman et al., 2007; Saal et al., 2002; Swertz et al., 2010)). One relatively well-known database is GeneNetwork (www.genenetwork.org) (Chesler et", + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "from co-regulation patterns found within tens of thousands of samples for which gene expression was measured. GeneNetwork provid es un- precedented resolution and predictive power across multip le cell types and tissues. Analogous to discovering patterns in expressi on data, the network of protein-protein interactions can also be comput ationally pre- dicted using various methods[381]. The combined current knowledge of how cells control functio ns", + "differentially expressed were further evaluated. Bioinformatic analyses were predominantly performed using tools available at GeneNetwork. org, and included gene ontology, presence of cis- regulation or polymorphisms, phenotype correlations, and principal component analyses. Comparisons of differential gene expression between groups showed little overlap. Gene Ontology demonstrated distinct biological processes in each group with the combined exposure (RSE) being" + ], + [ + "GeneNetwork.org is also a valuable teaching tool. While mainly designed for researchers interested in testing gene-to- phenotype relationships, GeneNetwork. orghas been adapted for dry-lab teaching in neuroscience and genetics ( Grisham et al., 2017 ). A useful approach is to assign sets of vetted questions, such as the exam- ples discussed above, and to help students work toward answers, solutions, or novelquestions. Several examples relating to the", + "GeneNetwork.org is also a valuable teaching tool. While mainly designed for researchers interested in testing gene-to- phenotype relationships, GeneNetwork. orghas been adapted for dry-lab teaching in neuroscience and genetics ( Grisham et al., 2017 ). A useful approach is to assign sets of vetted questions, such as the exam- ples discussed above, and to help students work toward answers, solutions, or novelquestions. Several examples relating to the", + "Category 1: Web Resources for Online Analysis of the Genetics of Alcoholism and More GeneNetwork (www.genenetwork.org): This is a comprehensive resource for learning about genetics, but users may", + "GeneNetwork also features a phenotype database, a public repository of data from over 700 traits previously measured across several laboratories in BXD RI (and other) strains. These include behavioral, biochemical, and anatomical traits. The data consist of strain means, not raw data from individual mice, and so we use the term genetic correlation. Using this database, we performed correlation and network analyses to identify relationships with", + "biological function of the new gene list. As mentioned previously, GeneNetwork (www.genenetwork.org) is a collaborative Web-based resource equipped with tools and features for studying gene/gene and exploring genetic correlates to neurobehavioral phenotypes (Chesler et al., 2003, 2004). The Web site is home to a growing collection of gene expression and phenotypic data from a variety of species and brain regions, with a host", + "(description of GeneNetwork provided by Dr. Robert W. Williams). Both of these websites focus to a large extent on correlations of behavioral phenotype with gene expression levels in recombinant inbred and inbred panels of mice and rats, and on QTL analyses, as a means to identify candidate genes for complex traits. What distinguishes PhenoGen, in addition to the tools for raw expression data analysis described above, is that the user can not only", + "with another database, GeneNetwork, correlating behavioral phenotypes with geneO'Brien et al. Page 11 Int Rev Neurobiol . Author manuscript; available in PMC 2014 July 21. NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript", + "interested in behavioral variation and in ways to exploit bioinformatic resources and methods to dissect and (we hope) reassemble and model behavior. You do not need to be a statistician or geneticist to use these tools. In order to use GeneNetwork, we have to start with some ground rules and assumptions. The first is that behavioral traits must vary significantly. This is a chapter about behavioral variation with an equal emphasis on both words. If a behavior is a \"fixed action pattern\" that", + "facilitated through the development of GeneNetwork(www.genenetwork.org), an Inte rnet resource for the multi- variate genetic analysis of complex traits in genetic reference populations (Chesler et al. 2003, 2004; Wang et al. 2003). GeneNetwork aids in identication of candidate genesand bio-molecular mechanisms underlying addiction-relatedphenotypes and includes a wealth of data on mRNAexpression proles from various tissues of the centralnervous system (Chesler et al. 2005; Peirce et al. 2006;", + "deposited in the GeneNetwork website (http://www.genenetwork.org) so that other investigators can look for correlations between gene expression patterns and phenotypic traits. The GeneNetwork is an open resource and consists of a set of linked resources for systems genetics. It has been designed for integration of networks of genes, transcripts, and traits such as toxicity, cancer susceptibility, and behavior for several species. Phenotypic QTLs using the" + ], + [ + "of importance in the emergence of precision medicine ( Curtis, 2015 ; Desautels et al., 2014 ; Glade Bender et al., 2015 ; Jorgensen, 2015 ; Kummar et al., 2015 ; Marquet et al., 2015 ; Rubin, 2014 ) wherein therapeutic strategies need to be aligned with specific properties of tumors. Methods GeneNetwork and WebGestalt GeneNetwork is an open access, online data analysis resource for systems biology and systems genetics. It contains a large number of microarray datasets from multiple tissues of", + "gathered together into an easily accessible format, not siloed into disparate data pools that cannot easily be integrated, valid ated, o r extended. This approach will allow us to make animal models of so called precision medicine, although perhaps more accurately, we want predictive medicine , where a phenotypic outcome (such as disease) can be predicted , and avoided . GeneNetwork (genenetwork.or g; GN) is one tool for systems genetics and predictive medicine,", + "The GeneNetwork site is supported by the University of Tennessee Center for Integrative and Translational Genomics, NI GMS Systems Genetics and Precision Medicine Project (R01 GM123489, 2017 -2021), NIDA Core Center of Excellence in Transcriptomics, Systems Genetics, and the Addictome (P30 DA044223, 2017 -2022), NIA Translational Systems Genetics of Mitochondria, Metabolism, and Aging (R01AG043930, 2013 -2018), NIAAA Integrative", + "The GeneNetwork site is supported by the University of Tennessee Center for Integrative and Translational Genomics, NI GMS Systems Genetics and Precision Medicine Project (R01 GM123489, 2017 -2021), NIDA Core Center of Excellence in Transcriptomics, Systems Genetics, and the Addictome (P30 DA044223, 2017 -2022), NIA Translational Systems Genetics of Mitochondria, Metabolism, and Aging (R01AG043930, 2013 -2018), NIAAA Integrative", + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "eron Genetics Center ( https://www.regeneron.com/ge - netics-center ), and aims to identify rare loss-of-function mutations in founder populations to delineate further the genetic factors that underpin health and disease. This ini - tiative is also addressed at developing countries and those in resource-limiting environments, under the coordina - tion of the Genomic Medicine Alliance ( http://www.ge - nomicmedicinealliance.org ), a founding partner of the", + "distributed neuroscience data sharing with ever expanding prospects for future breakthroughs. GeneNetwork.org : genetic analysis for all neuroscientists Originally named webqtl, GeneNetwork.org is the oldest contin- uously operating website in biomedical research ( Williams, 1994). This massive database contains ;40 million datasets. GeneNetwork.org also offers a powerful statistical platform for online network analyses and mapping, enabling numerous mo-", + "distributed neuroscience data sharing with ever expanding prospects for future breakthroughs. GeneNetwork.org : genetic analysis for all neuroscientists Originally named webqtl, GeneNetwork.org is the oldest contin- uously operating website in biomedical research ( Williams, 1994). This massive database contains ;40 million datasets. GeneNetwork.org also offers a powerful statistical platform for online network analyses and mapping, enabling numerous mo-", + "al., 2005). GeneNetwork is designed primarily as a web service for exploratory and statistical analysis of large published phenotype and genome datasets, and includes data from several species (see Supplementary Discussion). GeneNetwork includes extensive phenotype data extracted from the literature and submitted by users, which makes it practical to compare data on drug responses with gene expression patterns. Gene expression", + "deposited in the GeneNetwork website (http://www.genenetwork.org) so that other investigators can look for correlations between gene expression patterns and phenotypic traits. The GeneNetwork is an open resource and consists of a set of linked resources for systems genetics. It has been designed for integration of networks of genes, transcripts, and traits such as toxicity, cancer susceptibility, and behavior for several species. Phenotypic QTLs using the" + ], + [ + "mation on gene function and how altered function leads to disease. Elucidating the mechanisms of action for newly minted disease genes is amajor bottleneck in translating genetic discoveries into new therapeutics.Addressing this limitation, it has been shown that networks can provideinsight on gene function [71,72] . The premise behind this is simple dgenes", + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "of importance in the emergence of precision medicine ( Curtis, 2015 ; Desautels et al., 2014 ; Glade Bender et al., 2015 ; Jorgensen, 2015 ; Kummar et al., 2015 ; Marquet et al., 2015 ; Rubin, 2014 ) wherein therapeutic strategies need to be aligned with specific properties of tumors. Methods GeneNetwork and WebGestalt GeneNetwork is an open access, online data analysis resource for systems biology and systems genetics. It contains a large number of microarray datasets from multiple tissues of", + "gathered together into an easily accessible format, not siloed into disparate data pools that cannot easily be integrated, valid ated, o r extended. This approach will allow us to make animal models of so called precision medicine, although perhaps more accurately, we want predictive medicine , where a phenotypic outcome (such as disease) can be predicted , and avoided . GeneNetwork (genenetwork.or g; GN) is one tool for systems genetics and predictive medicine,", + "vidual patients. For the time being, the contribu - tion of genetic information to therapy is most likely to come through the drug-discovery pipe - line. Information from genetic studies could be used to identify new targets for pharmaceutical intervention that have validated effects on physi - ological characteristics, to provide information about new and existing targets (e.g., clues about the long-term safety of pathway intervention), 32", + "GeneNetwork.org is also a valuable teaching tool. While mainly designed for researchers interested in testing gene-to- phenotype relationships, GeneNetwork. orghas been adapted for dry-lab teaching in neuroscience and genetics ( Grisham et al., 2017 ). A useful approach is to assign sets of vetted questions, such as the exam- ples discussed above, and to help students work toward answers, solutions, or novelquestions. Several examples relating to the", + "GeneNetwork.org is also a valuable teaching tool. While mainly designed for researchers interested in testing gene-to- phenotype relationships, GeneNetwork. orghas been adapted for dry-lab teaching in neuroscience and genetics ( Grisham et al., 2017 ). A useful approach is to assign sets of vetted questions, such as the exam- ples discussed above, and to help students work toward answers, solutions, or novelquestions. Several examples relating to the", + "al., 2005). GeneNetwork is designed primarily as a web service for exploratory and statistical analysis of large published phenotype and genome datasets, and includes data from several species (see Supplementary Discussion). GeneNetwork includes extensive phenotype data extracted from the literature and submitted by users, which makes it practical to compare data on drug responses with gene expression patterns. Gene expression", + "biological function of the new gene list. As mentioned previously, GeneNetwork (www.genenetwork.org) is a collaborative Web-based resource equipped with tools and features for studying gene/gene and exploring genetic correlates to neurobehavioral phenotypes (Chesler et al., 2003, 2004). The Web site is home to a growing collection of gene expression and phenotypic data from a variety of species and brain regions, with a host", + "is tackling this immense challenge bystudying networks of genes, proteins,metabolites, and other biomarkers thatrepresent models of genuine biologicalpathways. Studying complex diseasesin terms of gene networks rather thanindividual genes or genomic loci shouldaid in uncovering disease genes. Withthis approach, the effects of multiplegenes in the network are combined,producing a stronger signal and reducingthe number of statistical tests of associ-ation that must be performed." + ], + [ + "considering single genes in the context of a whole gene network may provide thenecessary context within which to interpr et the disease role a given gene may play. Constructing gene networks can provide a convenient framework for exploring the context within which single genes operate. A network is simply a graphicalmodel comprised of nodes and edges. For gene networks associated with biological systems, the nodes in the network typically represent genes, gene products, or other", + "Genes do not carry out their functions in isolation of other genes, but instead oper- ate in complex networks that together, in a context-specic way, dene the complex behavior that emerges from biological systems. Therefore, understanding gene net- works in a diversity of contexts will lead to an increased understanding of complex system behavior, including disease. The reductionist approach to elucidating the complexity of biological systems", + "of links to external resources for tracing the interrelationships of a gene among multiple Web-based resources. GeneNetwork also offers a number of correlation and mapping strategies for assessing associations among multiple genes and QTLs. GeneNetwork aims to make the study of complex traits through the use of systems genetics widely available to the scientific community. A powerful tool that can be integrated with GeneNetwork or used on", + "genotypes and phenotypes, geneticists hope to discover and interpret the network of causal genotype-phenotype relationships that determine a trait of interest. Systems genetics research often follows a workow of nding a gene network, nding regulators of that network, and then performing a focused ge ne perturbation experiment to determine the role of the associated network on gene expre ssion or function. To be- gin, a large gene correlation graph must be sifted through , to nd a highly connected", + "genetics approaches can not only provide insights into the roles of individual genes or developmental pathways but also illuminate relationships between different levels of a biologic system, such as the genome, transcriptome, and phenome [ 10]. One such resource of systems genetics is the GeneNetwork website and resource (www.genenetwork.org ) that provides access to a wide variety of data such as genotypes (e.g., SNPs), phenotypes that are obtained", + "the risk of missing important biological phenomena [43]. 8.4 Defining gene and QTL networks In addition to the genetic dissection of phenotypic variation using QTL mapping techniques, systems geneticists are interested in r econstructing the biological net- works that connect genes, proteins and other traits based on their observed genetic (co-)variation. In this context, biological network s are often defined by graphical", + "GeneNetwork http://www.genenetwork.org is anexample of a bioinformatics tool that can be used to explore systems genetics data. The importance of defining biological networks and predicting molecular interactions has been emphasized by several reports [1,2]. Such studies emphasize that when knowledge about DNA variation within popula- tions is interfaced with data on gene expression, protein interactions and DNA-protein binding, biological networks can be constructed that are predictive of the", + "It is important to integrate the gene variants and environmental factors to the trait to understand the network controlling that trait. In systems genetics approach, different trait networks are related to different networks of gene and environmental variants to find global genetic modulation of the complex phenotype. The availability of genetic reference panels makes it easy to acquire diverse phenotypic data and advanced computational models make it possible to analyse their relationship. 2.2.1.", + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "genetic variants (SNPs, insertions, deletions, duplications, etc.) that segregate in the family [ 13]. The strains are appropriate for systems genetics /systems biology analysis [ 14], genetic mapping and genetic correlations of parameter means, and thus constitute an ideal platform for toxicogenomic research [ 15]. All data are available at www.genenetwork.org. GeneNetwork exists in two forms, GN1 and GN2 [ 16]. GN2 is an expansion and renement of the features of GN1. A tutorial of how to use GN1 may be" + ], + [ + "Fig. 2. GeneNetwork main search page and organization. Most analyses in GeneNetwork will follow the steps shown in panels A through D. In this workfl ow, a data set is selected ( A) and mined for traits of interest based on user search queries ( B). Traits are then selected from the search ( C) and placed in a collection for further inspection and quantitative analysis (D). The banner menu contains additional search options and helpful resources under the", + "Fig. 2. GeneNetwork main search page and organization. Most analyses in GeneNetwork will follow the steps shown in panels A through D. In this workfl ow, a data set is selected ( A) and mined for traits of interest based on user search queries ( B). Traits are then selected from the search ( C) and placed in a collection for further inspection and quantitative analysis (D). The banner menu contains additional search options and helpful resources under the", + "Another powerful feature of GeneNetwork is the ability to create and analyze whole collections of data. In Figure 3 there are boxes within the table that can be selected in order to form a trait collection. To do this, select the boxes in the table that su it the interests of the study, and press Add. This function allows groups of traits to be saved for later analysis such as the generation of a QTL, a network graph, and correlation matrix, some of which will be investigated further in", + "analysis in GeneNetwork, but there is an even more direct way to answer the same question. It is possible to query data sets in GeneNetwork from the Select and Search page using advanced options to locate the highest trait LRS values for any genomic interval, in this case the region within 2 Mb of Comt . (Note: You can explore this and other search options further by clicking the Advanced Search button and reading the section Advanced", + "is shown in Figure 1A. Associations between transcript abundance, phenotypic traits and genotype can be estab- lished either using correlation or genetic linkage mapping functions [29,30]. The main page of GeneNetwork at http://www.genenetwork.org provides access to subsets of data through pull-down menus that allow specific data sets to be queried. The datasets can be further restricted using a single text box for specific database entries to query probe set or trait ID, or annotations associated with", + "genetic mapping, and correlation of quantitative traits such as gene expression data and behavioral parameters (Wang et al, 2003) . GeneNetwork employs genotype data from 3809 markers, selected based on their being informative (i.e., different between progenitor strains). GeneNetwork outputs peak likelihood ratio statistic (LRS) locations for each trait, whic h can be directly converted to", + "GeneNetwork provides users with an array of analyticaltools to compare a given trait with a number of data setsavailable from other experimenters. Microarray data ofgene expression in the brain and data of other phenotypes are two such examples of possible tools. For this study, we", + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "(description of GeneNetwork provided by Dr. Robert W. Williams). Both of these websites focus to a large extent on correlations of behavioral phenotype with gene expression levels in recombinant inbred and inbred panels of mice and rats, and on QTL analyses, as a means to identify candidate genes for complex traits. What distinguishes PhenoGen, in addition to the tools for raw expression data analysis described above, is that the user can not only", + "of links to external resources for tracing the interrelationships of a gene among multiple Web-based resources. GeneNetwork also offers a number of correlation and mapping strategies for assessing associations among multiple genes and QTLs. GeneNetwork aims to make the study of complex traits through the use of systems genetics widely available to the scientific community. A powerful tool that can be integrated with GeneNetwork or used on" + ], + [ + "GeneNetwork provides users with an array of analyticaltools to compare a given trait with a number of data setsavailable from other experimenters. Microarray data ofgene expression in the brain and data of other phenotypes are two such examples of possible tools. For this study, we", + "genetics approaches can not only provide insights into the roles of individual genes or developmental pathways but also illuminate relationships between different levels of a biologic system, such as the genome, transcriptome, and phenome [ 10]. One such resource of systems genetics is the GeneNetwork website and resource (www.genenetwork.org ) that provides access to a wide variety of data such as genotypes (e.g., SNPs), phenotypes that are obtained", + "201 5Nature America, Inc. All rights reserved. 6 ADVANCE ONLINE PUBLICATION Nature Ge Neticsa n a ly s i s 11. Yang, J. et al. Common SNPs explain a large proportion of the heritability for human height. Nat. Genet. 42, 565569 (2010). 12. Yang, J., Lee, S.H., Goddard, M.E. & Visscher, P.M. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 7682 (2011). 13. Lee, S.H., Yang, J., Goddard, M.E., Visscher, P.M. & Wray, N.R. Estimation of", + "addition to this, GeneNetwork can be used to study correlations between traits and to perform data mining in genomic regions containing candidates for quantitative trait genes (Hoffman et al., 2011). All datasets in GeneNetwork are linked to a materials and methods information page that summarizes experimental details relating to the dataset. Databases within GeneNetwork include the transcriptome database, the BXD published", + "medicine. GeneNetwork.org is a tool for quantitative genetics that started in 2001 as WebQTL [38]. It evolved from analyses of forward genetics in the BXD mouse family, to phenome-wide association studies and reverse genetics in a variety of species. Although GeneNetwork contains data for many species and populations, it most prominently contains data for the BXD family. Over 10,000 classical phenotypes, measured under a variety of environmental conditions, and", + "is shown in Figure 1A. Associations between transcript abundance, phenotypic traits and genotype can be estab- lished either using correlation or genetic linkage mapping functions [29,30]. The main page of GeneNetwork at http://www.genenetwork.org provides access to subsets of data through pull-down menus that allow specific data sets to be queried. The datasets can be further restricted using a single text box for specific database entries to query probe set or trait ID, or annotations associated with", + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "GeneNetwork http://www.genenetwork.org is anexample of a bioinformatics tool that can be used to explore systems genetics data. The importance of defining biological networks and predicting molecular interactions has been emphasized by several reports [1,2]. Such studies emphasize that when knowledge about DNA variation within popula- tions is interfaced with data on gene expression, protein interactions and DNA-protein binding, biological networks can be constructed that are predictive of the", + "GeneNetwork.org is also a valuable teaching tool. While mainly designed for researchers interested in testing gene-to- phenotype relationships, GeneNetwork. orghas been adapted for dry-lab teaching in neuroscience and genetics ( Grisham et al., 2017 ). A useful approach is to assign sets of vetted questions, such as the exam- ples discussed above, and to help students work toward answers, solutions, or novelquestions. Several examples relating to the", + "GeneNetwork.org is also a valuable teaching tool. While mainly designed for researchers interested in testing gene-to- phenotype relationships, GeneNetwork. orghas been adapted for dry-lab teaching in neuroscience and genetics ( Grisham et al., 2017 ). A useful approach is to assign sets of vetted questions, such as the exam- ples discussed above, and to help students work toward answers, solutions, or novelquestions. Several examples relating to the" + ], + [ + "logical phenomena is often facilitated by the study of genetic mutants, and, in the case of humans, genetic disorders. Accordingly, a search was made, over the years, for genetic disorders characterized by premature aging. If DNA dam- age and repair has anything to do with aging it should be evidenced in such individuals. Martin (1978) listed 162 genetic syndromes in humans with some or many signs of premature aging. About 21 feahares are considered as markers for", + "[315] Szilard, L. On the nature of the aging process. Proc. Natl. Acad. Sci. USA 45:3545; 1959. [316] Vijg, J.; Dolle, M. E. Large genome rearrangements as a primary cause of aging. Mech. Ageing Dev. 123:907915; 2002. [317] Vijg, J. Somatic mutations and aging: a re-evaluation. Mutat. Res. 447:117135; 2000. [318] Martin, G. M. Genetic syndromes in Man with potential relevance to the pathobiology of aging. Birth Defects Orig. Artic. Ser. 14:539; 1978.", + "19 6. Milholland B, Suh Y , Vijg J.Mutation and catastrophe in the aging genome. Exp Gerontol. 2017;94:3440. 7. Maslov AY , Ganapathi S, Westerhof M, Quispe-Tintaya W, White RR, Van Houten B, etal. DNA damage in normally and prematurely aged mice. Aging Cell. 2013;12:46777. 8. Blokzijl F, de Ligt J, Jager M, Sasselli V , Roerink S, Sasaki N, etal. Tissue-specific mutation accumulation in human adult stem cells during life. Nature. 2016;538:2604.", + "143 Gonzalo S, Kreienkamp R & Askjaer P (2017) Hutchinson -Gilford Progeria Syndrome: A premature aging disease caused by LMNA gene mutations. Ageing Res. Rev. 33, 1829. 144 Lu L, Jin W & Wang LL (2017) Aging in Ro thmund -Thomson syndrome and related RECQL4 genetic disorders. Ageing Res. Rev. 33, 3035. 145 de Renty C & Ellis NA (2017) Blooms syndrome: Why not premature aging? Ageing Res. Rev. 33, 3651. 146 Shiloh Y & Lederman HM (2017) Ataxia -telangiectasia (A -T): An emerging", + "genetic disease model of premature aging, In: Harrison,D.E., eds, Genetic Effects on Aging II (Telford Press, Caldwell,NJ), pp. 521542. [2] Djawdan, M., Sugiyama, T., Schlaeger, L., Bradley, T.J. and Rose, M.R. (1996) Metabolic aspects of the trade-off between fecundity and longevity in Drosophila melanogaster ,Physiol. Zool. 69, 11751195. [3] Fleming, J.E., Spicer, G.S., Garrison, R.C. and Rose, M.R.", + "genes of a whole chromosome ineffective, couldbe a main causal factor in aging (Szilard, 1959).According to Maynard Smith, such types of mu-tations do not seem likely to be common enoughto be the main cause of aging. However, at thetime quantitative information on the possible age-related accumulation of different types of muta-tions in various tissues of mammals wascompletely lacking. The question, therefore,whether somatic mutations are a cause of aging,has not been resolved, more than four decadesafter", + "features of premature aging (16, 17). Subsequent experiments conrmed that mitochondrial DNA mutations and deletions were the driving force behind the observed accelerated aging phenotypes(18). THE LINK BETWEEN NUCLEAR GENOME INTEGRITY AND PREMATURE AGING The notion that the majority of currently identied progeria syndromes originate from defects in genome maintenance highlights the importance of the condition of DNA in the process of", + "Tryggvason K,ZhouZ.Genomicinstability inlaminopathy based premature aging,NatMed. 2005;11:780 785. 13.MisteliT,ScaffidiP.Genomeinstability inprogeria:when repairgetsold,NatMed. 2005;11:718 719. 14.PereiraS,Bourgeois P,NavarroC,EstevesVieiraV,CauP,De SandreGiovannoli A,LvyN.HGPSandrelatedpremature aging disorders: Fromgenomicidentification tothefirsttherapeutic approaches, MechAgeingDev.2008;129:449 459. 15.SmithED,Kudlow BA,FrockRL,KennedyBK.Atypenuclear", + "Nature Genetics | Volume 55 | February 2023 | 268279 278 Article https://doi.org/10.1038/s41588-022-01279-621. Tiwari, V. & Wilson, D. M. 3rd. DNA damage and associated DNA repair defects in disease and premature aging. Am. J. Hum. Genet. 105, 237257 (2019). 22. Tamae, D., Lim, P., Wuenschell, G. E. & Termini, J. Mutagenesis and repair induced by the DNA advanced glycation end product N2-1-(carboxyethyl)-2-deoxyguanosine in human cells. Biochemistry 50, 23212329 (2011).", + "[36] J. de Boer, J.O. Andressoo, J. de Wit, J. Huijmans, R.B. Beems, H. van Steeg, et al., Premature aging in mice decient in DNA repair and transcription, Science 296 (2002) 12761279. [37] S.M. Schuh-Huerta, N.A. Johnson, M.P. Rosen, B. Sternfeld, M.I. Cedars, R.A. Reijo Pera, Genetic markers of ovarian follicle number and menopause in women of multiple ethnicities, Hum. Genet. 131 (2012) 17091724." + ], + [ + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "GeneNetwork.org is also a valuable teaching tool. While mainly designed for researchers interested in testing gene-to- phenotype relationships, GeneNetwork. orghas been adapted for dry-lab teaching in neuroscience and genetics ( Grisham et al., 2017 ). A useful approach is to assign sets of vetted questions, such as the exam- ples discussed above, and to help students work toward answers, solutions, or novelquestions. Several examples relating to the", + "GeneNetwork.org is also a valuable teaching tool. While mainly designed for researchers interested in testing gene-to- phenotype relationships, GeneNetwork. orghas been adapted for dry-lab teaching in neuroscience and genetics ( Grisham et al., 2017 ). A useful approach is to assign sets of vetted questions, such as the exam- ples discussed above, and to help students work toward answers, solutions, or novelquestions. Several examples relating to the", + "GeneNetwork http://www.genenetwork.org is anexample of a bioinformatics tool that can be used to explore systems genetics data. The importance of defining biological networks and predicting molecular interactions has been emphasized by several reports [1,2]. Such studies emphasize that when knowledge about DNA variation within popula- tions is interfaced with data on gene expression, protein interactions and DNA-protein binding, biological networks can be constructed that are predictive of the", + "subnetworks GeneNetwork (www.genenetwork.org) is a depository of data- sets and tools for use in complex systems biology approaches in order to generate or predict higher order gene function ( 23, 24 ).", + "GeneNetwork (www.genenetwork.org). The web -based software further allows extraction of sets of", + "resources, gene expression pro les, and gene network constructions, methods for the analysis of gene function have been revolutionised in the past few years. One great resource for the analysis of gene networks is the databaseGeneNetwork, which consists of a set of linked resources for systems genetics (Andreux et al., 2012). It has been designed for multiple scale integration of networks of genes,transcripts in multiple tissues. GeneNetwork is an interac-", + "files on GeneNetwork) will also reduce the energy barrier of adopting powerful systems genetics and systems behavioral approaches. Web services such as GeneNetwork and its companionsGeneWeaver ( Baker et al., 2012 ), WebGestalt ( Zhang et al., 2005 ), DAVID (Huang et al., 2009a ; Huang et al., 2009b ), and the Allen Brain Atlas ( Lein et al., 2007 ) can now be used as virtual and free laboratories to test specific biological hypothesis, or they can be used to generate new ideas ab initio .", + "Its use is centred upon user-specied genes and can identify novel potential master regulatory genes for further investigation. We are working to increase the functionality and power of the GeneNet- work and systems genetics further in a number of areas. In partic- ular, increasing the number of strains studied can increase the mapping resolution. By increasing the genetic diversity of the founders of an RI set, the potential for observing regulatory poly-", + "gration enhances the chance to detect genuine modi ers across organs. GeneNetwork is a valuable platform that can be used by researchers without advanced skills of bioinformatics to perform systems genetics analyses. The next step would be to establish soft- ware tools that allow researchers to combine datasets from multiple resources and mapping analyses in different crosses and species (e.g. intercross, recombinant inbred lines, and human data). References" + ], + [ + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "This paper analyzes existing, publicly available data. These data sets accession numbers are provided in the Key Resource Table , and throughout the manuscript. Genotype les can be found at http://www.genenetwork.org/webqtl/main.py?FormID= sharinginfo&GN_AccessionId=600 . GeneNetwork.org original code is publicly available at https://github.com/genenetwork/genenetwork2 and https://github.com/ genenetwork/genenetwork1 .", + "Fig. 2. GeneNetwork main search page and organization. Most analyses in GeneNetwork will follow the steps shown in panels A through D. In this workfl ow, a data set is selected ( A) and mined for traits of interest based on user search queries ( B). Traits are then selected from the search ( C) and placed in a collection for further inspection and quantitative analysis (D). The banner menu contains additional search options and helpful resources under the", + "Fig. 2. GeneNetwork main search page and organization. Most analyses in GeneNetwork will follow the steps shown in panels A through D. In this workfl ow, a data set is selected ( A) and mined for traits of interest based on user search queries ( B). Traits are then selected from the search ( C) and placed in a collection for further inspection and quantitative analysis (D). The banner menu contains additional search options and helpful resources under the", + "1. Data Once you have navigated to genenetwork.org, t here are two ways to search for data in GN. The first is to use the global search bar located at the top of the page (Figure 1 ). This is a new feature in GN that allows researchers to search for genes, mRNAs, or proteins across all of the datasets. This will give the user data for that search term across many different species, groups, and types of data. Because of this, the global search bar is a good area to start ones searches if", + "data are entered into GeneNetwork after they have been shepherded through a system like PhenoGen that has extensive capabilities for normalization and quality control. A comparison of the brain gene expression datasets and some of the tools for data analysis available on PhenoGen and GeneNetwork is shown in Table 3, and more detailed information on features provided by each site is outlined in the Supplementary DiscussionHoffman et al. Page 5 Addict Biol . Author manuscript; available in PMC 2012 July 1.", + "abundance data sets directly within GeneNetwork's ana- lytical environment we provide simple web access to the data for the research community. In this environment, a combination of correlation analysis and linkage mapping provides the potential to identify and substantiate gene targets for saturation mapping and positional cloning. By integrating datasets from an unsequenced crop plant (bar- ley) in a database that has been designed for an animal model species (mouse) with well established genome", + "GeneNetwork (www.genenetwork.org). The web -based software further allows extraction of sets of", + "need to read the help files, FAQs, or one of the references(Chesler et al., 2003; Grisham et al., 2010, www.lifescied.org/content/9/2/98.full.pdf). GeneNetwork is one ofan interlinked trio of sites built up by NIAAA (GeneWeaverand WebGestalt are the other two) to house extensivedata for human, monkey, rat, mouse, and fruit fly. Itincludes hundreds of data sets on responsesto alcohol,particularly in a family of mice called the BXDs. Dataare linked with powerful gene analysis and mappingtools. Think of it as", + "al., 2005). GeneNetwork is designed primarily as a web service for exploratory and statistical analysis of large published phenotype and genome datasets, and includes data from several species (see Supplementary Discussion). GeneNetwork includes extensive phenotype data extracted from the literature and submitted by users, which makes it practical to compare data on drug responses with gene expression patterns. Gene expression" + ], + [ + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "18 GeneNetwork Time Machine : Full versions from 2009 to 2016 (mm9); UTHSC Genome Browser Classic and Newest ; UTHSC Galaxy Servic e; UTHSC Bayesian Network Web Server ; GeneNetwork Classic on Amazon Cloud ; GeneNetwork Classic Code on GitHub ; GeneNetwork 2.0 Development Code on GitHub ; and GeneNetwork 2.0 Development. Technologies or techniques: None Inventions, patent applications, and/or licenses: None Other products: None", + "GeneNetwork http://www.genenetwork.org is anexample of a bioinformatics tool that can be used to explore systems genetics data. The importance of defining biological networks and predicting molecular interactions has been emphasized by several reports [1,2]. Such studies emphasize that when knowledge about DNA variation within popula- tions is interfaced with data on gene expression, protein interactions and DNA-protein binding, biological networks can be constructed that are predictive of the", + "addition to this, GeneNetwork can be used to study correlations between traits and to perform data mining in genomic regions containing candidates for quantitative trait genes (Hoffman et al., 2011). All datasets in GeneNetwork are linked to a materials and methods information page that summarizes experimental details relating to the dataset. Databases within GeneNetwork include the transcriptome database, the BXD published", + "GeneNetwork (www.genenetwork.org). The web -based software further allows extraction of sets of", + "subnetworks GeneNetwork (www.genenetwork.org) is a depository of data- sets and tools for use in complex systems biology approaches in order to generate or predict higher order gene function ( 23, 24 ).", + "distributed neuroscience data sharing with ever expanding prospects for future breakthroughs. GeneNetwork.org : genetic analysis for all neuroscientists Originally named webqtl, GeneNetwork.org is the oldest contin- uously operating website in biomedical research ( Williams, 1994). This massive database contains ;40 million datasets. GeneNetwork.org also offers a powerful statistical platform for online network analyses and mapping, enabling numerous mo-", + "distributed neuroscience data sharing with ever expanding prospects for future breakthroughs. GeneNetwork.org : genetic analysis for all neuroscientists Originally named webqtl, GeneNetwork.org is the oldest contin- uously operating website in biomedical research ( Williams, 1994). This massive database contains ;40 million datasets. GeneNetwork.org also offers a powerful statistical platform for online network analyses and mapping, enabling numerous mo-", + "1 GeneNetwork: a continuously updated tool for systems genetics analyses Pamela M. Watson1, David G. Ashbrook1 1Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN 38163, USA Abstract GeneNetwork and its earlier iteration , WebQTL, have now been an important database and toolkit for quantitative trait genetics research for two decades. Recent improvements to", + "resources, gene expression pro les, and gene network constructions, methods for the analysis of gene function have been revolutionised in the past few years. One great resource for the analysis of gene networks is the databaseGeneNetwork, which consists of a set of linked resources for systems genetics (Andreux et al., 2012). It has been designed for multiple scale integration of networks of genes,transcripts in multiple tissues. GeneNetwork is an interac-" + ], + [ + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "files), and GeneNetwork (a free scientific web resource, http://www.genenetwork.org/). Statistical analysis was performed using GraphPad Prism (GraphPad Software, Inc., CA, USA).", + "data are entered into GeneNetwork after they have been shepherded through a system like PhenoGen that has extensive capabilities for normalization and quality control. A comparison of the brain gene expression datasets and some of the tools for data analysis available on PhenoGen and GeneNetwork is shown in Table 3, and more detailed information on features provided by each site is outlined in the Supplementary DiscussionHoffman et al. Page 5 Addict Biol . Author manuscript; available in PMC 2012 July 1.", + "thank the members of the GeneNetwork.org team for their assistance, excellent data curation, and informatics support. Conicts of Interest: The authors declare no conict of interest. References 1. Wilkinson, M.D.; Dumontier, M.; Aalbersberg, I.J.; Appleton, G.; Axton, M.; Baak, A.; Blomberg, N.; Boiten, J.W.; da Silva Santos, L.B.; Bourne, P .E.; et al. The FAIR Guiding Principles for scientic data management and stewardship. Sci. Data 2016 ,3, 160018. [CrossRef]", + "thank the members of the GeneNetwork.org team for their assistance, excellent data curation, and informatics support. Conicts of Interest: The authors declare no conict of interest. References 1. Wilkinson, M.D.; Dumontier, M.; Aalbersberg, I.J.; Appleton, G.; Axton, M.; Baak, A.; Blomberg, N.; Boiten, J.W.; da Silva Santos, L.B.; Bourne, P .E.; et al. The FAIR Guiding Principles for scientic data management and stewardship. Sci. Data 2016 ,3, 160018. [CrossRef]", + "thank the members of the GeneNetwork.org team for their assistance, excellent data curation, and informatics support. Conicts of Interest: The authors declare no conict of interest. References 1. Wilkinson, M.D.; Dumontier, M.; Aalbersberg, I.J.; Appleton, G.; Axton, M.; Baak, A.; Blomberg, N.; Boiten, J.W.; da Silva Santos, L.B.; Bourne, P .E.; et al. The FAIR Guiding Principles for scientic data management and stewardship. Sci. Data 2016 ,3, 160018. [CrossRef]", + "9 Scientific Data | (2019) 6:258 | https://doi.org/10.1038/s41597-019-0171-x www.nature.com/scientificdata www.nature.com/scientificdata/with more than 10% missing information, low quality ( <5000), and redundant information were removed. GeneNetwork genotypes, which were discrepant with our RNA-seq experiment, were tagged as unknown (mean of 1% of the GeneNetwork genotypes/strain [0.05% n 8%]). Finally, GeneNetwork and our RNA-seq", + "1. Phenotypic data should be quality checked and preprocessed before being uploaded to GeneNetwork. This includes nor- malization of data, removal of outliers or windsorization, even- tually transformation of data to obtain normal distribution. 2. When uploading data to GeneNetwork for permanent and public storage, make sure to follow the GeneNetwork naming guide for phenotypes. 3. When uploading your own data make sure that for any pheno-", + "1. Phenotypic data should be quality checked and preprocessed before being uploaded to GeneNetwork. This includes nor- malization of data, removal of outliers or windsorization, even- tually transformation of data to obtain normal distribution. 2. When uploading data to GeneNetwork for permanent and public storage, make sure to follow the GeneNetwork naming guide for phenotypes. 3. When uploading your own data make sure that for any pheno-", + "analysis of behavior and for neurologic diseases are provided in the study by Mulligan et al. (2017) . GeneNetwork.org is committed to data and code workflows that are FAIR compliant, ensuring that those who generate data and key ideas get the deserved credit. To further ensure effective and secure dissemination of data and ideas, as well as improved reproducibility, the GeneNetwork.org infrastructure is currently being redesigned using more modular structures and APIs that" + ], + [ + "considering single genes in the context of a whole gene network may provide thenecessary context within which to interpr et the disease role a given gene may play. Constructing gene networks can provide a convenient framework for exploring the context within which single genes operate. A network is simply a graphicalmodel comprised of nodes and edges. For gene networks associated with biological systems, the nodes in the network typically represent genes, gene products, or other", + "is tackling this immense challenge bystudying networks of genes, proteins,metabolites, and other biomarkers thatrepresent models of genuine biologicalpathways. Studying complex diseasesin terms of gene networks rather thanindividual genes or genomic loci shouldaid in uncovering disease genes. Withthis approach, the effects of multiplegenes in the network are combined,producing a stronger signal and reducingthe number of statistical tests of associ-ation that must be performed.", + "traditional genetical genomics approaches. It should also be noted that our approach is different from studying gene-gene regulation within a pathway, which focuses on the interactive activities of individual gene pairs genes within a pathway. A biological pathway is defined as a series of molecular interactions and reactions. If there are subtle changes in the expression level of a few genes located in the upper cascade of a", + "genes rapidly that may be in the same genetic network as the gene you are interested in. Then you need to validate the role of that gene and to identify its function in that network. The point is this is a powerful methodology that can provide data in half an hour that allows you to form hypotheses that you can then spend years investigating. Reference Lee PD, Ge B, Greenwood CM et al 2006 Mapping cis-acting regulatory variation in recombi- nant congenic strains. Physiol Genomics 25:294302", + "ment to determine the role of the associated network ongene expression or function. To begin, a large genecorrelation graph must be sifted through, to find a highlyconnected subgraph that corresponds biologically to a genenetwork in which genes are expressed together, presumablyto regulate or subserve a common function. They must thenfind a small set of causative genes, highly correlated withthe subgraph and likely to regulate coexpression, to be usedas targets of focused investigation. By manipulating the", + "Confronted with this daunting complexity, the field often progresses in small steps. A study may identify one or two relevant genes and assess their interactions with other factors. Gradually, genetic knowledge from many studies then can be assembled into a larger system of interactants that enables us to understand a set of related behaviors. We term this perspective behavioral genomics ( Fig. 2b ).2005 Nature Publishing Group http://www.nature.com/natureneuroscience", + "Confronted with this daunting complexity, the field often progresses in small steps. A study may identify one or two relevant genes and assess their interactions with other factors. Gradually, genetic knowledge from many studies then can be assembled into a larger system of interactants that enables us to understand a set of related behaviors. We term this perspective behavioral genomics ( Fig. 2b ).2005 Nature Publishing Group http://www.nature.com/natureneuroscience", + "From the network, modules of coexpressed genes can be obtained, i.e. com- munities of highly interconnected nodes within the graph. Such coexpressed modules can then be studied as putative functional units, thereby considerably reducing the dimensionality of the data. Different approaches have been proposed, many of which are inspired by social network resear ch. Chesler et al. choose to focus on sets of genes in which all nodes are inter connected; such sets are termed", + "large-scale human and experimental populations, focusing on how a single protein or RNA impacts disease will ultimately give way to how a network of gene interac- tions impacts disease. The integration of genetic, molecular proling, and clinical data has the potential to paint a more detailed picture of the particular network statesthat drive disease, and this in turn has the potential to lead to more progressive treat- ments of disease that may ultimately invol ve targeting of whole networks as opposed", + "from co-regulation patterns found within tens of thousands of samples for which gene expression was measured. GeneNetwork provid es un- precedented resolution and predictive power across multip le cell types and tissues. Analogous to discovering patterns in expressi on data, the network of protein-protein interactions can also be comput ationally pre- dicted using various methods[381]. The combined current knowledge of how cells control functio ns" + ] + ], + "task_id": [1,2,3,4,5,6,7,8,9,10,1,2,3,4,5,6,7,8,9,10] +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/gpt4o_de_aging.json b/gnqa/paper2_eval/data/dataset/gpt4o/gpt4o_de_aging.json new file mode 100644 index 0000000..5f14f2a --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/gpt4o_de_aging.json @@ -0,0 +1,289 @@ +{ + "question": [ + "How do recent single-cell transcriptomics studies enhance our understanding of cellular heterogeneity in aging tissues?", + "What are the latest findings on the role of senescence-associated secretory phenotype (SASP) factors in age-related tissue dysfunction?", + "How do age-related changes in chromatin architecture contribute to the decline in cellular function?", + "What insights have been gained from studying the epigenetic reprogramming of aged cells to a more youthful state?", + "How do alterations in the mitochondrial genome and bioenergetics influence the aging process in humans?", + "What are the therapeutic potentials and challenges of targeting the insulin/IGF-1 signaling pathway for extending healthspan and lifespan?", + "How can the integration of proteomics and metabolomics data shed light on age-associated metabolic shifts?", + "What role do long non-coding RNAs (lncRNAs) play in the regulation of aging and age-related diseases?", + "How do recent advancements in CRISPR/Cas9 technology open new avenues for studying and potentially reversing aging?", + "What is the significance of the DNA damage response (DDR) in the context of both replicative and chronological aging?", + "How do age-dependent changes in the immune system, such as immunosenescence, contribute to increased susceptibility to diseases?", + "How do advancements in machine learning and artificial intelligence aid in the identification of biomarkers for biological aging?", + "What recent discoveries have been made regarding the impact of systemic factors, such as circulating microvesicles, on aging phenotypes?", + "How do changes in the gut microbiome composition correlate with aging and longevity?", + "What are the key molecular mechanisms through which caloric restriction exerts its lifespan-extending effects across different species?", + "How do oxidative stress and the subsequent accumulation of damaged macromolecules contribute to cellular aging?", + "How are extracellular matrix remodeling and tissue stiffness implicated in the aging process?", + "How do recent developments in autophagy research contribute to our understanding of its role in aging and longevity?", + "What are the implications of age-related shifts in stem cell niche composition and function for tissue regeneration capacity?", + "How do cross-links and advanced glycation end-products (AGEs) accumulation affect the structural integrity and function of aging tissues?" + ], + "answer": [ + "Recent single-cell transcriptomics studies have significantly enhanced our understanding of cellular heterogeneity in aging tissues by providing detailed insights into the molecular and cellular changes that occur with age. These studies allow for the examination of small populations of cells, enabling the detection of low-represented mutations and transcriptional events that may alter cellular identity, supporting hypotheses about the cellular and genetic origins of age-related dysfunctions [1]. Single-cell transcriptomics has also been used to gain a more nuanced view of cell type-specific molecular changes during aging, which is not possible with bulk tissue analysis that neglects variations in cell compositions and cell-to-cell aging heterogeneity [2]. This approach has revealed that different cell identities express unique genes with aging, indicating cell-identity-specific aging phenotypes [5]. Furthermore, similar cell types across different tissues have shown broadly similar aging trajectories, suggesting inherent characteristics of cell types play a significant role in shaping aging patterns [5], [8]. Additionally, single-cell RNA-seq has been applied to study transcriptional heterogeneity within specific cell types, such as hematopoietic stem cells (HSCs), and how this heterogeneity changes with age [4]. This level of detail helps to understand the diversity of cell types and states in young and old tissues, providing insights into the intrinsic complexity of aging at the cellular level [9]. Overall, these studies are revolutionizing our understanding of cellular heterogeneity in aging tissues, opening new avenues for early disease detection and personalized treatments [6].", + "The latest findings on the role of senescence-associated secretory phenotype (SASP) factors in age-related tissue dysfunction highlight several key aspects: 1. SASP factors, which include cytokines, chemokines, proteases, and other inflammatory molecules, are secreted by senescent cells and disrupt tissue homeostasis through paracrine mechanisms [1]. These factors contribute to a deleterious microenvironment that promotes aging and age-related diseases [2]. 2. SASP is associated with chronic inflammation and exacerbates age-associated degeneration and hyperplasia in many tissues [4]. This chronic inflammation is a significant contributor to tissue dysfunction as organisms age. 3. The accumulation of senescent cells and their SASP factors is closely linked to aging-related diseases. These factors can induce chronic inflammation and cell proliferation, leading to cell dysfunction and potentially cancer [8]. 4. SASP factors exert their effects in both autocrine and paracrine manners, influencing not only the senescent cells themselves but also the surrounding tissue environment [8]. Overall, SASP factors play a critical role in driving the chronic inflammation and tissue dysfunction associated with aging, highlighting their importance in the study of age-related diseases and potential therapeutic targets.", + "Age-related changes in chromatin architecture contribute to the decline in cellular function through several mechanisms: 1. **Loss of Chromatin Homeostasis**: Sustained alterations in the chromatin landscape, such as changes in DNA methylation and histone modifications, can mediate the propagation of age-associated functional decline [1]. These changes are relatively stable and can persist through cell division, affecting cellular function over time. 2. **Changes in Chromatin Distribution**: During aging, there is an extensive change in the global distribution of euchromatin and heterochromatin. Specifically, there is an overall closing of chromatin in euchromatic gene-rich regions, which contributes to tissue dysfunction and the eventual decline of cellular function [2]. 3. **Increased DNA Damage**: Aging-associated defects in chromatin structure lead to increased DNA damage and persistent DNA breaks. This is possibly due to structural changes that increase the genome's susceptibility to damage, further contributing to the decline in cellular function [5]. 4. **Histone Loss and Chromatin Remodeling**: There is a general loss of histones and chromatin remodeling, leading to an imbalance of activating and repressive histone modifications. This results in transcriptional changes that are observed in all aging models, contributing to the decline in cellular function [9]. 5. **Epigenetic Changes and Gene Expression**: Age-related chromatin dysregulation and epigenetic changes drive the loss of cellular function by altering gene expression patterns. These changes can lead to increased transcriptional activity in certain chromosomal regions, ultimately driving the aging process [10]. These changes in chromatin architecture collectively contribute to the decline in cellular function observed with aging.", + "Studying the epigenetic reprogramming of aged cells to a more youthful state has provided several insights: 1. **Reversal of Aging-Associated Epigenetic Features**: Experiments have shown that epigenetic features associated with aging can be reversed. For instance, in successfully reprogrammed induced pluripotent stem cells (iPSCs), the chromatin state of the CDKN2A locus, which is associated with aging, is erased and restored to that of youthful cells [1]. 2. **Potential for Longevity**: Proper epigenetic gene silencing is required for longevity, as observed in multiple model organisms. This suggests that the process of epigenetic reprogramming might be evolutionarily conserved and could play a role in extending lifespan [1]. 3. **Rewinding the Aging Clock**: There is an apparent ability to rewind the aging clock without losing cellular differentiation. However, this requires clear epigenetic signatures of young and old cells and evidence that aged cells have regained a youthful signature [2]. 4. **Risks and Uncertainties**: While reprogramming the epigenome to a youthful state holds promise, it also carries inherent risks and uncertainties, highlighting the need for further research to understand the full implications and safety of such interventions [2]. 5. **Mechanisms of Rejuvenation**: The study of epigenetic reprogramming provides a framework for understanding the mechanisms of rejuvenation, suggesting that aging is at least partly a manifestation of epigenetic changes. This offers opportunities to alter the trajectory of age-related diseases [8], [10]. 6. **Prolonging Healthy Life Expectancy**: There are at least two ways to reverse or inhibit senescence through epigenetic mechanisms, which could prolong healthy life expectancy. One involves rejuvenation through effective epigenetic reprogramming in cells undergoing senescence or derived from very aged patients [7]. These insights collectively suggest that epigenetic reprogramming holds significant potential for reversing aging processes and extending healthy lifespan, although further research is needed to fully understand and safely harness these capabilities.", + "Alterations in the mitochondrial genome and bioenergetics significantly influence the aging process in humans through several mechanisms: 1. **Mitochondrial DNA Mutations**: As humans age, there is an increase in mitochondrial DNA (mtDNA) mutations. These mutations can lead to a decline in mitochondrial function, which is a fundamental mechanism in the physiological declines associated with aging [3]. Specifically, the aged heart shows a significant increase in mtDNA mutations compared to younger hearts, which may contribute to the failure in mitochondrial metabolism observed in aging [2]. 2. **Respiratory Function Decline**: Aging is associated with a decline in respiratory function and increased oxidative stress, which can lead to further DNA mutations and altered gene expression in mitochondria [6]. This decline in mitochondrial respiratory function is linked to the production of reactive oxygen species (ROS), which can damage mtDNA and exacerbate mitochondrial dysfunction [7]. 3. **Mitochondrial Dynamics**: Changes in mitochondrial dynamics, such as increased fragmentation and decreased fusion, are observed in aging tissues like skeletal muscle, heart, and brain. These alterations can impair mitochondrial biogenesis and mitophagy, leading to reduced energy production and increased cellular stress [5]. 4. **Bioenergetic Shifts**: The aging process involves shifts in mitochondrial metabolism, particularly in high-energy-demand tissues. For example, the brain experiences a decline in energy production due to mitochondrial dysfunction, which can affect cognitive function and overall brain health [9]. Overall, the accumulation of mtDNA mutations, decline in mitochondrial respiratory function, and alterations in mitochondrial dynamics and bioenergetics contribute to the aging process by impairing cellular energy production and increasing oxidative stress, leading to cellular and tissue dysfunction.", + "The insulin/IGF-1 signaling pathway has been identified as a significant target for extending healthspan and lifespan due to its role as a nutrient sensor and its control over the transcription of stress response genes [1]. Here are the therapeutic potentials and challenges associated with targeting this pathway: ### Therapeutic Potentials: 1. **Treatment of Age-Related Diseases**: Lowering IGF signaling, such as by targeting IGF receptors, has been proposed as a treatment for age-related diseases including cancer, Alzheimer's disease, and autoimmune diseases [2]. This suggests that modulating this pathway could have broad therapeutic applications in managing diseases associated with aging. 2. **Lifespan Extension**: Genetic interference in the insulin-signaling pathway has been shown to prolong life in various organisms, including C. elegans, D. melanogaster, and certain mouse models [8]. This indicates a potential for extending lifespan through targeted interventions in this pathway. 3. **Improved Cellular Maintenance**: The insulin/IGF-1 signaling pathway is involved in processes such as cellular senescence, protein refolding, and autophagy, which are crucial for cellular maintenance and protection against aging-related diseases [3]. Enhancing these processes could lead to slowed aging and improved healthspan. ### Challenges: 1. **Complexity of the Pathway**: The role of IGF-1 in lifespan regulation is complex, and it is not fully understood how alterations in this pathway contribute to aging phenotypes [9]. This complexity poses a challenge in developing targeted therapies without unintended consequences. 2. **Balancing Growth and Longevity**: The insulin/IGF-1 pathway is also involved in regulating growth and development. Therefore, interventions that reduce IGF signaling must carefully balance the trade-offs between promoting longevity and maintaining necessary growth functions [2]. 3. **Species-Specific Responses**: While interventions in the insulin/IGF-1 pathway have shown promising results in model organisms, translating these findings to humans is challenging due to species-specific differences in the pathway's role and regulation [8]. Overall, while targeting the insulin/IGF-1 signaling pathway holds significant promise for extending healthspan and lifespan, it requires careful consideration of the pathway's complexity and the potential trade-offs involved.", + "The integration of proteomics and metabolomics data can provide a comprehensive understanding of age-associated metabolic shifts by revealing changes in protein expression and metabolite profiles that occur with aging. This multi-omics approach allows for the identification of specific pathways and molecular mechanisms that are altered as organisms age. 1. **Proteomics Insights**: Proteomics data can identify plasma proteins that predict age and are predominantly associated with immunity [1]. This suggests that changes in protein expression related to immune function are significant in the aging process. 2. **Metabolomics Insights**: Metabolomics approaches enable the study of age-related changes in metabolite profiles, providing new insights into the physiological mechanisms of aging [1]. For example, metabolomics has identified significant alterations in glutathione metabolism, a key antioxidant pathway, which is indicative of oxidative stress associated with aging [10]. 3. **Integrated Analysis**: By integrating transcriptome and metabolome data, researchers have identified transcriptionally-driven alterations in metabolism during aging, such as changes in glycolysis and glycerolipid biosynthesis, and reductions in protein and polyamine biosynthesis [4], [8]. These changes can affect cellular signaling, epidermal barrier function, and skin structure and morphology, highlighting the interconnected nature of metabolic pathways and their impact on aging. 4. **Functional Changes**: The integration of these datasets can also reveal age-dependent changes in the activity of metabolic enzymes, which are driven by altered gene expression [6]. This helps in understanding how mild adaptations in metabolite and transcript levels contribute to maintaining functions like epidermal homeostasis during aging. Overall, the integration of proteomics and metabolomics data provides a holistic view of the molecular changes that occur with aging, allowing for the identification of biomarkers and pathways that could be targeted to mitigate age-related decline.", + "Long non-coding RNAs (lncRNAs) play significant roles in the regulation of aging and age-related diseases through various mechanisms: 1. **Regulation of Age-Associated Cardiovascular Diseases**: LncRNAs are involved in the regulation of age-associated cardiovascular diseases by acting as non-canonical precursors for specific microRNAs, such as hsa-miR-4485 and hsa-miR-1973, which participate in tissue age-related changes [1]. 2. **Senescence-Associated lncRNAs**: Certain lncRNAs are associated with cellular senescence, a key process in aging. These senescence-associated lncRNAs are implicated in the regulation of aging mechanisms [2]. 3. **Telomere Length Regulation**: LncRNAs are involved in the regulation of telomere length by modulating TERT activity and the synthesis of telomeric repeats, which is crucial for cellular aging and longevity [3]. 4. **Gene Expression Regulation**: LncRNAs interact with proteins and nucleic acids to regulate gene expression through epigenetic mechanisms, acting as antisense transcripts or transcriptional coactivators. They also influence the nuclear location of transcription factors and stabilize ribonucleoprotein complexes, which are important in aging-associated mechanisms [4]. 5. **Disease Progression**: LncRNAs play roles in the progression of various age-related diseases, such as atherosclerosis, diabetic nephropathy, glomerular disease, and renal fibrosis. For example, lncRNA H19 is involved in the activation of signaling pathways that induce atherosclerosis [5]. 6. **Neurodegeneration**: LncRNAs are implicated in neurodegenerative diseases, such as Huntington's disease, by regulating transcriptional networks and chromatin states [6]. 7. **Impaired Learning and Senescence**: Specific lncRNAs, like Gas5, are associated with impaired learning in aged brains, and others, like H19, interact with methyl-CpG binding domains, influencing senescence and aging pathways [7]. 8. **Angiogenic Capacity**: The expression of lncRNA Meg3 is linked to age-related impairment of the angiogenic capacity of endothelial cells, indicating a role in vascular aging processes [9]. Overall, lncRNAs are crucial regulators of aging and age-related diseases through their diverse roles in gene expression, cellular senescence, disease progression, and other aging-related mechanisms.", + "Recent advancements in CRISPR/Cas9 technology have opened new avenues for studying and potentially reversing aging in several ways: 1. **Development of New Research Models**: CRISPR/Cas9 is significantly impacting research by enabling the creation of new models for studying age-related diseases. This includes manipulating disease-associated gene pathways, which can lead to a better understanding of the cellular and molecular origins of these diseases [1], [2]. 2. **Understanding Aging Processes Across Species**: The technology is beneficial in clarifying aging processes across different species. This improved understanding, particularly of epigenetic mechanisms affecting longevity, is crucial for identifying new potential therapeutic targets [3], [9]. 3. **Targeting Non-Proliferating Cells**: One notable contribution of CRISPR/Cas9 to aging research is its ability to target non-proliferating cells. This capability is important for studying cellular senescence, which is a key factor in both physiological aging and age-associated diseases [4]. 4. **Molecular Pathogenesis of Neurodegenerative Diseases**: CRISPR/Cas technologies have significantly contributed to studies on the molecular pathogenesis of age-related neurodegenerative conditions such as Alzheimer's and Parkinson's diseases. This includes developing new tools to study the molecular mechanisms underlying these diseases using patient-derived cell lines with pathogenic mutations [10]. These advancements suggest that CRISPR/Cas9 technology not only aids in understanding the mechanisms of aging but also holds potential for developing interventions that could reverse or mitigate age-related conditions.", + "The DNA damage response (DDR) plays a crucial role in both replicative and chronological aging by maintaining genomic stability and influencing cell fate in response to DNA damage. Here are the key points regarding its significance: 1. **Premature Aging and DDR Impairment**: Impaired DDR is directly correlated with premature aging phenotypes, as evidenced by studies on certain genetic models like Ercc1 [1]. This suggests that a functional DDR is essential for normal aging processes. 2. **Cellular Senescence and DDR**: Persistent DDR signaling is a shared mechanism that triggers cellular senescence, which is a hallmark of aging [4]. This indicates that DDR not only repairs damage but also influences aging by promoting senescence when damage is irreparable. 3. **Replicative Senescence**: DDR activation at telomeres, especially when they are critically short or damaged, triggers replicative cellular senescence or apoptosis [5]. This highlights the role of DDR in controlling the replicative lifespan of cells. 4. **Age-related DNA Damage Accumulation**: As organisms age, DNA damage accumulates, and the DDR pathway becomes increasingly important in managing this damage to prevent mutations and maintain cellular function [6]. 5. **Tumor Suppression and Aging**: While DDR mechanisms like apoptosis and senescence are potent tumor suppressors, they also contribute to aging by removing or halting the proliferation of damaged cells [7]. Overall, the DDR is significant in aging as it balances repair and cell fate decisions, influencing both the replicative capacity of cells and the overall aging process by managing DNA damage and maintaining genomic integrity.", + "Age-dependent changes in the immune system, such as immunosenescence, contribute to increased susceptibility to diseases through several mechanisms: 1. **Functional Decline of the Adaptive Immune System**: Immunosenescence is characterized by a decline in the adaptive immune system's function, which leads to reduced protection against infections and decreased effectiveness of vaccinations [1]. This decline is primarily due to changes in T and B lymphocytes, which are crucial for adaptive immunity [2]. 2. **Loss of Diversity in Immune Receptors**: There is a loss of diversity in the T-cell receptor (TCR) and B-cell receptor repertoire as people age. This is due to the accumulation of dysfunctional cells and decreased output from the thymus and bone marrow, which are essential for generating new immune cells [9]. This loss of diversity impairs the immune system's ability to recognize and respond to new pathogens effectively. 3. **Chronic Inflammation (Inflammaging)**: Aging is also associated with a state of low-grade chronic inflammation, known as inflammaging. This chronic inflammation can further compromise immune function and contribute to the development of age-related diseases [1], [4]. 4. **Overall Immune System Alterations**: All components of the immune system are affected by aging, not just the adaptive immune system. This widespread alteration can lead to a compromised defense against pathogens, making the elderly more susceptible to infectious diseases and less responsive to vaccinations [2], [9]. These changes collectively lead to an increased susceptibility to diseases in the elderly, highlighting the importance of understanding and potentially intervening in these age-related immune alterations to improve health outcomes in older populations.", + "Advancements in machine learning and artificial intelligence significantly aid in the identification of biomarkers for biological aging by enabling the development of predictive models and personalized medical treatments. These technologies allow for the integration and analysis of complex biological data, which can be used to forecast an individual's lifespan and potential age-related diseases, thereby facilitating personalized medical interventions [2]. Machine learning algorithms, such as linear regression and its variants, are employed to select aging-related biomarkers and construct aging clocks, which are predictors of chronological and biological age based on various omics datasets [3]. Additionally, computational methods have been developed to predict biological age from gene expression data, which can help in evaluating lifestyle changes and therapeutic strategies aimed at promoting healthy aging [8].", + "Recent discoveries regarding the impact of systemic factors, such as circulating microvesicles, on aging phenotypes include the following: 1. The importance of progeronic (aging-promoting) and antigeronic (aging-delaying) circulating factors in the development of vascular aging phenotypes has been discussed. This highlights the role of systemic factors in contributing to age-related vascular pathologies and suggests potential interventions to prevent or delay these conditions by targeting fundamental cellular and molecular aging processes [1]. 2. Studies using heterochronic parabiosis, which involves connecting the circulatory systems of young and aged mice, have demonstrated the impact of circulating factors on aging phenotypes. This research provides initial evidence that circulating factors can influence cerebromicrovascular density, which typically declines with advanced age [3]. These findings underscore the significant role that systemic factors, including circulating microvesicles, play in influencing aging phenotypes, particularly in the context of vascular aging and potential rejuvenation strategies.", + "Changes in the gut microbiome composition are closely linked to aging and longevity. As individuals age, the composition and function of the gut microbiome undergo significant modifications. These changes are thought to contribute to various age-related processes, including immunosenescence and inflammaging, which are associated with the aging immune system [6]. Research has shown that a healthy microbiota can promote survival and is linked to longevity. Specifically, certain bacterial families such as Christensenellaceae, Akkermansia, and Bifidobacterium have been associated with immunological and metabolic regulation, which may contribute to increased lifespan [1]. Additionally, the gut microbiota of older adults differs in type and number of microorganisms compared to younger adults, with Bacteroidetes and Firmicutes being the most prevalent species in older individuals [4]. These changes in microbial composition can be influenced by both intrinsic and extrinsic factors, which play a significant role in the health and function of the microbiome as people age [8]. Overall, maintaining a healthy gut microbiome is crucial for promoting longevity and mitigating some of the negative effects associated with aging.", + "Caloric restriction extends lifespan across various species through several key molecular mechanisms: 1. **Sirtuin Activation**: Caloric restriction may exert some of its effects through the sirtuin family of genes, particularly SIR2, which is known to prolong lifespan in organisms like yeast, worms, and flies [3], [4]. Sirtuins are involved in chromatin regulation and promoting DNA stability, which are crucial for maintaining cellular health and longevity [4]. 2. **Insulin-like Signaling Pathways**: In mammals, caloric restriction is thought to modulate aging through the insulin-like signaling pathways. This mechanism is also observed in organisms like C. elegans and Drosophila, where it plays a role in regulating lifespan [6]. 3. **Oxidative Stress Reduction**: Caloric restriction is associated with reduced oxidative damage, which is a significant factor in aging. This reduction in oxidative stress is a common mechanism observed across different species [9]. 4. **AMPK Activation**: In mammals, caloric restriction has been linked to the activation of AMP-activated protein kinase (AMPK), which plays a role in energy homeostasis and has protective effects on the aged myocardium [10]. These mechanisms highlight the complex interplay of genetic and metabolic pathways through which caloric restriction can extend lifespan across diverse species.", + "Oxidative stress contributes to cellular aging through the accumulation of oxidative damage in various macromolecules, which leads to a decline in cellular function. This process occurs due to an imbalance between prooxidants and antioxidants, resulting in a steady-state accumulation of oxidative damage that increases with age [1]. The oxidative stress theory of aging posits that damage caused by reactive oxygen species (ROS) plays a critical role in determining lifespan, as it leads to the deterioration of biochemical and physiological processes [4]. Oxidative damage affects all cellular macromolecules, including lipids, proteins, and DNA, and this damage increases with age [3]. The accumulation of such damage is a key hallmark of aging physiology [5]. Specifically, oxidative damage to mitochondrial DNA (mtDNA) and the generation of ROS from the mitochondrial electron transport chain are significant contributors to this process [6]. Overall, the accumulation of oxidative damage is causally linked to aging and death, as it impairs cellular processes and bioenergetics, leading to the progressive loss of functional efficiency in cells [2], [8].", + "Extracellular matrix (ECM) remodeling and tissue stiffness are significant factors in the aging process. As we age, several changes occur in the ECM that contribute to increased tissue stiffness. These changes include decreased elastin synthesis, elastin degradation and fragmentation, and alterations in the cross-linking of ECM components, such as increased presence of advanced glycation end products (AGEs) [1]. AGEs can interfere with collagenolysis by forming cross-links that confer resistance to enzymatic degradation, thereby contributing to increased arterial stiffness [2]. Additionally, the activity of transforming growth factor-beta (TGF-\u03b2) increases with age, stimulating the synthesis of interstitial collagen by vascular smooth muscle cells (VSMCs), which further augments arterial stiffness [2]. The renin-angiotensin-aldosterone system (RAAS) also plays a role in this process by augmenting collagen synthesis and promoting elastolysis [2]. The ECM is crucial for providing mechanical scaffolding and mediating biomechanical and biochemical signals necessary for tissue homeostasis and cell differentiation [4]. However, with aging, ECM stiffness increases, affecting various organs, including the larynx [6]. This increased stiffness is associated with a decline in tissue health, as seen with the accumulation of damage in long-lived proteins like collagens, which become resistant to proteolysis and affect their turnover [8]. Overall, these changes in ECM remodeling and tissue stiffness contribute to the aging process by affecting vascular and tissue elasticity, leading to conditions such as arterial stiffening and vascular remodeling [1], [3], [9].", + "Recent developments in autophagy research have significantly enhanced our understanding of its role in aging and longevity. Here are some key contributions: 1. **Energy Metabolism and Lifespan Extension**: Research has shown that the depletion of acetyl-coenzyme A, an energy metabolite, can stimulate autophagy and prolong lifespan. This suggests that autophagy is closely linked to energy metabolism and can be a mechanism for extending lifespan [1]. 2. **Autophagy as an Anti-Aging Mechanism**: Autophagy is increasingly recognized as an emerging anti-aging mechanism. It plays a crucial role in maintaining cellular homeostasis by degrading and recycling damaged cellular components, which is essential for longevity [1]. 3. **Genetic Regulation and Dietary Restriction**: Studies have identified autophagy genes as important for lifespan extension, particularly in the context of dietary restriction. This indicates that genetic regulation of autophagy is a key factor in promoting longevity [2]. 4. **Pharmacological Activation**: There is evidence that pharmacological activation of autophagy can increase lifespan in animal models, including mice. This highlights the potential for therapeutic interventions targeting autophagy to promote healthy aging [3]. 5. **Impaired Autophagy and Cellular Aging**: Impaired autophagy is considered a principal determinant of cellular aging. The decline in autophagy with age is linked to various age-related diseases, emphasizing the importance of maintaining autophagic activity for longevity [4]. 6. **Spermidine and Longevity**: The induction of autophagy by compounds like spermidine has been shown to promote longevity, further supporting the role of autophagy in extending lifespan [7]. These findings collectively underscore the critical role of autophagy in aging and longevity, suggesting that enhancing autophagic processes could be a viable strategy for promoting healthy aging and extending lifespan.", + "Age-related shifts in stem cell niche composition and function have significant implications for tissue regeneration capacity. As individuals age, the stem cell niche, which is crucial for maintaining stem cell function, undergoes changes that can adversely affect the regenerative potential of stem cells. Here are some key implications based on the provided context: 1. **Deterioration of the Stem Cell Niche**: The aging process leads to the deterioration of the stem cell niche, which can result in a failure to support the balance between stem cell self-renewal and differentiation. This is evident in the spermatogonial stem cell niche, which deteriorates with age, affecting its ability to maintain stem cell function [1]. 2. **Functional Impairments of Stem Cells**: The regenerative potential diminishes with age due to functional impairments in adult stem cells. This is linked to the phenomenon of replicative senescence, where cells lose their ability to proliferate after a certain number of divisions [3]. 3. **Changes in Gene Expression**: Age-related changes in gene expression have been observed in stem cells, such as mesenchymal stem cells (MSCs) and hematopoietic progenitor cells (HPCs). These changes can lead to declines in stem cell function and, consequently, a reduction in tissue regeneration capacity [6]. 4. **Loss of Stem Cell Pool Division Potential**: Aging is associated with a loss of stem cell pool division potential, which directly impacts the regenerative capacity of tissues. This loss can also indirectly affect adult stem and progenitor cells by altering the tissue microenvironment essential for stem cell support [8]. 5. **Reduction in Stem Cell Numbers**: There is evidence of a decline in the number of MSCs in the bone marrow with age, which can further hinder the ability of these cells to participate in tissue regeneration processes such as osteogenesis and chondrogenesis [10]. Overall, these age-related shifts in stem cell niche composition and function contribute to a decline in the body's ability to repair and regenerate tissues, which is a hallmark of aging and is linked to various degenerative conditions [9].", + "The accumulation of cross-links and advanced glycation end-products (AGEs) significantly impacts the structural integrity and function of aging tissues in several ways: 1. **Inflammation and Oxidative Stress**: AGEs accumulation leads to inflammation and oxidative stress, which can cause long-term vascular and end-organ damage [1], [4]. This is partly due to the interaction of AGEs with specific receptors such as RAGE, which perpetuates these adverse processes. 2. **Vascular Changes**: AGEs contribute to vascular hypertrophy, stiffening of collagen, and reduced arterial compliance, which are associated with aging and are accelerated by hyperglycemia [2]. This stiffening of collagen and reduction in arterial compliance can lead to decreased vascular function and increased risk of vascular complications. 3. **Cross-linking of Proteins**: AGEs cause cross-linking of proteins, which affects the structural integrity of tissues. For example, the cross-linking of collagen is associated with increased susceptibility to atherosclerosis, osteoporosis, decreased joint elasticity, and the formation of cataracts [10]. 4. **Endothelial Dysfunction**: AGEs impair endothelial function and vascular reactivity, which can lead to complications such as atherosclerosis and diabetic complications [5]. This impairment is due to the modification of lipoproteins and the release of cytokines and growth factors upon AGE interaction with receptors. 5. **Pathological Changes in Tissues**: AGEs induce various pathological changes, including increased basement membrane thickening, arterial stiffness, and glomerular sclerosis [7]. These changes contribute to the decline in tissue function and structure as they age. Overall, the accumulation of AGEs and the resulting cross-links compromise the structural integrity and function of tissues, contributing to the aging process and the development of age-related diseases." + ], + "contexts": [ + [ + "Single-cell sequencing has helped to support several hypotheses about the cel- lular and genetic origin of age-related dysfunctions. Since single-cell sequencing allows us to study small populations of cells, it has been possible to find low repre- sented mutations as well as transcriptional events that alter cellular identity. This newly generated data suggests that aging could be the result of mutational accumu- lation, epigenetic errors, and transcriptional noise that occurs in cells altering the", + "structed using data from bulk tissues, which neglect the variationsin cell compositions and cell-to-cell aging heterogeneity. To gain amore detailed and nuanced view of cell type specific molecular changes during aging, several studies have applied machine-learn- ing models to single-cell transcriptomics and DNA methylation", + "within whole tissues or individual cell types in aging (Rodwellet al. 2004; Jonker et al. 2013; Cosgrove et al. 2014; O Brown et al. 2015; Su et al. 2015; White et al. 2015; Keyes et al. 2016; Benayoun et al. 2019). However, it remains unclear to what degree age-related transcriptional changes are shared or unique across cellidentities. To address this outstanding question, we performed dif-ferential expression analysis within each cell identity betweenyoung and old mice.", + "populations. Furthermore, single cell analysis should allow us to relate prospective profiles of HSCs that have just been isolated with known heterogeneity in their retrospective functional capacity in transplantation assays. Here, we leveraged single cell RNA-seq to directly assess transcriptional heterogeneity within the HSCs and how it may change with age in the steady-state unperturbed hematopoiesis. Given that HSCs are", + "cells. Here, we used single-cell RNA-seq to investigate aging across a diverse set of murine cell identities in three tissues. We found that cell identities differentially express unique genes with aging, consistent with previous reports of cell-identi- ty-specific aging phenotypes (Angelidis et al. 2019). Similar celltypes (e.g., kidney capillary endothelial cells and lung endothelial cells) showed broadly similar aging trajectories across tissues, and", + "Cellular heterogeneity is revolutionizing the way to study, monitor and dissect complex diseases. This has been possible with the technological and computational advances associated to single-cell genomics and epigenomics. Deeper understanding of cell-to-cell variation and its impact on tissue function will open new avenues for early disease detection, accurate diagnosis and personalized treatments, all together leading to the next generation of health care. This review focuses on the recent dis-coveries", + "Genomics 114 (2022) 110379 2have been observed in multiple species and tissues [7,8]. Transcriptome analysis using aged oocyte samples have confirmed the impact of aging on transcriptome landscapes [9,10]. Advances in single-cell sequencing technology promote our understanding of intrinsic complexity to another level [11]. Recently, we have successfully applied single-cell transcriptome technique to reveal cellular and molecular transitions in", + "present in multiple tissues, such as endothelial cells andepithelial cells, also tended to belong to the same category acrosstissues ( Supplemental Fig. S23). These findings indicate that inherent characteristics of cell types play an important role in shaping cell aging patterns, even when situated in different tissue environments. Discussion Here we show that tissue-specific aging programs can be learnedfrom scRNA-seq data and applied to describe aging heterogeneity", + "creased in old lung stromal cells. Using matrix factorization andoptimal transport methods, we computed trajectories of agingfor each cell identity and assessed the influence of identity and en-vironment on these trajectories. Results Single-cell RNA-sequencing identifies a diversity of cell types and states in young and old mouse tissue We collected transcriptional profiles of young and old cells of many identities by isolating single cells from the kidney, lung,", + "during the last decades. However, different types of cells in the cardiovascular system may be highly heterogeneous dur - ing aging and disease progression. Single-cell genomics, such as massively parallel single-cell RNA-seq, facilitate detailed transcriptome analysis to identify variants of key epigen-etic enzymes/pathways in specific diseased cohorts or cell types. 54,57,58,146 Altogether, new sequencing technologies have" + ], + [ + "SASP (senescence-associated secretoryphenotype):cytokines, chemokines,proteases, and otherfactors secreted bysenescent cells, whichare inammatory anddisrupt tissuehomeostasis viaparacrine mechanisms ATM (ataxia-telangiectasiamutated):serine/threoninekinase and centralregulator of the DDR;activated by DNAdamage and transducesthat signal througheffectorphosphorylationphenotype (SASP) (84). SASP proteins include interleukin-6 (IL-6), transforming growth factor-", + "SASP is one of the most representative features of senescent cells and may explain the organismal expression of aging and age-related diseases. Senescent cells pro- duce a deleterious microenvironment through the production and secretion of pro- liferative and proinflammatory molecules such as IL-1 and -1, IL-6, IL-8, the chemotactic cytokine GRO, IGBP-7, growth factors, VEGF, TGF-, serine prote- ases, and matrix remodeling enzymes [146]. It has been determined that the activa-", + "context. For example, SASP likely contributes to early tumorigenesis (84), chemoresistance (94),and potentially neurodegenerative diseases (95). However, SASP is also important for mammalian development (96), tissue repair (97), and wound healing (98). SASP plays an important role in stimulating clearance of damaged, senescent cells by the innate immune system (99). However,inefcient immune clearance of senescent cells in aged organisms is thought to contribute to chronic inammation of aging.", + "many tissues, where theSASP promotes chronic inflammation and exacerbates age-associated degeneration and hyperplasia. Recent evidence suggests that neurological aging and neurode- generation areaccompanied byanaccumulation ofsecretory cells inbrain, suggesting that cel- lular senescence may contribute tobrain aging [2]through ashared mechanism. Overlapping mechanisms canbedetected using functional genomics studies ofboth thebiology ofcellular senescence and cognitive aging.", + "senescence-associated with the secretory phenotype (SASP) are other markers of cellular senescence. Inflammation andIntercellular Communication While senescent cells no longer replicate, they are still metabolically active and secrete proteins in a recognizable pattern known as SASP.This is a widely heteroge- neous group of proteins with autocrine and paracrine effects [47], including soluble signaling factors, such as interleukins, chemokines, and growth factors, as well as", + "matory mediators. This particular phenotype is termed the senescence- associated secretory phenotype (SASP). Replicative cellular aging includes biochemical, mor - phological, and functional modifications that lead to the irreversible impairment of cell proliferation associated with DNA damage, shortening of the telomeres, and changes in chromatin architecture, as previously described [135, 136]. The molecular mechanisms that drive cellular senescence in proliferative and", + "secretion of a range of proinammatory cyto- and chemokines, a state that has been dened asthe senescence-associated secretory phenotype (SASP) (103). Major SASP factors include IL1, IL6, IL8, and various matrix metalloproteases (MMPs), all of which individually are thought to drive aging and age-related diseases. Thus, DNA damage is a major determinant in controllingcell death, stem cell exhaustion, and cellular senescence, which are considered important events", + "senescent cells [150]. SASP factors exert their functions in either an autocrine or a paracrine manner and are responsible for the induction of the chronic inflammation and cell proliferation that contributes to cell dysfunction and cancer. Thus, the accu- mulation of senescent cells in tissue is closely associated with aging-related dis- eases. Recently, it was determined that senescent fibroblasts significantly increase the expression of HLA-E, which inhibits the receptor NKG2A in killer cells, and", + "Role of L1 and Alu in cellular senescence and age-related inflammation A key feature of cellular senescence is the senescence-associatedsecretory phenotype (SASP), whereby senescent cells secretenumerous proinflammatory cytokines, chemokines, growth factors, and proteases (Campisi, 2013). This altered secretome", + "8. Coppe JP, Patil CK, Rodier F, et al. Senescence-associated secretory phenotypes reveal cell-nonautonomous func- tions of oncogenic RAS and the p53 tumor suppressor. PLoS Biol2008; 6:285368. 9. Wiley CD, Liu S, Limbad C, et al. SILAC analysis reveals increased secretion of hemostasis-related factors by senes- cent cells. Cell Rep 2019; 28:33293337 e3325. 10. Basisty N, Kale A, Jeon OH, et al. A proteomic atlas of senescence-associated secretomes for aging biomarker" + ], + [ + "loss of chromatin homeostasis drives aspects of aging. As chroma-tin marks are relatively stable and can even persist through cell divi-sion (Kouskouti and Talianidis 2005), sustained alterations to thechromatin landscape may mediate the propagation of age-associat- ed functional decline. Age-dependent changes in chromatin marks (e.g., DNA meth- ylation, histone modifications) have been observed in multiple species and tissues (Benayoun et al. 2015; Booth and Brunet", + "contributes to the onset of tissue dysfunction and the eventual demise of organisms as they age. During replicative senescence of human fibroblasts chromatin is subject to extensive changes in the global distribution of euchromatin and heterochromatin [25,35]. We found that the fundamental architecture of the genome undergoes profound alterations: an overall closing of chromatin in euchromatic gene-rich regions, which is", + "impaired function of histone modifying activ-ities, which in turn lead to structural chroma- tin changes. The number of known diseasesOrganismal agingAging-associated gene expression programsCellular stress DNA damageChromatin remodelingEpigenetic status SusceptibilityHistone modifier redistribution Non-specific gene expression events Figure 3. Chromatin effects in aging. A complex network of interactions links chromatin structure to aging.", + "by Pelicci and colleagues in this issue). However, it could also be argued that chromatin structure is directly affected by the ageing process through an as-yet-unknown mecha - nism that leads to increased DNA damage and a perma - nent damage response that alters gene-expression patterns in a similar way to the model proposed in this review. o ver the coming years, as researchers use mammalian models to map the global pattern of chromatin modifi -", + "and peripheral heterochromatin blocks are lost during aging (Haithcock et al. 2005). The aging-associated defects in chromatin structure have various functional consequences.T o start with, aged genomes are characterized by increased DNA damage and high levels of per-sistent DNA breaks, possibly brought about by structural changes, which increase the suscepti- bility of the genome to damage. Furthermore,probably as a consequence of loss of pericentro- meric heterochromatin structure, physiologi-", + "related changes in gene expression and the ageing process4,5. Changes in gene expression were already known to contribute to cellular senescence6, a possible cause of ageing7, and may provide an explanation for the age-related decline in organ and tissue function in complex organisms.Although chromatin reorganization was linked to ageing in budding yeast over 10 years ago8,9, these ideas have remained untested. Recently, a growing appre - ciation for the importance of chromatin in regulating", + "tone loss in the ageing process has been attributed to alterations in heterochromatin, which are characterized by a decrease in its distribution in the genome and the content of characteristic heterochromatin histone marks (such as H3K9me3 and H3K27me3) as evidenced in fibroblasts cells from a HGS patient and healthy aged individuals [59, 60]. Interestingly, it has been suggested that the increase in chroma- tin opening in T cells from aged people could be related to histone loss, which in", + "long lifespan (Dang et al. 2009). Given theseextensive changes in histone modications, not surprisingly, aged cells show dramatic and global misregulation of gene expression. Al-though some of these changes are likely part of specic aging-related gene expression pro- grams including inammation and cellularstress responses, others likely occur largely sto- chastically because of random changes in epi- genetic modications and chromatin structure. The mechanisms that drive chromatin and", + "general loss of histones coupled with local and global chromatinremodeling, an imbalance of activating and repressive histone modications, and transcriptional change in all aging models. Additionally, particularly in mammalian systems, there is globaland local change in DNA methylation, site-specic loss and gain in heterochromatin, and signicant nuclear reorganization (Figure 1 ). It is as yet unclear whether changes in the activity of epigenetic", + "Amarcb1) as well as histone deacetylases (Hdac1, -5, and -6) and a DNA methyltransferace (Dnmt3b) were downregulated in aged cells. They also showed that several chromosomal regions changed with age in a coordinated manner resulting in an overall increase in transcriptional activity. They propos e that chromatin dysregulation and epigenetic changes drive the loss of cellular function and ultimately drive the aging process in HSCs. Consistent with these data, Polycomb proteins (transcriptional" + ], + [ + "experiments suggest that epigenetic features associated withaging can be reversed. In successfully reprogrammed iPSCs, the chromatin state of CDKN2A locus associated with aging is erased and restored to that of youthful cells ( Meissner, 2010 ). The requirement for proper epigenetic gene silencing for longevity has been observed in multiple model organisms, sug- gesting an evolutionarily conserved process ( Lin et al., 2000; Chen et al., 2005; Greer et al., 2010 ). The function of Polycomb", + "apparent rewinding of the aging clock without loss of differenti-ation. Formal demonstration will require clear epigenetic signa- tures of young and old cells and evidence that the aged cells have regained a youthful signature. It should be noted thatreprogramming of the epigenome to a youthful state in an aged cell has inherent risks and uncertainties. For example, the", + "et al., 2010 ). Clearly, inhibiting single signaling pathways (NF-k B and mTOR) is sufcient to restore some features of youthful cells, but the number of transcriptional regulatorsthat need to be modulated to result in full rejuvenation is unknown. Third, is the youthful state or the aged state domi- nant? It would be interesting to determine which epigeneticand transcriptional prole is more robust in experiments of fusion of young and old cells. Concluding Remarks", + "Rejuvenation: Is It Epigenetic Reprogramming?By analogy to the attainment of a pluripotent state by epigenetic reprogramming of a differentiated cell, is cellular rejuvenation byheterochronic parabiosis, NF- kB inhibition, or inhibition of mTOR signaling ( Figure 1 ) a form of epigenetic reprogramming from an aged state to a youthful state? If so, then these would be examples of an uncoupling of the differentiation program from the aging clock, with cells in each case manifesting an", + "with a healthy lifestyle may preserve a more intact epigenome and hence experi-ence longevity. Reprogramming of aged cells into iPSCs and regeneration of dif-ferentiated cells may provide a mechanism for epigenetic rejuvenation. In addition to epigenetic drift, telomere shortening has been associated with", + "tion through the lens of epigenetic reprogramming. By dening youthfulness and senescence as epigenetic states, a framework for asking new questions about the aging process emerges. Introduction The inexorable tolls of aging are evident in almost all living beings. From the onset of reproductive maturity, organismalaging is generally characterized by a decline in fecundity, an increased susceptibility to disease and tissue dysfunction, and increased risk of mortality ( Kirkwood, 2005; Hayick, 2007; Kirk-", + "others (i.e. DNA methylation influences chromatin structures, histones PTMs). Several important conclusions emerge from the presented findings: there are at least two ways to reverse or inhibit senescence by epigenetic mechanisms, whereby a healthy life expectancy could be prolonged. The first way involves rejuvenation through effective epigenetic reprogramming in cells undergoing senescence or cells derived from very aged patients or patients with progeroid syndromes, by which the", + "aging is at least in part, if not largely, a manifestation of epigeneticchanges, including those that may be secondary to genomicmutations, offers a theoretical construct for understanding the mechanisms of rejuvenation. If so, it should be possible to char- acterize young and old cells by specic transcriptional andepigenetic proles and states. Furthermore, the processes that underlie aging and rejuvenation should be identiable in terms", + "determinants of the aged state by genetically manipulatingspecic biochemical pathways. A recent example demonstratesthe power of transcriptional proling and bioinformatic analysis to reveal an aging signature that can be genetically engineered to reect a more youthful state ( Adler et al., 2007 ). In a compar- ison of old and young tissues from mice and humans, old tissues were found to express at signicantly higher levels a set of genes that contained sequences in their 5 0regulatory regions, indica-", + "Recently, studying the direct relationship between epigeneticmechanisms and the aging process itself is gaining increasing attention. The potential reversibility of these epigenetic changes that occur as a hallmark of aging offers excitingopportunities to alter the trajectory of age-related diseases. 8 This is especially important given the remarkable plasticityof aging. 9,10In the literature, age-associated epigenetic alter- ations have been identified by epigenome-wide association" + ], + [ + "abolic regulation through mitochondrial signaling. Am J Physiol Endocrinol Metab. 2014;306:E58191. 74. Zhang R, Wang Y , Ye K, Picard M, Gu Z.Independent impacts of aging on mitochondrial DNA quantity and quality in humans. BMC Genomics. 2017;18:890. 75. Hebert SL, Lanza IR, Nair KS.Mitochondrial DNA alterations and reduced mitochondrial function in aging. Mech Ageing Dev. 2010;131:45162. 76. Liu D, Li H, Lu J, Bai Y .Tissue-specific implications of mitochondrial alterations in aging.", + "mechanisms that lead to mitochondrial metabolism shifts in human aging are not completely understood, the literature reports that the failure in the mitochondrial metabolism of aged heart might be associated with mutations in the mtDNA.In this sense, the aged heart shows an increase over 15-fold on mtDNA mutations in com- parison to hearts from young people [101]. Mutations in genes that encode Polg-a, responsible for mtDNA repair machinery, cytochrome b, and several subunits of", + "22. Fleming JE, Miquel J, Cottrell SF, Yengoyan LS, Economos AC: Is cell aging caused by respiration-dependent injury to the mitochondrial genome?Gerontology 1982, 28:, 44-53. 23. Pak JW, Herbst A, Bua E, Gokey N, McKenzie D, Aiken JM: Mitochondrial DNA mutations as a fundamental mechanism in physiological declinesassociated with aging. Aging Cell 2003, 2:1-7. 24. Jacobs HT: The mitochondrial theory of aging: dead or alive. Aging Cell 2003, 2:11-17.", + "Sun., N, Youle, R. J. and Finkel, T. (2016). The mitochondrial basis of aging. Mol. Cell 61, 654-666. doi:10.1016/j.molcel.2016.01.028 Symer, D. E., Connelly, C., Szak, S. T., Caputo, E. M., Cost, G. J., Parmigiani, G. and Boeke, J. D. (2002). Human L1 retrotransposition is associated with genetic instability in vivo. Cell110, 327-338. doi:10.1016/S0092-8674(02)00839-5 Szabo, L., Morey, R., Palpant, N. J., Wang, P. L., Afari, N., Jiang, C., Parast,", + "limitations to study mitochondrial metabolism in human samples, in this section we briefly described the implications of mitochondrial metabolism for aging in the most studied and high energy demand human tissues, such as skeletal muscle, heart, and brain.Table 4.1 Main mitochondrial dynamics proteins that are altered in human tissues during the aging process Tissue/ organ Fission Fusion Biogenesis Mitophagy Refs Skeletal muscleIncreased fragmentation Decreased Drp1 proteinIncreased interconnected", + "96. Wei Y-H, Wu S-B, Ma Y-S, Lee H-C.Respiratory function decline and DNA mutation in mitochondria, oxidative stress and altered gene expression during aging. Chang Gung Med J. 2009;32:11332. 97. Kates AM, Herrero P, Dence C, Soto P, Srinivasan M, Delano DG, Ehsani A, Gropler RJ. Impact of aging on substrate metabolism by the human heart. J Am Coll Cardiol. 2003;41:2939. 98. Gmez LA, Monette JS, Chavez JD, Maier CS, Hagen TM.Supercomplexes of the mito-", + "phenotype, such as the Mitochondrial Free Radical Theory of Aging (MFRTA), and although these theories have been recently confronted, the role of mitochondria in the aging process is undeniable because of their versatile roles and implications for cellular function. MFRTA suggests that the oxidative damage of mtDNA is the key event disturbing the respiratory chain proteins to induce its dysfunction and increase ROS production in a vicious cycle [123]. However, alterations in mito-", + "102. Zhang R, Wang Y , Ye K, Picard M, Gu Z.Independent impacts of aging on mitochondrial DNA quantity and quality in humans. BMC Genomics. 2017;18:890. https://doi.org/10.1186/ s12864-017-4287-0. 103. Norddahl GL, et al. Accumulating mitochondrial DNA mutations drive premature hema- topoietic aging phenotypes distinct from physiological stem cell aging. Cell Stem Cell. 2011;8:499510. https://doi.org/10.1016/j.stem.2011.03.009.", + "78 p53, which regulate the catalytic subunits of ETC complexes [103]. Unfortunately, these data have only been observed in murine models of aging and require further verification in human samples. Mitochondrial Metabolism intheAged Brain In normal conditions, the brain consumes around 25% of the total body glucose via glycolysis and mitochondrial OxPhos [104]. So besides the mitochondrial dynam- ics dysfunctions described above, during aging there is also a decline in energy", + "mitochondrial DNA mutations can reduce lifespan. Sci Rep. 2014;4:6569. 20. Ross JM, Stewart JB, Hagstrm E, Bren S, Mourier A, Coppotelli G, Freyer C, Lagouge M, Hoffer BJ, Olson L. Germline mitochondrial DNA mutations aggravate ageing and can impair brain development. Nature. 2013;501(7467):412 5. 21. Sondheimer N, Glatz CE, Tirone JE, Deardorff MA, Krieger AM, Hakonarson H. Neutral mitochondrial heteroplasmy and the influence of aging. Hum Mol Genet. 2011;20(8):1653 9." + ], + [ + "the attention of researchers as a therapeutic target for age-related diseases [109]. Resveratrol, a phytochemical enriched in the skin of red grapes and wine, has been actively investigated to determine whether it promotesSIRTs activity with conse- quent beneficial effects on aging [110]. IGF Because insulin/IGF-1 function through signaling as a nutrient sensor and controls the transcription of stress response genes, the insulin/IGF-1 pathway provides a", + "the use of lowered IGF signaling (e.g., by target-ing IGF receptors) to treat certain age-related diseasessuch as cancer (Pollak et al., 2004), Alzheimers disease(Cohen et al., 2009), and autoimmune diseases (Smith,2010). Moreover, a number of genes and pathways associ-ated with longevity and CR are part of nutrient-sensingpathways that also regulate growth and development, in-cluding the insulin/IGF1/GH pathway (Narasimhan et", + "as insulinIGF-1 signalling [6], cellular senescence [4], protein refolding [4345] , autophagy [41] and phase 1 and 2 detoxication [36,37,52] . These represent major points of intervention against ageing-related disease. As shown here, lifespan pathways control improved cellular maintenance, which leads to slowed ageing(e.g. slowed normal cognitive ageing) and protection against diseases of ageing (e.g. neurodegenerative diseases of ageing, such as Alzheimers and Parkinsons", + "ent-sensing pathways such as insulin/insulin-likegrowth factor (IGF-1) signalling (IIS) and target of rapamycin (TOR) signalling mediated lifespan exten- sion, and also the extension of lifespan by DR [ 2]. An interesting observation from the perspective ofhuman ageing is that, in rodents and monkeys, dietsrestricted in glucose, fat or protein uptake reduced ordelayed the risk of cancer and metabolic disease,thus extending the healthspan of the animals [ 2]. Fol-", + "43. Svensson, J. et al. Liver-derived IGF-I regulates mean life span in mice. PLoS ONE 6, e22640 (2011). 44. Junnila, R. K., List, E. O., Berryman, D. E., Murrey, J. W. & Kopchick, J. J. The GH/IGF-1 axis in ageing and longevity. Nat. Rev. Endocrinol. 9, 366376 (2013). 45. Yuan, R. et al. Aging in inbred strains of mice: study design and interim report on median lifespans and circulating IGF1 levels. Aging Cell 8, 277287 (2009). 46. Zhu, H. et al. Reference ranges for serum insulin-like growth", + "5. Piper MD, Selman C, McElwee JJ, Partridge L: Separating cause from effect: how does insulin/I GF signalling control lifespan in worms, flies and mice? J Intern Med 2008, 263:179-191. 6. Holzenberger M, Kappeler L, De Magalhaes Filho C: IGF-1 signaling and aging. Exp Gerontol 2004, 39:1761-1764. 7. Zahn JM, Kim SK: Systems biology of aging in four species. Curr Opin Biotechnol 2007, 18:355-359. 8. McElwee JJ, Schuster E, Blanc E, Piper MD, Thomas JH, Patel DS,", + "humans enriched for familial longevity. Aging Cell. 2016;15(6):112631. 44. Lee WS, Kim J.Insulin-like growth factor-1 signaling in cardiac aging. Biochim Biophys Acta Mol basis Dis. 2018;1864(5 Pt B):19318. 45. Balasubramanian P, Longo VD. Growth factors, aging and age-related diseases. Growth Hormon IGF Res. 2016;28:668. 46. Suzuki K, etal. Serum insulin-like growth factor-1 levels in neurodegenerative diseases. Acta Neurol Scand. 2019;139(6):5637.", + "paradigms for lifespan extension (C. elegans, D. melanogaster), genetic interference in the insulin-signaling pathway can prolong life multi-fold [47,48]. In mammals, IGF1-decient, Ames and Snell dwarf mice (characterized by defects in the development of the anterior pituitary due to mutations in the Prop-1 and Pit1 loci and diminished levels of GH, thyroid stimulating hormone, and prolactin hormone) combine", + "the role of IGF-1 in life span regulation is complex. In theory,SIRT6 might play a role in insulin signaling, similar to Sir2 fac- tors in other lower organisms. However, as in the prematureaging mouse models described above, it remains unclear whether the altered serum IGF-1/insulin levels of SIRT-6- decient mice directly contribute to aging-like phenotypesor, alternatively, reect compensatory alterations. In this re- gard, it will be of interest to determine whether SIRT6 is", + "lin-like growth factors (IGFs), and receptors in theinsulin-signaling pathway has been shown to confergreater longevity in yeast (12, 16), nematodes (21, 44),fruit ies (10, 43), mutant long-lived mice (4, 11), and caloric-restricted mice (40). Therefore, the as-yet un-identi ed mechanism of insulin signaling on lifespan" + ], + [ + "learning to show that plasma proteins that predict age are predominantly associated with immunity [91]. State-of-the-art metabolomics approaches are also now allowing age-related changes in me- tabolite pro les to be studied, which provide new insights into the physiological mechanisms of age- ing [ 92,93]. The integration of multiple datasets generated from genomes, epigenomes, transcriptomes, proteomes, and metabolomes, an approach termed multi-omics , offers great", + "13. Menni C, Kastenmuller G, Petersen AK, et al. Metabolomic markers reveal novel pathways of ageing and early development in human populations. Int J Epidemiol 2013;42:1111- 9. 14. Evans AM BB, Liu Q, Mitchell MW, Robinson RJ, et al. . High Resolution Mass Spectrometry Improves Data Quantity and Quality as Compared to Unit Mass Resolution Mass Spectrometry in High- Throughput Profiling Metabolomics. Metabolomics 2014;4:132.", + "Due to the mild adaptions, the identification of func- tionally altered metabolic activity in aged skin interpret- ation of significant metabolite and transcript changes of small magnitude is especially challenging. Therefore, we employed the previously presented locality scoring ap- proach [60] to identify age-dependent transcriptional al- terations of enzymes that functionally effect proximal metabolic activity and thus metabolite levels. This inte- grated analysis revealed age-dependent, concerted me-", + "matched transcriptome and metabolome data highlighted transcriptionally-driven alterations of metabolism during aging such as altered activity in upper glycolysis and glycerolipid biosynthesis or decreased protein and polyamine biosynthesis. Together, we identified several age-dependent metabolic alterations that might affect cellular signaling, epidermal barrier function, and skin structure and morphology.", + "used to assess biological responses provides new oppor - tunities to understand the impact of the environment on the risk of age-related diseases. For example, the multi - omics analysis and integration method produces a pri - ority list of multiple sets of biomarkers, which together reflect the molecular responses of the exposome. Each of these data warrants integration into a biomarker panel to aid physicians in developing age-related disease diagno - ses and prognoses [78].", + "summary, we identified age-dependent changes in gene expression in different metabolic pathways that have been associated with epidermal homeostasis and there- fore might be important to sustain epidermal function. Integrated analysis of transcriptome and metabolome data Since the age-dependent adaptations of metabolite and transcript levels are only mild, we set out to identify metabolic enzymes that featured an age-dependent and functional change in activity driven by altered gene ex-", + "These high throughput prof iling experiments have gener- ated large amounts of data for meta-analysis [24], which can compare molecular functions and expression patterns that change during aging in different systems. However, such studies are far from exhaustive, as they only describe the molecular changes during aging, which could in fact be the consequence of aging, rather than the cause of aging. Thus to explore the causal factors for aging, studies are increasingly", + "over, the integration of trans criptome and metabolome data revealed a transcriptionally re gulated reduction in protein as well as polyamine biosynthesis and adaptation in upper glycolysis and glycerolipid biosynthesis in aged skin. Results Differences in the epidermal skin metabolome of young and old human volunteers To chart metabolic adaptations in human skin during aging in vivo , we performed non-targeted metabolomicsanalysis of epidermal skin tissue samples obtained from", + "proteomes overlap significantly with the waves of aging proteins (Supplementary Table 15). Accounting for heterogeneous and com - plex changes to the plasma proteome during life will likely improve the sensitivity and specificity of prognostic and diagnostic tests. Moreover, these results are pertinent when considering the use of blood or blood products to treat aging and age-related diseases 39. Specifically, identifying plasma proteins that promote or antagonize", + "rmed using authentic standards. One of the key nodes identi ed by metabolomics as signi cantly altered with accelerated and normal aging was glutathione metabolism ( Fig. 4A), a key antioxidant and index of oxidative stress [71]. Dierential MS was used for proteomics analysis to identify redox- related proteins signi cantly altered in the livers of 3 4 month-old progeroid Ercc1/mice and old WT mice (> 2 years-old) vs. adult WT mice. Expression of catalase, SOD1 (CuZnSOD) and SOD2 (MnSOD)" + ], + [ + "lncRNA which overexpression participates in the regulation of age-associated car - diovascular diseases as it is a non-canonical precursor for hsa-miR-4485 and hsa- miR- 1973 microRNAs [62]. These studies demonstrate that not only coding genes (which represent only 2% of the genome sequence) are implicated in aging regula- tion, but also lncRNAs and microRNAs participate in tissue age-related changes. circRNAs are non-coding covalently closed single-stranded transcripts produced", + "(2008). 192. K. Abdelmohsen, A. Panda, M.-J. Kang, J. Xu, R. Selimyan, J.-H. Yoon, J. L. Martindale, S. De, W. H. Wood III, K. G. Becker, M. Gorospe, Senescence-associated lncRNAs: Senescence- associated long noncoding RNAs. Aging Cell 12, 890 900 (2013). 193. S. Kour, P. C. Rath, Long noncoding RNAs in aging and age-related diseases. Ageing Res. Rev. 26,1 21 (2015). 194. R. Johnson, Long non-coding RNAs in Huntington s disease neurodegeneration. Neurobiol. Dis. 46,2 4 5 254 (2012).", + "155 Premature ageing has been associated with altered expression of lncRNAs that participate in the regulation of the telomere length by modulating the TERT activity and synthesis of telomeric repeats [155, 161]. Furthermore, it has been reported that changes in the expression levels of some lncRNAs are associated with the develop- ment of AD [162]. Circular RNAs andAgeing Circular RNAs (circRNAs) are highly conserved covalently closed non-coding", + "interacting with proteins and nucleic acids in order to regulate gene expression (by indirect epigenetic mechanisms or by direct mechanisms acting as antisense tran- scripts or transcriptional coactivators), nuclear location of transcription factors and stabilization of ribonucleoprotein complexes [155]. It has been reported that lncRNAs are important in the regulation of ageing-associated mechanisms in humans and ani-", + "progression. LncRNA H19 was recently reported to play a crucial role in the activation of MAPK and the NF-kB signaling pathway and the induction of atherosclero - sis [3]. lncRNAs play crucial roles in the progression of diabetic nephropathy [12], glomerular disease [13] and renal fibrosis [14]. The lncRNA Arid-IR promotes NF- kB-mediated kidney inflammation by targeting NLRC5 transcription [15]. The cell cycle changes during aging. Previous studies have shown that lncRNAs are related to", + "expression of SIRT1 and are decreased in lymphoblastic cell lines generated from centenarians compared with those of AD patients, suggesting a protective effect of these miRNAs against neurodegeneration [66]. Long noncoding RNAs are important regulators of transcriptional networks and the closed or opened chromatin state [2]. One interesting example of an lncRNA is that associated with aging, H19. This lncRNA interacts with MBD1 (a methyl-", + "associated factors, modulating aging and senescence directly or in-directly. One such example includes a specific lncRNA, Gas5 ,w h i c h is highly expressed in aged mice brain and has been associated with im-paired learning ( 189). Another bona fide example is H19lncRNA, a dif- ferentially spliced product from the H19gene located at the IGF2/H19 imprinted locus, which interacts with methyl-CpG binding domain", + "tempting to speculate that these lncRNAs may exert some regulatory control of this locus, possibly contributing to senescent phenotypes. Together, these findings point to- wards a host of age-related ncRNAs as regulators of aging pathways and networks. Interaction network analysis The increased accuracy and breadth of our RNA-seq data sets allowed us to generate networks of gene func- tional change in aging liver, above and beyond what was observed using DAVID or GOrilla. Using Ingenuity", + "RNAs interact with proinflammatory signaling pathways and regulate senescence; however, their role on regulation of vas-cular aging processes is virtually unknown. 151 Interestingly, there is initial evidence linking the expression of the long noncoding RNA Meg3 (maternally expressed 3) to age-related impairment of angiogenic capacity of endothelial cells.152 Further studies are definitely needed to understand the", + "Page 2 of 11 Lietal. BMC Genomics (2022) 23:254 mechanism of kidney aging will be of great significance for delaying the occurrence and development of renal aging. Although a small number of studies have been conducted on renal aging, it is still meaningful to com - prehend the mechanism of renal aging. Long chain noncoding RNAs (lncRNAs) are more than 200 nucleotides in length. LncRNAs regulate transcrip - tional and posttranscriptional RNA processing, transla -" + ], + [ + "models of ageing, but it will also drastically accelerate the generation of refined ver - sions of those models or even allow the development of new research approaches in non-model organisms. Moreover, CRISPR-based genome editing is already having a significant impact in research aiming to understand the cellular and molecular origins of age-related diseases, as well as developing potential treatments against 11 Applications ofCRISPR-Cas inAgeing Research", + "of ageing. Finally, we will review how CRISPR-Cas has been used for creating new models for the study of age-related diseases, as well as for manipulating disease- associated gene pathways. S. Haston et al.", + "ularly Interspaced Short Palindromic Repeats (CRISPR)/Cas9) will be beneficial in clari- fying aging-processes across species. An improved understanding of epigenetic mechanisms affecting longevity will be deciding crucial step towards the identification of new potential therapeutic targets. In fact, epigenetic drugs are of particular interest to the clinic due to their reversible and transient effect. A limitation of manifold epigenetic studies, however, are the variations among sin-", + "224 high-throughput assays able to further delineate important molecular pathways involved in inducing and maintaining cellular senescence in both physiological ageing and age-associated diseases. Applications ofCRISPR-Cas intheStudy ofAgeing-Related Disease Cardiovascular Disease One of the most notable contributions of CRISPR-Cas to ageing research is its ability to target non-proliferating cells (contrary to HDR-directed gene targeting),", + "219 Applications ofCRISPR-Cas inBasic Research oftheMolecular Causes ofAgeing Investigating theMechanisms ofLongevity Currently there have been no studies exploring the utility of the CRISPR-Cas sys- tem on experimentally extending the lifespan of physiologically aged laboratory animals. A main issue in this regard is that established vertebrate models already possess relatively long lifespans that make longevity extension studies economi-", + "CRISPR-Cas genome- editing tools will provide feasible implementation of 11 Applications ofCRISPR-Cas inAgeing Research", + "the basis for future investigations into the spatio-temporal dynamics of the telom- erase protein invivo.11 Applications ofCRISPR-Cas inAgeing Research", + "induced by telomere erosion. Protein Cell. 2019;10:3705.11 Applications ofCRISPR-Cas inAgeing Research", + "using bulk mRNA or even analyzing single cells (scRNA-seq). In addition, advances in molecular biology and cell culture approaches (for instance Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/Cas9) will be benecial in clarifying aging-processes across species. An improved understanding of epigenetic mechanisms affecting longevity will be deciding crucial step towards the identication of new potential therapeutic targets. In", + "In recent years, CRISPR-Cas technologies have significantly contributed to studies addressing the molecular pathogenesis of age-related neurodegenerative conditions such as Alzheimers disease (AD) and Parkinsons disease (PD). Currently, it has mostly been utilised for developing new or improved tools in which to study the molecular mechanisms underlying these diseases, such as in patient-derived cell lines carrying pathogenic mutations." + ], + [ + "Chromatin Remodeling, DNA Damage Repair and Aging Current Genomics, 2012 , Vol. 13, No. 7 539 Ercc1 also show premature aging phenotypes, providing evi- dence of a direct correlation between impaired DDR and premature aging [137, 138]. The relationship between DNA damage accumulation and aging has gained maximum credibility through studies", + "genome is being transcribed or replicated, the threshold of damage needed to activate DDRs, and the choice of cell fate in response to genotoxic stress. It is important to point out that cross-sectional studies, which are largely all we have to date, yield information about the burden of DNA damage and cannot inform as to whether lesions accumulate over time. Longitudinal studies on tissues that can be serially accessed are desperately needed. DNA Repair Capacity Decreases with Aging", + "INTRODUCTION Damage to DNA occurs with surprising frequency. DNA lesions can cause mutations, blocktranscription and replication, and trigger the DNA damage response (DDR). The DDR arrests cell cycle progression and activates signaling pathways that impact cell fate: repair, apoptosis, or cellular senescence. DNA damage is widely recognized as a cause of cancer, and strong evidencenow links DNA damage to aging and diseases associated with aging.", + "DNA damage and persistent DDR signalling as a shared causative mechanism of cellular senescence andageing. Curr. Opin. Genet. Dev. 26:8995 103. Rodier F, Coppe JP, Patil CK, Hoeijmakers WA, Munoz DP, et al. 2009. Persistent DNA damage signalling triggers senescence-associated inammatory cytokine secretion. Nat. Cell Biol. 11:97379 104. Garinis GA, Uittenboogaard LM, Stachelscheid H, Fousteri M, van Ijcken W, et al. 2009. Persistent", + "persistent DNA damage response (DDR) at telomeres and that even long telomeres may be a target for the accu-mulation of irreparable DNA damage. Therefore, DDR activation either at critically short telomeres or caused by persistent telomeric DNA damage represents the trigger of replicative cellular senescence or apoptosis 48, 50. The analysis of apoptosis by TUNEL assay showed that leukocytes from untrained T2D subjects were more sensitive to H", + "E) (2931) and have alleviated the dependency on invitro and invivo models by using direct human samples. AGe-ReLATeD DNA DAMAGe AND DNA DAMAGe ReSPONSe (DDR) ACTiviTY Age-related accumulation of DNA damage has been studied thoroughly, showing correlation between age and damage levels or mutation frequency (32, 33). In the presence of DNA lesions or abnormalities, the DDR, a complex multigenic pathway, is", + "Spontaneous damage is stochastic. But the response to DNA damage is highly conserved, geneti-cally controlled, and with evolution exceedingly more complex. DNA damage triggers activation of signaling pathways termed the DDR, which facilitates repair and arrests cell cycle progression until repair is complete. If DNA damage is extensive or irreparable, DDR effectors trigger celldeath (apoptosis) or cell senescence. These are potent tumor suppressor mechanisms. However,", + "to senescence. Genetic attenuation of the DDR enables reversal of cellular senescence (81). Incontrast, introduction of DSBs in mouse liver, using a tetracycline-inducible SacI restriction endonuclease system, increases the burden of senescent cells in vivo and triggers hallmarks of liver aging (82), illustrating a clear path for how DNA damage can play a causal role in aging. Markers of senescence are detected at higher levels in tissues of older mice, humans, and other", + "mechanisms. In general, it appears that DDR signaling enhances DNA repair and autophagy tocontrol the level of damage in the cell. Interestingly, evidence, albeit early evidence, has been found that DNA damage is linked to proteostasis. Expression of proteins containing polyglutamine tracts that drive protein aggrega- tion linked to neurodegeneration activates the DDR and H2AX foci (148). Interestingly, DNA breaks in cells and H2AX foci in brain of a murine model of Huntington disease are detected", + "its relevance to age -related functional decline at the molecular and cellular level. The importance of oxidative stress and key DNA damage response (DDR) pathways in cellular aging is discussed, with a special focus on poly (ADP -ribose) polymerase 1, whose persistent activation depletes cellular energy reserves, leading to mitochondrial dysfunction, loss of energy homeostasis , and altered cellular metabolism. Elucidation of the relationship between genomic instability ," + ], + [ + "immune system are one of the hallmarks of the aging body. Immunosenescence is the functional decline of the adaptive immune system brought on by natural agingwhereby protection against infection by pathogens and the effectiveness of vaccination decline [45,46]. The sec- ond aging-induced change in the immune system iscalled inflammaging which is characterized by a low- grade chronic inflammation process that contributes to", + "the increased susceptibility of the elderly to infectious disease and tothe poor outcome of vaccination. Defence against pathogens is com-promised mainly because of changes in adaptive immunity mediatedby T and B lymphocytes; however, all components of the immunesystem are affected (Fig 1). Dissecting the crucial alterations responsi-ble for dysfunctional immunity in old age will facilitate the develop-ment of rational interventions to reconstitute appropriate immunefunction. Given the increasing", + "[39] C. Castelo-Branco, I. Soveral, The immune system and aging: a review, Gynecol. Endocrinol. 30 (2014) 1622. [40] S.A. Johnson, S.J. Rozzo, J.C. Cambier, Aging-dependent exclusion of antigen-in - experienced cells from the peripheral B cell repertoire, J. Immunol. 168 (2002) 50145023 . [41] D.P. Shanley, D. Aw, N.R. Manley, D.B. Palmer, An evolutionary perspective on the mechanisms of immunosenescence, Trends Immunol. 30 (2009) 374381.", + "immunosenescence: the decline in immune efficacy of both the innate and the adaptive immune systems. Age-relatedimmune decline also links to the concept of inflamm-aging, whereby aging is accompanied by sterile chronic inflammation. Along with a decline in immune function, aging is accompanied by a widespread of omics remodeling.", + "ence the development of inflamm-aging and immunosenes- cence phenotypes. Finally, although discussed studies have reported age-related changes in innate immune cell processes, there is still little known about how these changes are influenced by biologicalsex. Indeed, both the adult mammalian immune system [ 80,125] and the aging process [ 126] are sex-dimorphic, suggesting that", + "tion has also been implicated in ageing across a range of non-model organisms, including mice,nematode worms ( Caenorhabditis elegans ), and primates [ 4042]. The damage caused by the ageing adaptive and innate immune systems gives us insights into how these different arms of the immune system may in uence longevity. In general, adaptive im- mune function diminishes with age, whereas innate immune function is maintained [ 34,4346].", + "development to senescence, innate immunity to adaptive immunity,and genes to environments, in organisms ranging from mice to monkeys and humans. Understanding and eventually modulatingimmune dysfunction in the elderly now beckons. Lymphocyte development and ageing", + "an age-related decline in the capacity of adaptive immunity,consisting of more specic responses carried out by B andT cells [ 7]. Thus, with advanced age, the immune system undergoes a gradual remodeling in the attempt to reestablisha new balance that assures survival, however, favoring thedevelopment of chronic inammatory conditions [ 5,6,8,9]. DNA damage and inammation are inevitably linked by", + "All components of the immune system are altered as ageing pro-ceeds (Fig 1); however, the T-cell and B-cell compartments seem tobe particularly susceptible. The most severe clinical impact is proba-bly a result of the loss of diversity in the TCR and B-cell-receptorrepertoire, owing to the accumulation of dysfunctional cells, anddecreased thymic and bone-marrow output. Several interventionsdiscussed at the meeting could conceivably contribute to therestoration of appropriate immune function in the near", + "more susceptible to DNA damage. One of the major rea-sons are the impaired DNA repair mechanisms which havebeen described in several studies and have been associated with the initiation of age-associated diseases and progeroidsyndromes ( Hasty et al., 2003; Lieber and Karanjawala, 2004). Furthermore, dysregulated immune and inamma- tory responses have been already documented both inhumans and mouse with increasing age ( Badawi et al., 2004; Kovaiou et al., 2007 )." + ], + [ + "tifications of biological aging: do they measure the same thing? Am J Epidemiol. 2018;187(6):122030. 74. Putin E, etal. Deep biomarkers of human aging: application of deep neural networks to bio- marker development. Aging (Albany NY). 2016;8(5):102133. 75. Rehkopf DH, etal. Leukocyte telomere length in relation to 17 biomarkers of cardiovascular disease risk: a cross-sectional study of US adults. PLoS Med. 2016;13(11):e1002188.", + "studied (Table 13.1). Thus, due to the generation of these data and technological advances, possibly in the future, artificial intelligence programs will be able to reliably forecast the life of an individual, as well as the possible diseases that he may suffer in ageing; so these advances and discoveries will allow us to achieve a personalized medical treatment as a result of to the integration of biomarkers of ageing. Ageing Is aTreatable Condition", + "the data. However, construction of such models is often highlydegenerate, yielding little overlap of identified biomarkers be-tween studies and thus making results difficult to interpret(Thompson et al. 2018; Galkin et al. 2020). Among the many computational algorithms, linear regres- sion and its variants have been widely used to select aging-relatedbiomarkers and build aging clocks, namely, predictors of chro- nological age and biological age, in various omics data sets and ag-", + "states, which can be monitored using various biomarkers (Belskyet al. 2015). These markers are usually measurable indicators of aparticular outcome or source of aging, such as phenotypical mea-sures like frailty and molecular measures like DNA methylation dy- namics (Schumacher et al. 2021; Lpez-Otn et al. 2023). Although informative, they are not always quantitatively predictive of anindividual s true biological age, nor are they easy to obtain. The ad-", + "biomarkers of the aging process.", + "supervisedmachinelearningappliedtoageingresearch. Biogerontology ,18,171188. 47. Kriete,A.,Lechner,M.,Clearfield,D.andBohmann,D.(2011) Computationalsystemsbiologyofaging. WileyInterdiscip.Rev.Syst. Biol.Med. ,3,414428.Downloaded from https://academic.oup.com/nar/article/46/D1/D1083/4599180 by guest on 14 October 2023", + "associated with age, such as mouth width, nose width, and eye corner droop. This type of bioimage analysis has rendered relatively accurate calculations of the actual age, although this accuracy tended to fall with increasing age after 40years [71]. Integration ofBiomarkers ofAgeing Biomarkers of ageing allow estimating the biological age of an organism (Table 13.1) while providing information on their health status. Different studies are looking for", + "Background There is a marked heterogeneity in human lifespan and health outcomes for people of the same chronological age. Thus, one fundamental challenge is to identify mo- lecular and cellular biomarkers of aging that could pre- dict lifespan and be useful in evaluating lifestyle changes and therapeutic strategies in the pursuit of healthy aging. Here, we developed a computational method to predict biological age from gene expression data in skin fibro-", + "Background Ageing is a major risk for diseases and mortality [ 1,2]. Chronological age has been widely used as a marker of ageing due to ease and accuracy of measurement [ 1]. However, it is not necessarily a good predictor of biological ageing since individuals with the same chronological age can vary in health, especially in later life [ 3]. Therefore, researchers have attempted to search for biomarkers of ageing that can predict functional cap- ability at a later age [ 4,5]. In 2013, Hannum et al. and", + "discriminate between adverse aging-related events, such as frailty (Mitnitski et al. 2002 ), immobility (Simonsick et al. 2001 ), and propensity to fall (Lord et al.1994 ). There are additional considerations when choosing biomarkers to characterize aging. First, biomarkers measured at a given age are merely snapshots of important regulatory systems (Seeman et al. 2004 ); there is no information on system dynamics if each biomarker is measured only once. Having longitudinal" + ], + [ + "in the vascular system are considered in terms of their contribution to the pathogenesis of both microvascular and macrovascular diseases associated with old age. The importance of progeronic and antigeronic circulating factors in relation to development of vascular aging phenotypes are discussed. Finally, future directions and opportunities to develop novel interventions to prevent/delay age-related vascular pathologies by targeting fundamental cellular and molecular aging processes are presented. (Circ", + "pression of numerous mRNAs, some of which directly influence aging and age-related diseases. Jung and Suh describe what we know about the importance of microRNAs in aging and how this exciting new field is just starting to become explored. The last review in this special issue by Hou et al. brings things together nicely with a systems biology perspective of aging. In order to model the immense complexity of aging, we require systems-level approaches. This review describes how several", + "autoregulation of blood flow,218 vascular structural remodel- ing, atherogenesis,219 and angiogenic processes.220 The impact of circulating factors on aging phenotypes was also demonstrated by studies using mice with heter - ochronic parabiosis, which involves surgically connecting the circulatory system of a young and an aged mouse. 221 Cerebromicrovascular density typically declines with ad-vanced age, 222 and there is initial evidence that circulating an-", + "components, particularly chemokines and cytokines, in theblood and tissues ( Villeda et al., 2011 ). In addition to illuminating the inuence of the systemic environment on cellular function,such heterochronic studies emphasize the potential role of envi-ronmental factors in rejuvenating aged cells. Molecular signatures of aging have been directly tested as", + "related diseases. Ageing Res Rev. 2018;47:21477. 115. Kumar S, Vijayan M, Bhatti JS, Reddy PH.MicroRNAs as peripheral biomarkers in aging and age-related diseases. Prog Mol Biol Transl Sci. 2017;146:4794. 116. Smith-Vikos T, Liu Z, Parsons C, Gorospe M, Ferrucci L, Gill TM, etal. A serum miRNA profile of human longevity: findings from the Baltimore Longitudinal Study of Aging (BLSA). Aging (Albany NY). 2016;8(11):297187.", + "in the endothelium and the VSMCs and specific disease pro-cesses. There is evidence that the senescence-associated se-cretory phenotype can also induce paracrine senescence and alter the function of neighboring cells, and the role of this mechanism in vascular aging should be further evaluated. The possibility of paracrine transmission of senescence from microvascular endothelial cells to parenchymal cells also requires further investigations. It should be noted that many", + "protein VSIG4 as a biomarker of aging in murine adiposetissue. Aging Cell 2020; 19:e13219. 128. Angelidis I, Simon LM, Fernandez IE, et al. An atlas of the aging lung mapped by single cell transcriptomics and deeptissue proteomics. Nat Commun 2019; 10:963. 129. Clark D, Brazina S, Yang F, et al. Age-related changes to macrophages are detrimental to fracture healing in mice. Aging Cell 2020; 19:e13112. 130. Tabula Muris Consortium. A single-cell transcriptomic", + "Ungvari et al Mechanisms of Vascular Aging 861 mechanisms of vascular aging and identify translationally relevant treatments for the promotion of vascular health in older adults. The same cellular and molecular aging processes that af- fect arterial vessels and capillaries also affect veins and the lymphatic/glymphatic system, likely contributing to various disease pathologies. Examples include the potential role of cerebral venules in neuroinflammation, Alzheimer disease, and cerebral microhemorrhages", + "et al., Plasma proteomic signature of age in healthy humans, Aging Cell 17 (2018). [17] D. Mari, P.M. Mannucci, R. Coppola, B. Bottasso, K.A. Bauer, R.D. Rosenberg, Hypercoagulability in centenarians - the paradox of successful aging, Blood 85 (1995) 31443149. [18] S.A. Phillips, The vasculature in cardiovascular diseases: will the vasculature tell us what the future holds? Prog. Cardiovasc. Dis. 57 (2015) 407408. [19] R.A. Gibbs, J. Rogers, M.G. Katze, R. Bumgarner, G.M. Weinstock, E.R. Mardis,", + "16Lidzbarsky et al. Genomic Instabilities, Cellular Senescence, and Aging Frontiers in Medicine | www.frontiersin.org April 2018 | Volume 5 | Article 104 177. Smith-Vikos T, Slack FJ. MicroRNAs and their roles in aging. J Cell Sci (2012) 125:717. doi:10.1242/jcs.099200 178. Lanceta J, Prough RA, Liang R, Wang E. MicroRNA group disorganiza- tion in aging. Exp Gerontol (2010) 45:26978. doi:10.1016/j.exger.2009. 12.009" + ], + [ + "the adaptation of the microbiota to the physiological changes of the long aging process. It has been demonstrated that the microbiota on this population maintains the health and promotes the survival. Additionally, a relationship between a healthy microbiota and longevity had been proposed [44]. A possible pathway is an immu- nological and metabolic regulation linked to the increase of bacterial compounds like Christensenellaceae, Akkermansia, and Bifidobacterium [44, 45].", + "Marchesi JR, Falush D, Dinan T, Fitzgerald G, et al:Composition, variability, and temporal stability of the intestinal microbiota of the elderly. Proc Natl Acad Sci USA 2011, 108(Suppl 1):4586 4591. 21. Maegawa S, Hinkal G, Kim HS, Shen L, Zhang L, Zhang J, Zhang N, Liang S, Donehower LA, Issa JP: Widespread and tissue specific age-related DNA methylation changes in mice. Genome Res 2010, 20(3):332 340. 22. Englander EW: Gene expression changes reveal patterns of aging in the", + "microbiota present in infants, adults, and the elderly. Appl. Environ. Microbiol. 73, 77677770 (2007). 40. Kong, F. et al. Gut microbiota signatures of longevity. Curr. Biol. 26, R832R833 (2016). 41. Tremaroli, V. et al. Roux-en-Y gastric bypass and vertical banded gastroplasty induce long-term changes on the human gut microbiome contributing to fat mass regulation. Cell Metab. 22, 228238 (2015). 42. Everard, A. et al. Microbiome of prebiotic-treated mice reveals novel targets involved", + "Therefore, research in the field has demonstrated that aging is a potential modi- fier of the composition and function of the human microbiome. Figure 9.3 shows the local composition of the microbiome in an average older adult. It can be seen that Bacteroidetes and Firmicutes species are the most prevalent in this age. Recent data has shown that older people hide a microbiota that differs in the type and number of microorganisms from that of younger adults [38]. Young people", + "related malnutrition. Furthermore, it has been shownthat aging can cause bacterial overgrowth in the smallintestine [16,17] and promote changes in microbial com- position in the colon [18-20]. In addition, reported age- related changes in DNA methylation of the mouseintestine [21] might play a role in the altered gene expression levels observed in the duodenum and colon of aging mice [22]. Together these observations demon-strate that although certain aspects of the aging intestine", + "detectable. Changes in the gut microbiota in terms of compos- ition and functionality during the process of aging have previously been reported [19,20,51] and it hasbeen postulated that these changes might contribute to the development of immunosenescence and inflam- maging [18,52]. To establish whether the enhanced expression of genes playing a role in the immune sys- tem are due to modifications in the microbiota wemeasured the total number of all bacteria and of the", + "37. Li H, Qi Y , Jasper H.Preventing age-related decline of gut compartmentalization limits micro- biota Dysbiosis and extends lifespan. Cell Host Microbe. 2016;19(2):24053. 38. Mihajlovski A, Dor J, Levenez F, Alric M, Brugre J.Molecular evaluation of the human gut methanogenic archaeal microbiota reveals an age-associated increase of the diversity. Environ Microbiol Rep. 2010;2(2):27280. 39. Quercia S, Candela M, Giuliani C, Turroni S, Luiselli D, Rampelli S, etal. From lifetime to", + "[26], but at advanced ages, dramatic changes in its composition are associated with various diseases and frailty [27, 28]. Regarding pathological processes, it is known that cancer, obesity, diabetes, and inflammatory bowel disease (IBD) are associated with specific microbial alterations [29, 30]. In older ages, a burden of intrinsic and extrinsic factors affects the compo- sition of the microbiome and plays a determining role in every tract and tissue. Such mentioned factors can be seen in Fig.9.2.", + "Osawa R. Age-related changes in gut microbiota composition from newborn to centenarian: a cross-sectional study. BMC Microbiol. 2016;16:90. 14. Dugue PA, Bassett JK, Joo JE, Jung CH, Ming Wong E, Moreno-Betancur M, Schmidt D, Makalic E, Li S, Severi G, et al. DNA methylation-based biological aging and cancer risk and survival: pooled analysis of seven prospective studies. Int J Cancer. 2018;142(8):1611 9. 15. Levine ME, Hosgood HD, Chen B, Absher D, Assimes T, Horvath S. DNA", + "survival advantage that is age- and site-specific: Results from a large multi-site study. Aging Cell 18, e12905 (2019). [PubMed: 30801953] 51. Houtkooper RHet al.The metabolic footprint of aging in mice. Sci. Rep. 1, 134 (2011). [PubMed: 22355651] 52. Morrison KE, Jaarevi E, Howard CD & Bale TL Its the fiber, not the fat: significant effects of dietary challenge on the gut microbiome. Microbiome 8, 15 (2020). [PubMed: 32046785]" + ], + [ + "Metabolism Studies show that calorie restriction is the most consistent means to prolong life expectancy and health across several experimental models [55], ranging from yeasts to primates. It not only increases life expectancy, but it also delays the onset of many features and hallmarks of ageing, including age-related diseases. Transcriptional profiles are currently being applied and investigated. One of them is a caloric restric-", + "Keywords: caloric restriction; hepatic expression profiling; lifespan prolongation; metabolic signaling;microarray analysis; nutrition response. Introduction", + "(154, 155). Caloric restriction has been shown to sig- nicantly increase life span and promote resis-tance to a broad range of age-related pathol-ogy in worms, ies, and mice. Some of theeffects of caloric restriction may be mediatedthrough the sirtuin family of genes, as exem-plied by SIR2, which prolongs life span in", + "Calorie restriction, a dietary regimen that extends the lifespan of numerous organisms, also delays the majority of age-related gene-expression changes in mice and, to a certain extent, in flies45,50. It is currently unclear whether the effect of calorie restriction on gene expression underlies its beneficial effect on lifespan or is merely a consequence thereof. Findings in yeast suggest that there may be a causal link: Sir2 not only facilitates heterochromatin and promotes DNA stability, but is", + "life-span extension by calorie restriction in Saccharomyces cerevisiae. Science 289:21262128. Mair W, Goymer P, Pletcher SD, and Partridge L (2003) Demography of dietary restriction and death in Drosophila. Science 301:17311733. Masoro EJ (2005) Overview of caloric restriction and ageing. Mech Ageing Dev 126:913922. Mathers JC (2006) Nutritional modulation of ageing: genomic and epigenetic ap- proaches. Mech Ageing Dev 127:584589. Meric-Bernstam F and Gonzalez-Angulo AM (2009) Targeting the mTOR signaling", + "that caloric restriction also regulates mammalian aging, perhaps via the modulationof insulin-like signaling pathways. The nervous system has been implicated as a keytissue where insulin-like signaling and free radical protective pathways regulate lifespan inC. elegans andDrosophila . Genes that determine the life span could act in", + "extension by dietary restriction. Annu Rev Biochem 2008, 77:727-54. 8. Harper JM, Leathers CW, Austad SN: Does caloric restriction extend life iin wild mice? Aging Cell 2006, 5:441-9. 9. Forster MJ, Morris P, Sohal RS: Genotype and age influence the effect of caloric intake on mortality in mice. FASEB J 2003, 17:690-2. 10. Spindler SR, Mote PL: Screening candidate longevity therapeu- tics using gene-e xpression arrays. Gerontology 2007, 53:306-21.", + "Corton JC, Apte U, Anderson SP, Limaye P, Yoon L. Mimetics of caloric restriction include agonists of lipid-activated nuclear receptors. J Biol Chem 2004;279:4620446212. [PubMed: 15302862] Ferguson M, Sohal BH, Forster MJ, Sohal RS. Effect of long-term caloric restriction on oxygen consumption and body temperature in two different strains of mice. Mech Ageing Dev 2007;128:539545. [PubMed: 17822741] Forster MJ, Morris P, Sohal RS. Genotype and age influence the effect of caloric intake on mortality in", + "A key question still unresolved is to what extent the mechanisms of aging are conserved between species with vastly different lifespans. Some studies suggest that similar mechanisms are involved in aging in many species. Forexample, caloric restriction extends lifespan in yeast, worms,ies, mice, and primates (Weindruch 2003). Additionally,signaling through the insulin-like growth factor pathway,chromatin regulation by sir2,and oxidative damage have each", + "10.1111/acel.12103 241. Edwards AG, Donato AJ, Lesniewski LA, Gioscia RA, Seals DR, Moore RL. Life-long caloric restriction elicits pronounced protection of the aged myocardium: a role for AMPK. Mech Ageing Dev. 2010;131:739 742. doi: 10.1016/j.mad.2010.09.007 242. Colman RJ, Beasley TM, Kemnitz JW, Johnson SC, Weindruch R, Anderson RM. Caloric restriction reduces age-related and all- cause mortality in rhesus monkeys. Nat Commun. 2014;5:3557. doi: 10.1038/ncomms4557" + ], + [ + "under normal physiological conditions because of an imbal-ance between prooxidants and antioxidants. The imbalanceleads to a steady-state accumulation of oxidative damage in avariety of macromolecules t hat increases during aging, resulting in a progressive loss in the functional efficiency ofvarious cellular processes. In a recent review, Beckman andAmes made a useful addition to this debate by dividing the", + "tributing to impaired bioenergetics in aged cells include oxida-tion/nitration of mitochondrial proteins, destabilization of the macromolecular organization of electron transport chain com-plexes, and impaired mitophagy (a mitochondria-specific form of autophagy). The combination of increased mitochondrial Figure 2. Proposed scheme for mechanisms and pathological consequences of age-related oxidative stress in vascular endothelial cells. The", + "over the years to become the oxidative stress theory of aging, but the principle is the same, inthat the accumulation of oxidative damage drives aging. In support of this theory, a large body of literature indicates that oxidative damage to all cellular macromolecules increases with age. Furthermore, overexpression of antioxidant enzymes that detoxify ROS, such as copper- andzinc-containing superoxide dismutase (SOD), manganese-containing SOD, or catalase, increase", + "predicted from the oxidative stress theory of aging. Thistheory,whichisbasedonthetenetthatdamagecausedbyROSplays a critical role in determining life span, has been one ofthe most popular theories to explain the deterioration in bio-chemical and physiological processes that occur during theaging process. A large number of studies have producedcorrelative data in support of this theory, e.g., an increase inoxidativedamagetolipid,protein,andDNAwithagehasbeendemonstrated in a variety of tissues and organisms", + "during\tthe\taging\tprocess\t(Yi,\tChang,\t&\tShong,\t2018).\tOxidative\tdam - age to cellular macromolecules, or stress arising from mitochondrial DNA\t(mtDNA)\tmutation\tand\tincreased\treactive\toxygen\tspecies\t (ROS),\tis\ta\tkey\thallmark\tof\taging\tphysiology\t(Yi\tet\tal.,\t2018).\tAlthough", + "radical theory of aging, which argues that oxidative damageplays a key role in senescence. Among the numerousmechanisms known to generate oxidants, leakage of super-oxide anion and hydrogen peroxide from the mitochondrialelectron transport chain are the chief candidates. Increased damage to mtDNA could exacerbate this leakage of reactive oxygen species (ROS) (4). It is not known how mtDNA deletions accumulate during", + "most plausible explanation for aging. But, as we have discussed, not all types of damage contribute equally to aging. From this point of view, it seems that ROS generated by complex I (at sulfur iron clusters or flavin sites) may damage specific targets that can alter homeosta - sis in a significant enough way to influ - ence aging. The most obvious target for this damage is mtDNA. The generation of ROS specifically by complex I corre - lates with levels of oxidative damage in mtDNA.", + "increase lifespan also confer resistance to oxidative stress (1).This finding supports the free-radical hypothesis of aging, whichsuggests that reactive oxygen species that accumulate withincreasing age cause oxidative damage to macromolecules (in-cluding nucleic acids, proteins, and lipids) and are causally linkedto aging and death (8, 9). Free radicals have been found toregulate the expression of a number of genes that includeantioxidant defense genes involved in repairing oxidative dam-age, as well as", + "Molecular Biomarkers forOxidative Stress There are many theories that try to explain the nature of aging; however, none of them can explain every aspect of the biology of aging. One of the most accepted and studied is the one proposed by Denham Harman in 1956. This theory proposed that during lifespan organisms accumulate oxidative damage in their biomolecules. Oxidative damage is generated by reactive oxygen species (ROS), which are the", + "production by mitochondria and increased 8-oxo-dG con-tent in the mtDNA are frequently detected in aged tissues [40,4750], suggesting that progressive accumulation of oxidative DNA damage is a contributory factor to the agingprocess. Consistently, many studies have found that increasedoxidative damage in cells is associated with aging [ 5153]. Furthermore, genetic studies in worm, y, and mouse havelinked enhanced stress resistance or reduced free radical" + ], + [ + "208 Additional features that contribute to increased ar - terial stiffness include decreased elastin synthesis, elastin degradation and fragmentation, elastin calcification, al-terations in cross-linking of extracellular matrix compo-nents (eg, by increased presence of advanced glycation end products). 208,210,211 The pathophysiological consequences of age-related ECM remodeling and arterial stiffening have been the sub-ject of a recent comprehensive review by AlGhatrif and Lakatta.", + "collagen. AGE-mediated cross-links can confer resis-tance to enzymatic degradation, and thus interferewith collagenolysis (56). In addition, increased ac- tivity of TGF- bwith aging stimulates the synthesis of interstitial collagen by vascular smooth muscle cells(VSMCs), and thereby augments arterial stiffness (57). Likewise, increased activity of the RAAS may augment collagen synthesis and heighten elastolysis (58). Endothelial dysfunction and arterial stiffness are", + "that many of these age-related ECM alterations are governed by circulating factors and factors produced in the vascular wall, including the extended renin-angiotensin-aldosterone system (see above) and an age-related decline in circulating IGF-1. 209 Collagen synthesis is also dysregulated with age in the vascular wall likely because of the effects of increased para-crine action of TGF- (transforming growth factor- ), 123 which contributes to vascular fibrosis and arterial stiffen-ing.", + "Ungvari et al Mechanisms of Vascular Aging 859 Role of Extracellular Matrix Remodeling in Vascular Aging The extracellular matrix (ECM) is an important contribu- tor to health and longevity. This noncellular compartment, ubiquitous to all tissues and organs does not only provide es-sential mechanical scaffolding but mediates highly dynamic biomechanical and biochemical signals required for tissue homeostasis, morphogenesis, and cell differentiation. Studies", + "1996;25(3):20915. 79. Bonnans C, Chou J, Werb Z. Remodelling the extracellular matrix in development and disease. Nat Rev Mol Cell Biol. 2014;15(12):786801. 80. Swift J, Ivanovska IL, Buxboim A, Harada T, Dingal PCDP , Pinter J, et al. Nuclear Lamin-A scales with tissue stiffness and enhances matrix- directed differentiation. Science. 2013;341(6149):1240104. 81. Vogel C, Marcotte EM. Insights into the regulation of protein abun- dance from proteomic and transcriptomic analyses. Nat Rev Genet.", + "result in extracellular matrix stiffness in aging larynx and other organs [59, 79]. Finally, Lamin A was upregulated by dehydration, by a smaller magnitude, especially when observing the mean difference within the young groups. Previous data has identified that Lamin proteins A and C are important for imparting the nucleus with its stiff - ness, and their expression has been reported to scale with", + "aging. Annu Rev Biomed Eng. 2015;17:113141. doi: 10.1146/ annurev-bioeng-071114-040829 208. Jacob MP. Extracellular matrix remodeling and matrix metalloprotein- ases in the vascular wall during aging and in pathological conditions. Biomed Pharmacother. 2003;57:195202. 209. Tarantini S, Valcarcel-Ares NM, Yabluchanskiy A, Springo Z, Fulop GA, Ashpole N, Gautam T, Giles CB, Wren JD, Sonntag WE, Csiszar A, Ungvari Z. Insulin-like growth factor 1 deficiency exacerbates hyperten-", + "able human diseases such as osteoporosis and musculo- skeletal diseases [53]. Collagens are long-lived proteins known to accumulate damage during aging, leading to a decline in tissue health [54]. Also, type I collagens be- come resistant to proteolysis upon age [55, 56], affecting their turnover. Interestingly, mice expressing cleavage- resistant type I collagen go through an accelerated aging process [57]. Thus, cellular aging can be affected by the state of the extracellular matrix in mammals.", + "the characteristics of endothelial dysfunction and pheno- typic transition of smooth muscle cells, resulting in in- creased vascular stiffness and increased thickness of vascular walls. It has been reported that the age- associated phenotypic transition of VSMCs is a crucial contributor to vascular remodeling [ 17,25]. However, the mechanism that drives phenotypic transition ofVSMCs with aging remains unclarified. In this study, using RNAs extracted from the in vitro cultured VSMCs,", + "downregulation with aging of genes involved in the synthesisof the ECM and in particular of different forms of collagen(Table 2). In addition, aging males but not females showed adecrease in collagen type III. Interestingly, collagen type IIIdecreases the size of collagen bundles and thereby increasesvascular elasticity (11). Therefore, a decreased expression ofcollagen type III can participate in the increased stiffness thatcharacterizes the aging aorta (23). An interesting observationfrom our study that" + ], + [ + "D. Carmona-Gutierrez, C. Ruckenstuhl, J. Ring, W. Reichelt, K. Schimmel, T. Leeb,C. Moser, S. Schatz, L.-P. Kamolz, C. Magnes, F. Sinner, S. Sedej, K.-U. Frhlich,G. Juhasz, T. R. Pieber, J. Dengjel, S. J. Sigrist, G. Kroemer, F. Madeo, Nucleocytosolic de-pletion of the energy metabolite acetyl-coenzyme a stimulates autophagy and prolongs lifespan. Cell Metab. 19, 431 444 (2014). 225. S. Gelino, M. Hansen, Autophagy An emerging anti-aging mechanism. J. Clin. Exp. Pathol. (Suppl. 4), pii: 006 (2012).", + "[73] Vellai, T. Autophagy genes and ageing . Cell Death Differ. , 2009 , 16(1), 94-102. [74] Kaeberlein, M.; Kapahi, P. Cell signaling. Aging is RSKy business . Science , 2009 , 326(5949), 55-6. [75] Hansen, M.; Chandra, A.; Mitic, L.L.; Onken, B.; Driscoll, M.; Kenyon, C. A role for autophagy genes in the extension of lifespan by dietary restriction in C. elegans. PLoS Genet. , 2008 . [76] Hansen, M.; Taubert, S.; Crawford, D.; Libina, N.; Lee, S.J.;", + "chinery and upstream regulators provide evidence for a transcriptional decline in autophagy gene expression with age in human monocytes. The identification of key genes contributing to a decline in autophagy are of great interest, as pharmacologic activation of au- tophagy has been linked with increasing lifespan in animal models, including mice [45]. Further, dysfunc- tional autophagy is now widely implicated in patho- physiological processes of many age-related diseases", + "invasive pathogens, and to transport these cargos to the lysosomes for degradation [25]. In the aging field, im- paired autophagy is considered one of the principal de- terminants of cellular aging, which is supported by in vitro and animal study findings that autophagy de- clines with age [26]. However, studies of autophagy and age in humans are sparse. One of the most significant age-gene expression asso- ciations we observed in monocytes from 1,264 individ-", + "226. F. Madeo, N. Tavernarakis, G. Kroemer, Can autophagy promote longevity? Nat. Cell Biol. 12, 842 846 (2010). 227. J. Fllgrabe, M. A. Lynch-Day, N. Heldring, W. Li, R. B. Struijk, Q. Ma, O. Hermanson, M. G. Rosenfeld, D. J. Klionsky, B. Joseph, The histone H4 lysine 16 acetyltransferase hMOF regulates the outcome of autophagy. Nature 500, 468 471 (2013). 228. F. Ng, B. L. Tang, Sirtuins modulation of autophagy. J. Cell. Physiol. 228, 2262 2270 (2013).", + "(2013) The hallmarks of aging. Cell 153(6):11941217. doi: 10. 1016/j.cell.2013.05.039 3. Vellai T, Takacs-Vellai K, Sass M, Klionsky DJ (2009) The regulation of aging: does autophagy underlie longevity? TrendsCell Biol 19(10):487494. doi: 10.1016/j.tcb.2009.07.007 4. Kirkwood TB (2008) A systematic look at an old problem. Nature 451(7179):644647. doi: 10.1038/451644a 5. Koubova J, Guarente L (2003) How does calorie restriction work? Genes Dev 17(3):313321. doi: 10.1101/gad.1052903", + "Eisenberg, T., Knauer, H., Schauer, A., Bu ttner, S., Ruckenstuhl, C., Carmona- Gutierrez, D., Ring, J., Schroeder, S., Magnes, C., Antonacci, L., et al. (2009).Induction of autophagy by spermidine promotes longevity. Nat. Cell Biol. 11, 13051314. Enns, L.C., Morton, J.F., Treuting, P.R., Emond, M.J., Wolf, N.S., Dai, D.F., McKnight, G.S., Rabinovitch, P.S., and Ladiges, W.C. (2009). Disruption of protein kinase A in mice enhances healthy aging. PLoS ONE 4, e5963.", + "its essential part in the anti-aging mechanism of caloric restriction. Ann N Y Acad Sci. 2007;1114:69 78. 41. Cuervo AM, Bergamini E, Brunk UT, Droge W, Ffrench M, Terman A. Autophagy and aging: the importance of maintaining clean cells. Autophagy. 2005;1:131 40. 42. Terman A. The effect of age on formation and elimination of autophagic vacuoles in mouse hepatocytes. Gerontology. 1995;41 Suppl 2:319 26. 43. Donati A, Recchia G, Cavallini G, Bergamini E. Effect of aging and anti-aging", + "103 Experimental findings showing increased oxidative stress, impaired bioavailability of NO, and upregulation of in-flammatory mediators in autophagy-deficient endothelial cells support this view. 104 Further, pharmacological interventions that stimulate autophagy (eg, trehalose or spermidine treat-ment) were reported to reverse aspects of arterial aging. 105,106 Proteasomes degrade unneeded or damaged proteins by pro-teolysis. There is evidence that proteasome activity declines in advanced aging", + "Phosphorylation of ULK1 (hATG1) by AMP-activated protein kinase connects energy sensing to mitophagy. Science. 2011;331:456 61. 38. Xiao B, Sanders MJ, Underwood E, Heath R, Mayer FV, Carmena D, et al. Structure of mammalian AMPK and its regulation by ADP. Nature. 2011;472:230 3. 39. Tang D, Kang R, Livesey KM, Cheh CW, Farkas A, Loughran P, et al. Endogenous HMGB1 regulates autophagy. J Cell Biol. 2010;190:881 92. 40. Bergamini E, Cavallini G, Donati A, Gori Z. The role of autophagy in aging:" + ], + [ + "into old versus young recipients (Liang et al., 2005 ). Further experiments demonstrated that the muscle stem cell niche adversely effects stem cell function as evidenced by the restoration of old stem cell regenerative potential upon expos ure to a young systemic microenvironment (Conboy et al., 2005; Conboy and Rando, 2005). It has also been reported that the spermatogoni al stem cell niche deteriorates with age, causing the failure to suppor t an appropriate balance between stem cell self-renewal and", + "matopoietic stem cells is regulated by the stemcell niche. Exp Gerontol. 2008;43(11):974-980. 18. Geiger H, Rudolph KL. Aging in the lympho- hematopoietic stem cell compartment. Trends Immunol. 2009;30(7):360-365. 19. Muller-Sieburg C, Sieburg HB. Stem cell aging: survival of the laziest? Cell Cycle. 2008;7(24): 3798-3804. 20. Beerman I, Maloney WJ, Weissmann IL, Rossi DJ. Stem cells and the aging hematopoieticsystem. Curr Opin Immunol. 2010;22(4):500-506. 21. Teschendorff AE, Menon U, Gentry-Maharaj A,", + "Abstract The regenerative potential diminishes with age and this has been ascribed to functional impairments of adult stem cells. Cells in culture undergo senescence after a certain number of cell divisions whereby the cells enlarge and finally stop proliferation. This observation of replicative senescence has been extrapolated to somatic stem cells in vivo and might", + "Because of their plasticity and accessibility these cells are also prime candidates for regenerative medicine. The contribution of stem cell aging to organismal aging is un der debate and one theory is that reparative processes deteriorate as a consequence of stem cell aging and/or de crease in number. Age has been linked with changes in osteogenic and adipogen ic potential of MSCs. Results: Here we report on changes in global gene expression of cultured MSCs isolated from the bone marrow of", + "suggesting that stem cells are not likely to be a factor limiting hematopoietic regeneration with age. However, their func-tional decits do show that HSCs are impacted by the forces of aging in a manner similar to that of differentiated cells [3134]. In our molecular analysis, we identied global age-related changes in gene expression in murine HSCs, with a view to identifying mechanisms that could be responsible for these age-associated declines in HSC function. Genes involved in", + "Discussion The deterioration of the regenerative potential upon aging might be due to functional changes in adult stem cells. To test this hypothesis we have investigated differential gene expression in primary, human MSC and HPC derived from different agegroups. In this study, we demonstrate for the first time age-related gene expression changes in human MSC and HPC and that there", + "cells, which may explain the observed decline of stem cell function with age. Age-associated increases inDNAm target developmental genes, overlapping those associated with environmental disease risk factors and with disease itself, notably cancer. In particular, cancers and precursor cancer lesions exhibit aggravated", + "tion associated with age: loss of stem cell pool division potential (loss of regenerative capacity) and loss ofdierentiated somatic cell function, which directly leads to loss of organ function. Loss of dierentiated somatic cell function can additionally indirectly aect adult stem and progenitor cells by altering the tissue microenviron- ment that is essential for stem cell support (the stem cellniche). In general, loss of stem cell pool division potential", + "1. Introduction Stem cell aging is regarded as one of the contributors to several degenerative conditions af icting the elderly because it underlies the physiological decline in tissue maintenance and regenerative capacity of many organs ( Rossi et al., 2008 ). The brain is one such organ that contains discrete populations of stem cells and their precursors (collectively referred to as neural progenitor cells [NPCs]) that continue to generate new neurons throughout life", + "spective of tissue regeneration and repair because there isevidence that these beneficial functions may becomehandicapped with age. Age-related decline in the numberof MSCs in the bone marrows of rodents, monkeys, andhumans have been reported [26-33]. Most studies to datefocused on the effects of aging on the ability of MSCs toenter osteogenic, chondrogenic and adipogenic pro-grams. Some, but not all studies suggest that agingreduces osteogenesis and chondrogenesis while enhanc-" + ], + [ + "vascular and kidney diseases [47]. Advanced glycation end-products (AGE) are the result of nonenzymatic glyca- tion, which produces heterogeneous bioactive molecules, such as lipids, proteins, and nucleic acids [59]. The accumulation of AGEs in aged tissues leads to several processes, such as inflammation, obesity, apoptosis, and other adverse processes related to ageing [47]. These AGEs are detected by various techniques, such as", + "and leading to vascular hypertrophy and stiffening of collagen with subsequent reduction of arterial compliance. These are processes that are associated with aging but seem to be accelerated by hyperglycemia. These cross-linked macromolecules, called advanced glycosylation end products (AGEs), are implicated in the pathogenesis of vascular complications. Once", + "proposed mechanisms are the development of advanced glycosylation end products and sorbitol accumulation. Advanced glycosylation end products (AGEs) comprise a heterogeneous group of molecules that accumulate in plasma and tissues with advancing age, diabetes and renal failure. They are characterized by browning, fluorescence, cross-linking and biological response through specific AGE receptors and were first described in 1912 by French chemist L.C. Maillard (Fig. 5).", + "the accumulation of AGEs which can further perp etuate and amplify local inflammation and 197 oxidant stress through irreversible glycation of the various protei ns and lipids to promote long 198 term vascular and end-organ damage. Thus AGEs, acting through receptors such as RAGE, 199 could also contribute to hyperglycemic memo ry (18, 96, 147). These studies have begun to 200", + "AGEs are taken up by specific AGE receptors (RAGE), cytokines, growth factors, and adhesion factors are released, leading to further cellular changes. AGEs also can impair endothelial function and vascular reactivity, such as in response to nitric oxide. Modification of LDL as a result of glycation may contribute to foam cell formation.4 Thus, AGEs appear to be main players not only in the development of diabetic complications and atherosclerosis,", + "geneous group of macromolecules that are formed by the nonenzymatic glycation of proteins, lipids, and nucleic acids. Overproduction of AGEs is considered the most important pathophysiological mechanism that induces diabetic complications (Semba etal. 2010). On one hand, AGEs mediate intracellular glycation of mitochondrial respiratory chain proteins and increase ROS levels, thus triggering oxidative stress (Coughlan etal. 2009) and endoplasmic reticulum stress (Piperi etal. 2012). On the", + "Introduction In individuals with diabetes, nonenzymatic glycation of proteins leads to the formation of advanced glycation end products (AGE) and this process occurs at an accelerated rate in chronic hyperglycaemia1, and also the levels are found to be increased in complications of diabetes, such as diabetic retinopathy (DR).2 AGE induces a variety of pathological changes, such as increased basement membrane thickening, arterial stiffness, and glomerular sclerosis.3,4AGEs bind to a specic receptor", + "AGEs accelerate atherosclerosis through cross-linking of proteins, platelet aggregation, defective vascular relaxation, and abnormal lipoprotein metabolism. 30 AGEs have a vital role in pathogenesis of diabetic nephropathy and progression of renal failure. Renal failure, in turn, results in decreased excretion and increased generation of AGEs (Figure 6). 629", + "vessels show enhanced subintimal protein and lipoprotein deposition; increased vascular permeability, e.g. to albumin; inactivation of nitric oxide; activation of endothelial receptors, leading to vasoconstriction and thrombosis; altered proteoglycan milieu; altered basement membrane cellular structure; proliferation of matrix. Strategies directed at the prevention of formation or the disruption of AGE cross-links may be promising. REFERENCES:", + "proteins and nucleic acids, leads to modification and then decline in structure and function of these molecules, as the cross-links accumulate both extracellularly and intracellularly over time. A prime example would be the crosslinking of collagen, which is thought to lead to typical phenomena observed in aging, such as increased susceptibility to atherosclerosis, osteoporosis, decreased joint elasticity, the formation of cataracts, and" + ] + ], + "task_id": [1,2,3,4,5,6,7,8,9,10,1,2,3,4,5,6,7,8,9,10] +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/gpt4o_de_diabetes.json b/gnqa/paper2_eval/data/dataset/gpt4o/gpt4o_de_diabetes.json new file mode 100644 index 0000000..f95e6a6 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/gpt4o_de_diabetes.json @@ -0,0 +1,289 @@ +{ + "question": [ + "How do recent advancements in multi-omics approaches, including proteomics and metabolomics, contribute to our understanding of Type 2 diabetes pathogenesis?", + "What novel diabetic loci have been identified through the latest meta-analyses of large-scale genome-wide association studies (GWAS)?", + "How do epigenetic modifications, such as DNA methylation and histone modification, influence the expression of diabetes-related genes?", + "Can you elaborate on the role of the gut microbiome in modulating host genetic predispositions to diabetes?", + "How effective are machine learning algorithms in integrating genomic data to predict individual risk and progression of diabetes?", + "What are the implications of recent findings on the role of long non-coding RNAs (lncRNAs) in the regulation of insulin secretion and sensitivity?", + "How do post-translational modifications of proteins affect key signaling pathways involved in glucose homeostasis?", + "What insights have been gained from studying the genetic basis of syndromic forms of diabetes, such as Wolfram Syndrome and Alstr\u00f6m Syndrome?", + "How do genetic and epigenetic differences between monozygotic twins discordant for diabetes inform our understanding of its etiology?", + "What potential therapeutic targets have been identified through recent studies on the interaction between genetic variants and environmental factors in diabetes development?", + "How do rare variants identified through whole-genome sequencing contribute to the heritability of Type 2 diabetes?", + "What are the latest findings on the role of non-coding RNAs in the pathogenesis of diabetes?", + "How does the interaction between multiple polygenic risk scores (PRS) improve the prediction of Type 1 and Type 2 diabetes?", + "What are the mechanistic insights into the beta-cell failure pathways gleaned from recent single-cell RNA-sequencing studies?", + "How does the epigenetic landscape of key metabolic tissues change in diabetic versus non-diabetic individuals?", + "What recent advancements have been made in leveraging CRISPR-based approaches to correct monogenic forms of diabetes in vivo?", + "How do genome-wide association studies (GWAS) integrate with multi-omics data to elucidate the complex genetic architectures of diabetes?", + "What is the impact of genomic imprinting on the susceptibility and progression of diabetes?,", + "How do longitudinal genomics studies help in understanding gene-environment interactions in diabetes onset and management?", + "How have recent integrative genomics approaches, such as the use of single-cell RNA sequencing combined with epigenomic profiling, advanced our understanding of cellular heterogeneity and gene regulatory networks in pancreatic beta cells under diabetic conditions?" + ], + "answer": [ + "Recent advancements in multi-omics approaches, including proteomics and metabolomics, have significantly enhanced our understanding of the pathogenesis of Type 2 diabetes (T2D). These approaches allow for a comprehensive analysis of the molecular components involved in T2D, providing insights into the disease's complexity and heterogeneity. 1. **Integration of Omics Data**: The integration of data from proteomics, genomics, and transcriptomics helps in understanding the pathophysiology of T2D and the varied responses to different glucose-lowering therapies. This holistic view is crucial for identifying the molecular underpinnings of T2D and tailoring personalized treatment strategies [1]. 2. **Metabolomics Insights**: Metabolomics studies have identified specific metabolites, such as branched-chain and aromatic amino acids, that are associated with obesity and T2D. These findings suggest that metabolic alterations play a significant role in the development of T2D, offering potential targets for therapeutic intervention [1]. 3. **Complex Interactions**: T2D is caused by complex interactions between genetic and environmental factors. Metabolomics has helped elucidate these interactions by identifying metabolic footprints associated with T2D, thus moving from mere information gathering to actionable knowledge [2], [3]. 4. **Biomarker Discovery**: Advances in omics technologies have led to the discovery of numerous candidate biomarkers for T2D. Although only a small proportion of these biomarkers have been thoroughly investigated, they hold promise for improving the prediction and management of T2D [9]. 5. **Beyond Traditional Models**: The use of metabolomics has expanded our understanding of T2D beyond traditional models focused on the pancreas, liver, and muscle. This broader perspective is essential for developing more effective prevention and treatment strategies [10]. Overall, these advancements in multi-omics approaches provide a more detailed and nuanced understanding of T2D pathogenesis, paving the way for improved diagnostic and therapeutic options.", + "The latest meta-analyses of large-scale genome-wide association studies (GWAS) have identified several novel loci associated with diabetes. Specifically, the DIAbetes Genetics Replication and Meta-analysis (DIAGRAM) Consortium, using genotype data from almost 150,000 individuals predominantly of European descent, identified 10 novel type 2 diabetes (T2D) susceptibility loci that reached genome-wide significance [8].", + "Epigenetic modifications, such as DNA methylation and histone modification, play significant roles in influencing the expression of diabetes-related genes. 1. **DNA Methylation**: This modification can lead to the silencing of key genes involved in diabetes. For example, increased DNA methylation of the promoter of the PPARGC1A gene, which is crucial for regulating mitochondrial genes, has been observed in diabetic islets. This methylation can modulate diabetes by altering gene expression [1]. Additionally, altered DNA methylation patterns can change the expression of inflammatory and pathological genes, contributing to the development of diabetic complications [2]. 2. **Histone Modifications**: These modifications can affect chromatin structure and gene expression. In the context of diabetes, histone post-translational modifications (PTMs) have been associated with the regulation of genes susceptible to type 1 diabetes [3]. Changes in chromatin histone modifications can regulate pathological gene expression in diabetes, influencing the disease's progression [2]. Overall, both DNA methylation and histone modifications can lead to the repression or activation of genes that are critical in the pathogenesis and progression of diabetes, affecting insulin production, inflammation, and other metabolic processes.", + "The gut microbiome plays a significant role in modulating host genetic predispositions to diabetes through various mechanisms. The interactions between host genetics, metabolism, and the immune system are crucial in shaping the microbiome and influencing the predisposition to diseases like diabetes [1]. Research suggests that the gut microbiota affects numerous biological functions throughout the body, and its characterization has become a major research area in biomedicine. Studies have shown that gut bacteria play a fundamental role in diseases such as obesity and diabetes [6]. Dysbiosis, or an imbalance in the gut microbiota, is associated with obesity and type 2 diabetes (T2D), indicating that the composition of the gut microbiome can influence metabolic diseases [6]. Moreover, genetic control of the gut microbiota composition has been observed, suggesting that host genetics can influence the microbiome, which in turn can modulate metabolic traits and potentially affect the risk of developing diabetes [10]. This implies a bidirectional relationship where genetics can shape the microbiome, and the microbiome can influence the expression of genetic predispositions to diabetes. In summary, the gut microbiome interacts with host genetics and metabolism, potentially modulating the risk of diabetes by influencing metabolic processes and immune responses [1], [6], [10].", + "Machine learning algorithms show promise in integrating genomic data to predict individual risk and progression of diabetes, but there are challenges and limitations to consider. 1. Genomic data is considered to yield better patient-centric outcomes than traditional tabular data for predicting diabetic illnesses [1]. This suggests that machine learning models that incorporate genomic data may provide more accurate predictions. 2. Machine learning has been applied to integrate various types of data, including genomic and epigenomic biomarkers, to determine type 2 diabetic status. This approach has revealed connections between diabetic classification and other biological functions, indicating the potential of machine learning in this area [5]. 3. The integration of physiological, biochemical, genetic, and epigenetic features with machine learning algorithms has shown potential for more informative diagnostics and personalized treatment approaches for diabetes [8]. 4. However, there are limitations, such as the need for larger sample sizes and extensive training to achieve considerable accuracy when using polygenic scores-based approaches with genomic data [4]. Overall, while machine learning algorithms have demonstrated potential in integrating genomic data for diabetes prediction, further research and development are needed to overcome current limitations and improve accuracy and applicability in clinical settings.", + "Recent findings highlight the significant role of long non-coding RNAs (lncRNAs) in the regulation of insulin secretion and sensitivity, with several implications for understanding and potentially treating diabetes. 1. **Regulation of Islet Function**: LncRNAs have been shown to regulate the development and function of pancreatic islets, which are crucial for insulin secretion. For instance, the lncRNA H19 is involved in this regulatory process [1]. This suggests that lncRNAs could be critical in maintaining normal insulin secretion and could be targets for therapeutic intervention in diabetes. 2. **Impact on Insulin Synthesis and Secretion**: Specific lncRNAs, such as Meg3, have been found to affect insulin synthesis and secretion in pancreatic beta cells [4]. This indicates that lncRNAs play a direct role in the cellular mechanisms that control insulin production, which is essential for maintaining glucose homeostasis. 3. **Therapeutic Potential**: Due to their specific functions in regulating cellular pathways, lncRNAs are considered promising therapeutic targets. Their expression patterns in tissues often correlate with the progression of diabetes, making them potential biomarkers for diagnosis and prognosis [3]. 4. **Association with Insulin Resistance and Diabetes**: Altered levels of lncRNAs are closely associated with the onset and progression of insulin resistance and diabetes [5]. This association underscores the potential of lncRNAs as targets for interventions aimed at improving insulin sensitivity and managing diabetes. 5. **Research and Clinical Implications**: The diverse roles of lncRNAs in insulin resistance and diabetes suggest their importance in future research for diagnosis, prognosis, and therapy of the disease [2]. This calls for further investigations and collaborations among researchers, clinicians, and patients to fully understand and harness the potential of lncRNAs in diabetes management. Overall, these findings imply that lncRNAs are integral to the regulation of insulin secretion and sensitivity, offering new avenues for research and treatment strategies in diabetes.", + "Post-translational modifications (PTMs) of proteins play a significant role in regulating key signaling pathways involved in glucose homeostasis. Here are some ways PTMs affect these pathways: 1. **Protein Kinases and Glucose Homeostasis**: The PI3K/AKT, MAPK, and AMPK signaling pathways are crucial for glucose homeostasis, and these pathways are regulated by protein kinases, which can be modulated by PTMs such as phosphorylation [1]. 2. **Histone Modifications**: Histone post-translational modifications are involved in the regulation of genes associated with diabetes pathogenesis, including those related to insulin and islet-specific transcription factors. These modifications can influence gene expression and thereby affect glucose metabolism [3]. 3. **N-glycosylation**: The glycosylation of glucose transporter 2 is an example of a PTM that promotes insulin secretion, which is vital for maintaining glucose levels and suppressing diabetes [6]. 4. **Sirtuins and Deacetylation**: The sirtuin family, particularly SIRT1, is involved in regulating factors related to metabolism and insulin secretion. Sirtuins are deacetylases, and their activity represents a form of PTM that can influence glucose homeostasis [10]. These examples illustrate how PTMs can modulate signaling pathways and protein functions, ultimately impacting glucose homeostasis and related metabolic processes.", + "Studying the genetic basis of syndromic forms of diabetes, such as Wolfram Syndrome, has provided several insights: 1. **Genotype-Phenotype Correlation**: Research has highlighted the importance of understanding genotype-phenotype correlations in Wolfram Syndrome. This involves identifying specific genetic mutations and understanding how they manifest in clinical symptoms, which can aid in more accurate diagnosis and management of the syndrome [1], [6]. 2. **Genetic Mutations**: Wolfram Syndrome is primarily caused by mutations in the WFS1 gene, which encodes the wolframin ER transmembrane glycoprotein. These mutations lead to a range of symptoms including diabetes mellitus, optic atrophy, and deafness. Some severe cases are associated with dominant heterozygous variants [4]. 3. **Early Onset and Symptoms**: The syndrome is characterized by early onset of diabetes mellitus, typically around the age of 6, followed by other symptoms such as optic atrophy and sensorineural deafness in later years [5]. 4. **Potential for Broader Implications**: Insights from studying Wolfram Syndrome have implications for basic science and clinical practice. They emphasize the need for accurate clinical descriptions and early recognition of symptoms, which can improve patient outcomes and inform treatment strategies [6]. 5. **Risk Assessment**: There is ongoing research to determine if heterozygotes for Wolfram Syndrome are at risk for maturity-onset diabetes, which could have implications for understanding genetic risk factors in broader populations [2]. These insights not only enhance our understanding of Wolfram Syndrome but also contribute to the broader field of genetic research in diabetes, potentially informing personalized medicine approaches for more common forms of the disease.", + "The study of monozygotic twins discordant for diabetes provides valuable insights into the etiology of the disease by highlighting the roles of both genetic and epigenetic factors. Since monozygotic twins share identical genomes, any differences in disease manifestation between them can often be attributed to non-genetic factors, such as epigenetic modifications or environmental influences. 1. **Genetic Component**: The high concordance rates for diabetes in monozygotic twins, compared to dizygotic twins, underscore a significant genetic component to the disease [1], [6]. This suggests that genetic predispositions, particularly those affecting insulin sensitivity and secretion, play a crucial role in the development of diabetes [1]. 2. **Epigenetic Influences**: Despite identical genetic makeup, monozygotic twins can exhibit differences in disease susceptibility due to epigenetic variations. These variations can arise from environmental factors and gene-environment interactions, which are crucial in understanding the etiology of diabetes [4]. Epigenetic differences, such as DNA methylation patterns, have been observed in monozygotic twins and may contribute to discordance in disease states [9]. 3. **Environmental and Lifestyle Factors**: The discordance in diabetes among monozygotic twins also points to the influence of environmental factors and lifestyle choices, such as diet and physical activity, which can modify epigenetic marks and affect disease outcomes [6]. In summary, the study of monozygotic twins discordant for diabetes highlights that while genetic predispositions are significant, epigenetic modifications and environmental factors also play critical roles in the disease's etiology. This understanding can help in developing more targeted prevention and treatment strategies that consider both genetic and non-genetic factors.", + "Recent studies on the interaction between genetic variants and environmental factors in diabetes development have identified several potential therapeutic targets. These include: 1. **Primary Regulators of Insulin Secretion and Action**: Several type 2 diabetes (T2D) risk variants have been identified as primary regulators of insulin secretion, insulin action, and pancreatic islet transcription factors. This suggests that targeting these pathways could be a potential therapeutic strategy [6]. 2. **Specific Genetic Variants**: Newly discovered single nucleotide variants (SNVs) allow for better characterization of abnormalities in early insulin processing and secretion. Genes such as TCF7L2, SLC30A8, and C2CD4B have been highlighted as potential targets due to their roles in these processes [6]. 3. **Gene-Environment Interactions**: The interaction between genetic susceptibility and environmental factors such as physical activity and dietary fat has been shown to modify the risk of glucose homeostasis and T2D. This indicates that interventions targeting these environmental factors could potentially mitigate the genetic risk [7]. These findings underscore the importance of considering both genetic and environmental factors in developing therapeutic strategies for diabetes.", + "Rare variants identified through whole-genome sequencing contribute to the heritability of Type 2 diabetes by potentially explaining some of the \"missing heritability\" that common variants identified through genome-wide association studies (GWAS) do not account for. While GWAS have identified many common variants associated with Type 2 diabetes, these explain only a fraction of the heritability of the disease [4]. The missing heritability could be located in low-frequency and rare variants, particularly in noncoding regions of the genome [1]. However, studies have shown that rare coding variants, especially when clustered in a small number of genes, are unlikely to account for much of the missing heritability [10]. Instead, if rare coding variants are significant, they are likely scattered across many genes [10]. Therefore, while rare variants may contribute to the heritability of Type 2 diabetes, their exact role and impact remain to be fully elucidated, and larger multi-population studies are needed to reliably identify rare variants exclusively associated with Type 2 diabetes [6].", + "The latest findings on the role of non-coding RNAs in the pathogenesis of diabetes highlight several key aspects: 1. **Role of lncRNAs in Diabetes**: Long non-coding RNAs (lncRNAs) are implicated in mediating complex pathological mechanisms of diabetes. They are involved in post-transcriptional regulation and are associated with orchestrated networks that influence diabetes pathogenesis [5]. LncRNAs are considered better therapeutic targets due to their specific functions in regulating cellular pathways and their expression patterns that correlate with the progression of diabetes [7]. 2. **Epigenetic Influence**: Non-coding RNAs, including microRNAs and lncRNAs, can influence epigenetic mechanisms. They can promote the expression of pathological genes through post-transcriptional and post-translational mechanisms, contributing to metabolic memory and sustained gene expression in diabetic conditions [4]. 3. **Regulation of Islet Function**: LncRNAs have been shown to regulate pancreatic islet function, which is central to understanding diabetes pathophysiology. For instance, the lncRNA H19 has been implicated in islet development and function [8]. 4. **MicroRNAs in Disease**: MicroRNAs (miRs) play critical roles in various diseases, including diabetes, by influencing proliferation, differentiation, and development [2]. These findings underscore the importance of non-coding RNAs as regulatory players in diabetes and its complications, offering potential avenues for therapeutic intervention.", + "The interaction between multiple polygenic risk scores (PRS) can improve the prediction of Type 1 and Type 2 diabetes by combining information from various genetic loci associated with these diseases. This approach allows for a more comprehensive assessment of an individual's genetic risk. Specifically, combining information from common risk polymorphisms has been shown to improve disease prediction for Type 2 diabetes [3]. Additionally, partitioning polygenic scores according to factors of disease heterogeneity and mapping genetic loci to different immune-cell subtypes can enhance the predictive power of PRS, particularly for Type 2 diabetes [9]. These strategies leverage the aggregation of genetic risk from multiple sources, thereby capturing a larger proportion of the genetic variance underlying these traits and improving early diagnosis, intervention, and prevention efforts [4].", + "Recent single-cell RNA-sequencing studies have provided significant mechanistic insights into beta-cell failure pathways. These insights include: 1. **De-differentiation Signatures**: Single-cell analyses of human islet cells have revealed de-differentiation signatures, suggesting that beta cells may lose their specialized functions and revert to a more progenitor-like state, which contributes to their dysfunction in diabetes [1]. 2. **Transcriptional Regulation**: Advances in single-cell genomic profiling have enhanced our understanding of transcriptional regulation in non-beta cell types, which may play crucial roles in the hallmark features of beta-cell insufficiency and dysfunction in type 2 diabetes (T2D) [2]. 3. **ER Stress and Heterogeneity**: Single-cell transcriptomic analyses have identified subpopulations of beta cells experiencing endoplasmic reticulum (ER) stress. This stress is implicated in the dysfunction of both alpha and beta cells, contributing to diabetes pathogenesis [8]. These findings highlight the complexity of beta-cell failure and underscore the importance of single-cell technologies in unraveling the molecular mechanisms underlying diabetes.", + "The epigenetic landscape of key metabolic tissues shows several changes when comparing diabetic individuals to non-diabetic individuals: 1. **DNA Methylation Changes**: In diabetic individuals, increased DNA methylation has been observed in the promoter region of the PPARGC1A gene in both islets and skeletal muscle [3]. This suggests a potential mechanism by which gene expression related to metabolism is altered in diabetes. 2. **Histone Modifications**: There are disruptions in histone methylation patterns in diabetic states. While healthy individuals maintain stable histone methylation patterns, these can be disrupted in diabetes, indicating changes in the epigenome associated with inflammation and metabolic memory [2]. 3. **Impact on Gene Expression**: Epigenetic modifications, such as DNA methylation, have been linked to reduced expression of genes involved in diabetes and metabolism. Variations in DNA methylation have been noted near diabetes susceptibility genes and enhancers [6]. 4. **Tissue-Wide Epigenetic Changes**: Diabetes mellitus, characterized by high glucose stress, leads to epigenetic changes across most tissues impacted by the disease, including the cardiovascular system and immune system [7]. 5. **Adipose Tissue**: In subjects with type 2 diabetes, altered DNA methylation and differential expression of genes influencing metabolism and inflammation have been observed in adipose tissue [9]. These findings collectively suggest that diabetes is associated with specific epigenetic alterations across various metabolic tissues, which may contribute to the pathophysiology of the disease.", + "Recent advancements in leveraging CRISPR-based approaches to correct monogenic forms of diabetes in vivo include the use of CRISPR-mediated homology-directed repair (HDR) to correct specific genetic mutations associated with diabetes. For instance, CRISPR technology has been used to correct point mutations in patient-derived induced pluripotent stem cells (iPSCs) targeting diabetes-related gene defects. The most efficient method employed in iPSCs is CRISPR/Cas9-based HDR, where a Cas9-mediated cut is generated adjacent to the site of interest, and a homologous donor template with the intended nucleotide change is recombined by HDR [9]. Additionally, there has been a successful correction of a variant in the Wolfram syndrome 1 (WFS1) gene using CRISPR-mediated HDR, which improved insulin secretion in iPSC-differentiated beta-like cells [3]. These advancements highlight the potential of CRISPR-based genome editing to correct monogenic forms of diabetes by targeting specific genetic mutations in vivo.", + "Genome-wide association studies (GWAS) integrate with multi-omics data to elucidate the complex genetic architectures of diabetes by combining genetic, epigenetic, transcriptomic, and phenotypic information. This integration helps identify genes and novel metabolic pathway targets that are crucial for understanding mechanistic relationships with insulin resistance and pancreatic islet failure [1]. Additionally, complementary systems-level data, such as protein-protein interactions and gene expression, provide insights into the mechanisms underlying the pathogenesis of complex traits like type 2 diabetes (T2D) [8]. This multi-omics approach allows for a more comprehensive understanding of the genome-to-phenome correlation in T2D, which is essential for examining the disease's complex genetic architecture [9].", + "Genomic imprinting has a significant impact on the susceptibility and progression of diabetes. Imprinting can influence the expression of genes involved in metabolic processes, which are crucial in the development of diabetes. For instance, changes in imprinting status at specific loci, such as the KCNQ1 locus, have been linked to type 2 diabetes susceptibility, indicating that temporal changes in imprinting can affect the function of pancreatic islets and contribute to diabetes risk [6]. Additionally, imprinting defects have been associated with specific forms of diabetes, such as transient neonatal diabetes, suggesting that imprinted genes play a role in the disease's onset and progression [4]. Furthermore, the effects of maternal diabetes on the offspring's epigenome, including alterations in DNA methylation profiles, highlight the role of imprinting in the intergenerational transmission of diabetes risk [3], [7]. These epigenetic changes can lead to a permanent programming of the developing offspring, increasing the risk of diabetes in subsequent generations [8]. Overall, genomic imprinting is a critical factor in understanding the genetic and epigenetic mechanisms underlying diabetes susceptibility and progression.", + "Longitudinal genomics studies are crucial for understanding gene-environment interactions in diabetes onset and management because they allow researchers to observe how genetic variations interact with environmental factors over time. This approach helps in identifying temporal patterns and causal relationships that are not possible to discern from cross-sectional studies [2]. By incorporating genotype measurements into longitudinal studies, researchers can gain significant insights into the genetic basis of diseases like diabetes, which can lead to better prediction, targeted prevention, and stratified treatment of type 2 diabetes (T2D) [3]. Additionally, these studies can help stratify T2D into subclasses that can be treated more effectively by understanding gene-lifestyle interactions [8]. Overall, longitudinal genomics studies provide a comprehensive framework to explore how genetic predispositions and environmental exposures collectively influence diabetes-related traits and disease progression.", + "Recent integrative genomics approaches, such as the combination of single-cell RNA sequencing and epigenomic profiling, have significantly advanced our understanding of cellular heterogeneity and gene regulatory networks in pancreatic beta cells under diabetic conditions. These approaches have allowed researchers to delve deeper into the transcriptional regulation of non-beta cell types, which may play crucial roles in the hallmark features of beta cell insufficiency and dysfunction associated with Type 2 Diabetes (T2D) [1]. Single-cell RNA sequencing has been particularly instrumental in high-throughput diabetes research by enabling the sequencing of individual cells from human pancreatic islets. This is important given the heterogeneity within the islets of Langerhans, which consist of various cell types. By tracking genetic changes in individual cells, researchers can better understand the complex cellular landscape and the specific contributions of different cell types to diabetes pathogenesis [2]. Furthermore, epigenomic profiling adds another layer of complexity by revealing how epigenetic changes can modulate gene expression without altering the DNA sequence. These changes are crucial for maintaining the secretory capacity, survival, and functional identity of pancreatic islets, as well as their response to insulin [8]. The integration of these genomic and epigenomic data helps identify regulatory elements and pathways that could be targeted for therapeutic interventions, moving from correlation to causation in understanding diabetes [10]." + ], + "contexts": [ + [ + "proteomics, genomics, and transcriptomics) are based on the study of constituents of the cell or body in a collective way. The ndings made with use of these approaches are being integrated to better understand the pathophysiology of type 2 diabetes and the heterogeneity of responses to di erent glucose-lowering therapies. Findings from studies that used metabolomics and lipidomics showed that increases in branched-chain and aromatic aminoacids were associated with obesity and type 2 diabetes.", + "Metabolomics Applied to Diabetes Research Moving From Information to Knowledge James R. Bain, Robert D. Stevens, Brett R. Wenner, Olga Ilkayeva, Deborah M. Muoio, and Christopher B. Newgard Type 2 diabetes is caused by a complex set of interactions between genetic and environmentalfactors. Recent work has shown that human type2 diabetes is a constellation of disorders associ- ated with polymorphisms in a wide array of genes, witheach individual gene accounting for /H110211% of disease risk", + "between protein signals and type 2 diabetes incidence. Acta Diabetol. doi: 10.1007/s00592-012-0376-3 82. Bain JR, Stevens RD, Wenner BR, Ilkayeva O, Muoio DM, Newgard CB (2009) Metabolomics applied to diabetes re-search: moving from information to knowledge. Diabetes 58: 2429 244383. Suhre K, Meisinger C, Dring A et al (2011) Metabolic footprint of diabetes: a multiplatform metabolomics study in an epidemiological setting. PLoS One 5:e13953", + "The future: genetics, epigenetics, and omics Although understanding of the genetics of type 2 diabetes has advanced rapidly, much remains unknown. How genes interact with the environment to cause progressive loss of -cell function is unclear. Environmental factors and hyperglycaemia could contribute to epigenetic changes in DNA and histones, thereby modifying gene expression in organs implicated in the pathogenesis and progression of type 2 diabetes, including in cells. 82,83", + "potential to make far-reaching contributions to our understanding of molecular basis of T2D and the development of novel strategies for patient care. 2.1 Introduction Type 2 diabetes (T2D) is a common, chronic disorder whose prevalence is increas-ing rapidly across the globe. Like other complex diseases, T2D represents achallenge for genetic studies aiming to uncover the underlying pathophysiological mechanisms. It is predicted that T2D will affect 592 million individuals by 2035", + "inthepathogenesisoftype2diabetesandmetabolism, Current Opinion in Clinical Nutrition and Metabolic Care ,vol.10,no .4, pp .420426,2007 . [110] M.C.Cornelis,E.J.T.Tchetgen,L.Liangetal.,Gene-environ- ment interactions in genome-wide association studies: a com- parative study of tests applied to empirical studies of type 2 diabetes, American Journal of Epidemiology ,v o l.17 5,no .3,p p . 191202,2012. [111] M.L.Metzker,Sequencingtechnologiesthenextgeneration, Nature Reviews Genetics ,vol.11,no.1,pp.3146,2010.", + "meta-ana lysis provides insight intothegenetic architecture oftype2diabetes susceptibility. NatGenet. 2014; 46:234 244. https://doi.or g/10.103 8/ng.2897 PMID: 24509480 26. Morris AP,Voight BF,Teslovich TM,Ferreira T,Segr A-V, Steinthorsdot tirV,etal.Large-sc aleassoci- ation analysis provide sinsights intothegenetic architecture andpathophysi ology oftype2diabetes. NatGenet. 2012; 44:981 990. https://doi.or g/10.103 8/ng.2383 PMID: 228859 22", + "monitoring and preventing progression to costly co-morbidities. The principal concept of metabolomics being able to find some metabolites differing in a control and a type 2 diabetic group is established. It is not our goal here to show this once again. The questions we ask are rather How well are different approaches suited to attain this goal? and What are optimal settings under which such studies can be successful?. Others have already investigated these questions before [16,17,18]. However, we", + "Owing to current advances in -omics technologies, such as genomics, transcriptomics, proteomics and metabolomics, the number of candidate biomarkers keeps growing; however, only a small proportion of these has been investigated withreference to their potential to improve the prediction of type 2 diabetes. Genetic variants The heritability of glycaemic traits and type 2 diabetes is high [40], and the large genome-wide association studies published to date since the first in 2007, based on up to >10 5study", + "have improved our understanding of the complexity of T2DM pathophysiology, beyond the classic triumvirate of -cell, skeletal muscle and liver87. However, the ability of these biomarkers to predict future risk of T2DM beyond anthropometric measures, lifestyle factors and fasting levels of glucose and lipids is still debatable87. Within the past 7years, a complementary, novel set of T2DM biomarkers has largely been generated by metabo- lomic studies, which systematically analyse metabolites" + ], + [ + "wide association study identi es novel risk loci for type 2 diabetes. Nature (2007) 445:881 5. doi: 10.1038/nature05616 27. Scott LJ, Mohlke KL, Bonnycastle LL, Willer CJ, Li Y, Duren WL, et al. A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science (2007) 316:1341 5. doi: 10.1126/science.1142382 28. Fuchsberger C, Flannick J, Teslovich TM, Mahajan A, Agarwala V, Gaulton KJ, et al. The genetic architecture of type 2 diabetes. Nature (2016) 536:41 7.", + "novel loci for type 1 diabetes. Diabetes 58:290295. DOI: https://doi.org/10.2337/db08-1022, PMID: 18840781 Huang J, Ellinghaus D, Franke A, Howie B, Li Y . 2012. 1000 Genomes- based imputation identifies novel and refined associations for the Wellcome Trust Case Control Consortium phase 1 Data. European Journal of Human Genetics 20:801805. DOI: https://doi.org/10.1038/ejhg.2012.3, PMID: 22293688 Hundhausen C, Roth A, Whalen E, Chen J, Schneider A, Long SA, Wei S, Rawlings R, Kinsman M, Evanko SP ,", + "general population, these loci show limited effect in DKD, especially in individuals with type 1 diabetes [ 6]. Genome- wide association studies (GWAS) have previously identified ahandful of genetic loci for DKD at the genome-wide signifi- cance level ( p<510 8)[711]. Recently, a meta-analysis of GWAS, including up to 19,406 individuals with type 1 diabetes from the Diabetic Nephropathy Collaborative Research", + "Table 2.1 Major published T2D GWAS and meta-analyses StudyEthnicity/ origin NcasesaN controlsaNovel loci identiedGWAS or meta-analysis discoveryapproach GWAS arrayReference panel forimputationT2D phenotype denition/otherspecs Diabetes Gene Discovery Group (Sladek et al. 2007 ), NatureEuropean 694 645 SLC30A8 ,HHEX /IDE GWA Illumina 300k + Family history of T2D, AAO <45 years, BMI <30 kg/m 2 FinlandUS Investi-gation of NIDDMGenetics (FUSION)(Scott et al. 2007a ), ScienceEuropean 1161 1174 CDKN2A/2B ,", + "scale gene-centric meta-analysis across 39 studies identifies type 2diabetes loci. Am J Hum Genet. 2012;90(3):410 25. 13. Haiman C, Fesinmeyer M, Spencer K, Buzkova P, V oruganti V , Wan P, et al. Consistent directions ofeffect for established type 2 diabetes risk variants across populations: the Population Architectureusing Genomics and Epidemiology (PAGE) Consortium. Diabetes. 2012;61(6):1642 7.In the most complete trans-ethnic T2D GWAS", + "9. Sladek R, Rocheleau G, Rung J, Dina C, Shen L, et al. (2007) A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature 445:881885. 10. Zeggini E, Scott LJ, Saxena R, Voight BF, Marchini JL, et al. (2008) Meta- analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes. Nat Genet 40: 638645.11. Altshuler D, Daly MJ, Lander ES (2008) Genetic mapping in human disease. Science 322: 881888.", + "scale ongoing efforts to localize and characterize T2D susceptibility genes using genome-wide association study (GWAS) approaches. To date, the GWAS method has achieved substantial success in localizing novel T2D susceptibility loci and loci for T2D-related glycemic traits (about 90 loci), obesity loci (~90), and loci for metabolic syndrome or its components (~50 loci), e.g. reviews: [4,20,28,29,41,47,51,64,65,67] . However, common variants identi ed by GWAS explain only about", + "T2D GWA meta-analysis performed by the DIAbetes Genet-ics Replication and Meta-analysis (DIAGRAM) Consortium [6]. Using genotype data from almost 150,000 individuals, predominantly of European descent, the consortium was ableto define 10 novel T2D-susceptibility loci to genome-wide significance, and to highlight several hundreds more that, whilst failing to reach the stringent criteria typically regardedas proof, are nonetheless highly likely to reflect genuine", + "18. Sladek R, Rocheleau G, Rung J, Dina C, Shen L, Serre D, et al. A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature 2007;445:881-885. 19. Scott LJ, Mohlke KL, Bonnycastle LL, Willer CJ, Li Y, Duren WL, et al. A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science 2007; 316:1341-1345. 20. Diabetes Genetics Initiative of Broad Institute of Harvard and MIT , Lund University, and Novartis Institutes of BioMedical", + "additive, dominant, and recessive) and did not adjust for mul - tiple comparisons. The third study is the largest GWAS con - ducted to date and is a meta-analysis of two GWASs, Genetics of Kidneys in Diabetes (GoKinD) and Epidemiology of Dia - betes Interventions and Complications (EDIC) studies [24]. This study by Grassi et al. [24] involved 2,829 European sub - jects with T1DM. The most significant variant was rs476141 located in a long non-coding RNA ( LOC339529 ) in chromo -" + ], + [ + "diabetes due to epigenetic silencing of Pdx1, a key transcription factor that regulates insulin gene 301 expression and beta cell differentiation. Both hi stone modifications a nd DNA methylation were 302 implicated (111). In another study, it was shown th at, in diabetic islets , there was increased DNA 303 methylation of the promoter of PPAR-gamma co-activator 1 gene ( PPARGC1A ), a factor that 304 plays a key role in regulating mitochondrial ge nes and in the modulation of diabetes (87). 305", + "altered DNA methylation (DNA-me) at various genes in target cells all of which over time can 1009 result in changes to the expr ession patterns of inflammatory, sclerotic and other pathological 1010 genes and the ultimate developm ent of diabetic complications. 1011 1012 Figure 2: Model for epigenetic regulation of pa thological gene expressi on in diabetes via 1013 changes in chromatin histone modifications. Post translational modifications on the N- 1014", + "Dependent Demethylation of Regulatory Elements Correlates with Chromatin State and Improved Cell Function. Cell Metab. 2015 ,22, 619632. [CrossRef] 228. Zhang, H.; Pollin, T.I. Epigenetics Variation and Pathogenesis in Diabetes. Curr. Diab. Rep. 2018 ,18, 121. [CrossRef] 229. Miao, F.; Chen, Z.; Zhang, L.; Liu, Z.; Wu, X.; Yuan, Y.-C.; Natarajan, R. Proles of epigenetic histone post-translational modications at type 1 diabetes susceptible genes. J. Biol. Chem. 2012 ,287, 1633516345. [CrossRef]", + "Epigenetic Mechanisms in Diabetic Complications 14 DNA methylation at prom oter CpG islands has been associ ated with gene repression and 292 is a well studied epigenetic mark in the c ontext of tumor suppressor genes and cancer (129). 293 However, much less is known a bout DNA methylation in diabetes . A recent report has shown 294 that the insulin promoter DNA was methylated in mouse embryonic stem cells and only becomes 295", + "Epigenetics: deciphering its role in diabetes and its chronic complications. Clin. Exp. Pharmacol. Physiol. 38, 401409 (2011). 61. Cooper, M.E. & El-Osta, A. Epigenetics: mechanisms and implications for diabetic complications. Circ. Res. 107, 14031413 (2010). 62. Miao, F. etal. Profiles of epigenetic histone post- translational modifications at type1 diabetes susceptible genes. J.Biol. Chem. 287, 1633516345 (2012). 63. Sapienza, C. etal. DNA methylation profiling", + "Emerging evidence shows that epigenetic mecha-nisms in chromatin including histone PTMs, DNAme, and miRNAs also might play key roles in the etiology of diabetes and DN. The persistence ofepigenetic modi cations triggered by diabetic stim- uli could be one of the key mechanisms underlying metabolic memory. A role for several HMTs and thecorresponding histone PTMs has been shown in the expression of brotic and in ammatory genes asso-", + "inflammation-related epigenetic modifications: focus on DNA methylation. Exerc Immunol Rev. 2015;21:26 41. 17. Milagro FI, Mansego ML, De Miguel C, Martinez JA. Dietary factors, epigenetic modifications and obesity outcomes: progresses and perspectives. Mol Aspects Med. 2013;34(4):782 812. 18. Caramori ML, Kim Y , Goldfine AB, et al. Differential gene expres- sion in diabetic nephropathy in individuals with type 1 diabetes. J Clin Endocrinol Metab. 2015;100(6):E876 82.", + "elevated glucose level is not the only factor that leads to mal- adaptive epigenetic modifications in diabetes. DNA methyla- tion can also be influenced by reactive oxygen species, both directly through oxidative m odification DNA preventing methylation and indirectly through its effects on methylation writing/erasing enzymes [ 15]. Many other factors including hypoxia, inflammation, cytokines and growth factors, drugs, nutrition and even physical activity can modify epigenetic", + "1306 1313. 31. Miao F, et al.; DCCT/EDIC Research Group (2014) Evaluating the role of epigenetic histone modifications in the metabolic memory of type 1 diabetes. Diabetes 63(5): 1748 1762. 32. Reddy MA, Tak Park J, Natarajan R (2013) Epigenetic modifications in the patho- genesis of diabetic nephropathy. Semin Nephrol 33(4):341 353. 33. Bell CG, et al. (2010) Genome-wide DNA methylation analysis for diabetic nephrop- athy in type 1 diabetes mellitus. BMC Med Genomics 3:33.", + "ing that environment and diet may influence epigenetic mod-ifications that predispose individuals to diabetes [ 46]. Aber- rant DNAme has also been reported in the reduced expression of genes involved in diabetes and metabolism, and DNAme variations have also been noted near diabetes susceptibility genes and enhancers [ 15,47]. Genomic DNA from diabetic patients with nephropa- thy relative to those without displayed differential meth- ylation at several genes, including UNC13B , which had" + ], + [ + "diabetes? Is altered gut epithelial function and integrity important in the pathoge nesis of type 1 diabetes, and if so, what is the mechanism(s) and relation to dysbiosis and how do we demonstrate impaired function in humans? How important are the interactions between host genetics, metab olism and the immune system in shaping the microbiome and predilection to disease?", + "the gut, which might trigger an inflammatory response and play arole in the development of diabetes. In conclusion, our data suggest that the levels of glucose tolerance or severity of diabetes should be considered while linking microbiota with obesity and other metabolic diseases in humans. It is especially important for developing the strategies to modify the gut microbiota inorder to control metabolic diseases, since obesity and diabetes mightbe associated with different bacterial populations. Methods", + "2011;342:d35. [68] Hara N, Alkanani AK, Ir D, Robertson CE, Wagner BD, Frank DN, et al. The role of the intestinal microbiota in type 1 diabetes. Clin Immunol 2013;146:1129. [69] Beyan H, Wen L, Leslie RD. Guts, germs, and meals: the origin of type 1 diabetes. Curr Diab Rep 2012;12:45662. [70] Atkinson MA, Chervonsky A. Does the gut microbiota have a role in type 1 diabetes? Early evidence from humans and", + "diabetes. ISME J. 5,8291 (2011). 30. Brown, C. T. et al. Gut microbiome metagenomics analysis suggests a functional model for the development of autoimmunity for type 1 diabetes.PLoS ONE 6,e25792 (2011). 31. Endesfelder, D. et al. Compromised gut microbiota networks in children with anti-islet cell autoimmunity. Diabetes 63,2006 2014 (2014). 32. Kostic, A. D. et al. The dynamics of the human infant gut microbiome in development and in progression toward type 1 diabetes. Cell Host Microbe 17, 260273 (2015).", + "661678 (2007). 4. Scott, L. J. et al. A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science 316, 13411345 (2007). 5. Musso, G., Gambino, R. & Cassader, M. Interactions between gut microbiota and host metabolism predisposing to obesity and diabetes. Annu. Rev. Med. 62, 361380 (2011). 6. Eckburg, P. B. et al. Diversity of the human intestinal microbial flora. Science 308, 16351638 (2005).", + "The gut microbiota affects numerous biological functionsthroughout the body and its characterisation has becomea major research area in biomedicine. Recent studieshave suggested that gut bacteria play a fundamental rolein diseases such as obesity, diabetes and cardiovasculardisease. Data are accumulating in animal models andhumans suggesting that obesity and type 2 diabetes(T2D) are associated with a profound dysbiosis. Firsthuman metagenome-wide association studiesdemonstrated highly signi cant", + "18 Burcelin R. Regulation of metabolism: a cross talk between gut microbiota and its human host. Physiology (Bethesda) 2012;27:300 7. 19 Breen DM, Rasmussen BA, Cote CD, et al . Nutrient-sensing mechanisms in the gut as therapeutic targets for diabetes. Diabetes 2013;62:3005 13. 20 Karlsson F, Tremaroli V, Nielsen J, et al . Assessing the human gut microbiota in metabolic diseases. Diabetes 2013;62:3341 9. 21 Backhed F, Ding H, Wang T, et al . The gut microbiota as an environmental factor", + "interactions play a role in human obesity, insulin resistance and type 2 diabetes? Obes Rev 2011; 12: 27281. 47 Kootte RS, Vrieze A, Holleman F, et al. The therapeutic potential of manipulating gut microbiota in obesity and type 2 diabetes mellitus. Diabetes Obes Metab 2012; 14: 11220. 48 Qin J, Li Y , Cai Z, et al. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature 2012; 490: 5560. 49 Karlsson FH, Tremaroli V, Nookaew I, et al. Gut metagenome in", + "Other factors Interest in the role of the gut microbiome in the devel - opment of T2DM has exploded in the past few years, and variation in the diversity and composition of the gut microbiota has been tied to T2DM100. For example, levels of butyrate-producing bacteria are decreased in the gut microbiota of patients with T2DM compared with that of healthy individuals101. In addition, evidence suggests that ambient air pollution is an emerging risk factor for", + "52. Parks, B.W., et al., Genetic control of obesity and gut microbiota composition in response to high -fat, high -sucrose diet in mice. Cell Metab, 2013. 17(1): p. 141 -52. 53. Org, E., et al., Genetic and environmental c ontrol of host -gut microbiota interactions. Genome Res, 2015. 25(10): p. 1558 -69. 54. McKnite, A.M., et al., Murine gut microbiota is defined by host genetics and modulates variation of metabolic traits. PLoS One, 2012. 7(6): p. e39191." + ], + [ + "All the mentioned models rely on tabular datasets such as PIMA and ECG signals [ 47] in classifying the records with possible diabetic illnesses. The current study considers that genomic data yields a better patient-centric outcome than tabular data. 2.3. Genomics for Type 2 Diabetes Many research studies have been carried out on genetic-based illness prediction. Incorporating machine learning approaches with genetic-based illness prediction could", + "- chondrially rich, provides a direct connection between physiological dysfunction observed in the heart and the impact of altered genomic profiles in the mitochondrion and nucleus. Machine-learning, which at current has been applied to very few genetic applications, may play a significant role in defining the epigenome of those with diabetes mellitus, likely unveiling genes and molecular pathways first impacted by the pathology. The challenges ofmachine learning intheclinical setting", + "15. Ali, M.M.; Paul, B.K.; Ahmed, K.; Bui, F.M.; Quinn, J.M.W.; Moni, M.A. Heart disease prediction using supervised machine learning algorithms: Performance analysis and comparison. Comput. Biol. Med. 2021 ,136, 104672. [CrossRef] 16. Bell, C.G.; Teschendorff, A.E.; Rakyan, V .K.; Maxwell, A.P .; Beck, S.; Savage, D.A. Genome-wide DNA methylation analysis for diabetic nephropathy in type 1 diabetes mellitus. BMC Med. Genom. 2010 ,3, 33. [CrossRef]", + "Diagnostics 2022 ,12, 3067 6 of 30 Table 1. Various existing models for diabetes prediction. Approach Type of Data Applicability Limitations polygenic scores-based approach [12]Genomic DataUsed in the evaluation of clinical trials and illness screening mechanismsThe polygenic score approach needs larger samples and tremendous training for considerable Accuracy. Singular Value Decomposition [13]Genomic Data Tabular Data The image they are usedThey are used in ranking the feature", + "In the current study, machine-learning was used as a predictive tool to integrate cardiac physiological, bio - chemical, genomic, and epigenomic biomarker data in a patient-matched fashion and enable determination of type 2 diabetic status. In 50 patients, machine-learning algorithms revealed the interconnectedness between dia - betic classification, mitochondrial function, and methyla -", + "Diabetes mellitus is a multifaceted disease, consisting of systemic comorbidities which necessitate a variety of treatment modalities and stratify those affected with the disease [5]. Before the implementation of machine-learning algorithms in medicine, linear statistical models have highlighted measures, such as HbA1c, as diagnos - tic staples for the evaluation of diabetes mellitus onset and progression [6]. By exploring these previously pub -", + "tool that combines both genetic and clinical featur es in order to identify diabetic nephropathy in patients with T2D [81]. Leung et al . compared several machine learning methods that include partial least square regression, classification and regression tree, the C5.0 Decision Tree, Random For est, naive Bayes, neural networks and support vector machines [82]. The dataset used consists of both genetic (Single Nucleotide Polymorphisms - SNPs) and clinical data. Age, age of diagnosis, systolic", + "- ylation status and total nuclear methylation provided the best predictive measures for assessing type 2 diabetes mellitus. The incorporation of physiological, biochemical, genetic, and epigenetic features with machine-learning algorithms exemplifies the potential for more informa - tive diagnostics in the future, as well as personalized approaches to generalized treatment modalities (Fig.6). Discussion Machine-learning can be applied as a systems biol -", + "- tures is likely to occur, enhancing the diagnostic potential for the individual diabetic or prediabetic patient. Indeed, this is the advantage of using machine-learning models, in that they continue to learn and develop more accurate predictions as the number of features and sampled popu - lation grows. Conclusions Our work highlights the importance of identifying bio -", + "10 Meigs JB, Shrader P, Sullivan LM et al. Genotype score in addition to common risk factors for prediction of Type 2 diabetes. N. Engl. J. Med. 359, 22082219 (2008). 11 Scheuner MT, Sieverding P, Shekelle PG. Delivery of genomic medicine for common chronic adult diseases: a systematic review. JAMA 299, 13201334 (2008). \t Systematic\treview\tof\tearly\tresearch\tinto\tgenomic\tmedicine \t adoption\tin\tthe\tclinical\tcare\tof\tcommon\tchronic\tdiseases. \t Outlines\tboth\tphysician\tand\tpatient\tperspectives\ttowards" + ], + [ + "NAs to be mapped to diabetic susceptible loci [49 52], all suggesting towards critical roles of lncRNAs in insulin resistance, diabetes, and its associated complications. LncRNAs asregulators ofislet function The pancreatic islet is an important central node to researchers to understand the pathophysiology of diabe-tes [53]. The possible regulation of islet development and function by lncRNAs was first demonstrated by Ding etal., where the lncRNA, H19 (Fig. 4), was shown to be involved", + "this would require further investiga-tions, both invivo and invitro and critical networking among researchers, clinicians, and patients. Nevertheless, the implications of lncRNAs in diverse facets of insulin resistance and diabetes are indicative of their roles in the diagnosis, prognosis, and therapy of this disease in future.", + "To conclude, it would be apt to state that lncRNAs are widely implicated in diverse domains of cell metabolism and their altered expression is associated with diabetes and its complications. Although originally thought to be non-functional, lncRNA genes transcribe into lncRNAs that exert important and specific functions in regulating cellular pathways. Due to this specificity, lncRNAs are considered better therapeutic targets. In addition, their expression patterns in tissues quite follow the progress of", + "58. You L, Wang N, Yin D etal (2016) Downregulation of long noncoding RNA Meg3 affects insulin synthesis and secretion in mouse pancreatic beta cells. J Cell Physiol 231:852862 59. Arnes L, Akerman I, Balderes DA, Ferrer J, Sussel L (2016) betalinc1 encodes a long noncoding RNA that regulates islet beta-cell formation and function. Genes Dev 30:502507 60. Akerman I, Tu Z, Beucher A etal (2017) Human pancreatic beta cell lncRNAs control cell-specific regulatory networks. Cell Metab 25:400411", + "of lncRNAs in the development and function of metabolic tissues, and therefore, their altered levels are closely asso-ciated with the onset and progression of insulin resistance and diabetes. Roles oflncRNAs indiabetic complications Apart from being involved in major metabolic tissues dur -", + "tion among researchers ( Knoll et al., 2015 ). As an important post-transcriptional pathogenesis of diabetes, lncRNAs and their associated orchestrated networks are implicated in mediating complex pathological mechanisms of diabetes ( Kato et al., 2016; Liu et al., 2014 ). To delineate the inuence of lncRNAs and 172 iScience 19, 162176, September 27, 2019", + "in transgenerational transmission of gestational diabetes mellitus which leads to impaired islet structure and func-tion [ 54]. To understand the roles of lncRNAs in regu- lating pancreatic function, several research groups have profiled lncRNA expression in mouse and human pancre-atic islets [55, 56]. Transcriptome analysis in pancreatic -cells of type 2 diabetes patients identified tissue-specific and dynamically regulated abnormally expressed lncR -", + "1831 Lnc-ing non- coding RNAs withmetabolism anddiabetes: roles oflncRNAs 1 3 endocrine hormones, insulin and glucagon, where insulin is the anabolic master regulator which controls periph -", + "Vol.:(0123456789)1 3Cellular and Molecular Life Sciences (2018) 75:18271837 https://doi.org/10.1007/s00018-018-2760-9 REVIEW Lncing noncoding RNAs withmetabolism anddiabetes: roles oflncRNAs NehaGoyal1,2 DeveshKesharwani1,2 MalabikaDatta1,2 Received: 18 September 2017 / Revised: 29 December 2017 / Accepted: 24 January 2018 / Published online: 31 January 2018 Springer International Publishing AG, part of Springer Nature 2018 Abstract", + "(2013). A novel mechanism regulating insulin secretion involving Herpud1 inmice. Diabetologia 56, 15691576 . Zhao, X.Y., and Lin, J.D. (2015). Long noncoding RNAs: a new regulatory code in metabolic control. Trends Biochem. Sci. 40, 586596 . 1806 Cell Reports 17, 17951806, November 8, 2016" + ], + [ + "regulates glucose-induced biological responses in pancreatic beta-cells. Diabetes. 2008;57:2708-17. 29. Schultze SM, Hemmings BA, Niessen M, Tschopp O. PI3K/AKT, MAPK and AMPK signalling: protein kinases in glucose homeostasis. Expert Rev Mol Med. 2012;14:e1. 30. White MF. IRS proteins and the common path to diabetes. Am J Physiol Endocrinol Metab. 2002;283:E413-22. 31. Erener S, Marwaha A, Tan R, Panagiotopoulos C, Kieffer TJ. Profiling of circulating microRNAs in children with", + "pathological processes involved in glucose metabolism by post transcriptional regulation of gene expression. Particular microRNAs can regulate cell function271, exposing key regulatory signalling pathways involved in restoration of cell mass, and provide a promising strat egy for improving insulin secretion and cell health in T2DM. Identification of novel insulin secretagogues that act directly on cells and enteroendocrine Kcells and Lcells in the intestine are under investigation, and", + "can result in diabetes and its complications including DN. Several studies show that key histone post- translational modifications are involved in the regulation of genes associated with the pathogenesis of diabetes, such as insulin and islet-specific transcription factors.48,60 Inaddi - tion, several groups are examining the role of histone post-translational modifications in adipocytes related to type2 diabetes, obesity and the metabolic syndrome.48,60", + "cascade of protein kinases and regulatory proteins of which IRS-1 and IRS-2 are most important. This causes suppression of glucose release from liver and kidney/ translocation of glucose transporters in muscle and adipose tissue to increase their glucose uptake, and inhibition of release of FF A into the circulation due to suppression of the activity of hormone-sensitive lipase and a simultaneous increase in their clearance from the circulation. Although", + "Magnan C, Postic C, Prip-Buus C, Vasseur-Cognet M (2008) The transcription factor COUP-TFII is negatively regulated by insulin and glucose via Foxo1- and ChREBP-controlled pathways. Mol Cell Biol 28: 65686579Rodgers JT, Lerin C, Haas W, Gygi SP, Spiegelman BM, Puigserver P (2005) Nutrient control of glucose homeostasis through a complex ofPGC-1alpha and SIRT1. Nature 434: 113118 Schwer B, Verdin E (2008) Conserved metabolic regulatory functions of sirtuins. Cell Metab 7:104112", + "of glucose transporter 2 glycosylation promotes insulin secretion in suppressing diabetes. Cell 123:1307 1321. PMID: 16377570 47. Whitaker GM, Lynn FC, McIntosh CH, Accili EA (2012) Regulation of GIP and GLP1 receptor cell sur- face expression by N-glycosylation and receptor heteromerization. PLoS One 7: e32675. doi: 10.1371/ journal.pone.0032675 PMID: 22412906 48. Johswich A, Longuet C, Pawling J, Abdel Rahman A, Ryczko M, et al. (2014) N-glycan remodeling on", + "strate 1), Pde3b (phosphodiesterase 3B), Hk2 (hexokinase 2), Foxo1 (forkhead box O1), Socs6 (suppressor of cytokine signaling 6), and Ogt (O-linked N-acetylglucosamine (GlcNAc) transferase). Impaired insulinsignaling is well known to negatively in uence glucose and lipid metabolism [62]. In adipose tissue, insulin stimulates glucose uptake by inducing translocation of GLUT4 to the cell surface, it increasesglycolysis rate by stimulating hexokinases ( Hk2) and suppresses lipolysis ( Acaca and Prkaa1 )[63].", + "signalling pathways by reducing insulin induced tyro sine phosphorylation of IRS1 and IRS2 (REF. 161) and by increasing degradation of IRS1 (REF. 162). Recent studies have demonstrated that the p85 regulatory subunit of PI3K interacts with XBP1s (the spliced, transcription ally active isoform of XBP1) and promotes the trans location of XBP1s into the nucleus to initiate the ER stress response163.Diabetic complications Diabetic microvascular complications are closely related", + "activated protein kinase. J Biol Chem. 2007;282:9777 -88. [44] Chakrabarti S, Davidge ST. High glucose -induced oxidative stress alters estrogen effects on ERalpha and ERbeta in human endothelial cells: reversal by AMPK activator. J Steroid Biochem Mol Biol. 2009;117:99 -106. [45] Mortuza R, Chen S, Feng B, Sen S, Chakrabarti S. High glucose induced alteration of SIRTs in endothelial cells causes ra pid aging in a p300 and FOXO regulated pathway. PLoS One. 2013;8:e54514.", + "Epigenetic Mechanisms in Diabetic Complications 17 Interestingly, the sirtuin (SIRT) family of deacetylases, specifically SIRT1, has been found to 360 regulate several factors involved in metabolism, adipogenesis a nd insulin secretion (86). HATs 361 and HDACs can also modulate NF- B transcriptional activity (4, 44) resulting in changes in 362" + ], + [ + "WFS1 and genotype-phenotype correlation in Wolfram syndrome. Am J Med Genet A. 2007;143A(14):1605 12. 61. McCarthy MI. Painting a new picture of personalised medicine for diabetes. Diabetologia. 2017;60(5):793 9. 62. Fuchsberger C, Flannick J, Teslovich TM, et al. The genetic architecture of type 2 diabetes. Nature. 2016;536(7614):41 7. 63. Patch AM, Flanagan SE, Boustred C, Hattersley AT, Ellard S. Mutations in the ABCC8 gene encoding the SUR1 subunit of the KATP channel cause", + "enable physicians to ameliorate some of the complications that so devastate the lives of these patients. Three questions need answers from further studies: is there really a lack of diabetic complications in Wolfram syndrome patients compared with other diabetics? What is the nature of the neurodegeneration and its relation to diabetes mellitus? Are heterozygotes for Wolfram syndrome at risk of maturity-onset diabetes? This paper is dedicated to the memory of Robin Smith, a Wolfram", + "Monogenic and syndromic forms account for only a small,though highly informative, proportion of cases of nonau-toimmune diabetes. The challenge for medical science liesin bringing equivalent mechanistic insights and transla-tional benets to the hundreds of millions of peoplealready affected by, or at risk of, more common, typicalforms of diabetes. For type 2 diabetes, there is abundantevidence that individual susceptibility is inuenced byboth the combination of genetic variation at multiple sitesand a", + "responding to two causative genes have been identified to date. Wolfram syndrome 1 (WS1), characterized by diabetes insipidus, DM, optic atrophy, and deafness, is a rare autosomal recessive disease caused by variants in wolframin ER transmembrane gly- coprotein (WFS1). Severe cases with dominant heterozygous vari- ants are also reported (92). Often, patients first manifestation is DM at an average age of 6 years. Though most WS1 patients", + "finding study to describe the natural history, complications, prevalence, and inheritance of the syndrome. We identified 45 patients with Wolfram syndrome—a prevalence of one per 770000. Non-autoimmune, insulin- deficient diabetes mellitus presented at a median age of 6 years, followed by optic atrophy (11 years). Cranial diabetes insipidus occurred in 33 patients (73%) with sensorineural deafness (28, 62%) in the second decade; renal-tract abnormalities (26, 58%) presented in the third", + "Wolfram patients have a mitochondrial genome abnormality, but this has not yet been shown. The differential diagnosis indicates the importance of accurate clinical descriptions when presenting cases of the syndrome. Our study has implications for basic science and practice: more accurate characterisation of the syndrome will allow assessment of genotype/phenotype correlations; and earlier recognition of diabetes insipidus, gastrointestinal dysfunction, and central apnoeas should", + "onset diabetes of the young, multiple causes of neonatal DM, and syndromic diabetes such as Wolfram syndrome and lipodystrophy. We also review methods of prioritizing patients undergoing genetic testing, and highlight existing challenges facing sequence data interpretation that can be addressed by forming collaborations of expertise and by pooling cases.Monogenic diabetes: a gateway to precision medicine in diabetes Haichen Zhang,1 Kevin Colclough,2 Anna L. Gloyn,3,4 and Toni I. Pollin1", + "WFS1 mutations underlie a genetic syndrome of neonatal/infancy-onset diabetes, congenital sensorineural deafness, and congenital cataracts. Diabetes . 2017;66(7):20442053. 93. Rigoli L, Di Bella C. Wolfram syndrome 1 and Wolfram syndrome 2. Curr Opin Pediatr. 2012;24(4):512517 . 94. Bansal V, et al. Identification of a missense vari- ant in the WFS1 gene that causes a mild form of Wolfram syndrome and is associated with risk for type 2 diabetes in Ashkenazi Jewish individuals.", + "established. It has been corroborated by a series of obser-vations that include ethnic differences, familial aggrega-tion, twin studies, admixture studies, linkage studies, monogenic cases (e.g., MODY), mitochondrial cases of diabetes, and a constantly growing number of molecular markers [5] . On the other hand, the genetics of the meta- bolic syndrome remains complex [6] . It is highly unlikely that a single gene will account for a substantial portion", + "diabetes (0.5% carrier frequency) compared to controls (0.035%). One individual with early onset diabetes was homozygous for a rare pathogenic missense variant in the WFS1 gene but did not have the additional phenotypes associated with Wolfram syndrome. Conclusion: Targeted sequencing of genes linked with monogenic diabetes can identify disease-relevant mutations in individuals diagnosed with type 2 diabetes not suspected of having monogenic forms of the disease. Our data suggests" + ], + [ + "Studies of twins also provide compelling evidence for a genetic component to T2D. Estimates for concordance rates range from 0.29 to 1.00 in monozygotic (MZ) twins, while in dizygotic (DZ) twins the range is 0.100.43 [57, 58, 6164]. The high levels of heritability observed for insulin sensitivity and insulin secretion [6567] further reinforce the role of genetics in diabetes and indicate the primary genetic lesions for diabetes are likely to localize to genes in beta-cell-centric pathways.", + "It is therefore intriguing that A1C levels are signicantly correlated in monozygotic twins whether they are concor- dant for type 1 diabetes or not (4): in a discordant twin pairone twin is treated with insulin, whereas the other oneisnt, and thus this degree of correlation suggests thatgenetic contributors to A1C may be detectable despite thesuperimposition of a strong environmental modier. Rig-orous estimates of heritability of treated A1C, however, are not available.", + "Concordance rate for type II diabetes mellitus in monozy-gotic twins: actuarial analysis. Diabetologia 42:146150 3. Lehtovirta M, Kaprio J, Forsblom C, Eriksson J, Tuomilehto J, Groop L (2000) Insulin sensitivity and insulin secretionin monozygotic and dizygotic twins. Diabetologia43:285293 4. Florez JC, Hirschhorn J, Altshuler D (2003) The inherited basis of diabetes mellitus: implications for the genetic anal-ysis of complex traits. Annu Rev Genomics Hum Genet4:257291", + "disease susceptibility is not explained by genetics alone; environ- mental factors, gene by environment interactions, and epigenetic inuences are likely to play important roles in the etiology of T1D [5,6] . Monozygotic (MZ) twin pairs, discordant for T1D, represent an ideal system to test susceptibility factors not attributable to genetic variation, especially epigenetic variation, since the ge- nomes of the twins are identical. The ascertainment of disease-", + "epigenetic differences among monozygotic twins. A critical question is whether epigenetic marks are transmitted intactfrom parent to offspring and whether DNAm is allele- specific and covaries with allele-specific gene expression. For example, can we develop an epigenetic transmissiontest comparable to the transmission disequilibrium test used in genetic epidemiology? Finally, and most excitingly, we", + "their dietary and physical activity habits (Maes et al, 1997 ). There is also ample evidence that diabetes has a substantial genetic component. The con- cordance of type 2 diabetes in monozygotictwins ranges between 50 and 70% compared to 2037% in dizygotic twins (Kaprio et al, 1992 ; Newman et al, 1987 ; Poulsen et al 1999). Further evidence comes from studies that compare therisk in offspring with a family history of type 2 diabetes with offspring without such a fam-", + "monozygotic and dizygotic Danish twin pairs withinsulin dependent diabetes mellitus. Bmj 1997: 314:1575 1579. 30. R EDONDO MJ, R EWERS M, Y UL et al. Genetic deter- mination of islet cell autoimmunity in monozygotictwin, dizygotic twin, and non-twin siblings of patientswith type 1 diabetes: prospective twin study. Bmj 1999:318: 698 702. 31. L EVY-M ARCHAL C, P ATTERSON C, G REEN A. Variation", + "Studies in twins have demonstrated that 5070 % in the body mass index (BMI) variance may be explained by genetics ( Allison et al., 1996 ), and T2DM concordance was reported ranging from 1737 % in dizygotic to 5070 % in monozygotic twins ( Kaprio et al., 1992 ; Medici et al., 1999 ; Poulsen et al., 1999 ). In addition, family and adoption studies have reported heritability ranging from 2060 % for obesity ( Rice et al., 1999 ; Stunkard et al., 1986 ) and 3070 % for T2DM ( Meigs", + "Monozygotic twins exhibit numerous epigenetic differences: clues to twindiscordance? Schizophr Bull 29: 169178. 8. Oates NA, van Vliet J, Duffy DL, Kroes HY, Martin NG, et al. (2006) Increased DNA methylation at the AXIN1 gene in a monozygotic twin from a pair discordant for a caudal duplication anomaly. Am J Hum Genet 79: 155162. 9. Kuratomi G, Iwamoto K, Bundo M, Kusumi I, Kato N, et al. (2008) Aberrant DNA methylation associated with bipolar disorder identified from discordant", + "5 E/C128orts to estimate the heritability of T2D by a comparison of the concordance rates in mono- and dizygotic twins have varied greatly as a result of di/C128erences in ascertainment scheme, diagnostic criteria and follow-up duration.69Concordance for diabetes is generally higher in identical twins (supporting a genetic basis for disease), although the extremely high concordance rates in some early studies6were undoubtedly inated by ascertainment bias. Evidence from population studies" + ], + [ + "that genetic studies will ultimately identify key genetic elements that help determine susceptibility to diabetes,disease progression, and responsiveness to specific therapies, as well as help identify novel targets for futureintervention. A substantial number of genetic loci, gene polymorphisms, and mutations have already beenreported as having variable degrees of association with one or other type of diabetes (type 1, type 2, maturityonset diabetes of the young [MODY]), while others appear to be involved", + "ponse to thiazolidinedione therapy and candidate genes [100103]. Results from pharmacogenetic studies could potentially provide physicians with a powerful tool to adjust therapy appropriately for those individuals carry ing variants known to affect a given medication. Distefano and Watanabe have recently reviewed the pharmaco genetics of diabetes [104]. Genegene and geneenvironment interactions are also likely to be helpful to the clinician in making therapeutic", + "Genomics of T2D Diet, lifestyle, environment, and even genetic variation influence an individuals response to disease therapy. Like GWAS which identify genetic variants conferring risk for a disease, studies have been carried out for iden - tifying genetic variants responsible for patient differ -", + "ease caused by interactions between multiple genetic and environmental factors. Significant progress has been made in understanding the genetic architecture of T2D over the past 10 years [1]. A number of genome-wide as- sociation studies in diverse human populations have identified more than 60 common variants and loci asso- ciated with risk for T2D [2]. These studies have also revealed a significant overlap between traits and pheno- types of monogenic diabetes with related common", + "21582171 (2014). 29. Wood, A. R. et al. A genome-wide association study of IVGTT-based measures of first-phase insulin secretion refines the underlying physiology of type 2 diabetes variants. Diabetes 66, 22962309 (2017). 30. Pickrell, J. K. Joint analysis of functional genomic data and genome- wide association studies of 18 human traits. Am. J. Hum. Genet. 94, 559573 (2014). 31. Plenge, R. M., Scolnick, E. M. & Altshuler, D. Validating therapeutic targets", + "by GWASs [ 16,28,29]. A wide variety of network-based approaches have been applied to investigate the extent to which the genetics of T2D predisposition converge on a restricted set of biological pathways. Several T2D risk variants have been identied as primary regulators of insulin secretion, insulin action, and pancreatic islet transcription factors. [ 10,16]. The newly discovered SNVs allow the better characterization of abnormalities in early insulin processing and secretion. TCF7L2 ,SLC30A8 ,C2CD4B ,", + "[10] , many environmental factors [11] , and the interac- tions among those genetic and environmental factors. Physical activity and dietary fat have been reported to be important modifiers of the associations between glucose homeostasis and well-known candidate genes for T2DM [12] and there is reason to believe that a significant pro- portion of the susceptibility genes identified by GWASs will interact with these environmental factors to influ-ence the disease risk. Florez et al.", + "interactions suggest a way by which genetic risk may beameliorated, these environmental factors are of great relevanceto public health, and are the focus of a growing number of studies [7]. Environmental factors, such as diet and lifestyle, are important in the onset, development and progression of T2D and its related phenotypes [8,9]. The interactions of environmental factors with", + "cases. J Am Med Assoc. 1956;161:1628 30. 3. Duncan LE, Keller MC. A critical review of the first 10 years of candidate gene-by-environment interaction research in psychiatry. Am J Psychiatry. 2011;168:1041 9. 4. Brito EC et al. Previously associated type 2 diabetes variants may interact with physical activity to modify the risk of impaired glu- cose regulation and type 2 diabetes: a study of 16,003 Swedish adults. Diabetes. 2009;58:1411 8.", + "this occurs. Findings to date, however, indicate that behavioral changes can substantially mitigate diabetogenic and obesogenic effects of individual or multiple risk alleles, which has much broader clinical and public health implications.We have seen considerable progress in our understanding of the role that both environ- ment and genetics play in the development of T2D. Recent work suggests that the adverse effect of some established T2D-associated loci may be greatly attenuated by appropriate" + ], + [ + "and rare coding variants do not account for much of theheritability of type 2 diabetes. Under this scenario, themissing heritability could be located in common orlow-frequency and rare variants in noncoding regionsof the genome. Recent studies that jointly modeled dia-betes or obesity risk as a function of genetic relatednessacross all of the GWAS SNPs have suggested that much of the heritability of these traits can be explained by", + "T2D heritability. 3. Uncovering the Signicance of Rare-Coding and Non-Coding Genetic Variants in the Etiology of Type 2 Diabetes As previously stated, GWASs have uncovered many new genetic associations that are relevant to T2D, but GWAS ndings represent common and mid-frequency genetic variations, thus excluding rare frequency variants and also cumulative effect of many variants with small effect sizes. Missing heritability refers to the portion of genetic variance that cannot be explained by all signicant", + "could be accounted for by low-frequency and rare variants of moderate effect in a small number of genes. Our whole-exome sequencing study has explicitly addressed thisquestion. Additionally, we did not examine whether thereare fewer than 20 genes involved in type 2 diabetes butrather looked at whether rare coding variants in fewerthan 20 genes account for much of the heritability. In such a model, any number of other genes that do not", + "contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of the heritability of this disease. Here, to test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole-genome sequencing in 2,657 European individuals with and without diabetes, and exome", + "One common disease that has been subjected to intense genetic study is type 2 diabetes. 32The heritability of type 2 diabetes has been estimated to be around 30%.3335 Through GWASs, 63 loci have been reproducibly associ-ated with type 2 diabetes. 36However, as for other complex traits, the associated SNPs can only account for <20% of the heritability estimated from family studies.36 Here, we seek to evaluate the role that rare coding vari-", + "prevalence of T2D. These authors found rare variants that were not detected previously in population studies, but none of them were associated with T2D [ 49]. Larger multi-population studies and more advanced study methods are needed to reliably identify rare variants that are exclusively associated with T2D to eventually uncover missing T2D heritability. 3.2. Genetic Variants in Familial Studies of Type 2 Diabetes The development of T2D is driven by the combined effect of environmental factors and a", + "variance in disease risk that can be accounted for bythe 63 previously identied associations with commonvariants. Our empirical and simulation results are compatible with a variety of different genetic architectures for type2 diabetes. First, if rare coding variants are responsiblefor the majority of the heritability of the trait, the variants are most likely scattered across many ( >20) different", + "Genome-wide association studies (GWAS) have been helpful in identifying a large number of genetic variants conferring risk to T2D. However, only close to 10% heritability is explained by these variants. Other genetic variants, particularly those which are rare but with significant effects need to be identified.", + "and rare sequence variants associated with elevated or reduced risk of type 2 diabetes. Nat. Genet. 46, 294298 (2014). 168. Lek, M. etal. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285291 (2016).169. Xue, A. etal. Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes. Nat. Commun. 9, 2941 (2018). 170. Huyghe, J. R. etal. Exome array analysis identifies", + "diabetes. In particular, our study suggests that when clus-tered in a small number of genes, rare coding variants ofmoderate to strong effect are unlikely to account formuch of the missing heritability. Rather, if rare coding var-iants are an important factor in type 2 diabetes risk, theyare most likely scattered across many genes. Our resultshave important implications for the design and interpreta- tion of future medical resequencing studies. Subjects and Methods Study Populations" + ], + [ + "13 De Rosa et al. Type 2 Diabetes and CVD Frontiers in Endocrinology | www.frontiersin.org January 2018 | Volume 9 | Article 2176. Fatica A, Bozzoni I. Long non-coding RNAs: new players in cell differentia- tion and development. Nat Rev Genet (2014) 15:721. doi:10.1038/nrg3606 177. Wang KC, Chang HY . Molecular mechanisms of long noncoding RNAs. Mol Cell (2011) 43:90414. doi:10.1016/j.molcel.2011.08.018 178. Esteller M. Non-coding RNAs in human disease. Nat Rev Genet (2011) 12:86174. doi:10.1038/nrg3074", + "Epigenetic Mechanisms in Diabetic Complications 16 other non-coding RNAs can also in teract with transcriptional co -regulators and thereby further 337 influence epigenetics and tran scriptional regulation (82, 104). 338 Recent findings have demonstrated a critical role for miRs in various diseases. They have 339 been found to play key roles in proliferation, di fferentiation, development, and in cancer, where 340", + "Beltrami, C., Angelini, T.G., Emanueli, C., 2015. Noncoding RNAs in diabetes vascular complications. J. Mol. Cell. Cardiol. 89, 42 50.https://doi.org/10.1016/j.yjmcc. 2014.12.014 . Brookheart, R.T., Michel, C.I., Listenberger, L.L., et al., 2009. The non-coding RNA gadd7 is a regulator of lipid-induced oxidative and endoplasmic reticulum stress. J. Biol.Chem. 284, 7446 7454. https://doi.org/10.1074/jbc.M806209200 . Carter, G., Miladinovic, B., Patel, A.A., et al., 2015. Circulating long noncoding RNA", + "Noncoding RNAs that are induced by diabetic conditions can also promote theexpression of pathological genes via various post-transcriptional and post-translational mechanisms These epigenetic mechanisms and noncoding RNAs can lead to persistently open chromatin structures at pathological genes and sustained gene expression, which can also be a mechanism for metabolic memory Key epigenetic regulators, microRNAs and long noncoding RNAs could serve", + "tion among researchers ( Knoll et al., 2015 ). As an important post-transcriptional pathogenesis of diabetes, lncRNAs and their associated orchestrated networks are implicated in mediating complex pathological mechanisms of diabetes ( Kato et al., 2016; Liu et al., 2014 ). To delineate the inuence of lncRNAs and 172 iScience 19, 162176, September 27, 2019", + "coding RNAs [18]. A number of indirect lines of evi-dence point to the involvement of epigenetic changes indiabetic nephropathy. Murine models of disease progres-sion displaying temporal variation in gene expressionhave indicated these supra-sequence devices may beinvolved in the pathogenesis [19]. Gene expressionchanges reflect dynamic alterations in gene transcription and also messenger RNA stabi lity, which may be influ-", + "To conclude, it would be apt to state that lncRNAs are widely implicated in diverse domains of cell metabolism and their altered expression is associated with diabetes and its complications. Although originally thought to be non-functional, lncRNA genes transcribe into lncRNAs that exert important and specific functions in regulating cellular pathways. Due to this specificity, lncRNAs are considered better therapeutic targets. In addition, their expression patterns in tissues quite follow the progress of", + "NAs to be mapped to diabetic susceptible loci [49 52], all suggesting towards critical roles of lncRNAs in insulin resistance, diabetes, and its associated complications. LncRNAs asregulators ofislet function The pancreatic islet is an important central node to researchers to understand the pathophysiology of diabe-tes [53]. The possible regulation of islet development and function by lncRNAs was first demonstrated by Ding etal., where the lncRNA, H19 (Fig. 4), was shown to be involved", + "expected to rise due to the increasing incidence of diabetes, which necessitates the need for exploration of new molecular aspects of DR to expand the current scope of therapy. In the last two decades, the rapid advent of high-throughput genomic technology has made it evident that more than 97% of the human genome is comprised of non-protein-coding elements, such as non-coding RNAs (ncRNAs) 6. Although significant research has been conducted in annotating the transcripts that arise from these", + "regulation, control of mRNA decay, and sequestration of transcription factors. Although the underlying causes that define the diabetic phenotype are extremely intricate, most of the studies in the last decades were mostly centered on protein-coding genes. However, current opinion in the recent past has authenticated the contributions of diverse lncRNAs as critical regula - tory players during the manifestation of diabetes. The current review will highlight the importance of lncRNAs in regulating" + ], + [ + "review of polygenic risk scores for type 1 and type 2 diabetes. Int J Mol Sci. 2020;21(5):1703. 48. Khera AV, Chaffin M, Aragam KG, Haas ME, Roselli C, Choi SH, et al. Genome wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat Genet. 2018;50:121924. 49. Ding Y, Hou K, Burch KS, Lapinska S, Priv F, Vilhjalmsson B, et al. Large uncertainty in individual polygenic risk score estimation impacts PRS", + "(GWAS), polygenic risk scores (PRS) have shown promise to complement established clinical risk factors and inter vention paradigms, and improve early diagnosis and prevention of T2D. However, to date, T2D PRS have been most widely developed and validated in individuals of European descent. Comprehensive assessment of T2D PRS in non European populations is critical for equitable deployment of PRS to clinical practice that benefits global populations.", + "prediction of type 2 diabetes. N. Engl. J. Med. 359, 22082219 (2008). 45. Weedon, M. N. et al. Combining information from common type 2 diabetes risk polymorphisms improves disease prediction. PLoS. Med. 3, e374 (2006). 46. Euesden, J., Lewis, C. M. & OReilly, P . F. PRSice: Polygenic Risk Score software. Bioinformatics 31, 14661468 (2015). 47. Gatineau, M. et al. Adult obesity and type 2 diabetes (Public Health England,", + "(GWAS) in diverse populations have identified hundreds of genetic loci associated with T2D [79]. Polygenic risk scores (PRS), which aggregate the genetic risk of individ - ual alleles across the genome, are thus promising to pre - dict future T2D occurrence and improve early diagnosis, intervention, and prevention of T2D [1015]. However, to date, T2D PRS were most widely developed and vali - dated in individuals of European descent. Given that the predictive performance of PRS often attenuates in non-", + "in advance. Polygenic Risk Scores (PRS) were proposed by Duncan L. et al. [ 8] for risk analysis using the sum of the weight of each risk-associated locus of genomic sequence obtained from the corresponding evidence. These weights are assessed from the regression coefcient associated with each locus. These combined genetics features and correlation matrices would signicantly assist the entire eld of genomics study [ 9]. These studies on", + "performance. Conclusions: By integrating T2D GWAS from multiple populations, we developed and validated a transancestry PRS, and demonstrated its potential as a meaningful index of risk among diverse patients in clinical settings. Our efforts represent the first step towards the implementation of the T2D PRS into routine healthcare. Keywords: Polygenic risk score, Type 2 diabetes, Diverse populations, Clinical implementation", + "Owing to their small effect sizes, SNP associations have very little clinical applicability for risk prediction. A polygenic risk score (PRS) attempts to estimate the combined risk from multiple SNPs that have been associated with a certain trait with genome-wide sig-nificance. By accounting for a large proportion of the genetic variance underlying a trait, the overall effect size", + "8.Padilla-Mart nez, F., Collin, F., Kwasniewski, M., and Kretow- ski, A. (2020). Systematic review of polygenic risk scores for type 1 and type 2 diabetes. Int. J. Mol. Sci. 21, 1703 . 9.Rao, A., and Knowles, J. (2019). Polygenic risk scores in coro- nary artery disease. Curr. Opin. Cardiol. 34, 435440 . 10.Dikilitas, O., Schaid, D.J., Kosel, M.L., Carroll, R.J., Chute, C.G., Denny, J.A., Fedotov, A., Feng, Q., Hakonarson, H., Jar-vik, G.P., et al. (2020). Predictive utility of polygenic risk scores", + "partitioned polygenic scores according to factors of disease heteroge- neity, as successfully demonstrated for type 2 diabetes (32). Another strategy could be the mapping of statistically associated genetic loci to different immune-cell subtypes according to gene expression patterns derived from single-cell RNA sequencing (33). Autoimmune PRS, possibly in combination with other genetic and nongenetic predictors, may be of importance to manage the risk of", + "genome-wide polygenic risk scores (PRSs) for four lipid traits. We validated ( n= 4271) and subsequently tested associations of these scores with 3-year lipid changes in adolescents ( n= 620), carotid intima-media thickness (cIMT) in adult women ( n= 781), dyslipidemia ( n= 7723), and coronary heart disease (CHD) ( n= 2374 cases and 6246 controls) in type 2 diabetes (T2D) patients. (Continued on next page)" + ], + [ + "Tang X, Huang Y, Lei J, Luo H, Zhu X (2019) The single-cell sequenc- ing: new developments and medical applications. Cell Biosci 9:53. https ://doi.org/10.1186/s1357 8-019-0314-y Teo AKK etal (2018) Single-cell analyses of human islet cells reveal de-differentiation signatures. Cell Death Discov 4:14. https ://doi. org/10.1038/s4142 0-017-0014-5 Theis FJ, Lickert H (2019) A map of beta-cell differentiation pathways supports cell therapies for diabetes. Nature 569:342343. https ://", + "4. PRECISE CELLULAR GENOMICS Elucidating the molecular mechanisms that lead to beta cell dysfunction and T2D pathogenesis has been a major focus of diabetes research for decades. However, advances in single cell genomic proling techniques have led to greater understanding of non-beta cell type transcriptional regulation and suggest that they may play important roles in hallmark features of beta cell insuf ciency and", + "53. Eliasson L, Esguerra JL (2014) Role of non-coding RNAs in pancreatic beta-cell development and physiology. Acta Physiol (Oxf) 211:273284 54. Ding GL, Wang FF, Shu J etal (2012) Transgenerational glucose intolerance with Igf2/H19 epigenetic alterations in mouse islet induced by intrauterine hyperglycemia. Diabetes 61:11331142 55. Ku GM, Kim H, Vaughn IW etal (2012) Research resource: RNA-Seq reveals unique features of the pancreatic beta-cell tran-scriptome. Mol Endocrinol 26:17831792", + "understand each cell type s genomic architecture and better charac- terize their roles in islet resilience and failure. Experimental manipu- lation of the regulatory elements and/or the target genes identi ed by (epi)genomic approaches described above and modeling the putativepathways and processes they implicate in human islet cell lines (e.g., EndoC- bH1-H3) is essential to progress from correlation to causation. Similarly, transitioning from themouse (C57BL/6) to multiple mouse", + "therapeutic pathways for beta cell regeneration. An integrative analysis of whole-exome andRNA-sequencing data was employed to extensively characterize the genomic and molecularlandscape of insulinomas relative to normal beta cells. Here, we show at the pathway levelthat the majority of the insulinomas display mutations, copy number variants and/or dys-regulation of epigenetic modifying genes, most prominently in the polycomb and trithoraxfamilies. Importantly, these processes are coupled to co-expression", + "gesting that changes in alpha cell identity may ultimately lead to theirdysfunction. Analysis of normal and T2D islet single cells with simultaneous RNA-seq and patch clamping (patch-seq) also revealed subpopulations of alpha cells with varying enrichment for ER stressresponse genes (e.g., DDIT3, XBP1, PPP1R15A )[30]. Interestingly, this transcriptomic heterogeneity was consistent in normal and T2D islets", + "RNA-seq analysis: a tutorial. Mol Syst Biol 15:e8746. https ://doi.org/10.15252 /msb.20188 746 Ma L, Zheng J (2018) Single-cell gene expression analysis reveals -cell dysfunction and deficit mechanisms in type 2 diabe-tes. BMC Bioinform 19:515. https ://doi.org/10.1186/s1285 9-018-2519-1 Macaulay IC, Ponting CP, Voet T (2017) Single-cell multiom- ics: multiple measurements from single cells. Trends Genet 33:155168. https ://doi.org/10.1016/j.tig.2016.12.003", + "peak current. Prior single cell transcriptomic analyses have also notedsubpopulations of ER-stressed beta cells [31,32] which implicates the dysfunction of both alpha and beta cells in diabetes pathogenesis.Similarly, the integrity of beta and alpha cell functions seem to beReview S18MOLECULAR METABOLISM 27 (2019) S15 eS24/C2112019 Published by Elsevier GmbH. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ). www.molecularmetabolism.com", + "to understanding human development using single-cell tran-scriptomics. Development 144:1584. https ://doi.org/10.1242/dev.15045 8 Camp JG, Wollny D, Treutlein B (2018) Single-cell genomics to guide human stem cell and tissue engineering. Nat Methods 15:661667. https ://doi.org/10.1038/s4159 2-018-0113-0 Carrano AC, Mulas F, Zeng C, Sander M (2017) Interrogating islets in health and disease with single-cell technologies. Mol Metab 6:9911001. https ://doi.org/10.1016/j.molme t.2017.04.012", + "Advances ofsingle -cell genomics andepigenomics inhuman disease: whereare we now? 1 3 Brissova etal. 2018; Tritschler etal. 2017). Moreover, an increase in hyperglycaemia has been associated with a loss of beta-cell mass, function and organization and is the cell type most frequently studied for insulin resistance (Carrano etal. 2017; Lawlor etal. 2017b; Segerstolpe etal. 2016; Theis and Lickert 2019; Tritschler etal. 2017). Notably, single-cell transcriptome profiling has been" + ], + [ + "To date, the overwhelming majority of studies including and assessing genetic variation have pro led the steady state patterns of epigeneticmodi cations and gene expression in islets or their constituent cell types. Others have compared how these steady state measures differ between T2D and non-diabetic (ND) individuals [13,16,40 e44]. Sur- prisingly, these studies, especially transcriptome analyses, haveidenti ed only modest alterations despite clear phenotypic differences", + "T1D and resulting complications (99). These epig enomic profiling studies suggest that, while a 415 reasonably stable histone methylation pattern is maintained in healthy individuals over time in a 416 cell-type specific setting, this pa ttern can be disrupted in a dis ease state. Moreover, they also 417 provide a glimpse of the inflammatory cell epig enome under the diabetic state and suggest that 418 new information about diabetes, its complicatio ns and metabolic memory can be obtained by 419", + "hyperglycaemia, epigenetic changes have also been noted in other experimental settings of hyperglycaemia. For example, increased DNA methylation has been described for the promoter region of the peroxisome proliferator-activated receptor- g(PPAR g) coactivator-1 agene (PPARGC1A) in diabetic islets ( Ling et al., 2008 ). Similar hypermethylation in the promoter region of the PPARGC1A gene has been noted in the skeletal muscle from diabetic patients,", + "and correlated with mitochondrial content ( Barr /C18es et al., 2009 ). Epigenetic changes have also been suggested to be responsible forthe legacy effect of reduced risk of vascular complications after a period of sustained tight glucose control, or metabolic memory of transient hyperglycaemia and increased risk of diabetic vascular injury ( Pirola et al., 2010 ). Histone methylation variations have been noted in monocytes cultured in high glucose, as well as blood", + "Epigenetic Mechanisms in Diabetic Complications 17 Interestingly, the sirtuin (SIRT) family of deacetylases, specifically SIRT1, has been found to 360 regulate several factors involved in metabolism, adipogenesis a nd insulin secretion (86). HATs 361 and HDACs can also modulate NF- B transcriptional activity (4, 44) resulting in changes in 362", + "ing that environment and diet may influence epigenetic mod-ifications that predispose individuals to diabetes [ 46]. Aber- rant DNAme has also been reported in the reduced expression of genes involved in diabetes and metabolism, and DNAme variations have also been noted near diabetes susceptibility genes and enhancers [ 15,47]. Genomic DNA from diabetic patients with nephropa- thy relative to those without displayed differential meth- ylation at several genes, including UNC13B , which had", + "of diabetes mellitus on the body is a high glucose stressed condition, altering substrate metabolism and causing systemic inflammation [60]. Due to this environmental change, researchers have shown how epigenetic changes occur across most, if not all, tissues that are impacted by diabetes mellitus [49, 61]. In the cardiovascular system, the heart, circulatory system, and regulating immune system are all tran -", + "nephropathy. Exp. Physiol. 98, 934945 (2013). 48. Reddy, M.A., Tak Park, J. & Natarajan, R. Epigenetic modifications in the pathogenesis ofdiabetic nephropathy. Semin. Nephrol. 33, 341353 (2013). 49. Li, S.L. etal. Enhanced proatherogenic responses in macrophages and vascular smooth muscle cells derived from diabetic db/db mice. Diabetes 55, 26112619 (2006). 50. El-Osta, A. etal. Transient high glucose causes persistent epigenetic changes and altered gene", + "exhibit decreased plasticity of genome-wide muscle DNA methylation by high-fatoverfeeding. Diabetologia 2014;57:1154-1158. 53. Nilsson E, Jansson PA, Perfilyev A, et al. Altered DNA methylation and differential expression of genes influencing metabolism and inflammation in adipose tissue from subjects with type 2 diabetes. Diabetes 2014;63:2962-2976. 54. Aslibekyan S, Demerath EW, Mendelson M, et al. Epigenome-wide study identifies", + "etal. Hyperglycemia induces a dynamic cooperativity of histone methylase and demethylase enzymes associated with gene-activating epigenetic marks that coexist on the lysine tail. Diabetes (2009) 58:122936. doi:10.2337/ db08-1666 111. Keating S, Plutzky J, El-Osta A. Epigenetic changes in diabetic and cardio-vascular risk. Circ Res (2016) 118:170622. doi:10.1161/CIRCRESAHA. 116.306819 112. Paneni F, Volpe M, Lscher TF, Cosentino F. SIRT1, p66(Shc), and Set7/9 in" + ], + [ + "A variety of cellular and animal models have been developed and applied over the past few years to experimentally manipulate cis-regulatory elements and their target gene function as it related to beta cell/isletfunction, glucose homeostasis, and T2D pathogenesis. CRISPR/Cas9 hasrevolutionized our ability to modify genomes and epigenomes almost at will. Unsurprisingly, CRISPR (epi)genome editing tools can and have been used to target putative T2D target genes [54] orcis-REs[55] in beta", + "(276279). Through CRISPR-mediated HDR and base editing, it is possible to correct the vast majority of genetic variants, if notall. Conversion of GWAS-identi ed non-coding variants has not been conducted/documented in the diabetes eld, but it seems inevitable that such work will be carried out in the near futureHu et al. Genome Editing of Pancreatic Beta Cells Frontiers in Endocrinology | www.frontiersin.org October 2020 | Volume 11 | Article 576632 11", + "Cas9 editing to restore insulin production in differentiated iPSCcells that mimicked neonatal diabetes ( 251,252). Likewise, Shi et al. converted a patient-speci c mutation in GATA6 gene and showed that the mutation involved (GATA6 R456C) has a similar effect to GATA6 knockout ( 21). Most recently, correction of a variant in the Wolfram syndrome 1 ( WFS1 ) gene by CRISPR- mediated HDR improved insulin secretion in iPSC-differentiatedb-like cells ( 253). Studies on GWAS identi ed genetic variants", + "in response to various stimuli including glucose aftertransplantation in an immunocompromised mouse model (230,231). However, the use of iPSC is controversial and there are some concerns over genetic and epigenetic variations iniPSCs which might affect cell function after differentiation ( 275). Manipulation of hESC/iPSC cells via CRISPR-Cas9 technology provides a platform for the correction of genomic mutations not only in diabetes but in other disease elds as well", + "hPSCs [48,49] for correcting the COL7A1 [50] anda1-antitrypsin genes [51]. Given the superior cutting ef ciency, CRISPR/Cas9 is increasingly becoming the favored choice for genome editing inhPSCs [16,52] . 3.2. Employing hPSCs and genome editing tools to study diabetes and metabolic syndromes In general, the strategy to carry out in vitro disease modeling of dia-", + "Due to its simplicity and adaptability, CRISPR has rapidly become the most popular genome editing tool available for the mammalian genome ( 50,63). Because NHEJ DNA repair often introduces unwanted indels at the Cas9 cutting site, CRISPR hasbeen used to knock-out genes by introducing frameshiftmutations, resulting in protein depletion ( 156,157). In the diabetes eld, CRISPR has also been adopted to study several genes in bcell lines and in human ES-derived bcells ( 21,151,", + "RNP and single strand edDNA (ssDNA) donor which carriesdesired changes such as insertion of loxP site ( 255,259265). Using CRISPR-Cas9, leptin and leptin receptor knockout mice have been established as tools in diabetes and obesity research ( 160,255,256). Knock-in mouse models have also been established via HDR to achieve cell-speci c deletion of the gene ( 266). Genome Editing: Clinical Application in Diabetes An important goal in genetic research is to identify the genetic", + "to how CRISPR/Cas9 technology may nd clinical application in patients with diabetes. Keywords: genome editing, beta cell, genome-wide association studies, maturity onset of diabetes of the young, stem cells, mouse models INTRODUCTION Type 2 diabetes (T2D) affects an estimated 425 million people worldwide, a number predicted to rise to 629 million by 2045 ( 1). The disease usually involves insulin resistance but is ultimately the result", + "samples ( 236). CRISPR technology has been used recently to correct point mutations in patient-derived iPSCs to target diabetes-relatedgene defects. To date, the most ef cient method used in iPSC is CRISPR/Cas9-based homology-directed repair (HDR). Here, a Cas9-mediated cut is generated adjacent to the site of interest. A homologous donor template with the intended nucleotidechange containing silent mutations in the gRNA sequence(167) can then be recombined by HDR. This approach has", + "free IPSCs from Human Pancreatic Cells Using the CRISPR-Cas9 System. J Vis Exp JoVE (2017). doi: 10.3791/56260 277. Millette K, Georgia S. Gene Editing and Human Pluripotent Stem Cells: Tools for Advancing Diabetes Disease Modeling and Beta-Cell Development. Curr Diabetes Rep (2017) 17:116. doi: 10.1007/s11892-017-0947-3Hu et al. Genome Editing of Pancreatic Beta Cells Frontiers in Endocrinology | www.frontiersin.org October 2020 | Volume 11 | Article 576632 19" + ], + [ + "The integration of genetic, epigenetic, transcriptomic and phenotypic information allows to identify genes and novel metabolic pathway targets that deserve further attention to elucidate mechanistic relationships with insulin resistance and pancreatic islet failure. Although the GWASs and EWASs shed light onto (epi)genomic landscape of T2D to a great extent, these methods have still explicit limitations to conquer, such as sample size, small effect size, low allele frequency, genetic heterogeneity", + "map of the human genome, spurred larger multi-institutional programs (e.g., 1000 Genomes Projects, Encyclopedia of DNA Elements [ENCODE], and Roadmap Epigenomics), that have the goal of tracking genomic and epigenomic changes across multiple populations [ 8]. Aforementioned studies enabled GWASs for complex diseases such as T2D. DNA amplication, Sanger sequencing, and microarray studies have shed light on the genetics of diabetes but have only provided a limited amount of data. An", + "Abstract While genome-wide association studies (GWAS) and candidate gene approaches have identified many genetic variants that contribute to disease risk as main effects, the impact of genotype by environment (GxE) interactions remains rather under- surveyed. To explore the importance of GxE interactions for diabetes-related traits, a tool for Genome-wide Complex Trait", + "The advancement that has taken place in Genome-Wide Association Studies (GWAS) holds tremendous information related to various gene patterns associated with divergent illnesses that are complex and challenging to perform reductive analysis from a single locus, as stated by Cho Ys [6] and Coron [7]. The evolution of GWAS has focused on integrating data related to multi-locus across the gene that would assist in predicting complex illnesses", + "1. Genome-wide association studies (GW AS) have made considerable progress in identifying genetic risk factors and in providing evidence for more in-depth understanding of the biological and pathological pathways underlying T2D. A recent study performed a meta-analysis of T2D across 32 GW AS of European ancestry par - ticipants and identified 243 genome-wide significant loci (403 distinct genetic variants) associated with T2D risk", + "1. Introduction Genome wide association studies (GWAS) of type 2 diabetes mellitus and relevant endophenotypes have shed new light on the complex etiology of the disease and underscored the multiple molecular mechanisms involved in the pathogenic processes leading to hyperglycemia [1]. Even though these studies have successfully mapped many diabetes risk genetic loci that could not be detected by linkage analysis, the risk single nucleotide poly-", + "how they will continue to expand our understanding of the genetic risk factors and underlying biology of diabetes. Keywords Genotyping .Genome-wide association . Sequencing .Imputation .Exome .Genome . Fine-mapping .Diabetes .Quantitative traits .Metabochip . Single nucleotide polymorphism Introduction GWA studies have made progress toward understanding the inherited basis of type 1 and type 2 diabetes by detecting disease-associated DNA variants, usually with allele fre-", + "complementary systems level data such as that related to protein- protein interactions and to and gene expression can provideinsights into the mechanisms underlying pathogenesis of complextraits [2224]. Here, we have combined these approaches towarddeciphering genome to phenome correlation in T2D ( Figure 1 ). Given that T2D GWAS genes do not directly relate to disease", + "phenotypes [2,6]. The recently accomplished deep sequencing of human exomes has indeed suggested that rare variations contribute substantially to human phenotypic variation and disease susceptibility [73]. Availability of post-GWASs era data for T2D will be crucial in examining genome to phenomecorrelation in greater details. Emerging methods in pathway-wide analysis and integrative network based analysis of genetic association data in complex disorders will further help accelerate", + "Abstract Genome-wide association studies (GWASs) have discovered association of several loci with Type 2 diabetes (T2D), a common complex disease characterized by impaired insulin secretion by pancreatic bcells and insulin signaling in target tissues. However, effect of genetic risk variants on continuous glycemic measures in nondiabetic subjects mainly elucidatesperturbation of insulin secretion. Also, the disease associated genes do not clearly converge on functional categories" + ], + [ + "maternal diabetes reduces the precision of gene regulation in exposed individuals. Loss of precision in embry-onic gene regulation may include changes to the epigenome via deregulated expression of chromatin-modify-ing factors. Unraveling the mechanisms underlying such epigenetic modications in diabetic pregnancies willhelp to understand how teratogenic insults compromise embryonic development and possibly provide ave-nues for therapeutic intervention. Birth Defects Research (Part A) 88:601611, 2010.", + "and metabolic imprinting: the ongoing effects of maternal hyper-glycemia. Diabetes Care 30:2287 2292 9. Clausen TD, Mathiesen ER, Hansen T et al (2008) High prevalence of type 2 diabetes and pre-diabetes in adult offspring of women withgestational diabetes mellitus or type 1 diabetes: the role of intrauter- ine hyperglycemia. Diabetes Care 31:340 346 10. Solomon CG, Willett WC, Carey VJ et al (1997) A prospective study of pregravid determinants of gestational diabetes mellitus. JAMA 278:1078 1083", + "M. Gestational diabetes alters offspring DNA methylation profiles in human and rat: Identification of key pathways involved in endocrine system disorders, insulin signaling, diabetes signaling, and ILK signaling. Endocriniology 2015;156:2222 -38. [33] Murphy SK, Huang Z, Hoyo C. Differentially methylated regions of imprinted genes in prenatal, perinatal and postnatal human tissues. PLOS ONE 2012;7:e40924.", + "12. Kim JK, Samaranayake M, Pradhan S. Epigenetic mechanisms in mammals. Cell Mol Life Sci. 2009;66:596-612. 13. Horsthemke B, Buiting K. Genomic imprinting and imprinting defects in humans. Adv Genet. 2008;61:225-246. 14. Iacobuzio-Donahue CA. Epigenetic Changes in Cancer. Annu Rev Pathol. 2009;4:229-249. 15. Temple IK. Imprinting in human disease with special reference to transient neonatal diabetes and Beckwith-Wiedemann syn- drome. Endocr Dev. 2007;12:113-123.", + "and Knowler W C. Intrauterine exposure to diabetes conveys risks for type 2 diabetes and obesity: A study of discordant sibships. Diabetes 2000;49:2208 -11. [11] Feil R and Fraga MF. Epigenetics and the environment: Emerging patterns and implications. Nature Reviews Genetics 2012;13:97 -109. [12] Recillas -Targa F. DNA Methylation, Chromatin boundaries, and mechanisms of genomic imprinting. Archives of Medical Research 2002;33:428 -38.", + "53. T ravers,M.E. etal. Insights into the molecular mechanism for type2 diabetes susceptibility at the KCNQ1 locus from temporal changes in imprinting status in human islets. Diabetes 62, 987992 (2013). 54. Gulli,G., Ferrannini,E., Stern,M., Haffner,S. &DeFronzo,R.A. The metabolic profile of NIDDM isfully established in glucose-tolerant offspring of twoMexican-American NIDDM parents. Diabetes 41, 15751586 (1992). PRIMER NATURE REVIEWS | DISEASE PRIMERS VOLUME 1 | 2015 | 17", + "Gaudet, D., Hivert, M.F., Brisson, D., Bouchard, L., 2013 Sep. Gestational diabetesmellitus epigenetically affects genes predominantly involved in metabolic dis- eases. Epigenetics 8 (9), 935 e943. Salbaum, J.M., Kappen, C., 2012 Oct. Responses of the embryonic epigenome to maternal diabetes. Birth Defects Res. A Clin. Mol. Teratol. 94 (10), 770 e781. Salbe, A.D., Lindsay, R.S., Collins, C.B., Tataranni, P.A., Krakoff, J., Bunt, J.C., 2007 Feb.", + "environment are probably mediated by a permanent program-ming of the developing offspring, e.g. by the mechanism ofimprinting. Of interest, the increased risk of diabetes continuesinto subsequent generations, suggesting the changes also affectthe germ cell line [143]. Conclusions There is little doubt that some animal models of diabetes have", + "tal diabetes and later onset diabetes: a case of inher - ited insulin resistance. Arch. Dis. Child. 72:5657. 6. Temple, I.K., et al. 1995. An imprinted gene(s) for diabetes? Nat. Genet. 9:110112. 7. Temple, I.K., et al. 1996. Further evidence for an imprinted gene for neonatal diabetes localised to chro -", + "1994; Martinez-Frias et al., 1998). The underlying mecha-nisms are not well understood, but are thought to involve various responses of the embryonic genome to the adverse intrauterine environment (Greene, 2001;Loeken, 2008). To explore how conditions of maternal diabetes affect gene expression in the embryo, we recently conducted expression proling experiments on embryos from dia-betic dams compared to embryos from normal dams(Pavlinkova et al., 2009). We were able to demonstrate" + ], + [ + "genome-wide association scans on type 2 dia-betes (Lango et al, 2008 ; van Hoek et al, 2008 ). Both studies found a similar predictive value showing only a marginal improvement in the prediction of type 2 diabetes beyond classicalclinical characteristics. Thus, despite overwhelming signicances and repeated replications, the explained variance andpredictive value of the currently identied sus- ceptibility loci is too low to be clinically useful. 5 GeneEnvironment Interactions in Obesity and Diabetes", + "actions between genetic variation and environmental exposures and medical therapies has important implications for the predic- tion, targeted prevention, and s tratified treatment of T2D and many other diseases. The literature on gene-e nvironment interactions in diabetes-related traits is extensive, but few studies are accom- panied by adequate replication data or compelling mechanistic explanations. Moreover, most studies are cross-sectional, from which temporal patterns and causal effects cannot be", + "ined for a range of disorders, from diabetes, cancer and in ammatory bowel disease to depression. We refute the contention that incorporating the measurement of genotype into longitudinal-epidemiological studies is wasteful or unlikely to yield signi cant bene ts. 2008 Genetic effects on environmental vulnerability to disease. Wiley, Chichester (Novartis Foundation Symposium) p 128142 Slow progress understanding the genetic basis of many common diseases has been", + "In principle, each of these loci provides an opportunity to define the genetic architecture and pathophysiology of these traits. The earliest successes for genetic discovery in diabetes and obesity arose from the study of monogenic and syndromic forms of disease, for which the segregation of rare, but highly penetrant, alleles could be tracked using family-based linkage approaches that are well suited to that setting. Maturity-onset diabetes of the young, for example, accounts for ~12% of cases", + "wide GxE interactions in explaining the variance of diabetes-related traits. Citation: Zheng J-S, Arnett DK, Lee Y-C, Shen J, Parnell LD, et al. (2013) Genome-Wide Contribution of Genotype by Environment Interaction to Variation of Diabetes-Related Traits. PLoS ONE 8(10): e77442. doi:10.1371/journal.pone.0077442 Editor: Maria Eugenia Saez, CAEBi, Spain Received April 10, 2013; Accepted September 3, 2013; Published October 28, 2013", + "data sharing to advance complex disease research. Nat. Rev. Genet. 17, 535549 (2016). 82. Franks,P .W., Pearson,E. & Florez,J.C. Gene- environment and gene-treatment interactions in type2 diabetes: progress, pitfalls, and prospects. Diabetes Care 36, 14131421 (2013). 83. Hagberg,J.M., Jenkins,N.T . & Spangenburg,E. Exercise training, genetics and type2 diabetes- related phenotypes. Acta Physiol. 205, 456471 (2012). 84. Langenberg,C. etal. Gene-lifestyle interaction and", + "Genomics and geneenvironment interactions Even though many cases of T2DM could be prevented by maintaining a healthy body weight and adhering to a healthy lifestyle, some individuals with prediabetes mel - litus are more susceptible to T2DM than others, which suggests that individual differences in response to life - style interventions exist76. Substantial evidence from twin and family studies has suggested a genetic basis of T2DM77. Over the past decade, successive waves of", + "DNA variation with disease processes in a range of settings, from cell lines to human populations, and major advances have been made in coupling these complex datasets with information about extrinsic environmental exposures including drug prescription in ways that allowthe logical interrogation of gene-drug and gene-lifestyle interactions. Doing so may teach us about disease etiology and help stratify type 2 diabetes (T2D) into subclasses that can be treated more effectively, with", + "fuel subsequent functional and clinical translation studies. This is important, because diabetes medicine may rely increas- ingly on genomic stratification of patient populations and disease phenotype, for which gene-environment interaction studies might prove highly informative. How Are Gene-Environment Interactions Defined? The term gene-environment interaction has different meanings to different biomedical re searchers (see Supplement 1for glossary of terms used). However, here, we focus on the", + "Nutrients 2014, 6 5362 48. Cornelis, M.C.; Hu, F.B. Gene -enviroment interactions in the development of type 2 diabetes: Recent progress and continuing challenges. Annu. Rev. Nutr. 2012, 32, 245259. 49. Lee, Y.C.; Lai, C.Q.; Ordovas, J.M.; Parnell, L.D. A database of gene -enviroment interactions pertaining to blood lipid traits, cardiovascular disease and type 2 diabetes. J. Data Mining Genomics Proteomics 2011, 2, 106, doi:10.4172/2153- 0602.1000106." + ], + [ + "4. PRECISE CELLULAR GENOMICS Elucidating the molecular mechanisms that lead to beta cell dysfunction and T2D pathogenesis has been a major focus of diabetes research for decades. However, advances in single cell genomic proling techniques have led to greater understanding of non-beta cell type transcriptional regulation and suggest that they may play important roles in hallmark features of beta cell insuf ciency and", + "Genes 2018 ,9, 374 7 of 19 4. Single-Cell RNA-seq as a Novel Approach in High-Throughput Type 2 Diabetes Research Islets of Langerhans are heterogeneous structures that consist of different cell types. Further research is needed to track genetic changes in individual pancreatic islet cells and in sorted cell populations. The massive development of NGS allowed the sequencing of single cells from human pancreatic islets. Considering the cell-type heterogeneity within Langerhans islets, such an approach", + "Advances ofsingle -cell genomics andepigenomics inhuman disease: whereare we now? 1 3 Brissova etal. 2018; Tritschler etal. 2017). Moreover, an increase in hyperglycaemia has been associated with a loss of beta-cell mass, function and organization and is the cell type most frequently studied for insulin resistance (Carrano etal. 2017; Lawlor etal. 2017b; Segerstolpe etal. 2016; Theis and Lickert 2019; Tritschler etal. 2017). Notably, single-cell transcriptome profiling has been", + "Tang X, Huang Y, Lei J, Luo H, Zhu X (2019) The single-cell sequenc- ing: new developments and medical applications. Cell Biosci 9:53. https ://doi.org/10.1186/s1357 8-019-0314-y Teo AKK etal (2018) Single-cell analyses of human islet cells reveal de-differentiation signatures. Cell Death Discov 4:14. https ://doi. org/10.1038/s4142 0-017-0014-5 Theis FJ, Lickert H (2019) A map of beta-cell differentiation pathways supports cell therapies for diabetes. Nature 569:342343. https ://", + "53. Eliasson L, Esguerra JL (2014) Role of non-coding RNAs in pancreatic beta-cell development and physiology. Acta Physiol (Oxf) 211:273284 54. Ding GL, Wang FF, Shu J etal (2012) Transgenerational glucose intolerance with Igf2/H19 epigenetic alterations in mouse islet induced by intrauterine hyperglycemia. Diabetes 61:11331142 55. Ku GM, Kim H, Vaughn IW etal (2012) Research resource: RNA-Seq reveals unique features of the pancreatic beta-cell tran-scriptome. Mol Endocrinol 26:17831792", + "24. Nica, A. C. et al. Cell-type, allelic, and genetic signatures in the human pancreatic beta cell transcriptome. Genome Res. 23, 1554 1562 (2013). 25. Takane, K. K., Bender, A. & Stewart, A. F. Speci c targeting and sorting of puried human beta cells: de ning the human beta cell transcriptome. ADA Scienti c Sessions, San Francisco (2014). 26. Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008).", + "5. Genome-Wide Proling of Epigenetic Changes in Pancreatic Islets and Peripheral Tissues Epigenetic data added another layer of complexity to our understanding of the genomic bases of T2D. Given that a variable epigenetic pattern can modulate the link between the SNP and trait, consideration of this interplay is critically important. Molecular epigenetics involves changes in gene function that occur without a change in the nucleotide sequence via DNA methylation, histone", + "and model organisms. The combination of data from high-throughput approaches and association studies has provided compelling evidence that some epigenetic markers contribute to the risk of T2D [ 57,58]. Epigenetic alterations have been shown to affect the expression of genes that are crucial for maintaining pancreatic islet secretory capacity, survival, and functional identity and the proper response to insulin in peripheral tissues [ 59,60]. Furthermore, several epigenetic signatures, such", + "Epigenomic approaches: applications in diabetic complications research Epigenetic studies in human disease have been greatly accel- erated as a result of advances in whole-genome and epige- nome profiling technologies as well as bioinformatics andgenomic data analysis platforms [ 99,100]. DNAme is analysed using bisulfite conversion of genomic DNA, immu- noprecipitation of methylated DNA, followed byhybridisation to arrays or next-generation sequencing to ob-", + "understand each cell type s genomic architecture and better charac- terize their roles in islet resilience and failure. Experimental manipu- lation of the regulatory elements and/or the target genes identi ed by (epi)genomic approaches described above and modeling the putativepathways and processes they implicate in human islet cell lines (e.g., EndoC- bH1-H3) is essential to progress from correlation to causation. Similarly, transitioning from themouse (C57BL/6) to multiple mouse" + ] + ], + "task_id": [1,2,3,4,5,6,7,8,9,10,1,2,3,4,5,6,7,8,9,10] +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/gpt4o_de_gn.json b/gnqa/paper2_eval/data/dataset/gpt4o/gpt4o_de_gn.json new file mode 100644 index 0000000..67d6287 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/gpt4o_de_gn.json @@ -0,0 +1,289 @@ +{ + "question": [ + "How do recent advancements in network-based integrative genomics alter our understanding of complex trait architectures?", + "What are the latest methodological improvements in evaluating gene-environment interactions using GeneNetwork.org?", + "How do multi-omics data integration techniques enhance the prediction accuracy of phenotypic traits in GeneNetwork datasets?", + "What are the computational challenges and solutions in analyzing large-scale transcriptomic data within GeneNetwork.org?", + "How has the inclusion of data from diverse populations impacted the generalizability of findings on GeneNetwork.org?", + "What novel insights have been obtained from GeneNetwork.org regarding the genetic basis of psychiatric disorders?", + "How do advancements in machine learning algorithms contribute to the deconvolution of gene expression data in complex tissues?", + "What role do enhancer-promoter interactions play in the regulation of gene networks uncovered through GeneNetwork.org?", + "How can the integration of ATAC-seq data with RNA-seq data on GeneNetwork.org inform about chromatin accessibility and gene regulation?", + "What are the latest strategies for inferring causal relationships within gene networks using data from GeneNetwork.org?", + "How do advancements in single-nucleus RNA sequencing provide more granular insights into cell-type-specific gene expression networks?", + "What impact have recent discoveries in non-coding RNA regulation had on refining gene interaction maps on GeneNetwork.org?", + "How are spatial transcriptomics approaches being integrated into GeneNetwork.org to enhance understanding of tissue architecture and function?", + "How do recent developments in quantitative trait locus (QTL) mapping refine our understanding of gene regulatory variants?", + "What are the implications of incorporating epigenomic data, such as histone modification maps, into the gene networks on GeneNetwork.org?", + "How do recent findings on 3D genome organization contribute to our understanding of functional genomic networks?", + "What are the potential applications of artificial intelligence in improving the annotation and interpretation of gene networks?", + "How has the study of genetic pleiotropy been advanced by data available on GeneNetwork.org?", + "What novel genetic pathways have been identified in GeneNetwork.org studies related to aging and lifespan?", + "How do polygenic risk scores (PRS) developed using GeneNetwork.org data enhance the prediction and prevention of complex diseases?" + ], + "answer": [ + "Recent advancements in network-based integrative genomics have significantly enhanced our understanding of complex trait architectures by enabling a more comprehensive analysis of the interactions between genetic and environmental factors. These advancements allow for the integration of gene variants and environmental factors to better understand the networks controlling complex traits [1]. The use of systems genetics approaches facilitates the connection of different trait networks with networks of gene and environmental variants, which helps in identifying the global genetic modulation of complex phenotypes [1]. Moreover, the integration of genotypic, expression, and other data within a Bayesian network framework has been shown to improve our mechanistic understanding of how DNA perturbations can induce changes in gene networks that ultimately affect disease outcomes [2]. This integrative approach provides a more detailed view of the polygenic and pleiotropic networks that modulate complex traits, such as those affecting the nervous system [5]. Overall, these advancements in network-based integrative genomics offer a more nuanced and interconnected view of complex trait architectures, moving beyond single-gene analyses to consider the broader network of interactions that contribute to phenotypic outcomes.", + "The latest methodological improvements in evaluating gene-environment interactions using GeneNetwork.org include the addition of data from 10 species, multi-omics analysis, updated code, and new tools, making it a valuable resource for predictive medicine and systems genetics [1]. These enhancements allow for more comprehensive analyses and facilitate the study of correlations between traits and data mining in genomic regions containing candidates for quantitative trait genes [4]. Additionally, GeneNetwork is continuously updated to support systems genetics analyses, which can aid in the discovery, replication, validation, and translation of gene-environment interactions [8].", + "Multi-omics data integration techniques enhance the prediction accuracy of phenotypic traits in GeneNetwork datasets by allowing for the construction of biological networks that are predictive of molecular interactions and phenotypic outcomes. When data on DNA variation is combined with gene expression, protein interactions, and DNA-protein binding information, it enables the creation of comprehensive models that can predict complex phenotypes more accurately [6]. This integration facilitates the study of relationships between genes, pathways, and phenotypes, providing a more holistic view of the biological systems involved [2]. Additionally, the use of sophisticated statistical and computational tools in conjunction with multi-omics data allows for the genetic dissection and synthesis of traits, further enhancing predictive accuracy [7].", + "Analyzing large-scale transcriptomic data within GeneNetwork.org presents several computational challenges and solutions: 1. **Computational Complexity and Memory Requirements**: The analysis of large networks is computationally complex and requires significant memory resources. Traditional methods may not be ideal for handling such large datasets [5]. 2. **Scalable Algorithms**: To address these challenges, scalable parallel algorithms have been developed. These algorithms are designed to handle fixed-parameter tractable (FPT) problems, which are crucial for managing large-scale data efficiently [6]. 3. **High-Performance Implementations**: Genome-scale algorithms and high-performance implementations are utilized to facilitate the genetic analysis of gene transcription, making it possible to manage and analyze large volumes of data effectively [2]. 4. **Visualization and Biological Validation**: Visualization methods and biological validation are essential to translate complex data into understandable results. These methods help in comparing graphs collected in a systems genetic context, reflecting differences in time, tissue, and treatment effects [3]. 5. **Tools for Large-Scale Network Construction**: Tools like WGCNA, GeneNet, ARACNE, and SPACE are specifically designed to construct gene networks at very large scales. These tools also provide functionalities for analyzing and visualizing resulting networks, including identifying subnetworks [5]. These solutions collectively help in overcoming the computational challenges associated with large-scale transcriptomic data analysis within GeneNetwork.org.", + "The inclusion of data from diverse populations on GeneNetwork.org aims to enhance the generalizability of findings by addressing the limitations associated with genetic studies that predominantly focus on less diverse populations. The goal is to increase the diversity of underrepresented populations, with targeted recruitment aimed at over 50% non-European ancestry [9]. This effort is expected to improve the applicability of genetic findings across different populations, thereby enhancing the generalizability of the research outcomes. By incorporating data from a broader range of genetic backgrounds, GeneNetwork.org can provide more comprehensive insights into genetic networks and pathways, which are applicable to a wider array of populations.", + "The insights obtained from GeneNetwork.org regarding the genetic basis of psychiatric disorders include the identification of two fundamental yet distinct genetic components shared by major neuropsychiatric disorders. The first component is involved in central nervous system (CNS) development, neural projections, and synaptic transmission [1]. Additionally, the polygenicity of psychiatric illnesses has been highlighted, indicating that psychiatric disorders are influenced by multiple genes, and there is a degree of single nucleotide polymorphism (SNP) sharing among disease cases, which helps estimate the common, inherited portion of these disorders [2]. Furthermore, shared and unique genetic factors have been identified, which highlight key gene sets and molecular processes that may lead to improved diagnosis and treatment of psychiatric disorders [3].", + "Advancements in machine learning algorithms contribute to the deconvolution of gene expression data in complex tissues by enabling the prediction of cell-type proportions from bulk genomics data. This computational deconvolution is crucial for understanding the relative abundance of various cell types within a tissue, which is a key step in analyzing gene expression data from complex tissues [1]. Additionally, machine learning methods, such as decision tree methods, are explored to model functional dependencies and predict co-expressed gene profiles, which can further aid in the deconvolution process by identifying regulatory elements and signals that vary with disease status [4]. These advancements allow for more accurate and insightful analysis of gene expression data, facilitating the identification of transcriptional changes and regulatory networks in complex tissues.", + "Enhancer-promoter interactions play a significant role in the regulation of gene networks by influencing gene expression levels and patterns. These interactions are crucial for determining cell-specific gene expression, as enhancers can regulate genes over long distances and are involved in complex regulatory networks [4]. Approximately 90,000 enhancer-promoter interactions have been identified, with a majority occurring within the same topologically associating domains (TADs), which suggests a structured and hierarchical organization of these interactions within the genome [3]. Genes with more enhancers tend to have higher expression levels, indicating that enhancers contribute to the regulation of gene expression by interacting with promoters [3]. Additionally, enhancer-promoter interactions can involve long-range interactions, making the prediction of specific enhancer-target relationships challenging [1]. These interactions are part of the broader gene networks that include various regulatory elements and factors, highlighting their importance in the regulation of gene networks as uncovered through platforms like GeneNetwork.org.", + "The integration of ATAC-seq data with RNA-seq data can provide valuable insights into chromatin accessibility and gene regulation by combining information about open chromatin regions with gene expression profiles. ATAC-seq is a technique that characterizes accessible chromatin regions, which are often associated with transcriptional activity [1]. This method can simultaneously profile open chromatin, transcription factor-binding footprints, and nucleosome positioning [2]. By integrating this data with RNA-seq, which measures gene expression levels, researchers can relate chromatin accessibility to gene expression patterns. For example, by creating a reference map using single-cell RNA sequencing (scRNA-seq) and assigning cell-type identities, researchers can relate cell-type-resolved accessible chromatin to gene expression [3]. This integration helps in identifying cis-regulatory programs by aggregating reads from cells within each ATAC-seq cluster and linking them to gene expression data. Overall, the integration of ATAC-seq and RNA-seq data allows for a comprehensive understanding of how chromatin accessibility influences gene regulation, providing insights into the regulatory elements that control gene expression in different cellular contexts.", + "The latest strategies for inferring causal relationships within gene networks using data from GeneNetwork.org involve several advanced methodologies. One approach is the use of Bayesian network inference, which has been advanced to generate causal networks from observational biological data [2]. This method allows for the modeling of probabilistic relationships between genes and can help infer causality from complex datasets. Additionally, there is a focus on using genetic markers to orient causal inference in genome-wide association studies, which is critical for understanding the genetic basis of phenotypes [5]. This involves identifying genetic variants that can serve as markers to infer causal pathways. Another strategy involves the use of network deconvolution, a general method to infer direct dependencies in networks, which can be applied to gene networks to clarify causal relationships [2]. Furthermore, the integration of multi-omics data and the use of updated tools on platforms like GeneNetwork.org enhance the ability to perform predictive medicine and systems genetics analyses, which are crucial for inferring causal relationships in gene networks [10]. These strategies collectively leverage statistical, computational, and biological insights to improve the inference of causal relationships in gene networks.", + "Advancements in single-nucleus RNA sequencing (snRNA-seq) provide more granular insights into cell-type-specific gene expression networks by allowing for the analysis of gene expression within the nuclei of cells, rather than relying on intact cells as in single-cell RNA sequencing (scRNA-seq) [1]. This method is particularly useful for profiling gene expression in complex tissues from frozen samples at the single-cell level, which can be challenging with other techniques [1]. Additionally, snRNA-seq can help clarify cell-type proportions and corresponding transcriptional profiles, as demonstrated in studies involving postmortem human brain tissue [9]. By isolating nuclei and performing snRNA-seq, researchers can achieve finer cell subtype resolution, which is crucial for understanding the heterogeneity within cell populations [7]. This level of detail is essential for constructing accurate cell-type-specific gene expression networks, as it allows for the identification of transcriptional changes and cell-type-specific gene expression patterns that might be obscured in bulk tissue analyses [3]. Overall, snRNA-seq enhances our ability to dissect complex tissues into their constituent cell types and understand the unique gene expression networks within each type, providing a more detailed and nuanced view of cellular function and interaction.", + "The context provided does not explicitly mention the impact of recent discoveries in non-coding RNA regulation on refining gene interaction maps on GeneNetwork.org. However, there are some relevant points that can be inferred: 1. GeneNetwork.org has been updated with new tools and data, including multi-omics analysis, which could potentially incorporate non-coding RNA data to enhance gene interaction maps [2]. 2. The integration of gene expression data sets, particularly for mouse and rat, into GeneNetwork.org suggests that the platform is equipped to handle complex genetic data, which may include non-coding RNA interactions [7]. 3. There is ongoing research and data collection on non-coding RNAs, as indicated by references to databases like Rfam and cisRED, which could contribute to refining gene interaction maps by providing insights into regulatory networks [9], [10]. While the specific impact of non-coding RNA discoveries on GeneNetwork.org is not detailed, the platform's enhancements and the broader research context suggest that such discoveries could play a role in improving the accuracy and depth of gene interaction maps.", + "The provided context does not explicitly mention the integration of spatial transcriptomics approaches into GeneNetwork.org. However, it does describe GeneNetwork as a resource for systems biology and systems genetics, which includes large transcriptome datasets from multiple tissues [2], [9]. The platform is used to study relationships among markers, genes, and phenotypes, and to analyze genetic regulatory commonality and tissue structure and function [3], [4]. While spatial transcriptomics is not directly referenced, the existing capabilities of GeneNetwork in handling multi-omics data and performing systems genetics mapping [1], [5] suggest that it could potentially support spatial transcriptomics approaches to enhance understanding of tissue architecture and function.", + "Recent developments in quantitative trait locus (QTL) mapping have significantly refined our understanding of gene regulatory variants in several ways: 1. **Identification of eQTLs**: QTL mapping of gene expression traits allows for the identification of expression quantitative trait loci (eQTLs), which are genomic regions that have a regulatory effect on gene expression traits. These eQTLs can be categorized into local eQTLs, which are located near the gene encoding the transcript, and distant eQTLs, which are located elsewhere in the genome [2]. This distinction helps in understanding the regulatory architecture of the genome. 2. **Increased Resolution and Confidence**: With advancements in DNA sequencing and the availability of whole-genome databases and gene expression data from various tissues, researchers can use bioinformatic tools to identify candidate genes with greater confidence for further functional validations [1]. This enhances the precision of QTL mapping in pinpointing regulatory variants. 3. **Functional Mapping and Hypothesis Generation**: QTL mapping studies, such as those beginning with yeast, have used gene expression as the phenotype to infer regulatory control. Although these studies are not conclusive, they help narrow down potential regulatory candidates, generate hypotheses for further testing, and construct regulatory networks in silico [3]. 4. **Detection of Secondary QTLs and Epistatic Interactions**: Recent developments allow for the identification of large numbers of less strong, secondary QTLs that were previously obscured by background noise. This opens up new possibilities for analyses, such as identifying epistatic interactions, which can reveal pathways of genetic control within the studied tissue [4]. 5. **Integration with Expression Analysis**: The integration of genetic variation in associated loci with expression analysis data through eQTL studies helps define regulatory relationships. This approach provides insights into the physiological consequences of causal variants, aiding in the translation of findings into diagnostic tests and risk evaluation [8]. Overall, these advancements in QTL mapping enhance our understanding of the complex regulatory mechanisms underlying gene expression and trait variation.", + "Incorporating epigenomic data, such as histone modification maps, into the gene networks on GeneNetwork.org has several implications: 1. **Enhanced Functional Analysis**: By integrating epigenetic data, the predictive functional analysis of SNPs can be improved. This is because epigenetic data can highlight regions of DNA that are accessible or inaccessible to protein binding by transcription factors and other regulatory proteins, which can affect gene expression and regulation [4]. 2. **Prioritization of Regulatory Variants**: The incorporation of genome-wide histone modification signatures, as revealed by collaborative efforts like the ENCODE Project and Roadmap Epigenomics, allows for the prioritization of functional regulatory variants. This can be particularly useful in mapping studies and databases, enhancing the understanding of regulatory features in various tissues and cell lines [2]. 3. **Understanding Gene Expression Variation**: By studying the genetics of epigenetics, it is possible to reveal genes that directly or indirectly affect epigenetic gene states. This approach can help estimate the percentage of variation in gene expression that can be explained by different epigenetic conformations, thus providing a deeper understanding of gene regulation [3]. 4. **Resource for Predictive Medicine and Systems Genetics**: The integration of multi-omics data, including epigenomic data, makes GeneNetwork.org a valuable resource for predictive medicine and systems genetics. This integration supports more comprehensive analyses and enhances the platform's utility for research and clinical applications [7]. Overall, incorporating epigenomic data into GeneNetwork.org enriches the platform's analytical capabilities, offering deeper insights into gene regulation and expression, and supporting advanced research in genetics and epigenetics.", + "Recent findings on 3D genome organization have significantly enhanced our understanding of functional genomic networks in several ways: 1. **Co-regulation through Spatial Organization**: The 3D chromatin structure is known to couple nuclear compartmentalization of chromatin domains with the control of gene activity, which contributes to cell-specific gene expression [1]. This spatial organization within the nucleus suggests that chromosomal and spatial co-localization may indicate co-regulation of genes, thereby influencing functional genomic networks. 2. **Regulation by Distant Elements**: There is a growing awareness that the three-dimensional juxtaposition of DNA regions within nuclei allows genes to be regulated by elements located at a distance from the gene itself [4]. This understanding helps explain how disease-associated SNPs can fall within gene regulatory elements, thus affecting genomic networks and potentially leading to disease. 3. **Integration with Functional Annotations**: Advances in identifying functional genomic elements through various annotations, such as those from the ENCODE project, have been complemented by insights into 3D genome organization. This integration helps in identifying potential regulatory variants and understanding their roles within genomic networks [2]. These findings collectively contribute to a more comprehensive understanding of how genes are regulated within the complex spatial architecture of the genome, thereby enhancing our knowledge of functional genomic networks.", + "Artificial intelligence (AI) has several potential applications in improving the annotation and interpretation of gene networks: 1. **Inference of Gene Functions**: AI techniques, such as network inference algorithms, can help infer the putative functions of unknown genes by linking them to genes with known functions that exhibit similar expression patterns. This approach can also prioritize candidate variants and predict disease inheritance modes to some extent [3]. 2. **Network Inference Techniques**: AI-driven network inference techniques can be utilized to infer biological processes and the potential phenotypic impact of variants in genes of unknown function. These techniques can provide powerful approaches to inferring phenotypic information where direct links to phenotype do not exist [4]. 3. **Computational Approaches**: AI, particularly through computational approaches using statistical, machine learning, or soft-computing techniques, serves as a discovery tool for finding gene networks. These approaches can complement literature-based methods that gather published information on genes and their interrelationships [6]. 4. **Pattern Recognition and Predictive Modeling**: Deep learning models, a subset of AI, can be used for pattern recognition in gene sequences to identify potential future illnesses. There is also a demand for explainable AI models that are interpretable in decision-making, which can enhance the understanding and application of genomic data [8]. These applications demonstrate how AI can significantly enhance the annotation and interpretation of gene networks by providing insights into gene functions, biological processes, and potential phenotypic impacts.", + "The study of genetic pleiotropy has been advanced by data available on GeneNetwork.org through several key developments: 1. **Multi-Omics Analysis and Data from Multiple Species**: GeneNetwork.org has incorporated data from 10 different species and supports multi-omics analysis, which allows researchers to explore genetic pleiotropy across a wide range of organisms and biological data types. This comprehensive approach provides a richer understanding of how genes can influence multiple traits or diseases [4]. 2. **Systems Genetics Approach**: The platform enables a systems genetics approach, which contrasts with the traditional candidate gene approach. Instead of focusing on single gene mutations, it explores the relationships between diverse genetic and molecular markers and their resulting phenotypes and diseases. This approach is particularly useful for studying pleiotropy, as it considers the complex interactions and shared pathways that can lead to multiple phenotypic effects from a single genetic locus [5]. 3. **Open Web Resource**: GeneNetwork.org is an open web resource, making it accessible to a wide range of researchers. This accessibility facilitates collaborative research and data sharing, which are crucial for advancing the study of pleiotropy by allowing researchers to build on each other's findings and methodologies [8]. These features collectively enhance the ability to study genetic pleiotropy by providing comprehensive data, advanced analytical tools, and a collaborative platform for researchers.", + "GeneNetwork.org studies have identified novel genetic pathways related to aging and lifespan through various approaches. One notable method is the use of network identification by regression (NIR), which has been applied to identify novel pathways in the context of aging and lifespan [2]. Additionally, network-based approaches have revealed six pathways and six key genes that might play pivotal roles in regulating longevity, providing new insights into the mechanisms of longevity [6]. These findings highlight the potential of network-based methods to uncover novel genetic pathways associated with aging and lifespan.", + "Polygenic risk scores (PRS) developed using GeneNetwork.org data enhance the prediction and prevention of complex diseases by providing a quantitative metric of an individual's inherited risk based on the cumulative impact of many common polymorphisms [7]. These scores aggregate the genetic risk of individual alleles across the genome, which can significantly improve the prediction of future disease occurrence and aid in early diagnosis, intervention, and prevention strategies [5]. PRS can complement established clinical risk factors and intervention paradigms, thereby enhancing early diagnosis and prevention efforts for diseases such as type 2 diabetes (T2D) [6]. Additionally, PRS have emerged as promising biomarkers for predicting disease risk in various areas, including cardiovascular disorders and oncology [8]. By utilizing data from large consortia and genome-wide genotypes, the predictive value of these scores has substantially improved, allowing for a more comprehensive assessment of genetic risk [3]." + ], + "contexts": [ + [ + "It is important to integrate the gene variants and environmental factors to the trait to understand the network controlling that trait. In systems genetics approach, different trait networks are related to different networks of gene and environmental variants to find global genetic modulation of the complex phenotype. The availability of genetic reference panels makes it easy to acquire diverse phenotypic data and advanced computational models make it possible to analyse their relationship. 2.2.1.", + "Processing Large-Scale, High-Dimension Genetic 325 another. We anticipate these types of networks becoming increasingly important in the human genetics space to gain a mechanistic understanding of how a given DNAperturbation induces changes in one or more genes that go on to affect networks that cause disease. The integration of genotypic and expression and other data have recently been shown, in a Bayesian network framework [76], to enhance the overall", + "2. GENETICAL GENOMICS In recent years, there has been growing interest in uniting genetic and genomic approaches to enable more comprehensive dissections of complex traits and their genetic architecture. Jansen and Nap (2001) termed this synthesis genetical ge-", + "2. GENETICAL GENOMICS In recent years, there has been growing interest in uniting genetic and genomic approaches to enable more comprehensive dissections of complex traits and their genetic architecture. Jansen and Nap (2001) termed this synthesis genetical ge-", + "42.Chesler EJ, et al. 2005. Complex trait analysis of gene expression uncovers polygenic and pleiotropic networks that modulate nervous system func-tion. Nat. Genet. 37:233242. 43.Iraqi FA, Churchill G, Mott R. 2008. The Collaborative Cross, develop- ing a resource for mammalian systems genetics: a status report of theWellcome Trust cohort. Mamm. Genome 19:379 381. 44.Xiao J, et al. 2010. A novel strategy for genetic dissection of complex traits:", + "multiple-SNP analysis of GWAS summary statistics identiesadditional variants inuencing complex traits. Nat Genet 44(369375):S1S3. doi: 10.1038/ng.2213 Yang J, Zaitlen NA, Goddard ME et al (2014) Advantages and pitfalls in the application of mixed-model association methods. NatGenet 46:100106. doi: 10.1038/ng.2876 Yazbek SN, Buchner DA, Geisinger JM et al (2011) Deep congenic", + "10. The power of integrating all these genetic and genomic data has now been well documented, offering a glimpse of what the future of com-plex trait genetics will look like. Model systems that are genetically more complex, including extensive eight-strain crosses 11,12 and haplotype association studies using large panels of regular inbred strains of mice, and even humans, are", + "tive analysis of omics summary data reveals putative mechanisms underlying complex traits. Nat Commun 9:918 33. Yang J, Hong Lee S, Goddard ME, Visscher PM (2011) GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet 88:7682 34. Zeisel A, Hochgerner H, Lnnerberg P, Johnsson A, Memic F, van der Zwan J etal (2018) Molecular architecture of the mouse nervous system. Cell 174:999.e221014.e22 35. Zhan X, Hu Y, Li B, Abecasis GR, Liu DJ (2016) RVTESTS:", + "used to identify molecular traits involved in the p athology of diseases and to eluci- date the networks underlying complex phenotypes. Re cent studies have pushed the genetical genomics concept further towards data int egration and interpretation within and across molecular levels, and have also r evealed remaining challenges. The focus of this review is to discuss these challe nges and their possible solutions in", + "2 large populations. The new methods have allowed us to dissect the genetic architecture of complex disorders including the identification of the causal genomic loci, estimation of the disease heritability, estimation of effect sizes of different loci and their non-additive interactions. Linkage analysis The earlier breakthroughs in linking genotype with phenotype involved studies of Mendelian disorders that can be mapped to a single gene and a single mutation. These" + ], + [ + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "analytical method, have been used to discover gene- environment interactions; some approaches address similar objectives, whilst others are complementary and can be ap- plied in sequence. Below we describe several of these ap- proaches, and refer the reader to another excellent review of gene-environment interaction methods [ 31]. (a)Established statistical approaches Until 2008, almost all studies of gene-environment interac- tions focused on testing hypotheses based on existing biolog-", + "ulated by non-genetic factors. Thus, the once esoteric topic of gene-environment interaction is now becoming mainstream and appealing to investigators across diversedisciplines; this has propelled major methodological in- novations for the discovery, replication, validation and translation of gene-environment interactions. The expo- nentiation of data resources for these purposes has demanded analytical solutions that address data dimen- sionality reduction. Although not yet extensively imple-", + "addition to this, GeneNetwork can be used to study correlations between traits and to perform data mining in genomic regions containing candidates for quantitative trait genes (Hoffman et al., 2011). All datasets in GeneNetwork are linked to a materials and methods information page that summarizes experimental details relating to the dataset. Databases within GeneNetwork include the transcriptome database, the BXD published", + "Eaves LJ 2006 Genotype x environment interaction in psychopathology: fact or artifact? Twin Res Hum Genet 9:18 Hunter DJ 2005 Geneenvironment interactions in human diseases. Nat Rev Genet 6:287298 Ioannidis JP, Ntzani EE, Trikalinos TA, Contopoulos-Ioannidis DG 2001 Replication validity of genetic association studies. Nat Genet 29:306309 Ioannidis JP, Gwinn M, Little J et al 2006 A road map for ef cient and reliable human genome epidemiology. Nat Genet 38:35", + "GeneNetwork is an open-access database that collates genomic information of diverse experimental crosses and reference panels as well as phenotypic data from miscellaneous research groups [26]. Statistics Data generation, statistical analysis and graph creation were performed with SPSS Statistics 21 (IBM, Ehningen, Germany). As appropriate, mean and median values were further used for QTLanalysis. Phenotypic robustness for each strain was assessed by the", + "NU32CH13-Hu ARI 18 June 2012 13:45 effectively scan the entire genome for interac- tions with environment. Although innovative, the most effective study design and statistical approach for conducting gene-environment- wide interaction studies (GEWIS) remains unresolved (88). The greatest challenge for GEWIS involves nding a balance between rejecting true ndings resulting from stringent multiple-testing correction and reporting false-positive results. Several novel methods", + "1 GeneNetwork: a continuously updated tool for systems genetics analyses Pamela M. Watson1, David G. Ashbrook1 1Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN 38163, USA Abstract GeneNetwork and its earlier iteration , WebQTL, have now been an important database and toolkit for quantitative trait genetics research for two decades. Recent improvements to", + "13 132. Geneenvironment interaction: overcoming methodological challenges Rudolf Uher MRC Social, Genetic and Developmental Psychiatry Research Centre, Institute of Psychiatry, Kings College London, UK Abstract. While interacting biological effects of genes and environmental exposures (G E) form a natural part of the causal framework underlying disorders of human health, the detection of G E relies on inference from statistical interactions observed at popu-", + "A number of recent developments in twin methodology have taken place based on the incorporation of measured genotype information. Thisenables twin models to estimate how much of the genetic variation is dueto variation in a specific gene. Gene-environment interaction studies, link-Copyright National Academy of Sciences. All rights reserved.Cells and Surveys: Should Biological Measures Be Included in Social Science Research? http://www.nap.edu/catalog/9995.html" + ], + [ + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "Conclusion GeneNetwork is an excellent tool for exploring complex phenotypes with systems genetics. Here we have used GeneNetwork to explore an inflammatory phenotype, and identified a small number of plausible candidate genes. A similar workflow can be used for any trait on GeneNetwork, or for any phenotype collected by an investigator in a genetically diverse population. GeneNetwork can allow users to study relationships between genes, pathways, and phenotypes in an easy to use format.", + "Conclusion GeneNetwork is an excellent tool for exploring complex phenotypes with systems genetics. Here we have used GeneNetwork to explore an inflammatory phenotype, and identified a small number of plausible candidate genes. A similar workflow can be used for any trait on GeneNetwork, or for any phenotype collected by an investigator in a genetically diverse population. GeneNetwork can allow users to study relationships between genes, pathways, and phenotypes in an easy to use format.", + "addition to this, GeneNetwork can be used to study correlations between traits and to perform data mining in genomic regions containing candidates for quantitative trait genes (Hoffman et al., 2011). All datasets in GeneNetwork are linked to a materials and methods information page that summarizes experimental details relating to the dataset. Databases within GeneNetwork include the transcriptome database, the BXD published", + "connect Genotype with Gene2 and Phenotype, knowledge of the Genotype still influences the predicted values of these variables. For example, Genotype = 1 may cause a decrease in Gene1 and this decrease in Gene1 will subsequently cause a reduction in Gene2. 4 Discussion Network modeling of biological datasets is often limited by the number of samples within a dataset, and the available data does not support the construction of precise and reliable large-scale networks", + "GeneNetwork http://www.genenetwork.org is anexample of a bioinformatics tool that can be used to explore systems genetics data. The importance of defining biological networks and predicting molecular interactions has been emphasized by several reports [1,2]. Such studies emphasize that when knowledge about DNA variation within popula- tions is interfaced with data on gene expression, protein interactions and DNA-protein binding, biological networks can be constructed that are predictive of the", + "metadata (data about the data) are combined with sophisticated statistical and computation tools for the genetic dissection and synthesis of single traitsor entire systems of traits. One challenge facing investigators in the inter- pretation of the massive data sets on GeneNetworkand elsewhere is deciding how much confidence toplace in QTL extracted from still noisy array andproteomic platforms after having conducted many thousands of statistical tests with poorly understood", + "accuracy of predictive networks [40, 5153]. We have also recently demonstrated how this class of network can be used to inform associations identied in GW Astudies [40]. 9 Summary The signicant challenge we face in the post-genome era is deciphering the bio-logical function of individual genes, pathways, and networks that drive complexphenotypes like disease. The availability of low-cost, high-throughput technologies", + "members o f pathway modules [78]. Other studies applied gene network modeling algorithms to identify the potential regulators in complex di seases, for example cardiomyopathy [79], hepatic steatosis [80], as well as coronary artery disease [81]. Finally, there are many other integrative approaches available for the analysis of multi -omics data, but have not yet been applied in mouse systems genetics studies. Examples include the transcriptome -wide", + "gathered together into an easily accessible format, not siloed into disparate data pools that cannot easily be integrated, valid ated, o r extended. This approach will allow us to make animal models of so called precision medicine, although perhaps more accurately, we want predictive medicine , where a phenotypic outcome (such as disease) can be predicted , and avoided . GeneNetwork (genenetwork.or g; GN) is one tool for systems genetics and predictive medicine," + ], + [ + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "Combinatorial Genetic Regulatory Network Analysis Tools for High Throughput Transcriptomic Data Elissa J. Chesler1and Michael A. Langston2 1Life Sciences Division, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831-6124, USA 2Department of Computer Science, University of Tennessee, Knoxville, TN 379963450, USA Abstract: A series of genome-scale algorithms and high-performance implementations is described and shown to be useful in the genetic analysis of gene transcription. With", + "Combinatorial Genetic Regulatory Network Analysis Tools 163 In addition to expansive volumes of data, there is a growing complexity to the types of research questions that can be asked. We are presently developing approaches to compare graphs collected in a systems gene tic context to reect differences in time, tissue and treatment effects. Visualizatio n methods and compelling biological validation of novel results are essential to translate these methods and deliver them to the broader", + "al., 2005). GeneNetwork is designed primarily as a web service for exploratory and statistical analysis of large published phenotype and genome datasets, and includes data from several species (see Supplementary Discussion). GeneNetwork includes extensive phenotype data extracted from the literature and submitted by users, which makes it practical to compare data on drug responses with gene expression patterns. Gene expression", + "larger networks well. Because of the computational complexity aswell as the memory requirements, these methods as currentlyimplemented are not the ideal choice for such large networks.WGCNA, GeneNet, ARACNE and SPACE, on the other hand,were designed to construct the gene network at very large scales.Also, it worth mentioning that the WGCNA package providesseveral useful tools to facilitate the analysis and visualization of resulting networks, including tools to identify subnetworks and an", + "Proc Natl Acad Sci U S A 100: 94409445. 32. Chesler E, Langston MA (2005) Combinatorial Genetic Regulatory Network Analysis Tools for High Throughput Transcriptomic Data. Proceedings,RECOMB Satellite Workshop on Systems Biology and Regulatory Genomics. 17 p.33. Abu-Khzam F, Langston M, Shanbhag P, Symons C (2006) Scalable Parallel Algorithms for FPT Problems. Algorithmica 45. 34. Langston M, Perkins A, Saxton A, Scharff J, Voy B (2006) Innovative", + "computational methods for transcriptomic data analysis. SAC 06: Proceedings of the 2006 ACM symposium on Applied computing. 35. Csardi G, Nepusz T (2006) The igraph software package for complex network research. InterJournal Complex Systems 1695. 36. Chen J, Bardes EE, Aronow BJ, Jegga AG (2009) ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res 37:W305311. 37. Williams RW, Gu J, Qi S, Lu L (2001) The genetic structure of recombinant", + "plenary lecture, with a focus on the computational challengesin analyzing large datasets. The type of datasets discussed by Williams included the microarray type outputs first suggestedby Jansen and Nap ( 2001 ) for inclusion in genetical genomics analyses and are now extended to cross-platform datasets (Damerval et al. 1994; Ciobanu et al. 2010 ). A framework for carrying out the genetic analyses was described as being available through the GeneNetwork and WebQTL software", + "32. Zhu J, Zhang B, Smith EN, Drees B, Brem RB, Kru glyak L, Bumgarner RE, Schadt EE: Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks . Nat Genet 2008, 40 (7):854-861. 33. Vera G, Jansen RC, Suppi RL: R/parallel--speeding up bioinformatics analysis with R . BMC bioinformatics 2008, 9:390. 34. Alberts R, Terpstra P, Bystrykh LV, de Haan G, Jansen RC: A statistical multiprobe model for analyzing cis and trans genes in genetical", + "Processing Large-Scale, High-Dimension Genetic and Gene Expression Data Cliona Molony, Solveig K. Sieberts, and Eric E. Schadt Abstract The now routine generation of large-scale, high-throughput data in mul- tiple dimensions (genotype, gene expression, and so on) provides a signicant challenge to researchers who desire to integrate data across these dimensions in" + ], + [ + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "GeneNetwork provided the platform for correlation analysis, principal component generation, and linkage analysis. In general, datasets were queried for gene symbols, downloaded from GeneNetwork, and additional analysis was performed in R whenever necessary. P-values mentioned in relation to Pearsons coecient throughout this paper are based on pair- wise comparisons. All p-values were Bonferroni-adjusted for 36,012 genes, which is equal to the number of genes captured", + "GeneNetwork provided the platform for correlation analysis, principal component generation, and linkage analysis. In general, datasets were queried for gene symbols, downloaded from GeneNetwork, and additional analysis was performed in R whenever necessary. P-values mentioned in relation to Pearsons coecient throughout this paper are based on pair- wise comparisons. All p-values were Bonferroni-adjusted for 36,012 genes, which is equal to the number of genes captured", + "including correlation and network analysis to compare associations between tissues and between other rodent or human data sets[32] Many of the Data Sets are amenable to systems genetics mapping and other methods and are accessible at GeneNetwork. The Description and Usage column provides details about the data set and potential", + "including correlation and network analysis to compare associations between tissues and between other rodent or human data sets[32] Many of the Data Sets are amenable to systems genetics mapping and other methods and are accessible at GeneNetwork. The Description and Usage column provides details about the data set and potential", + "network. Cell 9, 12121226 (2014). 12. Hirschhorn, J.N. Genomewide association studiesilluminating biologic pathways. N. Engl. J. Med. 0, 16991701 (2009). 13. Cantor, R.M., Lange, K. & Sinsheimer, J.S. Prioritizing GWAS results: a review of statistical methods and recommendations for their application. Am. J. Hum. Genet. 8, 622 (2010). 14. Lee, I., Date, S.V., Adai, A.T. & Marcotte, E.M. A probabilistic functional network of yeast genes. Science 0, 15551558 (2004).", + "addition to this, GeneNetwork can be used to study correlations between traits and to perform data mining in genomic regions containing candidates for quantitative trait genes (Hoffman et al., 2011). All datasets in GeneNetwork are linked to a materials and methods information page that summarizes experimental details relating to the dataset. Databases within GeneNetwork include the transcriptome database, the BXD published", + "al., 2005). GeneNetwork is designed primarily as a web service for exploratory and statistical analysis of large published phenotype and genome datasets, and includes data from several species (see Supplementary Discussion). GeneNetwork includes extensive phenotype data extracted from the literature and submitted by users, which makes it practical to compare data on drug responses with gene expression patterns. Gene expression", + "limit the applicability of genetic ndings in more diversepopulations. In the next phase of the network, the goalis to increase the diversity of underrepresented popula-tions, with targeted recruitment aimed at over 50% non-European ancestry. The lessons from enrollment andRoRs to diverse populations, even limited, will inform our next phase as we continue to strive for a more represen-", + "data available across all contributing consortia will facilitate systematic exploration of these correlated phenotypes with more sophisticated statistical methods for joint analysis5254, yielding greater insight into the underlying pathways and genetic networks they represent. As data from human genetic networks accrue, we will be better placed to test whether there is support for the notion of hub genesthat is, genes highly connected with others in the network, proposed by experi" + ], + [ + "Lotan et al. Neuroinformatics of major neuropsychiatric disorders We demonstrated that although these disorders share a rela- tively small set of genes, there are two fundamental yet distinctgenetic components, or vectors, that are both shared by all sixdisorders. While the rst component is involved in CNS develop- ment, neural projections and synaptic transmission, the second", + "genetic variation) for any psychiatric disorder (Fig. 1), there is sufficient information to drawsome general conclusions. The polygenicity of psychiatric illness In addition to finding specific genes, molecu- lar genetics can provide information about theheritability of psychiatric disease, an approach that has led to some important insights about the genetic architecture of psychiatric illness.The degree of SNP sharing among disease cases estimates the common, inherited portion of a", + "of shared and unique genetic factors highlights key gene sets and molecular processesthat may ultimately translate into improved diagnosis and treatment of these debilitating disorders. Keywords: major neuropsychiatric disorders, neuroinformatics, cross-species, translational, genetic components, genome wide association studies, enrichment INTRODUCTION Common psychiatric disorders including attention-", + "6. D. H. Geschwind, J. Flint, Genetics and genomics of psychiatric disease. Science 349, 1489 1494 (2015). doi: 10.1126/science. aaa8954 ; pmid: 26404826 7. S. Cichon et al ., Genomewide association studies: History, rationale, and prospects for psychiatric disorders. Am. J. Psychiatry 166, 540 556 (2009). doi: 10.1176/ appi.ajp.2008.08091354 ; pmid: 19339359 8. A. Battle et al., Genetic effects on gene expression across human tissues. Nature 550, 204 213 (2017). doi: 10.1038/ nature24277 ; pmid: 29022597", + "the Psychiatric Genomics Consortium found that the results were highly correlated between methods in a comparison of methods applied across several psychiatric disorders ( Network Pathway Analysis Subgroup of Psychiatric Genomics Consortium 2015 ). A second limitation of pathway-based analysis is that it is still biased by our incomplete prior knowledge of gene function in the etiology of psychiatric illness. Despite these challenges, pathway-based analyses have identified biological pathways", + "Lotan et al. Neuroinformatics of major neuropsychiatric disorders GENES FROM THE NHGRI-CROSS-DISORDER SET CLUSTER IN THREE CO-EXPRESSION MODULES WITH DISTINCT SPATIO-TEMPORALEXPRESSION PATTERNS AND FUNCTIONAL BIASES One of the major properties of genes involved in regulation of", + "Genet. 2009; 85:847861. [PubMed: 19931040] Brownlee DJ, Fairweather I. Exploring the neurotransmitter labyrinth in nematodes. Trends Neurosci. 1999; 22:1624. [PubMed: 10088995] Bucholz KK, Cadoret R, Cloninger CR, Dinwiddie SH, Hesselbrock VM, Nurnberger JI Jr, Reich T, Schmidt I, Schuckit MA. A new, semi-structured psychiatric interview for use in genetic linkage studies: a report on the reliability of the SSAGA. J Stud Alcohol. 1994; 55:149158. [PubMed: 8189735]", + "with shared effects on ve major psychiatric disorders: a genome- wide analysis. Lancet 381(9875):13711379 Davis S, Meltzer P (2007) Geoquery: a bridge between the gene expression omnibus (geo) and bioconductor. Bioinformatics 14:18461847 de Mooij-van Malsen AJG, Vinkers CH, Peterse DP, Olivier B, Kas MJH (2011) Cross-species behavioural genetics: a starting point for unraveling the neurobiology of human psychiatric disorders. Prog Neuropsychopharmacol Biol Psychiatr 35(6):13831390", + "systems biology approach based on gene co-expression networks and genotype-gene expression (rather than genotype-disease)associations, these results further validate our methodology to construct polygenic scores linked to the overall biological function of tissue-speci c gene networks. Molecular Psychiatry (2022) 27:27422750; https://doi.org/10.1038/s41380-022-01533-7 INTRODUCTION Several psychiatric disorders of developmental origin are char-", + "systems biology approach based on gene co-expression networks and genotype-gene expression (rather than genotype-disease)associations, these results further validate our methodology to construct polygenic scores linked to the overall biological function of tissue-speci c gene networks. Molecular Psychiatry (2022) 27:27422750; https://doi.org/10.1038/s41380-022-01533-7 INTRODUCTION Several psychiatric disorders of developmental origin are char-" + ], + [ + "The method takes as input a large cohort of individuals, wherethe input for each individual includes: (1) genotyping; (2) bulk ex-pression of genes in a certain tissue; (3) the relative abundance(proportions) of the various cell types in the tissue (it is possible to use computational deconvolution methods to predict cell-type proportions from bulk genomics data ( Newman et al. 2015 )). In", + "Filtering out the latter class of technical difficulty im-proved the recovery of genuine cis-modulated transcripts and thus to identify genes that are relevant to further down-stream regulation of gene expression and more complex phe-notypes (Ciobanu et al. 2010 ). Williams also discussed the power of a structured mapping population in model organisms and presented the Complex4 Funct Integr Genomics (2012) 12:1 9", + "genomic hybridization microarrays (8), can complement RNA expression data and result in novel discoveries. With the evolution and maturation of proteom ics, certainly combining serum- or tissue-based patterns of protein expression with RNA expression holds promise. Finally, other rich sources of complex data such as the literature can be used to complement our analysis of microar ray data (39). These analyses face significant challenges with respect to gene", + "data. To model the functional dependence we shall explore machine learning methods16, such as decision tree methods to predict the co-expressed gene profiles. As part of this study and in (E) Future work, see below, we will investigate the benefit of using comparative genomics in helping to lo cate and characterise the regul atory elements and signals. D(d) Integration and Modelling to infer regulato ry systems co-varying with disease status", + "derived from complex tissue such as brain show a high level of correspondence24,25. Such structure can be used to inform a new level of neuroscientific investigation that is not possible using standard analysis of differential expression2225. For example, one of the first such studies23 showed that gene networks could be used to provide a unifying method of identifying transcriptional targets of human brain evolution in", + "profiling of a multicellular organism,\" Science, vol. 357, no. 6352, pp. 661 -667, 2017. [68] X. Guo, W. Li, and F. Iorio, \"Convolutional neural networks for steady flow approximation,\" in Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining , 2016, pp. 481 -490. [69] V. Ntranos, L. Yi, P. Melsted, and L. Pachter, \"A discriminative learning approach to differentia l expression analysis for single -cell RNA -seq,\" Nature Methods, vol. 16,", + "levels can influence the ability to call differential gene expression (Oshlack and Wakefield 2009), we also included, as a feature, the average expression level of the genes in the young samples. All machine-learning algorithms assigned genes to the correct transcriptional change with age 67% 81% of the time on average, significantly above that of a random classification (50%) (Fig. 3B,C; Supplemental Fig. S3B,C ;Supplemental Table S3A,B ). Models de-", + "DNA. Microarray technology is helpful in capturing biological genetic information to computer data. Computational techniques can be applied on those large set of genetic data of every individuals with or without disease, so that the genes that are responsible for the disease occurrence can be po inted out. Differentially Expressed Genes (DEG) are identified using many techniques. Machine Learning (ML) algorithms plays a significant role in identifying the distinction between normal", + "mapping, several sophisticated analyses will be required to extract full value fromthe enormous amount of collected data, and gain valuable insight into geneticcontrol of gene expression. As recently noted by Ariel Darvasi (2003), I expect thatthe combining of genetic information and gene expression will hasten the day whengenomics delivers on its promise to improve health care. But we must continuestriving to develop and apply sophisticated analytical tools for interpreting the vast,complex data sets that", + "mapping, several sophisticated analyses will be required to extract full value fromthe enormous amount of collected data, and gain valuable insight into geneticcontrol of gene expression. As recently noted by Ariel Darvasi (2003), I expect thatthe combining of genetic information and gene expression will hasten the day whengenomics delivers on its promise to improve health care. But we must continuestriving to develop and apply sophisticated analytical tools for interpreting the vast,complex data sets that" + ], + [ + "dynamic16,17, and several studies have proposed that impaired enhancer activation could be at the origin of disease1821. Besides interacting with nearby promoters, enhancers also engage in long-range interactions. Indeed, it is estimated that approximately 3540% of all promoter-enhancer interactions are intervened by at least one gene22, which makes exact enhancer-target prediction challenging. Long-range enhancers interactions can be identi ed by chromosome conformation capture methods23,24.", + "motifs found in its promoter (gene-to-sequence). We will referto the ensemble of these inuence interactions as genenetworks. The interaction between two genes in a gene network does not necessarily imply a physical interaction, but can also referto an indirect regulation via proteins, metabolites and ncRNA that have not been measured directly. Inuence interactions include physical interactions, if the two interacting partnersare a transcription factor, and its target, or two proteins in the", + "~90,000 enhancer-promoter interactions (fig.S36). As expected, ~75% of enhancer-promoterinteractions occurred within the same TAD, and genes with more enhancers tended to have high- er expression (Fig. 5B and fig. S36). We inte-grated the Hi-C data with QTLs; surprisingly, QTLs involving SNPs distal to eGenes but linked by Hi-C interactions showed significantly stron-ger associations (as indicated by the QTL Pvalue) than those with SNPs directly in the eGene pro- moter or exons (Fig. 5C and fig. S37).", + "histone-modifying proteins, and other factors to regulate polymerase-II activity. Such factors can bind in close prox- imity to promoters to influence gene expression. However, there is substantial evidence that additional genetic elements referred to as enhancers play major roles in determining cell- specific patterns of gene expression. 1517 Initially identified >30 years ago, enhancer elements can be located at various distances from promoters, typically between 1 and 50 kilo-", + "involved in the regulation of the target genes of both networks, but that the interaction partners through which this regulation is established differs for both target genes.", + "variants in epigenomic features using a systematic, data-driven approach. Bioinformatics 31,26012606 (2015). 13. Schug, J. et al. Promoter features related to tissue specicity as measured by Shannon entropy. Genome Biol. 6,R33 (2005).14. He, B., Chen, C., Teng, L. & Tan, K. Global view of enhancer-promoter interactome in human cells. Proc. Natl Acad. Sci. USA 111, E2191E2199 (2014). 15. Parker, S. C. J. et al. Chromatin stretch enhancer states drive cell-specic gene", + "regulation and harbor human disease risk variants. Proc. Natl Acad. Sci. USA 110, 1792117926 (2013). 16. Quang, D. X., Erdos, M. R., Parker, S. C. J. & Collins, F. S. Motif signatures in stretch enhancers are enriched for disease-associated genetic variants. Epigenet. Chromatin 8,23 (2015). 17. Whyte, W. A. et al. Master transcription factors and mediator establish super-enhancers at key cell identity genes. Cell153, 307319 (2013).", + "networks. In fact, several of the higher-order networks we describe below rely on having multiple reliable and interoperable transcriptional activators and repressors for proper functioning. Even so, these engineered transcription factors have not yet been fully characterized, and if they are to be used as building blocks for complex gene networks, then knowledge of their in vivo kinetics and", + "BMC Genomics 2008, 9:310 http://www.biomedcen tral.com/1471-2164/9/310 Page 10 of 17 (page number not for citation purposes)A gene regulatory network comprising the regulatory interactions of the significant genes and the significant and enriched TFs is shown in Figure 5. Obvious are the five hubs, the core regulatory circuit derived from [17]. Well-regulated candidates can be identified like Acly and Fabp4 . Target and regulator at the same time is Ipf1. Discussion", + "32. Kheradpour P, Ernst J, Melnikov A, Rogov P, Wang L, Zhang X, et al. Systematic dissection of regulatory motifs in 2,000 predicted human enhancers using a massively parallel reporter assay. Genome research. 2013:gr. 144899.112. 33. Rands CM, Meader S, Ponting CP, Lunter G. 8.2% of the human genome is constrained: variation in rates of turnover across functional element classes in the human lineage. PLoS genetics. 2014;10(7):e1004 525." + ], + [ + "high-throughput sequencing (ATAC-seq) allows the characterization of accessible chromatin re- gions,whichcorrespondtoareasoftranscriptionactivity(149).Examiningthethree-dimensional organization of the genome can facilitate the association between regulatory elements and their target genes by dividing the genome into discrete functional blocks, commonly known as topologically associating domains (139). The Encyclopedia of DNA Elements (ENCODE) and", + "variants, it is still unclear how multiple independent variants influence gene networks through changes in chromatin states. The Assay for Transpose Accessible Chromatin (ATAC-seq) was recently developed to address the need for sensitive as- says requiring less starting material, which also has the ability to simultaneously profile open chromatin, transcription factor- binding footprints, as well as nucleosome positioning in a single assay [ 57]. Given the limited availability of primary", + "Data Fig.4a). To relate cell-type-resolved accessible chromatin to gene expression, we created a single-cell RNA sequencing (scRNA-seq) refer - ence map of peripheral blood and pancreas. We assigned cell-type identi - ties for 90,495 cells to 29 clusters, which identified similar cell types and proportions to snATACseq (Extended Data Fig.5ac). To characterize cis-regulatory programs, we aggregated reads from cells within each snATACseq cluster and identified accessible chroma -", + "DNA methylation and ATAC-seq data (Supplementary Fig. 3). Integration across gene- and coordinate-centric views helps users examine genomic events in different chromosome contexts. For example, Xenas Visual Spreadsheet can help elucidate whether a gene amplification is part of a chromosomal arm duplication or a focal amplification (Supplementary Fig. 6).", + "matin accessibility assay ATAC-seq has been applied to single cells and has been shown to capture a higher order chromatin structure resembling the profiles generated by Hi-C [ 72]. Additionally, for CAD candidate genes that are transcrip- tion factors (TF), such as TCF21 and STAT3, protein-DNA interactions could be studied on a genome-wide scale using chromatin immunoprecipitation sequencing (ChIP-Seq). Recently, ChIP-Seq performed against TCF21 in human cor-", + "seq), Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq), Formaldehyde- Assisted Isolation of Regulatory Elements (FAIRE-seq) and DNase I hypersensitive sites sequencing (DNase-seq). The integration of DNA methylation data (WGBS) and chromatin accessibility data (ATAC-seq) with established ChIP-seq mark ers have provided an opportunity to create high-resolution", + "94. Mumbach MR, et al. HiChIP: efficient and sensitive analysis of protein-directed genome architecture. Nat Methods. 2016;13:919922. doi: 10.1038/nmeth.3999. 95. Kumasaka N, et al. Fine-mapping cellular QTLs with RASQUAL and ATAC- seq. Nat Genet. 2016;48:206213. doi: 10.1038/ng.3467. 96. Buenrostro JD, et al. ATAC-seq: a method for assaying chromatin acces- sibility genome-wide. Curr Protoc Mol Biol. 2015;109:21.29.121.29.9. doi: 10.1002/0471142727.mb2129s109.", + "CpG sites. Single nucleus Assay for Transposase-Accessible Chromatinusing sequencing (snATACseq) was informative of chromatin opennessin various kidney cell types. The RegulomeDB is a database with exten-sive epigenetic annotation for SNPs. The promoter capture HiC (PCHiC) sequencing data identified sequence interaction with gene promoters,", + "a method for assaying chromatin accessibility genome-wide. Curr Protoc Mol Biol 109:21.29.2121.29.29. https ://doi.org/10.1002/04711 42727 .mb212 9s109 Bysani M etal (2019) ATAC-seq reveals alterations in open chromatin in pancreatic islets from subjects with type 2 diabetes. Sci Rep 9:7785. https ://doi.org/10.1038/s4159 8-019-44076 -8 Camp JG etal (2015) Human cerebral organoids recapitulate gene expression programs of fetal neocortex development. Proc Natl", + "genes are involved with multiple biological features. RNA sequencing has been coupled with protein quantication (DNA barcoded antibodies to quantify protein expression) and ATAC-seq to facilitate the study of genes involved with chromatin accessibility remodeling. their environment [14 , 31 , 88 , 95 , 105] . Advances in multiplexed gene editing and transcriptional programing will also enable CRISPR screens" + ], + [ + "genetic data which are shifting the paradigm of net work inferences by providing statistical evidence to support directed links betw een genes, proteins, metabolites or diseases. In Chapter 6 , different approaches using genetic data for gene network inference that have been proposed are reviewed. Chapter 7 examines the statistical potential of such methods under different realistic settings: varying population sizes and in the presence or absence of hidden factor var iation and suggests ways to", + "73. Yu,J., Smith,V.A., Wang,P .P ., Hartemink,A.J. & Jarvis,E.D. Advances to Bayesian network inference for generating causal networks from observational biological data. Bioinformatics 20, 35943603 (2004). 74. Sachs,K., Perez,O., Peer,D., Lauffenburger,D. A. & Nolan,G. P . Causal protein signaling networks derived from multiparameter single cell data. Science 308, 523529 (2005). 75. Feizi,S., Marbach,D., Mdard,M. & Kellis,M. Network deconvolution as a general method to", + "Causal Inference of Regulator-Target Pairs by Gene Mapping 97 1.2 Background: Inferring Regula tory Networks from Correlated Gene Expression Independent of the data sets described so far, large collections of gene expres- sion over time course (Spellman et al., 1998) or varying environmental con- ditions (Gasch et al., 2000; Hughes et al., 2000) have been studied to reveal dependent variation among genes and thereby deduce regulatory relationships.", + "data, to infer possible pathways and help build a link from the phe-notype back to a causal gene. In many cases, such interaction data are already available in public archives and need not be generated anew by the researcher [ 1 ]. These different sources of interaction data can be collated into network models ( see Note 1 ) which allow analysis using techniques borrowed from graph theory.", + "relationships with a causal inference test . BMC Genet 2009, 10 :23. 60. Chaibub Neto E, Ferrara CT, Attie AD, Yandell B S: Inferring causal phenotype networks from segregating populations . Genetics 2008, 179 (2):1089-1100. 61. Li Y, Tesson BM, Churchill GA, Jansen RC: Critical preconditions for causal inference in genome-wide association studies under review 2010. 62. Aten JE, Fuller TF, Lusis AJ, Horvath S: Using genetic markers to orient", + "T, Samson L, T I (2006) A systems approach to mapping DNAdamage response pathways. Science 312:10541059 Yu J, Smith V A, Wang PP, Hartemink AJ, Jarvis ED (2004) Advances to bayesian network inference for generating causal networks fromobservational biological data. Bioinformatics 20:35943603How to infer gene networks from expression proles M Bansal et al 10Molecular Systems Biology 2007 &2007 EMBO and Nature Publishing Group", + "with the data. To cope with this problem, Siegenthaler et al. proposed a novel assessment procedure that incorporates the inferability of gene regulatory interactions by redening the confusion matrix interms of inferability of the network, i.e., the possibility of the network to be determined from data. The inferability of GRNs was analyzed based on the causal information that could beextracted from experiments. Authors used data from the DREAM", + "and can thus be helpful in determining the causal structure of gene networks. Often, such data have already been gathered previously in the form of single-gene experiments and other links can be gleaned from a search of the published literature. In a few cases, a relevant database exists which can be used as a data source. Links of this type will all be directed edges from gene to phenotype (where the phenotype is the same as used as the seed).", + "tional methodologies in gene regulatory net-works. IGI Global, Hershey, PA, pp 127 11. Roy S, Das D, Choudhury D, Gohain GG, Sharma R, Bhattacharyya DK (2013) Causality inference techniques for in-silico gene regu-latory network, Mining intelligence and knowl-edge exploration. Springer, New York, pp 432443 12. Olsen C, Meyer PE, Bontempi G (2009) Infer- ring causal relationships using information the-oretic measures. In Proceedings of the 5th Benelux Bioinformatics Conference (BBC09)", + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small" + ], + [ + "On the other hand, single-nucleus RNA-seq (snRNA-seq) provides an alternative method for gene expression proling in complex tissues from frozen samples at single cell levels (Grindberg et al., 2013). Compared to scRNAseq, snRNA-seq analyze gene expression within the nuclei instead of intact cells. It should be noted that there could be potential dierences between the RNA type and expression levels between nucleus and cytosol. As observed in a previous study comparing nuclear", + "most genetic and epigenetic mechanisms are yet to be probed with single-cell resolution. To understand the finer details at the level of a singular cell, sophisticated genomic and epigenomic next-generation sequencing (NGS) technologies have increased the potential for research output immensely (see Clark etal. 2018; Clark etal. 2016; Kelsey etal. 2017; Macaulay etal. 2017; Stuart and Satija 2019). These would", + "of the disease, profiling gene expression in only bulk tissue sam-ples may obscure biologically relevant cell-type specific changes. While single-cell RNA-seq allows us to evaluate transcriptional changes within cell-types, it is prohibitively costly to executeon large cohorts (i.e. hundreds of individuals). To circumvent this issue, we developed a framework that leverages single-", + "2019). The traditional RNA sequencing technology (bulk RNA-seq) is applied to determine gene expression pro les, isoform expression, alternative splicing and single-nucleotide polymorphisms on basis oftissue samples, which contains various cell types ( Kuksin et al., 2021 ). On the contrast, single-cell RNA sequencing (scRNA-seq), a noveltechnology can detect the gene expre ssion patterns for each transcript within single cell and distinguish cell subtypes ( Lhnemann et al., 2020 ).", + "sion from smaller amounts of RNA enabled cell typespecific analyses.Specific cell types can beisolated using flow cytometry, for example, using endogenously expressed fluorescent markers, with or without combining with antibodies for cell surface proteins. Transcriptomic analysis by either microarray or bulk RNA sequencing then follows (39,67,68,104,145).Such analyses can 280 Taiberetal. Annu. Rev. Genom. Hum. Genet. 2022.23:275-299. Downloaded from www.annualreviews.org", + "Recent applications Single-cell RNA sequencing has had a profound impact on our understanding of neuronal and hematopoietic cell types, as well as the immune system. Examples of novel insights in immunity include a window on to an unexpected plethora of dendritic cells in mouse immun- ity [25] and new regulators and subpopulations of CD4+ T cells [26 28]. In hematopoiesis, much single-cell tran- scriptomics work has focused on hematopoetic stem cells and the single-cell perspective has provided reso-", + "single- nucleus RNAseq makes them a valuable complement to the find- ings published by Orozco, Chen et al. (Orozco et al., 2020 ). Furthermore, Yan et al. (2020) used cell sorting to enrich for cell types with a high degree of heterogeneity, resulting in finer cell subtype resolution for non-photoreceptor cell types such as RGCs. In addition to neural retina, our understanding of the choroidal", + "using sequencing (ATAC-seq),95,96 that can map chro- matin interactions and accessibility with higher resolu-tion than previous methods will improve our ability to disentangle GWAS loci; while single-cell RNA sequenc- ing 97,98 and CRISPR-based pooled gene perturbation methods99103 provide unprecedented opportunities for studies of how RNA expression patterns differ between cells within tissues and how those tissues and cells react to perturbation of multiple genes in parallel.", + "cell RNA-seq data from a smaller cohort in conjunction withco-expression network analysis in order to estimate cell-typespecific transcriptomic changes in large, bulk tissue RNA-seq datasets. We isolated nuclei and performed single-nuclei RNA-seq (snRNA-seq, n= 27 321 nuclei) on postmortem human brain tissue from aged, neurologically healthy controls ( n=5 ,6 7t o9 0 + years old, PFC, Supplementary Material, Table S1 ) to clarify cell- type proportions and the corresponding transcriptional profiles", + "without the biases of probe sequence selection and hybridization reactions. The second innovation is cell-specific RNA profiling methods [79] that make it practical to generate comparatively accurate expression data for individual cell types in genetically engineered lines of mice. We can soon expect far more comprehensive and specific lists of genes for several important cell and tissue types that can be used to assemble multicellular expression networks in eye.ACKNOWLEDGMENTS Dr. Eldon E." + ], + [ + "52.Zhu J et al. (2007) Increasing the power to detect causal associations by combining genotypicand expression data in segregating populations. PLoS Comput Biol 3:e69 53.Zhu J et al. (2008) Integrating large-scale functional genomic data to dissect the complexity ofyeast regulatory networks. Nat Genet 40:854861 54.Kim JK et al. (2005) Functional genomic analysis of RNA interference in C. elegans. Science308:11641167", + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "expression and its effect on disease . Nature 2008, 452 (7186):423-428. 12. Chen LS, Emmert-Streib F, Storey JD: Harnessing naturally randomized transcription to infer regulatory relationships amo ng genes . Genome Biol 2007, 8(10):R219. 13. Aten JE, Fuller TF, Lusis AJ, Horvath S: Using genetic markers to orient the edges in quantitative trait networks: the NEO s oftware . BMC Syst Biol 2008, 2:34. 14. Millstein J, Zhang B, Zhu J, Schadt EE: Disentangling molecular", + "and unknown function by large-scale coexpression analysis. Plant Physiol 2008, 147:41-57. 98. Wolfe CJ, Kohane IS, Butte AJ: Systematic survey reveals gen- eral applicability of \"guilt-by-a ssociation\" within gene coex- pression networks. BMC Bioinformatics 2005, 6:227. 99. Lee NH: Genomic approaches for reconstructing gene net- works. Pharmacogenomics 2005, 6:245-58. 100. Goutsias J, Lee NH: Computational and experimental approaches for modeling ge ne regulatory networks. Curr", + "the discovery of interface genes. These mRNA transcripts regulate expression of genes in those structures, and thereby couple multiple networks a nd biological processes. The detection of these transcripts and the analysis of their gen es regulatory polymorphisms 37", + "Rev. Genet 2007;8:437449. [PubMed: 17510664] A review of theory and approaches to mapping genetic interaction networks. 16. Bork P, et al. Protein interaction networks from yeast to human. Curr. Opin. Struct. Biol 2004;14:292 299. [PubMed: 15193308] 17. Ewing B, Hillier L, Wendl MC, Green P. Base-calling of automated sequencer traces using phred. I. Accuracy assessment. Genome Res 1998;8:175185. [PubMed: 9521921]", + "CC represents a dramatic improvement over existinggenetic resources for mammalian systems biology appli- cations (Adam et al. 2007 ; Chesler et al. 2008 ). A number of gene expression data sets from microarray experiments,particularly those for mouse and rat, have been integrated into GeneNetwork ( http://www.genenetwork.org ), which is essentially a web knowledgebase in which the entire dataset and relevant metadata (data about the data) are com- bined with sophisticated statistical and computation tools", + "gene, and the first f unctional anti -sense miRNA, Lastly, we have used comparative genomics to infer regulatory networks based on individual conserved instances of regulatory motifs, which show functional enrichments similar and sometimes higher to genome -scale experimental met hods such as ChIP -chip. As part of the ENCODE and modENCODE projects, we are now studying dynamics of developmental and cell -differentiation networks in", + "(ncRNAs) from the Rfam database (Grifths-Jones et al. , 2005) and predicted regu- latory sites from the cisRED database (Robertson et al. , 2006). There is much to do in both of these emerging areas but even preliminary data have already given new insights into mammalian biology: it seems there is high lineage specic expansion of some ncRNA classes relative to protein-coding genes (Birney et al. , 2006). Another growing area of activity is in cataloguing the genetic variation present in human", + "(ncRNAs) from the Rfam database (Grifths-Jones et al. , 2005) and predicted regu- latory sites from the cisRED database (Robertson et al. , 2006). There is much to do in both of these emerging areas but even preliminary data have already given new insights into mammalian biology: it seems there is high lineage specic expansion of some ncRNA classes relative to protein-coding genes (Birney et al. , 2006). Another growing area of activity is in cataloguing the genetic variation present in human" + ], + [ + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "of importance in the emergence of precision medicine ( Curtis, 2015 ; Desautels et al., 2014 ; Glade Bender et al., 2015 ; Jorgensen, 2015 ; Kummar et al., 2015 ; Marquet et al., 2015 ; Rubin, 2014 ) wherein therapeutic strategies need to be aligned with specific properties of tumors. Methods GeneNetwork and WebGestalt GeneNetwork is an open access, online data analysis resource for systems biology and systems genetics. It contains a large number of microarray datasets from multiple tissues of", + "GeneNetwork, a public web source used to study relations amongmarkers, genes, and phenotypes. We made use of large transcriptomedata sets for the amygdala, hippocampus, ventral tegmental area", + "ject to mapping analysis. We examine the connectivity among these sets and analyze the molecular, biochemical and genetic regulatory commonality of connected genes us-ing novel and existing bioinformatics tools. We also develop data-driven hypotheses to explain the mechanisms of genetic perturbations and variation as a means of dening global consequences of individual differences on tissue structure and function. Much of our work is motivated by prior studies of brain gene expression and mRNA", + "including correlation and network analysis to compare associations between tissues and between other rodent or human data sets[32] Many of the Data Sets are amenable to systems genetics mapping and other methods and are accessible at GeneNetwork. The Description and Usage column provides details about the data set and potential", + "including correlation and network analysis to compare associations between tissues and between other rodent or human data sets[32] Many of the Data Sets are amenable to systems genetics mapping and other methods and are accessible at GeneNetwork. The Description and Usage column provides details about the data set and potential", + "weighted gene co-expression network are described in[54]. Consensus network analysis was carried out with Rfunction blockwiseConsensusModules in the WGCNA R package [54]. Our online R software tutorial easily permits the user to identify tissue-specific age related modules and CpGs. Gene ontology enrichment analysis", + "approach employed in the construction of large expression data sets, such as those provided by GeneNetwork,39treats gene expression as a continuous variable across RI strains, rather than asa categorical one (knockout model). Hence, we believe that using these complementary, yet conceptually distinct, approaches enhanced our ability to propose mechanistic insights. A limitation of the current study relates to the non-trivial relationship between structural and functional brain connectivity.4", + "GeneNetwork ( http://www.genenetwork.org ; Williams and Mulligan, 2012)). These databases 180 represent transcriptome datasets for different tissues of recombinant inbred mice. If several probes 181 for the same gene were available, probes with higher maximum likelihood ratio statistic (LRS, a 182 measurement of the association or linkage between differences in traits and differences in particular 183 genotype markers values) were used. 184", + "pathways.TheGeneNetworkdatabaseisauniqueresourceforco-expressionanalysisusingdatafromavarietyof tissues acrossgeneticallydistinctinbredmice.However,extractionofbiologicallymeaningfulco-expressedgenesets ischallengingduetovariabilityinmicroarrayplatforms,probequality,normalizationmethods,andconfounding biologicalfactors.Inthisstudy,wetestedwhetherliteraturederivedfunctionalcohesioncouldbeusedasanobjectivemetricinlieuofgroundtruthtoevaluatethequalityofprobesandmicroarraydatasets." + ], + [ + "to as quantitative trait loc us (QTL) mapping study. QTL studies inform us region s on the chromosome where existing polymorphisms or SNPs are highly correlated with variation of the trait of interest. With the advancement in DNA sequencing, whole genome database of several mouse strains as well as gene expression data from several tiss ues are available. This allows us to use bioinformatic tools to identify candidate genes with greater confidence for further functional validations .", + "differences, allows for a far more comprehensive understanding of the genetic regulatory links underlying this variation. QTL mapping of gene expression traits allows us to identify eQTLs; genomic regions that have a regulatory effect on those expression traits. Two types of eQTLs can be distinguished, i.e., those that map near (less than 10 Mb from) the gene which encodes the transcript (local ) and those that map elsewhere in the genome ( distant ). 18 Together, local", + "simultaneously. Beginning with a study in yeast (Brem et al. 2002), QTL mapping has been done with gene expression as the phenotype. In such a study, the genomic loci responsible for variation in gene expression can be used to infer regulatory control. While such a study is not conclusive, it can be used to narrow the potential regulatory candidates, generate hypotheses for further testing and construct regulatory networks in s ilico.", + "is that one can now identify large numbers of less strong, second-ary QTLs which were previously lost to background noise, and this information opens up a whole new range of possible analy-ses, such as the identi cation of epistatic interactions ( Figure 5), that promise to uncover pathways of genetic control within the tissue studied. Traditionally, QTL mapping starts with a phenotype of inter-", + "and quantitative trait loci (QTL) regulatory models. A major goal is to identify which,among a set of candidate genes, are the most likely regulators of trait variation. These methods are applied in an effort to identify multiple-QTL regulatory models for large groups of genetically co-expressed genes, and to extrapolate the consequences of thisgenetic variation on phenotypes observed across levels of biological scale through the", + "distal regions into even finer regulatory loci. This influence on gene expression may be the reason why so many classical QTLs have been mapped to Qrr1 . The complexity highlighted by Qrr1 may very well be the rule rather than the exception for loci that modulate complex traits. Efforts to fine -map a single QTL have often been confronted by clusters of multiple small effect QTLs within the original interval (Legare et al., 2000; Demarest et al., 2001) . This poses a serious challenge, and", + "genotypes, availing of genetic markers across the whole genome, and allow the identication of QTLs with signi- cant effects on the disease (Darvasi 1998 ; Manolio 2010 ). QTLs are genetic regions closely linked to a gene with a quantitative effect on the phenotype. QTL mapping is based on the concept that phenotypic differences between inbred mouse strains can be used to demonstrate theimportance of genetic effects on complex phenotypes (Andreux et al. 2012 ; Hillebrandt et al. 2002 ). The standard", + "of the variants within associated loci through expression-quantitative trait locus (eQTL) studies will combine the genetic variation in associate d loci with expression analysis data to define regulatory relationships. Studies designed to understand the functional effect of any causal variants in relevant cell systems and an imal models will give insight to physiological consequence. These advances will underpin efforts to translate the findings through development of diagnostic tests, ris k evaluation and", + "illustrating the potential of functional mapping for effici ently establishing associations between existing QTL, as well as for novel QTL discovery. References 1. Damerval C, Maurice A, Josse JM, De Vienne D: Quantitative trait loci underlying gene product va riation: a novel perspective for analyzing regulation of genome expression. Genetics 1994, 137:289-301. 2. Brem RB, Yvert G, C linton R, Kruglyak L: Genetic dissection of transcriptional regulation in budding yeast. Science 2002, 296:752-755.", + "over a decade ago, using new genometypes for the BXD family of murine strains, as well as new statistical tools, showing that we can identify new quantitative trait loci (QTLs), resulting in highly plausible candidate genes. Quantitative trait locus (QTL) mapping has been carried out in numerous species to associate regions of the genome to phenotypes even before the structure of the genome was well understood (e.g., [ 3]). Rodents, especially mice, have been the species most prominently used for biomedi-" + ], + [ + "frequent usage of terms like epigenetic or chromatin land-scape. New methods for high-throughput mapping ofgenome-wide histone modifications and protein-DNA inter- actions were developed over the last few years (Blecher-Gonen et al., 2013; Garber et al., 2012). Histone Modifications Associated with Gene EnhancersChromatin can be modulated by covalent histone modifica-", + "orative efforts of the ENCODE Project [ 42] and Roadmap Epigenomics [ 43] consortia have already revealed a compendia of genome-wide histone modification signatures for various regulatory features in multiple primary tissues and cell lines. These datasets have been applied to global mapping studies and databases to prioritize functional regula- tory variants [ 44,45]. While these assays have been employed extensively in LCLs, and tumor cell lines to follow-up auto-", + "genetical genomics) and the genetics of epigeneticscould be studied simultaneously, thus revealing genes that directly or indirectly affect epigenetic gene states. An additional issue that could be addressed by such anapproach is to estimate the percentage of variation in gene expression that can be explained by different epigenetic conformations. The level of complexity could be further increased by including different cell types in the analysis, such as the", + "Incorporating epigenetics into genetic analysis can also enhance the predictive functional analysis of SNPs by highlighting regions of DNA that are accessible or inaccessible to protein binding by transcription factors and other regulatory pro- teins. SNPs may also lead to loss or gain of cytosineguanine dinucleotide (CpG) methylation sites. Rakyan et al. (2004) suggested that such an event might affect the overall methylation prole of a locus and, consequently, promoter activity and gene", + "Incorporating epigenetics into genetic analysis can also enhance the predictive functional analysis of SNPs by highlighting regions of DNA that are accessible or inaccessible to protein binding by transcription factors and other regulatory pro- teins. SNPs may also lead to loss or gain of cytosineguanine dinucleotide (CpG) methylation sites. Rakyan et al. (2004) suggested that such an event might affect the overall methylation prole of a locus and, consequently, promoter activity and gene", + "Incorporating epigenetics into genetic analysis can also enhance the predictive functional analysis of SNPs by highlighting regions of DNA that are accessible or inaccessible to protein binding by transcription factors and other regulatory pro- teins. SNPs may also lead to loss or gain of cytosineguanine dinucleotide (CpG) methylation sites. Rakyan et al. (2004) suggested that such an event might affect the overall methylation prole of a locus and, consequently, promoter activity and gene", + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "374. Bernstein, B.E., Stamatoyannopoulos, J.A., Costello, J.F ., Ren, B. et al. (2010), The NIH Roadmap Epigenomics Mapping Consortium, Nat. Biotechnol. V ol. 28, pp. 10451048. 375. Portela, A. and Esteller, M. (2010), Epigenetic modications and human disease, Nat. Biotechnol. V ol. 28, pp. 10571068. 376. Esteller, M. (2007), Cancer epigenomics: DNA methylomes and histone-modication maps, Nat. Rev . Genet. V ol. 8, pp. 286298. 377. Gilad, Y ., Rifkin, S.A. and Pritchard, J.K. (2008), Revealing the archi-", + "likely to be part of regulatory elements. Our global map of histone marks will serve as an important resource forunderstanding the epigenetic basis of type 2 diabetes. [Supplemental material is available online at http:/ /www.genome.org. The ChIP-seq and gene expression data from this study have been submitted to ArrayExpress (http:/ /www.ebi.ac.uk/microarray-as/ae/) under accession nos. E-MTAB-189 and E-MTAB-191, respectively.] Genetic and epigenetic factors determine cell fate and function.", + "these with other epigenetic mechanisms. This section will describe each method and provide the reader with technologies and recommendations to aide in the design and implementation of an epigenetic study . Histone Modifi cation Analysis Histone modi cation signals can be captured with chromatin immunoprecipita- tion (ChIP), which provides modi cation position approximation on the genome" + ], + [ + "genomes. Hence, chromosomal and spatial co-localization in the nucleus may indicate co-regulation. It was previously shown that 3D chromatin structure couples nuclear compartmentaliza-tion of chromatin domains with the control of gene activity ( Gue- len et al., 2008 ) and thus contributes to cell-specic gene expression ( Zullo et al., 2012 ). In this context, it is noteworthy that cellular senescence is associated with modications of theglobal chromatin interaction network ( Chandra et al., 2015 ). To", + "2 Introduction Recent scientific advances have enabled the identification of functional genomic elements through a diverse set of functional annotations, including proteins functional scores (1, 2) , evolutionary conservation scores (3-5), and epigenetics scores from the Encyclopedia of DNA Elements (ENCODE) (6). Other initiatives such as the R oadmap Epigenomics project (7) and FANTOM5 project (8, 9) also provide evidence for potential regulatory v ariants in the human", + "accuracy of predictive networks [40, 5153]. We have also recently demonstrated how this class of network can be used to inform associations identied in GW Astudies [40]. 9 Summary The signicant challenge we face in the post-genome era is deciphering the bio-logical function of individual genes, pathways, and networks that drive complexphenotypes like disease. The availability of low-cost, high-throughput technologies", + "a growing awareness that the three-dimensional juxtaposition of DNAregions within nuclei means that genes can be regulated by regulatory elements that are located at some distance from the gene ( Fig. 5 ) (Javierre et al., 2016 ;Kadauke and Blobel, 2009 ). As a result of this, disease associated SNPs have been shown to fall in gene regulatory elements ( Chen and Tian, 2016; Fadason et al., 2017; Farh et al., 2014; Lee et al., 2014; Schierding et al., 2015 ).", + "network. Cell 9, 12121226 (2014). 12. Hirschhorn, J.N. Genomewide association studiesilluminating biologic pathways. N. Engl. J. Med. 0, 16991701 (2009). 13. Cantor, R.M., Lange, K. & Sinsheimer, J.S. Prioritizing GWAS results: a review of statistical methods and recommendations for their application. Am. J. Hum. Genet. 8, 622 (2010). 14. Lee, I., Date, S.V., Adai, A.T. & Marcotte, E.M. A probabilistic functional network of yeast genes. Science 0, 15551558 (2004).", + "Processing Large-Scale, High-Dimension Genetic 325 another. We anticipate these types of networks becoming increasingly important in the human genetics space to gain a mechanistic understanding of how a given DNAperturbation induces changes in one or more genes that go on to affect networks that cause disease. The integration of genotypic and expression and other data have recently been shown, in a Bayesian network framework [76], to enhance the overall", + "regions correlated with functional noncoding elements, including enhancers, better than did regions identified solely on the basis of nucleotide sequence. These results support the idea that the molecular shape of DNA is under selection and can identify evolutionary history. Genomic sequences that code for proteinsare relatively well understood but make up only ~2% of the human genome ( 1). Many functions are encoded in the remaining ~98% noncoding portion of the genome, but little", + "gene, and the first f unctional anti -sense miRNA, Lastly, we have used comparative genomics to infer regulatory networks based on individual conserved instances of regulatory motifs, which show functional enrichments similar and sometimes higher to genome -scale experimental met hods such as ChIP -chip. As part of the ENCODE and modENCODE projects, we are now studying dynamics of developmental and cell -differentiation networks in", + "References 1. Cremer T, Cremer M, Dietzel S, Muller S, Solovei I, Fakan S. Chromosome territoriesa function-al nuclear landscape. Curr Opin Cell Biol 2006; 18:307-16. 2. Misteli T. Beyond the sequence: cellular organization of genome function. Cell 2007; 128:787-800. 3. Schneider R, Grosschedl R. Dynamics and interplay of nuclear architecture, genome organization and gene expression. Genes Dev 2007; 21:3027-43.", + "enhancers in the control of cell identity and disease. Cell(2013) 155:934 47. doi: 10.1016/j.cell.2013.09.053 45. de Wit E, de Laat W. A decade of 3C technologies: insights into nuclear organization. Genes Dev (2012) 26:11 24. doi: 10.1101/gad.179804.111 46. Schmitt AD, Hu M, Ren B. Genome-wide mapping and analysis of chromosome architecture. Nat Rev Mol Cell Biol (2016) 17:743 55. doi: 10.1038/nrm.2016.104 47. Javierre BM, Burren OS, Wilder SP, Kreuzhuber R, Hill SM, Sewitz S, et al." + ], + [ + "[111], and for generation of networks based on known gene interactions such as GeneMania [112] and Cytoscape [113], as well as for identifying cross-species orthology relation-ships [114], network-based thinking has been increasingly applied to the study of aging and lifespan [115-118]. Re-cently, the novel computational method of network identifi- cation by regression (NIR) [119] has been used to identify", + "Here we will focus on gene network inference algorithms (the inuence approach). A description of other methods based on the physical approach and more details oncomputational aspects can be found in (Beer and Tavazoie,2004; Tadesse et al, 2004; Faith and Gardner, 2005; Prakash and Tompa, 2005; Ambesi and di Bernardo, 2006; Foat et al, 2006). We will also briey describe two improper reverse-engineering tools (MNI and TSNI), whose main focus is not", + "NIA[360] may help to infer a putative function by linking unkn own genes to genes known from previous studies to show a similar e xpres- sion pattern. We can also characterize unknown genes by thei r evolu- tionary, loss-of-function and network interaction proper ties to prioritize candidate variants[184] and even predict disease inherita nce mode to a certain degree[153]. Taking this approach a step further, GeneNetwork[99] is con structed", + "network inference techniques can be utilized to infer biologicalprocess and the potential phenotypic impact of variants in genes of unknown function [71 78]. Thus, pathway and network based annotation approaches can be powerful approaches to inferring phenotypic information where direct links to phenotype do not exist. 2.12. De novo association analyses involving multiple genomes In the absence of prior information one might leverage to annotate", + "interaction may be difficult to quantify. Conversely the directions and signs that accompany signalling or regula- tory pathways are generally known, but their incorpora- tion requires more work. It could nevertheless lead to important advances for the interpretation of microarray data in cancer studies, for example. Conclusion We have presented a general framework to analyse gene expression data when a gene network is known a priori . The approach involves the attenuation of the high-fre-", + "A number of techniques have been proposed for network inference. Existing techniques for nding gene networks can be broadly cate-gorized as (i) computational approaches, and (ii) literature-based approaches. The computational approach mainly uses statistical, machine learning, or soft-computing techniques [ 14,15] as discov- ery tools. On the other hand, a literature-based approach gathers relevant published information on genes and their interrelation-", + "addition, data from linkage or association studies (e.g. GWAS), or from high -throughput genetic screening experiments (e.g. CRISPR screening), or from animal gain -or-loss- of function studies, or from the gene -drug interactions, can also be exploited to predict potential gene functions. Integration of GeneBridge with data from these sources will further enhance the performance for gene function prediction, as is done in STRING [253], GeneMANIA [254] and Mitocarta [190, 255].", + "include the deep learning-driven pattern recognition models for analyzing the gene se- quences for identifying the possible future illness and developing mobile applications that can generalize the information from the genomic data. However, there is great demand for explainable Articial Intelligence models that are interpretable in decision-making. Author Contributions: The authors contributions are as follows, Conceptualization of the study,", + "Gene network inference algorithms are becoming accurate enough to be practically useful, at least when steady-state gene expression data are available, but efforts must be directedin assessing algorithm performances. In a few years, gene network inference will become as common as clustering for microarray data analysis. These algorithms will become moreTable IV Results of the application of network inference algorithms on the experiment data sets Data sets ARACNE BANJO NIR Clustering Random", + "accuracy of predictive networks [40, 5153]. We have also recently demonstrated how this class of network can be used to inform associations identied in GW Astudies [40]. 9 Summary The signicant challenge we face in the post-genome era is deciphering the bio-logical function of individual genes, pathways, and networks that drive complexphenotypes like disease. The availability of low-cost, high-throughput technologies" + ], + [ + "920 Diabetologia. 2020;63: 977986. doi:10.1007/s00125-020-05101-y 921 9. Stearns FW. One hundred years of pleiotropy: A retrospective. Genetics. Genetics; 922 2010. pp. 767773. doi:10.1534/genetics.110.122549 923 10. Geiler-Samerotte KA, Li S, Lazaris C, Taylor A, Ziv N, Ramjeawan C, et al. Extent and 924 context dependence of pleiotropy revealed by high-throughput single-cell phenotyping. 925 PLoS Biol. 2020;18. doi:10.1371/journal.pbio.3000836", + "920 Diabetologia. 2020;63: 977986. doi:10.1007/s00125-020-05101-y 921 9. Stearns FW. One hundred years of pleiotropy: A retrospective. Genetics. Genetics; 922 2010. pp. 767773. doi:10.1534/genetics.110.122549 923 10. Geiler-Samerotte KA, Li S, Lazaris C, Taylor A, Ziv N, Ramjeawan C, et al. Extent and 924 context dependence of pleiotropy revealed by high-throughput single-cell phenotyping. 925 PLoS Biol. 2020;18. doi:10.1371/journal.pbio.3000836", + "advances, the more examples become known which canbe explained only under the assumption of pleiotropy (Plate 1910, quoted from M cKusick 1976, pp. 301302). His assertion of the extent and importance of pleiotropyhas been a central theme that has been challenged andstrengthened throughout the past 100 years as the way inwhich we study pleiotropy has changed. DEVELOPMENT OF PLEIOTROPIC RESEARCH One of the rst experimental studies of the mecha-", + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "users can take advantage of a systems genetics approach (Rosen et al., 2003, 2007). While the candidate gene approach asks which one gene mutation causes a particular disease, the systems genetics approach explores which phenotypes and diseases result from diverse sets of genetic and molecular markers (Rosen et al., 2003, 2007). The majority of data sets in GeneNetwork are collected from GRPs consisting of hundreds of diverse, inbred strains of", + "34. Pyeritz, R.E. (1989) Pleiotropy revisited: molecular explanations of a classic concept. Am. J. Med. Genet. ,34, 124134. 35. Gruneberg, H. (1938) An analysis of the pleiotropic effects of a lethal mutation in the rat. Proc. R. Soc. Lond. B. ,125, 123144. 36. Wagner, G.P. and Zhang, J. (2011) The pleiotropic structure of the genotypephenotype map: the evolvability of complex organisms. Nat. Rev. Genet. ,12, 204213. 37. Solovieff, N., Cotsapas, C., Lee, P.H., Purcell, S.M. and Smoller, J.W.", + "21. Byars, S. G. et al. Genetic loci associated with coronary artery disease harbor evidence of selection and antagonistic pleiotropy. PLoS Genet. 13, e1006328 (2017). 22. Rodrguez, J. A. et al. Antagonistic pleiotropy and mutation accumulation inuence human senescence and disease. Nat. Ecol. Evol. 1, 0055 (2017). 23. Institute for Health Metrics and Evaluation. Findings from the Global Burden of Disease Study 2017 (IHME, 2018).", + "traits can be due to shared molecular mechanisms and processes (true gene pleiotropy)or covariance can be due to statistical error or to linkage of neighboring, but mechanis-tically independent gene variants. This latter effect is particularly serious and is described in more length by Gerlai 4and in Wang5in the context of RI strains. GeneNetwork GeneNetwork (GN, www.genenetwork.org ) is an open web resource that enables", + "2019;20 .https://doi.or g/10.118 6/s13059 -019-1628-0 PMID: 30678704 19. Chesmo reK,Bartlett J,Williams SM.Theubiquity ofpleiotropy inhuman disease. Hum Genet. 2018; 137: 3944. https://doi.or g/10.100 7/s00439 -017-1854 -zPMID: 29164333 20. Bulik-Sulli vanB,Finucane HK,Anttila V,Gusev A,DayFR,LohPR,etal.Anatlas ofgenetic correla- tions across human diseases andtraits. NatGenet 2015 4711. 2015; 47:12361241. https://doi.or g/ 10.1038 /ng.3406 PMID: 26414676", + "2019;20 .https://doi.or g/10.118 6/s13059 -019-1628-0 PMID: 30678704 19. Chesmo reK,Bartlett J,Williams SM.Theubiquity ofpleiotropy inhuman disease. Hum Genet. 2018; 137: 3944. https://doi.or g/10.100 7/s00439 -017-1854 -zPMID: 29164333 20. Bulik-Sulli vanB,Finucane HK,Anttila V,Gusev A,DayFR,LohPR,etal.Anatlas ofgenetic correla- tions across human diseases andtraits. NatGenet 2015 4711. 2015; 47:12361241. https://doi.or g/ 10.1038 /ng.3406 PMID: 26414676" + ], + [ + "the different pathways linked with aging and even study genenetworks. In such works, GenAge is an adequate resource asit provides a framework for the functional genomics of aging.For example, Xue et al . (2007) used GenAge to construct a modular network of aging and obtain insights into aging, including thefact that genes connecting different modules are more likely toaffect longevity and/or aging, an hypothesis the authors validatedexperimentally in worms (Xue et al", + "[111], and for generation of networks based on known gene interactions such as GeneMania [112] and Cytoscape [113], as well as for identifying cross-species orthology relation-ships [114], network-based thinking has been increasingly applied to the study of aging and lifespan [115-118]. Re-cently, the novel computational method of network identifi- cation by regression (NIR) [119] has been used to identify", + "network analysis is a useful approach toward identifying genetic determinants of longevity . PLoS One , 2008 , 3(11), e3802. [38] Bell, R.; Hubbard, A.; Che ttier, R.; Chen, D.; Miller, J.P.; Kapahi, P.; Tarnopolsky, M.; Sahasrabuhde, S.; Melov, S.; Hughes, R.E. A human protein interaction network shows conservation of aging processes between human and invertebrate species . PLoS Genet , 2009 , 5(3), e1000414. [39] Budovsky, A.; Abramovich, A.; Cohen, R.; Chalifa-Caspi, V.;", + "genes (http://genomics.senescence.info/genes/), more than700 genes have been identified that regulate lifespan inmodel organisms (de Magalha es et al., 2009a). Many ofthese genes and their associated pathwayssuch as theinsulin/IGF1/GH pathwayhave been shown to affect lon-gevity across different model organisms (Kenyon, 2010).Therefore, at least some mechanisms of aging are evolu-tionarily conserved and may have potential therapeuticapplications (Baur et al., 2006). For example, evidencesuggests the use of", + "30. Vartiainen, S., Aarnio, V., Lakso, M. & Wong, G. Increased lifespan in transgenic Caenorhabditis elegans overexpressing human -synuclein. Exp. Gerontol. 41, 871 876 (2006). 31. Lpez-Otn, C. et al. The hallmarks of aging. Cell153, 1194 1217 (2013). 32. Kenyon, C. J. The genetics of ageing. Nature 464, 504 512 (2010). 33. Liberzon, A. et al. The molecular signatures database hallmark gene set collection. Cell Syst. 1, 417 425 (2015).", + "1118 compared to young ones. Overall, our results revealed that six pathways and six key genes might play pivotal roles in regulating longevity, and three interacting genes might be implicated in longevity. The results will not only provide new insight into the mechanisms of longevity, but also provide novel ideas for network-based approaches for longevity-related research. Keywords Drosophila melanogaster Longevity Gene Pathway Network Introduction", + "During the past century, remarkable progress has been made in unveiling the mechanisms of aging. Genetic and molecular pathways that regulate healthspan and lifespan have been identified in various model organisms, provid-ing a rich knowledge base (Longo etal. 2015; Lopez-Otin etal. 2013, 2016; Singh etal. 2019). However, the focus on", + "In addition to aging- and CR-related genes, another source of candidate genes and pathways for drug designare human longevity-associated genes (Barzilai andShuldiner, 2001; Browner et al., 2004; Kenyon, 2010).Dozens of genes have now been associated with humanlongevity (de Magalha es et al., 2009a), although only ahandful of genes have been shown to have consistenteffects across populations. Many longevity-associated genes are related to spe-", + "been associated with human longevity in genetic asso-ciation studies. The parallel emergence of networkapproaches offers prospects to develop multitargetdrugs and combinatorial therapies. Understandinghow the environment modulates aging-related genesmay lead to human applications and disease therapiesthrough diet, lifestyle, or pharmacological interven-tions. Unlocking the capacity to manipulate humanaging would result in unprecedented health benefits. I. Introduction", + "Network approaches are instrumental in discerning global properties of aging/lifespan regulators, making com- putational predictions and inferring the modularity and rela-tionships of various aging regulators. However, they should be applied with great caution as to avoid bias introduced by the literature, the lack of spatial and temporal information, or the limited coverage of the network [44]. 4. EPIGENETIC REGULATION OF AGING In addition to gene expression changes, the states of epi-" + ], + [ + "in advance. Polygenic Risk Scores (PRS) were proposed by Duncan L. et al. [ 8] for risk analysis using the sum of the weight of each risk-associated locus of genomic sequence obtained from the corresponding evidence. These weights are assessed from the regression coefcient associated with each locus. These combined genetics features and correlation matrices would signicantly assist the entire eld of genomics study [ 9]. These studies on", + "Owing to their small effect sizes, SNP associations have very little clinical applicability for risk prediction. A polygenic risk score (PRS) attempts to estimate the combined risk from multiple SNPs that have been associated with a certain trait with genome-wide sig-nificance. By accounting for a large proportion of the genetic variance underlying a trait, the overall effect size", + "of genome-wide genotypes and publicly available data from large consortia, GRSs with a larger number of vari- ants are being used, and the predictive value of these genome-wide polygenic risk scores (PRSs) has substantially improved 50,51. PRSs can be derived using different approaches, however, these require both summary statistics from an exter -", + "use for estimation of polygenic risk scores (PRS) has grownin recent years. PRS screening may be used to determine therisk of common complex diseases for individuals and theiroffspring, and although it is not widely clinically availablenow, there is an ongoing interest in increasing its utility. Useof GWAS data from European populations for PRS esti-mation would subsequently impose a bias in favor of in- dividuals with similar ancestry, whereas limited bene ti s", + "(GWAS) in diverse populations have identified hundreds of genetic loci associated with T2D [79]. Polygenic risk scores (PRS), which aggregate the genetic risk of individ - ual alleles across the genome, are thus promising to pre - dict future T2D occurrence and improve early diagnosis, intervention, and prevention of T2D [1015]. However, to date, T2D PRS were most widely developed and vali - dated in individuals of European descent. Given that the predictive performance of PRS often attenuates in non-", + "(GWAS), polygenic risk scores (PRS) have shown promise to complement established clinical risk factors and inter vention paradigms, and improve early diagnosis and prevention of T2D. However, to date, T2D PRS have been most widely developed and validated in individuals of European descent. Comprehensive assessment of T2D PRS in non European populations is critical for equitable deployment of PRS to clinical practice that benefits global populations.", + "Letters NATure GeNeTicsMethods Polygenic score derivation. Polygenic scores provide a quantitative metric of an individuals inherited risk based on the cumulative impact of many common polymorphisms. Weights are generally assigned to each genetic variant according to the strength of their association with disease risk (effect estimate). Individuals are scored based on how many risk alleles they have for each variant (for example, zero, one, or two copies) included in the polygenic score.", + "(Fig. 1B ). Polygenic risk scores (PRS) have emerged as promising biomarkers for the prediction of disease risk, not only in the area of cardiovascular disorders, but also oncology (21). These risk scores also have become increasingly available for a multitude of phenotypes and are systematically curated in a free online database (22). It has been shown that certain preexisting autoimmune diseases as well as the occurrence of imAE upon treatment are associated with", + "eases identify individuals with risk equivalent to monogenicmutations. Nat. Genet. ,50, 12191224. 13. Euesden, J., Lewis, C.M. and OReilly, P.F. (2015) PRSice: poly- genic risk score software. Bioinformatics ,31, 14661468. 14. Belsky, D.W., Moffitt, T.E., Sugden, K., Williams, B., Houts, R., McCarthy, J. and Caspi, A. (2013) Development and evalu- ation of a genetic risk score for obesity. Biodemography Soc. Biol.,59, 85100. 15. De Jager, P.L., Chibnik, L.B., Cui, J., Reischl, J., Lehr, S., Simon,", + "in tissue-specic regions or use gene co-expression information may provide a more comprehensive view of a specic gene or a gene networks role in modulating an individuals response to environmental variations, compared to that provided by the single candidate gene approach (Gamazon et al., 2015; Barth et al., 2020). Expression-based polygenic risk scores (ePRS) oer one such approach to understand the underlying genetic background linked to behavioral outcomes (Hari Dass" + ] + ], + "task_id": [1,2,3,4,5,6,7,8,9,10,1,2,3,4,5,6,7,8,9,10] +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_1 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_1 new file mode 100644 index 0000000..8bc7dfe --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_1 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2009 - The Human Ageing Genomic Resources online.pdf", + "2018 - Sex Differences in Aging Genomic Instability.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2016 - Progress on the role of DNA methylation in aging.pdf", + "2001 - The genetics of aging.pdf", + "2011 - A genome-wide association study confirms APOE as the major gene influencing.pdf", + "2021 - Footprints in the Sand Deep Taxonomic Comparisons in Vertebrate Genomics to Unveil the Genetic Programs of Human Longevity.pdf", + "2012 - Genome-Environment Interactions That Modulate.pdf", + "2017 - Genome-wide transcriptomics of aging.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf" + ], + "extraction_id": [ + "7ada6b55-99c2-5e20-bf96-d153f927256c", + "0104338d-cc9c-538f-be29-8343a64da37d", + "4ea8424f-1cd8-569c-a1df-3f0f54206e70", + "bcb3c620-b960-5af6-95ea-13215c31672e", + "76bae746-eabf-51ed-a01f-d32ecc89c11b", + "210aa417-372c-5bf6-b961-e281a1817458", + "34223e0e-590c-5f26-b120-b7250cd91b99", + "d59d7882-333d-5576-86ab-3cfa6354b946", + "c7d6d597-a9c7-5db2-888d-5f9297f0af47", + "517379dd-d351-5e9a-8e78-72e543bb2945" + ], + "document_id": [ + "e43cd3b6-ad8e-5422-ba7c-ceb6e66cc529", + "8cfb5529-7f0c-58fc-b6e4-b3ee800fb72f", + "62b635c3-040e-512a-b016-6ef295308a1e", + "e4cdc02f-4415-5638-aab8-f848b4d64a22", + "aa9a9193-b6f3-5ef8-aefd-e01ec44abb46", + "63b27b06-db2c-5542-9b1a-cb9ebe64d339", + "0dc45abe-ab02-5b07-9916-7093b53323c0", + "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "1a2a3737-b0a6-58b9-908f-50753241a309", + "62b635c3-040e-512a-b016-6ef295308a1e" + ], + "id": [ + "chatcmpl-AIFgRqvOB8PnpNpKMnpdr80oxf2MI", + "3117c019-7311-53ae-8ab1-927ca822c709", + "a9434032-4a9d-54f8-a7a6-16110d1b3118", + "a0672677-71ad-5603-8427-a0648eec407f", + "c1b5a31a-066d-571b-af1f-db746d9d17f6", + "e09c33ea-4139-5cc2-9cf5-a40045f26a0c", + "2d0a20b8-4196-5451-9d99-282f82234464", + "8bcb7ae0-ac45-5b4c-8a4b-626564e8ec11", + "786d2756-4c4d-5ac0-8d3d-63f914d51664", + "d811de8c-b666-5bb5-b0eb-a9b17fa16a8e", + "081e12f9-359c-5a2c-b740-714d637367d3" + ], + "contexts": [ + "It is undisputed that genetic factors influence aging. In a remarkable", + "males: what are the molecular and evolutionary causes? Aging Cell. 2007;6:225233. doi:10.1111/j.1474-9726.2007.00279.x 63. Benayoun BA, Pollina EA, Brunet A. Epigenetic regulation of ageing: link- ing environmental inputs to genomic stability. Nat Rev Mol Cell Biol. 2015;16:593610. doi:10.1038/nrm4048 64. Sen P, Shah PP, Nativio R, Berger SL. Epigenetic mechanisms of longevity and aging. Cell. 2016;166:822839. doi:10.1016/j.cell.2016.07.050", + "Clinical Genetics and Genomics of Aging", + "standing the cause and mechanisms of aging is imperative in assisting to suppress age-related diseases and promote healthylongevity. It is well-known that aging is influenced by a combin- ation of genetic and environmental factors. Previous twin stud- ies have shown that the genetic contribution to general human longevity is about 2030% [ 4,5], whereas environmental factors in human aging and longevity still account for the largest effect. Epigenetic factors influence the regulation of gene expres-", + "Recent developments on the genetics of aging can be seen as several streams of effort. In general, humans show a relatively modest ( <50%) heritability of", + "effect genetic variants on human longevity. Aging 2, 612620. Yu, C.E., Seltman, H., Peskind, E.R., Galloway, N., Zhou, P.X., Rosenthal, E., Wijsman, E.M., Tsuang, D.W., Devlin, B., Schellenberg, G.D., 2007. Comprehensive analysis of APOE and selected proximate markers for late-onset Alzheimers disease: patterns of linkage disequilibrium and disease/marker association. Genomics", + "factors shape a complex scenario for which clear answers of the regulation of longevity have been dicult to distill. With the discovery of genetic factors underlying aging in experimental laboratory models, forays into the genetic regulation of these properties have rapidly expanded, uncovering conserved mechanisms across diverse metazoa that inuence expression of aging phenotypes and lifespan. Yet, the story gets muddled in that these factors are often", + "In addition to aging- and CR-related genes, another source of candidate genes and pathways for drug designare human longevity-associated genes (Barzilai andShuldiner, 2001; Browner et al., 2004; Kenyon, 2010).Dozens of genes have now been associated with humanlongevity (de Magalha es et al., 2009a), although only ahandful of genes have been shown to have consistenteffects across populations. Many longevity-associated genes are related to spe-", + "tion for decades, the underlying molecular genetic causes of and responses to aging remain an area of active study. Research from model systems hascharacterized a range of physiological and molecular phenotypes associated with aging. These include genomic instability caused by accumulation of DNA damage, dysregulation of repair mechanisms, and telomere attri- tion; epigenetic alterations; dysregulation of transcription; loss of proteostasis; cellular senescence; and deregulated", + "143 The molecular bases of ageing are multi factorial, but there are nine distinctive features related to this process, which include genomic instability, telomere shorten- ing, de-regulated nutrient sensing, mitochondrial dysfunction, cellular senescence, stem cell exhaustion, altered cellular senescence, loss of proteostasis and a change in the patterns of epigenetic modifications [4, 5]. Epigenetics andAgeing Epigenetics is considered as a dynamic interface between the genome and the envi-" + ], + "answer": "The main genetic factors that influence aging include: 1. **Genomic Instability**: Aging is associated with the accumulation of DNA damage and the dysregulation of repair mechanisms, leading to genomic instability [9]. 2. **Telomere Attrition**: The shortening of telomeres, which are protective caps at the ends of chromosomes, is a significant factor in aging [9], [10]. 3. **Epigenetic Alterations**: Changes in epigenetic marks, which regulate gene expression without altering the DNA sequence, play a crucial role in aging [2], [4], [9], [10]. 4. **Deregulated Nutrient Sensing**: The pathways that sense and respond to nutrients become deregulated with age, affecting longevity [10]. 5. **Mitochondrial Dysfunction**: Mitochondria, the energy-producing organelles in cells, become less efficient with age, contributing to the aging process [10]. 6. **Cellular Senescence**: The process by which cells lose the ability to divide and function properly is a hallmark of aging [9], [10]. 7. **Loss of Proteostasis**: The ability of cells to maintain protein homeostasis declines with age, leading to the accumulation of damaged proteins [9], [10]. 8. **Stem Cell Exhaustion**: The decline in the regenerative capacity of stem cells contributes to aging [10]. These factors collectively shape the complex genetic landscape of aging, influencing the expression of aging phenotypes and lifespan [7].", + "question": "What are the main genetic factors that influence aging?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_10 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_10 new file mode 100644 index 0000000..d9ddb50 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_10 @@ -0,0 +1,65 @@ +{ + "titles": [ + "1993 - Genomic Damage and Its Repair.pdf", + "2007 - Trends in oxidative aging theories.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2019 - Towards Understanding Genomic Instability, Mitochondrial.pdf", + "2002 - Pharmacology, Genomics, and the Evolutionary Biology.pdf", + "2002 - Large genome rearrangements as a primary cause of aging.pdf", + "2016 - Genome Integrity in Aging.pdf", + "2009 - Genomic instability and DNA damage responses in progeria arising.pdf", + "2023 - Genome-wide RNA polymerase stalling.pdf", + "2016 - Menopause Genome stability as new paradigm.pdf" + ], + "extraction_id": [ + "64063108-0ff2-54e5-9801-bc1c49cbdee4", + "752c6f1a-0c4d-5419-86cd-687d2aed7817", + "ead14808-bfb7-5e32-9830-28efaae71151", + "d620ea24-4422-5636-86f5-0943371a4a18", + "e501662f-ffca-563b-97a7-b682a5d7f6ba", + "8f1a0875-8179-5d45-abc0-bbd4c9ac8da5", + "17b26647-4659-5f2d-a9b0-7c122d4b5d1a", + "72beba0d-8c77-5aa9-82ac-ddf6a19355ac", + "31088092-778f-59e0-a9de-5ec25c241aab", + "0855231d-cb95-540c-a3dd-c93729efb34c" + ], + "document_id": [ + "d049f302-a130-5ee4-a1b5-5091605d5173", + "0d752c1a-706a-5b9e-88ef-ba7c51735c3c", + "62b635c3-040e-512a-b016-6ef295308a1e", + "9b34514d-3d0e-52b5-8e5e-2f3c0708fd82", + "1bc636a3-6ce0-5fea-b549-0dae90a78f1b", + "8a8926dc-2360-5a54-b586-8acc34e51c32", + "85d5fcbb-5385-5a01-8139-d11fc8b1fe3a", + "b7d96f9f-8ad4-5f8f-94f9-60404806d478", + "78812a12-8d31-5159-8367-b0d38e5bc84b", + "564dead1-2737-572f-860c-f00de4d0395e" + ], + "id": [ + "chatcmpl-AIFhaX8SGHyXXi1vHCPCMEW3CahGu", + "a9f7eda5-1b64-507e-95dd-07c81f2d603b", + "882149e3-8186-5577-a2a7-79f2659ff9b4", + "da4e59b7-d5b6-5992-9607-f6697c8f5276", + "4841d806-98b4-513e-94a2-714df6c896f5", + "fc10c968-3108-5c4b-a49c-cb0feabd18c5", + "eb8b89de-422a-5e9e-9ac8-60af4cd718c2", + "34e6b3c4-63bf-5198-ab09-2a7200a7c19a", + "beed04cc-28c7-5dc7-b334-51226a217439", + "badf3a36-1f99-58aa-b80c-725eccf4e8f3", + "c35d1f43-c3bd-5cac-ae4d-937be35f1121" + ], + "contexts": [ + "logical phenomena is often facilitated by the study of genetic mutants, and, in the case of humans, genetic disorders. Accordingly, a search was made, over the years, for genetic disorders characterized by premature aging. If DNA dam- age and repair has anything to do with aging it should be evidenced in such individuals. Martin (1978) listed 162 genetic syndromes in humans with some or many signs of premature aging. About 21 feahares are considered as markers for", + "[315] Szilard, L. On the nature of the aging process. Proc. Natl. Acad. Sci. USA 45:3545; 1959. [316] Vijg, J.; Dolle, M. E. Large genome rearrangements as a primary cause of aging. Mech. Ageing Dev. 123:907915; 2002. [317] Vijg, J. Somatic mutations and aging: a re-evaluation. Mutat. Res. 447:117135; 2000. [318] Martin, G. M. Genetic syndromes in Man with potential relevance to the pathobiology of aging. Birth Defects Orig. Artic. Ser. 14:539; 1978.", + "19 6. Milholland B, Suh Y , Vijg J.Mutation and catastrophe in the aging genome. Exp Gerontol. 2017;94:3440. 7. Maslov AY , Ganapathi S, Westerhof M, Quispe-Tintaya W, White RR, Van Houten B, etal. DNA damage in normally and prematurely aged mice. Aging Cell. 2013;12:46777. 8. Blokzijl F, de Ligt J, Jager M, Sasselli V , Roerink S, Sasaki N, etal. Tissue-specific mutation accumulation in human adult stem cells during life. Nature. 2016;538:2604.", + "143 Gonzalo S, Kreienkamp R & Askjaer P (2017) Hutchinson -Gilford Progeria Syndrome: A premature aging disease caused by LMNA gene mutations. Ageing Res. Rev. 33, 1829. 144 Lu L, Jin W & Wang LL (2017) Aging in Ro thmund -Thomson syndrome and related RECQL4 genetic disorders. Ageing Res. Rev. 33, 3035. 145 de Renty C & Ellis NA (2017) Blooms syndrome: Why not premature aging? Ageing Res. Rev. 33, 3651. 146 Shiloh Y & Lederman HM (2017) Ataxia -telangiectasia (A -T): An emerging", + "genetic disease model of premature aging, In: Harrison,D.E., eds, Genetic Effects on Aging II (Telford Press, Caldwell,NJ), pp. 521542. [2] Djawdan, M., Sugiyama, T., Schlaeger, L., Bradley, T.J. and Rose, M.R. (1996) Metabolic aspects of the trade-off between fecundity and longevity in Drosophila melanogaster ,Physiol. Zool. 69, 11751195. [3] Fleming, J.E., Spicer, G.S., Garrison, R.C. and Rose, M.R.", + "genes of a whole chromosome ineffective, couldbe a main causal factor in aging (Szilard, 1959).According to Maynard Smith, such types of mu-tations do not seem likely to be common enoughto be the main cause of aging. However, at thetime quantitative information on the possible age-related accumulation of different types of muta-tions in various tissues of mammals wascompletely lacking. The question, therefore,whether somatic mutations are a cause of aging,has not been resolved, more than four decadesafter", + "features of premature aging (16, 17). Subsequent experiments conrmed that mitochondrial DNA mutations and deletions were the driving force behind the observed accelerated aging phenotypes(18). THE LINK BETWEEN NUCLEAR GENOME INTEGRITY AND PREMATURE AGING The notion that the majority of currently identied progeria syndromes originate from defects in genome maintenance highlights the importance of the condition of DNA in the process of", + "Tryggvason K,ZhouZ.Genomicinstability inlaminopathy based premature aging,NatMed. 2005;11:780 785. 13.MisteliT,ScaffidiP.Genomeinstability inprogeria:when repairgetsold,NatMed. 2005;11:718 719. 14.PereiraS,Bourgeois P,NavarroC,EstevesVieiraV,CauP,De SandreGiovannoli A,LvyN.HGPSandrelatedpremature aging disorders: Fromgenomicidentification tothefirsttherapeutic approaches, MechAgeingDev.2008;129:449 459. 15.SmithED,Kudlow BA,FrockRL,KennedyBK.Atypenuclear", + "Nature Genetics | Volume 55 | February 2023 | 268279 278 Article https://doi.org/10.1038/s41588-022-01279-621. Tiwari, V. & Wilson, D. M. 3rd. DNA damage and associated DNA repair defects in disease and premature aging. Am. J. Hum. Genet. 105, 237257 (2019). 22. Tamae, D., Lim, P., Wuenschell, G. E. & Termini, J. Mutagenesis and repair induced by the DNA advanced glycation end product N2-1-(carboxyethyl)-2-deoxyguanosine in human cells. Biochemistry 50, 23212329 (2011).", + "[36] J. de Boer, J.O. Andressoo, J. de Wit, J. Huijmans, R.B. Beems, H. van Steeg, et al., Premature aging in mice decient in DNA repair and transcription, Science 296 (2002) 12761279. [37] S.M. Schuh-Huerta, N.A. Johnson, M.P. Rosen, B. Sternfeld, M.I. Cedars, R.A. Reijo Pera, Genetic markers of ovarian follicle number and menopause in women of multiple ethnicities, Hum. Genet. 131 (2012) 17091724." + ], + "answer": "Yes, there are known genetic mutations that cause premature aging. Specifically, mutations in the LMNA gene are associated with Hutchinson-Gilford Progeria Syndrome, a disease characterized by premature aging [4]. Additionally, mutations in the RECQL4 gene are linked to Rothmund-Thomson syndrome, which also involves premature aging [4]. These genetic disorders highlight the connection between genome integrity and premature aging [7].", + "question": "Are there any known genetic mutations that cause premature aging?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_11 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_11 new file mode 100644 index 0000000..5488b92 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_11 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Mitonuclear genomics and aging.pdf", + "2009 - The Human Ageing Genomic Resources online.pdf", + "2001 - The genetics of aging.pdf", + "2009 - Garland_and_Rose_Experimental_Evolution.pdf", + "2009 - Experimental_Evolution.pdf", + "2015 - Great Is Their Sin.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2021 - Career Retrospective Tom Johnson?Genetics, Genomics.pdf", + "2019 - Genomics of 1 million parent lifespans.pdf", + "2018 - Sex Differences in Aging Genomic Instability.pdf" + ], + "extraction_id": [ + "e6fb876b-e91c-505a-aa16-7b428ec61f10", + "7ada6b55-99c2-5e20-bf96-d153f927256c", + "76bae746-eabf-51ed-a01f-d32ecc89c11b", + "5aa7f5b9-df70-54ec-a95c-dcaefa3b617f", + "c9d59e72-f068-58da-be7a-71b2f51a23f3", + "44c57701-0d0e-5ef8-afa1-ea3a6c4742d6", + "ead14808-bfb7-5e32-9830-28efaae71151", + "dd4a6239-2e79-5b99-89ef-3e4939b87805", + "ff0adc7c-70ff-5b14-ba7d-a9dda60fac80", + "0104338d-cc9c-538f-be29-8343a64da37d" + ], + "document_id": [ + "e05fdc09-c8d8-5134-a1fd-bf07a1564981", + "e43cd3b6-ad8e-5422-ba7c-ceb6e66cc529", + "aa9a9193-b6f3-5ef8-aefd-e01ec44abb46", + "496faa7f-9623-5ab7-9816-7c3755abb3aa", + "34821353-1b74-5ee2-ac39-66dd46f145bf", + "e5ae9710-3049-5327-82e4-e6626eb670c2", + "62b635c3-040e-512a-b016-6ef295308a1e", + "f3a26f44-f5af-5b2b-aa1c-aec2fd99f17e", + "f68b939c-847b-5eac-8926-24713ae43478", + "8cfb5529-7f0c-58fc-b6e4-b3ee800fb72f" + ], + "id": [ + "chatcmpl-AIFhez5FFXsDDkyj8CmiEuE5k6YSr", + "c96b67f8-ad31-50fd-b053-07b127938ef2", + "1c4286b6-ede2-568b-9c18-b1e99ede17a6", + "e09c33ea-4139-5cc2-9cf5-a40045f26a0c", + "f7120061-9773-5f74-9760-5442d49fbaae", + "d0e74ffd-034d-5e0e-86b6-4cf0de57d774", + "217c3592-1622-503f-a140-fd1452083301", + "b3e21ac9-8df8-5119-a769-a9da82db78da", + "fd811aec-6e33-5078-83d5-b68bd59b5a61", + "de7c30f6-cce9-563d-83f4-809f2aab781b", + "a9434032-4a9d-54f8-a7a6-16110d1b3118" + ], + "contexts": [ + "During the past century, remarkable progress has been made in unveiling the mechanisms of aging. Genetic and molecular pathways that regulate healthspan and lifespan have been identified in various model organisms, provid-ing a rich knowledge base (Longo etal. 2015; Lopez-Otin etal. 2013, 2016; Singh etal. 2019). However, the focus on", + "series of recent breakthroughs, a number of genes capable ofaltering the aging process as a whole or at least to a largedegree have been identified in animal models and even a fewin humans (Finch & Ruvkun, 2001; de Magalhes, 2005; Kenyon,2005). Furthermore, multiple alleles have been examined fortheir association with human exceptional longevity (Vijg & Suh,2005). This is a fascinating and important area of research, yetthere are now so many genes being associated with aging andlongevity that keeping", + "Recent developments on the genetics of aging can be seen as several streams of effort. In general, humans show a relatively modest ( <50%) heritability of", + "One approach that has become increasingly common in the characterization of the ge-netics of aging is to isolate aging mutants, usually from mutagenesis experiments, andthen to determine the mechanistic basis for the unusual life span in the mutants. Thisapproach has led to the discovery of genes that can enhance (e.g., Maynard Smith 1958;Lin et al. 1988; reviewed in Guarente and Kenyon 2000, Kim 2007) or reduce life span(e.g., Pearl and Parker 1922). Most of the large-effect mutants affecting aging", + "One approach that has become increasingly common in the characterization of the ge-netics of aging is to isolate aging mutants, usually from mutagenesis experiments, andthen to determine the mechanistic basis for the unusual life span in the mutants. Thisapproach has led to the discovery of genes that can enhance (e.g., Maynard Smith 1958;Lin et al. 1988; reviewed in Guarente and Kenyon 2000, Kim 2007) or reduce life span(e.g., Pearl and Parker 1922). Most of the large-effect mutants affecting aging", + "genetics of aging I. What is aging? Frontiers in Genetics. doi:10.3389/fgene.2012.00134. r ose, Michael r ., Anthony D. Long, Laurence D. Mueller, Cristina L. r izza, Kennedy C. Matsagas, LeeF. Greer, and Bryant villeponteau. 2009. e volutionary nutrigenomics. In The future of aging, eds. G. M. Fahy, M. D. West, L. S. Coles, and S. B. h arris. Berlin: Springer. r ushton, J. p hillippe. 1995. Race, evolution, and behavior: A life history approach. New Brunswick, NJ: Transaction p ublishers.", + "informed by age-related disease identifies loci for exceptional human longevity. Li H, editor. PLoS Genet. 2015. https://doi.org/10.1371/journal.pgen. 15. Polderman TJC, Benyamin B, de Leeuw CA, Sullivan PF, van Bochoven A, Visscher PM, etal. Meta-analysis of the heritability of human traits based on fifty years of twin studies. Nat Genet. 2015;47:7029. 16. Cellerino A, Ori A.What have we learned on aging from omics studies? Semin Cell Dev Biol. 2017;70:17789.", + "eries that have inspired thousands of researchers across the world to study aging, and we acknowledge the wider significance of the creation of a field that has the potential to transform human health. Genetics Aging is influenced by genetic factors. It may be surprising to know that as recently as the 1970s and 1980s, the concept of modulating Downloaded from https://academic.oup.com/biomedgerontology/article/76/7/e85/6145792 by guest on 15 October 2023", + "discover specific genes that directly influence how quickly people age, beyond diseases. If such genes exist, their effects were too small to be detected in this study. The next step will be to expand the study to include more participants, which will hopefully pinpoint further genomic regions and help disentangle the biology of ageing and disease. DOI: https://doi.org/10.7554/eLife.39856.002", + "males: what are the molecular and evolutionary causes? Aging Cell. 2007;6:225233. doi:10.1111/j.1474-9726.2007.00279.x 63. Benayoun BA, Pollina EA, Brunet A. Epigenetic regulation of ageing: link- ing environmental inputs to genomic stability. Nat Rev Mol Cell Biol. 2015;16:593610. doi:10.1038/nrm4048 64. Sen P, Shah PP, Nativio R, Berger SL. Epigenetic mechanisms of longevity and aging. Cell. 2016;166:822839. doi:10.1016/j.cell.2016.07.050" + ], + "answer": "Recent discoveries in the genetics of aging include the identification of a number of genes capable of altering the aging process significantly in animal models and even in some humans [2]. Additionally, recent efforts have focused on isolating aging mutants through mutagenesis experiments to determine the mechanistic basis for unusual life spans, leading to the discovery of genes that can either enhance or reduce life span [4]. These findings contribute to a growing understanding of the genetic factors influencing aging and longevity.", + "question": "What recent discoveries have been made about the genetics of aging?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_12 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_12 new file mode 100644 index 0000000..8332dac --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_12 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2021 - Epigenetics of Aging and Aging-Associated Diseases.pdf", + "2017 - Epigenetic aging signatures in mice livers.pdf", + "2008 - GENETIC REGULATION OF HEMATOPOIETIC STEM CELL AGING (3).pdf", + "2010 - Higher-order Genome Organization.pdf", + "2007 - Aging Hematopoietic Stem Cells Decline in Function and Exhibit Epigenetic Dysregulation.pdf", + "2007 - Aging Hematopoietic Stem Cells Decline in Function and Exhibit Epigenetic Dysregulation.pdf", + "2016 - Epigenetic drift in the aging genome a ten-year.pdf", + "2021 - Epigenetics of Aging and Aging-Associated Diseases.pdf", + "2007 - Aging Hematopoietic Stem Cells Decline in Function and Exhibit Epigenetic Dysregulation.pdf", + "2012 - Aging, Rejuvenation, and Epigenetic.pdf" + ], + "extraction_id": [ + "8db25d5e-25bd-5873-a53d-3815badbfd32", + "8e77571a-60ea-5e53-877d-08169e86d553", + "6c8b334f-2e14-5099-85fe-93f9ed6046ad", + "1a3a302a-4009-5ccf-aafa-f5f5a258ffde", + "42a32702-d380-56e7-8309-6ab1efbcd479", + "eb735cbe-b7f5-56cb-8cd6-1f2fb2b990a4", + "2f6d20f0-addc-51e8-979d-1aac7ac26694", + "e2bc9b8e-2349-509b-a148-fbd86f0455f4", + "0a77e94f-85de-5ab3-b23f-129b08839502", + "0e274732-b0df-53b8-999b-30b798af92e2" + ], + "document_id": [ + "70945353-4808-539a-80f9-5632c27913e5", + "b20b11a6-1490-51b8-9218-c441a2e65ba7", + "7412a162-ee3b-5f09-9886-8e9172dd3ee8", + "91339298-860e-57d0-b58d-5a4571b4fc2b", + "a6fabf0c-e4a5-59f6-82c5-ebabce24fd0a", + "a6fabf0c-e4a5-59f6-82c5-ebabce24fd0a", + "52f09ef3-4e4c-538f-909c-d28eb72d91f3", + "70945353-4808-539a-80f9-5632c27913e5", + "a6fabf0c-e4a5-59f6-82c5-ebabce24fd0a", + "bde26feb-f423-51b0-89ec-6f079bfc8b17" + ], + "id": [ + "chatcmpl-AIFhiQpAn2q80Ivtr6ZFWK3Q6aNNm", + "e97baf15-d14f-563f-875f-a2a6bd314ad4", + "38dc7635-499d-52ba-a35f-f57ba8f8df22", + "975250c9-7b4b-5ce5-b907-606ca6c70641", + "7fad6aa1-d4d9-585c-bdbd-394a9552ec0e", + "b43c1348-b982-59c2-9685-af7bb9fd0c4a", + "33f6a665-bb01-5c9f-9325-0f9acf312b54", + "0aede05b-f0dd-595a-a11d-acac0970d25d", + "5e3a0748-9dc0-55b1-ac4d-d8b2291fa297", + "c35ad17b-fe97-5ce5-bae1-59fd08201a7b", + "dea115e3-3d9b-5d08-a604-ab227fcd1b71" + ], + "contexts": [ + "Figure 1. Epigenetics of aging and aging-relate d diseases. During aging, various ep igenetic alterations occur including accumulation of histone variants, change s in chromatin accessibility mediated by chromatin remodeling complexes, loss of histones and heterochroma tin, imbalance of activating /repressing histone modifications and aberrant expres- sion/activity of miRNAs. These deregulations can affect transcrip tion and, subsequently, transl ation, as well as the stabi-", + "ment of 5 years corresponded to a 21% increased risk of mortality overall [7]. Thus, predictions of epigenetic agemay be an indication of an individual s biological state of aging. Beyond these examples of advanced epigenetic aging, a complementary but unanswered question is whether epigenetic clocks can also be slowed. Epigenetic aging studies in humans have not thus far been well suited to address questions of slowed aging, given the lack of well-documented interventions that enhance health or", + "al., 2005 ). The epigenetic changes that accumulated with age had a dramatic effect on gene expression, thus the authors propos e that a so-called epigenetic drift accompanies the aging process. Epigenetic modifications can result in the cumulative loss of gene regulation over time, ultimately impairing cellular and tissue function. Further, recent data sugge st that epigenetic disruption of tissue specific stem and progenitor cells may play a role in cancer development (Feinberg et al., 2006 ). The", + "epigenetic changes during aging are currentlyunknown (Fig. 3). It has been suggested thatthe epigenetic alterations are largely triggered by DNA damage (reviewed in Oberdoerffer and Sinclair 2007). In this scenario, randomlyoccurring DNA damage leads to chromatin remodeling and to redistribution of chromatin modiers within the genome with modiersbeing recruited away from their normal sites so that they can engage in the repair of the", + "Epigenetic Dysregulation with Age", + "Epigenetic Dysregulation with Age", + "Recently, studying the direct relationship between epigeneticmechanisms and the aging process itself is gaining increasing attention. The potential reversibility of these epigenetic changes that occur as a hallmark of aging offers excitingopportunities to alter the trajectory of age-related diseases. 8 This is especially important given the remarkable plasticityof aging. 9,10In the literature, age-associated epigenetic alter- ations have been identified by epigenome-wide association", + "in gene transcription and, as a consequence, translation as well as the stabilization or degradation of molecular factors. While mechanisms underlying aging-related pathologies remain to be elucidated in detail, various studies demonstrate an epigenetic component. In fact, the aforementioned epigenetic modications were shown to play essential roles in diseases including inammation, cancer, osteoporosis, neurodegenerative diseases, and diabetes.", + "PLoS Biology | www.plosbiology.org August 2007 | Volume 5 | Issue 8 | e201 1759 Epigenetic Dysregulation with Age", + "and increased expression of proaging genes such as the cell-cycle inhibitor p16, which drives cell senescence. Additional consequences of epigenetic dys-regulation include increased transcriptional noise and decreased coordination of gene expression that contributes to organismal aging. Cell148, January 20, 2012 2012 Elsevier Inc. 53" + ], + "answer": "Epigenetic changes affect aging through several mechanisms: 1. **Alterations in Chromatin Structure**: During aging, there are various epigenetic alterations such as the accumulation of histone variants, changes in chromatin accessibility, loss of histones and heterochromatin, and imbalances in activating/repressing histone modifications. These changes can affect transcription and translation processes, impacting cellular function [1]. 2. **Epigenetic Drift**: As individuals age, epigenetic changes accumulate, leading to a phenomenon known as epigenetic drift. This drift results in the cumulative loss of gene regulation over time, impairing cellular and tissue function. It is suggested that this disruption may also play a role in the development of age-related diseases, such as cancer [3]. 3. **DNA Damage and Chromatin Remodeling**: It is suggested that epigenetic alterations during aging are largely triggered by DNA damage. This damage leads to chromatin remodeling and redistribution of chromatin modifiers, which are recruited away from their normal sites to engage in DNA repair [4]. 4. **Increased Expression of Pro-aging Genes**: Epigenetic dysregulation can lead to increased expression of pro-aging genes, such as the cell-cycle inhibitor p16, which drives cell senescence. This contributes to increased transcriptional noise and decreased coordination of gene expression, further contributing to organismal aging [10]. 5. **Potential for Reversibility**: Despite these changes, there is potential reversibility in epigenetic modifications, offering opportunities to alter the trajectory of age-related diseases. This highlights the plasticity of aging and the potential for interventions that could slow down the aging process [7]. Overall, epigenetic changes play a crucial role in the aging process by affecting gene expression, cellular function, and the development of age-related diseases.", + "question": "How do epigenetic changes affect aging?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_13 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_13 new file mode 100644 index 0000000..6b99b03 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_13 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2011 - Genomics of human longevity.pdf", + "2015 - A Chromosome 13 locus is associated with male-specific mortality in mice.pdf", + "2012 - Genome-Environment Interactions That Modulate.pdf", + "2013 - Genome Instability and Aging.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2011 - A genome-wide association study confirms APOE as the major gene influencing.pdf", + "2019 - A meta-analysis of genome-wide association.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2015 - A Chromosome 13 locus is associated with male-specific mortality in mice.pdf" + ], + "extraction_id": [ + "7c183ae5-f10e-5f0c-962e-32135887b3bd", + "5cc56e3b-53ab-5299-814d-014e2ed31d2f", + "d59d7882-333d-5576-86ab-3cfa6354b946", + "3091bce3-8eb6-593d-8a92-ee3570e8e9a9", + "68deea31-59de-5665-9c97-df57d72d0b52", + "7555b8ec-cf4e-54a4-b654-6ae7e63d150c", + "210aa417-372c-5bf6-b961-e281a1817458", + "68c41fe5-4413-5cfc-846b-a0097f994bcd", + "bdfc934a-d31b-57e4-9a78-15c719049c4f", + "5cc56e3b-53ab-5299-814d-014e2ed31d2f" + ], + "document_id": [ + "2e038219-fdaa-506f-9cd3-51379054130e", + "ad8f2626-87fb-520e-8cef-ee9a9cc3ab0b", + "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "71e08916-8cc8-5d96-8c06-4461b972b54d", + "62b635c3-040e-512a-b016-6ef295308a1e", + "62b635c3-040e-512a-b016-6ef295308a1e", + "63b27b06-db2c-5542-9b1a-cb9ebe64d339", + "9d36fc35-9708-5d1a-9514-9ce3469d7591", + "62b635c3-040e-512a-b016-6ef295308a1e", + "ad8f2626-87fb-520e-8cef-ee9a9cc3ab0b" + ], + "id": [ + "chatcmpl-AIFhpW2QcT6L6LqU3pI7kcz7hsxkv", + "77c88648-7807-5606-8793-4389378a82fd", + "9c463b71-be3a-5f01-bc6f-d1d29b7a162f", + "2f98af09-5895-545a-b36f-c05b70beee07", + "c6e1f317-e421-5f6b-ab4e-034f1aa94ba1", + "34dfec26-9828-56c8-be82-69eb114fa9e3", + "6dd65017-bb91-5a1a-9d85-c1c1cfcd5780", + "160acccd-d5c5-5e54-8f88-ada1d413e91b", + "aceb74e0-8b79-587f-9dd0-e260eeb90ab5", + "049ee89e-2f05-595b-9112-725976cb4ab3", + "f6636c31-1105-5ea2-9b3b-ae8b21e08bee" + ], + "contexts": [ + "27 Willcox, B. J. et al. 2008 FOXO3A genotype is strongly associated with human longevity. Proc. Natl Acad. Sci. USA 105, 13 98713 992. ( doi:10.1073/ pnas.0801030105 ) 28 Flachsbart, F., Caliebe, A., Kleindorp, R., Blanche, H., von Eller-Eberstein, H., Nikolaus, S., Schreiber, S. & Nebela, A. 2009 Association of FOXO3A variationwith human longevity conrmed in GermanGenomics of human longevity P . E. Slagboom et al. 41", + "3. Willcox BJ, Donlon TA, He Q et al (2008) FOXO3A genotype is strongly associated with human longevity. Proc Natl Acad Sci USA 105(37):1398713992. doi: 10.1073/pnas.0801030105 4. Anselmi CV, Malovini A, Roncarati R et al (2009) Association of the FOXO3A locus with extreme longevity in a southern Italian centenarian study. Rejuvenation Res 12(2):95104. doi: 10.1089/ rej.2008.0827 5. Flachsbart F, Caliebe A, Kleindorp R et al (2009) Association of FOXO3A variation with human longevity conrmed in German", + "are, in fact, part of the same insulin/IGF1/GH pathway(Fig. 1) that modulates lifespan across organisms (Ke-nyon, 2010). A strong association between FOXO3 and human longevity has been reported (Willcox et al., 2008)and subsequently validated in other populations (forreview, see Kenyon, 2010). FOXO3 was also associatedAGING GENES AS TARGETS FOR DRUG DISCOVERY 95", + "Biogerontology 11:28797 117. Willcox BJ, Donlon TA, He Q, Chen R, Grove JS, et al. 2008. FOXO3A genotype is strongly associated with human longevity. Proc. Natl. Acad. Sci. USA 105:1398792 118. Soerensen M, Dato S, Christensen K, McGue M, Stevnsner T, et al. 2010. Replication of an association of variation in the FOXO3A gene with human longevity using both case-control and longitudinal data. Aging Cell 9:101017 119. Mardis ER. 2011. A decades perspective on DNA sequencing technology. Nature 470:198203", + "FOXO3 locus is associated with extreme longevity in humans (centenarians) [2, 58, 59]. NRF/SKN-1 activates the expression of genes involved in protecting the cell in response to ROS, toxins, and metabolic changes through mTOR and insulin/IGF signaling, and it is also dysregulated later in life [60, 61]. Increasing the levels of L. Garca-Velzquez and C. Arias", + "A. 2003;100:406671. https://doi.org/10.1073/pnas.2628028100. 24. van den Akker EB, Deelen J, Slagboom PE, Beekman M. Exome and whole genome sequencing in aging and longevity. Adv Exp Med Biol. 2015;847:12739. https://doi. org/10.1007/978-1-4939-2404-2_6. 25. Flachsbart F, etal. Association of FOXO3A variation with human longevity confirmed in German centenarians. Proc Natl Acad Sci U S A. 2009;106:27005. https://doi.org/10.1073/ pnas.0809594106. A. Garca-Venzor and E. A. Mandujano-Tinoco", + "X.L., 2009. Genetic association of FOXO1A and FOXO3A with longevity trait in Han Chinese populations. Hum. Mol. Genet. 18, 48974904. Lunetta, K.L., DAgostino Sr., R.B., Karasik, D., Benjamin, E.J., Guo, C.Y., Govindaraju, R., Kiel, D.P., Kelly-Hayes, M., Massaro, J.M., Pencina, M.J., Seshadri, S., Murabito, J.M., 2007. Genetic correlates of longevity and selected age-related phenotypes:", + "theFOXO3 locus is not surprising, since this locus was previously reported in the longevity GWA study from the CHARGE con- sortium 7, from which many cohorts are included in these meta- analyses. So far, three functional longevity-associated variants have been identi ed at the FOXO3 locus (rs2802292, rs12206094, and rs4946935). For all of them, an allele-speci c response to cellular stress was observed. Consistently, the longevity-associated alleles of all three variants were shown to induce FOXO3", + "exceptional longevity with no significant genetic contribution. Interestingly, the authors found that FOXO3A, a longevity allele, may not be related to healthy aging phenotype [29]. Aging is a complex process usually accompanied by the onset of different dis- eases like neurodegenerative disorders (Alzheimers disease and Parkinsons dis- ease), cardiovascular illnesses, and cancer. The study of the genetic basis of these aging-related diseases is another approach in the study of the genomic basis of", + "centenarians. Proc Natl Acad Sci USA 106(8):27002705. doi: 10. 1073/pnas.0809594106 6. Li Y, Wang WJ, Cao H et al (2009) Genetic association of FOXO1A and FOXO3A with longevity trait in Han Chinese populations. Hum Mol Genet 18(24):48974904. doi: 10.1093/ hmg/ddp459 7. Soerensen M, Dato S, Christensen K et al (2010) Replication of an association of variation in the FOXO3A gene with human longevity using both case-control and longitudinal data. AgingCell 9(6):10101017. doi: 10.1111/j.1474-9726.2010.00627.x" + ], + "answer": "The gene FOXO3 plays a significant role in human longevity. Multiple studies have shown a strong association between variations in the FOXO3 gene and increased lifespan. For instance, the FOXO3A genotype has been strongly linked with human longevity, as demonstrated in studies by Willcox et al. (2008) and confirmed in various populations, including German and Southern Italian centenarians [1], [2], [3]. The FOXO3 locus is associated with extreme longevity in humans, particularly among centenarians [5]. Additionally, specific variants at the FOXO3 locus have been identified that respond to cellular stress, which may contribute to their role in promoting longevity [8].", + "question": "What is the role of the gene FOXO3 in longevity?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_14 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_14 new file mode 100644 index 0000000..97bfeb6 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_14 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2012 - Genome-Environment Interactions That Modulate.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2009 - The role of epigenetics in aging and age-related diseases.pdf", + "2011 - How pleiotropic genetics of the musculoskeletal system.pdf", + "2012 - Genome-Environment Interactions That Modulate.pdf", + "2015 - Age and sun exposure-related widespread.pdf", + "2012 - Genome-Environment Interactions That Modulate.pdf", + "2016 - Progress on the role of DNA methylation in aging.pdf", + "2018 - Genomics New Light on Alzheimer?s.pdf", + "2012 - Genome-Environment Interactions That Modulate.pdf" + ], + "extraction_id": [ + "68b12e10-d643-570a-aa63-eda62de81928", + "53f4a434-3d4b-5ae5-a788-9b32c94fc0ab", + "5a231e39-0927-56e5-970c-e86d69490a69", + "677eccb2-c51a-59dc-9690-7d02c51fbb7a", + "68b12e10-d643-570a-aa63-eda62de81928", + "aa62f800-8e83-5033-889a-64ff3f453fca", + "a01ca925-4ccf-5863-a162-7bd4c754fe89", + "bcb3c620-b960-5af6-95ea-13215c31672e", + "05bcb479-ca17-57eb-9674-1c2fecb5726c", + "df213743-7428-59be-ba19-2563f8ce5c70" + ], + "document_id": [ + "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "62b635c3-040e-512a-b016-6ef295308a1e", + "f7b452fc-0115-5582-b0c0-c2829f090e9d", + "ed31486c-a651-5894-bd96-21fbd78f2646", + "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "acf06062-9ca8-50be-a543-ef3b34ad6ad3", + "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "e4cdc02f-4415-5638-aab8-f848b4d64a22", + "940593d2-04c3-59b9-a5bf-976febbc6f71", + "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec" + ], + "id": [ + "chatcmpl-AIFhuoRML5l0E69TztcoQUZAgCOF2", + "a4773f1a-f2d3-5950-a81e-d22357e97a0f", + "3d657599-d2c8-518d-aee3-46c0643a88ec", + "49127379-fac4-525a-bf90-5c3bae66860a", + "7ce9af40-0bf8-58e1-ad7c-cd55ba0a7cf8", + "3f37774f-e56b-5350-93e8-371948bf3e23", + "3466f905-760d-5d0b-a3e1-b39f506e6289", + "3c369292-4b9c-5156-a80f-4b3301026f30", + "c1b5a31a-066d-571b-af1f-db746d9d17f6", + "90f9e09f-f339-5d59-ae24-fcbdd2ca6ceb", + "c44c36ad-fcca-540a-a4f3-3965e48e3948" + ], + "contexts": [ + "of multiple genes with each other and withthe environment. Evidence from animal systems showsa major impact of the environment on aging, yet envi-ronmental manipulations of aging act through genesand proteins, usually by triggering signaling pathwaysand modulating gene expression. In fact, some geneshave been shown in model organisms to have varyingeffects on lifespan depending on diet (Heikkinen et al.,2009). Genes that can regulate aging in model organ-isms cannot be directly applied to humans through", + "Several studies show the influence of the environment on the ageing process [24]. Environmental factors may affect homeostasis and lead to the development of dis- eases, thus affecting the quality of life in older age [25]. They also produce cellular damage, which causes an accelerated shortening of the telomeres at the genetic level, accompanied by changes in DNA methylation, acetylation or deacetylation of histones, among others. Altogether, these changes induce an aberrant gene", + "changes are generated during the aging process. For a long time it has been believed that epigenetic modications occurring during aging may depend on environmental factors. This idea is attractive because, if true, epigenetics could provide a link between the environment, disease and aging. It also opens the possibility of targeted intervention aimed, for example, at improving healthspan or healthy aging. Thus, the rst question is whether specic environmental factors can directly induce specic epigenetic", + "In addition, environmental factors influence the organism s ability to withstand the increase in entropy with aging: for example, caloric restriction and smoking can exert opposite effects on the rate ofaging (Colman et al. 2009 ; Fraser and Shavlik 2001 ). Both protective alleles and a benevolent environment contribute to excess physiological capacity, which in turn indirectly determines an individual s healthy life span and longevity (Martin et al. 2007 ). The well-", + "to humans through ge-netic manipulations for numerous legal, ethical, andtechnical reasons. If we could understand how the envi-ronment modulates these aging-related genes, we mightbe able to create antiaging therapies applicable to hu-mans, potentially through diet, lifestyle, and even phar-macological interventions. Therefore, understanding ge-nome-environment interactions in the context of agingcan be a powerful approach to identify attractive targetsfor drug design.", + "ing human life span have been identified [2,3]. At the same time, there is a growing realization that environ- mental factors are major contributors to aging and age- associated illness. Epigenetics is the study of chemical modifications of the genome, heritable by cell progeny, and it has been an attractive target for studies of aging and environmentally influenced disease. Several groups have shown differences in DNA methylation - a covalent", + "al., 2009; Stanfel et al., 2009). Many of these genesmodulate the response to environmental signals, such asfood availability, and act in signaling pathways that ifunderstood can be targeted (Fig. 1). The genetic regula-tion of aging is therefore an emerging field with multipleapplications in the human nutrition, cosmetic, and phar-maceutical industries. AGING GENES AS TARGETS FOR DRUG DISCOVERY 91", + "standing the cause and mechanisms of aging is imperative in assisting to suppress age-related diseases and promote healthylongevity. It is well-known that aging is influenced by a combin- ation of genetic and environmental factors. Previous twin stud- ies have shown that the genetic contribution to general human longevity is about 2030% [ 4,5], whereas environmental factors in human aging and longevity still account for the largest effect. Epigenetic factors influence the regulation of gene expres-", + "known to affect the function of epigenetic regulators, this may be an example of how aging interacts with our genome to inuence AD development.", + "consequently the incidence of age-related diseasessuch as heart disease, cancer, and neurodegenerativediseases, is projected to increase considerably in thecoming decades. Findings from model organisms haverevealed that aging is a surprisingly plastic processthat can be manipulated by both genetic and environ-mental factors. Here we review a broad range of find-ings in model organisms, from environmental to ge-netic manipulations of aging, with a focus on thosewith underlying gene-environment interactions" + ], + "answer": "The environment interacts with genes to influence aging through several mechanisms: 1. **Signaling Pathways and Gene Expression**: Environmental factors can trigger signaling pathways and modulate gene expression, impacting aging. For example, certain genes have varying effects on lifespan depending on environmental factors like diet [1]. 2. **Epigenetic Modifications**: Environmental factors can lead to changes in DNA methylation, acetylation, or deacetylation of histones, which are epigenetic modifications that influence gene expression. These changes can result in cellular damage and accelerated aging, such as the shortening of telomeres [2], [6]. 3. **Epigenetics as a Link**: Epigenetic modifications provide a potential link between the environment, disease, and aging. This suggests that specific environmental factors might directly induce specific epigenetic changes, which could be targeted for interventions aimed at improving healthspan or promoting healthy aging [3]. 4. **Physiological Capacity and Longevity**: Environmental factors, along with protective genetic alleles, contribute to an individual's physiological capacity, indirectly determining healthy lifespan and longevity. For instance, caloric restriction and smoking have opposite effects on the rate of aging [4]. 5. **Plasticity of Aging**: Aging is a plastic process that can be manipulated by both genetic and environmental factors. Understanding these interactions can help identify targets for anti-aging therapies, potentially through diet, lifestyle, and pharmacological interventions [5], [10]. Overall, the interaction between the environment and genes is complex and involves multiple pathways and mechanisms that collectively influence the aging process.", + "question": "How does the environment interact with genes to influence aging?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_15 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_15 new file mode 100644 index 0000000..e458f6f --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_15 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2018 - Nuclear Genomic Instability.pdf", + "2020 - A multidimensional systems biology.pdf", + "2022 - Functional genomics of inflamm-aging.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2018 - Nuclear Genomic Instability.pdf", + "2007 - Two faces of p53 aging and tumor suppression.pdf", + "2015 - Cellular and Molecular Biology of Aging Endothelial Cells.pdf", + "2018 - Nuclear Genomic Instability.pdf", + "2016 - Genome Integrity in Aging.pdf", + "2020 - A multidimensional systems biology.pdf" + ], + "extraction_id": [ + "4b00515d-e599-5ce1-84e3-012d7efe1a30", + "95744ef5-34b9-5540-a5e5-01fd580539e6", + "1635dbe1-1dcb-5213-9446-74129d50c5f8", + "6a2a94de-cfc0-50eb-b50e-bf3a0f813c78", + "2b1396d1-ea5d-5708-a6b1-2adf1712c7b4", + "4a95fed4-61db-58e9-96d7-3a9dcf87ef7f", + "10f1fcbd-35a6-507d-880f-1f3f303737ea", + "029ae7be-b0ab-55f8-84a2-5a74681e454d", + "102fcfb3-b333-5b67-ab94-08033f04ba5c", + "fe4ec57e-6ae7-59c4-b8fa-da73fe77ce96" + ], + "document_id": [ + "54d28a91-8db6-56b1-baaa-b67274c93a36", + "d040bfe3-e409-5b5c-b8f8-f3dd4fc060e3", + "435dc081-e3d1-52c5-93a1-caa11206422f", + "62b635c3-040e-512a-b016-6ef295308a1e", + "54d28a91-8db6-56b1-baaa-b67274c93a36", + "b1ef905a-c145-5270-9110-ae6954ea3d72", + "815d7f3e-e219-502f-aba0-57a68ae787d3", + "54d28a91-8db6-56b1-baaa-b67274c93a36", + "85d5fcbb-5385-5a01-8139-d11fc8b1fe3a", + "d040bfe3-e409-5b5c-b8f8-f3dd4fc060e3" + ], + "id": [ + "chatcmpl-AIFi4Qsa1GjY5azJi3IYJdr8DLXln", + "2f35de05-41ee-5471-870d-a4e663cf32f6", + "1efa76cb-2289-5dd3-9fa5-776083aa5cd5", + "9faa9b6b-6a97-5979-bf49-8bbdb4bb383d", + "6d4a1a0b-2af3-5cc4-b7c0-a7223ce3edfa", + "45f74737-847a-52c2-a0b9-bf9de335a7ce", + "bd5fffd3-cf7a-5f67-b581-6cb803a48de4", + "27d74137-3987-571d-87ab-2c12ec66d1f7", + "180adffa-397c-599b-adb3-64a7f464aaaa", + "93b3cc74-a414-5097-802a-7dc2ad10171d", + "3593241d-677d-5042-a1e9-dd92760a8c0e" + ], + "contexts": [ + "senescence, exhausting the ability for a tissue to regenerate after injury, impacting mitochondrial function,and inducing protein aggregation. Senescent cells have altered metabolism, and they can secreteproinammatory factors and alter the local tissue environment, thereby contributing to aging andage-related degenerative diseases. In addition, stem cell function can be impacted by DNA damage by bothcell autonomous and nonautonomous mechanisms. Proper function of mitochondria is dependent upongenome", + "[87] and the accumulation of senescent cells in human tissues with age has been implicated as a driver of aging- related diseases. Indeed, pharmacological approaches targeting senescent cells, like senolytics, are a major and timely area of research that could result in human clin- ical applications [ 5,88]. It is imperative that we fully understand and deconstruct cellular senescence in order to target aging-related diseases. We hope that CellAge will help researchers understand the role that CS plays", + "An important source of inflammatory signals in aged organ- isms is thought to be the accumulation of senescent cells across tissues [ 5,7]. Indeed, accumulating evidence has shown that senescent cells are characterized by a senescence-associatedsecretory phenotype [ 810], which includes a panoply of pro-inflammatory cytokines, proteases, growth factors and metabolites [ 10,11]. The impact of senescent cells on age-related inflammation, and their potential role as a target for pro-", + "senescent cells [150]. SASP factors exert their functions in either an autocrine or a paracrine manner and are responsible for the induction of the chronic inflammation and cell proliferation that contributes to cell dysfunction and cancer. Thus, the accu- mulation of senescent cells in tissue is closely associated with aging-related dis- eases. Recently, it was determined that senescent fibroblasts significantly increase the expression of HLA-E, which inhibits the receptor NKG2A in killer cells, and", + "atherosclerosis, osteoarthritis, sarcopenia, ulcer formation, cancer, and Alzheimer disease, which is suggestive of a causative role. However, the most convincing evidence that senescent cells causeaging comes from recent genetic (85) and pharmacologic studies (86) revealing that clearance of senescent cells can prevent or delay tissue dysfunction and extend health span. Senescent cells induce autocrine, as well as paracrine, signaling by secretion of proinamma-", + "senescence can deplete both stem (5153) and stromal (10,11) cell pools. Moreover, because senescent cellspersist, they have the ability to alter the tissue micro-environment, and can therefore also promote the degen-eration of organs and stem cell niches (14,46). Finally, senescent cells secrete factors such as matrix metallopro- teinase-3 (MMP-3), which favors extra-cellular matrixremodeling, promotes defects in epithelial cell dierentia-tion and stimulates cancer cell growth (46,54,55).", + "potential role of senescence in in vivo aging and disease has been difficult to assess and somewhat controversial [146]. However, recent studies have shown that senescent cells accumulate in normal arterial tissue over the lifespan of humans [147, 148]. Likewise, the accumulation of senescent cells has been reported in diseased tissues, such as atherosclerotic plaques [149] and abdominal aortic aneurysms [150]. Baker et al. showed that", + "51. Jeyapalan JC, Ferreira M, Sedivy JM, Herbig U. 2007. Accumulation of senescent cells in mitotic tissue of aging primates. Mech. Ageing Dev. 128:3644 52. Boyle J, Kill IR, Parris CN. 2005. Heterogeneity of dimer excision in young and senescent human dermal broblasts. Aging Cell 4:24755 53. Seluanov A, Mittelman D, Pereira-Smith OM, Wilson JH, Gorbunova V. 2004. DNA end joining becomes less efcient and more error-prone during cellular senescence. PNAS 101:762429", + "in many accelerated-aging mouse models and in a plethora of human age-associated pathologies, including osteoporosis, atherosclerosis, glomerular disease, diabetic venous ulcers, chronic ob-structive pulmonary disease and emphysema, osteoarthritis, herniated intervertebral discs, and vascular calcication (112). Senescent cells are resistant to apoptosis and accumulate exponen- tially with age as a consequence of inefcient clearance. Unlike apoptotic tissues, senescent tissues 436 VermeijHoeijmakersPothof", + "wound healing [ 8], and immune clearance [ 9,10]. By contrast, the gradual accumulation and chronic persistence of senescent cells with time promotes dele- terious effects that are considered to accelerate deterior- ation and hyperplasia in aging [ 11]. Senescent cells secrete a cocktail of inflammatory and stromal regula- torsdenoted as the senescence-associated secretory phenotype, or SASP which adversely impact neighbor- ing cells, the surrounding extracellular matrix, and other" + ], + "answer": "Senescent cells are cells that have stopped dividing and have entered a state of permanent growth arrest. They are characterized by an altered metabolism and the secretion of pro-inflammatory factors, a phenomenon known as the senescence-associated secretory phenotype (SASP) [1], [3]. These cells accumulate in tissues over time and are resistant to apoptosis, meaning they are not easily cleared from the body [9]. Senescent cells contribute to aging and age-related diseases in several ways. They impact the tissue environment by secreting inflammatory cytokines, proteases, and growth factors, which can lead to chronic inflammation and tissue dysfunction [3], [4]. This chronic inflammation is a significant factor in the development of age-related degenerative diseases [1], [4]. Additionally, senescent cells can alter the tissue microenvironment, promoting the degeneration of organs and stem cell niches, and potentially stimulating cancer cell growth [6]. The accumulation of senescent cells is associated with various age-related pathologies, such as atherosclerosis, osteoarthritis, and Alzheimer's disease [5], [9]. Recent studies have shown that clearing senescent cells can prevent or delay tissue dysfunction and extend health span, highlighting their causative role in aging [5].", + "question": "What are senescent cells and how do they contribute to aging?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_16 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_16 new file mode 100644 index 0000000..12b6b0f --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_16 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2017 - Dietary restriction protects from age-associated DNA methylation and induces epigenetic reprogramming of lipid metabolism.pdf", + "2021 - Gene-by-environment modulation of lifespan and weight gain in the murine BXD family.pdf", + "2012 - Aging, Rejuvenation, and Epigenetic.pdf", + "2016 - The dog aging project translational geroscience in companion.pdf", + "2012 - Aging, Rejuvenation, and Epigenetic.pdf", + "2020 - Mitonuclear genomics and aging.pdf", + "2021 - Epigenetics of Aging and Aging-Associated Diseases.pdf", + "2017 - Dietary restriction protects from age-associated DNA methylation and induces epigenetic reprogramming of lipid metabolism.pdf", + "2021 - Gene-by-environment modulation of lifespan and weight gain in the murine BXD family.pdf", + "2012 - Genome-Environment Interactions That Modulate.pdf" + ], + "extraction_id": [ + "6364d669-4b96-5d2f-8ce8-526b065dce72", + "30ba3324-6e19-58c2-9e32-508f827af3e5", + "b9f038dd-97af-51ea-bb32-d73bf66c3dcb", + "e433208e-665d-550c-b8e8-c9fb400f1159", + "915ca931-d49d-5837-97fd-f06c145764d0", + "e6fb876b-e91c-505a-aa16-7b428ec61f10", + "9770f6f4-b86a-514f-9cce-c23d2963aeae", + "21efa872-9d89-5dee-9dd1-27dcaa1208cf", + "bca61863-81b3-5ef7-850d-10cc9577a9e1", + "13ca8905-ddbb-5437-b6a8-4012969daa43" + ], + "document_id": [ + "fe573bb0-3d37-55e5-93fa-65b3fbc5f532", + "4d082da4-fa48-5170-8147-c4fea47a5d4b", + "bde26feb-f423-51b0-89ec-6f079bfc8b17", + "e841c6bd-78b8-56e1-b3dd-e2bcc8a0f590", + "bde26feb-f423-51b0-89ec-6f079bfc8b17", + "e05fdc09-c8d8-5134-a1fd-bf07a1564981", + "70945353-4808-539a-80f9-5632c27913e5", + "fe573bb0-3d37-55e5-93fa-65b3fbc5f532", + "4d082da4-fa48-5170-8147-c4fea47a5d4b", + "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec" + ], + "id": [ + "chatcmpl-AIFjcxBUYW02ZQfh6Ogs975bGNDi2", + "5fc33fac-ab39-5ec1-9fb9-dcaa93a595d3", + "4bf7307d-d8a0-5594-b0b5-487fe0f265ca", + "afc304d1-dd43-55ec-811d-27ca27fc4e5d", + "3fc1603d-dd9e-5bcf-96e6-6b927d344be1", + "7ca45b81-3f97-5b1b-9a84-84cfffc4cc08", + "c96b67f8-ad31-50fd-b053-07b127938ef2", + "193d98c7-8d37-5f83-b1b2-84aee242f079", + "7460a40c-8723-5de9-9f2e-c781f4872f1f", + "38c89363-89a1-56d5-82f2-28c19fa0fbcb", + "b9240ab4-370f-5bc1-8c33-9755ab788aac" + ], + "contexts": [ + "Dietary interventions, including starvation and protein deprivation, can also alter patterns of DNA methyla- tion, potentially in a long-lasting manner [42, 43], including transgenerationally [26, 44]. Dietary, genetic and pharmacological interventions that improve health during aging and extend lifespan induce long-lasting changes in gene expression that mediate their effects. Here we have asked if and how age-related DNA methylation, transcription and lipid", + "Longev. Heal. 2, 10 (2013). 7. Kreienkamp Ret al.Doubled lifespan and patient-like pathologies in progeria mice fed high-fat diet. Aging Cell18, e12852 (2019). [PubMed: 30548460] 8. Heilbronn LK & Ravussin E Calorie restriction and aging: review of the literature and implications for studies in humans. Am. J. Clin. Nutr. 78, 361369 (2003). [PubMed: 12936916] 9. Liang Yet al.Calorie restriction is the most reasonable anti-ageing intervention: a meta-analysis of", + "a medical intervention), without changing the fundamental rateof organismal aging. Nevertheless, it does seem that manyso-called longevity genes, as well as dietary restriction, appear to extend not only life span, but also health span (Kauffman et al., 2010; Luo et al., 2010 ). In that regard, it does appear that it is possible to experimentally slow the rate of aging. Still, in each case, aging does continue on as if there is some", + "As we describe above, a small but growing number ofinterventions has been shown to reproducibly increase lifespan in laboratory animals and, in a few cases, to also delay or reverse age-related declines in multiple organsystems. These healthy aging interventions could, in prin- ciple, be tested to determine whether they also increase lifespan and promote healthspan in dogs (Table 1). There are several questions that immediately present themselves when considering the design of a healthy aging interven-", + "be linked to the biology of stem cell quiescence and self-renewal. Although genetic and environmental interventions have clearly proven to be effective in prolonging life span, we postulate thatthose interventions, as well as the rejuvenating interventions described above, are, in fact, acting primarily to modify theepigenome. Consistent with this, genetic interventions directlytargeting the epigenome can extend life span ( Greer et al., 2010 ). Studying aging and rejuvenation through the lens of", + "During the past century, remarkable progress has been made in unveiling the mechanisms of aging. Genetic and molecular pathways that regulate healthspan and lifespan have been identified in various model organisms, provid-ing a rich knowledge base (Longo etal. 2015; Lopez-Otin etal. 2013, 2016; Singh etal. 2019). However, the focus on", + "205. Li, Y.; Tollefsbol, T.O. p16INK4a Suppression by Glucose Restriction Contributes to Human Cellular Lifespan Extension through SIRT1-Mediated Epigenetic and Genetic Mechanisms. PLoS ONE 2011 ,6, e17421. [CrossRef] 206. Daniel, M.; Tollefsbol, T.O. Epigenetic linkage of aging, cancer and nutrition. J. Exp. Biol. 2015 ,218, 5970. [CrossRef] 207. Kapahi, P .; Kaeberlein, M.; Hansen, M. Dietary restriction and lifespan: Lessons from invertebrate models. Ageing Res. Rev. 2017 , 39, 314. [CrossRef]", + "as diabetes, cancer and neurodegenerative disorders [1, 2]. Environmental and genetic interventions can ameliorate the effects of aging, with nutrition, nutrient-sensing signaling networks and metabolism playing evolutionarily conserved roles [1, 3 5]. Diet- ary restriction (DR), in which food intake is reducedwhile avoiding malnutrition, extends lifespan in di- verse model and non-model organisms [3, 6]. DR induces a remarkably broad-spectrum improvement in", + "53. Mair W & Dillin A Aging and survival: the genetics of life span extension by dietary restriction. Annu. Rev. Biochem. 77, 727754 (2008). [PubMed: 18373439] 54. Masoro EJCaloric restriction-induced life extension of rats and mice: a critique of proposed mechanisms. Biochim. Biophys. Acta1790, 10401048 (2009). [PubMed: 19250959] 55. Weindruch R, Walford RL, Fligiel S & Guthrie D The retardation of aging in mice by dietary", + "In addition to genes associated with aging, research has focused on identifying genes associated with the life- extending effects of CR. One method is to identify genesthat decrease or cancel out the life-extending effects of CRwhen mutated (Gems et al., 2002; Bishop and Guarente,2007). More than 100 such genes have been identified inmodel organisms (D. Wuttke, C. Vora, J. P. de Magalhes,unpublished observations). The growth hormone receptor(GHR) is the only gene so far identified in mammals that" + ], + "answer": "Yes, there are known lifestyle interventions that can positively impact genes related to aging. Dietary interventions, such as dietary restriction (DR) and calorie restriction, have been shown to alter patterns of DNA methylation and induce long-lasting changes in gene expression that improve health during aging and extend lifespan [1], [8]. These interventions can modify the epigenome, which is linked to the biology of aging [5]. Additionally, glucose restriction has been shown to extend human cellular lifespan through SIRT1-mediated epigenetic and genetic mechanisms [7].", + "question": "Are there any known lifestyle interventions that can positively impact genes related to aging?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_17 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_17 new file mode 100644 index 0000000..744f7be --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_17 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2022 - Functional genomics analysis identifies.pdf", + "2021 - Career Retrospective Tom Johnson?Genetics, Genomics.pdf", + "2021 - Career Retrospective Tom Johnson?Genetics, Genomics.pdf", + "2021 -Mozhui- Epigenetic aging.pdf", + "2021 - Genetic loci and metabolic states associated with murine epigenetic aging.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2019 - Improved precision of epigenetic clock.pdf", + "2021 - Genome-wide association studies identify.pdf" + ], + "extraction_id": [ + "a81cc7a6-0cc6-5909-9192-ac0fab26fbc2", + "63c7bfe5-a409-5435-91ea-487534957b81", + "6d7c1694-2c53-554c-9070-2db848fc5a42", + "c6cc3d8b-3736-5fe8-a4ff-eb186679a37e", + "c6cc3d8b-3736-5fe8-a4ff-eb186679a37e", + "6dfd0c51-91dd-5bb3-b7ae-a9c86ea22c35", + "68ee1ea3-5caf-5df5-8efc-134943a456cb", + "8f22afaf-a5fb-5f44-9fc2-18d4aeceede7", + "487cf1b1-1190-5d14-8b24-ba92f75aa6aa", + "53db6715-4f12-50ad-8fb9-acba4e2f4f37" + ], + "document_id": [ + "62b635c3-040e-512a-b016-6ef295308a1e", + "62b635c3-040e-512a-b016-6ef295308a1e", + "1fe1c748-9e73-51ba-8521-de924cc133d4", + "f3a26f44-f5af-5b2b-aa1c-aec2fd99f17e", + "f3a26f44-f5af-5b2b-aa1c-aec2fd99f17e", + "d23daa43-4176-54e6-b3c3-b889843e92f1", + "b82bd9e1-2373-577b-a942-164565eaca6b", + "62b635c3-040e-512a-b016-6ef295308a1e", + "556d0179-023f-581f-9c2d-febe4e75722f", + "60c2e869-1fee-53ea-b332-26d9c2abc747" + ], + "id": [ + "chatcmpl-AIFji2gbFHCW8aj8mLegsooXneEeb", + "e2522b52-d927-5c1a-8569-8fcb706ecc1e", + "c76f4517-c117-56e6-96b9-218f0fdae9f3", + "4edf498a-20de-593a-b301-73c799b07691", + "99532996-c835-534a-b6e7-a2f95ec00e2c", + "cb09d819-b809-5844-a111-5c7c7b9f9a99", + "2d08a161-7a62-5d3f-b300-1ca93ee5b751", + "66c03d04-0af5-50e5-8d4a-9a645493db46", + "35c83256-6072-5e6a-b15e-0cae1991b034", + "39dfbf42-78ec-5b0a-8448-55f47c22830e", + "d5ae06ad-3d88-5c4f-972a-0510d2fc67f3" + ], + "contexts": [ + "vided one of the most reliable aging biomarkers. An epigenetic clock is a group of CpG sites with particular methylation patterns that are highly related to the chrono- logical age of an individual. This correlation is very robust (r=0.9) for individuals between 20 and 100years. The epigenetic clock is a breakthrough discovery that will allow novel experimental approaches to understand the biological basis of aging [113]. For example, by using the epigenetic clock as a measure of cellular", + "Epigenetic Clock Chronological age is the number of years a person has lived, and biological or phys- iological age refers to a measure of how well your body functions compared to your chronological age. Biological age is influenced by multiple factors (genes, lifestyle, behavior, environment, among others) and correlates with mortality and health sta- tus. The epigenetic clock is one potentially reliable predictor of biological age.", + "Background Epigenetic clocks are sets of CpG dinucleotides whose DNA methylation (DNAm) can be used to accurately predict a person s chronological age [ 1]. In recent years, various epigenetic clocks have been developed [ 25]. Well-known examples are the clocks de- veloped by Hannum et al., trained on blood samples and containing 71 CpGs [ 2], and Horvath, a multi-tissue predictor consisting of 353 CpGs [ 3]. A popular application of", + "An EpigeneticClock The aging transcriptome could be used to gauge the physiological age of worms, and in that way serve as an epigenetic clock revealing how much of life span has been spent and how much remains (23). Middle-aged worms show an aging transcriptome half-way between the aging expression profiles of young and old worms. This provides an independent way to assess the age of an animal independent of its life span. This is important as there are at least 2 explanations to", + "The epigenetic aging clock measures the sum of all the age-related pathways affecting cellular physiology in old age. The aging epigen- etic clock is heavily enriched for germline- and intestinal-expressed genes, but lack muscle- and neuronal-expressed genes (23, 25). Expression changes in the germline and intestine were expected as there are massive changes in the morphology of gonad at the end of fertility and the intestine in old age. The aging transcriptome pro-", + "etic mouse aging and may be used to inform future studies in other model organisms and humans focused on studying the relationship between epigenetic aging and metabolism. Introduction Epigenetic clocks are widely used molecular biomarkers of aging (Horvath and Raj, 2018). These DNA methylation (DNAm) age predictors are based on the methylation levels of select CpGs that are RESEARCH ARTICLE *For correspondence: kmozhui@uthsc.edu Competing interest: See page 22 Funding: See page 22", + "etic mouse aging and may be used to inform future studies in other model organisms and humans focused on studying the relationship between epigenetic aging and metabolism. Introduction Epigenetic clocks are widely used molecular biomarkers of aging (Horvath and Raj, 2018). These DNA methylation (DNAm) age predictors are based on the methylation levels of select CpGs that are RESEARCH ARTICLE *For correspondence: kmozhui@uthsc.edu Competing interest: See page 22 Funding: See page 22", + "estimators epigenetic clocks; telomere length; transcriptomic-, proteomic-, and metabolomic-based estimators; and composite biomarkers concluded that the epi- genetic clock is the most promising molecular estimator of biological age [26]. Epigenetic age estimators are sets of CpGs (also known as clock CpGs) that are coupled with a mathematical algorithm to estimate the age of a DNA source, such as cells, tissues, or organs. This estimated age, also referred to as epigenetic age or", + "proved epigenetic clock. It should be noted that building a biological age predictor is difficult since there is no clear definition of biological age. Nevertheless, one of the essential features of biological age is its ability to in- dicate the different ageing rates between individuals with the same chronological age. A previous study has re- ported a number of CpG sites that show variation in the longitudinal changing rates between individuals [ 40].", + "ranging from 0.15 to 0.19 [ 8,9]. Individuals with epigenetic clock estimates greater than their chronological age display age acceleration and have been shown to be at a greater risk of all-cause mortality and multiple adverse health outcomes [ 10]. Conse- quently, identification of genetic and environmental contributors to the variation in these measures in populations has become a major goal in the field [ 11]. The first generation of epigenetic aging clocks used penalized regression models to" + ], + "answer": "The 'epigenetic clock' is a molecular biomarker of aging that is based on the DNA methylation levels of specific CpG sites. These methylation patterns are highly correlated with an individual's chronological age, with a robust correlation coefficient of approximately 0.9 for individuals aged between 20 and 100 years [1]. The epigenetic clock serves as a reliable predictor of biological age, which refers to how well a person's body functions compared to their chronological age [2]. In aging research, the epigenetic clock is used to estimate the biological age of cells, tissues, or organs by analyzing the methylation levels of select CpGs, often referred to as clock CpGs [8]. This estimated age, known as the epigenetic age, can indicate different aging rates between individuals with the same chronological age, providing insights into the biological basis of aging [9]. The epigenetic clock has been applied in various studies to understand the relationship between epigenetic aging and factors such as metabolism, and it is considered one of the most promising molecular estimators of biological age [6], [8].", + "question": "What is the 'epigenetic clock,' and how is it used in aging research?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_18 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_18 new file mode 100644 index 0000000..4fa7cac --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_18 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2007 - Longevity Genomics Across Species.pdf", + "2016 - Epigenetics and aging.pdf", + "2022 - Genome-wide screens in yeast models towards.pdf", + "2018 - Genomic Instabilities, Cellular Senescence, and Aging In Vitro, In Vivo and Aging-Like Human Syndromes.pdf", + "2012 - Replicative and Chronological Aging.pdf", + "2012 - Genomics and Successful Aging Grounds for Renewed.pdf", + "2016 - Unraveling the message insights into comparative genomics.pdf", + "2016 - Epigenetic Mechanisms of Longevity and Aging.pdf", + "1989 - Genetic instability and aging theories, facts, and future perspectives.pdf", + "2016 - Epigenetics and aging.pdf" + ], + "extraction_id": [ + "789baa84-292c-547a-8600-8568f34ea9de", + "6b4e8da8-7d82-5531-b1a8-21935a6f4a51", + "6977180a-bec7-5d39-ba97-b0fd664b6c96", + "fbb608d4-8b2b-502d-88cc-d1fcb0b89afb", + "cb936ad1-1a4f-5b1c-89f1-f8991f059969", + "3d18e792-3d83-5cc3-b9ab-309322ecf55d", + "0dc4e4f4-cd48-5ccd-991b-edd688533b5e", + "5ff88a27-4525-5e7f-99bd-e781cedd8005", + "b0531531-f629-512b-9835-24cc870b4ef3", + "6b4e8da8-7d82-5531-b1a8-21935a6f4a51" + ], + "document_id": [ + "1ab0b63f-d97c-5f5c-98ee-0bde785fa630", + "71b206ec-81bd-5194-8b21-ae522f8cbc2d", + "be6ba237-7f13-5aac-8cb1-6a43e82d326b", + "7de8d462-8a3c-5625-8cbb-374f3bb46425", + "496e387e-4278-5f74-8ecc-4edc1cee7dfe", + "6d2b82c3-4256-562a-9b23-ff7c71e9fd93", + "0deba7bb-c27a-5d9e-b1b2-e48a5574882c", + "588185a0-e157-552f-a304-4beefb85d398", + "4d5b1800-b676-5865-a555-09ea740cc14a", + "71b206ec-81bd-5194-8b21-ae522f8cbc2d" + ], + "id": [ + "chatcmpl-AIFlJI3UdMQFpFb34IWrf77DCGGJY", + "f125dd77-ecd5-59aa-9cf0-ae89333159d2", + "35414229-a946-525c-b508-4b8f49a2702c", + "1b3d84fb-c799-5d19-b3bd-a9032b7980fc", + "5caecfbd-14ef-59e2-a281-2bc524ca0353", + "c14402ec-2ad7-5857-9f09-39c71656bf0f", + "c103f3f8-b155-5787-bdd9-16f9d390369d", + "b19ebe3b-e87e-5cab-baef-24deddd303bb", + "c32f3dbe-95d5-531a-9165-d4da7b2dc2a8", + "91375d45-be1d-5c54-8d0f-a9b1dded69bb", + "ae5be149-52ad-5854-b40a-c24374545cf0" + ], + "contexts": [ + "the nematode Caenorhabditis elegans , and the budding yeast Saccharomyces cerevisiae , have emerged as the most widely used and, hence, best characterized, model organisms in bio- gerontology. When considering the use of simple eukaryotes to study aging and age-related disease, it is pertinent to ask whether, and to what degree, the aging process is evolutionarily con- served. Does a yeast cell age by the same mechanism(s) as a", + "Studies on the aging of mammals are rather limited by the long life span of the commonly used model organisms. Thus, both nonverte-brate and invertebrate organisms, with their shorter life span and ease of genetic and environmental manipulations, gained popularity amongresearchers in the aging field as experimental models for aging studies. Among them, budding yeast or Saccharomyces cerevisiae is a highly in- formative organismal model for aging studies with its genetic tools,", + "Abstract Cellular models such as yeasts are a driving force in biogerontology studies. Their simpler genome, short lifespans and vast genetic and genomics resources make them ideal to characterise pro-ageing and anti-ageing genes and signalling pathways.Over the last three decades, yeasts have contributed to the understanding of fundamental aspects of lifespan regulation including the roles of nutrient response, global protein translation rates and quality, DNA damage, oxidative stress,", + "usually chosen for convenience rather than for specific features applicable to human aging. Hence, choosing the suitable animal model to answer the specific question we aim to understand is of high importance in these types of studies. Among the most prevalent aging model organisms are Saccharomyces cerevisiae , Caenorhabditis elegans, Drosophila melanogaster, and Mus mus - culus . As a single-celled organism, S. cerevisiae is easily grown,", + "mammalian genes that affect aging than any other model organism. Aging in yeast is assayed primarily by measurement of replicative or chronological life span. Here, we review the genes and mechanisms implicated in these two aging model systems and key remaining issues that need to be addressed for their optimization.", + "be more exaggerated in more distantly related species (such as the worm and mouse models). There are, however, simi - larities between aged humans and aged model organisms; they all tend to have decreasing overall fitness, and there - fore, studies using model organisms continue as they may be at least indicative of some aging mechanisms in humans. Extensions to life span in model organisms are mostly associated with disruption to fundamental metabolic path -", + "eukaryote model organisms, namely yeast, worms, ies,and sh, as well as mice and rats, to explore both genetic and environmental determinants of lifespan. While these short-lived models have each yielded a number of fasci- nating ndings and insights into hypotheses surrounding extended lifespan and healthspan, they may also haveconstrained this complex, multifactorial eld to areas in which they are best suited, most notably short-term inter-", + "et al., 2010 ). These effects require an intact germline, andTable 2. Repositories and Tools for Aging Research Models Description Link/Reference Yeast Saccharomyces genome database http://www.yeastgenome.org/ published lifespan data http://lifespandb.sageweb.org/ (McCormick et al., 2015 ) Wilcoxon rank sum test to test signicance of lifespan differenceshttp://data.kaeberleinlab.org/scripts/ranksum.php yeast outgrowth data analyzer (YODA) for chronological lifespan assayshttp://yoda.sageweb.org/", + "for molecular biological studies on aging. Although material from humans should be employed where possible, for prac- tical reasons animal model systems like rats and mice are indispensible. There is evidence that, provided their health sta- tus and husbandry is optimal, rodents age much in the same way as humans do (Burek 1978). For studying certain funda- mental processes, such as the occurrence of various types of DNA rearrangement, lower organisms and cell lines can also", + "short life span, and fully sequenced genome (20 ,21). Despite being uni- cellular, yeast has been an excellent model to identify and characterize conserved basic biological processes, including aging. Yeast has beenextensively used to identify genes and interventions responsible for lifespan extension and to gain insights into the aging processes of all eu- karyotic organisms. In parallel, over the years, studies on invertebrate organisms, such as Drosophila melanogaster (flies) and Caenorhabditis" + ], + "answer": "Researchers use model organisms like yeast and worms to study human aging due to their simpler genomes, short lifespans, and the ease with which they can be genetically and environmentally manipulated. These characteristics make them ideal for identifying and characterizing genes and signaling pathways involved in aging [3]. Yeast, specifically Saccharomyces cerevisiae, is a highly informative model for aging studies because of its genetic tools and the ability to measure aging through replicative or chronological lifespan assays [2], [5]. Yeast has been extensively used to identify genes and interventions responsible for lifespan extension, providing insights into the aging processes of all eukaryotic organisms [10]. Similarly, the nematode Caenorhabditis elegans is another widely used model organism in biogerontology. Researchers study these organisms to understand whether the aging process is evolutionarily conserved and to what degree mechanisms in these simpler organisms can be indicative of aging mechanisms in humans [1], [6]. These model organisms help explore both genetic and environmental determinants of lifespan, contributing to hypotheses surrounding extended lifespan and healthspan [7].", + "question": "How do researchers use model organisms like yeast or worms to study human aging?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_19 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_19 new file mode 100644 index 0000000..f94bd0d --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_19 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2012 - Genome-Environment Interactions That Modulate.pdf", + "2001 - Demography in the age of genomics.pdf", + "2020 - Protecting the Aging Genome.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2018 - Spontaneous DNA damage to the nuclear genome promotes senescence.pdf", + "2009 - The Human Ageing Genomic Resources online.pdf", + "2009 - Adaptation, aging, and genomic information.pdf", + "2012 - Genome-Environment Interactions That Modulate.pdf", + "2009 - MicroRNAs in C. elegans Aging Molecular Insurance for Robustness.pdf", + "2002 - Pharmacology, Genomics, and the Evolutionary Biology.pdf" + ], + "extraction_id": [ + "68b12e10-d643-570a-aa63-eda62de81928", + "e3014138-3d5b-58bc-a1a5-5ac6f04cac1c", + "e5067ce2-69a6-5433-bed4-b95daeaa691e", + "822571e2-b05d-5e17-9eaa-431151851111", + "005e73b5-7a93-53ff-946c-735fb4588de5", + "7ada6b55-99c2-5e20-bf96-d153f927256c", + "c2a8f947-44f2-5100-99e5-9c3a2f1284e9", + "8650652a-1765-563b-a98e-2e9336bcf29a", + "c8d6f90d-a25c-590a-a546-4500df09aa28", + "6c9e1997-bfe6-5708-a476-07c833eed8fa" + ], + "document_id": [ + "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "0f07fa43-feb6-5656-b7e7-b8faa86f5623", + "bb774030-2570-5596-b2ab-b8f57ff81086", + "62b635c3-040e-512a-b016-6ef295308a1e", + "08be7274-78a3-5e93-9e8c-3d4f6dbeacf9", + "e43cd3b6-ad8e-5422-ba7c-ceb6e66cc529", + "54a993af-b86b-5cc3-a04b-bab03c244534", + "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "dff49223-ac74-5419-a190-a0c7f43a5ee5", + "1bc636a3-6ce0-5fea-b549-0dae90a78f1b" + ], + "id": [ + "chatcmpl-AIFlT2nob40QrExWjGMMqZ4fSc8yC", + "78733c6a-d870-5154-9128-eb66291fa967", + "9da7c5dc-0deb-577c-bb22-83f987bd76dd", + "3c636897-c47e-505d-9203-306124b73e0e", + "265126e3-2a4d-518f-93cf-21a201747eef", + "dcc13291-f18b-5094-83b6-4609322bc242", + "1c4286b6-ede2-568b-9c18-b1e99ede17a6", + "2c5241f1-1655-5e36-a787-b966767b2534", + "f20fd517-5f05-53ca-93a5-916bc891ad92", + "69681eeb-6629-5091-b2b4-b4444e570913", + "5d8cc04f-7e13-5dbc-80c2-a35643954e9a" + ], + "contexts": [ + "need to develop approaches and therapies targeting theaging process and age-related diseases (Butler et al.,2008). Delaying the process of aging, even slightly,would have profound social, medical and economic ben-efits (Olshansky et al., 2006; Butler et al., 2008). Forexample, slowing aging by a mere 7 years would cutmortality of age-related diseases by half at every age.Therefore, the potential benefits from research on thebasic biology and genetics of aging are unparalleled interms of improving quality", + "raises the possibility of therapies to slow aging. Therefore the discoveryof a gerontogene with even very rare mutations that increased longevitywould cause speculation about future trends in mortality. However, thediscovery of such a gene would be relevant only to long-term (and, there-fore, very speculative) projections. Prospective Epidemiologic Surveys that Include Genetic Information Some epidemiologic cohort studies of populations have collected", + "Interestingly, when senescent cells are abolished either through genetic manipulation or via senolytic drugs, biological aging is signicantly halted in mice [ 53,54]. Therefore, trials are now under way to test the ability of senolytics to postpone age-associated pathologies in humans [ 55]. Notably, multi- ple drugs are being pursued that either directly or indirectly impact DNA repair or the consequenceof DNA damage. Future Prospects: Developing Interventions through DNA Repair", + "5. Goldman DP, etal. Substantial health and economic returns from delayed aging may warrant a new focus for medical research. Health Aff (Millwood). 2013;32(10):1698705. 6. Esplin ED, Oei L, Snyder MP.Personalized sequencing and the future of medicine: discov- ery, diagnosis and defeat of disease. Pharmacogenomics. 2014;15(14):177190. 7. Marian AJ.Clinical applications of molecular genetic discoveries. Transl Res. 2016;168:614.", + "J.L. Kirkland, Barriers to the Preclinical Development of Therapeutics that Target Aging Mechanisms, J. Gerontol. A Biol. Sci. Med Sci. 71 (11) (2016) 1388 1394 . [2]D.J. Baker, B.G. Childs, M. Durik, M.E. Wijers, C.J. Sieben, J. Zhong, R.A. Saltness, K.B. Jeganathan, G.C. Verzosa, A. Pezeshki, K. Khazaie, J.D. Miller, J.M. van Deursen, Naturally occurringp16(Ink4a)-positive cells shorten healthy lifespan, Nature 530 (7589) (2016) 184 189.", + "series of recent breakthroughs, a number of genes capable ofaltering the aging process as a whole or at least to a largedegree have been identified in animal models and even a fewin humans (Finch & Ruvkun, 2001; de Magalhes, 2005; Kenyon,2005). Furthermore, multiple alleles have been examined fortheir association with human exceptional longevity (Vijg & Suh,2005). This is a fascinating and important area of research, yetthere are now so many genes being associated with aging andlongevity that keeping", + "pharmaceutical and other interventions for human aging based on research that starts with the genomic information required to sustain adaptation, and thus health, in older fruit flies [36-39]. Naturally, any such genomic short-cut to reverse-engineering the evolution of slowed aging from fruit flies to humans is fraught with potential for error. Such evolutionarily deep orthologies are sure to supply", + "century. Manipulation of aging-related genes by diet,lifestyle, and pharmaceuticals could dramatically im-prove human health and could be used to develop drugsagainst age-related diseases such as cancer, heart dis-ease, type 2 diabetes, obesity, and neurodegenerativediseases. The hundreds of aging-related genes and genesrelated to CR already identified offer enormous oppor-tunities for target discovery (Fig. 2). Although aging-related genes cannot be modified in humans, under-standing how these can be", + "[7] Hughes, S.E., Evason, K., Xiong, C., Kornfeld, K. Genetic and pharmacological factors that influence reproductive aging in nema- todes. PLoS Genet. 2007 , 3: e25. [8] Vijg, J., Campisi, J. Puzzles, promises and a cure for ageing. Na- ture 2008 , 454: 1065-1071. [9] Rolland, Y., Czerwinski, S., Abellan Van Kan, G., Morley, J.E., Cesari, M., Onder, G., Woo, J., Baumgartner, R., Pillard, F., Boirie, Y., Chumlea, W.M., Vellas, B. Sarcopenia: its assessment, etiol-", + "for the aging process during the 20th Century. Thissituation poses a fundamental challenge to anti-aging medicine: how to develop effective therapies for a genomically complex pathology. We propose such astrategy. As a rst step, we recommend the use of modelsystems in which signicant genetic intervention is not proscribed or impractical. Second, we propose that work" + ], + "answer": "Yes, there are promising anti-aging therapies being developed based on genetic research. Several approaches are being explored: 1. **Senolytic Drugs**: Research has shown that abolishing senescent cells through genetic manipulation or senolytic drugs can significantly halt biological aging in mice. Trials are underway to test the ability of senolytics to postpone age-associated pathologies in humans [3]. 2. **Genetic Discoveries in Aging**: A number of genes capable of altering the aging process have been identified in animal models and even in humans. This area of research is promising as it explores the association of multiple alleles with human exceptional longevity [6]. 3. **Manipulation of Aging-Related Genes**: There is potential in manipulating aging-related genes through diet, lifestyle, and pharmaceuticals to improve human health and develop drugs against age-related diseases such as cancer, heart disease, type 2 diabetes, obesity, and neurodegenerative diseases [8]. These developments indicate that genetic research is paving the way for potential anti-aging therapies.", + "question": "Are there any promising anti-aging therapies being developed based on genetic research?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_2 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_2 new file mode 100644 index 0000000..58edb8f --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_2 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2012 - Human Ageing Genomic Resources Integrated.pdf", + "2012 - Genome-Environment Interactions That Modulate.pdf", + "2012 - Genome-Environment Interactions That Modulate.pdf", + "1999 - Molecular Biology of Aging.pdf", + "2009 - The Human Ageing Genomic Resources online.pdf", + "2013 - Gene expression changes with age in skin.pdf", + "2011 - Clinical aspects and molecular diagnostics of skin aging.pdf", + "2013 - Gene expression changes with age in skin.pdf", + "2012 - Genome-Environment Interactions That Modulate.pdf", + "2007 - Characterization of a bidirectional promoter shared between two human.pdf" + ], + "extraction_id": [ + "25e9d8a3-54ac-5412-8efb-3b56d93f363f", + "68b12e10-d643-570a-aa63-eda62de81928", + "a01ca925-4ccf-5863-a162-7bd4c754fe89", + "66c6e911-2b6e-5d79-9e13-9f0e370c6fa6", + "52c67b46-63f2-54ae-a78e-e9d54a55f6e4", + "18e9f96b-9af8-59bf-9323-084df9a4f9b6", + "39cc0d86-f175-5fe3-990f-8471c7395ba4", + "dfb687b2-f1ff-5e22-8a67-4a1db9ebeb3c", + "68b12e10-d643-570a-aa63-eda62de81928", + "a5581fce-8af9-5131-a94b-8fce7fbf9557" + ], + "document_id": [ + "5f554cc7-c94d-5fbd-9567-528499663ed6", + "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "6f122d3a-d8ca-598f-8767-c059a941cef3", + "e43cd3b6-ad8e-5422-ba7c-ceb6e66cc529", + "5c121bbb-57b8-51cc-8461-effa1bfd87b9", + "e32f8f2c-d3ad-5dae-a393-9bd87c370ebe", + "5c121bbb-57b8-51cc-8461-effa1bfd87b9", + "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "e9a81a0d-52b4-5ced-be05-130eed317085" + ], + "id": [ + "chatcmpl-AIFgbQ1pPISWJITI2YHN6poeJtTyV", + "0ae63c75-df5f-59b0-9561-30d5115f0f74", + "c258bd44-b5b3-5eb5-9c72-60b365f18f81", + "4d6876c5-9226-587c-8d3e-d4957ee42dba", + "0671ed95-2909-54ac-baea-b156ee0ea8f8", + "650300e1-898c-56e2-9358-0bb6625b0073", + "cc78a209-081f-5e0c-877a-36b85d5105a6", + "a3859151-a94e-5085-8e7a-3c1d50716319", + "d53018ae-0881-5ef4-9c49-48623e8aa342", + "a4773f1a-f2d3-5950-a81e-d22357e97a0f", + "91530253-7015-5b7a-bb7b-506ddeb3c3f8" + ], + "contexts": [ + "potentially associated with human ageing. For eachgene, a description compiled from the studies that linkthe gene to ageing is provided. It should be noted thatour focus is on genes that might affect the ageingprocess, rather than individual age-related pathologies; genes affecting multiple, even if not all, age-related", + "showing that single genes can regulate aging in modelorganisms demonstrate that aging can be geneticallymanipulated (Finch and Ruvkun, 2001; Kenyon, 2010).Hundreds of genes that modulate longevity have nowbeen identified in model organisms (de Magalha es et al.,2009a). In some cases (e.g., in worms), mutations insingle genes can extend lifespan by almost 10-fold (Ayy-adevara et al., 2008). Nonetheless, aging is a complexprocess that derives not from single genes but from theinteractions of multiple genes", + "genes (http://genomics.senescence.info/genes/), more than700 genes have been identified that regulate lifespan inmodel organisms (de Magalha es et al., 2009a). Many ofthese genes and their associated pathwayssuch as theinsulin/IGF1/GH pathwayhave been shown to affect lon-gevity across different model organisms (Kenyon, 2010).Therefore, at least some mechanisms of aging are evolu-tionarily conserved and may have potential therapeuticapplications (Baur et al., 2006). For example, evidencesuggests the use of", + "key genes and pathways important in aging; geneticstudies of heritable diseases that cause the appearanceof premature aging in affected people; physiological ex-Introductionperiments that relate the pace of aging to caloric intake;Is aging the final act in the script of developmental biol-and advances in human genetics, as well as cell andogy? The characteristic changes that are part and parcelmolecular biology leading to an understanding of theof aging appear similar to developmentally regulatedbasis of", + "shown that genes associated with aging and/or longevity inmodel organisms are evolutionary conserved in terms of havingmore homologues than predicted by chance (Budovsky et al .,2007, 2008) and exhibiting slower molecular evolution rates (de Magalhes & Church, 2007). Therefore, it is now clear that atleast some genes identified in model organisms may be relevantto human aging. To allow researchers to focus specifically on human aging,", + "expression of certain genes have an effect upon longevity. Although similar aging processes are likely to operateacross multiple species [30], it has been much more diffi-cult to identify longevity candidate genes in human studies[30]. A key question in human aging is to what extent asignature of aging may be detectable across tissues. Until now there has been a lack of large transcriptional profiles from the same human individuals in multiple tissues. TheMuTHER study provides ins ight into the human aging", + "complex.108,109Studies on models such as the yeast Sac- charomyces cerevisiae110the nematode Caenorhabditis elegans,111the fly Drosophila melanogaster,112-114the mouse Mus musculus,115and humans116show that single gene mutations can contribute to the initiation of aging andinduce premature aging syndromes. There are, however, nospecial genes that can cause aging-associated damages. Themanifestation of aging is mostly due to the failure of main-tenance and repair mechanisms. 117,118", + "on model organisms [3] or have been confined to specificaging-associated disorders such as progeria syndromes [4]. A study of postmortem human brain tissue from 30 individuals aged 26 to 106 years [5] showed that approxi- mately 4% of approximately 11,000 genes analyzed show a significant age-related expression change (1.5-fold or more) in individuals aged >40 years. These genes were reported to play central roles in synaptic plasticity, vesi- cular transport, and mitoch ondrial function. Another", + "of multiple genes with each other and withthe environment. Evidence from animal systems showsa major impact of the environment on aging, yet envi-ronmental manipulations of aging act through genesand proteins, usually by triggering signaling pathwaysand modulating gene expression. In fact, some geneshave been shown in model organisms to have varyingeffects on lifespan depending on diet (Heikkinen et al.,2009). Genes that can regulate aging in model organ-isms cannot be directly applied to humans through", + "[2] L. Partridge, D. Gems, Mechanisms of ageing: public or private? Nat. Rev. Genet. 3 (2002) 165 175. [3] A.M. Leroi, et al., What evidence is there for the existence of individual genes with antagonistic pleiotropic effects? Mech. Ageing Dev. 126 (2005)421429. [4] S.N. Austad, Is aging programmed? Aging Cells 3 (2004) 249 251. [5] V.D. Longo, J. Mitteldorf, V.P. Skulachev, Opinion: programmed and altruistic ageing, Nat. Rev. Genet. 6 (2005) 866 872." + ], + "answer": "Genes affect the aging process in humans through complex interactions and pathways. Research has shown that single genes can regulate aging in model organisms, indicating that aging can be genetically manipulated [2]. Hundreds of genes have been identified that modulate longevity in these organisms, and some of these genes and their associated pathways, such as the insulin/IGF1/GH pathway, have been shown to affect longevity across different species, suggesting that some mechanisms of aging are evolutionarily conserved [3]. In humans, it has been more challenging to identify specific longevity candidate genes, but studies have shown that certain genes associated with aging in model organisms are evolutionarily conserved and may be relevant to human aging [5]. Additionally, approximately 4% of genes analyzed in a study of postmortem human brain tissue showed significant age-related expression changes, indicating that these genes play central roles in processes like synaptic plasticity, vesicular transport, and mitochondrial function [8]. Overall, while aging is a complex process involving multiple genes and their interactions with the environment, genetic studies in model organisms provide insights that may be applicable to understanding human aging [9].", + "question": "How do genes affect the aging process in humans?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_20 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_20 new file mode 100644 index 0000000..3f2af79 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_20 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2005 - Rapid and reversible induction of the longevity, anticancer.pdf", + "2009 - Genes and gene expression modules associated with caloric.pdf", + "2017 - Dietary restriction protects from age-associated DNA methylation and induces epigenetic reprogramming of lipid metabolism.pdf", + "2007 - The role of nuclear architecture.pdf", + "2016 - Epigenetics and aging.pdf", + "2012 - Genome-Environment Interactions That Modulate.pdf", + "2005 - Rapid and reversible induction of the longevity, anticancer.pdf", + "2005 - Rapid and reversible induction of the longevity, anticancer.pdf", + "2008 - Longevity mutation in SCH9 prevents recombination.pdf", + "2008 - DNA damage and ageing.pdf" + ], + "extraction_id": [ + "db18d213-92ed-5f3e-a242-60d3ed0ec8c8", + "0b45ae60-562c-5e48-a1c1-9eb29614a63c", + "a29560f5-a9e0-56d0-95f2-138eef516ef5", + "9655c555-838e-5cdf-85cf-13736c3cf028", + "91434549-bda3-5154-b089-28efed9c1089", + "5745c701-a549-51c3-adcc-b19c47436740", + "f4edcf22-ca64-5fe6-9bfa-f97a017d2d08", + "381d5314-5aa1-525f-a2a5-352b70ad86fa", + "bc856edb-7526-5424-a822-47075459a607", + "554b2b00-d006-5b97-aeb1-70ec31482641" + ], + "document_id": [ + "0b1bf178-21e4-5382-97c9-c93cdc1a9e66", + "893ba204-2e69-563f-9046-7246ca61494f", + "fe573bb0-3d37-55e5-93fa-65b3fbc5f532", + "578e2f7d-ddd4-56c8-a5b0-670969f8ff1e", + "71b206ec-81bd-5194-8b21-ae522f8cbc2d", + "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "0b1bf178-21e4-5382-97c9-c93cdc1a9e66", + "0b1bf178-21e4-5382-97c9-c93cdc1a9e66", + "a6b022ba-653f-51d3-845a-dd6b3d61d4a4", + "f170e9cf-dfab-5758-ab23-08daff2af694" + ], + "id": [ + "chatcmpl-AIFlaWmRr9Bl0RbY7T3uxkIANSmkA", + "56e72d29-31c2-5096-b4f8-06c740bce06e", + "2b081115-d36e-57ec-aedc-2fd9691bc5e9", + "bb028469-8295-5657-8061-a715cb314a4a", + "e01c4c58-342d-5369-89e6-98344af55000", + "1e116f55-36fd-525f-9950-9a1354c32f7b", + "b990eb0a-709a-500c-836e-83e202e0d6a6", + "833c9ddc-ec27-5301-9c3b-025eab95e28c", + "8151fbcb-f498-56a7-84ce-8af647fd2245", + "537cff80-380d-5c6a-a25a-04b32e9b1bd7", + "4f28b643-04a4-59e2-8226-ab050e698b64" + ], + "contexts": [ + "caloric restriction. Physiol. Genom. 17, 307 315.Van Remmen, H., Ward, W.F., Sabia, R.V ., Richardson, A., 1995. Gene expression and protein degradation. In: Masoro, E.J. (Ed.), Handbook ofPhysiology. Section 11: Aging. Oxford University Press, New York, pp. 171234. Weindruch, R., Walford, R.L., 1982. Dietary restriction in mice beginning at 1 year of age: effect on life-span and spontaneous cancer incidence.Science 215, 1415 1418.S.R. Spindler / Mechanisms of Ageing and Development 126 (2005) 960 966 966", + "extension by dietary restriction. Annu Rev Biochem 2008, 77:727-54. 8. Harper JM, Leathers CW, Austad SN: Does caloric restriction extend life iin wild mice? Aging Cell 2006, 5:441-9. 9. Forster MJ, Morris P, Sohal RS: Genotype and age influence the effect of caloric intake on mortality in mice. FASEB J 2003, 17:690-2. 10. Spindler SR, Mote PL: Screening candidate longevity therapeu- tics using gene-e xpression arrays. Gerontology 2007, 53:306-21.", + "analysis in calorie-restricted rats implicates epigenetic and post-translational mechanisms in neuroprotection and aging. Genome Biol. 2015;16:285. 21. Gillespie ZE, Pickering J, Eskiw CH. Better living through chemistry: caloric restriction (CR) and CR mimetics alter genome function to promote increased health and lifespan. Front Genet. 2016;7:142. 22. Jiang T, Liebman SE, Lucia MS, Phillips CL, Levi M. Calorie restriction modulates renal expression of sterol regulatory element binding proteins, lipid", + "Calorie restriction, a dietary regimen that extends the lifespan of numerous organisms, also delays the majority of age-related gene-expression changes in mice and, to a certain extent, in flies45,50. It is currently unclear whether the effect of calorie restriction on gene expression underlies its beneficial effect on lifespan or is merely a consequence thereof. Findings in yeast suggest that there may be a causal link: Sir2 not only facilitates heterochromatin and promotes DNA stability, but is", + "Transcriptome analysis in calorie-restricted rats implicates epigenetic and post- translational mechanisms in neuroprotection and aging. Genome Biol. 16,2 8 (2015). 204. M. V. Blagosklonny, Calorie restriction: Decelerating mTOR-driven aging from cells to or- ganisms (including humans). Cell Cycle 9, 683 688 (2010). 205. D. K. Ingram, G. S. Roth, Calorie restriction mimetics: Can you have your cake and eat it, too? Ageing Res. Rev. 20,4 662 (2015).", + "life-span extension by calorie restriction in Saccharomyces cerevisiae. Science 289:21262128. Mair W, Goymer P, Pletcher SD, and Partridge L (2003) Demography of dietary restriction and death in Drosophila. Science 301:17311733. Masoro EJ (2005) Overview of caloric restriction and ageing. Mech Ageing Dev 126:913922. Mathers JC (2006) Nutritional modulation of ageing: genomic and epigenetic ap- proaches. Mech Ageing Dev 127:584589. Meric-Bernstam F and Gonzalez-Angulo AM (2009) Targeting the mTOR signaling", + "Keywords: Caloric restriction; Short-term; Longevity; Cancer; Microarray; Affymetrix Aging is widely assumed to result from the gradual age- related accumulation of essentially irreversible moleculardamage. In this context, CR is often viewed as preventing orslowing the accumulation of such damage, thereby slowingthe process of aging ( Bokov et al., 2004 ). This view is intuitively appealing, as it provides a straightforwardexplanation for the stochastic nature of aging and the onset", + "of short- and long-term caloric restriction effects in the liver of agingmice. Proc. Natl. Acad. Sci. U.S.A. 98, 10630 10635.Capstick, F., Brooks, B.A., Burns, C.M., Zilkens, R.R., Steinbeck, K.S., Yue, D.K., 1997. Very low calorie diet (VLCD): a useful alternative inthe treatment of the obese NIDDM patient. Diab. Res. Clin. Pract. 36, 105111. Chen, H., 2004. Gene expression by the anterior pituitary gland: effects of age and caloric restriction. Mol. Cell. Endocrinol. 222, 21 31.", + "genomic effects of caloric restriction. Mech. Ageing Dev. 126 : 960 966 . Sun , H. , R.J. Bennett , and N. Maizels . 1999 . The Saccharomyces cerevisiae Sgs1 helicase effi ciently unwinds G-G paired DNAs. Nucleic Acids Res. 27 : 1978 1984 . Thompson , L.H. , and D. Schild . 2002 . Recombinational DNA repair and human disease. Mutat. Res. 509 : 49 78 .", + "L. & Spindler, S. R. Genomic profiling of short- and long-term caloric restriction effects in the liver of aging mice. Proc. Natl Acad. Sci. USA 98, 1063010635 (2001). 62. Harman, D. The aging process. Proc. Natl Acad. Sci. USA 78, 71247128 (1981). 63. van der Pluijm I, G. G.et.al. Impaired genome maintenance suppresses the growth hormoneinsulin-like growth factor 1 axis in mice with cockayne syndrome. PLoS Biol. 5, 2338 (2006). 64. Yang, H., Baur, J." + ], + "answer": "Caloric restriction and diet have significant impacts on the genetics of aging through various mechanisms: 1. **Gene Expression and Lifespan Extension**: Caloric restriction (CR) has been shown to delay age-related gene-expression changes in mice and, to some extent, in flies. This suggests that CR may influence the genetic pathways associated with aging, potentially contributing to lifespan extension [4]. 2. **Epigenetic and Post-Translational Mechanisms**: In calorie-restricted rats, transcriptome analysis indicates that CR involves epigenetic and post-translational mechanisms, which are implicated in neuroprotection and aging. These mechanisms may alter genome function to promote increased health and lifespan [3], [5]. 3. **mTOR Pathway**: Caloric restriction is associated with decelerating mTOR-driven aging, which is a significant pathway involved in cellular growth and metabolism. By modulating this pathway, CR may influence the genetic regulation of aging processes [5]. 4. **Genomic and Epigenetic Approaches**: Nutritional modulation, including caloric restriction, can impact aging through genomic and epigenetic approaches. This suggests that diet can influence the genetic and epigenetic landscape, potentially affecting the aging process [6]. Overall, caloric restriction and diet can modulate genetic pathways and mechanisms that are crucial for aging, potentially leading to increased lifespan and improved health during aging.", + "question": "How do caloric restriction and diet impact the genetics of aging?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_3 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_3 new file mode 100644 index 0000000..32565a3 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_3 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2017 - Dietary restriction protects from age-associated DNA methylation and induces epigenetic reprogramming of lipid metabolism.pdf", + "2009 - DNA Damage, Aging, and Cancer.pdf", + "2021 - Gene-by-environment modulation of lifespan and weight gain in the murine BXD family.pdf", + "2012 - Systems Biology Approaches to Nutrition.pdf", + "2020 - Mitonuclear genomics and aging.pdf", + "2011 - A genomic analysis of chronological longevity.pdf", + "2004 - A Transcriptional Profile of Aging.pdf", + "2017 - Dietary restriction protects from age-associated DNA methylation and induces epigenetic reprogramming of lipid metabolism.pdf", + "2015 - Cellular and Molecular Biology of Aging Endothelial Cells.pdf", + "2003 - Lifelong voluntary exercise in the mouse prevents.pdf" + ], + "extraction_id": [ + "21efa872-9d89-5dee-9dd1-27dcaa1208cf", + "b03f4297-85f4-5011-8dcf-ec169d3051d3", + "30ba3324-6e19-58c2-9e32-508f827af3e5", + "791bae8d-8d24-5873-b611-9c289591d11d", + "e6fb876b-e91c-505a-aa16-7b428ec61f10", + "d7daf4ea-f57a-5f7b-b6f7-afae08c35b45", + "b382fe8a-0267-5515-ac4b-07be55420040", + "6364d669-4b96-5d2f-8ce8-526b065dce72", + "86f9502b-7a3a-501f-9053-8af1d37043b4", + "e6c82594-27ba-5754-a106-69ae8b5e72ae" + ], + "document_id": [ + "fe573bb0-3d37-55e5-93fa-65b3fbc5f532", + "630c29c7-1dd7-509e-9b6b-b4af98b4ea48", + "4d082da4-fa48-5170-8147-c4fea47a5d4b", + "6955478b-950d-5d29-b24c-3a5ca656f3ae", + "e05fdc09-c8d8-5134-a1fd-bf07a1564981", + "a2e69cf7-8475-55f6-8fab-a572c12de9f0", + "4ab656a7-9656-526b-94e1-422875409b44", + "fe573bb0-3d37-55e5-93fa-65b3fbc5f532", + "815d7f3e-e219-502f-aba0-57a68ae787d3", + "24d4f270-f45b-5830-84f9-b1e5bcd3c070" + ], + "id": [ + "chatcmpl-AIFgiWkzt5opfBd5VTvAKGVKegG8y", + "7460a40c-8723-5de9-9f2e-c781f4872f1f", + "d78564d5-d785-554a-bb2c-d71917ccfe19", + "4bf7307d-d8a0-5594-b0b5-487fe0f265ca", + "da620f88-db92-5267-af81-d6b548e9f29c", + "c96b67f8-ad31-50fd-b053-07b127938ef2", + "a4e0cb76-8950-5471-a3c1-1ed43094fdf3", + "1da274d3-c789-5af5-a8b5-72cdc9a01899", + "5fc33fac-ab39-5ec1-9fb9-dcaa93a595d3", + "321d14fd-f2ae-5904-b502-dae3491cd370", + "4c3d343d-d443-5bb4-a9ef-dd1eecaf9fac" + ], + "contexts": [ + "as diabetes, cancer and neurodegenerative disorders [1, 2]. Environmental and genetic interventions can ameliorate the effects of aging, with nutrition, nutrient-sensing signaling networks and metabolism playing evolutionarily conserved roles [1, 3 5]. Diet- ary restriction (DR), in which food intake is reducedwhile avoiding malnutrition, extends lifespan in di- verse model and non-model organisms [3, 6]. DR induces a remarkably broad-spectrum improvement in", + "limiting exposure to exogenous genotoxins and by suppressing metabolism thereby producing fewer reactive species. However, DNA damage, like caloric restriction, can also elicit a protective survival response that promotes longevity and healthy aging. Recently, the use of sirolimus in mice was found to extend their life span and de - lay the development of conditions associated with aging, including cancer. 1 Sirolimus is one of pre -", + "Longev. Heal. 2, 10 (2013). 7. Kreienkamp Ret al.Doubled lifespan and patient-like pathologies in progeria mice fed high-fat diet. Aging Cell18, e12852 (2019). [PubMed: 30548460] 8. Heilbronn LK & Ravussin E Calorie restriction and aging: review of the literature and implications for studies in humans. Am. J. Clin. Nutr. 78, 361369 (2003). [PubMed: 12936916] 9. Liang Yet al.Calorie restriction is the most reasonable anti-ageing intervention: a meta-analysis of", + "can be slowed down to some extent by eating a healthy diet and taking physical exercise, and many of the chronic diseases prevalent in older adults are either preventable or modi able with healthy lifestyle habits. Thus, older adults can experience successful aging that allows them to achieve physical, social and mental well - being over the life course and to participate in society. Much research has been conducted in recent years to", + "During the past century, remarkable progress has been made in unveiling the mechanisms of aging. Genetic and molecular pathways that regulate healthspan and lifespan have been identified in various model organisms, provid-ing a rich knowledge base (Longo etal. 2015; Lopez-Otin etal. 2013, 2016; Singh etal. 2019). However, the focus on", + "13,14 Prior studies have identified dozens of genetic and environ - mental modifiers of chronological or replicative longevity, some of which are now known to function similarly to modulate life span in multicellular eukaryotes. 15-17 One example of such a con - served longevity intervention is dietary restriction, which has been shown to slow aging in many different species including yeast, nematodes, fruit flies and rodents, 18,19 and most recently", + "Genetic studies have shown that aging can be slowed in mutants that are defective in a wide range of cellularprocesses (such as mitochondrial function, chromatin regu- lation, insulin signaling, tran scriptional regulation, and genome stability). This indicates that aging is a complex process driven by diverse molecular pathways and biochem- ical events. As such, a powerful approach to study aging is touse systems biology, which allows a multitude of factors", + "Dietary interventions, including starvation and protein deprivation, can also alter patterns of DNA methyla- tion, potentially in a long-lasting manner [42, 43], including transgenerationally [26, 44]. Dietary, genetic and pharmacological interventions that improve health during aging and extend lifespan induce long-lasting changes in gene expression that mediate their effects. Here we have asked if and how age-related DNA methylation, transcription and lipid", + "in yeast , Drosophila, and C. elegans is able to slow aging and increase lifespan [252-255]. Follow -up stud ies out of Richard Millers laboratory reproduced these findings in mice fed a diet with rapamycin incorporated [256, 257]. These studies suggested that inhibiting mTOR via rapamycin could delay age-associated diseases and extend lifespan in mammals. A subsequent study replicated these findings by genetically manipulating a", + "appears to retard aging at the molecular level as indi-cated by the gene expression analysis? Most likely,aging retardation at the molecular level by exercise isnot observed in all tissues, including some that maylimit lifespan. For example, if exercise does not reduceaging rates in replicative tissues, then it will not retardage-related tumor onset, which tends to limit maxi-mum lifespan. Another possibility relates to the obser-vation that wheel running decreased to an average 680m/day at 33 mo of age" + ], + "answer": "To slow down genetic aging, several lifestyle choices can be beneficial: 1. **Dietary Restriction (DR)**: Reducing food intake without causing malnutrition has been shown to extend lifespan in various organisms. This approach improves healthspan by influencing nutrient-sensing signaling networks and metabolism [1]. 2. **Healthy Diet and Physical Exercise**: Engaging in regular physical activity and maintaining a healthy diet can slow down aging and prevent or modify many chronic diseases prevalent in older adults. This contributes to successful aging, allowing individuals to achieve physical, social, and mental well-being [4]. 3. **Caloric Restriction**: This is considered a reasonable anti-aging intervention, as it has been shown to slow aging in multiple species, including yeast, nematodes, fruit flies, and rodents [6]. 4. **Exercise**: While exercise may not retard aging in all tissues, it can contribute to molecular-level aging retardation, which is beneficial for overall health and longevity [10]. These lifestyle choices, when combined, can help mitigate the effects of genetic aging and promote a longer, healthier life.", + "question": "What lifestyle choices can help slow down genetic aging?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_4 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_4 new file mode 100644 index 0000000..b8cde0c --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_4 @@ -0,0 +1,65 @@ +{ + "titles": [ + "1989 - Genetic instability and aging theories, facts, and future perspectives.pdf", + "2006 - Specific age related signatures in Drosophila body parts.pdf", + "2016 - The dog aging project translational geroscience in companion.pdf", + "2009 - MicroRNAs in C. elegans Aging Molecular Insurance for Robustness.pdf", + "2012 - Genomics and Successful Aging Grounds for Renewed.pdf", + "2018 - Mechanisms of Vascular Aging.pdf", + "2021 - Gene-by-environment modulation of lifespan and weight gain in the murine BXD family.pdf", + "2011 - Genomics of human longevity.pdf", + "2001 - Demography in the age of genomics.pdf", + "2021 - Lifespan-Associated Gene Expression Signatures of Recombinant BXD Mice Implicates Coro7 and Set in Longevity.pdf" + ], + "extraction_id": [ + "b0531531-f629-512b-9835-24cc870b4ef3", + "efba6890-9b12-567c-b3f0-4e6ff5c6e9c4", + "9c8bc002-4f7d-5c53-9736-70f59a6ee518", + "c8d6f90d-a25c-590a-a546-4500df09aa28", + "3d18e792-3d83-5cc3-b9ab-309322ecf55d", + "bfeb5c38-4fa6-5df5-90ce-63204deba3a8", + "396683f9-b2e3-5942-bec8-f96fa798c341", + "89586b79-902d-5e2b-9b8a-b7a8c4971783", + "94acf45b-980d-5273-8a09-5d748c94a51b", + "e3eb627c-15f4-5713-92a4-e92a891b7136" + ], + "document_id": [ + "4d5b1800-b676-5865-a555-09ea740cc14a", + "24f073af-ef97-5ba3-9923-9a7d958bd411", + "e841c6bd-78b8-56e1-b3dd-e2bcc8a0f590", + "dff49223-ac74-5419-a190-a0c7f43a5ee5", + "6d2b82c3-4256-562a-9b23-ff7c71e9fd93", + "659b84b6-63dd-5bb1-80ee-7478ed3c47e3", + "4d082da4-fa48-5170-8147-c4fea47a5d4b", + "2e038219-fdaa-506f-9cd3-51379054130e", + "0f07fa43-feb6-5656-b7e7-b8faa86f5623", + "6277f22c-f56d-51a7-add1-1fe7674dda74" + ], + "id": [ + "chatcmpl-AIFgqiJDPdSbdoRhIXU84YMtAnqaJ", + "91375d45-be1d-5c54-8d0f-a9b1dded69bb", + "a32e8775-583f-5827-a590-b7058b255d26", + "aba78d88-b097-52fe-8246-66301e39cdd5", + "741dc9f2-2e8e-5fe3-9e6f-806a5a93213b", + "0916cf4a-a863-5c5d-b687-2ae5fa80bac0", + "b3e0de69-763f-5f19-aeb7-ea1df79a143b", + "e58a6718-dfef-58f6-9417-4abd793fe74d", + "71eb66cb-130c-5183-ba9e-038637582775", + "a0aa0b47-91a6-5f3e-b8a2-9ccdfcd79865", + "322613d7-921b-5e2e-b410-57ab4acc4130" + ], + "contexts": [ + "for molecular biological studies on aging. Although material from humans should be employed where possible, for prac- tical reasons animal model systems like rats and mice are indispensible. There is evidence that, provided their health sta- tus and husbandry is optimal, rodents age much in the same way as humans do (Burek 1978). For studying certain funda- mental processes, such as the occurrence of various types of DNA rearrangement, lower organisms and cell lines can also", + "Until now most of the genomic studies of invertebrate models have been performed on whole animals. Several studies, however, recently performed on specialized mammalian tissues, either post-mitotic (heart or nervous system) or mitotic (liver), show that the effects of aging are tissue-specific [19-25]. In addition, effects of caloric restriction on age related transcriptional changes are also tissue- or species-specific [19]. To better understand the aging process in invertebrate", + "opportunities for assessing the efcacy of interventions onaging. When considering the advantages and disadvantages of dogs as a model for geroscience research, it is useful tonote that the vast majority of mammalian studies on thebasic biology of aging are performed in a relatively small number of inbred mouse strains. Typical average lifespan for most of these mouse strains is approximately 23 years,", + "[14] Gerstbrein, B., Stamatas, G., Kollias, N., Driscoll, M. In vivo spec- trofluorimetry reveals endogenous biomarkers that report health- span and dietary restriction in Caenorhabditis elegans . Aging Cell 2005 , 4: 127-137. [15] Kennedy, B.K. The genetics of ageing: insight from genome-wide approaches in invertebrate model organisms. J. Intern. Med. 2008 , 263: 142-152. [16] Kenyon, C., Chang, J., Gensch, E., Rudner, A., Tabtiang, R. A C.", + "the DNA level leads to changes in gross phenotype, we must now look downstream at changes in gene expression associ - ated with genetic variation, aging, and ARD. Comparison With Laboratory Models of Aging Laboratory models typically used to study aging, such as Caenorhabditis elegans (nematode worm) and Mus musculus (mice), have drastically shorter life spans than our own (~3 wk [ 51] and ~3 y [ 52], respectively, vs a 122 y maxi - mum for humans thus far; [ 53]). In some respects, these", + "ing studies on invertebrate models of aging, long-lived mam-mals, transgenic mouse strains, and interventional studies, have led to the identification of evolutionarily conserved path- ways involved in life span regulation, as well as common de- nominators of aging in different organisms. 4 In this review, the pathophysiological roles of these aging mechanisms, including oxidative stress, mitochondrial dysfunction, impaired resis-", + "chain triglyceride oil on life span of genetically heterogeneous mice. J. Gerontol. A. Biol. Sci. Med. Sci. 68, 616 (2013). [PubMed: 22451473] 24. Yuan R, Peters LL & Paigen B Mice as a mammalian model for research on the genetics of aging. ILAR J. Natl. Res. Counc. Inst. Lab. Anim. Resour. 52, 415 (2011). 25. Saul MC, Philip VM, Reinholdt LG & Chesler EJ High-diversity mouse populations for complex traits. Trends Genet. 35, 501514 (2019). [PubMed: 31133439]", + "lowing the discovery of genes and pathways involved inanimal lifespan extension, human research has focusedon the corresponding candidate human genes withgenetic, genomic and epigenetic studies into ageingand longevity. The designs of these studies differwith respect to the selection of naturally occurringphenotypes and the study populations, which includepopulation-based, patient-based, family-based andexposure-based cohorts. Studies into human age-related disease phenotypes", + "Animal studies as stalking horses for human biogerontology. For the most part, studies on the biology of aging are as difficult and imprac-tical in humans as are studies of health insurance in rodents. It is fairlyCopyright National Academy of Sciences. All rights reserved.Cells and Surveys: Should Biological Measures Be Included in Social Science Research? http://www.nap.edu/catalog/9995.html", + "review of the evidence for genotype-dependent eects on lifespan. Ageing Res. Rev. 11, 254270. doi: 10.1016/j.arr.2011.12.006 Turturro, A., Witt, W. W., Lewis, S., Hass, B. S., Lipman, R. D., and Hart, R. W. (1999). Growth curves and survival characteristics of the animals used in the biomarkers of aging program. J. Gerontol. Ser. Biol. Sci. Med. Sci 54, B492B501. doi: 10.1093/gerona/54.11.b492 Vertti-Quintero, N., Berger, S., Solvas, X. C. I, Statzer, C., Annis, J., Ruppen," + ], + "answer": "Scientists study the genetics of aging in animals using a variety of approaches and model organisms. Here are some key methods and models mentioned in the context: 1. **Animal Model Systems**: Rodents, such as rats and mice, are indispensable for molecular biological studies on aging due to their practical advantages and similarities in aging processes to humans [1]. 2. **Tissue-Specific Studies**: Recent genomic studies have been performed on specialized mammalian tissues, both post-mitotic (e.g., heart, nervous system) and mitotic (e.g., liver), to understand the tissue-specific effects of aging. These studies also explore how caloric restriction affects age-related transcriptional changes, which are tissue- or species-specific [2]. 3. **Laboratory Models**: Common laboratory models for studying aging include Caenorhabditis elegans (nematode worm) and Mus musculus (mice), which have shorter lifespans than humans, allowing for more rapid observation of aging processes [5]. 4. **Transgenic Mouse Strains and Interventional Studies**: These studies have identified evolutionarily conserved pathways involved in lifespan regulation and common denominators of aging across different organisms [6]. These methods and models help scientists uncover the genetic and molecular mechanisms underlying aging, providing insights that can be applied to understanding human aging and developing potential interventions.", + "question": "How do scientists study the genetics of aging in animals?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_5 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_5 new file mode 100644 index 0000000..4e41bb9 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_5 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2009 - The Human Ageing Genomic Resources online.pdf", + "2012 - Genome-Environment Interactions That Modulate.pdf", + "2001 - Demography in the age of genomics.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2010 - A Meta-analysis of Four Genome-Wide Association Studies.pdf", + "2013 - Gene expression changes with age in skin.pdf", + "2017 - Genome-wide meta-analysis associates HLA.pdf", + "2012 - Genome-Wide RNAi Longevity Screens in Caenorhabditis elegans.pdf", + "2014 - Whole-Genome Sequencing of the World?s Oldest People.pdf", + "2017 - Four Genome-Wide Association Studies Identify New.pdf" + ], + "extraction_id": [ + "52c67b46-63f2-54ae-a78e-e9d54a55f6e4", + "a01ca925-4ccf-5863-a162-7bd4c754fe89", + "0e71cb8b-4647-56bb-9b5f-d14d0ea2ff50", + "ead14808-bfb7-5e32-9830-28efaae71151", + "8bc54e5b-f45f-54f9-9591-1e26dd80b50d", + "18e9f96b-9af8-59bf-9323-084df9a4f9b6", + "c302507d-508e-5093-a877-1cec20f8440e", + "f160f818-03bf-5b4e-b1f4-bfbd3b0bfb99", + "eed176e2-226d-5638-b10c-4813cfbbd213", + "52557433-1cf1-5441-b44d-361963a61eca" + ], + "document_id": [ + "e43cd3b6-ad8e-5422-ba7c-ceb6e66cc529", + "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "0f07fa43-feb6-5656-b7e7-b8faa86f5623", + "62b635c3-040e-512a-b016-6ef295308a1e", + "8e452186-a71c-5b62-81b2-7681c87c8e1d", + "5c121bbb-57b8-51cc-8461-effa1bfd87b9", + "3a565ba9-ee5b-5596-b870-ce8c055cb1f1", + "7589fec2-e893-5a4d-9f0c-09abb35858ab", + "d2a5ec28-873a-5ff3-9cf4-dbec3b52dd21", + "c10653f6-b3d7-5b92-9271-ab8fcc7905a7" + ], + "id": [ + "chatcmpl-AIFgxEMof4wOrO9UyQT5bEPH8q8fk", + "9defe0af-80a1-56da-90df-551fd55baa13", + "4d6876c5-9226-587c-8d3e-d4957ee42dba", + "2f28f34e-bf5e-57d6-8a8c-dd946f574906", + "b3e21ac9-8df8-5119-a769-a9da82db78da", + "c2234f77-2268-57d0-a227-e931fc4802c1", + "cc78a209-081f-5e0c-877a-36b85d5105a6", + "726417dd-f626-5197-966d-6a6ad25ff718", + "300f0303-caec-52b9-852b-8e67cec5d326", + "025a94a9-595e-56f6-8c03-89ccea15a22c", + "68e705e1-54a1-578a-98ee-0c76b02ccf79" + ], + "contexts": [ + "genes analyzed for their possible association with human lon-gevity (http://genomics.senescence.info/genes/longevity.html).All longevity association studies in humans we could find by thetime of the latest update were added to this list. These includestudies reporting negative results, which we see as essentialsince many genes display population-specific associations withlongevity. Fig. 1 From the main page of the Human Ageing", + "genes (http://genomics.senescence.info/genes/), more than700 genes have been identified that regulate lifespan inmodel organisms (de Magalha es et al., 2009a). Many ofthese genes and their associated pathwayssuch as theinsulin/IGF1/GH pathwayhave been shown to affect lon-gevity across different model organisms (Kenyon, 2010).Therefore, at least some mechanisms of aging are evolu-tionarily conserved and may have potential therapeuticapplications (Baur et al., 2006). For example, evidencesuggests the use of", + "Exceptional Longevity One approach to identifying genes associated with low mortality is to examine the genes of those who survive to the oldest ages. Several studieshave examined gene frequencies among centenarians or nonagenariansand compared them with frequencies at younger ages. Since changes ingene frequencies are more rapid when mortality rates are high, cross-sectional comparisons must be adjusted for differences in mortality amongcohorts.", + "informed by age-related disease identifies loci for exceptional human longevity. Li H, editor. PLoS Genet. 2015. https://doi.org/10.1371/journal.pgen. 15. Polderman TJC, Benyamin B, de Leeuw CA, Sullivan PF, van Bochoven A, Visscher PM, etal. Meta-analysis of the heritability of human traits based on fifty years of twin studies. Nat Genet. 2015;47:7029. 16. Cellerino A, Ori A.What have we learned on aging from omics studies? Semin Cell Dev Biol. 2017;70:17789.", + "GENOME-WIDE ASSOCIATION STUDY OF LONGEVITY 479 INCREASES in longevity of the general population world - wide are an unprecedented phenomenon with significant health and social impact. Although environmental factors have led to an increase in life span, there is ample evidence that genetic factors are involved in extreme longevity both in humans (17) and in other organisms (8). The protective genetic factors that lead to longevity are likely to involve", + "expression of certain genes have an effect upon longevity. Although similar aging processes are likely to operateacross multiple species [30], it has been much more diffi-cult to identify longevity candidate genes in human studies[30]. A key question in human aging is to what extent asignature of aging may be detectable across tissues. Until now there has been a lack of large transcriptional profiles from the same human individuals in multiple tissues. TheMuTHER study provides ins ight into the human aging", + "4. Joshi, P. K. et al. Variants near CHRNA3/5 and APOE have age- and sex- related effects on human lifespan. Nat. Commun. 7, 11174 (2016). 5. Pilling, L. C. et al. Human longevity is in uenced by many genetic variants: evidence from 75,000 UK Biobank participants. Aging 8, 547560 (2016). 6. Deelen, J. et al. Genome-wide association meta-analysis of human longevity identi es a novel locus conferring survival beyond 90 years of age. Hum. Mol. Genet. 23, 4420 4432 (2014).", + "79-91. [97] Smith, E.D.; Kennedy, B.K.; Kaeberlein, M. Genome-wide identification of conserved longevity genes in yeast and worms . Mech. Ageing Dev. , 2007 , 128(1), 106-11. [98] Chen, D.; Pan, K.Z.; Palter, J.E.; Kapahi, P. Longevity determined by developmental arrest genes in Caenorhabditis elegans. Aging Cell, 2007 , 6(4), 525-33. [99] Curran, S.P.; Ruvkun, G. Lifespan regulation by evolutionarily conserved genes essential for viability . PLoS Genet. , 2007 , 3(4), e56.", + "9. vB Hjelmborg J, Iachine I, Skytthe A, Vaupel JW, McGue M, et al. (2006) Genetic influence on human lifespan and longevity. Hum Genet 119: 312321.doi:10.1007/s00439-006-0144-y. 10. Sebastiani P, Perls TT (2012) The genetics of extreme longevity: lessons from the new England centenarian study. Front Genet 3: 277. doi:10.3389/fgene.2012.00277.11. Perls TT, Wilmoth J, Levenson R, Drinkwater M, Cohen M, et al. (2002) Life-", + "39. Fortney K, Dobriban E, Garagnani P, etal. Genome-wide scan informed by age-related disease identifies loci for exceptional human longevity. PLoS Genet. 2015;11:e1005728. doi:10.1371/journal.pgen.1005728 40. Beekman M, Nederstigt C, Suchiman HE, et al. Genome-wide asso- ciation study (GWAS)-identified disease risk alleles do not compromise human longevity. Proc Natl Acad Sci U S A. 2010;107:1804618049. doi:10.1073/pnas.1003540107" + ], + "answer": "Yes, specific genes have been linked to longer lifespans. Research has identified more than 700 genes that regulate lifespan in model organisms, with many of these genes and their associated pathways, such as the insulin/IGF1/GH pathway, shown to affect longevity across different model organisms [2]. Additionally, variants near genes such as CHRNA3/5 and APOE have been found to have age- and sex-related effects on human lifespan [7]. Genome-wide association studies have also identified loci associated with exceptional human longevity [10].", + "question": "Are there specific genes that have been linked to longer lifespans?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_6 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_6 new file mode 100644 index 0000000..ad08e52 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_6 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2018 - Sex Differences in Aging Genomic Instability.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2008 - Telomeres and Aging.pdf", + "2018 - Genomic Instabilities, Cellular Senescence, and Aging In Vitro, In Vivo and Aging-Like Human Syndromes.pdf", + "2018 - Repetitive Fragile Sites Centromere Satellite DNA.pdf", + "2016 - Genome Integrity in Aging.pdf", + "2018 - Genomic Instabilities, Cellular Senescence, and Aging In Vitro, In Vivo and Aging-Like Human Syndromes.pdf", + "2018 - Genomic Instabilities, Cellular Senescence, and Aging In Vitro, In Vivo and Aging-Like Human Syndromes.pdf", + "2017 - The Aging Cardiovascular System.pdf" + ], + "extraction_id": [ + "396708f1-aa0a-571e-a8d3-7cb8404e9502", + "41b98643-1948-519b-8b27-ab0fa4041048", + "d4afa45a-5efa-577b-822e-7a82c2f6508d", + "55fd2e43-f58e-5d89-8730-7d82d3b6c44f", + "016d8de2-949f-511e-a9e1-d2d5fd2bede5", + "3b0cb0ab-421d-54d7-9816-c6a2e6f1ac68", + "5179130e-5fa6-5979-ba68-270e546e43d7", + "9fafad4c-f208-53e0-b2ac-f10569429a5e", + "016d8de2-949f-511e-a9e1-d2d5fd2bede5", + "82798504-5de9-513c-b3df-09968387cd42" + ], + "document_id": [ + "8cfb5529-7f0c-58fc-b6e4-b3ee800fb72f", + "62b635c3-040e-512a-b016-6ef295308a1e", + "62b635c3-040e-512a-b016-6ef295308a1e", + "61d9c326-d36e-55c1-a891-335dc943e70f", + "7de8d462-8a3c-5625-8cbb-374f3bb46425", + "262df0d6-ad68-544a-88ed-b4568f305858", + "85d5fcbb-5385-5a01-8139-d11fc8b1fe3a", + "7de8d462-8a3c-5625-8cbb-374f3bb46425", + "7de8d462-8a3c-5625-8cbb-374f3bb46425", + "d3ff8471-986b-5fa0-b9c4-96eaaa8fce7c" + ], + "id": [ + "chatcmpl-AIFh26X5nul0obtiAeqSkHmHNgJoq", + "53508a9e-d064-58a3-a4f9-0785470a1462", + "b532d055-ab02-5326-8eb4-67e7277a92b8", + "65fb74aa-f3c3-5c80-919f-329169db982f", + "ab6a6bda-490d-5b7e-a715-3b9b4f89243f", + "80a2162f-6208-5f97-a646-e8803d501f4e", + "f181e6da-58b6-5f26-87a2-355e25388673", + "6d0cccc5-3ed7-507e-9f7a-6035badacc00", + "72b978c7-44fc-530d-a1d2-eaffaf2c8782", + "0faa4fb9-efa7-5e92-8fe4-5e28c51dbee4", + "b1383516-a23e-5048-9cf3-944b5142e16b" + ], + "contexts": [ + "Telomeres are specialized structures that protect the ends of linear chromosomes. They shorten during aging due to the unidirectional activity of DNA polymerase, which leaves a section of DNA unrepli-cated on the lagging strand. Telomeres also are subject to shortening by genotoxic stress, such as oxidative damage (33). Among many eukaryotes, the enzyme telomerase maintains telomere length; but telomerase activity varies over the lifespan and between cell types, tissues, and species (34). In most human", + "that shorten their length with progressing age. This shortening of telomeres is the result of the absence of the activity of an enzyme called telomerase, and in turn it induces several processes, such as apoptosis, senescence, or oncogenic transforma- tion of somatic cells, affecting the health and lifespan of an individual [42]. Human telomere shortening has been mostly studied in leukocytes and linked not only to ageing and life expectancy [43] but also to age-related diseases, including cardio-", + "nization may directly affect telomere attrition, resulting in accelerated replicative senescence and progeroid phenotypes [180]. Telomeres are regions constituted by tandem repeats of non-coding DNA sequences 5-(TTAGGG)n-3 and a protein complex called shelterin, bound to them. This structure ensures the stability of the genome and protects the chromosomes from a wrong action of the DNA repair machinery [184] by allowing the formation of a chromatin loop called T-Loop [185].", + "Telomeres play a central role in cell fate and aging by adjusting the cellular response to stress and growth stimulation on thebasis of previous cell divisions and DNA damage. At least a few hundred nucleotides of telomere repeats must cap eachchromosome end to avoid activation of DNA repair pathways. Repair of critically short or uncapped telomeres by telomeraseor recombination is limited in most somatic cells and apoptosis or cellular senescence is triggered when too many uncappedtelomeres accumulate.", + "ing (84). This process is believed to be the trigger for the aging process, according to the telomere theory (11, 85, 86). It is further supported by Bodnar etal. who proved that telomere elongation caused by ectopic expression of telomerase avoids the senescence phenotype (87). His work relied on one of the earliest studies linking telomere shortening to aging which was performed", + "telomeres, the repetitive sequence at the end of linear chromosomes, has garnered much attention for its relation to aging. Telomere repeats serve as an internal clock for cycling cells because each round of replication results in the loss of telomeric DNA in the absence of active telomerase (reviewed in [66]). Eventually, this loss over cellular generations culminates in telomere crisis and a permanent state of", + "and consequently lose telomeric sequences, thereby limiting the number of cell cycles, which is important for preventing the onset of cancer. Cells perceive critically short telomeres as persistentDNA damage. This activates the DNA damage responses, including cell cycle checkpoints, which ultimately leads to a permanent cell cycle arrest (cellular senescence). Senescence protects from cancer but contributes to the aging process (37).", + "When the telomeres shorten, this loop is no longer able to form and in turn, the epigenetic regulation is changed to activation of the TPE-OLD genes. This happens before the telomeres reach the critical length that causes activation of DDR, thus leading to another earlier possible effect of telomere shortening on aging (138, 139). Interestingly, a following study by Kim etal. showed that one of the TPE-OLD sensitive genes is hTERT, the core reverse transcriptase component of telomerase (140). This is", + "to maintain proliferation potential (94). Cells with mutated telomerase exhibited irregular morphology and short telomeres, but these changes did not cause deadly damage and determinate senescence (95). One hypothesis connects aging to telomere erosion through the transcription of subtelomeric genes. Genes located in subtelomeric regions are affected by transcriptional silencing which was found to change in an age-related manner. Kim et al. (96) found that silencing of genes in subtelomeric", + "evidence implicates telomere shortening in cellularsenescence. Telomeres consist of repetitive nucleotides e q u e n c e s( T T A G G G )a tt h ee n d so fm a m m a l i a nc h r o -mosomes, that preserve chromosome stability andintegrity by preventing deterioration or fusion withneighboring chromosomes (76) (Central Illustration ).JACC VOL. 69, NO. 15, 2017 Paneni et al . APRIL 18, 2017:1952 67 The Aging Cardiovascular System1957" + ], + "answer": "Telomeres affect the aging process in several ways: 1. **Telomere Shortening**: Telomeres are specialized structures that protect the ends of linear chromosomes. They shorten during aging due to the unidirectional activity of DNA polymerase, which leaves a section of DNA unreplicated on the lagging strand [1]. This shortening is a natural part of the aging process and is exacerbated by genotoxic stress, such as oxidative damage [1]. 2. **Cellular Senescence and Apoptosis**: The shortening of telomeres can induce processes such as apoptosis (programmed cell death) and cellular senescence (a state where cells stop dividing), which affect the health and lifespan of an individual [2]. When telomeres become critically short, they trigger a DNA damage response, leading to cellular senescence or apoptosis [4]. 3. **Genome Stability**: Telomeres ensure the stability of the genome and protect chromosomes from incorrect actions by the DNA repair machinery [3]. When telomeres are too short, they can no longer form protective structures, leading to genome instability and potentially contributing to aging [8]. 4. **Cancer Prevention**: Short telomeres limit the number of cell cycles, which is important for preventing the onset of cancer. However, this also contributes to the aging process as cells enter a state of permanent cell cycle arrest (senescence) [7]. 5. **Telomerase Activity**: The enzyme telomerase can maintain telomere length, but its activity varies over the lifespan and between cell types, tissues, and species [1]. In most human somatic cells, telomerase activity is limited, which contributes to telomere shortening and aging [4]. Overall, telomere shortening acts as a biological clock that limits cellular replication, contributing to aging and age-related diseases [6].", + "question": "How do telomeres affect the aging process?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_7 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_7 new file mode 100644 index 0000000..7050781 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_7 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2007 - Caloric restriction and genomic stability.pdf", + "2018 - Nuclear Genomic Instability.pdf", + "2018 - Nuclear Genomic Instability.pdf", + "2018 - Nuclear Genomic Instability.pdf", + "2018 - Nuclear Genomic Instability.pdf", + "2017 - Mutation and catastrophe in the aging genome.pdf", + "2007 - Decline of nucleotide excision repair capacity in aging.pdf", + "2005 - DNA Repair, Genome Stability.pdf", + "2023 - Genomic Instability Evolutionary Footprints on Human Health.pdf", + "2005 - DNA Repair, Genome Stability.pdf" + ], + "extraction_id": [ + "a563be97-fd42-50ba-8a26-3e1ca3b738db", + "44047f31-85e4-587c-ba58-8c3494fb7d52", + "e3e52327-4a23-5003-b418-dafdcdcae82c", + "b934a2a9-a672-5d65-9d0d-bbc36652a148", + "2b406c50-28e1-5b8c-a39d-a26db15f8aaa", + "eb91e436-a1bb-5d10-b648-07224b9e5bff", + "a0e59df7-6a34-5f03-af2e-82bdc0edacb9", + "5ea2fb27-ddd7-50b4-b318-39ca71f1c7e2", + "57e201b2-a357-5cff-9555-49955299669e", + "67128b6e-9bd6-53fe-b1e7-d0721db8619d" + ], + "document_id": [ + "76c08863-1522-519b-8da6-65a872418fee", + "54d28a91-8db6-56b1-baaa-b67274c93a36", + "54d28a91-8db6-56b1-baaa-b67274c93a36", + "54d28a91-8db6-56b1-baaa-b67274c93a36", + "54d28a91-8db6-56b1-baaa-b67274c93a36", + "7ae205a2-e002-5e8b-bbf1-ea96ab599b37", + "c9bb2ba2-a001-5c1b-8be8-d1c184924362", + "e658e73b-2494-5fa3-ae39-9f4933bc037b", + "59dec4a5-f80a-5a82-b55a-b6b1b33b907f", + "e658e73b-2494-5fa3-ae39-9f4933bc037b" + ], + "id": [ + "chatcmpl-AIFhETjzplVDZqcInKYA6bobssz1r", + "566bd0c9-262e-543e-8934-1af5fa9edef5", + "b8c3720d-f697-5d2f-9728-49b7489d6509", + "9180d1c5-31b6-533e-bf2e-4b367dc2097d", + "ca253ce9-4661-5ca2-bf17-3a86ef3eff1d", + "494f865d-a7b6-5978-9b02-d5e628952a9d", + "a1370bf9-13f2-5c98-9d9d-9dfead21ebd7", + "8d2bc107-4d94-5dd8-8f67-b593aecc0478", + "4db748ed-7063-50e5-b42c-cb6fa3ecd9a2", + "4521b426-a67e-51e4-bc63-b6da5fab60cf", + "4c627903-8a25-5db0-8a60-1850a924a27b" + ], + "contexts": [ + "Effect of age on DNA repair Research over the past decades suggest that many steps in DNA metabolism are altered with age in a variety of tissues and animal models (56,57). The relation of DNArepair to aging has been studied by measuring the ability of cells from organisms of various life spans to repair DNA damage and by experiments that have comparedthe ability of cells from young and old organisms to repair DNA damage. Interest was peaked by the original", + "BI87CH14_Niedernhofer ARI 18 May 2018 15:1 SUMMARY POINTS 1. Evolutionarily conserved DNA repair pathways maintain the integrity and stability of the nuclear genome. Impairment of DNA repair mechanisms results in accelerated agingand/or cancer. 2. Evidence in humans and model organisms supports the conclusions that with age (a) endogenous sources of genotoxins increase, ( b) DNA repair capacity declines, and (c) levels of DNA damage and mutations increase.", + "Several lines of evidence suggest that DNA repair capacity might decrease with age. However,it should be noted that measuring DNA repair in tissues is challenging and that the validity ofsurrogate markers of repair capacity is not well established. For example, a reduction in expression of DNA repair genes/proteins is not proven to impact DNA repair. Frequently, the reduction in", + "improved DNA repair. Finally, there should be a plausible mechanism by which DNA damage can drive aging. Here, we review the evidence currently supporting each of these predictions. EVIDENCE THAT DNA DAMAGE INCREASES WITH AGE Sources of Damage Increase with Age The free radical theory of aging posits that aging is caused primarily by oxidative damage in- curred by ROS that chemically modify critical cellular biomolecules (13). This theory has evolved", + "All rights reservedKeywords DNA damage, aging, mutations, senescence, DNA damage response, DNA repair Abstract The nuclear genome decays as organisms age. Numerous studies demon- strate that the burden of several classes of DNA lesions is greater in older mammals than in young mammals. More challenging is proving this is acause rather than a consequence of aging. The DNA damage theory of aging, which argues that genomic instability plays a causal role in aging,", + "repaired; otherwise the genome would soon become saturated with damage and life would cease. There is some evidence that DNA damage accumulates with age in some tissues ( Maslov et al., 2013 ), but the exact nature of the damage remains unclear. Indeed, even these low levels of spontaneous DNA damage may represent a steady state due to continu- ous repair and induction of new damage. However, DNA damage can cause certain aging phenotypes by activating cellular responses, such", + "36:1049-1062. 66. Hasty P, Vijg J: Accelerating aging by mouse reverse genetics: a rational approach to understanding longevity. Aging Cell 2004, 3:55-65. 67. Bohr VA: Deficient DNA repair in the human progeroid dis- order, Werner syndrome. Mutat Res 2005, 577:252-259. 68. Nouspikel T, Hanawalt PC: DNA repair in term inally differenti- ated cells. DNA Repair 2002, 1:59-75. 69. Nouspikel T, Hanawalt PC: When parsimony backfires: neglect- ing DNA repair may doom neurons in Alzheimer's disease.", + "DNA repair. In the latterdifficult to arrive at a strict, experimentally useful defini-context, most premature aging syndromes are causedtion of aging. Factors implicated in organismal declineby mutations in genes encoding proteins involved inin genetic models might not play a role in the normalDNA repair ( Karanjawala and Lieber, 2004 ). Accumula-aging processes. A related difficulty is that prematuretion of mutations in critical genes may be one generalaging models fail to recapitulate all aspects of", + "escape the repair process and accumulate in the genome, impacting several processes and aging [67,145147]. There is little evidence of association between DNA repair improvement and life- time expansion [ 148,149], thus, indicating that such mechanism seems to have evolved to maintain DNA stabilityand therefore healthonly until reproductive age, without any regard for the fate of the individual in old age, both in terms of quality and length of", + "with age, and DNA repairtween different tissues. These differences likely reflectdefects can cause phenotypes resembling prematurefunctional characteristics of those tissues, such as mi-aging. We discuss how cellular DNA damage re-totic rate, transcriptional activity, metabolism, and thesponses may contribute to manifestations of aging.action of specific DNA repair systems.We review Sir2, a factor linking genomic stability, me-Reactive Oxygen Species: An Important Sourcetabolism, and aging. We conclude" + ], + "answer": "DNA repair plays a significant role in aging by maintaining the integrity and stability of the nuclear genome. Impairment of DNA repair mechanisms can result in accelerated aging and/or cancer [2]. As organisms age, endogenous sources of genotoxins increase, DNA repair capacity declines, and levels of DNA damage and mutations increase [2]. This accumulation of DNA damage is associated with aging phenotypes, as DNA damage can activate cellular responses that contribute to aging [6]. The DNA damage theory of aging suggests that genomic instability, caused by accumulated DNA damage, plays a causal role in aging [5]. Additionally, the burden of DNA lesions is greater in older mammals compared to younger ones, indicating that DNA repair is crucial for mitigating the effects of aging [5].", + "question": "What role does DNA repair play in aging?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_8 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_8 new file mode 100644 index 0000000..26e2861 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_8 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2001 - Demography in the age of genomics.pdf", + "2012 - Genome-Environment Interactions That Modulate.pdf", + "2020 - Protecting the Aging Genome.pdf", + "2012 - Peroxiredoxins, gerontogenes linking.pdf", + "2019 - Genomics of 1 million parent lifespans.pdf", + "2021 - Epigenetics of Aging and Aging-Associated Diseases.pdf", + "2012 - Genome-Environment Interactions That Modulate.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2012 - Aging, Rejuvenation, and Epigenetic.pdf", + "2016 - Progress on the role of DNA methylation in aging.pdf" + ], + "extraction_id": [ + "e3014138-3d5b-58bc-a1a5-5ac6f04cac1c", + "68b12e10-d643-570a-aa63-eda62de81928", + "e5067ce2-69a6-5433-bed4-b95daeaa691e", + "38ebdc6a-8e8e-5472-a3ed-9a0f06591474", + "ff0adc7c-70ff-5b14-ba7d-a9dda60fac80", + "e2bc9b8e-2349-509b-a148-fbd86f0455f4", + "8650652a-1765-563b-a98e-2e9336bcf29a", + "822571e2-b05d-5e17-9eaa-431151851111", + "b9f038dd-97af-51ea-bb32-d73bf66c3dcb", + "8829c724-73ff-582b-ab94-c9f1a906cfd5" + ], + "document_id": [ + "0f07fa43-feb6-5656-b7e7-b8faa86f5623", + "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "bb774030-2570-5596-b2ab-b8f57ff81086", + "2eaad7ba-b6ae-5382-ba79-84609080b53e", + "f68b939c-847b-5eac-8926-24713ae43478", + "70945353-4808-539a-80f9-5632c27913e5", + "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "62b635c3-040e-512a-b016-6ef295308a1e", + "bde26feb-f423-51b0-89ec-6f079bfc8b17", + "e4cdc02f-4415-5638-aab8-f848b4d64a22" + ], + "id": [ + "chatcmpl-AIFhM7HonwMIv1KCdMHKw9gGzAYlV", + "9da7c5dc-0deb-577c-bb22-83f987bd76dd", + "78733c6a-d870-5154-9128-eb66291fa967", + "3c636897-c47e-505d-9203-306124b73e0e", + "43cba086-7f03-529f-bcd0-6483202bf3c7", + "de7c30f6-cce9-563d-83f4-809f2aab781b", + "4eb34c07-921b-55bb-98eb-ff013bb2ace0", + "f20fd517-5f05-53ca-93a5-916bc891ad92", + "265126e3-2a4d-518f-93cf-21a201747eef", + "afc304d1-dd43-55ec-811d-27ca27fc4e5d", + "1c77b8dc-2fd6-5e3d-9cf0-5585e7c9fb57" + ], + "contexts": [ + "raises the possibility of therapies to slow aging. Therefore the discoveryof a gerontogene with even very rare mutations that increased longevitywould cause speculation about future trends in mortality. However, thediscovery of such a gene would be relevant only to long-term (and, there-fore, very speculative) projections. Prospective Epidemiologic Surveys that Include Genetic Information Some epidemiologic cohort studies of populations have collected", + "need to develop approaches and therapies targeting theaging process and age-related diseases (Butler et al.,2008). Delaying the process of aging, even slightly,would have profound social, medical and economic ben-efits (Olshansky et al., 2006; Butler et al., 2008). Forexample, slowing aging by a mere 7 years would cutmortality of age-related diseases by half at every age.Therefore, the potential benefits from research on thebasic biology and genetics of aging are unparalleled interms of improving quality", + "Interestingly, when senescent cells are abolished either through genetic manipulation or via senolytic drugs, biological aging is signicantly halted in mice [ 53,54]. Therefore, trials are now under way to test the ability of senolytics to postpone age-associated pathologies in humans [ 55]. Notably, multi- ple drugs are being pursued that either directly or indirectly impact DNA repair or the consequenceof DNA damage. Future Prospects: Developing Interventions through DNA Repair", + "and potentially important genetic markers for slow aging have been found in humans (Suh et al. 2008). Elucidating the function of such genes is believed to enable decipher- ing the core of the aging process, answer to what extentthe process is conserved, and pave the way for therapeutic interventions of age-related maladies, including cancers, neurodegeneration, and metabolic syndrome (Guarente 2011). The identity of the virtual gerontogenes so far discov-", + "discover specific genes that directly influence how quickly people age, beyond diseases. If such genes exist, their effects were too small to be detected in this study. The next step will be to expand the study to include more participants, which will hopefully pinpoint further genomic regions and help disentangle the biology of ageing and disease. DOI: https://doi.org/10.7554/eLife.39856.002", + "using bulk mRNA or even analyzing single cells (scRNA-seq). In addition, advances in molecular biology and cell culture approaches (for instance Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/Cas9) will be benecial in clarifying aging-processes across species. An improved understanding of epigenetic mechanisms affecting longevity will be deciding crucial step towards the identication of new potential therapeutic targets. In", + "century. Manipulation of aging-related genes by diet,lifestyle, and pharmaceuticals could dramatically im-prove human health and could be used to develop drugsagainst age-related diseases such as cancer, heart dis-ease, type 2 diabetes, obesity, and neurodegenerativediseases. The hundreds of aging-related genes and genesrelated to CR already identified offer enormous oppor-tunities for target discovery (Fig. 2). Although aging-related genes cannot be modified in humans, under-standing how these can be", + "5. Goldman DP, etal. Substantial health and economic returns from delayed aging may warrant a new focus for medical research. Health Aff (Millwood). 2013;32(10):1698705. 6. Esplin ED, Oei L, Snyder MP.Personalized sequencing and the future of medicine: discov- ery, diagnosis and defeat of disease. Pharmacogenomics. 2014;15(14):177190. 7. Marian AJ.Clinical applications of molecular genetic discoveries. Transl Res. 2016;168:614.", + "a medical intervention), without changing the fundamental rateof organismal aging. Nevertheless, it does seem that manyso-called longevity genes, as well as dietary restriction, appear to extend not only life span, but also health span (Kauffman et al., 2010; Luo et al., 2010 ). In that regard, it does appear that it is possible to experimentally slow the rate of aging. Still, in each case, aging does continue on as if there is some", + "genetic modification. Currently, emerging evidence suggeststhat certain interventions (e.g. CR, dietary supplementation andchemical drugs) can prevent age-related diseases and promote longevity, at least in part, through reversing the aberrant age- associated changes in DNA methylation, suggesting the greatpotential of DNA methylation in therapeutic strategies againstage-related diseases ( Figure 1B ).However, to further understand the roles of DNA methyla-" + ], + "answer": "Yes, genetic research can potentially lead to treatments that slow down aging. Several pieces of evidence from the context support this possibility: 1. The discovery of genetic markers for slow aging in humans suggests that understanding these genes could pave the way for therapeutic interventions for age-related maladies, including cancers, neurodegeneration, and metabolic syndrome [4]. 2. Research indicates that manipulating aging-related genes through various means, such as diet, lifestyle, and pharmaceuticals, could dramatically improve human health and lead to the development of drugs against age-related diseases [7]. 3. Advances in molecular biology, such as CRISPR/Cas9, are expected to clarify aging processes and identify new potential therapeutic targets, which could be crucial for developing treatments that slow aging [6]. 4. The use of senolytic drugs, which target senescent cells, has shown promise in halting biological aging in mice, and trials are underway to test their effectiveness in humans [3]. 5. There is a suggestion that interventions targeting DNA methylation and other genetic modifications could prevent age-related diseases and promote longevity, highlighting the potential of genetic research in developing therapeutic strategies against aging [10]. Overall, while the research is still ongoing and some findings are speculative, there is significant potential for genetic research to contribute to treatments that slow down the aging process.", + "question": "Can genetic research lead to treatments that slow down aging?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_9 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_9 new file mode 100644 index 0000000..799cb0c --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_9 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2018 - Sex Differences in Aging Genomic Instability.pdf", + "2017 - Independent impacts of aging.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2020 - Mitonuclear genomics and aging.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2020 - Transposable elements, circular RNAs and mitochondrial.pdf", + "2004 - Ageing, repetitive genomes and DNA.pdf", + "1999 - Molecular Biology of Aging.pdf", + "1998 - Neurodegeneration and Aging Role.pdf" + ], + "extraction_id": [ + "400784cf-bb7d-5bf8-b735-2142ebf7c953", + "1f0b6363-a045-53aa-a124-4cf89e61fc26", + "c8db1d28-f6c2-5896-95ec-bb01159ba483", + "385c192b-a416-5208-9615-20111ce782aa", + "381cc064-9970-5dcd-b959-c52a8e487fe7", + "ef9463cd-cf21-527f-ae4a-3df211c78435", + "5cbace8d-e538-5531-9311-ea9726ad2f15", + "9b7b2005-857b-5379-ba5c-3a8c6fd6c891", + "68c55aac-d8fa-5287-a420-2bb83a2c159e", + "21db434d-3cf5-5ff1-8257-0941ebe74822" + ], + "document_id": [ + "8cfb5529-7f0c-58fc-b6e4-b3ee800fb72f", + "d1d0b9ce-f827-5dfb-8e39-d87a9ca52f6d", + "62b635c3-040e-512a-b016-6ef295308a1e", + "62b635c3-040e-512a-b016-6ef295308a1e", + "e05fdc09-c8d8-5134-a1fd-bf07a1564981", + "62b635c3-040e-512a-b016-6ef295308a1e", + "7bebb41c-ac73-5917-91d3-4f59fbb3266a", + "1772d596-16a3-547a-9f76-2cb658e89746", + "6f122d3a-d8ca-598f-8767-c059a941cef3", + "694c44f3-6025-5a2c-9c72-d9c5f16c8b85" + ], + "id": [ + "chatcmpl-AIFhRRoCeBcURczVYxgfGYsJd8loh", + "9ec5a15f-8232-5e79-a78d-64eeba35747f", + "39019881-9b6d-5111-87ea-71c413bdf4ff", + "1a9d5c26-f606-5cb5-98ee-4120de3fbd1a", + "e0d41918-20fb-53f0-ac63-cd079c6dce1e", + "ef3be4e1-4cbc-5b61-a286-9b759df08cd9", + "4206977e-23df-5307-8d8a-cb2ed7b33595", + "7095cdbb-852e-541e-884b-a9e67c2c790c", + "512e09e3-c880-5bed-9071-bfa84d80a5a3", + "2e2de9a7-2e83-5f46-a4b7-08eddcd37baa", + "c35761f9-58e8-526b-94fd-96ed7599cb53" + ], + "contexts": [ + "In addition to nuclear DNA, mitochondrial DNA (mtDNA) also is affected by aging. Alterations in mitochondrial function and mito-chondrial-nuclear signaling occur during aging and have been linked to sex biases in aging and age-related diseases (28). Due to their role in energy production, mitochondria are at high risk of oxida-tive damage. Not surprisingly, accumulation of oxidative lesions is an important source of age-related mtDNA damage (29). In aged Wistar rats brains, DNA oxidation, as measured by", + "mitochondrial DNA mutations can reduce lifespan. Sci Rep. 2014;4:6569. 20. Ross JM, Stewart JB, Hagstrm E, Bren S, Mourier A, Coppotelli G, Freyer C, Lagouge M, Hoffer BJ, Olson L. Germline mitochondrial DNA mutations aggravate ageing and can impair brain development. Nature. 2013;501(7467):412 5. 21. Sondheimer N, Glatz CE, Tirone JE, Deardorff MA, Krieger AM, Hakonarson H. Neutral mitochondrial heteroplasmy and the influence of aging. Hum Mol Genet. 2011;20(8):1653 9.", + "102. Zhang R, Wang Y , Ye K, Picard M, Gu Z.Independent impacts of aging on mitochondrial DNA quantity and quality in humans. BMC Genomics. 2017;18:890. https://doi.org/10.1186/ s12864-017-4287-0. 103. Norddahl GL, et al. Accumulating mitochondrial DNA mutations drive premature hema- topoietic aging phenotypes distinct from physiological stem cell aging. Cell Stem Cell. 2011;8:499510. https://doi.org/10.1016/j.stem.2011.03.009.", + "other studies, the risk for metabolic disorders is highly associated with age-related diseases that affect lifespan, and interestingly these conditions exhibit mitochon- drial dysfunction [73]. Aging is a complex process as a time-dependent progressive loss of physiologi- cal integrity, leading to impaired function and increased vulnerability to death [74], and as we described above, aging is highly associated with mtDNA mutations; in", + "mt, and overall mitonuclear genomic compatibility. Given the uncertainty of mtDNA mutation accumulation in driving the natural aging process, it is plausible that mito - chondrial communication may be a significant evolutionarily conserved force that influences lifespan and/or healthspan. Acknowledgements Funding was provided by the American Federa- tion for Aging Research (AFAR), the National Institute on Aging (T32", + "abolic regulation through mitochondrial signaling. Am J Physiol Endocrinol Metab. 2014;306:E58191. 74. Zhang R, Wang Y , Ye K, Picard M, Gu Z.Independent impacts of aging on mitochondrial DNA quantity and quality in humans. BMC Genomics. 2017;18:890. 75. Hebert SL, Lanza IR, Nair KS.Mitochondrial DNA alterations and reduced mitochondrial function in aging. Mech Ageing Dev. 2010;131:45162. 76. Liu D, Li H, Lu J, Bai Y .Tissue-specific implications of mitochondrial alterations in aging.", + "Sun., N, Youle, R. J. and Finkel, T. (2016). The mitochondrial basis of aging. Mol. Cell 61, 654-666. doi:10.1016/j.molcel.2016.01.028 Symer, D. E., Connelly, C., Szak, S. T., Caputo, E. M., Cost, G. J., Parmigiani, G. and Boeke, J. D. (2002). Human L1 retrotransposition is associated with genetic instability in vivo. Cell110, 327-338. doi:10.1016/S0092-8674(02)00839-5 Szabo, L., Morey, R., Palpant, N. J., Wang, P. L., Afari, N., Jiang, C., Parast,", + "than ones that affect mitochondrial DNA12,57,58,71.So,this is an important reason for favouring nuclear DNA as the ultimate damage target in natural ageing. Nevertheless, it is conceivable that when mutations occur in the mitochondrial genome, mutant-protein production could increase the inefficiency of the mitochondrial respiratory chain, thereby resulting in more reactive oxygenspecies, which would then damage nuclear and mitochondrial DNA further.", + "generation animals as they grow older.Mitochondrial DNAGenetic instability outside of the nuclear genome mightalso contribute to aging (reviewed in Lee et al., 1997;Wallace et al., 1998). The mutation rate for mitochondrialDNA (mtDNA) is 10- to 20-fold greater than for nuclearDNA, and it is believed that mtDNA mutations may com-promise mitochondrial functions in different ways (Fig-ure 4). First, defects in electron transport and oxidativephosphorylation could lead to declines in ATP levelsand the NAD:NADH", + "of the human aging process(Corral-Debrinski et al., 1992; Soong et al., 1992;Wei etal., 1996b), and it has been demonstrated that certain pointmutations of mitochondrial DNA accumulate in the aginghuman brain (Zhang et al., 1993; Liu et al., 1997). However,thefunctionalimplicationsofthesendingsarecontroversial(Hayashietal.,1994).Tocomplicatethematterfurther,Takaiand co-workers discuss the possibility that the commonage-associated changes in human and mouse" + ], + "answer": "Mitochondrial DNA (mtDNA) influences aging through several mechanisms: 1. **Oxidative Damage**: Mitochondria are crucial for energy production and are highly susceptible to oxidative damage. The accumulation of oxidative lesions in mtDNA is a significant source of age-related damage [1]. 2. **Mutations and Lifespan**: Mutations in mtDNA can reduce lifespan. These mutations can aggravate aging and impair brain development, indicating a direct link between mtDNA mutations and the aging process [2]. 3. **Mitochondrial Dysfunction**: Aging is associated with mtDNA mutations, which contribute to mitochondrial dysfunction. This dysfunction is linked to age-related diseases and metabolic disorders, further influencing lifespan [4]. 4. **Genetic Instability**: The mutation rate for mtDNA is significantly higher than for nuclear DNA. These mutations can compromise mitochondrial functions, such as electron transport and oxidative phosphorylation, leading to declines in ATP levels and increased production of reactive oxygen species, which further damage both nuclear and mitochondrial DNA [9]. Overall, mtDNA influences aging by accumulating mutations and oxidative damage, leading to mitochondrial dysfunction and contributing to age-related physiological decline.", + "question": "How does mitochondrial DNA influence aging?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_1 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_1 new file mode 100644 index 0000000..edbcd4d --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_1 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2015 - Diabetes mellitus The epidemic of the century.pdf", + "2019 - How Recent Advances in Genomics Improve Precision Diagnosis.pdf", + "1994 - Isolation of the Human LIMTHomeodomain Gene Islet-1.pdf", + "2016 - Dissecting diabetes metabolic disease.pdf", + "2004 - Diabetes Genes a.pdf", + "1986 - Diabetes due to secretion of a structurally abnormal insulin.pdf", + "2004 - Diabetes Genes a.pdf", + "2001 - Genomic variation in pancreatic ion channel genes in Japanese type 2 diabetic patients.pdf", + "2011 - Genome-wide association studies (GWAS) impact.pdf", + "2019 - The clinical and genetic characteristics of permanent neonatal diabetes PNDM in the state of Qatar.pdf" + ], + "extraction_id": [ + "7d87ee73-2cc1-576c-8c0d-eb58479177b8", + "ebb48c39-f48a-5dce-a4dc-fcd34e6f17e1", + "b3b6cf5d-8cc3-5559-af5f-36780a303792", + "998a92ba-e7fc-5553-b629-7b5797fbfafe", + "ce0307a5-fae4-5b6d-9786-10619e49e075", + "ffdee7b8-ff45-57bf-973a-ca03ba595d23", + "508fd29c-5cf8-52bc-8bf2-5cebb1833cb1", + "2d845f58-a674-5e08-a68b-e8cfea8a9b84", + "a4721e1b-273b-5042-bc20-cf5602f0955a", + "d340209d-0773-5cfc-b0d1-e714f2ebe9f2" + ], + "document_id": [ + "e114dd28-fd39-56df-bdeb-8806474a6c10", + "42b64375-06af-5e09-9ae6-6bd0ecb782c7", + "c77b101b-9f78-5090-8be9-6f98d9380466", + "eee2f79d-e093-52fb-871a-798fd859235e", + "805ad0d5-6a3d-5e5f-b1ba-121e10c6daaa", + "5b884835-4cf4-5e80-a762-36582271e63e", + "805ad0d5-6a3d-5e5f-b1ba-121e10c6daaa", + "bff7795f-c109-5d7f-871d-ef1f4400a2c6", + "086c6869-7c70-5364-9269-760267fb458d", + "90268a68-3926-535e-952e-735e206eb3ab" + ], + "id": [ + "chatcmpl-AIFp3LBsYqGcLUvIYyVnKoJE32qw3", + "111f4875-7ccd-502f-bd5b-5d4ee88e5af6", + "3f7cc31a-dc57-568a-a3b0-602280c56428", + "3cb068b3-0761-5fd8-91bd-92744fa9ca9a", + "5d64fd7b-32d2-55ac-8586-c7ba9172a9ef", + "551f3603-6a4c-51e6-b568-7ae2fc9e7b33", + "2126e367-c1aa-56ae-aff4-0ba7e7070a22", + "487ca988-cce2-5b92-a05f-2e1cd11efea3", + "fa07c029-ad6e-5768-97da-a4bc5aa4e44f", + "644810c4-af08-5c60-b333-8c97ddadae8b", + "ac0df77e-c676-552b-b742-1591cb18fbbb" + ], + "contexts": [ + "Mutations that result in mutant insulin or the inability to convert proinsulin to insulin result in gl ucose intolerance in some of these cases. Genetic defects in the insulin receptor or in the signal transduction pathway of insulin have been demonstrated to result in hyperinsulinemia and modest hyperglycemia to severe diabetes[1]. Disease of the exocrine pancreas Damage of the cells of the pancreas due to diffused injury of the pancreas can cause diabetes. This damage", + "A, et al. Insulin gene mutations resulting in early-onset diabetes: marked differences in clinical presentation, metabolic status, and pathogenic effect through endoplasmic reticulum retention. Diabetes. 2010;59:653 61. 21. Steele AM, Shields BM, Wensley KJ, Colclough K, Ellard S, Hattersley AT. Prevalence of vascular complications among pa- tients with glucokinase mutations and prolonged, mild hyperglyce- mia. JAMA. 2014;311:279 86.22. Chakera AJ, Spyer G, Vincent N, Ellard S, Hattersley AT, Dunne FP.", + "presumed glucose toxicity (34). The finding that a mutation of a single nucleotide in the gene encoding the glucokinase enzyme can result in NIDDM lends credibility to the hypoth- esis that inherited defects in insulin production contribute to NIDDM (6). Increased insulin demand of obesity and insulin resistance is accompanied by enhanced insulin biosynthesis,", + "insulin synthesis and function while mutations in the insulin gene ( INS) obviously affect the key hormone made by pancreatic beta cells [62]. ATP synthesis defect (mitochondrial diabetes) and mutations in ATP- sensitive potassium channel subunits (channel-building Kir6.2 [po- tassium inwardly-rectifying channel, subfamily J, member 11;KCNJ11 ] and regulatory SUR1 [ATP-binding cassette transporter subfamily C member 8], ABCC8 ) all affect insulin secretion [63].", + "Insulin gene mutations Insulin is synthesized in 13-cells of the islets of Langerhans and is a central honnone that maintains glucose homeostasis. Insulin-deficient mice die shortly after birth due to severe hyperglycemia.53 All cell types of the endocrine pancreas are present in insulin deficient mice suggesting that insulin is not required for development and differentiation of the endocrine pancreas. 53 Naturally occurring mutations in the insulin gene that result in the", + "Theprevalenceofgeneticmutationsaffectingthestructure oftheinsulinmoleculeinthegeneralpopulationisunknown. Uptothepresent,onlythosepatientsmanifestingthemutant insulinsyndrome(5-8,36)withunusualorfamilialTypeII diabeteshavebeenscreenedanddiscovered.Thus,mutantin- sulinspecieswithnormalorrelativelywell-preservedbinding andbiologicalactivitycharacteristics,andthereforenormal metabolicclearances,areunlikelytobediscoveredbythisap- proachsincehyperinsulinemiawillbeabsentorsubtle.Future", + "at various steps, resulting in an impaired insulin action and potential development of extreme insulin resistant clinical conditions. Many mutations have been identified in the insulin receptor gene. These mutations may lead to: Decreased insulin receptor biosynthesis Premature chain termination in extracellular or intracellular domain Accelerated receptor degradation Defect in the receptor transport to plasma membranes Decreased insulin binding affinity Impaired tyrosine kinase activity", + "15. Steiner DF, Tager HS, Chan SJ, et al . Lessons learned from molecular biology of insulin-gene mutations. Diabetes Care 1990; 13: 600609. 16. Vionnet N, Stoffel M, Takeda J, et al . Nonsense mutation in the glucokinase gene causes early-onset non-insulin-dependent diabetes mellitus. Nature 1992; 356 : 721722. 17. Sakagashira S, Sanke T, Hanabusa T, et al . Missense mutation of amylin gene (S20G) in Japanese NIDDM patients. Diabetes 1996; 45: 12791281.", + "vating mutations in the gene encoding Kir6.2 alter fetal and postnatal growthand also cause neonatal diabetes. J Clin Endocrinol Metab 2006; 91(7): 27822788. 93. Stoy J, Edghill EL, Flanagan SE, et al. Insulin gene mutations as a cause of permanent neonatal diabetes. Proc Natl Acad Sci U S A 2007; 104(38): 1504015044. 94. Pulizzi N, Lyssenko V, Jonsson A, et al. Interaction between prenatal growth and high-risk genotypes in the devel-opment of type 2 diabetes. Diabetolo- gia2009; 52(5): 825829.", + "(Edghill et al., 2008; Garin et al., 2010; Stoy et al., 2007). Hyperglycemia occurs due to decreased insulin biosynthe-sis, in which most of the reported missense heterozygous mutations are expected to cause an abnormal proinsulin folding. An accumulation of the misfolded protein in the en-doplasmic reticulum (ER) consequently occurs, resulting in ER stress and betacell apoptosis (Liu, Hodish, Rhodes, & Arvan, 2007). Our identified de novo novel variant in INS is expected to result in aberrant proinsulin" + ], + "answer": "Genetic mutations in the insulin gene can significantly affect glucose metabolism in several ways: 1. Mutations that result in mutant insulin or the inability to convert proinsulin to insulin can lead to glucose intolerance. This is because the proper conversion and function of insulin are crucial for maintaining normal glucose levels in the blood [1]. 2. Insulin gene mutations can cause early-onset diabetes, with marked differences in clinical presentation and metabolic status. These mutations can lead to the retention of insulin in the endoplasmic reticulum, causing stress and potentially leading to beta-cell apoptosis, which reduces insulin production and secretion [2], [10]. 3. Insulin-deficient conditions, such as those caused by certain mutations, can lead to severe hyperglycemia, as insulin is a central hormone for maintaining glucose homeostasis. Without sufficient insulin, glucose cannot be effectively taken up by cells, leading to elevated blood glucose levels [5]. 4. Some mutations may result in abnormal proinsulin folding, leading to an accumulation of misfolded proteins in the endoplasmic reticulum. This accumulation can cause endoplasmic reticulum stress and beta-cell apoptosis, further impairing insulin production and secretion [10]. Overall, genetic mutations in the insulin gene can disrupt normal insulin synthesis and function, leading to impaired glucose metabolism and conditions such as diabetes.", + "question": "How do genetic mutations in the insulin gene affect glucose metabolism?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_10 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_10 new file mode 100644 index 0000000..4eb17e2 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_10 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2010 - The Genetics of Type 2 Diabetes.pdf", + "2010 - The Genetics of Type 2 Diabetes.pdf", + "2015 - Current and Best Practices of Genetic Testing for Maturity.pdf", + "2019 - How Recent Advances in Genomics Improve Precision Diagnosis.pdf", + "2008 - Glossary of Genetics Genomics Terms.pdf", + "2015 - Current and Best Practices of Genetic Testing for Maturity.pdf", + "2015 - Current and Best Practices of Genetic Testing for Maturity.pdf", + "2015 - Rare and Common Genetic Events in Type 2 Diabetes.pdf", + "2011 - Inherited destiny Genetics and gestational diabetes mellitus.pdf", + "2004 - Diabetes Genes a.pdf" + ], + "extraction_id": [ + "6c0f6484-fb94-5583-8c4a-f707983ff29f", + "6c0f6484-fb94-5583-8c4a-f707983ff29f", + "c5c209f5-e4dc-5eb2-a9f0-536a686efa96", + "7117f141-8841-5c11-ba1a-85039181b393", + "4668a316-6c87-5039-b55b-47fe0b8fbc71", + "3c09962c-226d-5271-a5c8-14f6327a079b", + "039d8c39-8fe3-5183-b952-3e8a25510b86", + "587c2476-a12a-5e32-b0cf-ada54ee4a1db", + "318e6c28-16d2-5d59-b047-cfdc6b2e7301", + "d4fcf425-4ac9-5918-973a-fe6c422b19bc" + ], + "document_id": [ + "a1d211d4-279e-51d7-b2b2-33bc2763d089", + "a1d211d4-279e-51d7-b2b2-33bc2763d089", + "076f1140-8992-536f-832b-65ebdb9232a2", + "42b64375-06af-5e09-9ae6-6bd0ecb782c7", + "c66d2572-071d-5aaf-829c-b3ca6cf6d697", + "076f1140-8992-536f-832b-65ebdb9232a2", + "076f1140-8992-536f-832b-65ebdb9232a2", + "641771c3-048c-5afb-a41c-05a883e99408", + "6d341cd2-ae56-5807-9aff-39298efc4d06", + "805ad0d5-6a3d-5e5f-b1ba-121e10c6daaa" + ], + "id": [ + "chatcmpl-AIFqHVwVjpSqrvqjfDgFColFbW31Q", + "7d0ed0e8-6967-5dbe-b57e-f1e3a5821fd0", + "e3ec46a7-b0c9-567d-ba4a-e1c4f1341364", + "72000563-bea1-562e-b5d6-ea2c82f74d53", + "b1c43f5d-53c4-58e5-ac10-a90ecdd9d576", + "1555d1c2-53e4-5f7f-8411-7bb11d990eed", + "25e3417d-4e7e-595c-bec6-6f6e3d697ab4", + "e479acca-9418-552b-98ae-edb6eb74ee6f", + "b964fb31-cf7f-5d5d-9d73-d737daa96b8d", + "847efd79-3919-5ec0-b5b3-9934cdb29c39", + "77d42dce-1bb6-577f-95f4-f8c7ece85c19" + ], + "contexts": [ + "studying the highly familial MODY form of young - onset diabetes or other rare forms of monogenic diabetes. Table 12.2 The different subtypes of maturity - onset diabetes of the young ( MODY ). MODY type Gene locus Gene name Year of discovery Distribution Onset of diabetes Primary defect Severity of diabetes Complications OMIM MODY1 20q HNF4A ( TCF14 ) 1996 Rare (2 3%) Adolescence/", + "penetrance and early - onset diabetes, allows the collection of multigenerational pedigrees, making MODY an attractive model for genetic studies. MODY usually develops in thin young adults (usually before 25 years of age; in childhood, adolescence or young adulthood), and is associated with primary insulin - secretion defects [4,5] . The prevalence of MODY is estimated to be less than 1 2% of patients with T2DM, although it could represent as many as 5% of European cases of diabetes [4,25] . MODY is not", + "[2] . Mutations in 13 genes are known to cause MODY; the most prevalent are HNF1A , GCK and HNF4A [3, 4] . The MODY subtypes differ in age of onset of diabetes, the pattern of hyperglycemia, response to treatment, and associated extrapancreatic manifesta-tions [5] . As compared to type 2 diabetes, the clinical Key Words Best practice Genetic testing Healthcare providers Interview study Maturity onset diabetes of the young Abstract", + "causal for MODY , although genetic or functional evidence of obvious pathogenicity is not fully compelling (Table 1). Despite these important advances in understanding the mo- lecular pathogenesis of MODY , the genetic determinants in many patients with young-onset diabetes resembling a MODY-like phenotype remain unknown, suggesting addi- tional locus heterogeneity and new pathogenic mechanismsto be yet discovered. This has particularly been observed in", + "MODY Maturity Onset Diabetes of the Young. This is an uncommon form of diabetes, inherited as an autosomal dominant condition, and displaysa slow onset of symptoms. It generally presents before 25 years of age, is not related to obesity, and appears to have no autoi mmune basis. Multiple forms of MODY have been characterised based on mutations affecting different genes involved in the control of -cellfunction, and display different degrees of disease severity Continued over page", + "Genetic Testing for MODY Public Health Genomics 2015;18:5259 DOI: 10.1159/00036796359 1 Singh R, Pearson ER: The importance of mak- ing a genetic diagnosis of diabetes. Can J Dia-betes 2006; 30: 183190. 2 Ledermann HM: Is maturity onset diabetes at young age (MODY) more common in Europe than previously assumed? Lancet 1995; 345: 648.", + "Genetic Testing for MODY Public Health Genomics 2015;18:5259 DOI: 10.1159/00036796353symptoms present often at a relatively young age in pa- tients without overweight, who have a positive family his-tory. As compared to type 1 diabetes, progression may be less severe, and the required dosage of insulin low. Many patients with MODY are currently undiagnosed or misdiagnosed with type 1 or 2 diabetes mellitus [4] . In", + "in 1992, through familial linkage analysis of French pedigreeswith early-onset, non-auto-immune, non-obese diabetes thatwas also called maturity-onset diabetes of the young (MODY) (Froguel et al., 1992 ). Mutations in GCK (encoding glucokinase) were shown to cause a relatively benign form of MODY. Inciden-tally, it was the rst time that the direct causative effect of rela- tive insulin deciency was demonstrated in T2D, when insulin", + "gene studies were under powered. However, studies of monogenic forms of diabetes, specifically maturity onset diabetes of the young 2 (MODY2), provided some of the first insights into the contribution of genetic variation to hyperglycemia observed during pregnancy and fetal outcomes. MODY2 is an autosomal dominant form of MODY due to mutations in glucokinase ( GCK ) [2527]. Table 1. Characteristics and treatment modalities of different forms of diabetes mellitus Characteristics Treatment modalities", + "is variable, underlining that this disorder is genetically heterogeneous. Table 1. Definition of MODY Impaired glucose tolerance Age of onset <25 years Autosomal-dominant inheritance Using genetic linkage and candidate gene approaches, mutations in genes on chromosomes 2, 7, 12, 13, 19, and 20 have been linked to MODY and collectively may represent up to 3% of all patients with type 2 diabetes (Table 2). The gene on chromosome 7 (MODY2) encodes the glycolytic" + ], + "answer": "Studying the genetic basis of MODY (Maturity Onset Diabetes of the Young) has provided several important insights: 1. **Genetic Heterogeneity**: MODY is caused by mutations in multiple genes, with at least 13 known genes implicated. The most prevalent mutations occur in the genes HNF1A, GCK, and HNF4A [3]. This genetic diversity leads to different subtypes of MODY, each with distinct clinical characteristics such as age of onset, pattern of hyperglycemia, response to treatment, and associated extrapancreatic manifestations [3]. 2. **Inheritance Pattern**: MODY is inherited in an autosomal dominant manner, which means that it can be passed down through families. This inheritance pattern allows for the collection of multigenerational pedigrees, making MODY an attractive model for genetic studies [2]. 3. **Clinical Presentation**: MODY typically presents in young adults, often before the age of 25, and is characterized by primary insulin secretion defects. It is not related to obesity or autoimmune processes, distinguishing it from other forms of diabetes like type 1 and type 2 diabetes [5]. 4. **Pathogenic Mechanisms**: Despite advances in understanding the molecular pathogenesis of MODY, there remain unknown genetic determinants in many patients with a MODY-like phenotype, suggesting additional locus heterogeneity and new pathogenic mechanisms yet to be discovered [4]. 5. **Impact on Treatment and Diagnosis**: Genetic testing for MODY can lead to more accurate diagnoses and tailored treatment plans. Many patients with MODY are currently undiagnosed or misdiagnosed with type 1 or type 2 diabetes, highlighting the importance of genetic testing in identifying this condition [7]. These insights underscore the complexity and variability of MODY, as well as the importance of genetic research in improving diagnosis and treatment strategies for this form of diabetes.", + "question": "What insights have been gained from studying the genetic basis of MODY (Maturity Onset Diabetes of the Young)?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_11 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_11 new file mode 100644 index 0000000..55d5138 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_11 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2017 - diabetes-mellitus-in-developing-countries-and-underserved-commun-2017.pdf", + "2010 - Pharmacogenetics of Anti-Diabetes Drugs.pdf", + "2008 - Clinical Risk Factors, DNA Variants.pdf", + "2010 - Diabetes in Asia.pdf", + "2015 -precision-medicine-for-managing-diabetes.pdf", + "2010 - Diabetes in Asia.pdf", + "2003 - Genome-wide screen in obese pedigrees with type 2 diabetes.pdf", + "2010 - Diabetes in Asia.pdf", + "2018 - Quantitative Relationship Between Cumulative Risk Alleles Based.pdf", + "2018 - Genetic variants of gestational diabetes mellitus a study of 112 SNPs among 8722 women in two independent populations.pdf" + ], + "extraction_id": [ + "d7bd898b-1d46-557a-b065-f94fc5310b2a", + "73e1aaff-7ef6-5ca2-9c94-23f5674a4f88", + "2643b341-8c50-5cea-af36-86a8b070a80e", + "11faf4fe-7b71-562e-9901-c428ab20b285", + "f53ccf4e-f47f-5b44-8b41-f7068efc8be3", + "11faf4fe-7b71-562e-9901-c428ab20b285", + "1110f7b4-ab5a-5b41-b37d-a992b29cb20c", + "e99fe157-eda9-5e56-9ec9-8f428de2a161", + "6db9f25e-36fd-51c0-be36-6dfacd963b1b", + "f6de8981-a79b-5817-b688-a20f76bff86c" + ], + "document_id": [ + "8a9451b9-d7e8-5417-b6a5-5fd1b791cc4d", + "ffeebaf9-ff76-5751-9b8b-7a2a4a4f1dc3", + "0018610a-9c86-5e2d-a27d-f66cf4f8519d", + "0be842b8-7f69-503b-baed-c336e5c834d6", + "80949bab-d085-5f61-b98a-4bee043bc4e2", + "0be842b8-7f69-503b-baed-c336e5c834d6", + "335a3c08-14d3-5511-ab84-340e64c6f993", + "0be842b8-7f69-503b-baed-c336e5c834d6", + "d585896e-1c32-51cb-827d-e4fd3b3943f3", + "3b301dd1-17bd-5632-9a96-d6294c6d7650" + ], + "id": [ + "chatcmpl-AIFqUmWTKdcimZ6Y2TYtW6SieUkPG", + "47e8bd94-fd61-57f2-b1d0-cc139d71936a", + "437a7129-63b8-5f34-8273-2eef9535e987", + "aa72551a-ac0c-5d7d-8057-34f229f68eb1", + "461b6f32-4dd8-5dc1-b69f-134f949fc021", + "263dc0cb-dfa0-5ee2-b927-f9a196294d46", + "78d81651-7215-596a-b128-37e429dc7edb", + "b0d3a09d-36a3-5c6e-a110-3fccddaa74b7", + "e6c0f12d-8136-5a16-b77c-88dd17c3a212", + "d632d486-4e04-5c2d-9cf0-9d614453cab3", + "e1ba568f-cc08-549a-9c87-a23285c3b5dc" + ], + "contexts": [ + "of Diabetes Results of several genome-wide association stud- ies (GWAS) have linked the following common gene variants with a 1520% increased risk of diabetes: reduced insulin secretion via reduce beta-cell mass (CDKAL1, CDKN2A, CDKN2B) and beta-cell dysfunction (MTNR1B, TCF7L2, KCNJ11) and increased insulin resistance related to obesity (FTO) and unrelated to obesity (IRS1, PPARG) [ 11 ]. While most of the early studies", + "gene are associated with NIDDM in Caucasians. Diabetes 1996 , 45, 825-831. 46. Tarasov, A.I.; Nicolson, T.J. ; Riveline, J.P.; Taneja, T.K. ; Baldwin, S.A.; Baldwin, J.M.; Charpentier, G.; Gautier, J.F. ; Froguel, P.; Vaxillaire, M.; et al. A rare mutation in ABCC8/SUR1 leading to altered ATP-sensitive K+ channel activ ity and beta-cell glucose sensing is associated with type 2 diabetes in adults. Diabetes 2008 , 57, 1595-1604.", + "ly associated with type 2 diabetes: TCF7L2, KCNJ11, and PPARG . 5-7 However, in 2007, a number of novel genetic variants ( CDKAL1, IGF2BP2, the locus on chromosome 9 close to CDKN2A/CDKN2B, FTO, HHEX, SLC30A8, and WFS1)8-14 were shown to in - crease susceptibility to type 2 diabetes in repro - ducible studies. Furthermore, a recent meta-analy - sis identified six novel variants ( JAZF1, CDC123/ CAMK1D, TSPAN8/LGR5, THADA, ADAMTS9, and NOTCH2 ) that are associated with type 2 dia - betes. 15", + "CDKAL1 in uences insulin response and risk of type 2 diabetes. Nat Genet 2007; 39: 77075. 69 Wu Y , Li H, Loos RJ, et al. Common variants in CDKAL1, CDKN2A/ B, IGF2BP2, SLC30A8, and HHEX/IDE genes are associated with type 2 diabetes and impaired fasting glucose in a Chinese Han population. Diabetes 2008; 57: 283442. 70 Sandhu MS, Weedon MN, Fawcett KA, et al. Common variants in WFS1 confer risk of type 2 diabetes. Nat Genet 2007; 39: 95153.", + "Genes signifying increased risk for both type 1 and type 2 dia-betes have been identified. Genomewide association studies have identified over 50 loci associated with an increased genetic risk of type 1 diabetes. Several T1D candidate genes for increased risk of developing type 1 diabetes have been sug-gested or identified within these regions, but the molecular basis by which they contribute to islet cell inflammation and beta cell destruction is not fully understood. 12 Also, several", + "associated with susceptibility to type 2 diabetes mellitus. Nat Genet 2008; 40: 109297 . 74 Unoki H, Takahashi A, Kawaguchi T, et al. SNPs in KCNQ1 are associated with susceptibility to type 2 diabetes in East Asian and European populations. Nat Genet 2008; 40: 1098102. 75 Lyssenko V, Lupi R, Marchetti P, et al. Mechanisms by which common variants in the TCF7L2 gene increase risk of type 2 diabetes. J Clin Invest 2007; 117: 215563. 76 Lyssenko V, Jonsson A, Almgren P, et al. Clinical risk factors, DNA", + "type 2 diabetes or the inability to replicate linkage withdened loci. However, at least one susceptibility gene, namelyCAPN10, was found using a genome-wide scan approach [3]. Obesity is the greatest risk factor for type 2 diabetes mellitus, as it is known to induce insulin resistance via variousmechanisms ( TNF release, free fatty acids, etc.). Both", + "50 most cases of type 2 diabetes are thought to be due to genetic variations that are more common but exert less e ect. In early studies, genetic variants in the peroxisome proliferator-activated receptor- gene (PPARG) 51 and the ATP-sensitive potassium channel Kir62 (KCNJ11) were reproducibly associated with type 2 diabetes. 52 In Asian populations, the protective e ect of the PPARG*A12Ala allele on insulin resistance and risk of type 2 diabetes was not consistently seen. 53", + "49. Cornelis MC, Qi L, Zhang C, et al. Joint e ects of common genetic variants on the risk for type 2 diabetes in U.S. men and women ofEuropean ancestry. Ann Intern Med . 2009;150:541 550(in eng). 50. Hu C, Zhang R, Wang C, et al. PPARG, KCNJ11, CDKAL1, CDKN2A-CDKN2B, IDE-KIF11-HHEX, IGF2BP2 and SLC30A8are associated with type 2 diabetes in a Chinese population. PLoS One. 2009;4:e7643 (in eng). 51. Lin X, Song K, Lim N, et al. Risk prediction of prevalent diabetes in", + "46. Sladek R, Rocheleau G, Rung J et al (2007) A genome-wide asso- ciation study identifies novel risk loci for type 2 diabetes. Nature 445:881 885 47. Lauenborg J, Grarup N, Damm P et al (2009) Common type 2 diabetes risk gene variants associate with gestational diabetes. J Clin Endocrinol Metab 94:145 150 48. Florez JC, Jablonski KA, Bayley N et al (2006) TCF7L2 polymor- phisms and progression to diabetes in the Diabetes Prevention Program. N Engl J Med 355:241 250" + ], + "answer": "The genes most commonly associated with an increased risk of developing diabetes, particularly type 2 diabetes, include: 1. **CDKAL1, CDKN2A, CDKN2B** - These genes are linked to reduced insulin secretion via reduced beta-cell mass [1]. 2. **MTNR1B, TCF7L2, KCNJ11** - These genes are associated with beta-cell dysfunction [1]. 3. **FTO** - This gene is related to increased insulin resistance associated with obesity [1]. 4. **IRS1, PPARG** - These genes are related to increased insulin resistance unrelated to obesity [1]. 5. **IGF2BP2, HHEX, SLC30A8, WFS1** - These genes have been shown to increase susceptibility to type 2 diabetes in reproducible studies [3]. 6. **JAZF1, CDC123/CAMK1D, TSPAN8/LGR5, THADA, ADAMTS9, NOTCH2** - These are additional variants identified in a recent meta-analysis as being associated with type 2 diabetes [3]. 7. **KCNQ1** - This gene is associated with susceptibility to type 2 diabetes in East Asian and European populations [6]. These genes have been identified through various genome-wide association studies (GWAS) and other genetic research efforts.", + "question": "What genes are most commonly associated with an increased risk of developing diabetes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_12 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_12 new file mode 100644 index 0000000..ba90dbe --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_12 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2009 - Pathomechanisms of Type 2 Diabetes Genes.pdf", + "2008 - Glossary of Genetics Genomics Terms.pdf", + "2016 - The Application of Genomics.pdf", + "2015 -precision-medicine-for-managing-diabetes.pdf", + "2019 - Genetic Risk Scores for Diabetes Diagnosis.pdf", + "2018 - Human Genetics of Obesity and Type 2 Diabetes Mellitus.pdf", + "2008 - Genotype Score in Addition to Common Risk Factors for Prediction of Type 2 Diabetes.pdf", + "2009 - Genetics of Type 1A Diabetes.pdf", + "2010 - Cardiovascular Disease Risk Factors, Type 2 Diabetes Mellitus, and the Framingham Heart Study.pdf", + "2014 - Impact of Delivery Models on Understanding Genomic Risk for Type 2 Diabetes.pdf" + ], + "extraction_id": [ + "9c49d40d-91d3-5f0d-8eaa-b3efa49ac200", + "53e868dd-b318-5cf3-8b2e-98a548aab7cf", + "7aa2ab48-620b-5b30-b2de-103e103579ba", + "f53ccf4e-f47f-5b44-8b41-f7068efc8be3", + "ba3abde6-0fac-587f-976e-bd0e08c48ae3", + "e9c258eb-26f2-5e33-87a2-7ac5a5b29989", + "e0f816e4-3c97-575e-8bbe-0e006c8c8e61", + "d3fa98dd-b7be-5192-9a7c-71742b1b05e4", + "5763fc63-1abb-5baf-b2ed-ad1b019bdb56", + "aafcb80d-7069-59da-8a21-d6a32f1a8820" + ], + "document_id": [ + "cf8ec75c-8ffe-5baa-830d-ac7a4a5964bd", + "c66d2572-071d-5aaf-829c-b3ca6cf6d697", + "2ec5c9c1-fe53-59ca-b36f-d360dfce0da5", + "80949bab-d085-5f61-b98a-4bee043bc4e2", + "8c66aca1-d4ba-534d-a037-4273de340ee1", + "2083de31-17c6-5d1e-9aa6-2efc6c1d9ac2", + "fb502e5b-7094-58aa-9508-103476a9c035", + "7a98f456-6c43-5e9e-b404-31122159eab8", + "134c506f-f66f-5a17-9e81-1f4c5923fe91", + "b2665466-da66-59f0-8581-a68131e924bf" + ], + "id": [ + "chatcmpl-AIFqbEtJpAtIpQor3Q5twvd1eoH61", + "28d6dfa3-28eb-537b-ad53-7d312f20fc88", + "54ff4672-bf7f-5158-b228-ca3d45e0cb0d", + "71ebe60b-4807-5b6f-887a-2ab897a46039", + "6cf756f6-bc3a-515a-a879-7270f663c516", + "59b0a653-0d03-582e-8fb5-009af723b984", + "9d44b00e-027f-557f-a851-e870605ea20f", + "f0ca71ce-f2bb-54f2-a933-dc9c952f1eb8", + "e32de26a-7ad6-51a9-860e-5df0b45d981d", + "b677fe54-5f7e-5d87-a16d-6694578c6f2b", + "530788ae-3a97-50d6-ad96-5463a3dc75e8" + ], + "contexts": [ + "genetic knowledge beyond its use for predic-tion of the individuals type 2 diabetes risk?One major advantage of knowing an at-riskpersons genotype could be to offer an individ-ually tailored lifestyle intervention program to prevent or, at least, to significantly retard the", + "Genetic factors appear to play a role in determining an individuals risk of developing diabetes. It is hoped", + "(35). If genetic tests are not helpful in the prediction and prevention of diabetes,they could have a role in discriminatingbetween type 1 and type 2 diabetes. Theepidemic of obesity (36) has made it moredifcult to distinguish diabetes type be- cause many children and young adultswith type 1 diabetes are also obese (37).Misclassi cation poses signi cant risks; an incorrect diagnosis of type 2 diabetes", + "geted at specific genetic mutations, it is likely that accompa-nying diagnostic tests for biomarkers will also become available to confirm whether the target biomarker is present. Genomic Analyses for Diabetes Risk", + "genes improves prediction of type 1 diabetes[published correction appears in Diabetologia. 2015; 58(1):206]. Diabetologia . 2014; 57(12):2521 2529. 57. Oram RA, Patel K, Hill A, Shields B, McDonald TJ, Jones A, Hattersley AT, Weedon MN. A type 1 diabetes genetic risk score can aid discrimination between type 1 and type 2 diabetes in young adults.Diabetes Care . 2016; 39(3):337 344. 58. Redondo MJ, Oram RA, Steck AK. Genetic risk", + "10.2337/db13-1663. 20. Vassy JL, et al. A genotype risk score predicts type 2 diabetes from young adulthood: the CARDIA study. Diabetologia. 2012;55:26042612. doi: 10.1007/s00125-012-2637-7. 21. Vassy JL, et al. Is genetic testing useful to predict type 2 diabe-tes? Best Pract Res Clin Endocrinol Metab. 2012;26:189201. doi: 10.1016/j.beem.2011.09.002. 22. Khera AV, et al. Genome-wide polygenic score to identify a monogenic risk-equivalent for coronary disease. bioRxiv. 2017. doi: 10.1101/218388.", + "Genotype Score for Prediction of Type 2 Diabetes n engl j med 359;21 www.nejm.org november 20, 2008 2209Type 2 diabetes mellitus is a m ajor health problem worldwide.1 Fortunately, its development can be prevented in many instances,2 and persons at risk can be readily identified with the measurement of a few com - mon risk factors.3-5 Type 2 diabetes is heritable, with a risk for people with familial diabetes as compared with those without familial diabetes that is increased by a factor of 2 to 6.", + "risk of type 1 diabetes offers the potential for improved prediction, stratification of patients according to risk, and selection of possible therapeutic targets. As germ-line factors, genetic risk variants are present and amenable to study at all times be -", + "offers the opportunity to test whetherknowledge of these genetic loci canimprove our ability to detect who willultimately develop diabetes. To answerthis question, we genotyped 18 well-validated single nucleotide polymorph-isms that had previously been associat- ed with diabetes in large genetics", + "Comprehension of Genomic Risk for Diabetes Public Health Genomics 2014;17:95104 DOI: 10.1159/000358413101their results in-person from a genetic counselor were able to correctly indicate their genomic or lifetime risk score for T2DM and interpret their genomic risk, compared to 50% of participants receiving their results online. This finding aligns with reports that suggest genetic counsel-ing (though limited to reporting of test results in this study) improves patients accuracy of risk perception" + ], + "answer": "Genetic testing can help predict a person's risk for diabetes in several ways: 1. **Tailored Interventions**: Knowing an individual's genotype can allow for the development of personalized lifestyle intervention programs aimed at preventing or significantly delaying the onset of type 2 diabetes [1]. 2. **Role of Genetic Factors**: Genetic factors play a role in determining an individual's risk of developing diabetes, suggesting that genetic testing can help identify those at higher risk [2]. 3. **Genetic Risk Scores**: A genotype risk score can predict type 2 diabetes from a young age, as demonstrated in studies like the CARDIA study [6]. This score can help identify individuals who are at increased risk due to their genetic makeup. 4. **Heritability and Risk Assessment**: Type 2 diabetes is heritable, and genetic testing can help identify individuals with a familial risk, which is increased by a factor of 2 to 6 compared to those without familial diabetes [7]. 5. **Improved Prediction and Stratification**: Genetic testing offers the potential for improved prediction and stratification of patients according to their risk, which can aid in selecting possible therapeutic targets [8]. 6. **Identification of Genetic Variants**: By genotyping specific single nucleotide polymorphisms (SNPs) associated with diabetes, genetic testing can improve the ability to detect who will ultimately develop the disease [9]. Overall, genetic testing provides valuable insights into an individual's risk for diabetes, enabling more targeted prevention and management strategies.", + "question": "How can genetic testing help predict a person's risk for diabetes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_13 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_13 new file mode 100644 index 0000000..756256e --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_13 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2008 - Glossary of Genetics Genomics Terms.pdf", + "2010 - Family History of Diabetes and Prevalence.pdf", + "2017 - diabetes-mellitus-in-developing-countries-and-underserved-commun-2017.pdf", + "2004 - Diabetes Genes a.pdf", + "2004 - Diabetes Genes a.pdf", + "2005 - Pathogenesis of Type 2 Diabetes Mellitus.pdf", + "2018 - High-Throughput Approaches onto Uncover (Epi)Genomic Architecture of Type 2 Diabetes.pdf", + "2011 - Type 2 diabetes across generations from pathophysiology to prevention and management.pdf", + "2004 - Diabetes Genes a.pdf", + "2010 - Diabetes in Asia.pdf" + ], + "extraction_id": [ + "53e868dd-b318-5cf3-8b2e-98a548aab7cf", + "b91922c6-7b5b-5fa1-a740-4564ec4cfa36", + "5ae0e120-7064-5ced-84ff-e74fb0f90047", + "40d292c1-03bc-5780-a2ae-9b0fe245f39c", + "8e5322e6-a8a2-5d98-b87d-1ba3846d5fe1", + "d62a1716-bd6a-5532-ab22-ee6e7ec4cf37", + "f6b9d6b9-a60b-56f5-9727-d90d43efe0ac", + "baec13ec-c42b-51b4-9974-8ef1c2d10ddc", + "5a2221e0-dabc-523c-8358-3e43789e8f7a", + "e99fe157-eda9-5e56-9ec9-8f428de2a161" + ], + "document_id": [ + "c66d2572-071d-5aaf-829c-b3ca6cf6d697", + "f16c4c6e-bb5f-5d4a-9945-8af4d0df19f4", + "8a9451b9-d7e8-5417-b6a5-5fd1b791cc4d", + "805ad0d5-6a3d-5e5f-b1ba-121e10c6daaa", + "805ad0d5-6a3d-5e5f-b1ba-121e10c6daaa", + "75b4ae7d-7abf-57b8-bda9-5b022d698ae6", + "1cb0c4ac-c1fe-55c2-919c-52cd5018c00d", + "0f49b102-1d7e-5702-af30-35e5f2ed93a6", + "805ad0d5-6a3d-5e5f-b1ba-121e10c6daaa", + "0be842b8-7f69-503b-baed-c336e5c834d6" + ], + "id": [ + "chatcmpl-AIFqiY2VOktGY4xVSkvpvMDbynoMw", + "54ff4672-bf7f-5158-b228-ca3d45e0cb0d", + "03dbb574-1b16-5300-af34-08b82263388e", + "13fa34fd-9bf6-5ae5-8a7e-e1998d56d084", + "527419f1-075d-5d53-a8b5-1685952ecdb0", + "3a807b66-fcae-5cae-b8ad-83a5c6815221", + "b63c48dd-b954-56d4-bdfa-8ab135e7bf47", + "ee3d0900-a422-59cd-a6db-308f20052cc0", + "2aa9f009-ae05-5c93-ac3a-58b1f516d844", + "353dc970-3106-5bbe-8a58-d65d13e5e6ee", + "6c14eef8-bb27-503a-9523-9e7a16d71021" + ], + "contexts": [ + "Genetic factors appear to play a role in determining an individuals risk of developing diabetes. It is hoped", + "Metabolic Syndrome and Family History of Diabetes Public Health Genomics 2010;13:353359 357able difference in the odds between these 2 risk levels. This table indicates that, compared with the average fa-milial risk, a moderate or high familial risk of diabetes increases the odds for each single component of the met-a b o l i c s y n d r o m e . T h e s e o d d s v a r y f r o m 1 . 1 9 ( 9 5 % C I : 0.881.61) to 1.53 (95% CI: 1.301.81). C o n c l u s i o n", + "For type 2 diabetes, there have been a few studies utilising a candidate-gene approach as well as genome-wide association studies, although some argue that genetic factors play only a minor role among Caribbean populations [ 90 ]. A family history of diabetes in any rst- degree relative (parent, sibling) or in a grandpar-ent is associated with a two- to fourfold increased risk of diabetes [ 10 , 91 ]. A family history of dia-", + "evidenced by a very high positive rate of family history of diabetes, and drastically different prevalence in various ethnic groups. Therefore, there is no doubt that type 2 diabetes is a disease with a strong genetic influence. However, the prediction of the relative contribution of genetic influence and number of genes involved in the pathogenesis of the disease has changed in the past few years. Initially, enthusiastic searches of diabetes genes were", + "can decrease risk of diabetes.22 Diet may also play a role. High calorie diets, including those high in fat, and especially saturated fat, have been implicated in the development of type 2 diabetes?4-26 Family history is a very strong risk factor for type 2 diabetes. A strong genetic component is suggested by the 58-75% concordance rates for type 2 diabetes observed in identical twins (Table 3).3 Table 3. Estimated risk of developing type 2 diabetes by family history One parent with type 2 diabetes", + "The fact that type 2 diabetes is a genetic disease is well known to clinicians by how it occurs in families, and by there being ethnic populations who are particularly high risk. The genetic link was clearly shown more than two decades ago by a famous study of identical twins in the U.K. that found essentially a 100% concordance rate for this disease if one twin developed type 2 diabetes, then the other one invariably developed it (9). However, this kind of study", + "genetic factors play an important role in the susceptibility to T2D. The risk of the disease developing at some point of life is ~70% when both parents are diabetic and ~40% when one parent has T2D [ 4]. Furthermore, latest data show that more than 400 genetic risk variants at 250 loci for T2D have been Genes 2018 ,9, 374; doi:10.3390/genes9080374 www.mdpi.com/journal/genes", + "36 Herder C, Roden M. Genetics of type 2 diabetes: pathophysiologic and clinical relevance. Eur J Clin Invest 2011; 41: 67992. 37 Dabelea D, Hanson RL, Lindsay RS, et al. Intrauterine exposure to diabetes conveys risks for type 2 diabetes and obesity: a study of discordant sibships. Diabetes 2000; 49: 220811. 38 Voight BF, Scott LJ, Steinthorsdottir V, et al. Twelve type 2 diabetes susceptibility loci identi ed through large-scale association analysis. Nat Genet 2010; 42: 57989.", + "long follow-up. Type 2 diabetes and impaired glucose tolerance (IGT) cluster in families. Thus, most patients have a positive family history, and the lifetime risk for developing type 2 diabetes is increased up to 40% (more than five times the background rate) by having a first degree relative with the disease. If both parents have type 2 diabetes the risk to the offspring may be as high as 70%. Available evidence supports a polygenic mode of inheritance with a considerable environmental input. 1", + "Genetic factors Type 2 diabetes has a strong genetic component and most Asian patients have a rst-degree relative with diabetes. 48,49 Much progress has been made in our understanding of the genetics of this disease. Importantly, most of the loci originally associated with diabetes in European populations have been replicated in Asian populations. Whereas monogenic forms of diabetes result from rare genetic mutations with large e ects, such as those seen in maturity-onset diabetes of young people," + ], + "answer": "Family genetics play a significant role in the likelihood of developing diabetes. Several studies and observations highlight this connection: 1. Genetic factors are important in determining an individual's risk of developing diabetes [1]. 2. A family history of diabetes, particularly in first-degree relatives such as parents or siblings, is associated with a two- to fourfold increased risk of developing diabetes [3]. 3. Type 2 diabetes is strongly influenced by genetics, as evidenced by high concordance rates in identical twins, with studies showing a 58-75% concordance rate [5]. In some studies, the concordance rate is reported to be nearly 100% [6]. 4. The risk of developing type 2 diabetes is approximately 70% if both parents have the disease and about 40% if one parent is affected [7], [9]. 5. Type 2 diabetes clusters in families, and having a first-degree relative with the disease increases the lifetime risk significantly, up to 40% or more [9]. Overall, these findings underscore the strong genetic component in the susceptibility to diabetes, particularly type 2 diabetes.", + "question": "What role do family genetics play in the likelihood of getting diabetes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_14 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_14 new file mode 100644 index 0000000..e573f24 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_14 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2018 - Global aetiology and epidemiology of type 2 diabetes mellitus and its complications.pdf", + "2020 - Precision Medicine in Diabetes.pdf", + "2020 - Precision Medicine in Diabetes.pdf", + "2008 - Glossary of Genetics Genomics Terms.pdf", + "2011 - Lifestyle and Genetics in Obesity and type 2 Diabetes.pdf", + "2010 - Interactions of Dietary Whole-Grain Intake.pdf", + "2013 - Gene-Environment and Gene-Treatment.pdf", + "2011 - Interaction Between Exercise and Genetics.pdf", + "2012 - Gene-Environment Interactions in the Development of Type 2 Diabetes.pdf", + "2010 - Candidate Gene and Genome-Wide Association Studies in Behavioral Medicine.pdf" + ], + "extraction_id": [ + "751ccb98-2846-5ca7-8ab8-2684100c28fa", + "0504a937-6b88-5004-a13e-5e9c3073eaf6", + "0504a937-6b88-5004-a13e-5e9c3073eaf6", + "53e868dd-b318-5cf3-8b2e-98a548aab7cf", + "93638ea5-6d1f-5b6a-9629-798804de24dd", + "6283c124-b479-5050-86ca-dc42390147a1", + "ee6a4bf3-6f68-58e7-a96f-c879b5269694", + "ed6dcfee-8273-5512-8fb4-fc51a9c921da", + "89bf4316-d0cc-5310-a45e-1dd8b8aefe1b", + "3bf3c6a7-de03-5114-bad8-d53fd76d0fba" + ], + "document_id": [ + "8bc8f3d4-968f-5252-ab4c-832b92e9ec0d", + "0ad5b2de-d782-5d43-b294-bff5c7befd2d", + "0ad5b2de-d782-5d43-b294-bff5c7befd2d", + "c66d2572-071d-5aaf-829c-b3ca6cf6d697", + "a16d3328-039c-530a-bfe5-f6f80ecf2ad0", + "e4d4a19e-18a0-5a08-9ab7-537f31b7cdc1", + "fe958fb1-5408-56ec-b102-ccf07b4bac2d", + "c36db75e-4b76-540d-9efb-d0e156e61541", + "ea9601ed-ad83-506e-b1b7-e7211671ff73", + "17637a6f-804e-50e4-9cf5-37318e17f15c" + ], + "id": [ + "chatcmpl-AIFqrzKmzcOBxhh6XTfMBqYsubXv7", + "a1c71566-1d75-551a-8588-9a05436545dc", + "fe89ba68-d709-5494-bcdc-82d81e1498d1", + "799f3578-a7ac-551f-b84a-b9fb3be53040", + "54ff4672-bf7f-5158-b228-ca3d45e0cb0d", + "be87703d-e7b2-5db5-9983-5412e09a57ba", + "89339b65-325f-588f-9f25-761124f0012f", + "fe35615a-6df7-548c-b313-4abca69b1e2d", + "68a382e9-85e0-548c-910e-5f24cb48f9c8", + "6b83f0af-1145-5679-9dae-0f645771d25d", + "1b364e28-08e2-5813-b066-7ce37eeb36cf" + ], + "contexts": [ + "of a given genetic variant is modified by the environ - mental milieu (and vice versa). Evidence that lifestyle factors modify the genetic effects on T2DM risk has been generated from both observational studies and clinical trials82. However, genetic background might also affect the individuals response to lifestyle interventions83. In addition, replication data are sparse, and comprehensive, large-scale studies have failed to provide a compelling", + "genetic risk for diabetes may not moti-vate improvements in lifestyle behaviors.Indeed, knowledge of increased geneticrisk for diabetes may decrease motiva-tion to modify behavior in genetic fatal-ists (83). Diet recommendations optimized to the individual have been shown to re-duce postprandial glycemic excursionsto a greater extent than standard approaches in healthy individuals (84).Meal compositions that induce the most favorable glycemic pro les have been", + "diabetes regardless of the underlying genetic risk. This contrasts with theextensive epidemiological evidence sug-gesting that the relationship of lifestylewith obesity is dependent on genetic risk(7881); however, with few exceptions (e.g., [74]), analyses in large randomizedcontrolled trials have failed to show thatthese same genetic variants modifyweight loss in response to lifestyle in-tervention (82). It is also important to recognize that knowledge of increased", + "Genetic factors appear to play a role in determining an individuals risk of developing diabetes. It is hoped", + "suggested to attenuate its negative e ect on metabolic pro le, body weight, and diabetes risk ( Franks et al., 2007 ; Kilpelainen et al., 2008 ; Lindi et al., 2002 ; Ruchat et al., 2010 ) ( Table 1 ). The notion that lifestyle modi cation can eliminate the increased risk for development of T2DM in subjects with genetic suscepti-bility is also supported by ndings of Barwell et al. (2008) who", + "proven particularly effective for preven-tion and management of type 2 diabetes.For example, improvement in dietaryquality, in conjunction with other lifestylemodications like increased physical ac-tivity, was shown to be more effectivethan pharmacological treatment in pre-vention of diabetes in individuals at highrisk (1). Further, lifestyle modicationmay mitigate the risk associated with thestrongest known diabetes risk loci (2).While the existence of environmental in-uences on genetic risk (and vice", + "who is lean, genetic risk factors are more likely to be present than in someone who is obese and develops the disease or that weight loss enhances the genetic risk ofdiabetes. Genetic analyses performed in clinical trials involving intensive lifestyle modi - cation provide an important adjunct to the epidemiological literature on gene- lifestyle interactions in type 2 diabetes.On one hand, a major advantage of ran- domized controlled trials is that interac-", + "Lifestyle behaviors and genetic loci have clear and distinguishable effects on T2D risk; however, the pattern of disease occurrence within and between popula-tions that differ in their genetic and environmental underpinnings suggests T2D is caused in part by the interaction between adverse lifestyle behaviors and the genetic profile of an individual. For many, this seems a reasonable assumption, but there is little robust empirical evidence supporting the presence of such interactions.", + "this occurs. Findings to date, however, indicate that behavioral changes can substantially mitigate diabetogenic and obesogenic effects of individual or multiple risk alleles, which has much broader clinical and public health implications.We have seen considerable progress in our understanding of the role that both environ- ment and genetics play in the development of T2D. Recent work suggests that the adverse effect of some established T2D-associated loci may be greatly attenuated by appropriate", + "Susceptibility to obesity and diabetes is deter- mined by both genetic and lifestyle factors.Suggestive evidence of genelifestyle interac- tion (Box 33.3) in the development of common diseases such as obesity and type 2 diabetes wasrst provided by descriptive epidemiological studies such as migration studies that compare the disease risk between genetically related pop-ulations who live different lifestyles. A classicalexample is the comparison of the risk of obesity" + ], + "answer": "Yes, lifestyle changes can affect genetic risk factors for diabetes. Evidence from both observational studies and clinical trials suggests that lifestyle factors can modify the genetic effects on the risk of developing type 2 diabetes mellitus (T2DM) [1]. Lifestyle modifications, such as improvements in dietary quality and increased physical activity, have been shown to be particularly effective in preventing and managing type 2 diabetes, even in individuals with a high genetic risk [6]. Furthermore, behavioral changes can substantially mitigate the diabetogenic effects of individual or multiple risk alleles, indicating that the adverse effects of some established T2D-associated loci may be greatly attenuated by appropriate lifestyle changes [9].", + "question": "Can lifestyle changes affect genetic risk factors for diabetes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_15 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_15 new file mode 100644 index 0000000..1214d93 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_15 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2007 - Genetic studies of diabetes following the advent of the genome-wide association study where do we go from here.pdf", + "2011 - Genome-wide association studies (GWAS) impact.pdf", + "2003 - The Inherited Basis of Diabetes Mellitus.pdf", + "2015 - Diabetes mellitus The epidemic of the century.pdf", + "2007 - Genetic studies of diabetes following the advent of the genome-wide association study where do we go from here.pdf", + "2017 - Differentiation of Diabetes by Pathophysiology.pdf", + "2010 - Interactions of Dietary Whole-Grain Intake.pdf", + "2007 - Genome\u2013wide association studies provide new insights into type 2 diabetes aetiology..pdf", + "2008 - Glossary of Genetics Genomics Terms.pdf", + "2018 - Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps.pdf" + ], + "extraction_id": [ + "1a155200-3610-528f-a51d-b2f27562037a", + "cf06774a-9e13-59fd-9652-d5013ef83387", + "238129d2-439f-5a25-8e86-297e7a69d81c", + "6b04dc27-e7ff-53c8-9021-a3cdb5415059", + "1a155200-3610-528f-a51d-b2f27562037a", + "a9accd40-eb89-5595-bf27-b6b82b49f4d4", + "40190f1d-aad5-5d71-b5ba-78331d5e3abb", + "cd034e2b-72bd-5cda-a456-48cf17ead1bf", + "53e868dd-b318-5cf3-8b2e-98a548aab7cf", + "9190d1c1-41a4-5af3-a570-7fea6a15e71a" + ], + "document_id": [ + "7b96d9b2-6494-5c20-9693-dc146a4e347c", + "086c6869-7c70-5364-9269-760267fb458d", + "7b85b290-d711-55d5-9b1e-b06e4d6f14a2", + "e114dd28-fd39-56df-bdeb-8806474a6c10", + "7b96d9b2-6494-5c20-9693-dc146a4e347c", + "9cfaef1e-fb60-5c2b-94f0-632c89b2eb16", + "e4d4a19e-18a0-5a08-9ab7-537f31b7cdc1", + "2ad9b6c6-56ed-5ba6-ad88-c1a6777f5196", + "c66d2572-071d-5aaf-829c-b3ca6cf6d697", + "ab2868dd-62f6-5350-994c-fcea4328e8a3" + ], + "id": [ + "chatcmpl-AIFqw6zPQKQT7tNlJNiyf2dx560ep", + "9250b1a6-26d8-5c38-840f-547a9647e809", + "9d55a0b9-d125-587d-b21e-f4bd55b8de28", + "0372a2d5-28c0-5369-8f05-18f7124eb4ae", + "5b134bfd-6af3-5189-b144-57bf70c2cf20", + "3cd5df03-7c2b-585c-a3bb-67dc0e1c615c", + "9b04e578-bfe5-5f3c-8556-aac26d6429cc", + "f3c6864c-7c06-5a61-bdda-d5730821c237", + "81e7ee8d-adb5-5fd7-a3b1-1f6bfb059974", + "b092c8b9-edb1-55fb-ae16-c67e3298946e", + "23321ca3-f73d-5542-a6c0-1133c3d3e9e5" + ], + "contexts": [ + "understanding of the genetic basis of diabetes, and the advances of recent months are arguably the most important made since the role of the HLA region was recognised in type1 diabetes. The number of genetic regions causally implicated is now 11 each for type 1 and type 2 diabetes [ 19], and is set to rise further. The bewildering pace of new discovery standsin stark contrast to the slow progress that characterised the previous two decades, with a total combined output of three", + "It has proven to be challenging to isolate the genes underlying the genetic components conferring susceptibility to type 1 and type 2 diabetes. Unlike previous approaches, genome-wide association studies have extensively delivered on the promise of uncovering genetic determinants of complexdiseases, with a number of novel disease-associated variants being largelyreplicated by independent groups. This review provides an overview of these recent breakthroughs in the context of type 1 and type 2 diabetes, and", + "The history of diabetes genetics traces human genetic research more broadly.Initially, only a few polymorphic genetic markers were known, and these werestudiedinpopulation-basedassociationstudies.Withthedevelopmentofgenome-wide maps for family-based linkage analysis and of positional cloning, attentionturned to monogenic forms of disease. The application of family-based linkagemethods to common forms of diabetes, however, met with less clear success.More recently, with progress in genome sequencing and", + "the elucidation of the wide spectrum of genes that played a role in the molecular mechanism of diabetes development[142-144]. However , despite the vast flow of genetic information including the identification of many gene mutations and a large array of single nucleotide polymorphisms (SNPs) in many genes involved in the metabolic pathways that affect blood glucose levels, the exact genetic mechanism of diabetes remains elusive[145,146]. Evidently, a major complication is the", + "confirmed genes for type 2 diabetes and six for type 1(Fig. 1). At last, it seems, our understanding of the genetic basis of complex, multifactorial forms of diabetes is catching up with that of rarer, single-gene disorders. This leap in knowledge is the result of major advances in technology plus an improved understanding of patterns of human genetic variation. Using single nucleotide polymor- phism (SNP) chips it is now possible to analyse up to a million", + "make dissection of the black box of genetics of diabetespossible in the near future, but at this point, apart fromthe pro les that distinguish between type 1 and type 2 diabetes and a limited number of speci c variants that identify small subgroups of patients (MODY), genetics has not been successful in further differentiating subclasses ofdiabetes. Research Gaps After consideration of the known genetic associations with diabetes risk, consensus developed that the eld is", + "studies provide new insights into type 2diabetes aetiology. Nat Rev Genet 2007;8:657662 11. Grant RW, Moore AF, Florez JC. Genetic architecture of type 2 diabetes: recentprogress and clinical implications. Diabe-tes Care 2009;32:11071114 12. Dupuis J, Langenberg C, Prokopenko I,", + "early results have been excellent, yielding six new replicating gene regions. Here I discuss the insights into type 2 diabetes genetics that have been provided by these new findings. I consider where diabe - tes genetic studies might go from here, and present a perspective that may be applicable to other common traits. I also briefly discuss the wider implications that surround the identification of a common gene that predis - poses to type", + "that genetic studies will ultimately identify key genetic elements that help determine susceptibility to diabetes,disease progression, and responsiveness to specific therapies, as well as help identify novel targets for futureintervention. A substantial number of genetic loci, gene polymorphisms, and mutations have already beenreported as having variable degrees of association with one or other type of diabetes (type 1, type 2, maturityonset diabetes of the young [MODY]), while others appear to be involved", + "24. Varshney, A. et al. Genetic regulatory signatures underlying islet gene expression and type 2 diabetes. Proc. Natl. Acad. Sci. USA 114, 23012306 (2017). 25. Thurner, M. et al. Integration of human pancreatic islet genomic data refines regulatory mechanisms at Type 2 diabetes susceptibility loci. eLife 7, e31977 (2018). 26. Gaulton, K. J. et al. Genetic fine mapping and genomic annotation defines causal mechanisms at type 2 diabetes susceptibility loci. Nat. Genet. 47, 14151425 (2015)." + ], + "answer": "Recent breakthroughs in understanding the genetic causes of diabetes include several key advancements: 1. The number of genetic regions causally implicated in both type 1 and type 2 diabetes has increased to 11 each, marking significant progress in identifying genetic factors associated with these conditions [1]. 2. Genome-wide association studies have been particularly successful in uncovering genetic determinants of complex diseases like diabetes. These studies have identified a number of novel disease-associated variants, which have been largely replicated by independent research groups, highlighting their reliability and significance [2]. 3. Advances in technology, such as the use of single nucleotide polymorphism (SNP) chips, have enabled the analysis of up to a million genetic variations. This technological leap has greatly enhanced our understanding of the genetic basis of complex, multifactorial forms of diabetes, bringing it closer to the understanding of rarer, single-gene disorders [5]. 4. Recent studies have yielded six new replicating gene regions associated with type 2 diabetes, providing new insights into the genetic architecture of the disease and suggesting potential directions for future research [8]. These breakthroughs represent a significant leap forward in the genetic understanding of diabetes, driven by technological advancements and collaborative research efforts.", + "question": "What recent breakthroughs have been made in understanding the genetic causes of diabetes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_16 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_16 new file mode 100644 index 0000000..8f5ae28 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_16 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2013 - Systems Biology Approach Reveals Genome to Phenome Correlation in Type 2 Diabetes.pdf", + "2013 - Diabetes genes identified by genome-wide association studies are regulated in mice by nutritional factors in metabolically relevant tissues and by glucose concentrations in islets.pdf", + "2013 - Diabetes genes identified by genome-wide association studies are regulated in mice by nutritional factors in metabolically relevant tissues and by glucose concentrations in islets.pdf", + "2013 - Diabetes genes identified by genome-wide association studies are regulated in mice by nutritional factors in metabolically relevant tissues and by glucose concentrations in islets.pdf", + "2015 - Gestational Diabetes Alters Offspring DNA.pdf", + "2010 - Diabetes in Asia.pdf", + "2016 - Genetic predisposition for beta cell fragility underlies type 1 and type 2 diabetes.pdf", + "2021 - Interpreting type 1 diabetes risk.pdf", + "2010 - Common Inherited Variation in Mitochondrial Genes.pdf", + "2018 - Global aetiology and epidemiology of type 2 diabetes mellitus and its complications.pdf" + ], + "extraction_id": [ + "9369222f-e125-58c0-8f2b-cf5daa867f77", + "c9f74729-056d-556f-8aa8-e0f7a7bd6b66", + "c9f74729-056d-556f-8aa8-e0f7a7bd6b66", + "a9ec4c4f-b038-52d2-90db-7bee1ef1f78c", + "af8de1bb-e71e-514f-a5eb-59f37498028e", + "510b7c7b-ccbb-5d0d-b654-e1dbcf859cb7", + "af25ae45-0c5f-5492-86d0-734eb0fbac12", + "0bd0fae3-9297-5da2-b3ae-21190af84094", + "930831e1-56c3-5ef6-b847-2f25f2567032", + "8248ba2b-335c-53ce-afbe-f31b68507443" + ], + "document_id": [ + "ea7c2799-c259-5d0e-b40b-ecebe0a9fc9f", + "98564dd2-424b-557a-a539-022508283567", + "98564dd2-424b-557a-a539-022508283567", + "98564dd2-424b-557a-a539-022508283567", + "59b51d61-b2c4-540b-a2fb-4c56badb26c1", + "0be842b8-7f69-503b-baed-c336e5c834d6", + "5b239c51-7b4c-58e0-acca-2061593fe317", + "9f13ec69-195b-55eb-a549-b3eb3dc0f321", + "9a5c8cba-06cb-5280-871f-1bbe128c3dc4", + "8bc8f3d4-968f-5252-ab4c-832b92e9ec0d" + ], + "id": [ + "chatcmpl-AIFr5iGpAfX0NjS78CDO8cOuBpwUj", + "3bbf736e-7d8b-5e67-a4bf-e1ae28738bf3", + "ccf2d9af-4dca-5021-9c9d-301f817f80e4", + "d580609b-d24b-5718-ab63-0e6088c8bfeb", + "3f90af62-9a1d-5ac2-b5ee-a616857b34df", + "c171a147-2cf6-5340-82d4-caa63cdafbbd", + "81eb21fb-488a-5b08-b883-cd8780110c66", + "9b60d258-714a-5e70-b2fa-b0a29fc0d672", + "dd3348a8-1f07-5e6d-8ba0-3c6c263c0799", + "9080e28b-1c0d-5bfa-8698-7ae677aa64ed", + "c2f1a416-7f04-55b0-b19b-8a8aa858b801" + ], + "contexts": [ + "genes relate directly to insulin secretion and indirectly, through collaborating with other genes, to insulin resistance. Thisseems to support the epidemiological evidence that environmentally triggered insulin resistance interacts with geneticallyprogrammed bcell dysfunction to precipitate diabetes. Citation: Jain P, Vig S, Datta M, Jindel D, Mathur AK, et al. (2013) Systems Biology Approach Reveals Genome to Phenome Correlation in Type 2 Diabetes. PLoS ONE 8(1): e53522. doi:10.1371/journal.pone.0053522", + "have been the subject of most follow-up studies to date.Specifically, we examined acute changes in expression of these genes in response to feeding and fasting and longer term changes in the expression of these genes inresponse to a diet high in fat and sugar, recognized as a critical environmental risk factor for type 2 diabetes. It has been hypothesized that most of the new genetic variants affect -cell function, development or survival but not insulin sensitivity [6]. Consistent with this,", + "or survival. However, we also found evidence that most of the genes could have potential roles in other metabolically-relevant tissues. Genes affecting insulinsensitivity may be expected to be expressed in peripheralinsulin sensitive tissues, such as liver and adipose tissue, and be responsive to metabolic status. Consumption of a high fat diet was associated with a tendency for the ex- pression of several of these genes to be decreased. Simi-larly, many of the genes were regulated by feeding and", + "secretion versus insulin sensitivity). We also sought todetermine whether any of these genes are regulated by conditions known to alter the expression of metabolic- ally relevant genes. We examined the expression of thesegenes under fasting and non-fasting conditions (e.g. in response to insulin), which might be altered if they affect peripheral insulin sensitivity. Consumption of diets high in fats and sugars is associated with risk of developing type 2 diabetes [34] and many genes that are critical for", + "regulating sugar metabolism. Moreover, genes that were", + "Figure 2: The role of type 2 diabetes genes in insulin secretion Pancreatic -cell genes associated with type 2 diabetes are in italics. G6P=glucose-6-phosphate. Adapted from Florez JC. Newly identi ed loci highlight beta cell dysfunction as a key cause of type 2 diabetes: where are the insulin resistance genes? Diabetologia 2008; 51: 110010, by kind permission of the author and Springer Science + Business Media. Positive calorie balance Cycle A++ Cycle B Liver fat Insulin suppression of", + "tive Glis3 expression, which in turn drive increased levels of beta cell apoptosis and senescence. Genetic susceptibility could be replicated by elevated levels of dietary fat. Transcriptional analysis of human islets identified the same genetic networks at play. Together, these findings demonstrate both the important role of genetic variation in beta cells for diabetes susceptibility and a mechanism by which the Western diet may contribute to the growing diabetes epidemic. RESULTS", + "associated with fasting proinsulin levels and provides new insights into the pathophysiology of type 2 diabetes. Diabetes 60, 26242634 (2011). 65. Saxena, R. etal. Genetic variation in GIPR influences the glucose and insulin responses to an oral glucose challenge. Nat. Genet. 42, 142148 (2010). 66. Tobacco and Genetics Consortium. Genome-wide meta-analyses identify multiple loci associated with smoking behavior. Nat. Genet. 42, 441447 (2010).", + "38. Saxena R, Hivert M, Langenberg C, Tanaka T, Pankow JS, et al. (2010) Genetic variation in GIPR influences the glucose and insulin responses to an oral glucose challenge. Nat Genet 42: 142148. doi:10.1038/ng.521. 39. Neale BM, Sham PC (2004) The future of association studies: gene-based analysis and replication. Am J Hum Genet 75: 353362. doi:10.1086/423901. 40. Saccone SF, Hinrichs AL, Saccone NL, Chase GA, Konvicka K, et al. (2007)", + "Nature Reviews | EndocrinologyFactors that aect insulin secretion and action Body weight Level of physical activity Smoking Heavy alcohol consumption Genetic predisposition Geneenvironment interaction Positive risk prole Negative risk prole Normoglycaemia/uni03B2-cell dysfunction and insulin resistanceAdipose tissue Skeletal muscle LiverInsulin-mediated glucose production /uni2191Insulin-mediated glucose uptake /uni2193 Insulin-mediated glucose uptake /uni2193 Hyperglycaemia Epigenetics" + ], + "answer": "Genes influence how our bodies respond to sugar and insulin in several ways: 1. **Insulin Secretion and Resistance**: Genes are directly related to insulin secretion and indirectly influence insulin resistance by interacting with other genes. This interaction supports the idea that environmentally triggered insulin resistance can interact with genetically programmed beta-cell dysfunction, leading to diabetes [1]. 2. **Beta-Cell Function**: Many genetic variants primarily affect beta-cell function, development, or survival, rather than insulin sensitivity. This suggests that genetic factors play a significant role in how beta cells respond to sugar and insulin [2]. 3. **Expression in Metabolically Relevant Tissues**: Genes affecting insulin sensitivity are often expressed in peripheral insulin-sensitive tissues, such as the liver and adipose tissue, and their expression can be responsive to metabolic status. For instance, a high-fat diet can decrease the expression of several of these genes, indicating a genetic influence on how the body responds to dietary changes [3]. 4. **Regulation by Metabolic Conditions**: The expression of certain genes can be altered by conditions such as fasting and feeding, which are known to affect peripheral insulin sensitivity. This suggests that genetic regulation can influence how the body responds to changes in sugar and insulin levels [4]. 5. **Genetic Variation and Insulin Response**: Specific genetic variations, such as those in the GIPR gene, can influence glucose and insulin responses to an oral glucose challenge, highlighting the role of genetic differences in individual responses to sugar intake [9]. Overall, genetic factors can influence both the secretion of insulin and the body's sensitivity to it, affecting how we metabolize sugar and respond to dietary changes.", + "question": "How do genes influence how our bodies respond to sugar and insulin?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_17 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_17 new file mode 100644 index 0000000..4824400 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_17 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2015 -precision-medicine-for-managing-diabetes.pdf", + "2010 - The Genetics of Type 2 Diabetes.pdf", + "2021 - Interpreting type 1 diabetes risk.pdf", + "2008 - Clinical Risk Factors, DNA Variants.pdf", + "2018 - Quantitative Relationship Between Cumulative Risk Alleles Based.pdf", + "2011 - Shared Genomics of Type 2 and Gestational Diabetes Mellitus.pdf", + "2019 - Biomarkers for type 2 diabetes.pdf", + "2015 - Diabetes mellitus The epidemic of the century.pdf", + "2008 - Clinical Risk Factors, DNA Variants.pdf", + "2008 - Clinical Risk Factors, DNA Variants.pdf" + ], + "extraction_id": [ + "f53ccf4e-f47f-5b44-8b41-f7068efc8be3", + "2610c9c1-5e75-528e-98d8-c4a543ea2f89", + "254be2dd-1b4f-5cf9-af93-dbf3d5867510", + "640f3749-a2bf-5b6b-adab-72ce7f029a28", + "6db9f25e-36fd-51c0-be36-6dfacd963b1b", + "41fefdf5-447e-556e-b95f-c132bdea7c41", + "bc4717c3-d353-5f44-9513-50634f8d5196", + "7cfe9f29-a0ee-56d3-be3b-1b238a43bc07", + "0aae948a-50f9-568a-b0dc-5960a2d2ceaa", + "38bacfcd-d182-5220-b8bc-18f6c74b14a8" + ], + "document_id": [ + "80949bab-d085-5f61-b98a-4bee043bc4e2", + "a1d211d4-279e-51d7-b2b2-33bc2763d089", + "9f13ec69-195b-55eb-a549-b3eb3dc0f321", + "0018610a-9c86-5e2d-a27d-f66cf4f8519d", + "d585896e-1c32-51cb-827d-e4fd3b3943f3", + "bef0cabe-0bca-5715-9ffc-0b825744fbcf", + "c8ee94fc-f9bc-5a32-9524-9d1d9cf37159", + "e114dd28-fd39-56df-bdeb-8806474a6c10", + "0018610a-9c86-5e2d-a27d-f66cf4f8519d", + "0018610a-9c86-5e2d-a27d-f66cf4f8519d" + ], + "id": [ + "chatcmpl-AIFrBAew5HsqHnMUVkuc9dpSmo0io", + "263dc0cb-dfa0-5ee2-b927-f9a196294d46", + "988cae28-e149-5190-8ff0-6ecce8d001bc", + "ca9e53b7-6e51-5ae6-9ef4-8f2f5f40acb5", + "61f523a8-f466-5148-afba-6400c44ed278", + "151d8a78-8aa8-5024-8e15-54fba4f1857b", + "f692be48-b905-5463-8101-22eaf14e6405", + "48c93a37-d0d5-51de-b2d1-5c6122c01ab1", + "82debd98-f2fe-51aa-931c-63e11249de7b", + "8469faae-c6c9-5fd4-8437-870eef394dd1", + "387e1774-0250-5c72-b11c-069bdf3ef9ea" + ], + "contexts": [ + "Genes signifying increased risk for both type 1 and type 2 dia-betes have been identified. Genomewide association studies have identified over 50 loci associated with an increased genetic risk of type 1 diabetes. Several T1D candidate genes for increased risk of developing type 1 diabetes have been sug-gested or identified within these regions, but the molecular basis by which they contribute to islet cell inflammation and beta cell destruction is not fully understood. 12 Also, several", + "Genetics of Type 2 Diabetes Chapter 12 197400 multiallelic markers (short tandem repeats or microsatellites, with a density of 1 marker/10 cmol) allows identi cation of polymorphic markers showing strong allele identity by descent in diabetic family members (i.e. allele sharing in sibships is signi - cantly higher than 50%). Once identi ed, such susceptibility genes for diabetes may then be positionally cloned in the intervals of linkage.", + "3. Katsarou, A. etal. Type 1 diabetes mellitus. Nat. Rev. Dis. Primers 3, 17016 (2017). 4. Onengut-Gumuscu, S. etal. Fine mapping of type 1 diabetes susceptibility loci and evidence for colocalization of causal variants with lymphoid gene enhancers. Nat. Genet. 47, 381386 (2015). 5. Barrett, J. C. etal. Genome-wide association study and meta-analysis find that over 40 loci affect risk of type 1 diabetes. Nat. Genet. 41, 703707 (2009).", + "Clinical Risk Factors, DNA Variants, and the Development of Type 2 Diabetes n engl j med 359;21 www.nejm.org november 20, 2008 2229(Fig. 3). An increase in the BMI and a concomi - tant decrease in insulin sensitivity during the 8-year period were consistent findings, with no differences between subjects at high and low genetic risk (Fig. 3A and 3B). However, subjects with a high genetic risk did not increase their insulin secretion (disposition index) to compen -", + "and genetic markers to improve the prediction of type 2 diabetes: theEPIC-Potsdam Study. Diabetes Care . 2009;32:2116 2119 (in eng). 56. Cauchi S, Meyre D, Durand E, et al. Post genome-wide association studies of novel genes associated with type 2 diabetes show gene-gene interaction and high predictive value. PLoS One . 2008;3(5): e2031 . 57. Lyssenko V, Jonsson A, Almgren P, et al. Clinical risk factors, DNA variants, and the development of type 2 diabetes. N Engl J Med . 2008;359:2220 2232 (in eng).", + "etically expressed homeobox variant (rs1111875) on type 2 diabetes risk. Molecular Genetics and Metabolism , 102 (2), 194199. Watanabe, R. M., Black, M. H., Xiang, A. H., Allayee, H., Lawrence, J. M., & Buchanan, T. A. (2007). Genetics of gestational diabetes mellitus and type 2 diabetes. Diabetes Care , 30 (Suppl. 2), S134S140. Williams, M. A., Qiu, C., Dempsey , J. C., & Luthy , D. A. (2003). Familial aggregation of type 2", + "markers, genetic markers do not change with disease progression.Dimas and collaborators examined the association of 37 establishedT2D susceptibility loci and indices of proinsulin processing, insulin secretion, and insulin sensitivity in 58,614 nondiabetic subjects [6]. Cluster analysis classi ed the risk loci into ve major categories on the basis of their association with glycemic phenotypes. The rst cluster was characterized by the effects of the risk alleles of PPARG ,KLF14 ,", + "recently, meta-analysis of GWAS data involving African American type 2 diabetes patients identified similar loci to the previous studies with the addition of two novel loci, HLA-B and INS-IGF[157]. These results provide strong evidence of common genetic determinants including common specific genes that are linked to diabetes. A small list of specific genetic markers seem strongly associated with the risk of developing type 2 diabetes including the TCF7L2[158] and CAPN10[159,160]", + "Clinical Risk Factors, DNA Variants, and the Development of Type 2 Diabetes n engl j med 359;21 www.nejm.org november 20, 2008 2231MPP subjects (P = 0.001) and from 0.79 to 0.83 in the Botnia subjects (P = 0.006). Of the 16 loci that have been associated with type 2 diabetes previously,8-15 we showed that 11 TCF7L2, PPARG, FTO, KCNJ11, NOTCH2, WFS1, CDKAL1, IGF2BP2, SLC30A8, JAZF1, and HHEX were associated with an enhanced risk of future", + "Clinical Risk Factors, DNA Variants, and the Development of Type 2 Diabetes n engl j med 359;21 www.nejm.org november 20, 2008 2227(Fig. 1B), whereas impaired fasting glucose or impaired glucose tolerance developed in 313 of 2039 subjects (15.4%). Clinical Factors Predicting Incidence of Diabetes In both the MPP and Botnia studies, a family his - tory of diabetes, an increased BMI, and increased levels of blood pressure and serum levels of tri -" + ], + "answer": "Yes, there are specific genetic markers that can indicate a higher risk for Type 1 versus Type 2 diabetes. For Type 1 diabetes, genome-wide association studies have identified over 50 loci associated with an increased genetic risk. Several candidate genes within these regions have been suggested or identified, although the exact molecular mechanisms by which they contribute to islet cell inflammation and beta cell destruction are not fully understood [1]. For Type 2 diabetes, specific genetic markers have also been identified. Some of the loci associated with an increased risk include TCF7L2, PPARG, FTO, KCNJ11, NOTCH2, WFS1, CDKAL1, IGF2BP2, SLC30A8, JAZF1, and HHEX [9]. Additionally, markers such as TCF7L2 and CAPN10 have been strongly associated with the risk of developing Type 2 diabetes [8]. These findings indicate that while both types of diabetes have genetic components, the specific markers and loci associated with each type differ, reflecting their distinct pathophysiological mechanisms.", + "question": "Are there specific genetic markers that can indicate a higher risk for Type 1 versus Type 2 diabetes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_18 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_18 new file mode 100644 index 0000000..fbd2614 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_18 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2004 - Diabetes Genes a.pdf", + "2015 - Recent Trends in Therapeutic Approaches for Diabetes Management A Comprehensive Updat.pdf", + "2010 - The Genetics of Type 2 Diabetes.pdf", + "2015 - Recent Trends in Therapeutic Approaches for Diabetes Management A Comprehensive Updat.pdf", + "2018 - A theoretical framework for interaction of nursing discipline.pdf", + "2015 - Recent Trends in Therapeutic Approaches for Diabetes Management A Comprehensive Updat.pdf", + "2005 - Animal models of diabetes mellitus.pdf", + "2017 - Painting a new picture of personalised medicine for diabetes.pdf", + "2015 - Recent Trends in Therapeutic Approaches for Diabetes Management A Comprehensive Updat.pdf", + "2015 - Recent Trends in Therapeutic Approaches for Diabetes Management A Comprehensive Updat.pdf" + ], + "extraction_id": [ + "a5ae065c-371f-5459-830b-7a34891ca091", + "6b2ac076-ee4b-53b3-b49b-1d15f46e6a98", + "c4de4c07-4749-5401-bbf3-16988c132852", + "48643e77-c5b4-5042-8f08-82c986d9f5b2", + "abf78c3a-ad53-5c86-979d-2d9d176a51a4", + "168e94e9-e8c2-547c-878a-1e5306564193", + "3dca156c-64c4-577f-b0a6-069de0f31234", + "1cd3076d-af86-55d7-903c-9065bc640af0", + "6b2ac076-ee4b-53b3-b49b-1d15f46e6a98", + "168e94e9-e8c2-547c-878a-1e5306564193" + ], + "document_id": [ + "805ad0d5-6a3d-5e5f-b1ba-121e10c6daaa", + "ec4921c2-af14-56cc-aed3-65f8ea236bde", + "a1d211d4-279e-51d7-b2b2-33bc2763d089", + "ec4921c2-af14-56cc-aed3-65f8ea236bde", + "4c90f95f-3365-522e-9eb4-9ea002beddb2", + "ec4921c2-af14-56cc-aed3-65f8ea236bde", + "2fd381ac-2898-5a8c-af93-bcc86e7dec14", + "e226b2b1-0bc4-5d79-b931-ad47f21be045", + "ec4921c2-af14-56cc-aed3-65f8ea236bde", + "ec4921c2-af14-56cc-aed3-65f8ea236bde" + ], + "id": [ + "chatcmpl-AIFrIc4qRPbtDcHbcrNOicZwU9hKr", + "59cce584-cd38-52d1-bdaa-d5500175eefb", + "d37e62ab-6261-5f14-8423-3b6e2574422e", + "f4e8a3c8-0b85-5595-8917-933aced8b3ba", + "cb7178a0-7015-555c-801a-cd2d258cf3dc", + "d5963c8e-686f-52f5-a6de-b978d5c40e20", + "0b4a495d-fdee-515a-a524-d9415b17f97e", + "13b73999-262c-50e1-b668-2d5f7ca02067", + "1299cc23-f6b0-5801-bead-b46ac90bc3a8", + "3201da93-5a34-5164-8bf4-c98d32019019", + "0418b345-7005-5d7d-a79f-570fb61bd14b" + ], + "contexts": [ + "unraveling the pathophysiological mechanisms of this disease, identifying candidate diabetic genes, and discovering and testing new therapeutic agents. The classical rodent models of diabetes allow unbiased discovery, while the new models made by genetic manipulation allow testing of the role of specific genes and tissues. Experimental animal models are an irreplaceable resource for diabetes research and are hastening the progress towards the goals of better treatment, prevention, and cure.", + "is absence of reliable methods for generating specific celltypes,immunologicalrejectionofthetransplantedcells,anddifficulty in purification of specific lineages [55]. Furtherconcernsincludetheuncontrolledproliferationofthetrans-planted embryonic stem cells into a specific type, once theyaretransplanted[56].Still,despiteofitsmanifoldlimitationsboth scientific and ethical, the application of stem cell tech-nologyholdsimmenseprospectsintreatmentofdiabetes. 6. Gene Therapy in Diabetes", + "T ogether, these discoveries will continue to improve our understanding of the biologic mechanisms that maintain glucose homeostasis, and of still hidden molecular defects leading to chronic hyperglycemia, and could also lead to the development of more speci cally targeted antidiabetic drugs or even gene - based therapies. Moreover, pharmacogenetic testing might then be used to predict, for each patient, the therapeutic response to different classes of drugs. The identi cation of T2DM genes will", + "Greatstrideshavebeenmadeclinicallyintheprevention, development,andtreatmentofthediseasebutnotherapeuticmethod have been completely successful till date. With newtechnologies revolutionizing the treatment possibilities, thesearch for an effective medication is not far ahead. Theextensive research leading to the discovery of the pathwaygenes contributing to the development of the disease andthe sequencing of complete genomes have revolutionized the diabetes research. The development of the techniques", + "into different genetic levels of disease categories, from which pre- vention or treatment methods could be provided accordingly [ 4]. For example, some forms of diabetes are directly related to a change in a single gene [ 34]. Some patients who are diagnosed with type 1 diabetes can now be tested for one of monogenic diabetes. The appropriate treatment for these patients is not injecting insulin, but giving oral sulfonylureas [ 34]. Moreover, it is now well understood", + "pp .430435,2003. [58] M. Zalzman, S. Gupta, R. K. Giri et al., Reversal of hyperglycemia in mice by using human expandable insulin- producing cells differentiated from fetal liver progenitor cells,Proceedings of the National Academy of Sciences of the United StatesofAmerica ,vol.100,no .12,pp .72537258,2003. [59] H.-S. Jun and J.-W. Yoon, Approaches for the cure of type 1 diabetes by cellular and gene therapy, Current Gene Therapy , vol.5,no.2,pp.249262,2005.", + "transgenics. It is likely that animal models will play an importantrole in the eventual cure of human diabetes mellitus. Competing interests None declared. References 1Sima AAF, Shafrir E, eds. Animal Models of Diabetes: A Primer. Amsterdam: Harwood Academic Publishers, 2000. 2British Union for the Abolition of Vivisection. Home page. Available from: http://www.buav.org. 3Patterson C. Eternal Treblinka. Our Treatment of Animals and the Holocaust . New York: Lantern Books, 2002. 4Regan T.", + "Third, this view of diabetes pathogenesis is consistent with the growing portfolio of available therapies. We have agents and interventions that can prevent or ameliorate diabetesthrough, for example, beneficial effects on islet function (e.g. sulfonylureas), obesity (weight loss), insulin resistance (e.g. exercise), fuel partitioning (e.g. thiazolidinediones) andmicrobiome content (metformin, possibly). Just as diabetes risk alleles influence metabolic phenotype through pushing", + "aprospectivetherapeuticapproachfortype1diabetes[59]. Thein vivogene therapy is the method of choice as a therapeutic strategy because it is simpler and the vectorcontaining the desired gene is directly inserted into thepatient, but the development of safe (not toxic to host)and effective vectors remains as a challenging task for genetherapist. Presently, the strategies for in vivotherapy involve", + "betacellulin gene therapy induces islet neogenesis in the liver a n dr e v e r s e sd i a b e t e si nm i c e , Nature Medicine ,v o l .9 ,n o .5 , pp.596603,2003. [73] S. Ferber, A. Halkin, H. Cohen et al., Pancreatic and duode- nal homeobox gene 1 induces expression of insulin genes inliver and ameliorates streptozotocin-induced hyperglycemia, Nature Medicine ,vol.6,no .5,pp .568572,2000. [74] P.A.Halban,S.E.Kahn, A.Lernmark,andC.J.Rhodes,Gene andcell-replacementtherapyinthetreatmentoftype1diabetes." + ], + "answer": "New gene therapies have the potential to cure or treat diabetes through several innovative approaches: 1. **Gene-Based Therapies**: Advances in understanding the biological mechanisms that maintain glucose homeostasis and the molecular defects leading to chronic hyperglycemia could lead to the development of gene-based therapies. These therapies aim to target specific genetic factors involved in diabetes, potentially offering more precise treatment options [3]. 2. **In Vivo Gene Therapy**: This approach involves directly inserting a vector containing the desired gene into the patient. It is considered a promising therapeutic strategy for type 1 diabetes, although challenges remain in developing safe and effective vectors [9]. 3. **Inducing Islet Neogenesis**: Gene therapy techniques, such as betacellulin gene therapy, have been shown to induce islet neogenesis in the liver and reverse diabetes in mice. This suggests that gene therapy can stimulate the body to produce insulin-producing cells, offering a potential cure for diabetes [10]. These strategies highlight the potential of gene therapies to address the underlying genetic causes of diabetes and restore normal insulin production and glucose regulation.", + "question": "How can new gene therapies potentially cure or treat diabetes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_19 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_19 new file mode 100644 index 0000000..f60ea60 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_19 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2021 - Genomic Medicine in Diabetes Improving the Diagnostic Rate of Monogenic Diabetes.pdf", + "2015 - Diabetes mellitus The epidemic of the century.pdf", + "2007 - Pharmacogenetics of metformin response a step in the path toward personalized medicine.pdf", + "2021 - Monogenic diabetes a gateway to precision medicine.pdf", + "2012 - Systems Biology Approaches to Nutrition.pdf", + "2021 - Monogenic diabetes a gateway to precision medicine.pdf", + "2004 - Diabetes Genes a.pdf", + "2019 - HDAC4 mutations cause diabetes and induce \u2010cell FoxO1 nuclear exclusion.pdf", + "2005 - Type 2 diabetes mellitus from genes to disease.pdf", + "2021 - Genomic Medicine in Diabetes Improving the Diagnostic Rate of Monogenic Diabetes.pdf" + ], + "extraction_id": [ + "29df597d-e40d-5bc8-8ee0-89141d8e7fc0", + "e119acfb-4ad6-515e-a1bb-7796d283befc", + "c66bcb9f-15af-5843-9e9c-168e8cf230d0", + "38df3fac-cb86-5e74-b270-1e1e9e12dcdb", + "8d7fefe4-325f-5c64-9fee-0587c545d5ab", + "5a39ee4b-ba00-56d6-ba6c-0edeac3b4f2e", + "c6bf083c-f045-55e2-9eae-ff96a4ceea4c", + "7f53ea65-79ed-5207-9397-68b6d14bc19c", + "d79047d9-58d0-5440-b63c-e648b5df5538", + "29df597d-e40d-5bc8-8ee0-89141d8e7fc0" + ], + "document_id": [ + "e315a891-ba59-57e9-856b-602544375324", + "e114dd28-fd39-56df-bdeb-8806474a6c10", + "12344230-0ed1-516f-bf2d-9c6e71ac76b5", + "0b6ff786-6a7b-5d24-ba5e-7a61fee7757f", + "6955478b-950d-5d29-b24c-3a5ca656f3ae", + "0b6ff786-6a7b-5d24-ba5e-7a61fee7757f", + "805ad0d5-6a3d-5e5f-b1ba-121e10c6daaa", + "7e4028b2-d5c1-5ddc-a06d-fd4f767d0f39", + "52687a38-6a4b-51d2-aafa-812c76981dfe", + "e315a891-ba59-57e9-856b-602544375324" + ], + "id": [ + "chatcmpl-AIFrQL8Phj0Q41oiXeOOZsabJcULg", + "c38627c6-0dd1-5dec-aeb6-ea1edb924480", + "d704148a-88c0-58fe-810a-89b767a1f53b", + "7bbf950d-cbf8-5221-8ea6-b3571fab4fad", + "35efeaf0-c6b6-509e-9426-d23c8727164f", + "55e16624-4a02-5fba-bbe7-a07db8559401", + "854afd34-91ed-5817-b24e-1fd5894261f3", + "a74728c6-2903-5035-afd9-0f6a0f0c295e", + "5b06a49e-7ef9-558a-b402-11866c555cd5", + "fedbf66e-cfd2-52bb-b9db-393d815aade7", + "c48920f3-1236-5921-b2a1-f09edba1e7ec" + ], + "contexts": [ + "to improve diagnosis. Monogenic vs. polygenic diabetes Monogenic and polygenic diabetes are traditionally considered distinct, with monogenic diabetes resulting from one highly penetrant variant in one gene in a given individual, and polygenic diabetes resulting from the contribution of several variants with smaller effects in the context of environmental/lifestyle factors. In T1D, autoimmune dysfunction is the prominent mechanism, with variation in the major histocompatibility", + "represent about 2%-5% of diabetes patients. Mono - genic diabetes results primarily from gene defects that lead to a decrease in beta cell number or function. Monogenic diabetes genes were identified using linkage studies or code for proteins that directly affected glucose homeostasis. The majority of genes responsible for monogenetic diabetes code for either transcription factors that participate in the control of nuclear gene expression or proteins that are located on the cell", + "diabetic patients inwhom rare, highly penetrant mutations ofasingle gene cause their diabetes (13). While com - mon variants ofthese genes that make a small contribution topolygenic diabetes may also exist (13), thevariants causing monogenic diabetes have limited util- ityinpharmacogenetics duetotheir low allele frequency. Thevast majority oftype 2diabetes patients have polygenetic forms ofthedisease that typically also require a permissive environment (e.g., obesity, sed-", + "diabetes exist along more of a continuum than previously appre - ciated. Therefore, knowledge about monogenic diabetes not only provides opportunities for etiology-based treatment of the minori- ty of individuals with highly penetrant variants, but also informs broader understanding of diabetes etiology. Types of monogenic diabetes Maturity-onset diabetes of the young MODY comprises most monogenic diabetes cases, with classical characteristics of young diagnosis age, family history of diabe -", + "Monogenic Diabetes Monogenic diabetes is a class of diabetes associated with genetic defects in beta - cell function. They are frequently associated with early onset of hyperglycemia (typically before 25 years of age). Three common forms of mono-genic diabetes include maturity - onset diabetes of the", + "HNF4A-MODY and requires genetic testing to diagnose. Here we will describe monogenic diabetes types, etiologies, diagnosis, management, and strategies to improve diagnosis. Monogenic versus polygenic diabetes Monogenic and polygenic diabetes are traditionally considered distinct, with monogenic diabetes resulting from one highly pene - trant variant in one gene in a given individual and polygenic diabe - tes resulting from the contribution of several variants with smaller", + "Monogenic inheritance is caused by mutation of a single gene. There are some well-defined monogenic rodent models. In humans, monogenic obesity and diabetes exist as well, but are extremely rare. Polygenic inheritance is the result of multiple contributing genes and is the predominant mode of inheritance in human type 2 diabetes. Multiple polygenic animal models are also available. However, even in monogenic animal models, genetic background plays an important influence. For", + "(Mendelian) that may also cause type 2 diabetes (Yang & Chan, 2016). More than twenty genes highly expressed in pancreatic cells have been identified within these mono-genic subtypes (AlkortaAranburu et al., 2014). Recently, two national surveys revealed that most patients with mono-genic diabetes are likely to be unrecognized and misdiag-nosed as type 1 or type 2 diabetes (Delvecchio et al., 2017; Johansson et al., 2017). Genetic diagnosis leads to improved treatment, better prediction of disease", + "Key words: diabetes, gene, polygenic, monogenic Introduction Diabetes is one of the most common metabolic disor - ders. It is estimated that the number of diabetes pa - tients worldwide has already exceeded 200 million [92]. This creates a need to understand the etiology ofthe disease, genetic and enviromental factors influ - encing development of diabetes. Diabetes is a group of metabolic diseases that are characterized by ele - vated glucose level. Poorly controlled or undiagnosed", + "2 1.1.2 Introduction Monogenic diabetes is caused by a single defect in one of over 40 genes1,2. Since MODY (maturity onset diabetes of the young) was named by Fajans for the T2D -like presentation in young people with an autosomal dominant pattern of inheritance3,4, our understanding of phenotypic and genetic heterogeneity in monogenic diabetes has increased. The major monogenic diabetes categories are MODY, neon atal diabetes" + ], + "answer": "Monogenic and polygenic diabetes are distinct forms of diabetes with different genetic underpinnings: 1. **Monogenic Diabetes**: This form of diabetes results from a mutation in a single gene that is highly penetrant, meaning it has a strong effect on the individual who carries it [1], [6]. Monogenic diabetes is often associated with defects in beta-cell function, leading to a decrease in the number or function of these cells [2]. It is typically characterized by early onset, often before the age of 25, and can include conditions like Maturity-Onset Diabetes of the Young (MODY) [5]. Monogenic diabetes is relatively rare, representing about 2%-5% of diabetes cases [2]. 2. **Polygenic Diabetes**: In contrast, polygenic diabetes results from the combined effect of multiple genetic variants, each contributing a small effect, along with environmental and lifestyle factors [1], [6]. This form of diabetes is more common and is the predominant mode of inheritance in type 2 diabetes [7]. The genetic variants involved in polygenic diabetes do not have as strong an effect individually as those in monogenic diabetes, but together they contribute to the disease risk in the presence of other factors like obesity and sedentary lifestyle [3]. In summary, monogenic diabetes is caused by a single gene mutation with a strong effect, while polygenic diabetes involves multiple genes with smaller effects combined with environmental influences.", + "question": "What is the difference between monogenic and polygenic diabetes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_2 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_2 new file mode 100644 index 0000000..2222b39 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_2 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2012 - Genome-wide meta-analysis of genetic susceptible genes for Type 2 Diabetes.pdf", + "2008 - Clinical Risk Factors, DNA Variants.pdf", + "2010 - A Genome-Wide Association Study Identifies.pdf", + "2012 - Association between type 2 diabetes genetic susceptibility loci and visceral and subcutaneous fat area as determined by computed tomography.pdf", + "2018 - High-Throughput Approaches onto Uncover (Epi)Genomic Architecture of Type 2 Diabetes.pdf", + "2010 - Genomics, Type 2 Diabetes, and Obesity.pdf", + "2008 - SNPs in KCNQ1 are associated with susceptibility to type 2 diabetes.pdf", + "2010 - Diabetes in Asia.pdf", + "2003 - Genome-wide screen in obese pedigrees with type 2 diabetes.pdf", + "2008 - Clinical Risk Factors, DNA Variants.pdf" + ], + "extraction_id": [ + "e61efd89-f844-5c3a-98b9-1a827b58b507", + "2643b341-8c50-5cea-af36-86a8b070a80e", + "f5b0ecdc-fdf2-5ac3-bebb-9c9ff5863935", + "e0bbfc0e-ae79-568c-b704-96febad87d6f", + "aba850e8-8c0d-5256-b2ba-fa1dfc221114", + "8a28c11f-e0d2-526b-ac85-2f2fbf054fc5", + "706cb4a1-57c4-5b63-9d4e-4a7ea027a8f1", + "11faf4fe-7b71-562e-9901-c428ab20b285", + "1110f7b4-ab5a-5b41-b37d-a992b29cb20c", + "0aae948a-50f9-568a-b0dc-5960a2d2ceaa" + ], + "document_id": [ + "f5096148-3f85-57c1-8414-2f240ea42068", + "0018610a-9c86-5e2d-a27d-f66cf4f8519d", + "0301881d-40dd-5343-b22e-927d58c2cb2a", + "b86d3101-f383-520b-8360-7d80bc7ec6fa", + "1cb0c4ac-c1fe-55c2-919c-52cd5018c00d", + "3d629777-f1b6-5450-94ef-56736e5a4e10", + "78702b1e-0f14-5757-b967-9bcb7852f6ac", + "0be842b8-7f69-503b-baed-c336e5c834d6", + "335a3c08-14d3-5511-ab84-340e64c6f993", + "0018610a-9c86-5e2d-a27d-f66cf4f8519d" + ], + "id": [ + "chatcmpl-AIFpDYOJMy59ARMmvejZqYaNW81X4", + "47558743-2803-51a7-856b-8f6606712d08", + "aa72551a-ac0c-5d7d-8057-34f229f68eb1", + "225792f4-c56b-5139-8bec-d5d1d393a6b2", + "8b718138-167a-50b0-afb7-4b507abc05ff", + "e3cbe02b-9a3c-5b66-a5fb-d9d75b5db3f9", + "f3ce8455-f123-5840-8a50-da7885c7e18d", + "dfba6b2e-1531-5ac4-a41d-aa4a6d76d7e0", + "78d81651-7215-596a-b128-37e429dc7edb", + "b0d3a09d-36a3-5c6e-a110-3fccddaa74b7", + "8469faae-c6c9-5fd4-8437-870eef394dd1" + ], + "contexts": [ + "novel risk loci for type 2 diabetes. Nature 2007, 445(7130) :881-885.5. Gaulton KJ, Willer CJ, Li Y, Scott LJ, Conneely KN, Jackson AU, Duren WL, Chines PS, Narisu N, Bonnycastle LL, et al:Comprehensive association study of type 2 diabetes and related quantitative traits with 222 candidate genes. Diabetes 2008, 57(11) :3136-3144. 6. Hu C, Zhang R, Wang C, Wang J, Ma X, Lu J, Qin W, Hou X, Bao Y, Xiang K, et al:PPARG, KCNJ11, CDKAL1, CDKN2A-CDKN2B, IDE-KIF11-HHEX,", + "ly associated with type 2 diabetes: TCF7L2, KCNJ11, and PPARG . 5-7 However, in 2007, a number of novel genetic variants ( CDKAL1, IGF2BP2, the locus on chromosome 9 close to CDKN2A/CDKN2B, FTO, HHEX, SLC30A8, and WFS1)8-14 were shown to in - crease susceptibility to type 2 diabetes in repro - ducible studies. Furthermore, a recent meta-analy - sis identified six novel variants ( JAZF1, CDC123/ CAMK1D, TSPAN8/LGR5, THADA, ADAMTS9, and NOTCH2 ) that are associated with type 2 dia - betes. 15", + "2009. There are now at least 19 loci containing genes that increase risk of T2D, including PPARG [27], KCNJ11 [27], KCNQ1 [28,29], PLoS Genetics | www.plosgenetics.org 1 February 2010 | Volume 6 | Issue 2 | e1000847", + "et al. Association between type 2 diabetes loci and measures of fatness. PLoS One 5, e8541 (2010). 22 Ng, M. C., Park, K. S., Oh, B., Tam, C. H., Cho, Y. M., Shin, H. D. et al. Implication of genetic variants near TCF7L2, SLC30A8, HHEX, CDKAL1, CDKN2A/B, IGF2BP2, and FTO in type 2 diabetes and obesity in 6,719 Asians. Diabetes 57,22262233 (2008). 23 Thorsby, P. M., Midthjell, K., Gjerlaugsen, N., Holmen, J., Hanssen, K. F., Birkeland, K. I.", + "Genome-wide association studies validated these old culprits of T2D and expanded them to include hundreds of single-nucleotide variants (SNVs) that represent more than 150 genomic loci that are associated with T2D, insulin secretion, and insulin resistance [ 11]. Besides TCF7L2 ,PP ARG , and KCNJ11 loci, the most replicated T2D susceptibility variants identied in GWASs were found in and around CDKN2A/2B ,IGF2BP2 ,SLC30A8 ,CDKAL1 and FTO genes [ 1215]. The variants that are most", + "Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes. Nat Genet 2008;40:638-45. 20. Dupuis J, Langenberg C, Prokopenko I, et al. New genetic loci implicated in fasting glucose homeostasis and their im - pact on type 2 diabetes risk. Nat Genet 2010;42:105-16. 21. Qi L, Cornelis MC, Kraft P, et al. Ge - netic variants at 2q24 are associated with susceptibility to type 2 diabetes. Hum Mol Genet 2010;19:2706-15.", + "multiple loci associated with susceptibility to type 2 diabetes, includ- ingTCF7L2 (transcription factor 7-like 2), which had been originally identied by a large-scale association mapping prompted by prior evidence of linkage in that area2,SLC30A8 (solute carrier family 30 member 8), HHEX (haematopoietically expressed homeobox), CDKAL1 (CDK5 regulatory subunit associated protein 1-like 1), CDKN2A/B (cyclin-dependent kinase inhibitor 2A/B) and IGF2BP2 (insulin-like growth factor 2 mRNA-binding protein 2)37.", + "associated with susceptibility to type 2 diabetes mellitus. Nat Genet 2008; 40: 109297 . 74 Unoki H, Takahashi A, Kawaguchi T, et al. SNPs in KCNQ1 are associated with susceptibility to type 2 diabetes in East Asian and European populations. Nat Genet 2008; 40: 1098102. 75 Lyssenko V, Lupi R, Marchetti P, et al. Mechanisms by which common variants in the TCF7L2 gene increase risk of type 2 diabetes. J Clin Invest 2007; 117: 215563. 76 Lyssenko V, Jonsson A, Almgren P, et al. Clinical risk factors, DNA", + "type 2 diabetes or the inability to replicate linkage withdened loci. However, at least one susceptibility gene, namelyCAPN10, was found using a genome-wide scan approach [3]. Obesity is the greatest risk factor for type 2 diabetes mellitus, as it is known to induce insulin resistance via variousmechanisms ( TNF release, free fatty acids, etc.). Both", + "Clinical Risk Factors, DNA Variants, and the Development of Type 2 Diabetes n engl j med 359;21 www.nejm.org november 20, 2008 2231MPP subjects (P = 0.001) and from 0.79 to 0.83 in the Botnia subjects (P = 0.006). Of the 16 loci that have been associated with type 2 diabetes previously,8-15 we showed that 11 TCF7L2, PPARG, FTO, KCNJ11, NOTCH2, WFS1, CDKAL1, IGF2BP2, SLC30A8, JAZF1, and HHEX were associated with an enhanced risk of future" + ], + "answer": "The most common genetic loci associated with an increased risk of Type 2 diabetes include: 1. **TCF7L2** [2], [5], [7] 2. **KCNJ11** [2], [5] 3. **PPARG** [2], [5] 4. **CDKAL1** [2], [5], [7] 5. **CDKN2A/CDKN2B** [2], [5], [7] 6. **IGF2BP2** [2], [5], [7] 7. **SLC30A8** [2], [5], [7] 8. **FTO** [2], [5] 9. **HHEX** [2], [5], [7] These loci have been identified and replicated in multiple studies as being associated with an increased susceptibility to Type 2 diabetes.", + "question": "What are the most common genetic loci associated with an increased risk of Type 2 diabetes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_20 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_20 new file mode 100644 index 0000000..f41bd36 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_20 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2004 - Diabetes Genes a.pdf", + "2003 - The Inherited Basis of Diabetes Mellitus.pdf", + "2010 - The Role of Epigenetics in the Pathology of Diabetic Complications.pdf", + "2010 - The Genetics of Type 2 Diabetes.pdf", + "2016 - The Genomics of Type 1 Diabetes.pdf", + "2015 - Epigenetic mechanisms in diabetic complications and metabolic memory.pdf", + "2011 - The identification of gene expression.pdf", + "2016 - Putting the Genome in Context Gene-Environment Interactions.pdf", + "2008 - Glossary of Genetics Genomics Terms.pdf", + "2011 - The identification of gene expression.pdf" + ], + "extraction_id": [ + "a19924b0-a834-5100-8b24-6b57dcddb82a", + "2aa8d99c-99d7-55de-aa2a-c24a46ea9058", + "919cb859-8f47-5930-8713-090520be523f", + "c4de4c07-4749-5401-bbf3-16988c132852", + "5e43ab7d-3e2b-551c-9a90-f91e970cb8d7", + "312b1856-e1b1-5ae7-8cba-370becf5f7cb", + "e5e4169a-56d8-539f-8ebc-ad44eb75433f", + "c362793d-c70f-5225-afe5-88098042daef", + "53e868dd-b318-5cf3-8b2e-98a548aab7cf", + "e5e4169a-56d8-539f-8ebc-ad44eb75433f" + ], + "document_id": [ + "805ad0d5-6a3d-5e5f-b1ba-121e10c6daaa", + "7b85b290-d711-55d5-9b1e-b06e4d6f14a2", + "766edfd5-4756-51bf-b636-c94b041d030c", + "a1d211d4-279e-51d7-b2b2-33bc2763d089", + "4933cdc2-7d36-5181-87c9-63b58498839f", + "470f1f94-792d-5273-a88f-7e06084951c5", + "61558082-f092-5a1d-abbb-a5a81e8a959b", + "ea43bb66-b6fe-5682-8f48-90568c080401", + "c66d2572-071d-5aaf-829c-b3ca6cf6d697", + "61558082-f092-5a1d-abbb-a5a81e8a959b" + ], + "id": [ + "chatcmpl-AIFrXDhQoyphIy4Ti77mFez47y0g5", + "e7d89095-ef66-5c11-982c-879791dd14a4", + "c21caf96-f04a-551d-92b2-f4ff084d43c8", + "fceee048-359b-5854-b45d-5531b9374ce8", + "f4e8a3c8-0b85-5595-8917-933aced8b3ba", + "df21554e-6053-53ae-aae5-e3d1dba1b1f5", + "db06230d-31c0-5947-8c1c-f58c48b6f439", + "e0b86e8e-4e1a-5f6b-9b41-e9a4f912790c", + "cc98a5b9-131e-5b60-919e-82e86b7a37a7", + "b092c8b9-edb1-55fb-ae16-c67e3298946e", + "efd7c210-858d-5125-8da9-46862e19a58a" + ], + "contexts": [ + "by performing a genetic profile on diabetic patients (pharmacogenetics). Furthermore, identification of genetic determinants of diabetic patients will better define the targets of current and future therapies, and will lead to therapies that are more specific for their genetic constitutes. SUMMARY With the advancement of the Human Genome Project, we enter the era of a sequence-based biology. Some progress has been made in the", + "Todate,studiesofdiabeteshaveplayedamajorroleinshapingthinkingabout thegeneticanalysisofcomplexdiseases.Basedontrendsingenomicinformationandtechnology,combinedwiththegrowingpublichealthimportanceofdiabetes,diabetes will likely continue to be an important arena in which methods will bepioneeredandlessonslearned.Itiswithgreatenthusiasmthatwelookforwardtothis effort, and with avid curiosity we await to see whether the lessons of todaywill be supported by the data of tomorrow.", + "DNA code. Therefore, greater unders tanding of the epigenetic basis of disease could enable the 576 discovery new therapeutic targets for the treat ment of numerous human diseases including 577 diabetes and its complications. 578 579 580", + "T ogether, these discoveries will continue to improve our understanding of the biologic mechanisms that maintain glucose homeostasis, and of still hidden molecular defects leading to chronic hyperglycemia, and could also lead to the development of more speci cally targeted antidiabetic drugs or even gene - based therapies. Moreover, pharmacogenetic testing might then be used to predict, for each patient, the therapeutic response to different classes of drugs. The identi cation of T2DM genes will", + "research will contribute positive ly to the life of people living with T1D . Being able pinpoint mutations, and then discover how they contribute to the genetic cause of a condition, can help to open up path s for pharmaceutical treatments. Currently, m ost treatment strategies for genetic disorders do not alter the underlying genetic mutation; but are designed to improve particular signs and symptoms associated with the disorder. For instance, T1D is managed by", + "Epigenomic approaches: applications in diabetic complications research Epigenetic studies in human disease have been greatly accel- erated as a result of advances in whole-genome and epige- nome profiling technologies as well as bioinformatics andgenomic data analysis platforms [ 99,100]. DNAme is analysed using bisulfite conversion of genomic DNA, immu- noprecipitation of methylated DNA, followed byhybridisation to arrays or next-generation sequencing to ob-", + "new therapeutic targets and identify potential diabetic neuropathy biomarkers. The genes identied in the current study conrm datagathered from experimental models of diabetes and provide a comprehensive picture of the expression of multiple targets in asingle human tissue sample. Our initial analyses of this data set classied the patient samples based on myelinated bre density and found that two large groups emerged; those with a loss of myelinated bre density 5500 bres/mm", + "DNA variation with disease processes in a range of settings, from cell lines to human populations, and major advances have been made in coupling these complex datasets with information about extrinsic environmental exposures including drug prescription in ways that allowthe logical interrogation of gene-drug and gene-lifestyle interactions. Doing so may teach us about disease etiology and help stratify type 2 diabetes (T2D) into subclasses that can be treated more effectively, with", + "that genetic studies will ultimately identify key genetic elements that help determine susceptibility to diabetes,disease progression, and responsiveness to specific therapies, as well as help identify novel targets for futureintervention. A substantial number of genetic loci, gene polymorphisms, and mutations have already beenreported as having variable degrees of association with one or other type of diabetes (type 1, type 2, maturityonset diabetes of the young [MODY]), while others appear to be involved", + "the onset and progression of diabetic neuropathy is of primeimportance. The current study takes an important rst step towards this goal by identifying specic sets of genes whose expression accurately classies patient samples with regard to diabetic neuropathy pro- gression and by analysing their interactions within known cellularpathways. Identifying common elements in these complex net-works will yield novel insights into disease pathogenesis, provide" + ], + "answer": "Studying the DNA of people with diabetes helps scientists find better treatments or cures in several ways: 1. **Identification of Genetic Determinants**: By performing genetic profiling on diabetic patients, scientists can identify genetic determinants that define the targets of current and future therapies. This leads to the development of therapies that are more specific to the genetic makeup of individuals with diabetes [1]. 2. **Understanding Disease Mechanisms**: Genetic studies improve our understanding of the biological mechanisms that maintain glucose homeostasis and reveal molecular defects leading to chronic hyperglycemia. This knowledge can lead to the development of more specifically targeted antidiabetic drugs or even gene-based therapies [4]. 3. **Pharmacogenetics**: Pharmacogenetic testing can be used to predict therapeutic responses to different classes of drugs for each patient, allowing for more personalized treatment plans [4]. 4. **Discovery of New Therapeutic Targets**: A greater understanding of the genetic and epigenetic basis of diabetes can enable the discovery of new therapeutic targets, potentially leading to novel treatments for diabetes and its complications [3]. 5. **Stratification of Diabetes Subclasses**: By analyzing DNA variations and their interactions with environmental factors, scientists can stratify type 2 diabetes into subclasses. This stratification allows for more effective treatment strategies tailored to specific genetic and lifestyle interactions [8]. 6. **Identification of Key Genetic Elements**: Genetic studies can identify key genetic elements that determine susceptibility to diabetes, disease progression, and responsiveness to specific therapies. This information helps in identifying novel targets for future interventions [9]. Overall, studying the DNA of people with diabetes provides critical insights that drive the development of more effective and personalized treatments.", + "question": "How does studying the DNA of people with diabetes help scientists find better treatments or cures?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_3 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_3 new file mode 100644 index 0000000..fc94ef5 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_3 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2009 - Prioritizing genes for follow-up from genome wide association studies using information on gene expression in tissues relevant for type 2 diabetes mellitus.pdf", + "2009 - Cohorts for Heart and Aging Research in Genomic.pdf", + "2014 - Identification of novel risk genes associated with type 1 diabetes mellitus.pdf", + "2020 - Genome-wide association analysis of type 2 diabetes in the EPIC-InterAct study.pdf", + "2007 - Genome\u2013wide association studies provide new insights into type 2 diabetes aetiology..pdf", + "2013 - Systems Biology Approach Reveals Genome to Phenome Correlation in Type 2 Diabetes.pdf", + "2021 - Genome-wide association studies identify two novel loci.pdf", + "2015 - Genome-wide studies to identify risk factors for kidney disease.pdf", + "2020 - Identification of novel functional CpG-SNPs associated with type 2 diabetes and coronary artery disease..pdf", + "2009 - Gene prioritization based on biological plausibility over genome wide association studies renders new loci associated with type 2 diabetes.pdf" + ], + "extraction_id": [ + "e2b46a32-6616-55ad-8511-31ee8f9cce45", + "746e7837-d0f3-5a73-bfef-adfd748e35d6", + "4b1681f4-4088-5b15-a704-040e35e31080", + "2c601441-443d-5c47-95bb-6343378dd5dc", + "aa94128a-99f6-59f3-b5fa-33ac97b858d5", + "9369222f-e125-58c0-8f2b-cf5daa867f77", + "fc9812ae-7b35-5dac-af9b-6d60f4faaa54", + "92bd58f8-6770-5c1c-8202-19b08bd57df8", + "2341dbc6-8084-5d51-a52e-f8f667b79bbb", + "0c5401ea-2a43-5578-af0b-6ad1e818fa42" + ], + "document_id": [ + "4b1a56e7-6821-5504-b6da-27dcdf57c6a5", + "9534989a-a5a5-52d8-95b8-0ad2926f228c", + "97fe33b0-a6c7-59b6-bd34-05528e77293f", + "5dd7d700-03db-595d-b1a5-beca77f9579e", + "2ad9b6c6-56ed-5ba6-ad88-c1a6777f5196", + "ea7c2799-c259-5d0e-b40b-ecebe0a9fc9f", + "7131256d-7d55-597d-aac5-a62956736923", + "3e696b99-6306-5429-bce9-8d04a2471b2d", + "f0385a45-ad3e-5813-ab1f-b3e227d5164b", + "0fd2b5c8-9bda-5cc8-adb4-231d3842d50f" + ], + "id": [ + "chatcmpl-AIFpJNprqmrM6nedwSTz4Aw1PacbM", + "b6827ec6-aa43-53e3-8d00-19e802bc3010", + "9abaf02e-eee2-504d-be20-d589cb9a3164", + "a1e3ca85-6fd1-5364-87c5-442c3f96ba74", + "263ea999-9662-5518-a606-939f69d09f90", + "53c3668c-95f8-5fb9-b978-e4c03ddfa40f", + "7fd80e84-ec0c-564c-8e8b-278b8c622abb", + "9afcf9a9-3abf-5441-a711-55e25f1ef9b7", + "ad7955f2-824c-59f8-8357-6ee201756ec9", + "5488da5b-5efa-55cd-92c3-a0d77e587fce", + "7f17fa56-1b7a-5d51-a111-3c74b31a5821" + ], + "contexts": [ + "BMC Medical Genomics 2009, 2:72 http://www.biomedcentral.com/1755-8794/2/72 Page 2 of 8 (page number not for citation purposes)Background Genome-wide association study (GWAS) offers unbiased ways to examine association of more than a million singlenucleotide polymorphisms (SNPs) with disease [1]. Sev-eral GWAS have indentified novel genomic regions influ-encing risk for type 2 diabetes mellitus (T2DM) [2-6].However, the challenge remains to prioritize SNPs from", + "GWAS have successfully identified genetic loci associ- ated with a variety of conditions such as type 2 diabetes2 and coronary disease.35The large number of statistical tests required in GWAS poses a special challenge because few studies that have DNA and high-quality phenotypedata are sufficiently large to provide adequate statisticalpower for detecting small to modest effect sizes. 6Meta- analyses combining previously published findings have im-proved the ability to detect new loci.", + "diabetes mellitus6,7. However, the traditional GWAS ignored a large number of loci with moderate effects, because of the strin-gent signi cance thresholds used. Gene-based analysis takes a gene as a basic unit for association analysis. As this method can combine genetic information given by all the SNPs in a gene to obtain moreinformative results 8, it is being used as a novel method com- plementing SNP-based GWAS to identify disease susceptibilitygenes. Notably, this method can increase our chance of nd-", + "1. Genome-wide association studies (GW AS) have made considerable progress in identifying genetic risk factors and in providing evidence for more in-depth understanding of the biological and pathological pathways underlying T2D. A recent study performed a meta-analysis of T2D across 32 GW AS of European ancestry par - ticipants and identified 243 genome-wide significant loci (403 distinct genetic variants) associated with T2D risk", + "that a genome-wide approach could uncover previously unexpected disease pathways. In early 2007, GW AS provided by far the biggest increment to date in our knowledge of the genetics of this common health problem. Six new gene regions identified Together, the six recent GW AS papers provide convincing evidence for six new gene regions involved in type 2 diabetes1621; a seventh publication describes how one of these variants alters BMI and represents by far the best example of an association", + "Abstract Genome-wide association studies (GWASs) have discovered association of several loci with Type 2 diabetes (T2D), a common complex disease characterized by impaired insulin secretion by pancreatic bcells and insulin signaling in target tissues. However, effect of genetic risk variants on continuous glycemic measures in nondiabetic subjects mainly elucidatesperturbation of insulin secretion. Also, the disease associated genes do not clearly converge on functional categories", + "mechanisms of DR remain poorly understood. A genome-wide association study (GWAS) is a powerful tool to identify genetic loci for complex diseases, and a large number of genetic loci for the susceptibility to various diseases, such astype 2 diabetes, have been successfully identified through GWAS (69). GWAS for DR have been performed, but most of the studies only reported suggestive signals with no replication ( 5)b e c a u s e of their limited sample sizes. Recently, several loci with genome-", + "kidney disease, several loci have been identi ed and validated, but the results were quite heterogenic across different popula- tions and depended on the type of diabetes and stage of disease. The major bene t of GWAS results is to be found in the in- creased understanding of disease mechanism and identi ca- tion of novel pathways and possibly new therapeutic targets.Follow-up studies are important in order to identify variants with speci c biological effect and may provide important", + "Abstract Genome-wide association studies (GWASs) have identified hundreds of single nucleotide polymorphisms (SNPs) associated with type 2 diabetes (T2D) and coronary artery disease (CAD), respectively. Nevertheless, these studies were generally per -", + "linkage or association data. But, none of these studies include in the analysis existing data from GWAs. Finally, a recent study identied additional susceptibility loci for type 2 diabetes by performing a meta-analysis of three published GWAs.21As acknowledged by the authors, GWAs are limited by the modest effect sizes of individual common variants and the need for stringent statistical thresholds. Thus, by combining data involving 10,128 samples, the authors found" + ], + "answer": "Genome-wide association studies (GWAS) help in identifying diabetes-related genes by examining associations between a large number of single-nucleotide polymorphisms (SNPs) and the disease, such as type 2 diabetes mellitus (T2DM) [1]. GWAS have successfully identified genetic loci associated with T2DM, providing insights into the genetic risk factors and biological pathways underlying the disease [2], [4]. These studies have identified hundreds of SNPs associated with T2DM, which can uncover previously unexpected disease pathways and contribute significantly to our understanding of the genetics of diabetes [5], [9]. Additionally, meta-analyses of GWAS data have improved the ability to detect new loci by combining findings from multiple studies, thereby increasing statistical power [2], [10]. Despite challenges such as the modest effect sizes of individual variants and stringent significance thresholds, GWAS remain a powerful tool for identifying genetic loci for complex diseases like diabetes [7], [10].", + "question": "How does genome-wide association studies (GWAS) help in identifying diabetes-related genes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_4 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_4 new file mode 100644 index 0000000..64e0f83 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_4 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2010 - Genetics of Type 1 Diabetes What\u2019s Next.pdf", + "2011 - Type 1 Diabetes Etiology, Immunology.pdf", + "2010 - Genetics of Type 1 Diabetes What\u2019s Next.pdf", + "2009 - Genetics of Type 1A Diabetes.pdf", + "2018 - The genetic architecture of type 1 diabetes mellitus.pdf", + "2003 - The Inherited Basis of Diabetes Mellitus.pdf", + "2007 - Localization of type 1 diabetes susceptibility to the MHC Class 1 Genes.pdf", + "2018 - The genetic architecture of type 1 diabetes mellitus.pdf", + "2010 - Genetics of Type 1 Diabetes What\u2019s Next.pdf", + "2007 - Localization of type 1 diabetes susceptibility to the MHC Class 1 Genes.pdf" + ], + "extraction_id": [ + "c584fa40-ecb0-57e0-8bd1-6761ae3b4d27", + "36ba6e4a-46c7-5a84-89ca-2b6e2a3c2a2c", + "508a0315-d23a-506d-bf66-b5c355576f96", + "79b78db5-d77a-51bd-84ff-14b558534df8", + "65402f67-0066-5d04-98c6-5e7b16087495", + "548d7a8d-5314-53f3-b646-108a4b1ac2f2", + "0ed848eb-6e55-5fab-82d0-f10b3c83d98f", + "dd2e688a-2f06-5911-9eb8-13c5d935885e", + "3e3ff61a-86ee-5e2a-9b14-9aa3b06937ff", + "92a54171-9f94-51ea-83cb-11698b1f0c21" + ], + "document_id": [ + "261cbb40-ed6b-554c-a70d-db6b9f14cf74", + "3c9823cd-3615-53b6-96c8-b7d2123d3eb0", + "261cbb40-ed6b-554c-a70d-db6b9f14cf74", + "7a98f456-6c43-5e9e-b404-31122159eab8", + "341261db-b38a-5bd2-8d8d-fc04a0b3da30", + "7b85b290-d711-55d5-9b1e-b06e4d6f14a2", + "3887995f-fa61-5472-b0a2-90b7b39592c2", + "341261db-b38a-5bd2-8d8d-fc04a0b3da30", + "261cbb40-ed6b-554c-a70d-db6b9f14cf74", + "3887995f-fa61-5472-b0a2-90b7b39592c2" + ], + "id": [ + "chatcmpl-AIFpPxGF7qh3CvkVYK1PXW0J3bHPe", + "f654b4ad-31d8-528c-800d-000a0f56438a", + "8480cb40-fb38-5f5e-98c5-41eb2b3bdad2", + "5157af28-d09f-5bbb-8984-61da49bed642", + "5c650a7a-98f1-5119-b66f-5a93db18faec", + "0fa5241d-e039-55b3-ba8c-aa14d0125967", + "f159c8b5-357c-57f6-98e4-5d5436f59925", + "277e2627-b99d-5b35-ae45-1fbaa2bf0710", + "69fb55b3-37a8-5fb6-9916-2ab5be15a0a8", + "3ccbc6c4-a2a3-53ab-b904-c4d5875e2e2c", + "2b1f2a05-4693-595c-94c0-fea40e19539c" + ], + "contexts": [ + "conferred by specic alleles, genotypes, and haplotypes ofthe HLA class II (and class I) genes. There are currentlyabout 50 non-HLA region loci that also affect the type 1diabetes risk. Many of the assumed functions of thenon-HLA genes of interest suggest that variants at theseloci act in concert on the adaptive and innate immunesystems to initiate, magnify, and perpetuate /H9252-cell destruc-", + "II HLA gene associated with type 1 diabetes maps to the 240-kbregion near HLA-B. Diabetes 49: 22172221, 2000. 303. Nejentsev S, Howson JM, Walker NM, Szeszko J, Field SF. Localization of type 1 diabetes susceptibility to the MHC class Igenes HLA-B and HLA-A. Nature 450: 887892, 2007. 304. Nejentsev S, Walker N, Riches D, Egholm M, Todd JA. Rare variants of IFIH1, a gene implicated in antiviral responses, protectagainst type 1 diabetes. Science 324: 387389, 2009.", + "Although the highly polymorphic HLA class II genesclearly play the most important single role in susceptibilityto type 1 diabetes, variation at these loci alone cannotexplain all of the evidence of genetic association andlinkage of the MHC with type 1 diabetes. To better denegenes within the MHC that may affect type 1 diabetes riskand would therefore merit further studies, the T1DGCundertook a comprehensive study of the genetics of theclassic 4-Mb MHC region. More than 3,000 SNPs and 66microsatellite", + "age to type 1 diabetes in the HLA region and suggestive evidence at a small number of other regions in the genome. In general, the emerging picture from linkage studies is that the class II genes encoding HLA-DR and HLA-DQ, as well as one or more additional genes within the HLA re - gion, confer most of the genetic risk for type 1 dia - betes. Genes outside the HLA region also con - tribute to the risk of type 1 diabetes, but their individual contributions are much smaller than that of HLA.", + "Benkalha and Polychronakos, 2008 ). Other genetic loci ( Table 1) are believed to in uence population-level risk for T1D, although it is poorly understood how these non-HLA loci contribute to disease susceptibility (Ram et al., 2016a ). 2.1. Human leukocyte antigen (HLA) The association between T1D and the HLA complex was rst de- monstrated in 1973 following observation of an increased frequency ofHL-W15 (HLA antigen) in T1D patients compared to controls ( Singal", + "cyte Antigen (HLA) gene region in immune regulation, and ready availability of serologic markers, led investigators to discover the association between certainHLAalleles and T1D in the early 1970s (33,130,158). The global importance of theHLAonT1Dhassincebeenconrmedingenome-widescansforlinkage:All suchscansperformedtodateshowamajorlocusatthe HLA(28,32,36,78,119). Thefractionofallgeneticrisk,whichcanbeattributedtothecontributionof HLA genes to T1D susceptibility, is about 44%, with a Sof3.4 (160).", + "The major histocompatibility complex (MHC) on chromosome 6 is associated with susceptibility to more common diseases than any other region of the human genome, including almost all dis- orders classified as autoimmune. In type 1 diabetes the major genetic susceptibility determinants have been mapped to the MHC class II genes HLA-DQB1 andHLA-DRB1 (refs 13), but these genes cannot completely explain the association between type 1 diabetes and the MHC region411.Owing to the regions", + "The HLA class I A locus a ects susceptibility to type 1 diabetes. Hum. Immunol. 63, 657 664. pii). https://doi.org/S0198885902004214 . Noble, J.A., Valdes, A.M., Cook, M., Klitz, W., Thomson, G., Erlich, H.A., 1996. The role of HLA class II genes in insulin-dependent diabetes mellitus: molecular analysis of 180 Caucasian, multiplex families. Am. J. Hum. Genet. 59, 1134 1148 . Noble, J.A., Valdes, A.M., Thomson, G., Erlich, H.A., 2000. The HLA class II locus DPB1", + "to type 1diabetes susceptibility, including within the MHC itself.Currently, there are over 50 non-HLA regions that signi-cantly affect the risk for type 1 diabetes (http://www.t1dbase.org). Many of these regions contain interesting,but previously unrecognized, candidate genes. A few re-gions contain genes of unknown function or no knownannotated genes, suggesting roles for long-distance generegulatory effects, noncoding RNAs, or unknown mecha-nisms. Against a background of ever-improving knowledgeof the", + "the 240-kb region near HLA-B. Diabetes 49,22172221 (2000). 6. Lie, B. A. et al. The predisposition to type 1 diabetes linked to the human leukocyte antigen complex includes at least one non-class II gene. Am. J. Hum. Genet. 64, 793800 (1999). 7. Valdes, A. M. et al. Extended DR3 D6S273-HLA-B haplotypes are associated with increased susceptibility to type 1 diabetes in US Caucasians. Tissue Antigens 65,115119 (2005). 8. Valdes, A. M., Erlich, H. A. & Noble, J. A. Human leukocyte antigen class I B and C" + ], + "answer": "The HLA region plays a significant role in the genetic predisposition to Type 1 diabetes. The major genetic susceptibility determinants for Type 1 diabetes have been mapped to the MHC class II genes, specifically HLA-DQB1 and HLA-DRB1 [7]. These genes are part of the highly polymorphic HLA class II genes, which play the most important single role in susceptibility to Type 1 diabetes [3]. The class II genes encoding HLA-DR and HLA-DQ, along with one or more additional genes within the HLA region, confer most of the genetic risk for Type 1 diabetes [4]. The contribution of HLA genes to Type 1 diabetes susceptibility accounts for about 44% of the genetic risk [6]. However, variation at these loci alone cannot explain all of the genetic association and linkage of the MHC with Type 1 diabetes, indicating that other genes within the MHC region may also affect Type 1 diabetes risk [3].", + "question": "What is the role of the HLA region in the genetic predisposition to Type 1 diabetes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_5 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_5 new file mode 100644 index 0000000..db215c5 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_5 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2017 - diabetes-mellitus-in-developing-countries-and-underserved-commun-2017.pdf", + "2017 - diabetes-mellitus-in-developing-countries-and-underserved-commun-2017.pdf", + "2005 - Type 2 diabetes mellitus from genes to disease.pdf", + "2016 - Association of genetic variants in INS (rs689), INSR (rs1799816) and PP1G.G (rs1799999) with type 2 diabetes (T2D) a case\u2013control study in three ethnic groups from N.pdf", + "2007 - Bioethnic Conscription Genes, Race.pdf", + "2019 - Machine-learning to stratify diabetic patients using novel cardiac biomarkers and integrative genomics.pdf", + "2011 - Dating the age of admixture via wavelet.pdf", + "2020 - Precision Medicine in Diabetes.pdf", + "2014 - Diabetes in Europe An update.pdf", + "2016 - TRPV1 Gene Polymorphisms Are Associated with Type 2 Diabetes by Their Interaction with Fat Consumption in the Korean Genome Epidemiology Study.pdf" + ], + "extraction_id": [ + "61fb4dd8-1428-5add-8c41-9ec2459ffd5a", + "090365f1-32e0-5adc-b589-b9331e0630a0", + "73278198-67af-5556-9414-86580dd07c48", + "4cbd4dfc-da8e-5432-b844-5f70d6f3811d", + "95f0e6f8-da7d-5997-ab8a-a1aad020c706", + "8d323598-fdf7-56cf-8290-be85929f0eaf", + "a5c137e5-84d2-5d75-8191-fa6b0be3d39e", + "9dc25bb6-787b-5e7a-af5d-d1353d122959", + "fa58324a-e5b7-538e-9cbb-0549887a2154", + "8276c974-f60b-5f59-943d-94a635160d1d" + ], + "document_id": [ + "8a9451b9-d7e8-5417-b6a5-5fd1b791cc4d", + "8a9451b9-d7e8-5417-b6a5-5fd1b791cc4d", + "52687a38-6a4b-51d2-aafa-812c76981dfe", + "5fe7c5f4-a209-56be-8504-c08073335c3b", + "d90126d9-fd87-5b38-87f7-08415f690836", + "332ac2ec-accc-5370-a4d2-6fec9ce7e072", + "786cebc5-c3cc-586e-bdc0-e7bee67edc19", + "0ad5b2de-d782-5d43-b294-bff5c7befd2d", + "81e1fc53-6768-590f-9b47-9a5105b6ddb5", + "521db985-2ce8-56c3-aed7-b38ef41cce45" + ], + "id": [ + "chatcmpl-AIFpUuEUTWxzzcta8xK3fjxfSUNPx", + "49748fe8-4351-5cd1-8367-957a160a59d9", + "80ad1f9c-4f67-5a68-9446-1f692b23f324", + "5fd9c60a-410f-5782-90a9-03d377a5f72b", + "d02a16ce-c62e-537d-9d32-266018c70415", + "684d1e26-b78a-5dde-b405-a79ee28087c3", + "8445ab0a-2287-5537-ab3a-cb058205e944", + "10c1db42-f724-5885-99e0-7637dfce63ca", + "d29cdd31-d214-52cf-b236-be4de1182b26", + "6fd138d2-6960-55fd-b656-05f4e84a0c6d", + "2771c343-be7b-51a2-a598-235647357416" + ], + "contexts": [ + "of diabetes when compared to the native population while not necessar-ily different from populations where they origi-nate from. Risk factors for diabetes appear to be similar between populations, mostly insulin resistance, obesity, and sedentary lifestyle with possible genetic differences contributing to the increased susceptibility. Some data suggest a greater prevalence of microvascular complica-", + "nants of type 2 diabetes between immigrant and native populations. Some studies in South Asian (Indian) populations suggest that genetic differ-ences may exist [ 17 , 30 ], but larger studies are needed to get better insight into this issue. Prevalence Estimates The prevalence of diabetes in minorities is affected by ethnicity and country of residence. In one study in the UK [ 59 ], standardized preva-", + "majority of cases it is difficult to replicate the findingsin other populations. One of the major problems in thesearch for genes responsible for common forms ofdiabetes is the genetic heterogeneity of the diseasewith different genes responsible for the developmentof T2DM in different populations. Furthermore, evenwithin the same ethnic group, different genes may beresponsible for different subtypes of diabetes (for in-stance with predominating failure in insulin secretionor insulin resistance). This is", + "across different races or populations but show ethnicity- specific differences. The pathogenesis of T2D involves genetic variants in the candidate genes. The interactions between the genes involved in insulin signaling and secre - tory pathways are believed to play an important role in determining an individuals susceptibility towards T2D. Therefore, the present study was initiated to examine the differences, if any, in the contribution of polymorphisms", + "That is, the minute genetic differences discernable with SNPs, patterns of single nu-cleotides (A,G,T ,C), and other mutation analysis technologies are now used to explainpatterns of disease between populations, which are in turn understood as the basisfor biological differences between the populations themselves. The case of diabetesgenetics research affords a more nuanced look at what is labeled genetic determinism.It is evident in diabetes research that SNPs and haplotypes, (an inherited pattern of 99", + "- tion for disease classification. This genetic component may be specifically important when understanding the pathogenesis of diabetes in ethnic groups, when BMI [14, 15] and HbA1c [16] show distinct differences between ethnicities. Though applying patient-matched, genomic information is currently unrealistic for disease diagnosis, it may hold the key for revealing commonalities across ethnic and demographic groups when classifying diabetic onset, progression, and severity.", + "particularly useful for understanding differences in dis-ease prevalence and drug response among differentpopulations. There is ample evidence that human popu-lations have different susceptibility to diseases, exhibit-ing substantial variation in risk allele frequencies [1].For example, genetic predisposition to asthma differsamong the differentially-admixed Hispanic populations of the United States, with the highest prevalence observed in Puerto Ricans. Ge netic variants responsible", + "populations and across countries. World-wide differences in prevalence of theforms of diabetes necessitates inclusion of currently understudied populationsfor the development of precision diag-nostics and therapeutics. As a result, theprecise subtype of diabetes a particularindividual is diagnosed with may vary indifferent populations based on subtypefrequency or genetic or dietary or life-style differences. The communication strategy used by the interventionalist and the patient s", + "were positively associated with country level income [49]. However, the drivers for the observed pattern with geographi- cal differences and varying time trends are still unclear. Susceptibility to type 1 diabetes denitely has a strong genetic component (HLA genotype) [50], but the heterogeneity of type 1 diabetes cannot be explained solely by the prevalence of susceptibility genes [5153] . Thus, the reasons for changes in", + "twice higher than that of 2010 [3] . The genetic influences on the prevalence of type 2 diabetes i n the Asian population are mainly related to insulin secretion capacity [4] ; other genes involved in the risk of type 2 diabetes are not substantially different in other ethnic groups [5] . The most relevant genes contributing to ethnic differences are associated with insulin secretion capacity, and they are" + ], + "answer": "Genetic differences contribute to variations in diabetes prevalence among different populations in several ways: 1. **Genetic Heterogeneity**: Different genes may be responsible for the development of type 2 diabetes mellitus (T2DM) in different populations, indicating genetic heterogeneity. Even within the same ethnic group, different genes might be responsible for different subtypes of diabetes, such as those with predominating failure in insulin secretion or insulin resistance [3]. 2. **Ethnicity-Specific Genetic Variants**: The pathogenesis of T2DM involves genetic variants in candidate genes, and these variants can show ethnicity-specific differences. The interactions between genes involved in insulin signaling and secretory pathways play a crucial role in determining an individual's susceptibility to T2DM [4]. 3. **Single Nucleotide Polymorphisms (SNPs) and Haplotypes**: Minute genetic differences, such as SNPs and haplotypes, are used to explain patterns of disease between populations. These genetic markers are understood as the basis for biological differences between populations, which can affect diabetes prevalence [5]. 4. **Risk Allele Frequencies**: Human populations exhibit substantial variation in risk allele frequencies, which contributes to different susceptibilities to diseases, including diabetes, among populations [7]. 5. **Insulin Secretion Capacity**: In some populations, such as the Asian population, genetic influences on the prevalence of type 2 diabetes are mainly related to insulin secretion capacity. This suggests that genes associated with insulin secretion capacity are particularly relevant in contributing to ethnic differences in diabetes prevalence [10]. Overall, genetic differences, including variations in specific genes, SNPs, and risk allele frequencies, contribute to the observed variations in diabetes prevalence among different populations. These genetic factors interact with environmental and lifestyle factors to influence diabetes risk.", + "question": "How do genetic differences contribute to variations in diabetes prevalence among different populations?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_6 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_6 new file mode 100644 index 0000000..656ba76 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_6 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2006 - Adiponectin, type 2 diabetes and the metabolic syndrome.pdf", + "2021 - PPAR\u03b3 and Diabetes Beyond the Genome and Towards Personalized Medicine.pdf", + "2010 - The Genetics of Type 2 Diabetes.pdf", + "2008 - Glossary of Genetics Genomics Terms.pdf", + "2018 - Association of PGC-1\u03b1 gene with type 2 diabetes in three unrelated endogamous groups of North-West India (Punjab) a case-control and meta-analysis study.pdf", + "2021 - PPAR\u03b3 and Diabetes Beyond the Genome and Towards Personalized Medicine.pdf", + "2021 - PPAR\u03b3 and Diabetes Beyond the Genome and Towards Personalized Medicine.pdf", + "2021 - PPAR\u03b3 and Diabetes Beyond the Genome and Towards Personalized Medicine.pdf", + "2013 - Gene-Environment and Gene-Treatment.pdf", + "2018 - Refining the accuracy of validated target identification through coding variant.pdf" + ], + "extraction_id": [ + "4647b43a-e4a0-5e8a-9cf5-6bf33cd6e672", + "2d610953-ea5c-5c01-ad19-60c607383da4", + "1df8f645-85c4-5832-8142-09bacafcd01d", + "f8b79de5-3e0c-5495-b6c2-8a3be6138223", + "94ee1317-d606-5921-8175-a86da2fa95d6", + "02cdfa1b-cc8f-5141-bde0-1079d252c6e8", + "4bdd6cdb-1f2a-585f-b08e-392a54c6dad8", + "2d610953-ea5c-5c01-ad19-60c607383da4", + "a6b92963-2cf0-51a4-8686-ce3a7515d443", + "d96545e5-f3a0-5765-9b06-27a41219d3b9" + ], + "document_id": [ + "6a46f7cf-e75b-5b72-b77b-7e0cc03f92d8", + "4ea83190-476d-5090-a461-abde1adccbc5", + "a1d211d4-279e-51d7-b2b2-33bc2763d089", + "c66d2572-071d-5aaf-829c-b3ca6cf6d697", + "2faa21d2-146e-568a-b881-63201819e99a", + "4ea83190-476d-5090-a461-abde1adccbc5", + "4ea83190-476d-5090-a461-abde1adccbc5", + "4ea83190-476d-5090-a461-abde1adccbc5", + "fe958fb1-5408-56ec-b102-ccf07b4bac2d", + "3362e616-f824-55fa-9b4d-3ee8dcf52ac0" + ], + "id": [ + "chatcmpl-AIFpdRa9QE6LvKot0urXoNDlpAF5x", + "6f046969-4e26-5dee-a310-cf32dc1f799c", + "c909cc5c-6fdb-5646-8332-973a92ac9486", + "c3ac7ed2-1b42-5c87-9104-b6da2e33b30b", + "02a160ba-95ee-5aa9-bc45-445b4706715b", + "4e415210-bf41-542f-841c-4bb17622d2e6", + "8d7fb270-e23f-5d89-b75c-50b8fbd22fe8", + "9f62a8cf-a14f-5989-a899-cf1f525905bf", + "818c1d6b-c1c1-570d-9e7a-87449fae279a", + "793e2430-fa2b-513a-a4ab-0c85a167de3f", + "7c375d6d-672d-594c-a56e-7391ed3e9daa" + ], + "contexts": [ + "The transcription factor peroxisome-proliferator- activated receptor gamma (PPAR g) is known to inuence insulin sensitivity, and acts partly via amodulation of the circulating adiponectin level (PPAR gagonists increase the adiponectin level) (Ref. 38). The PPAR gP12A SNP is a well- established genetic variant that modulates insulin sensitivity and the risk of type 2 diabetes (Ref. 39). In a Chinese family study, Yang et al.demonstrated a genetic interaction between the", + "intricate regulation of PPAR signaling to pave the way to tailored therapies in patients with insulin resistance and T2D. Keywords PPARG genetic variants .Dominant-negative isoforms .Post-tranlational modifications .Adipose tissue dysfunctions .Drug responsiveness .Type 2 diabetes Introduction Peroxisome proliferator activated receptor gamma (PPAR ) is a ligand-activated transcription factor belonging to the nu-", + "2 . A widespread Gly482Ser polymorphism of PGC1 - (known as PPARGC1 ), a transcriptional coactivator of a series of nuclear receptors includ-ing PPARG , has been associated with a 1.34 genotype relative risk of T2DM [93] . In this study, a test for interaction with the Pro12Ala variant in PPARG gave no indication for additive effects on diabetes status. Other genes have been shown to be implicated in the genetic", + "PPARG Peroxisome proliferator-activated receptor- gene. This gene is located on chromosome 3p25, and has been studied as a candidate genefor type 2 diabetes based on its role in adipocyte and lipid metabolism. The Pro12Ala variant in particular has been associated with adecrease in insulin sensitivity and a several-fold increased risk of type 2 diabetes. PPAR is a target for the thiazolidinedione class of oralantidiabetic agents", + "Genetic variation in the peroxisome proliferator-activated receptor (PPAR) and peroxisome proliferator-activated receptor gamma co-activator 1 (PGC1) gene families and type 2 diabetes. Ann Hum Genet 78:2332 Vimaleswaran KS, Radha V, Ghosh S, Majumder PP, Deepa R, Babu HN etal (2005) Peroxisome proliferator-activated receptor-gamma co-activator-1alpha (PGC-1alpha) gene polymorphisms and their relationship to type 2 diabetes in Asian Indians. Diabetic Med 22:15161521", + "Dali-Youcef N, et al. The Pro12Ala PPARgamma2 variant deter- mines metabolism at the gene-environment interface. Cell Metab. 2009;9:88 98. 53. Agostini M, Schoenmakers E, Mitchell C, Szatmari I, Savage D, Smith A, et al. Non-DNA binding, dominant-negative, human PPARgamma mutations cause lipodystrophic insulin resistance. Cell Metab. 2006;4:303 11. 54. Agostini M, Gurnell M, Savage DB, Wood EM, Smith AG, Rajanayagam O, et al. Tyrosine agonists reverse the molecular", + "associated with a marked increase in T2D risk in the general population, schematized in Fig. 1. The latter systematically tested all the possible PPAR protein variants by using a large-scale pooled functional assay based on a human macro- phage cell line. Using these in vitro data to train a classifier by supervised machine learning, they identified six pathogenic PPARG variants (R194Q, A417V, R212W, P387S, M203I, and T356R) in patients with partial lipodystrophy [ 109].", + "lipid metabolism, as well as insulin sensitivity and inflammatory pathways. These pleiotropic functions confer great relevance to PPAR in physiological regulation of whole-body metabolism, as well as in the etiology of metabolic disorders. Accordingly, PPARG gene mutations, nucleotide variations, and post-translational modifications have been associated with adipose tissue disorders and the related risk of insulin resistance and type 2 diabetes (T2D). Moreover, PPAR alternative splicing isoforms", + "the PPARgamma locus. Diabetes 2001;50:686 689 12. Kahara T, Takamura T, Hayakawa T, et al. PPARgamma gene polymorphism is as-sociated with exercise-mediated changes of insulin resistance in healthy men. Me- tabolism 2003;52:209 212 13. Franks PW, Luan J, Browne PO, et al. Does peroxisome proliferator-activated receptor gamma genotype (Pro12ala) modify the association of physical activityand dietary fat with fasting insulin level? Metabolism 2004;53:11 16 14. Memisoglu A, Hu FB, Hankinson SE, et al.", + "30. Majithia, A. R. et al. Rare variants in PPARG with decreased activity in adipocyte differentiation are associated with increased risk of type 2 diabetes. Proc Natl Acad Sci USA 111, 1312713132 (2014). 31. Majithia, A. R. et al. Prospective functional classification of all possible missense variants in PPARG . Nat. Genet. 48, 15701575 (2016). 32. Claussnitzer, M. et al. Leveraging cross-species transcription factor binding" + ], + "answer": "The PPAR-gamma gene encodes a transcription factor that plays a crucial role in regulating insulin sensitivity and lipid metabolism, which are important in the context of diabetes [4]. PPAR-gamma influences insulin sensitivity partly by modulating circulating adiponectin levels, with PPAR-gamma agonists known to increase these levels [1]. This gene is a target for the thiazolidinedione class of oral antidiabetic agents, which are used to improve insulin sensitivity in patients with type 2 diabetes [4]. Variants of the PPAR-gamma gene can significantly impact the risk and progression of type 2 diabetes. The Pro12Ala variant, for example, is a well-established genetic variant that modulates insulin sensitivity and increases the risk of type 2 diabetes [1], [4]. Other pathogenic PPARG variants, such as R194Q, A417V, R212W, P387S, M203I, and T356R, have been identified in patients with partial lipodystrophy and are associated with an increased risk of type 2 diabetes [7]. These variants can affect the gene's function in adipocyte differentiation and metabolism, thereby influencing the development of insulin resistance and type 2 diabetes [10].", + "question": "What is the function of the PPAR-gamma gene in diabetes, and how do its variants impact the disease?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_7 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_7 new file mode 100644 index 0000000..485f8f6 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_7 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2019 - (Epi)genomic heterogeneity of pancreatic islet function and failure in type 2 diabetes.pdf", + "2020 - Functional Genomics in Pancreatic \u03b2 Cells Recent Advances in Gene Deletion and Genome Editing Technologies for Diabetes Research.pdf", + "2016 - Dissecting diabetes metabolic disease.pdf", + "2020 - Functional Genomics in Pancreatic \u03b2 Cells Recent Advances in Gene Deletion and Genome Editing Technologies for Diabetes Research.pdf", + "2020 - Functional Genomics in Pancreatic \u03b2 Cells Recent Advances in Gene Deletion and Genome Editing Technologies for Diabetes Research.pdf", + "2020 - Functional Genomics in Pancreatic \u03b2 Cells Recent Advances in Gene Deletion and Genome Editing Technologies for Diabetes Research.pdf", + "2020 - Functional Genomics in Pancreatic \u03b2 Cells Recent Advances in Gene Deletion and Genome Editing Technologies for Diabetes Research.pdf", + "2019 - (Epi)genomic heterogeneity of pancreatic islet function and failure in type 2 diabetes.pdf", + "2020 - Functional Genomics in Pancreatic \u03b2 Cells Recent Advances in Gene Deletion and Genome Editing Technologies for Diabetes Research.pdf", + "2020 - Functional Genomics in Pancreatic \u03b2 Cells Recent Advances in Gene Deletion and Genome Editing Technologies for Diabetes Research.pdf" + ], + "extraction_id": [ + "57736895-897e-54e5-a735-aadcbd77cb63", + "5f8a0ddd-a0c7-5151-9b6a-e0980bb94aa6", + "998a92ba-e7fc-5553-b629-7b5797fbfafe", + "fe5bf2df-2eda-5ef0-8aad-79bbc5b898d6", + "0a3e3095-4789-505a-96b7-123a05078e95", + "ab61a462-21d3-50dc-afb3-3e1cdeb15b1f", + "ab61a462-21d3-50dc-afb3-3e1cdeb15b1f", + "4e73f54b-d265-594d-9fc1-9535a2d84672", + "a36cee80-5961-55e5-8ea4-8d4e1bc501a9", + "62d513ed-2dca-5f45-9da2-d847f92fc931" + ], + "document_id": [ + "b9bc63a5-e366-5685-bd7a-4732a8eeffb7", + "51350055-d53c-5692-ab53-337b8a8bafd6", + "eee2f79d-e093-52fb-871a-798fd859235e", + "51350055-d53c-5692-ab53-337b8a8bafd6", + "51350055-d53c-5692-ab53-337b8a8bafd6", + "51350055-d53c-5692-ab53-337b8a8bafd6", + "51350055-d53c-5692-ab53-337b8a8bafd6", + "b9bc63a5-e366-5685-bd7a-4732a8eeffb7", + "51350055-d53c-5692-ab53-337b8a8bafd6", + "51350055-d53c-5692-ab53-337b8a8bafd6" + ], + "id": [ + "chatcmpl-AIFppDyOUKllFXSAk1UvPBBd5ythq", + "f42c0f84-d2a8-5bf9-89c2-3dd182bfb235", + "1859f32b-8f5c-5c3c-9f4d-54193d37645d", + "df30dab3-a490-5497-a079-2741f9039f87", + "eadf2320-de70-5499-ade0-7aa9930ac091", + "99ccc9a2-865f-5d11-9b08-b26261d02fc9", + "1f114642-3f77-5346-89e8-394c433f66ff", + "57b9550d-0258-5a87-be57-976f471e5763", + "4b170851-2dbd-5c06-9e3a-188d30a00170", + "83053df5-47ac-59da-9c30-69740a64372d", + "6f0adc7f-54ce-5a70-a2ea-153e074ccbdf" + ], + "contexts": [ + "A variety of cellular and animal models have been developed and applied over the past few years to experimentally manipulate cis-regulatory elements and their target gene function as it related to beta cell/isletfunction, glucose homeostasis, and T2D pathogenesis. CRISPR/Cas9 hasrevolutionized our ability to modify genomes and epigenomes almost at will. Unsurprisingly, CRISPR (epi)genome editing tools can and have been used to target putative T2D target genes [54] orcis-REs[55] in beta", + "to how CRISPR/Cas9 technology may nd clinical application in patients with diabetes. Keywords: genome editing, beta cell, genome-wide association studies, maturity onset of diabetes of the young, stem cells, mouse models INTRODUCTION Type 2 diabetes (T2D) affects an estimated 425 million people worldwide, a number predicted to rise to 629 million by 2045 ( 1). The disease usually involves insulin resistance but is ultimately the result", + "hPSCs [48,49] for correcting the COL7A1 [50] anda1-antitrypsin genes [51]. Given the superior cutting ef ciency, CRISPR/Cas9 is increasingly becoming the favored choice for genome editing inhPSCs [16,52] . 3.2. Employing hPSCs and genome editing tools to study diabetes and metabolic syndromes In general, the strategy to carry out in vitro disease modeling of dia-", + "Due to its simplicity and adaptability, CRISPR has rapidly become the most popular genome editing tool available for the mammalian genome ( 50,63). Because NHEJ DNA repair often introduces unwanted indels at the Cas9 cutting site, CRISPR hasbeen used to knock-out genes by introducing frameshiftmutations, resulting in protein depletion ( 156,157). In the diabetes eld, CRISPR has also been adopted to study several genes in bcell lines and in human ES-derived bcells ( 21,151,", + "samples ( 236). CRISPR technology has been used recently to correct point mutations in patient-derived iPSCs to target diabetes-relatedgene defects. To date, the most ef cient method used in iPSC is CRISPR/Cas9-based homology-directed repair (HDR). Here, a Cas9-mediated cut is generated adjacent to the site of interest. A homologous donor template with the intended nucleotidechange containing silent mutations in the gRNA sequence(167) can then be recombined by HDR. This approach has", + "in response to various stimuli including glucose aftertransplantation in an immunocompromised mouse model (230,231). However, the use of iPSC is controversial and there are some concerns over genetic and epigenetic variations iniPSCs which might affect cell function after differentiation ( 275). Manipulation of hESC/iPSC cells via CRISPR-Cas9 technology provides a platform for the correction of genomic mutations not only in diabetes but in other disease elds as well", + "RNP and single strand edDNA (ssDNA) donor which carriesdesired changes such as insertion of loxP site ( 255,259265). Using CRISPR-Cas9, leptin and leptin receptor knockout mice have been established as tools in diabetes and obesity research ( 160,255,256). Knock-in mouse models have also been established via HDR to achieve cell-speci c deletion of the gene ( 266). Genome Editing: Clinical Application in Diabetes An important goal in genetic research is to identify the genetic", + "CRISPR-Cas9 epigenome editing enables high-throughput screening for functionalregulatory elements in the human genome. Nature Biotechnology 35(6):561 e568. [58] Hodson, D.J., Mitchell, R.K., Marselli, L., Pullen, T.J., Gimeno Brias, S., Semplici, F., et al., 2014. ADCY5 couples glucose to insulin secretion in humanislets. Diabetes 63(9):3009 e3021 . [59] Zhou, Y., Park, S.-Y., Su, J., Bailey, K., Ottosson-Laakso, E., Shcherbina, L.,", + "free IPSCs from Human Pancreatic Cells Using the CRISPR-Cas9 System. J Vis Exp JoVE (2017). doi: 10.3791/56260 277. Millette K, Georgia S. Gene Editing and Human Pluripotent Stem Cells: Tools for Advancing Diabetes Disease Modeling and Beta-Cell Development. Curr Diabetes Rep (2017) 17:116. doi: 10.1007/s11892-017-0947-3Hu et al. Genome Editing of Pancreatic Beta Cells Frontiers in Endocrinology | www.frontiersin.org October 2020 | Volume 11 | Article 576632 19", + "DNA donors as templates, it is possible the nCas9-RT will beable to convert all variants at once. This new technique, however,is still in early development, and its editing ef ciency and side- effects remain to be seen.FUTURE PROSPECTIVES Recent technological developments around CRISPR-Cas9 and itsderivative technologies, combined with advances in humancellular models, should accelerate our understanding of theinterplay between diabetes risk-associated genetic variants and" + ], + "answer": "CRISPR/Cas9 technology can be used to study or treat genetic forms of diabetes in several ways: 1. **Genome and Epigenome Editing**: CRISPR/Cas9 has revolutionized the ability to modify genomes and epigenomes, allowing researchers to target specific genes associated with Type 2 Diabetes (T2D) and manipulate cis-regulatory elements related to beta cell function and glucose homeostasis [1]. 2. **Gene Knockout Studies**: CRISPR has been used to knock out genes by introducing frameshift mutations, which result in protein depletion. This approach has been adopted to study several genes in beta cell lines and human embryonic stem cell-derived beta cells, providing insights into the genetic basis of diabetes [4]. 3. **Correction of Point Mutations**: CRISPR technology has been used to correct point mutations in patient-derived induced pluripotent stem cells (iPSCs) targeting diabetes-related gene defects. The most efficient method for this is CRISPR/Cas9-based homology-directed repair (HDR), which involves generating a Cas9-mediated cut near the mutation site and using a homologous donor template to introduce the intended nucleotide change [5]. 4. **Disease Modeling**: CRISPR/Cas9 can be employed in human pluripotent stem cells (hPSCs) to model diabetes and metabolic syndromes in vitro. This allows researchers to study the disease mechanisms and test potential treatments in a controlled environment [3]. 5. **Animal Models**: CRISPR/Cas9 has been used to create knockout and knock-in mouse models, such as leptin and leptin receptor knockout mice, which serve as tools for diabetes and obesity research. These models help in understanding the genetic factors contributing to diabetes and testing therapeutic interventions [7]. Overall, CRISPR/Cas9 technology provides a powerful platform for both studying the genetic underpinnings of diabetes and developing potential gene therapies to treat the disease.", + "question": "How can CRISPR/Cas9 technology be used to study or treat genetic forms of diabetes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_8 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_8 new file mode 100644 index 0000000..278b7cf --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_8 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2004 - Diabetes Genes a.pdf", + "2003 - The Inherited Basis of Diabetes Mellitus.pdf", + "2007 - Physical activity modifies the effect of SNPs in the SLC2A2 (GLUT2).pdf", + "2003 - The Inherited Basis of Diabetes Mellitus.pdf", + "2009 - Zinc and Diabetes - clinical links and molecular mechanisms.pdf", + "2020 - Genetics and Epigenetics New Insight on Gestational Diabetes Mellitus.pdf", + "2012 - Reduced Insulin Exocytosis in Human Pancreatic b-Cells.pdf", + "2000 - A High Fasting Plasma Insulin Concentration.pdf", + "2006 - Polymorphisms in the Ghrelin Gene Are Associated with Serum High-Density Lipoprotein.pdf", + "2018 - Genetic variants of gestational diabetes mellitus a study of 112 SNPs among 8722 women in two independent populations.pdf" + ], + "extraction_id": [ + "0734af87-4854-5a0f-b10c-2ea89376cb87", + "78e2a11a-4e89-5d14-b076-ef24c92b35b2", + "276a7b90-6325-59c8-b8b2-77f855aa2553", + "51702d4a-735b-5bc4-98a4-d26bf1e58b40", + "a482defd-8d6a-5966-8ec1-5aa7e49c14f1", + "7d315f2c-43f0-587a-9370-e0f205d6c611", + "e6e7fc9f-e4a4-5d51-9070-01ce34cffcd3", + "6aefb64e-b732-5742-90a4-f2aa43c8b866", + "00f6985d-f69b-50e3-b673-0ec508e6c025", + "a228ec1a-de5b-5e0c-b24f-db8249be4053" + ], + "document_id": [ + "805ad0d5-6a3d-5e5f-b1ba-121e10c6daaa", + "7b85b290-d711-55d5-9b1e-b06e4d6f14a2", + "6f5ced46-b777-563a-b644-432f4e7e2644", + "7b85b290-d711-55d5-9b1e-b06e4d6f14a2", + "72ab8458-928b-56b9-9547-1ba4b59dfab9", + "301a7093-a33a-57c9-8979-58146c57ced1", + "689e4fcc-99db-5798-8f1d-68c41d4638db", + "77375e8f-ca8e-5bbb-b310-910dd82ade9c", + "8ad4e1b5-9c29-5b20-bed8-afbf4a14862a", + "3b301dd1-17bd-5632-9a96-d6294c6d7650" + ], + "id": [ + "chatcmpl-AIFq1suHUy36hH4x2WAKqkv1fOq53", + "bdc8f1de-8c90-5e28-8a21-a5bb0182cfe1", + "bc93539a-df5f-5720-a4ce-0345fe4b66d4", + "0a7e6fb3-bf85-5440-adeb-c66fca9d170a", + "a4973968-2510-5f08-8252-f2be85be3c42", + "4bab532c-8b73-54b8-905a-d7b070af1da8", + "42eee55f-adfa-5a04-b3f0-a592b3b08a2c", + "65c8f702-eee5-550e-bd63-78892b158c93", + "a6ef60db-d564-5f55-a31a-db893879ab14", + "b91a2e1b-eb4d-5e1b-a85c-46a8f394603c", + "277f37e3-ee45-5619-b051-33d5ba95bd07" + ], + "contexts": [ + "Effectors Glucose transporters. A number of polymorphisms have been identified in the GLUT4 gene. None of them have been linked to or found to be associated with type 2 diabetes in a variety of populations. 5960 Interestingly, an association was found between a polymorphism in the human GLUT! gene and type 2 diabetes60 that was significant for obese women. Regulation of GLUT4 protein expression in diabetes occurs in a strongly tissue-specific", + "M,XiangKS,etal.1996.Geneticcontri-bution of polymorphism of the GLUT1and GLUT4 genes to the susceptibilityto type 2 (non-insulin-dependent) dia-betes mellitus in different populations.Acta Diabetologica 33:19397 141. Poulsen P, Kyvik KO, Vaag A, Beck- Nielsen H. 1999. Heritability of type II(non-insulin-dependent) diabetes melli-tus and abnormal glucose toleranceapopulation-basedtwinstudy. Diabetolo- gia42:13945 142. Pugliese A, Zeller M, Fernandez AJ,", + "A mutation in the Glut2 glucose transporter gene of a diabetic patientabolishes transport activity. J Biol Chem 269: 1776517767, 1994. 36.Patel P, Bell GI, Cook JT, Turner RC, Wainscoat JS. Multiple restriction fragment length polymorphisms at the GLUT2 locus: GLUT2haplotypes for genetic analysis of type 2 (non-insulin-dependent) diabetesmellitus. Diabetologia 34: 817821, 1991. 37.Pereira MA, FitzerGerald SJ, Gregg EW, Joswiak ML, Ryan WJ, Suminski RR, Utter AC, Zmuda JM. A collection of Physical Activity", + "NootherrecentassociationsofpolymorphismswithT2Dhavebeenreplicated to date (Table 5). However, a recent meta-analysis (106) identied some earlyreproducibilityofanassociationbetweenvariationin GLUT1andT2D,originally reportedin1988(104).Itislikelythatthisassociationhasnotbeenpursuedfurtherfor several reasons, but one possibility is a study that reported the rejection oflinkageto GLUT1athighlevelsofsignicance(46).However,linkagehaslimited", + "mechanism by which type 2 diabetes is influenced remains to be identified. There have been several attempts to clarify the role of the polymorphism in SLC30A8 in the development of type 2 diabetes and the focus has been set on insulin secretion dueto the importance of ZnT-8 for insulin storage in the granulaof pancreatic cells. The results are controversial, but there appears to be an association between the risk variant of rs13266634 and reduced insulin secretion. Interestingly, decreased insulin", + "glucose tolerance, suggesting a r ole for this polymorphism in the onset of GDM as well as type 2 diabetes mellitus ( 17). The switch on IRS-1 of the amino acid GLY972 Arg (rs1801278) impairs insulinsecretion, and a study on 1306 GDM patients and 1973 pregnantwomen without GDM found a signi cant association between the presence of this polymorphism and the risk of GDM ( 18). Intriguing results were generated by a study on the genetic", + "tients the EUGENE2 study. Diabetologia 2008;51:816 820 32. Kirchhoff K, Machicao F, Haupt A, et al. Polymorphisms in the TCF7L2, CDKAL1 and SLC30A8 genes are associated with impaired proinsulinconversion. Diabetologia 2008;51:597 601 33. Nicolson TJ, Bellomo EA, Wijesekara N, et al. Insulin storage and glucose homeostasis in mice null for the granule zinc transporter ZnT8 and studies of the type 2 diabetes-associated variants. Diabetes 2009;58:2070 2083", + "is markedly reduced in glucose-unresponsive islets from ani-mal models of type 2 diabetes (51). In a previous study in PimaIndians, we found that ~5% of this population carries a mis-sense polymorphism in exon 3 of the GLUT2 gene (52), but this polymorphism was not associated with the residual fast-ing plasma insulin concentration in the present study.Despite the fact that GLUT2 is an attractive candidate, it", + "polymorphisms in 24 DNA samples. Common variants were thengenotyped in 760 type 2 diabetic patients and 641 nondiabetic sub-jects. Genetic associations with diabetes-related phenotypes werealso analyzed. Results: Nine polymorphisms were identified, and four common poly- morphisms [g. /H110021500C /H11022G, g./H110021062G /H11022C, g./H11002994C/H11022T, g./H11001408C/H11022A (Leu72Met)] were genotyped in a larger study. The genotype distri-butions of these four common polymorphisms in type 2 diabetes pa-", + "in turn, result in a defective or poorly expressed glucagonprotein and lead to decreased insulin secretion and conse- quently hyperglycaemia [ 48]. The current study identified, for the first time, several type 2 diabetes-associated risk alleles associated with a higher riskof GDM, namely rs7957197 ( HNF1A ), rs10814916 ( GLIS3 ), rs3802177 ( SLC30A8 ) and rs7041847 ( GLIS3 ). These SNPs" + ], + "answer": "Genetic polymorphisms in the GLUT4 gene have been studied to determine their significance in relation to Type 2 diabetes. However, according to the provided context, none of the identified polymorphisms in the GLUT4 gene have been linked to or found to be associated with Type 2 diabetes across various populations [1]. This suggests that, despite investigations, there is no established connection between GLUT4 gene polymorphisms and the susceptibility to Type 2 diabetes.", + "question": "What is the significance of genetic polymorphisms in the GLUT4 gene for Type 2 diabetes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_9 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_9 new file mode 100644 index 0000000..d6dd48e --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_9 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2023 - Genetic Link Between Type 2 Diabetes Mellitus and Cardiovascular Disease.pdf", + "2010 - The Role of Epigenetics in the Pathology of Diabetic Complications.pdf", + "2009 - Obesity and genetics regulate microRNAs in islets, liver, and adipose of diabetic mice.pdf", + "2013 - The miRNA Profile of Human Pancreatic Islets and BetaCells and Relationship to Type 2 Diabetes Pathogenesis.pdf", + "2015 - Epigenetic mechanisms in diabetic complications and metabolic memory.pdf", + "2014 - Nutrigenetics and Nutrigenomics Insights into Diabetes Etiopathogenesis.pdf", + "2018 - MicroRNA profiling and their pathways in South African.pdf", + "2009 - Obesity and genetics regulate microRNAs in islets, liver, and adipose of diabetic mice.pdf", + "2016 - Epigenetic Mechanisms in Diabetic Kidney Disease.pdf", + "2018 - Type 2 Diabetes Mellitus and Cardiovascular Disease Genetic and Epigenetic Links.pdf" + ], + "extraction_id": [ + "2211fc04-119d-534b-8de8-dfa4d1bfbf09", + "b1d2c95c-d639-5c75-8c52-278f1e187675", + "7d22ecdf-dd9f-53e9-aa2b-df81bd03c3bc", + "65ad21df-f728-54b6-b329-9ed8793c33ce", + "593dfb70-8b55-5a74-abd5-446394a0bd23", + "0cb154ce-660d-54fa-a31f-0391434a5470", + "14577d73-d320-54dd-93f2-c55f986bc8bc", + "42c407dd-9f88-57b3-b47b-e467c486e3a4", + "767d65c7-b99d-5427-8f5a-4afa10669e11", + "9e010393-b98f-5f6c-a62d-fc0646ba8667" + ], + "document_id": [ + "c54f9f64-7e6d-5186-a1de-d487ba9d19b8", + "766edfd5-4756-51bf-b636-c94b041d030c", + "c3d2aced-4550-553f-abed-0d3a7ac1414f", + "05e7f076-6b4a-5ab0-b4d0-28e4b6eeef8f", + "470f1f94-792d-5273-a88f-7e06084951c5", + "ce4f171c-494c-53f2-a770-c3edd3561c40", + "b6bb090d-7176-59db-af04-582aa1d5cf10", + "c3d2aced-4550-553f-abed-0d3a7ac1414f", + "6f773bda-0b8f-5da2-a9b5-e6c013d75050", + "3e82a2e5-4b2c-59c0-99cd-f3b06d8dabf2" + ], + "id": [ + "chatcmpl-AIFq5UdPTHMiHxXb8m3RyqvBs55fY", + "c2fa8cbd-5f7f-5086-90ec-d1e5e6df0ee9", + "3cd8facc-0c2c-5a48-9f7c-cbd5685d914a", + "f35c5082-c877-5cdf-9ba8-a91dd72da2e8", + "abbcafb6-f502-5648-a9a4-196466452564", + "8347a530-d264-5d7a-81f6-704f8ed7bf57", + "f0bb404a-2062-584e-850d-cf49a1e0b4a7", + "a9695ed0-6f3d-5e79-ab99-514119637e0b", + "1d9d150b-27f9-55f7-8111-1f6de79a78bc", + "5bf6de7b-8b41-5a32-a513-843f0f71c640", + "01d78f49-9996-58ea-b076-e352ff22461c" + ], + "contexts": [ + "MicroRNAs (miRNA) ar e single -stranded, small RNA molecules that act at the post - transcriptional standard to regulate their target or source genes. Many biological processes are regulated by this Micro RNA. Since its discovery about two decades ago. It is correlated with a com prehensive set of diseases and described by numerous miRNAs, including T2DM and cardiovascular diseases. Specifically, with respect to T2DM, micro RNA plays a", + "they can act as oncogenes or tumor suppressors (8, 29, 72). miRs are associated with the 341 regulation of genes relevant to insulin secre tion, cholesterol biosynthesis, fat metabolism and 342 adipogenesis, crucial pathways in the pathogene sis of diabetes (53, 114, 115). miRs have also 343 been implicated in TGF- signaling related to th e pathogenesis of diabetic nephropathy with key 344 miRs such as miR-192, miR-216a, miR-217 and miR-377 being up-regula ted in glomerular 345", + "Lim LP, Lau NC, Garrett-Engele P, Grimson A, Schelter JM et al (2005) Microarray analysis shows that some microRNAs down-regulate large numbers of target mRNAs. Nature 433:769773 Lovis P, Roggli E, Laybutt DR, Gattesco S, Yang JY et al (2008) Alterations in microRNA expression contribute to fatty acid-induced pancreatic beta-cell dysfunction. Diabetes 57:27282736 Nadler ST, Stoehr JP, Schueler KL, Tanimoto G, Yandell BS et al", + "Abstract Recent advances in the understanding of the genetics of type 2 diabetes (T2D) susceptibility have focused attention on the regulation of transcriptional activity within the pancreatic beta-cell. MicroRNAs (miRNAs) represent an important component of regulatory control, and have proven roles in the development of human disease and control of glucose", + "evidence demonstrates that miRNAs and lncRNAs can alsoregulate the expression of genes and modulate the actions of growth factors and inflammatory factors related to diabetic complications [ 8]. These reports have been described in sev- eral reviews [ 8,8791] and are only briefly discussed here. Numerous recent reports have demonstrated abnormal ex- pression of various miRNAs in renal, vascular and retinal cellsunder diabetic conditions, and in vivo models of related", + "In addition, miRNAs have been shown to be involved in T2DM. For example, miRNAs play major roles in pancreatic islet development, cell dysfunction, insulin synthesis and secretion and insulin resistance [148] . Studies based on miRNA microarray analysis have identified many different miRNAs involved in the pathology of both T1DM and T2DM; these miRNAs include mi R-375, miR -29, miR -9, miR-124a, miR -195, miR -222, miR -126, miR -133a, miR -296, miR -96, miR -34a, miR -146b, miR -657,", + "26. He Y , Ding Y , Liang B, Lin J, Kim TK, Yu H, Hang H, Wang K. A Systematic Study of Dysregulated MicroRNA in Type 2 Diabetes Mellitus. Int J Mol Sci. 2017:18. 27. Dias S, Hemmings S, Muller C, Louw J, Pheiffer C. MicroRNA Expression Varies according to Glucose Tolerance, Measurement Platform, and Biological Source. Biomed Res Int. 2017;2017:1080157. 28. El Ouaamari A, Baroukh N, Martens GA, Lebrun P, Pipeleers D, van Obberghen E. miR-375 targets 3'-phosphoinositide-dependent protein kinase-1 and", + "nucleotide RNA molecules that potentially regulate the expression of thousands of genes. To understand therelationship between miRNA regulation and obesity- induced diabetes, we quantitatively proled approximately220 miRNAs in pancreatic islets, adipose tissue, and liver from diabetes-resistant (B6) and diabetes-susceptible (BTBR) mice. More than half of the miRNAs proled wereexpressed in all three tissues, with many miRNAs in each tissue showing signicant changes in response to genetic", + "11. Bartel DP. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell. 2004;116(2):281 97. 12. Pirola L, Balcerczyk A, Tothill RW, et al. Genome-wide analysis distinguishes hyperglycemia regulated epigenetic signatures of pri- mary vascular cells. Genome Res. 2011;21(10):1601 15. 13.Cooper ME, El-Osta A. Epigenetics: mechanisms and implications for diabetic complications. Circ Res. 2010;107(12):1403 13.Thispaper also provides a review of evidence pertaining to the role", + "128. Diao X, Shen E, Wang X, Hu B. Differentially expressed microRNAs and their target genes in the hearts of streptozotocin-induced diabetic mice. Mol Med Rep (2011) 4:63340. doi:10.3892/mmr.2011.489 129. La Sala L, Cattaneo M, De Nigris V , Pujadas G, Testa R, Bonfigli AR, et al. Oscillating glucose induces microRNA-185 and impairs an efficient antioxidant response in human endothelial cells. Cardiovasc Diabetol (2016) 15:71. doi:10.1186/s12933-016-0390-9" + ], + "answer": "MicroRNAs (miRNAs) regulate gene expression related to diabetes by acting at the post-transcriptional level to control their target genes. They are involved in several crucial pathways associated with diabetes, including insulin secretion, cholesterol biosynthesis, fat metabolism, and adipogenesis [2]. miRNAs also play significant roles in pancreatic islet development, beta-cell dysfunction, insulin synthesis and secretion, and insulin resistance, which are key factors in the pathology of both Type 1 and Type 2 Diabetes Mellitus (T1DM and T2DM) [6]. Additionally, specific miRNAs have been implicated in the pathogenesis of diabetic complications, such as diabetic nephropathy, where miRNAs like miR-192, miR-216a, miR-217, and miR-377 are up-regulated [2]. These miRNAs can modulate the actions of growth factors and inflammatory factors, further influencing diabetic complications [5].", + "question": "How do microRNAs regulate gene expression related to diabetes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_1 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_1 new file mode 100644 index 0000000..d66558b --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_1 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2010 - Using expression genetics to study the neurobiology of ethanol and alcoholism.pdf", + "2020 - GeneNetwork a toolbox for systems genetics.pdf", + "2017 - GeneNetwork a toolbox for systems genetics.pdf", + "2010 - Systems genetics analyses predict a transcription role for P2P-R Molecular confirmation that P2P-R is a transcriptional co-repressor.pdf", + "2021 -Highlights from the Era of Open Source Web-Based Tools.pdf", + "2020 -Highlights from the Era of Open Source Web-Based Tools.pdf", + "2011 - Peroxisomal L-bifunctional enzyme (Ehhadh) is essential for the production of medium-chain dicarboxylic acids.pdf", + "2014 - Systems Genetics of Liver Fibrosis Identification of Fibrogenic and Expression Quantitative Trait Loci in the BXD Murine Reference Population.pdf", + "2009 - Genetical Toxicogenomics in Drosophila Identifies Master Modulatory Loci that are Regulated by Developmental Exposure to Lead.pdf" + ], + "extraction_id": [ + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "9597c8b3-0d67-5192-9e08-1bccc5e2f75c", + "f28836b7-0091-59ff-8d31-2ccad7341718", + "f7d5751d-c84d-5332-9dde-f31293ff02e3", + "ec624ebb-489a-5437-a721-f01cf981d0a7", + "ffafdd06-808c-58be-bcb5-bd74d7ffa89a", + "14530ed7-e49e-5a1a-9df6-820c7495a8ce", + "a8b40857-7ae8-512a-9817-bea1ae3345ba", + "8c423789-3641-5853-9cf3-f4a026ffb446", + "3ca48658-ca83-5952-8f8d-eb7ae491e6b6" + ], + "document_id": [ + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "64469ae5-5eb6-5e45-ab23-7bafb63d486f", + "d11a87ca-4989-59af-95e3-ab90af7d9212", + "682c3a51-0aa5-54a3-a6e7-a09b81c0e8b6", + "e4d1e2e9-f267-5814-8c7b-dc11d7eec9bf", + "0a6f3d2e-70c3-5db7-bfc0-93ad04806104", + "f2f0dc2f-eaf5-57f6-9ffa-816d3fab6640", + "ac61753e-bcb2-55c3-804b-e821e3d1a4ad", + "125d9cd4-5297-5173-9b16-9073cd3bcc71", + "301d6469-2a9c-5960-88ac-8437212d78ab" + ], + "id": [ + "chatcmpl-AIGl833nLoD9fbsUoJ9TogtCBZo31", + "c63cfaee-749e-547b-9c0a-086266f10670", + "d8162fdc-326a-5f90-9fa4-24d86d701184", + "91e1f097-b446-5915-9fcb-d38640d8a14a", + "488b9f81-e94f-56ad-9f28-dd71f3acd31f", + "a9508122-3b14-5365-979c-ba580bdcb78f", + "a1124460-ae34-57fb-846b-e033f4bbf49c", + "7beda13b-1ea5-53c0-9380-72eee2df79fe", + "8b8a24da-a175-5cb8-91bd-8966fca5d344", + "1c26e6f6-680b-5877-9600-fee25a42c943", + "40ecbff2-5039-57f0-a01e-9d412d559dbe" + ], + "contexts": [ + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "of links to external resources for tracing the interrelationships of a gene among multiple Web-based resources. GeneNetwork also offers a number of correlation and mapping strategies for assessing associations among multiple genes and QTLs. GeneNetwork aims to make the study of complex traits through the use of systems genetics widely available to the scientific community. A powerful tool that can be integrated with GeneNetwork or used on", + "inbred strain; Reverse genetics; dbSNP; GeneWeaver; BioGPS; NCBI; GeneRIF; UCSC Genome Browser; Gemma; GEO; Allen Brain Atlas; GWAS Catalog; GTEx; WebGestalt; PLINK; Manhattan plot; eQTL analysis; R/qtl; WGCNA; Proteomics; Metabolomics; Metagenomics 1 Introduction GeneNetwork ( www.genenetwork.org , GN) is a web service for systems genetics. It started in 2001 as WebQTL an online version of Ken Manlys Map Manager QT program [ 1]", + "inbred strain; Reverse genetics; dbSNP; GeneWeaver; BioGPS; NCBI; GeneRIF; UCSC Genome Browser; Gemma; GEO; Allen Brain Atlas; GWAS Catalog; GTEx; WebGestalt; PLINK; Manhattan plot; eQTL analysis; R/qtl; WGCNA; Proteomics; Metabolomics; Metagenomics 1 Introduction GeneNetwork ( www.genenetwork.org , GN) is a web service for systems genetics. It started in 2001 as WebQTL an online version of Ken Manlys Map Manager QT program [ 1]", + "GeneNetwork http://www.genenetwork.org is anexample of a bioinformatics tool that can be used to explore systems genetics data. The importance of defining biological networks and predicting molecular interactions has been emphasized by several reports [1,2]. Such studies emphasize that when knowledge about DNA variation within popula- tions is interfaced with data on gene expression, protein interactions and DNA-protein binding, biological networks can be constructed that are predictive of the", + "GeneNetwork.org is also a valuable teaching tool. While mainly designed for researchers interested in testing gene-to- phenotype relationships, GeneNetwork. orghas been adapted for dry-lab teaching in neuroscience and genetics ( Grisham et al., 2017 ). A useful approach is to assign sets of vetted questions, such as the exam- ples discussed above, and to help students work toward answers, solutions, or novelquestions. Several examples relating to the", + "GeneNetwork.org is also a valuable teaching tool. While mainly designed for researchers interested in testing gene-to- phenotype relationships, GeneNetwork. orghas been adapted for dry-lab teaching in neuroscience and genetics ( Grisham et al., 2017 ). A useful approach is to assign sets of vetted questions, such as the exam- ples discussed above, and to help students work toward answers, solutions, or novelquestions. Several examples relating to the", + "subnetworks GeneNetwork (www.genenetwork.org) is a depository of data- sets and tools for use in complex systems biology approaches in order to generate or predict higher order gene function ( 23, 24 ).", + "GeneNetwork is an open-access database that collates genomic information of diverse experimental crosses and reference panels as well as phenotypic data from miscellaneous research groups [26]. Statistics Data generation, statistical analysis and graph creation were performed with SPSS Statistics 21 (IBM, Ehningen, Germany). As appropriate, mean and median values were further used for QTLanalysis. Phenotypic robustness for each strain was assessed by the", + "deposited in the GeneNetwork website (http://www.genenetwork.org) so that other investigators can look for correlations between gene expression patterns and phenotypic traits. The GeneNetwork is an open resource and consists of a set of linked resources for systems genetics. It has been designed for integration of networks of genes, transcripts, and traits such as toxicity, cancer susceptibility, and behavior for several species. Phenotypic QTLs using the" + ], + "answer": "GeneNetwork.org is a web service for systems genetics that started in 2001 as WebQTL, an online version of Ken Manly's Map Manager QT program [3]. It serves as a depository of datasets and tools for use in complex systems biology approaches to generate or predict higher-order gene function [8]. GeneNetwork.org is an open-access database that collates genomic information from diverse experimental crosses and reference panels, as well as phenotypic data from various research groups [9]. GeneNetwork.org helps scientists understand genetics by providing a platform for systems genetics, which involves the study of complex traits through the integration of networks of genes, transcripts, and traits such as toxicity, cancer susceptibility, and behavior across several species [10]. It offers tools for correlation and mapping strategies to assess associations among multiple genes and quantitative trait loci (QTLs), making the study of complex traits widely available to the scientific community [2]. Additionally, it supports predictive medicine and systems genetics by constantly being maintained and improved with data from multiple species and multi-omics analysis [1].", + "question": "What is GeneNetwork.org, and how does it help scientists understand genetics?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_10 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_10 new file mode 100644 index 0000000..2023cc2 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_10 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2015 - Cell cycle gene expression networks discovered using systems biology Significance in carcinogenesis.pdf", + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2020 - Gene network a continuously updated tool for systems genetics analyses.pdf", + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2019 - Implementation of Genomic Medicine.pdf", + "2021 -Highlights from the Era of Open Source Web-Based Tools.pdf", + "2020 -Highlights from the Era of Open Source Web-Based Tools.pdf", + "2011 - Using the PhenoGen Website for \u201cIn Silico\u201d Analysis of Morphine-Induced Analgesia Identifying Candidate Genes.pdf", + "2009 - Genetical Toxicogenomics in Drosophila Identifies Master Modulatory Loci that are Regulated by Developmental Exposure to Lead.pdf" + ], + "extraction_id": [ + "5b6d04d2-3aa2-5a43-814a-b13e60e3bb1d", + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "2fd15885-4e19-536f-a90a-3650bd23c37e", + "406a0217-5585-5daf-88d0-5904cfb04c3b", + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "90e220eb-61ba-56bd-b455-ac29a1df5867", + "62c12bdc-ae2b-5cc0-88f5-a3c1a264326b", + "28892088-5a95-56eb-822d-b12da3a612d0", + "0e3a5e40-06b0-58d4-b495-3093954ed17b", + "3ca48658-ca83-5952-8f8d-eb7ae491e6b6" + ], + "document_id": [ + "6f354254-4f4d-52ad-bed7-9356f43c0b20", + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "374fd6d3-e6c1-560c-a421-a4b393ba23b2", + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "a7faf15a-ed90-575b-805c-11f33fb2d6dd", + "0a6f3d2e-70c3-5db7-bfc0-93ad04806104", + "f2f0dc2f-eaf5-57f6-9ffa-816d3fab6640", + "eb266fa1-8dec-5c56-a3d5-b508bd6bd448", + "301d6469-2a9c-5960-88ac-8437212d78ab" + ], + "id": [ + "chatcmpl-AIGluZZhH7wm0mptVn5RRlhFxsJ3L", + "dcb29dfe-ba22-54bc-91f7-af3261a18fd2", + "d7e5ef8a-d43a-587d-8ffd-cd5e8e63f6ab", + "aaa9652c-64f3-53e7-ab24-48c5d19136d0", + "7dd2de60-d539-59e1-9c4c-2927367d650f", + "c63cfaee-749e-547b-9c0a-086266f10670", + "a9bbd320-eb89-5ae7-a3af-703ca68c8305", + "aaf19f3a-353a-595b-b7de-da9f01a7b0f1", + "7b626f27-5aed-5464-a20d-463954ff057d", + "0b2bd83d-680a-52d2-8116-50cce4f35cc3", + "40ecbff2-5039-57f0-a01e-9d412d559dbe" + ], + "contexts": [ + "of importance in the emergence of precision medicine ( Curtis, 2015 ; Desautels et al., 2014 ; Glade Bender et al., 2015 ; Jorgensen, 2015 ; Kummar et al., 2015 ; Marquet et al., 2015 ; Rubin, 2014 ) wherein therapeutic strategies need to be aligned with specific properties of tumors. Methods GeneNetwork and WebGestalt GeneNetwork is an open access, online data analysis resource for systems biology and systems genetics. It contains a large number of microarray datasets from multiple tissues of", + "gathered together into an easily accessible format, not siloed into disparate data pools that cannot easily be integrated, valid ated, o r extended. This approach will allow us to make animal models of so called precision medicine, although perhaps more accurately, we want predictive medicine , where a phenotypic outcome (such as disease) can be predicted , and avoided . GeneNetwork (genenetwork.or g; GN) is one tool for systems genetics and predictive medicine,", + "The GeneNetwork site is supported by the University of Tennessee Center for Integrative and Translational Genomics, NI GMS Systems Genetics and Precision Medicine Project (R01 GM123489, 2017 -2021), NIDA Core Center of Excellence in Transcriptomics, Systems Genetics, and the Addictome (P30 DA044223, 2017 -2022), NIA Translational Systems Genetics of Mitochondria, Metabolism, and Aging (R01AG043930, 2013 -2018), NIAAA Integrative", + "The GeneNetwork site is supported by the University of Tennessee Center for Integrative and Translational Genomics, NI GMS Systems Genetics and Precision Medicine Project (R01 GM123489, 2017 -2021), NIDA Core Center of Excellence in Transcriptomics, Systems Genetics, and the Addictome (P30 DA044223, 2017 -2022), NIA Translational Systems Genetics of Mitochondria, Metabolism, and Aging (R01AG043930, 2013 -2018), NIAAA Integrative", + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "eron Genetics Center ( https://www.regeneron.com/ge - netics-center ), and aims to identify rare loss-of-function mutations in founder populations to delineate further the genetic factors that underpin health and disease. This ini - tiative is also addressed at developing countries and those in resource-limiting environments, under the coordina - tion of the Genomic Medicine Alliance ( http://www.ge - nomicmedicinealliance.org ), a founding partner of the", + "distributed neuroscience data sharing with ever expanding prospects for future breakthroughs. GeneNetwork.org : genetic analysis for all neuroscientists Originally named webqtl, GeneNetwork.org is the oldest contin- uously operating website in biomedical research ( Williams, 1994). This massive database contains ;40 million datasets. GeneNetwork.org also offers a powerful statistical platform for online network analyses and mapping, enabling numerous mo-", + "distributed neuroscience data sharing with ever expanding prospects for future breakthroughs. GeneNetwork.org : genetic analysis for all neuroscientists Originally named webqtl, GeneNetwork.org is the oldest contin- uously operating website in biomedical research ( Williams, 1994). This massive database contains ;40 million datasets. GeneNetwork.org also offers a powerful statistical platform for online network analyses and mapping, enabling numerous mo-", + "al., 2005). GeneNetwork is designed primarily as a web service for exploratory and statistical analysis of large published phenotype and genome datasets, and includes data from several species (see Supplementary Discussion). GeneNetwork includes extensive phenotype data extracted from the literature and submitted by users, which makes it practical to compare data on drug responses with gene expression patterns. Gene expression", + "deposited in the GeneNetwork website (http://www.genenetwork.org) so that other investigators can look for correlations between gene expression patterns and phenotypic traits. The GeneNetwork is an open resource and consists of a set of linked resources for systems genetics. It has been designed for integration of networks of genes, transcripts, and traits such as toxicity, cancer susceptibility, and behavior for several species. Phenotypic QTLs using the" + ], + "answer": "GeneNetwork.org plays a significant role in personalized medicine by serving as an open-access, online data analysis resource for systems biology and systems genetics [1]. It is a tool for systems genetics and predictive medicine, which aims to predict and potentially avoid phenotypic outcomes such as diseases [2]. The platform supports the integration of networks of genes, transcripts, and traits, which is crucial for understanding complex genetic interactions and their implications for personalized medicine [10]. Additionally, GeneNetwork.org facilitates the comparison of data on drug responses with gene expression patterns, which is essential for tailoring therapeutic strategies to individual genetic profiles [9].", + "question": "What role does GeneNetwork.org play in personalized medicine?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_11 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_11 new file mode 100644 index 0000000..b0ea5b7 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_11 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2016 - A Systems-Level Understanding of Cardiovascular Disease through Networks.pdf", + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2015 - Cell cycle gene expression networks discovered using systems biology Significance in carcinogenesis.pdf", + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2010 - Genomics, Type 2 Diabetes, and Obesity.pdf", + "2020 -Highlights from the Era of Open Source Web-Based Tools.pdf", + "2021 -Highlights from the Era of Open Source Web-Based Tools.pdf", + "2011 - Using the PhenoGen Website for \u201cIn Silico\u201d Analysis of Morphine-Induced Analgesia Identifying Candidate Genes.pdf", + "2010 - Using expression genetics to study the neurobiology of ethanol and alcoholism.pdf", + "2012 - Identifying Gene Networks Underlying the Neurobiology of Ethanol and Alcoholism.pdf" + ], + "extraction_id": [ + "362cb4d9-306b-5bbe-a2b7-3fedfb2cae2a", + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "5b6d04d2-3aa2-5a43-814a-b13e60e3bb1d", + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "8dc4ac39-16e2-508e-aaa3-016c51410c79", + "14530ed7-e49e-5a1a-9df6-820c7495a8ce", + "ffafdd06-808c-58be-bcb5-bd74d7ffa89a", + "0e3a5e40-06b0-58d4-b495-3093954ed17b", + "9597c8b3-0d67-5192-9e08-1bccc5e2f75c", + "40850ed1-db52-594e-a9d6-0b661e0bc494" + ], + "document_id": [ + "96657025-7e50-571d-9a6b-1a202cb8a690", + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "6f354254-4f4d-52ad-bed7-9356f43c0b20", + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "3d629777-f1b6-5450-94ef-56736e5a4e10", + "f2f0dc2f-eaf5-57f6-9ffa-816d3fab6640", + "0a6f3d2e-70c3-5db7-bfc0-93ad04806104", + "eb266fa1-8dec-5c56-a3d5-b508bd6bd448", + "64469ae5-5eb6-5e45-ab23-7bafb63d486f", + "c02542c0-eff8-5ec7-8f73-78f5d28d4226" + ], + "id": [ + "chatcmpl-AIGlzcVkzqXfNsnzBiVpVbwVplFu6", + "70bf64a4-327c-517d-8ccb-a9012bcfc453", + "c63cfaee-749e-547b-9c0a-086266f10670", + "dcb29dfe-ba22-54bc-91f7-af3261a18fd2", + "d7e5ef8a-d43a-587d-8ffd-cd5e8e63f6ab", + "b0020ac6-de30-5090-817a-d14d770abd1e", + "7beda13b-1ea5-53c0-9380-72eee2df79fe", + "a1124460-ae34-57fb-846b-e033f4bbf49c", + "0b2bd83d-680a-52d2-8116-50cce4f35cc3", + "067136a5-b89e-5108-85b0-f638c041e68c", + "1cf9d2ee-62b7-5dc8-8f58-23cecab650dc" + ], + "contexts": [ + "mation on gene function and how altered function leads to disease. Elucidating the mechanisms of action for newly minted disease genes is amajor bottleneck in translating genetic discoveries into new therapeutics.Addressing this limitation, it has been shown that networks can provideinsight on gene function [71,72] . The premise behind this is simple dgenes", + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "of importance in the emergence of precision medicine ( Curtis, 2015 ; Desautels et al., 2014 ; Glade Bender et al., 2015 ; Jorgensen, 2015 ; Kummar et al., 2015 ; Marquet et al., 2015 ; Rubin, 2014 ) wherein therapeutic strategies need to be aligned with specific properties of tumors. Methods GeneNetwork and WebGestalt GeneNetwork is an open access, online data analysis resource for systems biology and systems genetics. It contains a large number of microarray datasets from multiple tissues of", + "gathered together into an easily accessible format, not siloed into disparate data pools that cannot easily be integrated, valid ated, o r extended. This approach will allow us to make animal models of so called precision medicine, although perhaps more accurately, we want predictive medicine , where a phenotypic outcome (such as disease) can be predicted , and avoided . GeneNetwork (genenetwork.or g; GN) is one tool for systems genetics and predictive medicine,", + "vidual patients. For the time being, the contribu - tion of genetic information to therapy is most likely to come through the drug-discovery pipe - line. Information from genetic studies could be used to identify new targets for pharmaceutical intervention that have validated effects on physi - ological characteristics, to provide information about new and existing targets (e.g., clues about the long-term safety of pathway intervention), 32", + "GeneNetwork.org is also a valuable teaching tool. While mainly designed for researchers interested in testing gene-to- phenotype relationships, GeneNetwork. orghas been adapted for dry-lab teaching in neuroscience and genetics ( Grisham et al., 2017 ). A useful approach is to assign sets of vetted questions, such as the exam- ples discussed above, and to help students work toward answers, solutions, or novelquestions. Several examples relating to the", + "GeneNetwork.org is also a valuable teaching tool. While mainly designed for researchers interested in testing gene-to- phenotype relationships, GeneNetwork. orghas been adapted for dry-lab teaching in neuroscience and genetics ( Grisham et al., 2017 ). A useful approach is to assign sets of vetted questions, such as the exam- ples discussed above, and to help students work toward answers, solutions, or novelquestions. Several examples relating to the", + "al., 2005). GeneNetwork is designed primarily as a web service for exploratory and statistical analysis of large published phenotype and genome datasets, and includes data from several species (see Supplementary Discussion). GeneNetwork includes extensive phenotype data extracted from the literature and submitted by users, which makes it practical to compare data on drug responses with gene expression patterns. Gene expression", + "biological function of the new gene list. As mentioned previously, GeneNetwork (www.genenetwork.org) is a collaborative Web-based resource equipped with tools and features for studying gene/gene and exploring genetic correlates to neurobehavioral phenotypes (Chesler et al., 2003, 2004). The Web site is home to a growing collection of gene expression and phenotypic data from a variety of species and brain regions, with a host", + "is tackling this immense challenge bystudying networks of genes, proteins,metabolites, and other biomarkers thatrepresent models of genuine biologicalpathways. Studying complex diseasesin terms of gene networks rather thanindividual genes or genomic loci shouldaid in uncovering disease genes. Withthis approach, the effects of multiplegenes in the network are combined,producing a stronger signal and reducingthe number of statistical tests of associ-ation that must be performed." + ], + "answer": "The information on GeneNetwork.org aids in developing new treatments for diseases in several ways: 1. **Insight into Gene Function**: GeneNetwork.org provides insights into gene function and how altered gene function can lead to disease. This understanding is crucial for translating genetic discoveries into new therapeutics, as it helps elucidate the mechanisms of action for newly identified disease genes, which is a major bottleneck in drug development [1]. 2. **Predictive Medicine and Systems Genetics**: The platform is an exciting resource for predictive medicine and systems genetics. It integrates data from multiple species and omics analyses, which can be used to predict phenotypic outcomes such as disease, potentially allowing for the development of treatments that can prevent these outcomes [2], [4]. 3. **Identification of Drug Targets**: Genetic information from GeneNetwork.org can be used to identify new targets for pharmaceutical intervention. This includes providing information about the long-term safety of pathway interventions, which is crucial for developing effective and safe treatments [5]. 4. **Exploratory and Statistical Analysis**: GeneNetwork.org is designed for exploratory and statistical analysis of large phenotype and genome datasets. This makes it practical to compare data on drug responses with gene expression patterns, facilitating the identification of potential therapeutic targets [8]. 5. **Studying Gene Networks**: By studying networks of genes, proteins, metabolites, and other biomarkers, GeneNetwork.org helps uncover disease genes. This network-based approach combines the effects of multiple genes, producing stronger signals and reducing the complexity of statistical analyses, which can accelerate the discovery of new treatments [10]. Overall, GeneNetwork.org serves as a comprehensive tool for researchers to explore genetic data and develop insights that are critical for the creation of new therapeutic strategies.", + "question": "How does the information on GeneNetwork.org help in developing new treatments for diseases?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_12 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_12 new file mode 100644 index 0000000..81e94df --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_12 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2009 - Processing Large-Scale, High-Dimension Genetic and Gene Expression Data.pdf", + "2009 - Processing Large-Scale, High-Dimension Genetic and Gene Expression Data.pdf", + "2010 - Using expression genetics to study the neurobiology of ethanol and alcoholism.pdf", + "2009 - Visual analytics for relationships in scientific data (1).pdf", + "2017 - Systems Genetics Analysis to Identify the Genetic Modulation of a Glaucoma-Associated Gene.pdf", + "2011 - Genetical genomics approaches for systems genetics.pdf", + "2010 - Systems genetics analyses predict a transcription role for P2P-R Molecular confirmation that P2P-R is a transcriptional co-repressor.pdf", + "2012 - Genetic regulation of adult hippocampal neurogenesis A systems genetics approach using BXD recombinant inbred mouse strains.pdf", + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2020 - Modeling the Genetic Basis of Individual Differences in Susceptibility to Gulf War Illness.pdf" + ], + "extraction_id": [ + "1d401588-b6dc-532f-8194-4667a7d31153", + "1d401588-b6dc-532f-8194-4667a7d31153", + "9597c8b3-0d67-5192-9e08-1bccc5e2f75c", + "697332a8-8630-50ff-aa2b-f33478931d24", + "2455cf6d-4c9b-5272-8650-da127cc329e8", + "a83ca198-3b9d-5355-aa82-30d89ebf018c", + "ec624ebb-489a-5437-a721-f01cf981d0a7", + "ebea9717-52a1-5eb8-8b5a-67afb90c95f8", + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "98aff04d-a5b2-5cca-bc1a-552055a74262" + ], + "document_id": [ + "17264155-b665-59db-94cb-f4d67eac20fc", + "17264155-b665-59db-94cb-f4d67eac20fc", + "64469ae5-5eb6-5e45-ab23-7bafb63d486f", + "a6642ef1-8aa2-5305-9cc8-8a6263bb2b0c", + "67e804db-8127-5938-8d7f-a5918cdf4f86", + "de78a01d-8d03-5afb-af5b-ce2ed2167766", + "e4d1e2e9-f267-5814-8c7b-dc11d7eec9bf", + "c54da858-9620-588e-8e41-76a960af2ff6", + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "d235d186-3d1c-5cde-90d5-9c140cd920f4" + ], + "id": [ + "chatcmpl-AIGm7DFsh1v2eeUURegyReODMaCec", + "509d3815-9994-5afc-9777-52eb80281dc8", + "9d6a0871-3235-5fd6-855a-897e6a177db4", + "d8162fdc-326a-5f90-9fa4-24d86d701184", + "e78c3922-952f-53ea-a1d5-8edd98f9b893", + "18c7c27b-b51f-5ab6-9d09-4235c57811b1", + "9c0d7bcf-242c-5ba7-86bb-df799e6e03a6", + "a9508122-3b14-5365-979c-ba580bdcb78f", + "2fe235ff-90ab-5f21-8e51-cbfb0e13713a", + "c63cfaee-749e-547b-9c0a-086266f10670", + "23de1e96-55b6-5062-a2e1-02bf06fd3565" + ], + "contexts": [ + "considering single genes in the context of a whole gene network may provide thenecessary context within which to interpr et the disease role a given gene may play. Constructing gene networks can provide a convenient framework for exploring the context within which single genes operate. A network is simply a graphicalmodel comprised of nodes and edges. For gene networks associated with biological systems, the nodes in the network typically represent genes, gene products, or other", + "Genes do not carry out their functions in isolation of other genes, but instead oper- ate in complex networks that together, in a context-specic way, dene the complex behavior that emerges from biological systems. Therefore, understanding gene net- works in a diversity of contexts will lead to an increased understanding of complex system behavior, including disease. The reductionist approach to elucidating the complexity of biological systems", + "of links to external resources for tracing the interrelationships of a gene among multiple Web-based resources. GeneNetwork also offers a number of correlation and mapping strategies for assessing associations among multiple genes and QTLs. GeneNetwork aims to make the study of complex traits through the use of systems genetics widely available to the scientific community. A powerful tool that can be integrated with GeneNetwork or used on", + "genotypes and phenotypes, geneticists hope to discover and interpret the network of causal genotype-phenotype relationships that determine a trait of interest. Systems genetics research often follows a workow of nding a gene network, nding regulators of that network, and then performing a focused ge ne perturbation experiment to determine the role of the associated network on gene expre ssion or function. To be- gin, a large gene correlation graph must be sifted through , to nd a highly connected", + "genetics approaches can not only provide insights into the roles of individual genes or developmental pathways but also illuminate relationships between different levels of a biologic system, such as the genome, transcriptome, and phenome [ 10]. One such resource of systems genetics is the GeneNetwork website and resource (www.genenetwork.org ) that provides access to a wide variety of data such as genotypes (e.g., SNPs), phenotypes that are obtained", + "the risk of missing important biological phenomena [43]. 8.4 Defining gene and QTL networks In addition to the genetic dissection of phenotypic variation using QTL mapping techniques, systems geneticists are interested in r econstructing the biological net- works that connect genes, proteins and other traits based on their observed genetic (co-)variation. In this context, biological network s are often defined by graphical", + "GeneNetwork http://www.genenetwork.org is anexample of a bioinformatics tool that can be used to explore systems genetics data. The importance of defining biological networks and predicting molecular interactions has been emphasized by several reports [1,2]. Such studies emphasize that when knowledge about DNA variation within popula- tions is interfaced with data on gene expression, protein interactions and DNA-protein binding, biological networks can be constructed that are predictive of the", + "It is important to integrate the gene variants and environmental factors to the trait to understand the network controlling that trait. In systems genetics approach, different trait networks are related to different networks of gene and environmental variants to find global genetic modulation of the complex phenotype. The availability of genetic reference panels makes it easy to acquire diverse phenotypic data and advanced computational models make it possible to analyse their relationship. 2.2.1.", + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "genetic variants (SNPs, insertions, deletions, duplications, etc.) that segregate in the family [ 13]. The strains are appropriate for systems genetics /systems biology analysis [ 14], genetic mapping and genetic correlations of parameter means, and thus constitute an ideal platform for toxicogenomic research [ 15]. All data are available at www.genenetwork.org. GeneNetwork exists in two forms, GN1 and GN2 [ 16]. GN2 is an expansion and renement of the features of GN1. A tutorial of how to use GN1 may be" + ], + "answer": "A gene network is a graphical model comprised of nodes and edges, where the nodes typically represent genes, gene products, or other biological entities [1]. These networks illustrate how genes do not function in isolation but operate in complex networks that define the behavior of biological systems [2]. Understanding gene networks is crucial for interpreting the roles of individual genes within the broader context of these networks, which can provide insights into complex system behaviors, including diseases [1], [2]. By considering genes within their networks, researchers can better understand the interrelationships and regulatory mechanisms that contribute to phenotypic traits and disease processes [4].", + "question": "What is a gene network, and why is it important for understanding genetics?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_13 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_13 new file mode 100644 index 0000000..b241543 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_13 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2017 - GeneNetwork a toolbox for systems genetics.pdf", + "2020 - GeneNetwork a toolbox for systems genetics.pdf", + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2012 - Genetic and Molecular Network Analysis of Behavior.pdf", + "2008 - Towards systems genetic analyses in barley Integration of phenotypic, expression and genotype data into GeneNetwork.pdf", + "2018 - Molecular Brain Adaptations to Ethanol_ Role of Glycogen Synthase (2).pdf", + "2008 - Genetic Analysis of Posterior Medial Barrel Subfield Size.pdf", + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2011 - Using the PhenoGen Website for \u201cIn Silico\u201d Analysis of Morphine-Induced Analgesia Identifying Candidate Genes.pdf", + "2010 - Using expression genetics to study the neurobiology of ethanol and alcoholism.pdf" + ], + "extraction_id": [ + "6cbea84e-4d8d-5ce0-8e58-45ee75f6f908", + "2bdd2f18-e4d0-53e9-b0fa-a7ed8d710961", + "3033b643-e51e-5467-b7d7-6a5c27061cab", + "dbfd3de6-3641-5430-b694-682fed7b32e9", + "a6c480d1-b384-5c6f-b21b-94fe0b3b0f4d", + "1047bf10-3878-5b70-8bb2-c0249f2a9c53", + "66aad1b1-a76d-58a8-aa40-76a6b58c4964", + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "308bef07-d720-5686-990d-d1e26a48e8a1", + "9597c8b3-0d67-5192-9e08-1bccc5e2f75c" + ], + "document_id": [ + "682c3a51-0aa5-54a3-a6e7-a09b81c0e8b6", + "d11a87ca-4989-59af-95e3-ab90af7d9212", + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "4b6759f8-fdaf-59a1-94bd-5a7cf184e1f9", + "8513abbe-65ed-5f35-9f86-ba93cfc5a194", + "cc2690a9-5a87-5f09-87d5-115a6a6b8349", + "76a715a4-8222-598b-8e65-6d5b6e807989", + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "eb266fa1-8dec-5c56-a3d5-b508bd6bd448", + "64469ae5-5eb6-5e45-ab23-7bafb63d486f" + ], + "id": [ + "chatcmpl-AIGmBVU8OOwhBDyIls65dlks2MJDd", + "1762dc59-0e50-5b7e-bdc2-b754e0e57797", + "e030ce79-6970-5300-a1d8-1623d07c2157", + "48cb54db-68ef-50f0-bc7c-83b7db2ec9a5", + "bd9e8c5d-405c-5b8b-b731-bf4fdaea1b3a", + "01a09a4e-3c30-53b1-8819-6085d4886079", + "d261c68c-c253-52c9-8e27-f76fb8d0b4f8", + "21936758-94b1-506f-9229-77e26001ae44", + "c63cfaee-749e-547b-9c0a-086266f10670", + "94f60899-c281-586e-8741-135a4fef2663", + "d8162fdc-326a-5f90-9fa4-24d86d701184" + ], + "contexts": [ + "Fig. 2. GeneNetwork main search page and organization. Most analyses in GeneNetwork will follow the steps shown in panels A through D. In this workfl ow, a data set is selected ( A) and mined for traits of interest based on user search queries ( B). Traits are then selected from the search ( C) and placed in a collection for further inspection and quantitative analysis (D). The banner menu contains additional search options and helpful resources under the", + "Fig. 2. GeneNetwork main search page and organization. Most analyses in GeneNetwork will follow the steps shown in panels A through D. In this workfl ow, a data set is selected ( A) and mined for traits of interest based on user search queries ( B). Traits are then selected from the search ( C) and placed in a collection for further inspection and quantitative analysis (D). The banner menu contains additional search options and helpful resources under the", + "Another powerful feature of GeneNetwork is the ability to create and analyze whole collections of data. In Figure 3 there are boxes within the table that can be selected in order to form a trait collection. To do this, select the boxes in the table that su it the interests of the study, and press Add. This function allows groups of traits to be saved for later analysis such as the generation of a QTL, a network graph, and correlation matrix, some of which will be investigated further in", + "analysis in GeneNetwork, but there is an even more direct way to answer the same question. It is possible to query data sets in GeneNetwork from the Select and Search page using advanced options to locate the highest trait LRS values for any genomic interval, in this case the region within 2 Mb of Comt . (Note: You can explore this and other search options further by clicking the Advanced Search button and reading the section Advanced", + "is shown in Figure 1A. Associations between transcript abundance, phenotypic traits and genotype can be estab- lished either using correlation or genetic linkage mapping functions [29,30]. The main page of GeneNetwork at http://www.genenetwork.org provides access to subsets of data through pull-down menus that allow specific data sets to be queried. The datasets can be further restricted using a single text box for specific database entries to query probe set or trait ID, or annotations associated with", + "genetic mapping, and correlation of quantitative traits such as gene expression data and behavioral parameters (Wang et al, 2003) . GeneNetwork employs genotype data from 3809 markers, selected based on their being informative (i.e., different between progenitor strains). GeneNetwork outputs peak likelihood ratio statistic (LRS) locations for each trait, whic h can be directly converted to", + "GeneNetwork provides users with an array of analyticaltools to compare a given trait with a number of data setsavailable from other experimenters. Microarray data ofgene expression in the brain and data of other phenotypes are two such examples of possible tools. For this study, we", + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "(description of GeneNetwork provided by Dr. Robert W. Williams). Both of these websites focus to a large extent on correlations of behavioral phenotype with gene expression levels in recombinant inbred and inbred panels of mice and rats, and on QTL analyses, as a means to identify candidate genes for complex traits. What distinguishes PhenoGen, in addition to the tools for raw expression data analysis described above, is that the user can not only", + "of links to external resources for tracing the interrelationships of a gene among multiple Web-based resources. GeneNetwork also offers a number of correlation and mapping strategies for assessing associations among multiple genes and QTLs. GeneNetwork aims to make the study of complex traits through the use of systems genetics widely available to the scientific community. A powerful tool that can be integrated with GeneNetwork or used on" + ], + "answer": "Researchers identify important genes for certain traits using GeneNetwork.org through a series of steps and tools provided by the platform: 1. **Data Selection and Trait Mining**: Researchers begin by selecting a data set and mining it for traits of interest based on user search queries [1]. This involves using the main search page to query specific data sets and identify traits that are relevant to their study. 2. **Trait Collection and Analysis**: Once traits are identified, they are selected and placed in a collection for further inspection and quantitative analysis [1]. This allows researchers to organize and focus on specific traits for deeper investigation. 3. **Advanced Search Options**: GeneNetwork offers advanced search options that enable researchers to query data sets for specific genomic intervals and locate traits with the highest likelihood ratio statistic (LRS) values, which are indicative of strong genetic associations [4]. 4. **Correlation and Genetic Linkage Mapping**: Researchers can establish associations between transcript abundance, phenotypic traits, and genotype using correlation or genetic linkage mapping functions [5]. This helps in identifying candidate genes linked to specific traits. 5. **QTL Analysis and Network Graphs**: The platform allows for the generation of quantitative trait loci (QTL) analyses, network graphs, and correlation matrices, which are essential for understanding the genetic architecture of complex traits [3]. By utilizing these tools and processes, researchers can effectively identify and analyze genes that are important for specific traits using GeneNetwork.org.", + "question": "How do researchers identify which genes are important for certain traits using GeneNetwork.org?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_14 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_14 new file mode 100644 index 0000000..f1352eb --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_14 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2008 - Genetic Analysis of Posterior Medial Barrel Subfield Size.pdf", + "2017 - Systems Genetics Analysis to Identify the Genetic Modulation of a Glaucoma-Associated Gene.pdf", + "2015 - An atlas of genetic correlations across human diseases.pdf", + "2012 - Using Genome-Wide Expression Profiling to Define Gene Networks Relevant to the Study of Complex Traits From RNA Integrity to Network Topology.pdf", + "2021 - Old data and friends improve with age Advancements with the updated tools.pdf", + "2008 - Towards systems genetic analyses in barley Integration of phenotypic, expression and genotype data into GeneNetwork.pdf", + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2010 - Systems genetics analyses predict a transcription role for P2P-R Molecular confirmation that P2P-R is a transcriptional co-repressor.pdf", + "2021 -Highlights from the Era of Open Source Web-Based Tools.pdf", + "2020 -Highlights from the Era of Open Source Web-Based Tools.pdf" + ], + "extraction_id": [ + "66aad1b1-a76d-58a8-aa40-76a6b58c4964", + "2455cf6d-4c9b-5272-8650-da127cc329e8", + "70e38f86-69b7-515d-919e-b8d93f5c709f", + "3e0c2a06-e6de-5888-a360-a2c483d9f744", + "46f604d3-ba70-5cca-8466-21381131697e", + "a6c480d1-b384-5c6f-b21b-94fe0b3b0f4d", + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "ec624ebb-489a-5437-a721-f01cf981d0a7", + "ffafdd06-808c-58be-bcb5-bd74d7ffa89a", + "14530ed7-e49e-5a1a-9df6-820c7495a8ce" + ], + "document_id": [ + "76a715a4-8222-598b-8e65-6d5b6e807989", + "67e804db-8127-5938-8d7f-a5918cdf4f86", + "7b1f602b-1534-5465-b026-03dedf01352d", + "1eb6f5b7-a3bc-5455-91f0-6f2eb37be861", + "55cb2c81-b699-54df-96ab-2bf0b888031e", + "8513abbe-65ed-5f35-9f86-ba93cfc5a194", + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "e4d1e2e9-f267-5814-8c7b-dc11d7eec9bf", + "0a6f3d2e-70c3-5db7-bfc0-93ad04806104", + "f2f0dc2f-eaf5-57f6-9ffa-816d3fab6640" + ], + "id": [ + "chatcmpl-AIGmJRrNQ5y45QTYEPosOFommIdfp", + "21936758-94b1-506f-9229-77e26001ae44", + "18c7c27b-b51f-5ab6-9d09-4235c57811b1", + "38f4e070-1a03-566c-b261-c61ed61963c1", + "312eae52-ede7-5c13-8974-fce0126426cf", + "ed2def7c-a3bb-5d45-ae88-5100874b0837", + "01a09a4e-3c30-53b1-8819-6085d4886079", + "c63cfaee-749e-547b-9c0a-086266f10670", + "a9508122-3b14-5365-979c-ba580bdcb78f", + "a1124460-ae34-57fb-846b-e033f4bbf49c", + "7beda13b-1ea5-53c0-9380-72eee2df79fe" + ], + "contexts": [ + "GeneNetwork provides users with an array of analyticaltools to compare a given trait with a number of data setsavailable from other experimenters. Microarray data ofgene expression in the brain and data of other phenotypes are two such examples of possible tools. For this study, we", + "genetics approaches can not only provide insights into the roles of individual genes or developmental pathways but also illuminate relationships between different levels of a biologic system, such as the genome, transcriptome, and phenome [ 10]. One such resource of systems genetics is the GeneNetwork website and resource (www.genenetwork.org ) that provides access to a wide variety of data such as genotypes (e.g., SNPs), phenotypes that are obtained", + "201 5Nature America, Inc. All rights reserved. 6 ADVANCE ONLINE PUBLICATION Nature Ge Neticsa n a ly s i s 11. Yang, J. et al. Common SNPs explain a large proportion of the heritability for human height. Nat. Genet. 42, 565569 (2010). 12. Yang, J., Lee, S.H., Goddard, M.E. & Visscher, P.M. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 7682 (2011). 13. Lee, S.H., Yang, J., Goddard, M.E., Visscher, P.M. & Wray, N.R. Estimation of", + "addition to this, GeneNetwork can be used to study correlations between traits and to perform data mining in genomic regions containing candidates for quantitative trait genes (Hoffman et al., 2011). All datasets in GeneNetwork are linked to a materials and methods information page that summarizes experimental details relating to the dataset. Databases within GeneNetwork include the transcriptome database, the BXD published", + "medicine. GeneNetwork.org is a tool for quantitative genetics that started in 2001 as WebQTL [38]. It evolved from analyses of forward genetics in the BXD mouse family, to phenome-wide association studies and reverse genetics in a variety of species. Although GeneNetwork contains data for many species and populations, it most prominently contains data for the BXD family. Over 10,000 classical phenotypes, measured under a variety of environmental conditions, and", + "is shown in Figure 1A. Associations between transcript abundance, phenotypic traits and genotype can be estab- lished either using correlation or genetic linkage mapping functions [29,30]. The main page of GeneNetwork at http://www.genenetwork.org provides access to subsets of data through pull-down menus that allow specific data sets to be queried. The datasets can be further restricted using a single text box for specific database entries to query probe set or trait ID, or annotations associated with", + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "GeneNetwork http://www.genenetwork.org is anexample of a bioinformatics tool that can be used to explore systems genetics data. The importance of defining biological networks and predicting molecular interactions has been emphasized by several reports [1,2]. Such studies emphasize that when knowledge about DNA variation within popula- tions is interfaced with data on gene expression, protein interactions and DNA-protein binding, biological networks can be constructed that are predictive of the", + "GeneNetwork.org is also a valuable teaching tool. While mainly designed for researchers interested in testing gene-to- phenotype relationships, GeneNetwork. orghas been adapted for dry-lab teaching in neuroscience and genetics ( Grisham et al., 2017 ). A useful approach is to assign sets of vetted questions, such as the exam- ples discussed above, and to help students work toward answers, solutions, or novelquestions. Several examples relating to the", + "GeneNetwork.org is also a valuable teaching tool. While mainly designed for researchers interested in testing gene-to- phenotype relationships, GeneNetwork. orghas been adapted for dry-lab teaching in neuroscience and genetics ( Grisham et al., 2017 ). A useful approach is to assign sets of vetted questions, such as the exam- ples discussed above, and to help students work toward answers, solutions, or novelquestions. Several examples relating to the" + ], + "answer": "GeneNetwork.org can assist in understanding complex traits like height or intelligence through several key features: 1. **Analytical Tools and Data Sets**: GeneNetwork provides a variety of analytical tools that allow users to compare traits with numerous datasets available from other researchers. This includes microarray data of gene expression in the brain and other phenotypic data, which can be crucial for studying complex traits [1]. 2. **Systems Genetics Approach**: The platform offers a systems genetics approach, which helps illuminate the relationships between different biological system levels, such as the genome, transcriptome, and phenome. This comprehensive view can provide insights into the roles of individual genes and developmental pathways involved in complex traits [2]. 3. **Correlation and Genetic Linkage Mapping**: GeneNetwork allows for the establishment of associations between transcript abundance, phenotypic traits, and genotype using correlation or genetic linkage mapping functions. This can help identify genetic factors contributing to complex traits like height or intelligence [6]. 4. **Data Mining and Trait Correlations**: The platform can be used to study correlations between traits and perform data mining in genomic regions containing candidates for quantitative trait genes. This feature is particularly useful for identifying genetic components of complex traits [4]. 5. **Multi-Omics Analysis**: GeneNetwork has been updated to include multi-omics analysis, which integrates various types of biological data. This holistic approach can enhance the understanding of complex traits by considering multiple layers of biological information [7]. Overall, GeneNetwork.org provides a comprehensive suite of tools and data that can facilitate the exploration and understanding of complex traits like height and intelligence through a systems genetics framework.", + "question": "How can GeneNetwork.org help in understanding complex traits like height or intelligence?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_15 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_15 new file mode 100644 index 0000000..c79414e --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_15 @@ -0,0 +1,65 @@ +{ + "titles": [ + "1993 - Genomic Damage and Its Repair.pdf", + "2007 - Trends in oxidative aging theories.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2019 - Towards Understanding Genomic Instability, Mitochondrial.pdf", + "2002 - Pharmacology, Genomics, and the Evolutionary Biology.pdf", + "2002 - Large genome rearrangements as a primary cause of aging.pdf", + "2016 - Genome Integrity in Aging.pdf", + "2009 - Genomic instability and DNA damage responses in progeria arising.pdf", + "2023 - Genome-wide RNA polymerase stalling.pdf", + "2016 - Menopause Genome stability as new paradigm.pdf" + ], + "extraction_id": [ + "64063108-0ff2-54e5-9801-bc1c49cbdee4", + "752c6f1a-0c4d-5419-86cd-687d2aed7817", + "ead14808-bfb7-5e32-9830-28efaae71151", + "d620ea24-4422-5636-86f5-0943371a4a18", + "e501662f-ffca-563b-97a7-b682a5d7f6ba", + "8f1a0875-8179-5d45-abc0-bbd4c9ac8da5", + "17b26647-4659-5f2d-a9b0-7c122d4b5d1a", + "72beba0d-8c77-5aa9-82ac-ddf6a19355ac", + "31088092-778f-59e0-a9de-5ec25c241aab", + "0855231d-cb95-540c-a3dd-c93729efb34c" + ], + "document_id": [ + "d049f302-a130-5ee4-a1b5-5091605d5173", + "0d752c1a-706a-5b9e-88ef-ba7c51735c3c", + "62b635c3-040e-512a-b016-6ef295308a1e", + "9b34514d-3d0e-52b5-8e5e-2f3c0708fd82", + "1bc636a3-6ce0-5fea-b549-0dae90a78f1b", + "8a8926dc-2360-5a54-b586-8acc34e51c32", + "85d5fcbb-5385-5a01-8139-d11fc8b1fe3a", + "b7d96f9f-8ad4-5f8f-94f9-60404806d478", + "78812a12-8d31-5159-8367-b0d38e5bc84b", + "564dead1-2737-572f-860c-f00de4d0395e" + ], + "id": [ + "chatcmpl-AIGmRJNSU1IpWwTrk2tDfmXqGWPRd", + "a9f7eda5-1b64-507e-95dd-07c81f2d603b", + "882149e3-8186-5577-a2a7-79f2659ff9b4", + "da4e59b7-d5b6-5992-9607-f6697c8f5276", + "4841d806-98b4-513e-94a2-714df6c896f5", + "fc10c968-3108-5c4b-a49c-cb0feabd18c5", + "eb8b89de-422a-5e9e-9ac8-60af4cd718c2", + "34e6b3c4-63bf-5198-ab09-2a7200a7c19a", + "beed04cc-28c7-5dc7-b334-51226a217439", + "badf3a36-1f99-58aa-b80c-725eccf4e8f3", + "c35d1f43-c3bd-5cac-ae4d-937be35f1121" + ], + "contexts": [ + "logical phenomena is often facilitated by the study of genetic mutants, and, in the case of humans, genetic disorders. Accordingly, a search was made, over the years, for genetic disorders characterized by premature aging. If DNA dam- age and repair has anything to do with aging it should be evidenced in such individuals. Martin (1978) listed 162 genetic syndromes in humans with some or many signs of premature aging. About 21 feahares are considered as markers for", + "[315] Szilard, L. On the nature of the aging process. Proc. Natl. Acad. Sci. USA 45:3545; 1959. [316] Vijg, J.; Dolle, M. E. Large genome rearrangements as a primary cause of aging. Mech. Ageing Dev. 123:907915; 2002. [317] Vijg, J. Somatic mutations and aging: a re-evaluation. Mutat. Res. 447:117135; 2000. [318] Martin, G. M. Genetic syndromes in Man with potential relevance to the pathobiology of aging. Birth Defects Orig. Artic. Ser. 14:539; 1978.", + "19 6. Milholland B, Suh Y , Vijg J.Mutation and catastrophe in the aging genome. Exp Gerontol. 2017;94:3440. 7. Maslov AY , Ganapathi S, Westerhof M, Quispe-Tintaya W, White RR, Van Houten B, etal. DNA damage in normally and prematurely aged mice. Aging Cell. 2013;12:46777. 8. Blokzijl F, de Ligt J, Jager M, Sasselli V , Roerink S, Sasaki N, etal. Tissue-specific mutation accumulation in human adult stem cells during life. Nature. 2016;538:2604.", + "143 Gonzalo S, Kreienkamp R & Askjaer P (2017) Hutchinson -Gilford Progeria Syndrome: A premature aging disease caused by LMNA gene mutations. Ageing Res. Rev. 33, 1829. 144 Lu L, Jin W & Wang LL (2017) Aging in Ro thmund -Thomson syndrome and related RECQL4 genetic disorders. Ageing Res. Rev. 33, 3035. 145 de Renty C & Ellis NA (2017) Blooms syndrome: Why not premature aging? Ageing Res. Rev. 33, 3651. 146 Shiloh Y & Lederman HM (2017) Ataxia -telangiectasia (A -T): An emerging", + "genetic disease model of premature aging, In: Harrison,D.E., eds, Genetic Effects on Aging II (Telford Press, Caldwell,NJ), pp. 521542. [2] Djawdan, M., Sugiyama, T., Schlaeger, L., Bradley, T.J. and Rose, M.R. (1996) Metabolic aspects of the trade-off between fecundity and longevity in Drosophila melanogaster ,Physiol. Zool. 69, 11751195. [3] Fleming, J.E., Spicer, G.S., Garrison, R.C. and Rose, M.R.", + "genes of a whole chromosome ineffective, couldbe a main causal factor in aging (Szilard, 1959).According to Maynard Smith, such types of mu-tations do not seem likely to be common enoughto be the main cause of aging. However, at thetime quantitative information on the possible age-related accumulation of different types of muta-tions in various tissues of mammals wascompletely lacking. The question, therefore,whether somatic mutations are a cause of aging,has not been resolved, more than four decadesafter", + "features of premature aging (16, 17). Subsequent experiments conrmed that mitochondrial DNA mutations and deletions were the driving force behind the observed accelerated aging phenotypes(18). THE LINK BETWEEN NUCLEAR GENOME INTEGRITY AND PREMATURE AGING The notion that the majority of currently identied progeria syndromes originate from defects in genome maintenance highlights the importance of the condition of DNA in the process of", + "Tryggvason K,ZhouZ.Genomicinstability inlaminopathy based premature aging,NatMed. 2005;11:780 785. 13.MisteliT,ScaffidiP.Genomeinstability inprogeria:when repairgetsold,NatMed. 2005;11:718 719. 14.PereiraS,Bourgeois P,NavarroC,EstevesVieiraV,CauP,De SandreGiovannoli A,LvyN.HGPSandrelatedpremature aging disorders: Fromgenomicidentification tothefirsttherapeutic approaches, MechAgeingDev.2008;129:449 459. 15.SmithED,Kudlow BA,FrockRL,KennedyBK.Atypenuclear", + "Nature Genetics | Volume 55 | February 2023 | 268279 278 Article https://doi.org/10.1038/s41588-022-01279-621. Tiwari, V. & Wilson, D. M. 3rd. DNA damage and associated DNA repair defects in disease and premature aging. Am. J. Hum. Genet. 105, 237257 (2019). 22. Tamae, D., Lim, P., Wuenschell, G. E. & Termini, J. Mutagenesis and repair induced by the DNA advanced glycation end product N2-1-(carboxyethyl)-2-deoxyguanosine in human cells. Biochemistry 50, 23212329 (2011).", + "[36] J. de Boer, J.O. Andressoo, J. de Wit, J. Huijmans, R.B. Beems, H. van Steeg, et al., Premature aging in mice decient in DNA repair and transcription, Science 296 (2002) 12761279. [37] S.M. Schuh-Huerta, N.A. Johnson, M.P. Rosen, B. Sternfeld, M.I. Cedars, R.A. Reijo Pera, Genetic markers of ovarian follicle number and menopause in women of multiple ethnicities, Hum. Genet. 131 (2012) 17091724." + ], + "answer": "Yes, there are known genetic mutations that cause premature aging. Some specific genetic syndromes associated with premature aging include: 1. Hutchinson-Gilford Progeria Syndrome, which is caused by mutations in the LMNA gene [4]. 2. Rothmund-Thomson syndrome and related disorders, which are associated with mutations in the RECQL4 gene [4]. 3. Ataxia-telangiectasia, which is another genetic disorder linked to premature aging [4]. Additionally, Martin (1978) listed 162 genetic syndromes in humans that exhibit some or many signs of premature aging [1]. These conditions highlight the connection between genetic mutations and premature aging.", + "question": "Are there any known genetic mutations that cause premature aging?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_16 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_16 new file mode 100644 index 0000000..cfa8250 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_16 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2020 -Highlights from the Era of Open Source Web-Based Tools.pdf", + "2021 -Highlights from the Era of Open Source Web-Based Tools.pdf", + "2010 - Systems genetics analyses predict a transcription role for P2P-R Molecular confirmation that P2P-R is a transcriptional co-repressor.pdf", + "2011 - Peroxisomal L-bifunctional enzyme (Ehhadh) is essential for the production of medium-chain dicarboxylic acids.pdf", + "2013 - Pathogenesis and reversal of liver fibrosis Effects of genes and environment.pdf", + "2015 - Gene network of a phosphoglycerate mutase in muscle wasting in mice.pdf", + "2012 - Genetic and Molecular Network Analysis of Behavior.pdf", + "2008 - Systems_genetics_can_provide_new_insight.pdf", + "2017 - Systems genetics of liver fibrosis.pdf" + ], + "extraction_id": [ + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "14530ed7-e49e-5a1a-9df6-820c7495a8ce", + "ffafdd06-808c-58be-bcb5-bd74d7ffa89a", + "ec624ebb-489a-5437-a721-f01cf981d0a7", + "a8b40857-7ae8-512a-9817-bea1ae3345ba", + "71f1aefb-6e32-5add-804e-6fbaa39ca720", + "b71c7c43-8bd9-5d7e-b337-a8c0396f96b7", + "833f822f-9080-5434-b9a1-2473531becfe", + "bfd98d6d-b026-5bbd-896f-b70045a032c8", + "1c595cd2-cdf9-58c7-a344-902eb2facf31" + ], + "document_id": [ + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "f2f0dc2f-eaf5-57f6-9ffa-816d3fab6640", + "0a6f3d2e-70c3-5db7-bfc0-93ad04806104", + "e4d1e2e9-f267-5814-8c7b-dc11d7eec9bf", + "ac61753e-bcb2-55c3-804b-e821e3d1a4ad", + "73724c99-98df-53b2-a378-29c8b4faa171", + "912c42f2-1802-5fe9-b9dd-871c5290dd18", + "4b6759f8-fdaf-59a1-94bd-5a7cf184e1f9", + "0bc5b1ee-f7fe-5d12-9ebf-ab7363119424", + "4efaa694-a204-54e3-8f4b-caa322574f0a" + ], + "id": [ + "chatcmpl-AIGmWH13RNXnSJZf14g9zwI9AF1X4", + "c63cfaee-749e-547b-9c0a-086266f10670", + "7beda13b-1ea5-53c0-9380-72eee2df79fe", + "a1124460-ae34-57fb-846b-e033f4bbf49c", + "a9508122-3b14-5365-979c-ba580bdcb78f", + "8b8a24da-a175-5cb8-91bd-8966fca5d344", + "f5c218f0-1280-55f8-912b-b32b833e93a3", + "a0c173a5-8685-50df-8110-8d7ec02cdbf3", + "27eda296-b0b7-5ae4-881e-31987ba63dec", + "55461d7c-995b-5f5a-be38-a64acd4a904a", + "fb6033cf-3df1-5dd6-b3ed-1d32913800b2" + ], + "contexts": [ + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "GeneNetwork.org is also a valuable teaching tool. While mainly designed for researchers interested in testing gene-to- phenotype relationships, GeneNetwork. orghas been adapted for dry-lab teaching in neuroscience and genetics ( Grisham et al., 2017 ). A useful approach is to assign sets of vetted questions, such as the exam- ples discussed above, and to help students work toward answers, solutions, or novelquestions. Several examples relating to the", + "GeneNetwork.org is also a valuable teaching tool. While mainly designed for researchers interested in testing gene-to- phenotype relationships, GeneNetwork. orghas been adapted for dry-lab teaching in neuroscience and genetics ( Grisham et al., 2017 ). A useful approach is to assign sets of vetted questions, such as the exam- ples discussed above, and to help students work toward answers, solutions, or novelquestions. Several examples relating to the", + "GeneNetwork http://www.genenetwork.org is anexample of a bioinformatics tool that can be used to explore systems genetics data. The importance of defining biological networks and predicting molecular interactions has been emphasized by several reports [1,2]. Such studies emphasize that when knowledge about DNA variation within popula- tions is interfaced with data on gene expression, protein interactions and DNA-protein binding, biological networks can be constructed that are predictive of the", + "subnetworks GeneNetwork (www.genenetwork.org) is a depository of data- sets and tools for use in complex systems biology approaches in order to generate or predict higher order gene function ( 23, 24 ).", + "GeneNetwork (www.genenetwork.org). The web -based software further allows extraction of sets of", + "resources, gene expression pro les, and gene network constructions, methods for the analysis of gene function have been revolutionised in the past few years. One great resource for the analysis of gene networks is the databaseGeneNetwork, which consists of a set of linked resources for systems genetics (Andreux et al., 2012). It has been designed for multiple scale integration of networks of genes,transcripts in multiple tissues. GeneNetwork is an interac-", + "files on GeneNetwork) will also reduce the energy barrier of adopting powerful systems genetics and systems behavioral approaches. Web services such as GeneNetwork and its companionsGeneWeaver ( Baker et al., 2012 ), WebGestalt ( Zhang et al., 2005 ), DAVID (Huang et al., 2009a ; Huang et al., 2009b ), and the Allen Brain Atlas ( Lein et al., 2007 ) can now be used as virtual and free laboratories to test specific biological hypothesis, or they can be used to generate new ideas ab initio .", + "Its use is centred upon user-specied genes and can identify novel potential master regulatory genes for further investigation. We are working to increase the functionality and power of the GeneNet- work and systems genetics further in a number of areas. In partic- ular, increasing the number of strains studied can increase the mapping resolution. By increasing the genetic diversity of the founders of an RI set, the potential for observing regulatory poly-", + "gration enhances the chance to detect genuine modi ers across organs. GeneNetwork is a valuable platform that can be used by researchers without advanced skills of bioinformatics to perform systems genetics analyses. The next step would be to establish soft- ware tools that allow researchers to combine datasets from multiple resources and mapping analyses in different crosses and species (e.g. intercross, recombinant inbred lines, and human data). References" + ], + "answer": "The research done through GeneNetwork.org has several practical applications: 1. **Predictive Medicine and Systems Genetics**: GeneNetwork is a valuable resource for predictive medicine and systems genetics, providing tools and data for multi-omics analysis across multiple species [1]. 2. **Teaching Tool**: It serves as a teaching tool in neuroscience and genetics, allowing educators to use it for dry-lab teaching and helping students explore gene-to-phenotype relationships [2]. 3. **Exploration of Systems Genetics Data**: GeneNetwork is used to explore systems genetics data, which is crucial for defining biological networks and predicting molecular interactions [4]. 4. **Complex Systems Biology Approaches**: It provides datasets and tools for complex systems biology approaches, aiding in the generation or prediction of higher-order gene functions [5]. 5. **Virtual Laboratory for Hypothesis Testing**: GeneNetwork can be used as a virtual laboratory to test specific biological hypotheses or to generate new ideas from scratch [8]. 6. **Identification of Regulatory Genes**: The platform can identify novel potential master regulatory genes for further investigation, enhancing the understanding of genetic regulation [9]. 7. **User-Friendly Systems Genetics Analyses**: It allows researchers without advanced bioinformatics skills to perform systems genetics analyses, making it accessible to a broader range of scientists [10].", + "question": "What are the practical applications of the research done through GeneNetwork.org?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_17 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_17 new file mode 100644 index 0000000..74708a3 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_17 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2020 - A platform for experimental precision medicine The extended BXD mouse family.pdf", + "2020 - GeneNetwork a toolbox for systems genetics.pdf", + "2017 - GeneNetwork a toolbox for systems genetics.pdf", + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2011 - Using the PhenoGen Website for \u201cIn Silico\u201d Analysis of Morphine-Induced Analgesia Identifying Candidate Genes.pdf", + "2008 - Towards systems genetic analyses in barley Integration of phenotypic, expression and genotype data into GeneNetwork.pdf", + "2013 - Pathogenesis and reversal of liver fibrosis Effects of genes and environment.pdf", + "2012 - Genetic and Genomic Web Resources for Research on Alcohol Use and Abuse.pdf", + "2011 - Using the PhenoGen Website for \u201cIn Silico\u201d Analysis of Morphine-Induced Analgesia Identifying Candidate Genes.pdf" + ], + "extraction_id": [ + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "d1c32c32-42c8-5065-b7f2-bd2a0baeae62", + "2bdd2f18-e4d0-53e9-b0fa-a7ed8d710961", + "6cbea84e-4d8d-5ce0-8e58-45ee75f6f908", + "779b4029-6cc7-535e-a8b7-0ee31fa97162", + "0e3a5e40-06b0-58d4-b495-3093954ed17b", + "dc001755-2e77-5b41-8617-263b3ba35af8", + "71f1aefb-6e32-5add-804e-6fbaa39ca720", + "83ae495f-31a2-5977-a63a-57e704c394e2", + "0e3a5e40-06b0-58d4-b495-3093954ed17b" + ], + "document_id": [ + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "dd4994b9-9546-59c0-bc71-60e2617b6bcd", + "d11a87ca-4989-59af-95e3-ab90af7d9212", + "682c3a51-0aa5-54a3-a6e7-a09b81c0e8b6", + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "eb266fa1-8dec-5c56-a3d5-b508bd6bd448", + "8513abbe-65ed-5f35-9f86-ba93cfc5a194", + "73724c99-98df-53b2-a378-29c8b4faa171", + "08b12d72-9776-5acb-b1ef-7ee402781897", + "eb266fa1-8dec-5c56-a3d5-b508bd6bd448" + ], + "id": [ + "chatcmpl-AIGmdOlKLAeARCOPtbkwth6fOr9HL", + "c63cfaee-749e-547b-9c0a-086266f10670", + "bd2eb0ef-24a1-55ff-8597-c21dff0ecf0a", + "e030ce79-6970-5300-a1d8-1623d07c2157", + "1762dc59-0e50-5b7e-bdc2-b754e0e57797", + "f574ef17-062c-5bc8-be3e-81184e141970", + "fa07b1bf-94e6-515b-8400-cf3afa8b8741", + "251de62d-6e8e-50c7-9616-7fea05a250fb", + "f5c218f0-1280-55f8-912b-b32b833e93a3", + "db6bfa4e-9612-5f7e-8b7f-162f60b91c9d", + "0b2bd83d-680a-52d2-8116-50cce4f35cc3" + ], + "contexts": [ + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "This paper analyzes existing, publicly available data. These data sets accession numbers are provided in the Key Resource Table , and throughout the manuscript. Genotype les can be found at http://www.genenetwork.org/webqtl/main.py?FormID= sharinginfo&GN_AccessionId=600 . GeneNetwork.org original code is publicly available at https://github.com/genenetwork/genenetwork2 and https://github.com/ genenetwork/genenetwork1 .", + "Fig. 2. GeneNetwork main search page and organization. Most analyses in GeneNetwork will follow the steps shown in panels A through D. In this workfl ow, a data set is selected ( A) and mined for traits of interest based on user search queries ( B). Traits are then selected from the search ( C) and placed in a collection for further inspection and quantitative analysis (D). The banner menu contains additional search options and helpful resources under the", + "Fig. 2. GeneNetwork main search page and organization. Most analyses in GeneNetwork will follow the steps shown in panels A through D. In this workfl ow, a data set is selected ( A) and mined for traits of interest based on user search queries ( B). Traits are then selected from the search ( C) and placed in a collection for further inspection and quantitative analysis (D). The banner menu contains additional search options and helpful resources under the", + "1. Data Once you have navigated to genenetwork.org, t here are two ways to search for data in GN. The first is to use the global search bar located at the top of the page (Figure 1 ). This is a new feature in GN that allows researchers to search for genes, mRNAs, or proteins across all of the datasets. This will give the user data for that search term across many different species, groups, and types of data. Because of this, the global search bar is a good area to start ones searches if", + "data are entered into GeneNetwork after they have been shepherded through a system like PhenoGen that has extensive capabilities for normalization and quality control. A comparison of the brain gene expression datasets and some of the tools for data analysis available on PhenoGen and GeneNetwork is shown in Table 3, and more detailed information on features provided by each site is outlined in the Supplementary DiscussionHoffman et al. Page 5 Addict Biol . Author manuscript; available in PMC 2012 July 1.", + "abundance data sets directly within GeneNetwork's ana- lytical environment we provide simple web access to the data for the research community. In this environment, a combination of correlation analysis and linkage mapping provides the potential to identify and substantiate gene targets for saturation mapping and positional cloning. By integrating datasets from an unsequenced crop plant (bar- ley) in a database that has been designed for an animal model species (mouse) with well established genome", + "GeneNetwork (www.genenetwork.org). The web -based software further allows extraction of sets of", + "need to read the help files, FAQs, or one of the references(Chesler et al., 2003; Grisham et al., 2010, www.lifescied.org/content/9/2/98.full.pdf). GeneNetwork is one ofan interlinked trio of sites built up by NIAAA (GeneWeaverand WebGestalt are the other two) to house extensivedata for human, monkey, rat, mouse, and fruit fly. Itincludes hundreds of data sets on responsesto alcohol,particularly in a family of mice called the BXDs. Dataare linked with powerful gene analysis and mappingtools. Think of it as", + "al., 2005). GeneNetwork is designed primarily as a web service for exploratory and statistical analysis of large published phenotype and genome datasets, and includes data from several species (see Supplementary Discussion). GeneNetwork includes extensive phenotype data extracted from the literature and submitted by users, which makes it practical to compare data on drug responses with gene expression patterns. Gene expression" + ], + "answer": "To access and use the data available on GeneNetwork.org, you can follow these steps: 1. **Navigating to the Website**: Start by visiting the GeneNetwork website at www.genenetwork.org [8]. 2. **Searching for Data**: There are two primary ways to search for data on GeneNetwork: - Use the global search bar located at the top of the page. This feature allows you to search for genes, mRNAs, or proteins across all datasets, providing data across various species, groups, and types of data [5]. - Alternatively, you can follow the main search workflow, which involves selecting a dataset, mining it for traits of interest based on user search queries, selecting traits from the search, and placing them in a collection for further inspection and quantitative analysis [3]. 3. **Analyzing Data**: Once you have selected the data, GeneNetwork provides an analytical environment where you can perform correlation analysis and linkage mapping. This environment helps identify and substantiate gene targets for further research [7]. 4. **Accessing Genotype Files**: If you need genotype files, they can be accessed directly via a specific URL: http://www.genenetwork.org/webqtl/main.py?FormID=sharinginfo&GN_AccessionId=600 [2]. 5. **Using Additional Resources**: The banner menu on the main search page contains additional search options and helpful resources to assist with your analyses [3]. By following these steps, you can effectively access and utilize the data available on GeneNetwork.org for your research needs.", + "question": "How can I access and use the data available on GeneNetwork.org?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_18 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_18 new file mode 100644 index 0000000..6db16f1 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_18 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2018 - Genetic Networks Activated by Blast Injury to the Eye.pdf", + "2010 - Systems genetics analyses predict a transcription role for P2P-R Molecular confirmation that P2P-R is a transcriptional co-repressor.pdf", + "2012 - Using Genome-Wide Expression Profiling to Define Gene Networks Relevant to the Study of Complex Traits From RNA Integrity to Network Topology.pdf", + "2013 - Pathogenesis and reversal of liver fibrosis Effects of genes and environment.pdf", + "2011 - Peroxisomal L-bifunctional enzyme (Ehhadh) is essential for the production of medium-chain dicarboxylic acids.pdf", + "2021 -Highlights from the Era of Open Source Web-Based Tools.pdf", + "2020 -Highlights from the Era of Open Source Web-Based Tools.pdf", + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2015 - Gene network of a phosphoglycerate mutase in muscle wasting in mice.pdf" + ], + "extraction_id": [ + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "0c76461f-34ff-5604-aa4c-12eb9d2877aa", + "ec624ebb-489a-5437-a721-f01cf981d0a7", + "3e0c2a06-e6de-5888-a360-a2c483d9f744", + "71f1aefb-6e32-5add-804e-6fbaa39ca720", + "a8b40857-7ae8-512a-9817-bea1ae3345ba", + "62c12bdc-ae2b-5cc0-88f5-a3c1a264326b", + "28892088-5a95-56eb-822d-b12da3a612d0", + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "b71c7c43-8bd9-5d7e-b337-a8c0396f96b7" + ], + "document_id": [ + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "57e3820f-7a5d-51f1-a0c6-ecfbdf546005", + "e4d1e2e9-f267-5814-8c7b-dc11d7eec9bf", + "1eb6f5b7-a3bc-5455-91f0-6f2eb37be861", + "73724c99-98df-53b2-a378-29c8b4faa171", + "ac61753e-bcb2-55c3-804b-e821e3d1a4ad", + "0a6f3d2e-70c3-5db7-bfc0-93ad04806104", + "f2f0dc2f-eaf5-57f6-9ffa-816d3fab6640", + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "912c42f2-1802-5fe9-b9dd-871c5290dd18" + ], + "id": [ + "chatcmpl-AIGmkanSOkp2jhcX3KUkfSSqCSJEs", + "c63cfaee-749e-547b-9c0a-086266f10670", + "105b8e0a-a88c-59a6-9d39-7d894d059b73", + "a9508122-3b14-5365-979c-ba580bdcb78f", + "312eae52-ede7-5c13-8974-fce0126426cf", + "f5c218f0-1280-55f8-912b-b32b833e93a3", + "8b8a24da-a175-5cb8-91bd-8966fca5d344", + "aaf19f3a-353a-595b-b7de-da9f01a7b0f1", + "7b626f27-5aed-5464-a20d-463954ff057d", + "f3f859bb-d066-5552-b07e-eefcb489d8f5", + "a0c173a5-8685-50df-8110-8d7ec02cdbf3" + ], + "contexts": [ + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "18 GeneNetwork Time Machine : Full versions from 2009 to 2016 (mm9); UTHSC Genome Browser Classic and Newest ; UTHSC Galaxy Servic e; UTHSC Bayesian Network Web Server ; GeneNetwork Classic on Amazon Cloud ; GeneNetwork Classic Code on GitHub ; GeneNetwork 2.0 Development Code on GitHub ; and GeneNetwork 2.0 Development. Technologies or techniques: None Inventions, patent applications, and/or licenses: None Other products: None", + "GeneNetwork http://www.genenetwork.org is anexample of a bioinformatics tool that can be used to explore systems genetics data. The importance of defining biological networks and predicting molecular interactions has been emphasized by several reports [1,2]. Such studies emphasize that when knowledge about DNA variation within popula- tions is interfaced with data on gene expression, protein interactions and DNA-protein binding, biological networks can be constructed that are predictive of the", + "addition to this, GeneNetwork can be used to study correlations between traits and to perform data mining in genomic regions containing candidates for quantitative trait genes (Hoffman et al., 2011). All datasets in GeneNetwork are linked to a materials and methods information page that summarizes experimental details relating to the dataset. Databases within GeneNetwork include the transcriptome database, the BXD published", + "GeneNetwork (www.genenetwork.org). The web -based software further allows extraction of sets of", + "subnetworks GeneNetwork (www.genenetwork.org) is a depository of data- sets and tools for use in complex systems biology approaches in order to generate or predict higher order gene function ( 23, 24 ).", + "distributed neuroscience data sharing with ever expanding prospects for future breakthroughs. GeneNetwork.org : genetic analysis for all neuroscientists Originally named webqtl, GeneNetwork.org is the oldest contin- uously operating website in biomedical research ( Williams, 1994). This massive database contains ;40 million datasets. GeneNetwork.org also offers a powerful statistical platform for online network analyses and mapping, enabling numerous mo-", + "distributed neuroscience data sharing with ever expanding prospects for future breakthroughs. GeneNetwork.org : genetic analysis for all neuroscientists Originally named webqtl, GeneNetwork.org is the oldest contin- uously operating website in biomedical research ( Williams, 1994). This massive database contains ;40 million datasets. GeneNetwork.org also offers a powerful statistical platform for online network analyses and mapping, enabling numerous mo-", + "1 GeneNetwork: a continuously updated tool for systems genetics analyses Pamela M. Watson1, David G. Ashbrook1 1Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN 38163, USA Abstract GeneNetwork and its earlier iteration , WebQTL, have now been an important database and toolkit for quantitative trait genetics research for two decades. Recent improvements to", + "resources, gene expression pro les, and gene network constructions, methods for the analysis of gene function have been revolutionised in the past few years. One great resource for the analysis of gene networks is the databaseGeneNetwork, which consists of a set of linked resources for systems genetics (Andreux et al., 2012). It has been designed for multiple scale integration of networks of genes,transcripts in multiple tissues. GeneNetwork is an interac-" + ], + "answer": "The provided context does not explicitly mention any specific recent discoveries made using GeneNetwork.org. However, it highlights the platform's capabilities and improvements, such as the addition of data from 10 species, multi-omics analysis, updated code, and new tools, which make it a valuable resource for predictive medicine and systems genetics [1]. Additionally, GeneNetwork.org is described as a powerful statistical platform for online network analyses and mapping, which could enable numerous breakthroughs in neuroscience and other fields [7]. While these improvements and capabilities suggest potential for discoveries, specific recent discoveries are not detailed in the context provided.", + "question": "What are some recent discoveries made using GeneNetwork.org?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_19 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_19 new file mode 100644 index 0000000..60d520c --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_19 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2014 - Sirt1 induction confers resistance to etoposide-induced genotoxic apoptosis in thyroid cancers.pdf", + "2011 - Using the PhenoGen Website for \u201cIn Silico\u201d Analysis of Morphine-Induced Analgesia Identifying Candidate Genes.pdf", + "2022 - New Insights on Gene by Environmental Effects of Drugs of Abuse in Animal Models Using GN.pdf", + "2022 - New Insights on Gene by Environmental Effects of Drugs of Abuse in Animal Models Using GeneNetwork.pdf", + "2022 -Chunduri- Drugs Animal Models.pdf", + "2019 - A multi-omics digital research object for the genetics of sleep regulation.pdf", + "2016 - Systems Genetics of Obesity.pdf", + "2017 - Systems genetics of obesity.pdf", + "2021 -Highlights from the Era of Open Source Web-Based Tools.pdf" + ], + "extraction_id": [ + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "e3d1b792-6241-5ba3-b06f-ee29eb0106fc", + "0e3a5e40-06b0-58d4-b495-3093954ed17b", + "50d920fa-3482-52ca-899f-15b182fdb4fd", + "ee874620-8c4e-55df-8274-2dcd4eba2ca9", + "4cafc4e9-69df-5a08-921c-de6c66267056", + "a002e2e0-b978-540d-b435-5701c30496b6", + "d214b44c-c033-59f7-b120-fa4d6bf35bb4", + "674a8666-6310-5df3-8539-e274cd629e9c", + "ffafdd06-808c-58be-bcb5-bd74d7ffa89a" + ], + "document_id": [ + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "18e62e2f-643c-5c42-b80a-bab5432a8894", + "eb266fa1-8dec-5c56-a3d5-b508bd6bd448", + "6f5d0c5b-0bbb-5eca-9e3e-73c3b0675472", + "d71efa0d-5de8-549c-964d-489ef6b73a1f", + "9cfa4f4c-37ce-5c0f-9da6-3bbb075fdc45", + "af97f766-ca4d-56c0-9eb8-ba6c5e7db1da", + "c38d1bad-8690-5d4d-a60a-dcbb4ac4aa93", + "f10cf311-0397-5c0a-81e0-3b84090e434b", + "0a6f3d2e-70c3-5db7-bfc0-93ad04806104" + ], + "id": [ + "chatcmpl-AIGmr7v0rrhLH7kaV38yDCwjdEEpc", + "c63cfaee-749e-547b-9c0a-086266f10670", + "a2875189-1592-59ad-ad10-f3c4911411e2", + "fa07b1bf-94e6-515b-8400-cf3afa8b8741", + "8f734e2a-cd29-5021-84be-a9e08bc21a99", + "219cfeab-8877-5c92-92d0-87b17c0d4206", + "8a3abc37-292a-5bd3-9527-bcf17dc9eafc", + "29c406c6-34e1-5f8a-8a6f-1b239dd633ae", + "45ce962b-f534-59a7-ab21-c5f858d4ec20", + "19ba23ee-9d24-55cc-85cb-bee95894f710", + "4188099c-aba1-5f0d-b2ec-a7c8f5bb1bc5" + ], + "contexts": [ + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "files), and GeneNetwork (a free scientific web resource, http://www.genenetwork.org/). Statistical analysis was performed using GraphPad Prism (GraphPad Software, Inc., CA, USA).", + "data are entered into GeneNetwork after they have been shepherded through a system like PhenoGen that has extensive capabilities for normalization and quality control. A comparison of the brain gene expression datasets and some of the tools for data analysis available on PhenoGen and GeneNetwork is shown in Table 3, and more detailed information on features provided by each site is outlined in the Supplementary DiscussionHoffman et al. Page 5 Addict Biol . Author manuscript; available in PMC 2012 July 1.", + "thank the members of the GeneNetwork.org team for their assistance, excellent data curation, and informatics support. Conicts of Interest: The authors declare no conict of interest. References 1. Wilkinson, M.D.; Dumontier, M.; Aalbersberg, I.J.; Appleton, G.; Axton, M.; Baak, A.; Blomberg, N.; Boiten, J.W.; da Silva Santos, L.B.; Bourne, P .E.; et al. The FAIR Guiding Principles for scientic data management and stewardship. Sci. Data 2016 ,3, 160018. [CrossRef]", + "thank the members of the GeneNetwork.org team for their assistance, excellent data curation, and informatics support. Conicts of Interest: The authors declare no conict of interest. References 1. Wilkinson, M.D.; Dumontier, M.; Aalbersberg, I.J.; Appleton, G.; Axton, M.; Baak, A.; Blomberg, N.; Boiten, J.W.; da Silva Santos, L.B.; Bourne, P .E.; et al. The FAIR Guiding Principles for scientic data management and stewardship. Sci. Data 2016 ,3, 160018. [CrossRef]", + "thank the members of the GeneNetwork.org team for their assistance, excellent data curation, and informatics support. Conicts of Interest: The authors declare no conict of interest. References 1. Wilkinson, M.D.; Dumontier, M.; Aalbersberg, I.J.; Appleton, G.; Axton, M.; Baak, A.; Blomberg, N.; Boiten, J.W.; da Silva Santos, L.B.; Bourne, P .E.; et al. The FAIR Guiding Principles for scientic data management and stewardship. Sci. Data 2016 ,3, 160018. [CrossRef]", + "9 Scientific Data | (2019) 6:258 | https://doi.org/10.1038/s41597-019-0171-x www.nature.com/scientificdata www.nature.com/scientificdata/with more than 10% missing information, low quality ( <5000), and redundant information were removed. GeneNetwork genotypes, which were discrepant with our RNA-seq experiment, were tagged as unknown (mean of 1% of the GeneNetwork genotypes/strain [0.05% n 8%]). Finally, GeneNetwork and our RNA-seq", + "1. Phenotypic data should be quality checked and preprocessed before being uploaded to GeneNetwork. This includes nor- malization of data, removal of outliers or windsorization, even- tually transformation of data to obtain normal distribution. 2. When uploading data to GeneNetwork for permanent and public storage, make sure to follow the GeneNetwork naming guide for phenotypes. 3. When uploading your own data make sure that for any pheno-", + "1. Phenotypic data should be quality checked and preprocessed before being uploaded to GeneNetwork. This includes nor- malization of data, removal of outliers or windsorization, even- tually transformation of data to obtain normal distribution. 2. When uploading data to GeneNetwork for permanent and public storage, make sure to follow the GeneNetwork naming guide for phenotypes. 3. When uploading your own data make sure that for any pheno-", + "analysis of behavior and for neurologic diseases are provided in the study by Mulligan et al. (2017) . GeneNetwork.org is committed to data and code workflows that are FAIR compliant, ensuring that those who generate data and key ideas get the deserved credit. To further ensure effective and secure dissemination of data and ideas, as well as improved reproducibility, the GeneNetwork.org infrastructure is currently being redesigned using more modular structures and APIs that" + ], + "answer": "Scientists ensure the accuracy of the data on GeneNetwork.org through several methods: 1. **Quality Control and Normalization**: Data are entered into GeneNetwork after being processed through systems like PhenoGen, which have extensive capabilities for normalization and quality control [3]. 2. **Quality Checking and Preprocessing**: Phenotypic data undergo quality checks and preprocessing before being uploaded to GeneNetwork. This includes normalization, removal of outliers, and transformation of data to achieve a normal distribution [8]. 3. **Data Curation and Informatics Support**: The GeneNetwork.org team provides excellent data curation and informatics support to maintain data accuracy [4]. These steps help ensure that the data on GeneNetwork.org is accurate and reliable for scientific research.", + "question": "How do scientists ensure the accuracy of the data on GeneNetwork.org?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_2 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_2 new file mode 100644 index 0000000..9510d12 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_2 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2012 - Using Genome-Wide Expression Profiling to Define Gene Networks Relevant to the Study of Complex Traits From RNA Integrity to Network Topology.pdf", + "2016 - A Systems-Level Understanding of Cardiovascular Disease through Networks.pdf", + "2011 - Using the PhenoGen Website for \u201cIn Silico\u201d Analysis of Morphine-Induced Analgesia Identifying Candidate Genes.pdf", + "2010 - Systems genetics analyses predict a transcription role for P2P-R Molecular confirmation that P2P-R is a transcriptional co-repressor.pdf", + "2020 - GeneNetwork a toolbox for systems genetics.pdf", + "2017 - GeneNetwork a toolbox for systems genetics.pdf", + "2009 - Detection and interpretation of expression quantitative trait loci (eQTL).pdf", + "2012 - Identifying Gene Networks Underlying the Neurobiology of Ethanol and Alcoholism.pdf", + "2011 - Peroxisomal L-bifunctional enzyme (Ehhadh) is essential for the production of medium-chain dicarboxylic acids.pdf" + ], + "extraction_id": [ + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "3e0c2a06-e6de-5888-a360-a2c483d9f744", + "82e07232-dd92-52f6-8230-d90a03c71b4f", + "0e3a5e40-06b0-58d4-b495-3093954ed17b", + "ec624ebb-489a-5437-a721-f01cf981d0a7", + "4ca2fc9e-7d42-5ea3-b1b7-a296bfbc6a09", + "7dd82b3f-58bd-5915-9eea-250f11412ff2", + "e2190b29-6d30-58fb-978f-d052582698bd", + "40850ed1-db52-594e-a9d6-0b661e0bc494", + "a8b40857-7ae8-512a-9817-bea1ae3345ba" + ], + "document_id": [ + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "1eb6f5b7-a3bc-5455-91f0-6f2eb37be861", + "96657025-7e50-571d-9a6b-1a202cb8a690", + "eb266fa1-8dec-5c56-a3d5-b508bd6bd448", + "e4d1e2e9-f267-5814-8c7b-dc11d7eec9bf", + "d11a87ca-4989-59af-95e3-ab90af7d9212", + "682c3a51-0aa5-54a3-a6e7-a09b81c0e8b6", + "ef974b09-4ea2-5382-85e5-c2169f440fda", + "c02542c0-eff8-5ec7-8f73-78f5d28d4226", + "ac61753e-bcb2-55c3-804b-e821e3d1a4ad" + ], + "id": [ + "chatcmpl-AIGlD8JegvZvagzZ7ZZc2o1BsPAjA", + "c63cfaee-749e-547b-9c0a-086266f10670", + "27bb3941-5a92-56a2-b67d-c5e64603c1a3", + "1c8d31d6-bd59-56da-83b8-f603b4a9ec2b", + "0b2bd83d-680a-52d2-8116-50cce4f35cc3", + "a9508122-3b14-5365-979c-ba580bdcb78f", + "7ce6c0fe-8b0a-5ce9-83d1-6e6b99b4f24d", + "30e2423f-2b2b-5c7d-8808-b025242fa0c7", + "d348dfa8-c6c5-5514-8c64-920f254b9571", + "1cf9d2ee-62b7-5dc8-8f58-23cecab650dc", + "8b8a24da-a175-5cb8-91bd-8966fca5d344" + ], + "contexts": [ + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "users can take advantage of a systems genetics approach (Rosen et al., 2003, 2007). While the candidate gene approach asks which one gene mutation causes a particular disease, the systems genetics approach explores which phenotypes and diseases result from diverse sets of genetic and molecular markers (Rosen et al., 2003, 2007). The majority of data sets in GeneNetwork are collected from GRPs consisting of hundreds of diverse, inbred strains of", + "Based on this, Goh et al. created networks using data from the Online Mendelian Inheritance in Man (OMIM) [18]database that houses lists of disease gene links. Two networks emerged: the human disease network inwhich disease nodes were connected if they were caused by mutations in the same gene, and the disease gene network where gene nodes were", + "al., 2005). GeneNetwork is designed primarily as a web service for exploratory and statistical analysis of large published phenotype and genome datasets, and includes data from several species (see Supplementary Discussion). GeneNetwork includes extensive phenotype data extracted from the literature and submitted by users, which makes it practical to compare data on drug responses with gene expression patterns. Gene expression", + "GeneNetwork http://www.genenetwork.org is anexample of a bioinformatics tool that can be used to explore systems genetics data. The importance of defining biological networks and predicting molecular interactions has been emphasized by several reports [1,2]. Such studies emphasize that when knowledge about DNA variation within popula- tions is interfaced with data on gene expression, protein interactions and DNA-protein binding, biological networks can be constructed that are predictive of the", + "including correlation and network analysis to compare associations between tissues and between other rodent or human data sets[32] Many of the Data Sets are amenable to systems genetics mapping and other methods and are accessible at GeneNetwork. The Description and Usage column provides details about the data set and potential", + "including correlation and network analysis to compare associations between tissues and between other rodent or human data sets[32] Many of the Data Sets are amenable to systems genetics mapping and other methods and are accessible at GeneNetwork. The Description and Usage column provides details about the data set and potential", + "atic way. Users begin by selecting one or more human diseases and clicking on Compare. The genes associated with the selected disease are tested for enrichment against all sets of known associat ed genes for worm phenotypes. The result reveals functionally coherent , evolution- arily conserved gene networks. Alternatively, users can also start by selecting worm pheno types, which are tested against human diseases. In addition to cross -species", + "is tackling this immense challenge bystudying networks of genes, proteins,metabolites, and other biomarkers thatrepresent models of genuine biologicalpathways. Studying complex diseasesin terms of gene networks rather thanindividual genes or genomic loci shouldaid in uncovering disease genes. Withthis approach, the effects of multiplegenes in the network are combined,producing a stronger signal and reducingthe number of statistical tests of associ-ation that must be performed.", + "subnetworks GeneNetwork (www.genenetwork.org) is a depository of data- sets and tools for use in complex systems biology approaches in order to generate or predict higher order gene function ( 23, 24 )." + ], + "answer": "Researchers use GeneNetwork.org to study diseases by leveraging its capabilities as a bioinformatics tool for systems genetics analysis. This platform allows researchers to explore large phenotype and genome datasets from multiple species, which are essential for understanding complex biological networks and predicting molecular interactions [4], [5]. GeneNetwork.org supports a systems genetics approach, which examines how diverse sets of genetic and molecular markers contribute to phenotypes and diseases, rather than focusing on single gene mutations [2]. This approach is facilitated by the extensive data available on the platform, including gene expression patterns and drug response data, which can be compared and analyzed statistically [4]. The platform also enables correlation and network analysis, allowing researchers to compare associations between tissues and across different species, such as rodents and humans [6]. By studying networks of genes, proteins, metabolites, and other biomarkers, researchers can model genuine biological pathways, which helps in uncovering disease genes and understanding complex diseases [9]. Overall, GeneNetwork.org provides a comprehensive resource for predictive medicine and systems genetics, aiding researchers in constructing biological networks that are predictive of disease outcomes [1], [5].", + "question": "How do researchers use GeneNetwork.org to study diseases?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_20 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_20 new file mode 100644 index 0000000..4ad1e6d --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_20 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2009 - Processing Large-Scale, High-Dimension Genetic and Gene Expression Data.pdf", + "2012 - Identifying Gene Networks Underlying the Neurobiology of Ethanol and Alcoholism.pdf", + "2007 - Identifying genomic regulators of set-wise co-expression.pdf", + "2007 - Systems genetics the next generation.pdf", + "2008 - Dynamic Visualization of Coexpression in Systems Genetics Data.pdf", + "2005 -Lovinger- Lab models of alcoholism.pdf", + "2005 - Laboratory models of alcoholism treatment target identification and insight into mechanisms.pdf", + "2011 - Genetical genomics approaches for systems genetics.pdf", + "2009 - Processing Large-Scale, High-Dimension Genetic and Gene Expression Data.pdf", + "2009 - Detection and interpretation of expression quantitative trait loci (eQTL).pdf" + ], + "extraction_id": [ + "1d401588-b6dc-532f-8194-4667a7d31153", + "40850ed1-db52-594e-a9d6-0b661e0bc494", + "51cb3178-b604-5869-98bd-cd32def3bd54", + "eff279b1-0d36-5dd4-9230-72adfe2ed79a", + "84072d11-c436-5405-a356-7dd9886db6e8", + "ee39bb1c-a55c-5aad-8e43-77eb8f38ff85", + "440c9c21-a03a-576f-8206-2a354508bb82", + "6ee23564-711a-5bc9-bb04-14a4b611c8bf", + "fad81c4c-0da6-54c0-898a-755ffb8870b6", + "223e442e-898d-5aea-866a-5cdc0ac915e8" + ], + "document_id": [ + "17264155-b665-59db-94cb-f4d67eac20fc", + "c02542c0-eff8-5ec7-8f73-78f5d28d4226", + "34b8aa80-7150-5c53-8cef-9f1d614ae886", + "a6202d00-514d-5b48-89cd-5fc9649c0ee4", + "ff6bf912-b590-582e-a841-6499cea56508", + "91621f34-9602-5cdc-91d8-c608c4e0b02c", + "148a4120-6ab8-554a-ab30-3394f61f98e1", + "de78a01d-8d03-5afb-af5b-ce2ed2167766", + "17264155-b665-59db-94cb-f4d67eac20fc", + "ef974b09-4ea2-5382-85e5-c2169f440fda" + ], + "id": [ + "chatcmpl-AIGmxl54ZXUf76PPUFFgbX3aFiiFS", + "509d3815-9994-5afc-9777-52eb80281dc8", + "1cf9d2ee-62b7-5dc8-8f58-23cecab650dc", + "85a8d5cf-0da0-5273-b1f2-f10e440ea24e", + "fc76d75c-37d3-5a29-9093-d25ff746b465", + "a06d0485-b264-53b2-8a13-0c13c600c026", + "8358a79b-ac66-510f-9ee2-9763a0f9d95e", + "343c3cb8-779b-571c-9633-cbd37a941f25", + "1b8201cc-bdc4-5cb7-ad3b-da9d6ae59fc8", + "c6c3f636-c6bb-539d-861f-6cf1145f4f50", + "72cac585-5de7-56ca-8ea5-c133d3ff7acf" + ], + "contexts": [ + "considering single genes in the context of a whole gene network may provide thenecessary context within which to interpr et the disease role a given gene may play. Constructing gene networks can provide a convenient framework for exploring the context within which single genes operate. A network is simply a graphicalmodel comprised of nodes and edges. For gene networks associated with biological systems, the nodes in the network typically represent genes, gene products, or other", + "is tackling this immense challenge bystudying networks of genes, proteins,metabolites, and other biomarkers thatrepresent models of genuine biologicalpathways. Studying complex diseasesin terms of gene networks rather thanindividual genes or genomic loci shouldaid in uncovering disease genes. Withthis approach, the effects of multiplegenes in the network are combined,producing a stronger signal and reducingthe number of statistical tests of associ-ation that must be performed.", + "traditional genetical genomics approaches. It should also be noted that our approach is different from studying gene-gene regulation within a pathway, which focuses on the interactive activities of individual gene pairs genes within a pathway. A biological pathway is defined as a series of molecular interactions and reactions. If there are subtle changes in the expression level of a few genes located in the upper cascade of a", + "genes rapidly that may be in the same genetic network as the gene you are interested in. Then you need to validate the role of that gene and to identify its function in that network. The point is this is a powerful methodology that can provide data in half an hour that allows you to form hypotheses that you can then spend years investigating. Reference Lee PD, Ge B, Greenwood CM et al 2006 Mapping cis-acting regulatory variation in recombi- nant congenic strains. Physiol Genomics 25:294302", + "ment to determine the role of the associated network ongene expression or function. To begin, a large genecorrelation graph must be sifted through, to find a highlyconnected subgraph that corresponds biologically to a genenetwork in which genes are expressed together, presumablyto regulate or subserve a common function. They must thenfind a small set of causative genes, highly correlated withthe subgraph and likely to regulate coexpression, to be usedas targets of focused investigation. By manipulating the", + "Confronted with this daunting complexity, the field often progresses in small steps. A study may identify one or two relevant genes and assess their interactions with other factors. Gradually, genetic knowledge from many studies then can be assembled into a larger system of interactants that enables us to understand a set of related behaviors. We term this perspective behavioral genomics ( Fig. 2b ).2005 Nature Publishing Group http://www.nature.com/natureneuroscience", + "Confronted with this daunting complexity, the field often progresses in small steps. A study may identify one or two relevant genes and assess their interactions with other factors. Gradually, genetic knowledge from many studies then can be assembled into a larger system of interactants that enables us to understand a set of related behaviors. We term this perspective behavioral genomics ( Fig. 2b ).2005 Nature Publishing Group http://www.nature.com/natureneuroscience", + "From the network, modules of coexpressed genes can be obtained, i.e. com- munities of highly interconnected nodes within the graph. Such coexpressed modules can then be studied as putative functional units, thereby considerably reducing the dimensionality of the data. Different approaches have been proposed, many of which are inspired by social network resear ch. Chesler et al. choose to focus on sets of genes in which all nodes are inter connected; such sets are termed", + "large-scale human and experimental populations, focusing on how a single protein or RNA impacts disease will ultimately give way to how a network of gene interac- tions impacts disease. The integration of genetic, molecular proling, and clinical data has the potential to paint a more detailed picture of the particular network statesthat drive disease, and this in turn has the potential to lead to more progressive treat- ments of disease that may ultimately invol ve targeting of whole networks as opposed", + "from co-regulation patterns found within tens of thousands of samples for which gene expression was measured. GeneNetwork provid es un- precedented resolution and predictive power across multip le cell types and tissues. Analogous to discovering patterns in expressi on data, the network of protein-protein interactions can also be comput ationally pre- dicted using various methods[381]. The combined current knowledge of how cells control functio ns" + ], + "answer": "The difference between looking at one gene and studying a whole gene network lies in the scope and context of the analysis. When examining a single gene, the focus is on understanding the role and function of that specific gene, often in isolation. This approach can be limited because it does not consider the interactions and relationships that gene may have with others. In contrast, studying a whole gene network involves analyzing a system of interconnected genes, which provides a broader context. A gene network is a graphical model where nodes represent genes or gene products, and edges represent interactions between them [1]. This approach allows researchers to explore how multiple genes interact within biological pathways, potentially uncovering complex relationships and combined effects that a single-gene analysis might miss [2]. By studying gene networks, researchers can gain insights into the collective behavior of genes, which can be crucial for understanding complex diseases and biological functions [2]. Additionally, gene networks can help identify highly connected subgraphs that correspond to biologically relevant networks, aiding in the identification of causative genes and their regulatory roles [5].", + "question": "What\u2019s the difference between looking at one gene and studying a whole gene network?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_21 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_21 new file mode 100644 index 0000000..1b36f73 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_21 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2020 -Highlights from the Era of Open Source Web-Based Tools.pdf", + "2021 -Highlights from the Era of Open Source Web-Based Tools.pdf", + "2008 - Systems_genetics_can_provide_new_insight.pdf", + "2007 - Systems genetics the next generation.pdf", + "2010 - Systems genetics analyses predict a transcription role for P2P-R Molecular confirmation that P2P-R is a transcriptional co-repressor.pdf", + "2012 - Genetic and Molecular Network Analysis of Behavior.pdf", + "2008 - Towards systems genetic analyses in barley Integration of phenotypic, expression and genotype data into GeneNetwork.pdf", + "2012 - Genetic and Genomic Web Resources for Research on Alcohol Use and Abuse.pdf", + "2009 - Herding cats the sociology of data integration.pdf" + ], + "extraction_id": [ + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "14530ed7-e49e-5a1a-9df6-820c7495a8ce", + "ffafdd06-808c-58be-bcb5-bd74d7ffa89a", + "bfd98d6d-b026-5bbd-896f-b70045a032c8", + "d7d6d0a8-db35-5be6-a3fc-82f347e5d37b", + "ec624ebb-489a-5437-a721-f01cf981d0a7", + "833f822f-9080-5434-b9a1-2473531becfe", + "b3c1f52a-f5be-586f-a3c9-04bed83fb12e", + "83ae495f-31a2-5977-a63a-57e704c394e2", + "c5e5623c-2373-535c-978d-3af1cec77f1a" + ], + "document_id": [ + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "f2f0dc2f-eaf5-57f6-9ffa-816d3fab6640", + "0a6f3d2e-70c3-5db7-bfc0-93ad04806104", + "0bc5b1ee-f7fe-5d12-9ebf-ab7363119424", + "a6202d00-514d-5b48-89cd-5fc9649c0ee4", + "e4d1e2e9-f267-5814-8c7b-dc11d7eec9bf", + "4b6759f8-fdaf-59a1-94bd-5a7cf184e1f9", + "8513abbe-65ed-5f35-9f86-ba93cfc5a194", + "08b12d72-9776-5acb-b1ef-7ee402781897", + "15e14cfc-dbeb-5998-b42a-9fbfb7464bf2" + ], + "id": [ + "chatcmpl-AIGn3AbKULjVBzRjcrJYXxi0av0dH", + "c63cfaee-749e-547b-9c0a-086266f10670", + "7beda13b-1ea5-53c0-9380-72eee2df79fe", + "a1124460-ae34-57fb-846b-e033f4bbf49c", + "55461d7c-995b-5f5a-be38-a64acd4a904a", + "f3821133-e965-535b-88d0-f43b14d311b6", + "a9508122-3b14-5365-979c-ba580bdcb78f", + "27eda296-b0b7-5ae4-881e-31987ba63dec", + "b82c0ee6-9e1b-595d-95b3-1cc9d7aff44d", + "db6bfa4e-9612-5f7e-8b7f-162f60b91c9d", + "dc06eebe-4d67-5bd0-9ed8-3dd9d5a588bd" + ], + "contexts": [ + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "GeneNetwork.org is also a valuable teaching tool. While mainly designed for researchers interested in testing gene-to- phenotype relationships, GeneNetwork. orghas been adapted for dry-lab teaching in neuroscience and genetics ( Grisham et al., 2017 ). A useful approach is to assign sets of vetted questions, such as the exam- ples discussed above, and to help students work toward answers, solutions, or novelquestions. Several examples relating to the", + "GeneNetwork.org is also a valuable teaching tool. While mainly designed for researchers interested in testing gene-to- phenotype relationships, GeneNetwork. orghas been adapted for dry-lab teaching in neuroscience and genetics ( Grisham et al., 2017 ). A useful approach is to assign sets of vetted questions, such as the exam- ples discussed above, and to help students work toward answers, solutions, or novelquestions. Several examples relating to the", + "Its use is centred upon user-specied genes and can identify novel potential master regulatory genes for further investigation. We are working to increase the functionality and power of the GeneNet- work and systems genetics further in a number of areas. In partic- ular, increasing the number of strains studied can increase the mapping resolution. By increasing the genetic diversity of the founders of an RI set, the potential for observing regulatory poly-", + "and can identify novel potential master regulatory genes for further investigation. We are working to increase the functionality and power of GeneNetwork and systems genetics in a number of areas. In particular, the mapping resolution can be increased by increasing the number of strains studied. By increasing the genetic diversity of the founders of an RI set, the potential for observing regulatory poly-morphisms increases dramatically. In this context, the availability of 1000 RI strains from The Gene", + "GeneNetwork http://www.genenetwork.org is anexample of a bioinformatics tool that can be used to explore systems genetics data. The importance of defining biological networks and predicting molecular interactions has been emphasized by several reports [1,2]. Such studies emphasize that when knowledge about DNA variation within popula- tions is interfaced with data on gene expression, protein interactions and DNA-protein binding, biological networks can be constructed that are predictive of the", + "files on GeneNetwork) will also reduce the energy barrier of adopting powerful systems genetics and systems behavioral approaches. Web services such as GeneNetwork and its companionsGeneWeaver ( Baker et al., 2012 ), WebGestalt ( Zhang et al., 2005 ), DAVID (Huang et al., 2009a ; Huang et al., 2009b ), and the Allen Brain Atlas ( Lein et al., 2007 ) can now be used as virtual and free laboratories to test specific biological hypothesis, or they can be used to generate new ideas ab initio .", + "within GeneNetwork's analytical en vironment we provide simple we b access to the data for the research community. In this envi ronment, a combination of correl ation analysis and linkage mapping provides the potential to identify and substant iate gene targets for saturation mapping and positional cloning. By integratin g datasets from an unsequenced cr op plant (barley) in a database that has been designed for an animal model species (mouse) with a well established genome", + "need to read the help files, FAQs, or one of the references(Chesler et al., 2003; Grisham et al., 2010, www.lifescied.org/content/9/2/98.full.pdf). GeneNetwork is one ofan interlinked trio of sites built up by NIAAA (GeneWeaverand WebGestalt are the other two) to house extensivedata for human, monkey, rat, mouse, and fruit fly. Itincludes hundreds of data sets on responsesto alcohol,particularly in a family of mice called the BXDs. Dataare linked with powerful gene analysis and mappingtools. Think of it as", + "will be convincing both the community and its leaders that it is doable today. acKnoWledgmenTs Thanks to Glenn D. Rosen and Kathryn Graehl for comments and editorial improvements. Thanks also to the NIH for support of many data sets for GRPs now being incorporated into GeneNetwork, a pilot project in systems genetics. references Carneiro, A. M., Airey, D. C., Thompson, B., Zhu, C. B., Lu, L., Chesler, E. J., Erikson, K. M., and Blakely, R. D. (2009). Functional coding variation" + ], + "answer": "GeneNetwork.org can contribute to advancements in genetic engineering in several ways: 1. **Predictive Medicine and Systems Genetics**: GeneNetwork.org is an exciting resource for predictive medicine and systems genetics, providing a platform for multi-omics analysis and data from multiple species [1]. This can help in understanding complex genetic interactions and predicting phenotypic outcomes, which are crucial for genetic engineering. 2. **Identification of Master Regulatory Genes**: The platform allows users to identify novel potential master regulatory genes for further investigation [4]. This capability is essential for genetic engineering as it can help in pinpointing key genes that control important traits or pathways. 3. **Increased Mapping Resolution**: By increasing the number of strains studied and the genetic diversity of the founders of a recombinant inbred (RI) set, GeneNetwork.org enhances the mapping resolution [5]. This improvement can lead to more precise genetic modifications and better understanding of genetic variations. 4. **Construction of Biological Networks**: GeneNetwork.org facilitates the construction of biological networks by integrating data on DNA variation, gene expression, protein interactions, and DNA-protein binding [6]. These networks are predictive of molecular interactions, which can guide genetic engineering efforts. 5. **Virtual Laboratory for Hypothesis Testing**: GeneNetwork.org, along with its companion tools, can be used as a virtual and free laboratory to test specific biological hypotheses or generate new ideas [7]. This feature supports experimental design and innovation in genetic engineering. Overall, GeneNetwork.org provides a comprehensive analytical environment and a wealth of data that can significantly aid in the advancement of genetic engineering by enabling detailed genetic analysis, hypothesis testing, and the discovery of key genetic regulators.", + "question": "How can GeneNetwork.org contribute to advancements in genetic engineering?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_3 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_3 new file mode 100644 index 0000000..6576029 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_3 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2010 - Systems genetics analyses predict a transcription role for P2P-R Molecular confirmation that P2P-R is a transcriptional co-repressor.pdf", + "2019 - Bioinformatic prediction of critical genes and pathways.pdf", + "2010 - Using expression genetics to study the neurobiology of ethanol and alcoholism.pdf", + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2008 - Towards systems genetic analyses in barley Integration of phenotypic, expression and genotype data into GeneNetwork.pdf", + "2017 - Systems Genetics Analysis to Identify the Genetic Modulation of a Glaucoma-Associated Gene.pdf", + "2011 - Prioritizing candidate disease genes by network-based boosting of genome-wide association data.pdf", + "2009 - Detection and interpretation of expression quantitative trait loci (eQTL).pdf", + "2019 - Different genetic mechanisms mediate spontaneous versus UVR-induced malignant melanoma.pdf", + "2012 - Using Genome-Wide Expression Profiling to Define Gene Networks Relevant to the Study of Complex Traits From RNA Integrity to Network Topology.pdf" + ], + "extraction_id": [ + "ec624ebb-489a-5437-a721-f01cf981d0a7", + "9383f177-92a5-5264-9d81-ff623d0614e3", + "9597c8b3-0d67-5192-9e08-1bccc5e2f75c", + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "a6c480d1-b384-5c6f-b21b-94fe0b3b0f4d", + "2455cf6d-4c9b-5272-8650-da127cc329e8", + "4eb8a5f8-5936-523d-971d-302348d6d62f", + "223e442e-898d-5aea-866a-5cdc0ac915e8", + "a771f252-00da-5f52-9c29-d006313c9e7b", + "3e0c2a06-e6de-5888-a360-a2c483d9f744" + ], + "document_id": [ + "e4d1e2e9-f267-5814-8c7b-dc11d7eec9bf", + "01201944-11f2-52d9-ac3e-7af685d4a4c4", + "64469ae5-5eb6-5e45-ab23-7bafb63d486f", + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "8513abbe-65ed-5f35-9f86-ba93cfc5a194", + "67e804db-8127-5938-8d7f-a5918cdf4f86", + "db0aa4b3-66ec-5d51-be72-2a1289db944a", + "ef974b09-4ea2-5382-85e5-c2169f440fda", + "8161c536-c996-5660-b6ae-2d33c5d4aa9a", + "1eb6f5b7-a3bc-5455-91f0-6f2eb37be861" + ], + "id": [ + "chatcmpl-AIGlKS6puXfNaWUwFF42aUVBShhJ7", + "a9508122-3b14-5365-979c-ba580bdcb78f", + "f1d40272-4a35-5b52-b3a8-3a0e7e8626d2", + "d8162fdc-326a-5f90-9fa4-24d86d701184", + "c63cfaee-749e-547b-9c0a-086266f10670", + "01a09a4e-3c30-53b1-8819-6085d4886079", + "18c7c27b-b51f-5ab6-9d09-4235c57811b1", + "a11bd1db-1c26-54fa-85c8-39bb745d2ebf", + "72cac585-5de7-56ca-8ea5-c133d3ff7acf", + "c0150694-7ee6-5e4f-a880-302cfd596718", + "312eae52-ede7-5c13-8974-fce0126426cf" + ], + "contexts": [ + "GeneNetwork http://www.genenetwork.org is anexample of a bioinformatics tool that can be used to explore systems genetics data. The importance of defining biological networks and predicting molecular interactions has been emphasized by several reports [1,2]. Such studies emphasize that when knowledge about DNA variation within popula- tions is interfaced with data on gene expression, protein interactions and DNA-protein binding, biological networks can be constructed that are predictive of the", + "Molecular Genetics and Genomics 1 3 as overexpression, knockdown, knockout and mutation (Online Resource 1). Gene network construction Genegene interaction data were extracted from the STRING database (http://strin g-db.org/) (Christian etal. 2003), a web resource that includes comprehensively predicted and known interaction information. Then, the genegene interaction pairs were imported into Cytoscape software (Version 3.5.1) (http://cytos cape.org/ ) (Smoot etal. 2011 ) to construct a", + "of links to external resources for tracing the interrelationships of a gene among multiple Web-based resources. GeneNetwork also offers a number of correlation and mapping strategies for assessing associations among multiple genes and QTLs. GeneNetwork aims to make the study of complex traits through the use of systems genetics widely available to the scientific community. A powerful tool that can be integrated with GeneNetwork or used on", + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "is shown in Figure 1A. Associations between transcript abundance, phenotypic traits and genotype can be estab- lished either using correlation or genetic linkage mapping functions [29,30]. The main page of GeneNetwork at http://www.genenetwork.org provides access to subsets of data through pull-down menus that allow specific data sets to be queried. The datasets can be further restricted using a single text box for specific database entries to query probe set or trait ID, or annotations associated with", + "genetics approaches can not only provide insights into the roles of individual genes or developmental pathways but also illuminate relationships between different levels of a biologic system, such as the genome, transcriptome, and phenome [ 10]. One such resource of systems genetics is the GeneNetwork website and resource (www.genenetwork.org ) that provides access to a wide variety of data such as genotypes (e.g., SNPs), phenotypes that are obtained", + "occurrence; GN, gene neighbor; GT, genetic interaction; LC, literature-curated protein interactions; MS, affinity purification/mass spectrome try; PG, phy- logenetic profiles; PI, fly protein interactions; TS, tertiary structure; and YH, yeast two-hybrid). Detailed descriptions are listed in Suppleme ntal Table S1. ( B) Essential genes were highly interconnected in HumanNet, and thus predictable from the network, as shown by ROC analysis. Genes were ranked by their sum", + "from co-regulation patterns found within tens of thousands of samples for which gene expression was measured. GeneNetwork provid es un- precedented resolution and predictive power across multip le cell types and tissues. Analogous to discovering patterns in expressi on data, the network of protein-protein interactions can also be comput ationally pre- dicted using various methods[381]. The combined current knowledge of how cells control functio ns", + "(http://string-db.org/ ). STRING creates networks representing the best available knowledge of gene interconnections. Each protein-protein interaction is annotated with scores indicating how likely an interaction should be true. Scores rank from 0 to 1, with one being the highest confidence. A score of 0.5 indicates roughly every second interaction might be erroneous. Gene-gene co-expression cor- relations were computed as Pearson product-moment correlations (r) in Genenetwork.org after removing outliers.", + "addition to this, GeneNetwork can be used to study correlations between traits and to perform data mining in genomic regions containing candidates for quantitative trait genes (Hoffman et al., 2011). All datasets in GeneNetwork are linked to a materials and methods information page that summarizes experimental details relating to the dataset. Databases within GeneNetwork include the transcriptome database, the BXD published" + ], + "answer": "GeneNetwork.org provides several tools and resources to explore how genes interact with each other. It is a bioinformatics tool that allows users to explore systems genetics data, which is crucial for defining biological networks and predicting molecular interactions [1]. GeneNetwork offers correlation and mapping strategies to assess associations among multiple genes and quantitative trait loci (QTLs), facilitating the study of complex traits [3]. Additionally, it provides access to a wide variety of data, including genotypes and phenotypes, which can illuminate relationships between different levels of a biological system, such as the genome, transcriptome, and phenome [6]. GeneNetwork also computes gene-gene co-expression correlations, which are essential for understanding gene interactions [9].", + "question": "What can GeneNetwork.org tell us about how genes interact with each other?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_4 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_4 new file mode 100644 index 0000000..b20b8a1 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_4 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2012 - Using Genome-Wide Expression Profiling to Define Gene Networks Relevant to the Study of Complex Traits From RNA Integrity to Network Topology.pdf", + "2016 - A Systems-Level Understanding of Cardiovascular Disease through Networks.pdf", + "2009 - Basic Genetics and Genomics A Primer for Nurses.pdf", + "2010 - Systems genetics analyses predict a transcription role for P2P-R Molecular confirmation that P2P-R is a transcriptional co-repressor.pdf", + "2010 - Using expression genetics to study the neurobiology of ethanol and alcoholism.pdf", + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2017 - Systems Genetics Analysis to Identify the Genetic Modulation of a Glaucoma-Associated Gene.pdf", + "2019 - Implementation of Genomic Medicine.pdf", + "2012 - Generating Embryonic Stem Cells from the Inbred Mouse Strain DBA2J, a Model of Glaucoma and Other Complex Diseases.pdf", + "2021 -Highlights from the Era of Open Source Web-Based Tools.pdf" + ], + "extraction_id": [ + "3e0c2a06-e6de-5888-a360-a2c483d9f744", + "82e07232-dd92-52f6-8230-d90a03c71b4f", + "a58546e6-fe89-5d04-8adb-08d1991dc53c", + "ec624ebb-489a-5437-a721-f01cf981d0a7", + "9597c8b3-0d67-5192-9e08-1bccc5e2f75c", + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "2455cf6d-4c9b-5272-8650-da127cc329e8", + "90e220eb-61ba-56bd-b455-ac29a1df5867", + "ee03f7c5-6eee-5c66-8174-688f06da1587", + "ffafdd06-808c-58be-bcb5-bd74d7ffa89a" + ], + "document_id": [ + "1eb6f5b7-a3bc-5455-91f0-6f2eb37be861", + "96657025-7e50-571d-9a6b-1a202cb8a690", + "c37e2ace-171b-5776-8969-86eda9736481", + "e4d1e2e9-f267-5814-8c7b-dc11d7eec9bf", + "64469ae5-5eb6-5e45-ab23-7bafb63d486f", + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "67e804db-8127-5938-8d7f-a5918cdf4f86", + "a7faf15a-ed90-575b-805c-11f33fb2d6dd", + "a9b08d55-2f85-5d3a-abbf-389eed34009c", + "0a6f3d2e-70c3-5db7-bfc0-93ad04806104" + ], + "id": [ + "chatcmpl-AIGlO1Tf6FzOyoXrb1Vnt5VYQUM0R", + "27bb3941-5a92-56a2-b67d-c5e64603c1a3", + "1c8d31d6-bd59-56da-83b8-f603b4a9ec2b", + "f8a32960-cfe3-5440-9d5c-b55dfe52ea6d", + "a9508122-3b14-5365-979c-ba580bdcb78f", + "d8162fdc-326a-5f90-9fa4-24d86d701184", + "c63cfaee-749e-547b-9c0a-086266f10670", + "18c7c27b-b51f-5ab6-9d09-4235c57811b1", + "a9bbd320-eb89-5ae7-a3af-703ca68c8305", + "504b72fb-9a5e-53a4-b6a6-0fc6be18ec4e", + "a1124460-ae34-57fb-846b-e033f4bbf49c" + ], + "contexts": [ + "users can take advantage of a systems genetics approach (Rosen et al., 2003, 2007). While the candidate gene approach asks which one gene mutation causes a particular disease, the systems genetics approach explores which phenotypes and diseases result from diverse sets of genetic and molecular markers (Rosen et al., 2003, 2007). The majority of data sets in GeneNetwork are collected from GRPs consisting of hundreds of diverse, inbred strains of", + "Based on this, Goh et al. created networks using data from the Online Mendelian Inheritance in Man (OMIM) [18]database that houses lists of disease gene links. Two networks emerged: the human disease network inwhich disease nodes were connected if they were caused by mutations in the same gene, and the disease gene network where gene nodes were", + "Genetics Home Reference - Genetics Home Reference provides consumer-friendly information about the effects of genetic variations on human health. http://ghr.nlm.nih.gov/ Gene Reviews Features expert-authored, peer-reviewed, current disease descriptions that apply genetic testing to the diagnosis, management, and genetic counseling of patients and families with specific inherited conditions. www.genetests.org/servlet/access?", + "GeneNetwork http://www.genenetwork.org is anexample of a bioinformatics tool that can be used to explore systems genetics data. The importance of defining biological networks and predicting molecular interactions has been emphasized by several reports [1,2]. Such studies emphasize that when knowledge about DNA variation within popula- tions is interfaced with data on gene expression, protein interactions and DNA-protein binding, biological networks can be constructed that are predictive of the", + "of links to external resources for tracing the interrelationships of a gene among multiple Web-based resources. GeneNetwork also offers a number of correlation and mapping strategies for assessing associations among multiple genes and QTLs. GeneNetwork aims to make the study of complex traits through the use of systems genetics widely available to the scientific community. A powerful tool that can be integrated with GeneNetwork or used on", + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "genetics approaches can not only provide insights into the roles of individual genes or developmental pathways but also illuminate relationships between different levels of a biologic system, such as the genome, transcriptome, and phenome [ 10]. One such resource of systems genetics is the GeneNetwork website and resource (www.genenetwork.org ) that provides access to a wide variety of data such as genotypes (e.g., SNPs), phenotypes that are obtained", + "eron Genetics Center ( https://www.regeneron.com/ge - netics-center ), and aims to identify rare loss-of-function mutations in founder populations to delineate further the genetic factors that underpin health and disease. This ini - tiative is also addressed at developing countries and those in resource-limiting environments, under the coordina - tion of the Genomic Medicine Alliance ( http://www.ge - nomicmedicinealliance.org ), a founding partner of the", + "to understand the genetics of a variety of diseases andbiological systems including aging, the immune system and ironregulation [26,27,28,29,30]. Much of this work has been madeavailable through GeneNetwork (formerly WebQTL ) an on-line", + "GeneNetwork.org is also a valuable teaching tool. While mainly designed for researchers interested in testing gene-to- phenotype relationships, GeneNetwork. orghas been adapted for dry-lab teaching in neuroscience and genetics ( Grisham et al., 2017 ). A useful approach is to assign sets of vetted questions, such as the exam- ples discussed above, and to help students work toward answers, solutions, or novelquestions. Several examples relating to the" + ], + "answer": "GeneNetwork.org aids in finding the genetic causes of common diseases by utilizing a systems genetics approach. This approach allows researchers to explore which phenotypes and diseases result from diverse sets of genetic and molecular markers, rather than focusing on single gene mutations [1]. GeneNetwork provides access to a wide variety of data, including genotypes (e.g., SNPs) and phenotypes, which can be used to study complex traits [7]. The platform integrates data on gene expression, protein interactions, and DNA-protein binding to construct biological networks that are predictive of disease [4]. Additionally, GeneNetwork offers correlation and mapping strategies for assessing associations among multiple genes and quantitative trait loci (QTLs), facilitating the study of complex traits [5]. This makes GeneNetwork a powerful tool for predictive medicine and systems genetics, helping researchers understand the genetic underpinnings of common diseases [6].", + "question": "How does GeneNetwork.org help in finding the genetic causes of common diseases?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_5 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_5 new file mode 100644 index 0000000..f4fd1c4 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_5 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2018 - Genome-wide polygenic scores for common diseases.pdf", + "2018 - Communication of cancer-related genetic and genomic information A landscape analysis of reviews.pdf", + "2012 - Population-based screening in the era of genomics.pdf", + "2009 - From Disease Association to Risk Assessment.pdf", + "2010 - Interactions of Dietary Whole-Grain Intake.pdf", + "2014 - Impact of Delivery Models on Understanding Genomic Risk for Type 2 Diabetes.pdf", + "2014 - Impact of Delivery Models on Understanding Genomic Risk for Type 2 Diabetes.pdf", + "2009 - Basic Genetics and Genomics A Primer for Nurses.pdf", + "2010 - Considerations for the Impact of Personal Genome Information.pdf", + "2004 - The emergence of epidemiology.pdf" + ], + "extraction_id": [ + "df306ee1-389f-56bb-bc5e-80ca8ff68fff", + "672e1f6a-25dd-5973-b19e-8d9371ec8973", + "706f0647-f63c-5383-9167-724c83faf79c", + "6f819601-6eea-54a4-ab88-27e1b0602287", + "37a4db8f-72a7-5e4e-b396-94bc0532a29d", + "3b79395f-0e1c-564c-9965-b04acf204132", + "074c3cae-ea97-5e74-8607-74c099df35cd", + "a58546e6-fe89-5d04-8adb-08d1991dc53c", + "694d74ca-68c2-5874-b143-113c6cc7802a", + "d7c4830a-8d69-531b-855a-eda3fa2ea5e7" + ], + "document_id": [ + "a8cefcf1-7edf-52cc-8aeb-b4d353acaef5", + "2a560126-b122-55dc-a213-a16bc00300b7", + "3a8d8722-9a3a-5062-9548-48e3c3bd6247", + "a61066d0-0d1a-5f10-96c3-aa96bacdad5e", + "e4d4a19e-18a0-5a08-9ab7-537f31b7cdc1", + "b2665466-da66-59f0-8581-a68131e924bf", + "b2665466-da66-59f0-8581-a68131e924bf", + "c37e2ace-171b-5776-8969-86eda9736481", + "e4f3c9ce-1cc1-56f1-a290-b874455e53f9", + "53cc9020-d5d9-5c5b-a8df-66b3f1019961" + ], + "id": [ + "chatcmpl-AIGlUj81qIxsp6ZB9jZOhAF3uBNfA", + "d31fc0e8-028a-5879-b262-ec03ca586488", + "d57998c0-b045-5f68-a2ad-2173add21137", + "65aa608a-7e60-54bb-a299-ae1e2e66d0cd", + "deab786b-11ed-5c75-8ff5-fd2812138917", + "946c47ae-bbaf-5151-88f0-afa898c28a66", + "563b865d-03a4-5607-a6c5-a0ee977010b4", + "8ac717f0-586c-5ee3-b4e3-4334657938b5", + "f8a32960-cfe3-5440-9d5c-b55dfe52ea6d", + "3c0229cb-f853-5ef6-b45f-5462f62ede91", + "60497a7a-5c86-51a3-bc73-e373ca716270" + ], + "contexts": [ + "Letters NATure GeNeTicsIn our testing dataset, 19.8% of participants were at threefold increased risk for at least 1 of the 5 diseases studied (Table 2). The potential to identify individuals at significantly higher genetic risk, across a wide range of common diseases and at any age, poses a number of opportunities and challenges for clinical medicine. Where effective prevention or early detection strategies are available, key issues will include the allocation of attention and", + "genetic risks of disease on risk-reducing health behaviour: Systematic review with meta-analysis. BMJ. 2016;352:i1102. 57. Vernarelli JA. Impact of genetic risk assessment on nutrition-related life- style behaviours. Proc Nutr Soc . 2013;72(1):153159. 58. Marteau TM, French DP , Griffin SJ, et al. Effects of communicating DNA- based disease risk estimates on risk-reducing behaviours. Cochrane Database Syst Rev . 2010;(10). 59. National Human Genome Research Institute. All about The Human", + "personalized screening based on age and polygenic risk profile. 12 Pashayan N, Pharoah P. Translating genomics into improved population screening: hype or hope? Hum. Genet. 130(1), 1921 (2011). 13 Pharoah PD, Antoniou A, Bobrow M, Zimmern RL, Easton DF, Ponder BA. Polygenic susceptibility to breast cancer and implications for prevention. Nat. Genet. 31(1), 3336 (2002). nn\t Examines the potential for prediction of risk based on common genetic variation and compares this with the prediction that", + "Eur J Hum Genet. 12. Janssens AC, van Duijn CM (2008) Genome-based prediction of common diseases: advances and prospects. Hum Mol Genet 17: R166173. 13. Wray NR, Goddard ME, Visscher PM (2007) Prediction of individual genetic risk to disease from genome-wide association studies. Genome Res 17:15201528. 14. Wray NR, Goddard ME, Visscher PM (2008) Prediction of individual genetic risk of complex disease. Curr Opin Genet Dev 18: 257263. 15. Jakobsdottir J, Gorin MB, Conley YP, Ferrell RE, Weeks DE (2009)", + "within the general population and toutedfor its potential contribution to personal-ized medicine (1315), although the un-derlying clinical utility has yet to bedemonstrated (16,17). Given the poten-tial for individual genetic risk to beempirically quantied and rapidly com-municated, it is of interest to both clini-cians and the general public to discover ifmodiable characteristics like diet canmitigate risk in individuals empiricallydened as high risk on the basis ofgenotype.", + "Comprehension of Genomic Risk for Diabetes Public Health Genomics 2014;17:95104 DOI: 10.1159/000358413103 9 Green MJ, Peterson SK, Baker MW, Harper GR, Friedman LC, Rubinstein WS, Mauger DT: Effect of a computer-based decision aid on knowledge, perceptions, and intentions about genetic testing for breast cancer suscep-tibility: a randomized controlled trial. JAMA 2004; 292: 442452. 10 Bernhardt JM, McClain J, Parrott RL: Online", + "Comparison of family history and SNPs for predicting risk of complex disease. PLoS Ge-net 2012; 8:e1002973. Downloaded from http://karger.com/phg/article-pdf/17/2/95/3426597/000358413.pdf by guest on 03 July 2023", + "Genetics Home Reference - Genetics Home Reference provides consumer-friendly information about the effects of genetic variations on human health. http://ghr.nlm.nih.gov/ Gene Reviews Features expert-authored, peer-reviewed, current disease descriptions that apply genetic testing to the diagnosis, management, and genetic counseling of patients and families with specific inherited conditions. www.genetests.org/servlet/access?", + "Khoury, M. J. (2006). Family history of type 2 diabetes: apopulation-based screening tool for prevention? Genetics in Medicine, 8 (2), 102 108. Hunter, D. J., Khoury, M. J., & Drazen, J. M. (2008). Letting the genome out of the bottle will we get our wish? The New England Journal of Medicine, 358 (2), 105 107. Ioannidis, J. P. A. (2009). Personalized genetic prediction: too limited, too expensive, or too soon? Annals of Internal Medicine, 150 (2), 139141.", + "genomic profiling for measuring susceptibility to common diseasesand targeting interventions. Genet Med 2004; 6:3847. 42Vineis P, Christiani DC. Genetic testing for sale. Epidemiology 2004; 15:35. 43Haga SB, Khoury MJ, Burke W. Genomic profiling to promote ahealthy lifestyle: not ready for prime time. Nat Genet 2003; 34:34750. 44Yang Q, Khoury MJ, Botto L et al. Improving the prediction of complex diseases by testing for multiple disease-susceptibility genes.Am J Hum Genet 2003; 72:63649." + ], + "answer": "The context provided does not specifically mention GeneNetwork.org or its capabilities in predicting health conditions based on genetic information. However, the context does discuss the general potential of genetic information to predict disease risk. For example, it mentions the ability to identify individuals at higher genetic risk for common diseases [1], and the potential for genomic profiling to measure susceptibility to diseases [10]. While these references indicate that genetic information can be used to assess disease risk, there is no direct mention of GeneNetwork.org's specific capabilities in this area. Therefore, based on the provided context, it is unclear if GeneNetwork.org itself offers such predictive services.", + "question": "Can GeneNetwork.org predict my risk of developing certain health conditions based on my genes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_6 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_6 new file mode 100644 index 0000000..85d09e5 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_6 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2013 - Pathogenesis and reversal of liver fibrosis Effects of genes and environment.pdf", + "2011 - Using the PhenoGen Website for \u201cIn Silico\u201d Analysis of Morphine-Induced Analgesia Identifying Candidate Genes.pdf", + "2014 - Systems Genetics of Liver Fibrosis Identification of Fibrogenic and Expression Quantitative Trait Loci in the BXD Murine Reference Population.pdf", + "2008 - Genetic Analysis of Posterior Medial Barrel Subfield Size.pdf", + "2010 - Systems genetics analyses predict a transcription role for P2P-R Molecular confirmation that P2P-R is a transcriptional co-repressor.pdf", + "2020 -Highlights from the Era of Open Source Web-Based Tools.pdf", + "2021 -Highlights from the Era of Open Source Web-Based Tools.pdf", + "2012 - Using Genome-Wide Expression Profiling to Define Gene Networks Relevant to the Study of Complex Traits From RNA Integrity to Network Topology.pdf", + "2008 - Towards systems genetic analyses in barley Integration of phenotypic, expression and genotype data into GeneNetwork.pdf" + ], + "extraction_id": [ + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "71f1aefb-6e32-5add-804e-6fbaa39ca720", + "0e3a5e40-06b0-58d4-b495-3093954ed17b", + "8c423789-3641-5853-9cf3-f4a026ffb446", + "66aad1b1-a76d-58a8-aa40-76a6b58c4964", + "ec624ebb-489a-5437-a721-f01cf981d0a7", + "28892088-5a95-56eb-822d-b12da3a612d0", + "62c12bdc-ae2b-5cc0-88f5-a3c1a264326b", + "3e0c2a06-e6de-5888-a360-a2c483d9f744", + "dc001755-2e77-5b41-8617-263b3ba35af8" + ], + "document_id": [ + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "73724c99-98df-53b2-a378-29c8b4faa171", + "eb266fa1-8dec-5c56-a3d5-b508bd6bd448", + "125d9cd4-5297-5173-9b16-9073cd3bcc71", + "76a715a4-8222-598b-8e65-6d5b6e807989", + "e4d1e2e9-f267-5814-8c7b-dc11d7eec9bf", + "f2f0dc2f-eaf5-57f6-9ffa-816d3fab6640", + "0a6f3d2e-70c3-5db7-bfc0-93ad04806104", + "1eb6f5b7-a3bc-5455-91f0-6f2eb37be861", + "8513abbe-65ed-5f35-9f86-ba93cfc5a194" + ], + "id": [ + "chatcmpl-AIGlYq5ocxHDndnTkp4uj3sxjmcHK", + "c63cfaee-749e-547b-9c0a-086266f10670", + "f5c218f0-1280-55f8-912b-b32b833e93a3", + "0b2bd83d-680a-52d2-8116-50cce4f35cc3", + "1c26e6f6-680b-5877-9600-fee25a42c943", + "21936758-94b1-506f-9229-77e26001ae44", + "a9508122-3b14-5365-979c-ba580bdcb78f", + "7b626f27-5aed-5464-a20d-463954ff057d", + "aaf19f3a-353a-595b-b7de-da9f01a7b0f1", + "312eae52-ede7-5c13-8974-fce0126426cf", + "251de62d-6e8e-50c7-9616-7fea05a250fb" + ], + "contexts": [ + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "GeneNetwork (www.genenetwork.org). The web -based software further allows extraction of sets of", + "al., 2005). GeneNetwork is designed primarily as a web service for exploratory and statistical analysis of large published phenotype and genome datasets, and includes data from several species (see Supplementary Discussion). GeneNetwork includes extensive phenotype data extracted from the literature and submitted by users, which makes it practical to compare data on drug responses with gene expression patterns. Gene expression", + "GeneNetwork is an open-access database that collates genomic information of diverse experimental crosses and reference panels as well as phenotypic data from miscellaneous research groups [26]. Statistics Data generation, statistical analysis and graph creation were performed with SPSS Statistics 21 (IBM, Ehningen, Germany). As appropriate, mean and median values were further used for QTLanalysis. Phenotypic robustness for each strain was assessed by the", + "GeneNetwork provides users with an array of analyticaltools to compare a given trait with a number of data setsavailable from other experimenters. Microarray data ofgene expression in the brain and data of other phenotypes are two such examples of possible tools. For this study, we", + "GeneNetwork http://www.genenetwork.org is anexample of a bioinformatics tool that can be used to explore systems genetics data. The importance of defining biological networks and predicting molecular interactions has been emphasized by several reports [1,2]. Such studies emphasize that when knowledge about DNA variation within popula- tions is interfaced with data on gene expression, protein interactions and DNA-protein binding, biological networks can be constructed that are predictive of the", + "distributed neuroscience data sharing with ever expanding prospects for future breakthroughs. GeneNetwork.org : genetic analysis for all neuroscientists Originally named webqtl, GeneNetwork.org is the oldest contin- uously operating website in biomedical research ( Williams, 1994). This massive database contains ;40 million datasets. GeneNetwork.org also offers a powerful statistical platform for online network analyses and mapping, enabling numerous mo-", + "distributed neuroscience data sharing with ever expanding prospects for future breakthroughs. GeneNetwork.org : genetic analysis for all neuroscientists Originally named webqtl, GeneNetwork.org is the oldest contin- uously operating website in biomedical research ( Williams, 1994). This massive database contains ;40 million datasets. GeneNetwork.org also offers a powerful statistical platform for online network analyses and mapping, enabling numerous mo-", + "addition to this, GeneNetwork can be used to study correlations between traits and to perform data mining in genomic regions containing candidates for quantitative trait genes (Hoffman et al., 2011). All datasets in GeneNetwork are linked to a materials and methods information page that summarizes experimental details relating to the dataset. Databases within GeneNetwork include the transcriptome database, the BXD published", + "abundance data sets directly within GeneNetwork's ana- lytical environment we provide simple web access to the data for the research community. In this environment, a combination of correlation analysis and linkage mapping provides the potential to identify and substantiate gene targets for saturation mapping and positional cloning. By integrating datasets from an unsequenced crop plant (bar- ley) in a database that has been designed for an animal model species (mouse) with well established genome" + ], + "answer": "GeneNetwork.org utilizes data from different populations around the world by integrating diverse genomic information and phenotypic data from various experimental crosses and reference panels. This allows for comprehensive exploratory and statistical analysis of large published phenotype and genome datasets [3], [4]. The platform includes data from multiple species, which facilitates the comparison of gene expression patterns with drug responses and other phenotypic traits [3]. Additionally, GeneNetwork.org provides analytical tools that enable users to compare traits across datasets from different experimenters, further enhancing the ability to study correlations and perform data mining in genomic regions [5], [9]. This integration of diverse datasets supports the construction of predictive biological networks by interfacing DNA variation data with gene expression, protein interactions, and DNA-protein binding information [6].", + "question": "How does GeneNetwork.org make use of data from different populations around the world?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_7 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_7 new file mode 100644 index 0000000..a8a3e28 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_7 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - A platform for experimental precision medicine The extended BXD mouse family.pdf", + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2014 - Systems Genetics of Liver Fibrosis Identification of Fibrogenic and Expression Quantitative Trait Loci in the BXD Murine Reference Population.pdf", + "2020 - Modeling the Genetic Basis of Individual Differences in Susceptibility to Gulf War Illness.pdf", + "2011 - Using the PhenoGen Website for \u201cIn Silico\u201d Analysis of Morphine-Induced Analgesia Identifying Candidate Genes.pdf", + "2013 - Pathogenesis and reversal of liver fibrosis Effects of genes and environment.pdf", + "2012 - Using Genome-Wide Expression Profiling to Define Gene Networks Relevant to the Study of Complex Traits From RNA Integrity to Network Topology.pdf", + "2008 - Genetic Analysis of Posterior Medial Barrel Subfield Size.pdf", + "2009 - Genetical Toxicogenomics in Drosophila Identifies Master Modulatory Loci that are Regulated by Developmental Exposure to Lead.pdf", + "2017 - Systems Genetics Analysis to Identify the Genetic Modulation of a Glaucoma-Associated Gene.pdf" + ], + "extraction_id": [ + "d1c32c32-42c8-5065-b7f2-bd2a0baeae62", + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "8c423789-3641-5853-9cf3-f4a026ffb446", + "98aff04d-a5b2-5cca-bc1a-552055a74262", + "0e3a5e40-06b0-58d4-b495-3093954ed17b", + "71f1aefb-6e32-5add-804e-6fbaa39ca720", + "3e0c2a06-e6de-5888-a360-a2c483d9f744", + "66aad1b1-a76d-58a8-aa40-76a6b58c4964", + "3ca48658-ca83-5952-8f8d-eb7ae491e6b6", + "2455cf6d-4c9b-5272-8650-da127cc329e8" + ], + "document_id": [ + "dd4994b9-9546-59c0-bc71-60e2617b6bcd", + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "125d9cd4-5297-5173-9b16-9073cd3bcc71", + "d235d186-3d1c-5cde-90d5-9c140cd920f4", + "eb266fa1-8dec-5c56-a3d5-b508bd6bd448", + "73724c99-98df-53b2-a378-29c8b4faa171", + "1eb6f5b7-a3bc-5455-91f0-6f2eb37be861", + "76a715a4-8222-598b-8e65-6d5b6e807989", + "301d6469-2a9c-5960-88ac-8437212d78ab", + "67e804db-8127-5938-8d7f-a5918cdf4f86" + ], + "id": [ + "chatcmpl-AIGlcT3nIUJnhMWAFTwtm7j28RbUR", + "bd2eb0ef-24a1-55ff-8597-c21dff0ecf0a", + "c63cfaee-749e-547b-9c0a-086266f10670", + "1c26e6f6-680b-5877-9600-fee25a42c943", + "23de1e96-55b6-5062-a2e1-02bf06fd3565", + "0b2bd83d-680a-52d2-8116-50cce4f35cc3", + "f5c218f0-1280-55f8-912b-b32b833e93a3", + "312eae52-ede7-5c13-8974-fce0126426cf", + "21936758-94b1-506f-9229-77e26001ae44", + "40ecbff2-5039-57f0-a01e-9d412d559dbe", + "18c7c27b-b51f-5ab6-9d09-4235c57811b1" + ], + "contexts": [ + "This paper analyzes existing, publicly available data. These data sets accession numbers are provided in the Key Resource Table , and throughout the manuscript. Genotype les can be found at http://www.genenetwork.org/webqtl/main.py?FormID= sharinginfo&GN_AccessionId=600 . GeneNetwork.org original code is publicly available at https://github.com/genenetwork/genenetwork2 and https://github.com/ genenetwork/genenetwork1 .", + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "GeneNetwork is an open-access database that collates genomic information of diverse experimental crosses and reference panels as well as phenotypic data from miscellaneous research groups [26]. Statistics Data generation, statistical analysis and graph creation were performed with SPSS Statistics 21 (IBM, Ehningen, Germany). As appropriate, mean and median values were further used for QTLanalysis. Phenotypic robustness for each strain was assessed by the", + "genetic variants (SNPs, insertions, deletions, duplications, etc.) that segregate in the family [ 13]. The strains are appropriate for systems genetics /systems biology analysis [ 14], genetic mapping and genetic correlations of parameter means, and thus constitute an ideal platform for toxicogenomic research [ 15]. All data are available at www.genenetwork.org. GeneNetwork exists in two forms, GN1 and GN2 [ 16]. GN2 is an expansion and renement of the features of GN1. A tutorial of how to use GN1 may be", + "al., 2005). GeneNetwork is designed primarily as a web service for exploratory and statistical analysis of large published phenotype and genome datasets, and includes data from several species (see Supplementary Discussion). GeneNetwork includes extensive phenotype data extracted from the literature and submitted by users, which makes it practical to compare data on drug responses with gene expression patterns. Gene expression", + "GeneNetwork (www.genenetwork.org). The web -based software further allows extraction of sets of", + "addition to this, GeneNetwork can be used to study correlations between traits and to perform data mining in genomic regions containing candidates for quantitative trait genes (Hoffman et al., 2011). All datasets in GeneNetwork are linked to a materials and methods information page that summarizes experimental details relating to the dataset. Databases within GeneNetwork include the transcriptome database, the BXD published", + "GeneNetwork provides users with an array of analyticaltools to compare a given trait with a number of data setsavailable from other experimenters. Microarray data ofgene expression in the brain and data of other phenotypes are two such examples of possible tools. For this study, we", + "deposited in the GeneNetwork website (http://www.genenetwork.org) so that other investigators can look for correlations between gene expression patterns and phenotypic traits. The GeneNetwork is an open resource and consists of a set of linked resources for systems genetics. It has been designed for integration of networks of genes, transcripts, and traits such as toxicity, cancer susceptibility, and behavior for several species. Phenotypic QTLs using the", + "genetics approaches can not only provide insights into the roles of individual genes or developmental pathways but also illuminate relationships between different levels of a biologic system, such as the genome, transcriptome, and phenome [ 10]. One such resource of systems genetics is the GeneNetwork website and resource (www.genenetwork.org ) that provides access to a wide variety of data such as genotypes (e.g., SNPs), phenotypes that are obtained" + ], + "answer": "GeneNetwork.org provides a variety of genetic data, including: 1. Genomic information from diverse experimental crosses and reference panels, as well as phenotypic data from various research groups [3]. 2. Genetic variants such as SNPs (single nucleotide polymorphisms), insertions, deletions, and duplications [4]. 3. Extensive phenotype data extracted from the literature and submitted by users, which allows for comparisons of drug responses with gene expression patterns [5]. 4. Microarray data of gene expression in the brain and data of other phenotypes [8]. 5. Genotypes, including SNPs, and phenotypes obtained from various studies [10]. These datasets are designed to support systems genetics research and include data from multiple species [2], [5].", + "question": "What kinds of genetic data are available on GeneNetwork.org?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_8 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_8 new file mode 100644 index 0000000..efc389c --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_8 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2008 - Genetic Analysis of Posterior Medial Barrel Subfield Size.pdf", + "2011 - Using the PhenoGen Website for \u201cIn Silico\u201d Analysis of Morphine-Induced Analgesia Identifying Candidate Genes.pdf", + "2011 - Using the PhenoGen Website for \u201cIn Silico\u201d Analysis of Morphine-Induced Analgesia Identifying Candidate Genes.pdf", + "2011 - Using the PhenoGen Website for \u201cIn Silico\u201d Analysis of Morphine-Induced Analgesia Identifying Candidate Genes.pdf", + "2020 - GeneNetwork a toolbox for systems genetics.pdf", + "2017 - GeneNetwork a toolbox for systems genetics.pdf", + "2011 - Using the PhenoGen Website for \u201cIn Silico\u201d Analysis of Morphine-Induced Analgesia Identifying Candidate Genes.pdf", + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2009 - Detection and interpretation of expression quantitative trait loci (eQTL).pdf", + "2017 - Analyses of differentially expressed genes after exposure to acute stress, acute ethanol, or a combination of both in mice.pdf" + ], + "extraction_id": [ + "66aad1b1-a76d-58a8-aa40-76a6b58c4964", + "0e3a5e40-06b0-58d4-b495-3093954ed17b", + "0e3a5e40-06b0-58d4-b495-3093954ed17b", + "308bef07-d720-5686-990d-d1e26a48e8a1", + "4ca2fc9e-7d42-5ea3-b1b7-a296bfbc6a09", + "7dd82b3f-58bd-5915-9eea-250f11412ff2", + "0e3a5e40-06b0-58d4-b495-3093954ed17b", + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "223e442e-898d-5aea-866a-5cdc0ac915e8", + "4f3d275e-f521-5ae9-b550-0411d2a1bb33" + ], + "document_id": [ + "76a715a4-8222-598b-8e65-6d5b6e807989", + "eb266fa1-8dec-5c56-a3d5-b508bd6bd448", + "eb266fa1-8dec-5c56-a3d5-b508bd6bd448", + "eb266fa1-8dec-5c56-a3d5-b508bd6bd448", + "d11a87ca-4989-59af-95e3-ab90af7d9212", + "682c3a51-0aa5-54a3-a6e7-a09b81c0e8b6", + "eb266fa1-8dec-5c56-a3d5-b508bd6bd448", + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "ef974b09-4ea2-5382-85e5-c2169f440fda", + "433904cc-23b8-50a5-ba84-0ee4d41d23c2" + ], + "id": [ + "chatcmpl-AIGljdYmj6PqUgXHWW6b3NFcoOufn", + "21936758-94b1-506f-9229-77e26001ae44", + "0b2bd83d-680a-52d2-8116-50cce4f35cc3", + "fa07b1bf-94e6-515b-8400-cf3afa8b8741", + "94f60899-c281-586e-8741-135a4fef2663", + "7ce6c0fe-8b0a-5ce9-83d1-6e6b99b4f24d", + "30e2423f-2b2b-5c7d-8808-b025242fa0c7", + "76ca1a96-ff40-515d-8d8b-5b1cde3c32b5", + "c63cfaee-749e-547b-9c0a-086266f10670", + "72cac585-5de7-56ca-8ea5-c133d3ff7acf", + "90151329-53f0-5d76-b428-da316848daf3" + ], + "contexts": [ + "GeneNetwork provides users with an array of analyticaltools to compare a given trait with a number of data setsavailable from other experimenters. Microarray data ofgene expression in the brain and data of other phenotypes are two such examples of possible tools. For this study, we", + "al., 2005). GeneNetwork is designed primarily as a web service for exploratory and statistical analysis of large published phenotype and genome datasets, and includes data from several species (see Supplementary Discussion). GeneNetwork includes extensive phenotype data extracted from the literature and submitted by users, which makes it practical to compare data on drug responses with gene expression patterns. Gene expression", + "data are entered into GeneNetwork after they have been shepherded through a system like PhenoGen that has extensive capabilities for normalization and quality control. A comparison of the brain gene expression datasets and some of the tools for data analysis available on PhenoGen and GeneNetwork is shown in Table 3, and more detailed information on features provided by each site is outlined in the Supplementary DiscussionHoffman et al. Page 5 Addict Biol . Author manuscript; available in PMC 2012 July 1.", + "(description of GeneNetwork provided by Dr. Robert W. Williams). Both of these websites focus to a large extent on correlations of behavioral phenotype with gene expression levels in recombinant inbred and inbred panels of mice and rats, and on QTL analyses, as a means to identify candidate genes for complex traits. What distinguishes PhenoGen, in addition to the tools for raw expression data analysis described above, is that the user can not only", + "including correlation and network analysis to compare associations between tissues and between other rodent or human data sets[32] Many of the Data Sets are amenable to systems genetics mapping and other methods and are accessible at GeneNetwork. The Description and Usage column provides details about the data set and potential", + "including correlation and network analysis to compare associations between tissues and between other rodent or human data sets[32] Many of the Data Sets are amenable to systems genetics mapping and other methods and are accessible at GeneNetwork. The Description and Usage column provides details about the data set and potential", + "by example in the Supplementary Methods, and in the Users Manual that can be downloaded from the website. There are a number of databases that investigators can use to assist in various aspects of gene expression data storage and mining (e.g., (Chesler et al., 2005; Galperin and Cochrane, 2009; Gentleman et al., 2004; Mailman et al., 2007; Saal et al., 2002; Swertz et al., 2010)). One relatively well-known database is GeneNetwork (www.genenetwork.org) (Chesler et", + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "from co-regulation patterns found within tens of thousands of samples for which gene expression was measured. GeneNetwork provid es un- precedented resolution and predictive power across multip le cell types and tissues. Analogous to discovering patterns in expressi on data, the network of protein-protein interactions can also be comput ationally pre- dicted using various methods[381]. The combined current knowledge of how cells control functio ns", + "differentially expressed were further evaluated. Bioinformatic analyses were predominantly performed using tools available at GeneNetwork. org, and included gene ontology, presence of cis- regulation or polymorphisms, phenotype correlations, and principal component analyses. Comparisons of differential gene expression between groups showed little overlap. Gene Ontology demonstrated distinct biological processes in each group with the combined exposure (RSE) being" + ], + "answer": "Scientists use GeneNetwork.org to study differences in gene expression by leveraging a variety of analytical tools and datasets available on the platform. GeneNetwork provides access to large published phenotype and genome datasets from several species, allowing for exploratory and statistical analysis [2]. The platform includes microarray data of gene expression in the brain and other phenotypes, which can be used to compare traits across different datasets [1]. GeneNetwork also facilitates the comparison of gene expression patterns with drug responses and other phenotypic data, making it practical for identifying candidate genes for complex traits through QTL analyses [2], [4]. The platform supports correlation and network analysis to compare associations between tissues and across rodent or human datasets, which is useful for systems genetics mapping [5]. Additionally, bioinformatic analyses on GeneNetwork.org include tools for gene ontology, presence of cis-regulation or polymorphisms, phenotype correlations, and principal component analyses, which help in evaluating differentially expressed genes and understanding distinct biological processes [10].", + "question": "How do scientists use GeneNetwork.org to study differences in gene expression?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_9 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_9 new file mode 100644 index 0000000..839bc7b --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_9 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 -Highlights from the Era of Open Source Web-Based Tools.pdf", + "2021 -Highlights from the Era of Open Source Web-Based Tools.pdf", + "2012 - Genetic and Genomic Web Resources for Research on Alcohol Use and Abuse.pdf", + "2012 - Systems genetic analysis of the effects of iron deficiency in mouse brain.pdf", + "2010 - Using expression genetics to study the neurobiology of ethanol and alcoholism.pdf", + "2011 - Using the PhenoGen Website for \u201cIn Silico\u201d Analysis of Morphine-Induced Analgesia Identifying Candidate Genes.pdf", + "2012 - Using Genome-Wide Expression Profiling to Define Gene Networks Relevant to the Study of Complex Traits From RNA Integrity to Network Topology.pdf", + "2012 - Genetic and Molecular Network Analysis of Behavior.pdf", + "2009 - High\u2010throughput behavioral phenotyping in the expanded panel of BXD recombinant inbred strains.pdf", + "2009 - Genetical Toxicogenomics in Drosophila Identifies Master Modulatory Loci that are Regulated by Developmental Exposure to Lead.pdf" + ], + "extraction_id": [ + "14530ed7-e49e-5a1a-9df6-820c7495a8ce", + "ffafdd06-808c-58be-bcb5-bd74d7ffa89a", + "83ae495f-31a2-5977-a63a-57e704c394e2", + "22ed818f-78a7-5409-9f6a-1b83284db25d", + "9597c8b3-0d67-5192-9e08-1bccc5e2f75c", + "308bef07-d720-5686-990d-d1e26a48e8a1", + "be1e859f-c4c7-576d-8a52-9588e15fab44", + "21f8c6e4-ef9b-582b-ac32-2679933c3b59", + "7a89fb44-80f8-5890-b2ae-a4643b587737", + "3ca48658-ca83-5952-8f8d-eb7ae491e6b6" + ], + "document_id": [ + "f2f0dc2f-eaf5-57f6-9ffa-816d3fab6640", + "0a6f3d2e-70c3-5db7-bfc0-93ad04806104", + "08b12d72-9776-5acb-b1ef-7ee402781897", + "99fc80f0-f3c3-5766-a604-921552bb3298", + "64469ae5-5eb6-5e45-ab23-7bafb63d486f", + "eb266fa1-8dec-5c56-a3d5-b508bd6bd448", + "1eb6f5b7-a3bc-5455-91f0-6f2eb37be861", + "4b6759f8-fdaf-59a1-94bd-5a7cf184e1f9", + "423c6929-9d69-5c95-b510-bff6757fed7d", + "301d6469-2a9c-5960-88ac-8437212d78ab" + ], + "id": [ + "chatcmpl-AIGlpL9doFmtJJWVoX5DvTMPsImCv", + "7beda13b-1ea5-53c0-9380-72eee2df79fe", + "a1124460-ae34-57fb-846b-e033f4bbf49c", + "d45f4d61-dfd4-57ef-9b52-ae6cbff0e6f4", + "2b47c0db-8e09-51a2-8689-defa87ee8ac1", + "067136a5-b89e-5108-85b0-f638c041e68c", + "94f60899-c281-586e-8741-135a4fef2663", + "4b91e1d0-f7ce-577c-bad2-b59bd75173b0", + "2f453c67-3f97-5d7b-b92d-0530f86e26ee", + "c61e7911-9138-5a2e-8b2f-e035f374e9e3", + "40ecbff2-5039-57f0-a01e-9d412d559dbe" + ], + "contexts": [ + "GeneNetwork.org is also a valuable teaching tool. While mainly designed for researchers interested in testing gene-to- phenotype relationships, GeneNetwork. orghas been adapted for dry-lab teaching in neuroscience and genetics ( Grisham et al., 2017 ). A useful approach is to assign sets of vetted questions, such as the exam- ples discussed above, and to help students work toward answers, solutions, or novelquestions. Several examples relating to the", + "GeneNetwork.org is also a valuable teaching tool. While mainly designed for researchers interested in testing gene-to- phenotype relationships, GeneNetwork. orghas been adapted for dry-lab teaching in neuroscience and genetics ( Grisham et al., 2017 ). A useful approach is to assign sets of vetted questions, such as the exam- ples discussed above, and to help students work toward answers, solutions, or novelquestions. Several examples relating to the", + "Category 1: Web Resources for Online Analysis of the Genetics of Alcoholism and More GeneNetwork (www.genenetwork.org): This is a comprehensive resource for learning about genetics, but users may", + "GeneNetwork also features a phenotype database, a public repository of data from over 700 traits previously measured across several laboratories in BXD RI (and other) strains. These include behavioral, biochemical, and anatomical traits. The data consist of strain means, not raw data from individual mice, and so we use the term genetic correlation. Using this database, we performed correlation and network analyses to identify relationships with", + "biological function of the new gene list. As mentioned previously, GeneNetwork (www.genenetwork.org) is a collaborative Web-based resource equipped with tools and features for studying gene/gene and exploring genetic correlates to neurobehavioral phenotypes (Chesler et al., 2003, 2004). The Web site is home to a growing collection of gene expression and phenotypic data from a variety of species and brain regions, with a host", + "(description of GeneNetwork provided by Dr. Robert W. Williams). Both of these websites focus to a large extent on correlations of behavioral phenotype with gene expression levels in recombinant inbred and inbred panels of mice and rats, and on QTL analyses, as a means to identify candidate genes for complex traits. What distinguishes PhenoGen, in addition to the tools for raw expression data analysis described above, is that the user can not only", + "with another database, GeneNetwork, correlating behavioral phenotypes with geneO'Brien et al. Page 11 Int Rev Neurobiol . Author manuscript; available in PMC 2014 July 21. NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript", + "interested in behavioral variation and in ways to exploit bioinformatic resources and methods to dissect and (we hope) reassemble and model behavior. You do not need to be a statistician or geneticist to use these tools. In order to use GeneNetwork, we have to start with some ground rules and assumptions. The first is that behavioral traits must vary significantly. This is a chapter about behavioral variation with an equal emphasis on both words. If a behavior is a \"fixed action pattern\" that", + "facilitated through the development of GeneNetwork(www.genenetwork.org), an Inte rnet resource for the multi- variate genetic analysis of complex traits in genetic reference populations (Chesler et al. 2003, 2004; Wang et al. 2003). GeneNetwork aids in identication of candidate genesand bio-molecular mechanisms underlying addiction-relatedphenotypes and includes a wealth of data on mRNAexpression proles from various tissues of the centralnervous system (Chesler et al. 2005; Peirce et al. 2006;", + "deposited in the GeneNetwork website (http://www.genenetwork.org) so that other investigators can look for correlations between gene expression patterns and phenotypic traits. The GeneNetwork is an open resource and consists of a set of linked resources for systems genetics. It has been designed for integration of networks of genes, transcripts, and traits such as toxicity, cancer susceptibility, and behavior for several species. Phenotypic QTLs using the" + ], + "answer": "Yes, GeneNetwork.org can be used to learn about genetic influences on behavior. It is a comprehensive resource equipped with tools and features for studying genetic correlates to neurobehavioral phenotypes [5]. The platform includes a phenotype database with data on behavioral traits, among others, which can be used for correlation and network analyses to identify relationships with genetic data [4]. Additionally, GeneNetwork focuses on correlations of behavioral phenotypes with gene expression levels in recombinant inbred and inbred panels of mice and rats, which helps in identifying candidate genes for complex traits [6]. The resource is designed for the multivariate genetic analysis of complex traits, including behavior, in genetic reference populations [9].", + "question": "Can GeneNetwork.org be used to learn about genetic influences on behavior?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_1 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_1 new file mode 100644 index 0000000..4693a1d --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_1 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2023 - A transcriptome-based single-cell biological age model.pdf", + "2019 - Murine single-cell RNA-seq reveals cell-identity.pdf", + "2015 - Single cell RNA-seq reveals changes in cell cycle and differentiation programs.pdf", + "2019 - Murine single-cell RNA-seq reveals cell-identity.pdf", + "2020 - Advances of single?cell genomics and epigenomics in human disease.pdf", + "2022 - Parallel bimodal single-cell sequencing of transcriptome and methylome.pdf", + "2023 - A transcriptome-based single-cell biological age model.pdf", + "2019 - Murine single-cell RNA-seq reveals cell-identity.pdf", + "2018 - Epigenetic Modifications in Cardiovascular Aging and Diseases.pdf" + ], + "extraction_id": [ + "7fcf2db7-a5cc-5ffe-aee1-98f447d9b8e5", + "f277b0c9-43b7-542a-828f-1bebcf0cddf9", + "6411b0be-b683-56dd-a3a5-e76618ede4e4", + "e6e9d12b-1784-5f0e-924e-442be1636afb", + "a0a95eb8-8214-5918-9b54-7f69eec9df53", + "60355441-16f5-53a2-9b24-9616624f8d00", + "1e4f0bd9-2e45-536b-ae84-33c3ed01ba34", + "548ece22-253e-512f-8dcd-7ffc6c95d482", + "4104881f-c452-54ba-b7ab-5c13f9dc8bad", + "7109021e-4b15-502a-a9d1-ef6055acdf9d" + ], + "document_id": [ + "62b635c3-040e-512a-b016-6ef295308a1e", + "9be234b7-f37d-5cd5-8895-bfe676441b2f", + "0d36d6a5-5c98-5c67-af47-4e00d32ce9c8", + "42f2b0ac-0991-58fb-bb24-128b368cc1d7", + "0d36d6a5-5c98-5c67-af47-4e00d32ce9c8", + "afe53f5a-3962-520f-be55-9df5bfdaad70", + "e112f7dc-0ca4-51df-82ed-2f9748f9a3b5", + "9be234b7-f37d-5cd5-8895-bfe676441b2f", + "0d36d6a5-5c98-5c67-af47-4e00d32ce9c8", + "6559d6a2-bd88-5538-a611-da6288439bed" + ], + "id": [ + "chatcmpl-AIHWB476q1MhqSkzMBJiel5B4S1fK", + "91b0d8d4-949d-528f-b56d-8e5d99655fbc", + "27854d68-63aa-5b56-9580-46c83cd121db", + "f74570ce-e096-50b2-8061-b9a538299b3a", + "04c9d851-f9d9-53e6-b1ba-1bf3398395f5", + "f037fb19-8998-5f24-ab7c-b1ecc0e1b7ef", + "cdc7970a-4c9a-55c8-accb-8f99d88b0f6c", + "9c580ff4-e422-56e4-b774-44cbc2e5d87c", + "225b6504-a754-5aa3-ae95-b4019dbcaa8b", + "4a1ef9aa-4fbc-5093-9c53-73937397c715", + "1a51a565-e5bf-5659-84a2-39e06def18fd" + ], + "contexts": [ + "Single-cell sequencing has helped to support several hypotheses about the cel- lular and genetic origin of age-related dysfunctions. Since single-cell sequencing allows us to study small populations of cells, it has been possible to find low repre- sented mutations as well as transcriptional events that alter cellular identity. This newly generated data suggests that aging could be the result of mutational accumu- lation, epigenetic errors, and transcriptional noise that occurs in cells altering the", + "structed using data from bulk tissues, which neglect the variationsin cell compositions and cell-to-cell aging heterogeneity. To gain amore detailed and nuanced view of cell type specific molecular changes during aging, several studies have applied machine-learn- ing models to single-cell transcriptomics and DNA methylation", + "within whole tissues or individual cell types in aging (Rodwellet al. 2004; Jonker et al. 2013; Cosgrove et al. 2014; O Brown et al. 2015; Su et al. 2015; White et al. 2015; Keyes et al. 2016; Benayoun et al. 2019). However, it remains unclear to what degree age-related transcriptional changes are shared or unique across cellidentities. To address this outstanding question, we performed dif-ferential expression analysis within each cell identity betweenyoung and old mice.", + "populations. Furthermore, single cell analysis should allow us to relate prospective profiles of HSCs that have just been isolated with known heterogeneity in their retrospective functional capacity in transplantation assays. Here, we leveraged single cell RNA-seq to directly assess transcriptional heterogeneity within the HSCs and how it may change with age in the steady-state unperturbed hematopoiesis. Given that HSCs are", + "cells. Here, we used single-cell RNA-seq to investigate aging across a diverse set of murine cell identities in three tissues. We found that cell identities differentially express unique genes with aging, consistent with previous reports of cell-identi- ty-specific aging phenotypes (Angelidis et al. 2019). Similar celltypes (e.g., kidney capillary endothelial cells and lung endothelial cells) showed broadly similar aging trajectories across tissues, and", + "Cellular heterogeneity is revolutionizing the way to study, monitor and dissect complex diseases. This has been possible with the technological and computational advances associated to single-cell genomics and epigenomics. Deeper understanding of cell-to-cell variation and its impact on tissue function will open new avenues for early disease detection, accurate diagnosis and personalized treatments, all together leading to the next generation of health care. This review focuses on the recent dis-coveries", + "Genomics 114 (2022) 110379 2have been observed in multiple species and tissues [7,8]. Transcriptome analysis using aged oocyte samples have confirmed the impact of aging on transcriptome landscapes [9,10]. Advances in single-cell sequencing technology promote our understanding of intrinsic complexity to another level [11]. Recently, we have successfully applied single-cell transcriptome technique to reveal cellular and molecular transitions in", + "present in multiple tissues, such as endothelial cells andepithelial cells, also tended to belong to the same category acrosstissues ( Supplemental Fig. S23). These findings indicate that inherent characteristics of cell types play an important role in shaping cell aging patterns, even when situated in different tissue environments. Discussion Here we show that tissue-specific aging programs can be learnedfrom scRNA-seq data and applied to describe aging heterogeneity", + "creased in old lung stromal cells. Using matrix factorization andoptimal transport methods, we computed trajectories of agingfor each cell identity and assessed the influence of identity and en-vironment on these trajectories. Results Single-cell RNA-sequencing identifies a diversity of cell types and states in young and old mouse tissue We collected transcriptional profiles of young and old cells of many identities by isolating single cells from the kidney, lung,", + "during the last decades. However, different types of cells in the cardiovascular system may be highly heterogeneous dur - ing aging and disease progression. Single-cell genomics, such as massively parallel single-cell RNA-seq, facilitate detailed transcriptome analysis to identify variants of key epigen-etic enzymes/pathways in specific diseased cohorts or cell types. 54,57,58,146 Altogether, new sequencing technologies have" + ], + "answer": "Recent single-cell transcriptomics studies have significantly enhanced our understanding of cellular heterogeneity in aging tissues by providing detailed insights into the molecular and cellular changes that occur with age. These studies allow for the examination of small populations of cells, enabling the detection of low-represented mutations and transcriptional events that may alter cellular identity, supporting hypotheses about the cellular and genetic origins of age-related dysfunctions [1]. Single-cell transcriptomics has also been used to gain a more nuanced view of cell type-specific molecular changes during aging, which is not possible with bulk tissue analysis that neglects variations in cell compositions and cell-to-cell aging heterogeneity [2]. This approach has revealed that different cell identities express unique genes with aging, indicating cell-identity-specific aging phenotypes [5]. Furthermore, similar cell types across different tissues have shown broadly similar aging trajectories, suggesting inherent characteristics of cell types play a significant role in shaping aging patterns [5], [8]. Additionally, single-cell RNA-seq has been applied to study transcriptional heterogeneity within specific cell types, such as hematopoietic stem cells (HSCs), and how this heterogeneity changes with age [4]. This level of detail helps to understand the diversity of cell types and states in young and old tissues, providing insights into the intrinsic complexity of aging at the cellular level [9]. Overall, these studies are revolutionizing our understanding of cellular heterogeneity in aging tissues, opening new avenues for early disease detection and personalized treatments [6].", + "question": "How do recent single-cell transcriptomics studies enhance our understanding of cellular heterogeneity in aging tissues?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_10 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_10 new file mode 100644 index 0000000..f6fa0d0 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_10 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2012 - Chromatin Remodeling, DNA Damage Repair and Aging.pdf", + "2018 - Nuclear Genomic Instability.pdf", + "2018 - Nuclear Genomic Instability.pdf", + "2016 - Genome Integrity in Aging.pdf", + "2017 - Regular exercise participation improves genomic stability in diabetic patients an exploratory study to analyse telomere length and DNA damage.pdf", + "2018 - Genomic Instabilities, Cellular Senescence, and Aging In Vitro, In Vivo and Aging-Like Human Syndromes.pdf", + "2018 - Nuclear Genomic Instability.pdf", + "2018 - Nuclear Genomic Instability.pdf", + "2018 - Nuclear Genomic Instability.pdf", + "2019 - Towards Understanding Genomic Instability, Mitochondrial.pdf" + ], + "extraction_id": [ + "d9a12bd9-c65e-547a-89aa-4e0231558ddc", + "e3e52327-4a23-5003-b418-dafdcdcae82c", + "3da50120-f7fd-5256-ae05-4ffd57876a5c", + "fdd9c5d5-2cca-5fe1-baed-c672f464dab0", + "a5caef7f-f1c1-55af-8807-3c9db425df7b", + "4cd1741b-c96f-592f-af69-95f3a10a157b", + "2b1396d1-ea5d-5708-a6b1-2adf1712c7b4", + "2b1396d1-ea5d-5708-a6b1-2adf1712c7b4", + "0a7a0a01-a262-51bf-bfaf-4f301a0a467b", + "93dbd5fc-d568-5b19-a9cd-fa192ed94ca7" + ], + "document_id": [ + "594e5dbe-b92a-5b0c-9f65-2a10670f9517", + "54d28a91-8db6-56b1-baaa-b67274c93a36", + "54d28a91-8db6-56b1-baaa-b67274c93a36", + "85d5fcbb-5385-5a01-8139-d11fc8b1fe3a", + "dcaf7b09-2d54-5cbf-b061-e3c4e6c6c518", + "7de8d462-8a3c-5625-8cbb-374f3bb46425", + "54d28a91-8db6-56b1-baaa-b67274c93a36", + "54d28a91-8db6-56b1-baaa-b67274c93a36", + "54d28a91-8db6-56b1-baaa-b67274c93a36", + "9b34514d-3d0e-52b5-8e5e-2f3c0708fd82" + ], + "id": [ + "chatcmpl-AIHXSI5xx8VWq2TPqps22AUgX04Pq", + "42a07dfa-c5ac-535f-9a65-8c53b8f10aec", + "86bd9226-94dc-5186-984e-3dd140de9af3", + "79535f3c-51b2-5696-9081-3fdf146e8e61", + "6b4d2b61-4c6b-5b9e-a175-7a3c53a923a5", + "609e97e2-babd-5a49-9451-1a6162eb01e4", + "9fac0777-2bcb-528c-9c16-cbcd85e28522", + "b9de772a-53c5-5128-a595-9baf9420e534", + "1d1662ae-28d6-514d-a600-8860b061a504", + "43c4d87f-c0ce-5148-b601-77e6fd8956b2", + "0acc43f6-5d5b-53f5-af2f-53077b26591a" + ], + "contexts": [ + "Chromatin Remodeling, DNA Damage Repair and Aging Current Genomics, 2012 , Vol. 13, No. 7 539 Ercc1 also show premature aging phenotypes, providing evi- dence of a direct correlation between impaired DDR and premature aging [137, 138]. The relationship between DNA damage accumulation and aging has gained maximum credibility through studies", + "genome is being transcribed or replicated, the threshold of damage needed to activate DDRs, and the choice of cell fate in response to genotoxic stress. It is important to point out that cross-sectional studies, which are largely all we have to date, yield information about the burden of DNA damage and cannot inform as to whether lesions accumulate over time. Longitudinal studies on tissues that can be serially accessed are desperately needed. DNA Repair Capacity Decreases with Aging", + "INTRODUCTION Damage to DNA occurs with surprising frequency. DNA lesions can cause mutations, blocktranscription and replication, and trigger the DNA damage response (DDR). The DDR arrests cell cycle progression and activates signaling pathways that impact cell fate: repair, apoptosis, or cellular senescence. DNA damage is widely recognized as a cause of cancer, and strong evidencenow links DNA damage to aging and diseases associated with aging.", + "DNA damage and persistent DDR signalling as a shared causative mechanism of cellular senescence andageing. Curr. Opin. Genet. Dev. 26:8995 103. Rodier F, Coppe JP, Patil CK, Hoeijmakers WA, Munoz DP, et al. 2009. Persistent DNA damage signalling triggers senescence-associated inammatory cytokine secretion. Nat. Cell Biol. 11:97379 104. Garinis GA, Uittenboogaard LM, Stachelscheid H, Fousteri M, van Ijcken W, et al. 2009. Persistent", + "persistent DNA damage response (DDR) at telomeres and that even long telomeres may be a target for the accu-mulation of irreparable DNA damage. Therefore, DDR activation either at critically short telomeres or caused by persistent telomeric DNA damage represents the trigger of replicative cellular senescence or apoptosis 48, 50. The analysis of apoptosis by TUNEL assay showed that leukocytes from untrained T2D subjects were more sensitive to H", + "E) (2931) and have alleviated the dependency on invitro and invivo models by using direct human samples. AGe-ReLATeD DNA DAMAGe AND DNA DAMAGe ReSPONSe (DDR) ACTiviTY Age-related accumulation of DNA damage has been studied thoroughly, showing correlation between age and damage levels or mutation frequency (32, 33). In the presence of DNA lesions or abnormalities, the DDR, a complex multigenic pathway, is", + "Spontaneous damage is stochastic. But the response to DNA damage is highly conserved, geneti-cally controlled, and with evolution exceedingly more complex. DNA damage triggers activation of signaling pathways termed the DDR, which facilitates repair and arrests cell cycle progression until repair is complete. If DNA damage is extensive or irreparable, DDR effectors trigger celldeath (apoptosis) or cell senescence. These are potent tumor suppressor mechanisms. However,", + "to senescence. Genetic attenuation of the DDR enables reversal of cellular senescence (81). Incontrast, introduction of DSBs in mouse liver, using a tetracycline-inducible SacI restriction endonuclease system, increases the burden of senescent cells in vivo and triggers hallmarks of liver aging (82), illustrating a clear path for how DNA damage can play a causal role in aging. Markers of senescence are detected at higher levels in tissues of older mice, humans, and other", + "mechanisms. In general, it appears that DDR signaling enhances DNA repair and autophagy tocontrol the level of damage in the cell. Interestingly, evidence, albeit early evidence, has been found that DNA damage is linked to proteostasis. Expression of proteins containing polyglutamine tracts that drive protein aggrega- tion linked to neurodegeneration activates the DDR and H2AX foci (148). Interestingly, DNA breaks in cells and H2AX foci in brain of a murine model of Huntington disease are detected", + "its relevance to age -related functional decline at the molecular and cellular level. The importance of oxidative stress and key DNA damage response (DDR) pathways in cellular aging is discussed, with a special focus on poly (ADP -ribose) polymerase 1, whose persistent activation depletes cellular energy reserves, leading to mitochondrial dysfunction, loss of energy homeostasis , and altered cellular metabolism. Elucidation of the relationship between genomic instability ," + ], + "answer": "The DNA damage response (DDR) plays a crucial role in both replicative and chronological aging by maintaining genomic stability and influencing cell fate in response to DNA damage. Here are the key points regarding its significance: 1. **Premature Aging and DDR Impairment**: Impaired DDR is directly correlated with premature aging phenotypes, as evidenced by studies on certain genetic models like Ercc1 [1]. This suggests that a functional DDR is essential for normal aging processes. 2. **Cellular Senescence and DDR**: Persistent DDR signaling is a shared mechanism that triggers cellular senescence, which is a hallmark of aging [4]. This indicates that DDR not only repairs damage but also influences aging by promoting senescence when damage is irreparable. 3. **Replicative Senescence**: DDR activation at telomeres, especially when they are critically short or damaged, triggers replicative cellular senescence or apoptosis [5]. This highlights the role of DDR in controlling the replicative lifespan of cells. 4. **Age-related DNA Damage Accumulation**: As organisms age, DNA damage accumulates, and the DDR pathway becomes increasingly important in managing this damage to prevent mutations and maintain cellular function [6]. 5. **Tumor Suppression and Aging**: While DDR mechanisms like apoptosis and senescence are potent tumor suppressors, they also contribute to aging by removing or halting the proliferation of damaged cells [7]. Overall, the DDR is significant in aging as it balances repair and cell fate decisions, influencing both the replicative capacity of cells and the overall aging process by managing DNA damage and maintaining genomic integrity.", + "question": "What is the significance of the DNA damage response (DDR) in the context of both replicative and chronological aging?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_11 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_11 new file mode 100644 index 0000000..7281b2f --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_11 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2012 - Structural, functional and molecular analysis.pdf", + "2007 - Immunosenescence comes of age.pdf", + "2020 - Age-related gene expression and DNA methylation changes in rhesus.pdf", + "2022 - Functional genomics of inflamm-aging.pdf", + "2022 - Functional genomics of inflamm-aging.pdf", + "2022 - Immunity and lifespan answering.pdf", + "2007 - Immunosenescence comes of age.pdf", + "2012 - Pleiotropic Cellular Functions of PARP1 in Longevity.pdf", + "2007 - Immunosenescence comes of age.pdf", + "2007 - The skin as a mirror of the aging process in the human organism.pdf" + ], + "extraction_id": [ + "d9ef944b-b9a5-5b45-aaa6-c48f6fe54893", + "1ec3aae0-b171-511c-8250-fc0731aa3ec8", + "245e6d14-fa43-5af6-92d3-c5d7bf0235c2", + "1635dbe1-1dcb-5213-9446-74129d50c5f8", + "72b29fff-be72-5ede-85c9-7dc81894c956", + "b7467732-698f-5ca4-be08-08b011b0d343", + "1ec3aae0-b171-511c-8250-fc0731aa3ec8", + "f12b7e5c-29bc-5f56-9303-ab9286f22d88", + "170e6d89-2624-5b49-a6d1-95d4f35f73f3", + "daf4bb0f-4be5-5c47-baa5-686cd61adc1a" + ], + "document_id": [ + "0e803003-d6e5-570e-a810-1aea89d7ea63", + "22313267-b0be-572f-8170-dcb814fe6140", + "0f1fe2f6-b9c8-514d-ac1c-4e7c07a19ff0", + "435dc081-e3d1-52c5-93a1-caa11206422f", + "435dc081-e3d1-52c5-93a1-caa11206422f", + "a834e7ee-7bab-5c4d-a236-b570d1ae635f", + "22313267-b0be-572f-8170-dcb814fe6140", + "e67324c0-474b-5280-8cbc-3778c6c0e5f0", + "22313267-b0be-572f-8170-dcb814fe6140", + "c429b80b-ad40-5fd3-b189-3982e5a8ab23" + ], + "id": [ + "chatcmpl-AIHXbfIiqBOfJAG67WB3RBf5qTOVk", + "65fe4bdc-890e-53bf-ad11-2d9c67adac7f", + "0c2a9ad8-054d-5a03-af43-704d2b7722d0", + "a8f4f7d2-85f9-5097-b588-614c7973c3b5", + "6822e1b6-b9bc-5e26-b6d5-d0d141854dd4", + "c0eedfc9-fd74-51f8-ace9-dfd79ad16b71", + "c4f7a0e2-0d13-5928-aaf2-8fc70dc9face", + "1683b89a-86bd-5439-9a6f-df120b67d0e8", + "fb4173c8-cf14-59d2-804c-3c2824a3fdc5", + "f16127b0-68dc-50bc-b39e-8ead81d723ee", + "ba9fdb3c-b9c2-57a2-9bb7-df5472d20e73" + ], + "contexts": [ + "immune system are one of the hallmarks of the aging body. Immunosenescence is the functional decline of the adaptive immune system brought on by natural agingwhereby protection against infection by pathogens and the effectiveness of vaccination decline [45,46]. The sec- ond aging-induced change in the immune system iscalled inflammaging which is characterized by a low- grade chronic inflammation process that contributes to", + "the increased susceptibility of the elderly to infectious disease and tothe poor outcome of vaccination. Defence against pathogens is com-promised mainly because of changes in adaptive immunity mediatedby T and B lymphocytes; however, all components of the immunesystem are affected (Fig 1). Dissecting the crucial alterations responsi-ble for dysfunctional immunity in old age will facilitate the develop-ment of rational interventions to reconstitute appropriate immunefunction. Given the increasing", + "[39] C. Castelo-Branco, I. Soveral, The immune system and aging: a review, Gynecol. Endocrinol. 30 (2014) 1622. [40] S.A. Johnson, S.J. Rozzo, J.C. Cambier, Aging-dependent exclusion of antigen-in - experienced cells from the peripheral B cell repertoire, J. Immunol. 168 (2002) 50145023 . [41] D.P. Shanley, D. Aw, N.R. Manley, D.B. Palmer, An evolutionary perspective on the mechanisms of immunosenescence, Trends Immunol. 30 (2009) 374381.", + "immunosenescence: the decline in immune efficacy of both the innate and the adaptive immune systems. Age-relatedimmune decline also links to the concept of inflamm-aging, whereby aging is accompanied by sterile chronic inflammation. Along with a decline in immune function, aging is accompanied by a widespread of omics remodeling.", + "ence the development of inflamm-aging and immunosenes- cence phenotypes. Finally, although discussed studies have reported age-related changes in innate immune cell processes, there is still little known about how these changes are influenced by biologicalsex. Indeed, both the adult mammalian immune system [ 80,125] and the aging process [ 126] are sex-dimorphic, suggesting that", + "tion has also been implicated in ageing across a range of non-model organisms, including mice,nematode worms ( Caenorhabditis elegans ), and primates [ 4042]. The damage caused by the ageing adaptive and innate immune systems gives us insights into how these different arms of the immune system may in uence longevity. In general, adaptive im- mune function diminishes with age, whereas innate immune function is maintained [ 34,4346].", + "development to senescence, innate immunity to adaptive immunity,and genes to environments, in organisms ranging from mice to monkeys and humans. Understanding and eventually modulatingimmune dysfunction in the elderly now beckons. Lymphocyte development and ageing", + "an age-related decline in the capacity of adaptive immunity,consisting of more specic responses carried out by B andT cells [ 7]. Thus, with advanced age, the immune system undergoes a gradual remodeling in the attempt to reestablisha new balance that assures survival, however, favoring thedevelopment of chronic inammatory conditions [ 5,6,8,9]. DNA damage and inammation are inevitably linked by", + "All components of the immune system are altered as ageing pro-ceeds (Fig 1); however, the T-cell and B-cell compartments seem tobe particularly susceptible. The most severe clinical impact is proba-bly a result of the loss of diversity in the TCR and B-cell-receptorrepertoire, owing to the accumulation of dysfunctional cells, anddecreased thymic and bone-marrow output. Several interventionsdiscussed at the meeting could conceivably contribute to therestoration of appropriate immune function in the near", + "more susceptible to DNA damage. One of the major rea-sons are the impaired DNA repair mechanisms which havebeen described in several studies and have been associated with the initiation of age-associated diseases and progeroidsyndromes ( Hasty et al., 2003; Lieber and Karanjawala, 2004). Furthermore, dysregulated immune and inamma- tory responses have been already documented both inhumans and mouse with increasing age ( Badawi et al., 2004; Kovaiou et al., 2007 )." + ], + "answer": "Age-dependent changes in the immune system, such as immunosenescence, contribute to increased susceptibility to diseases through several mechanisms: 1. **Functional Decline of the Adaptive Immune System**: Immunosenescence is characterized by a decline in the adaptive immune system's function, which leads to reduced protection against infections and decreased effectiveness of vaccinations [1]. This decline is primarily due to changes in T and B lymphocytes, which are crucial for adaptive immunity [2]. 2. **Loss of Diversity in Immune Receptors**: There is a loss of diversity in the T-cell receptor (TCR) and B-cell receptor repertoire as people age. This is due to the accumulation of dysfunctional cells and decreased output from the thymus and bone marrow, which are essential for generating new immune cells [9]. This loss of diversity impairs the immune system's ability to recognize and respond to new pathogens effectively. 3. **Chronic Inflammation (Inflammaging)**: Aging is also associated with a state of low-grade chronic inflammation, known as inflammaging. This chronic inflammation can further compromise immune function and contribute to the development of age-related diseases [1], [4]. 4. **Overall Immune System Alterations**: All components of the immune system are affected by aging, not just the adaptive immune system. This widespread alteration can lead to a compromised defense against pathogens, making the elderly more susceptible to infectious diseases and less responsive to vaccinations [2], [9]. These changes collectively lead to an increased susceptibility to diseases in the elderly, highlighting the importance of understanding and potentially intervening in these age-related immune alterations to improve health outcomes in older populations.", + "question": "How do age-dependent changes in the immune system, such as immunosenescence, contribute to increased susceptibility to diseases?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_12 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_12 new file mode 100644 index 0000000..29f4353 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_12 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2023 - A transcriptome-based single-cell biological age model.pdf", + "2023 - A transcriptome-based single-cell biological age model.pdf", + "2007 - Biological Aging Is No Longer.pdf", + "2018 - Human Ageing Genomic Resources new and updated.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2018 - Predicting age from the transcriptome.pdf", + "2019 - Improved precision of epigenetic clock.pdf", + "2011 - How pleiotropic genetics of the musculoskeletal system.pdf" + ], + "extraction_id": [ + "660d608e-8333-590f-8183-31b51779cec3", + "1af20df8-561f-59cb-9996-106a3be3f82f", + "f9312bd9-9f67-5e36-9986-f01d66d4b7ac", + "f9312bd9-9f67-5e36-9986-f01d66d4b7ac", + "5362f054-bb14-53fd-8d6d-9fb7aa41b3f3", + "62ff5c38-25a5-5729-a160-ce89e2ceb1c8", + "5a07784a-755c-598d-9d2d-3eb2ab8285cc", + "be79444e-743f-5289-9607-db6bc3b35493", + "6e048749-b423-54c0-9505-439db5595254", + "1b0806b9-729c-581f-9e3f-a98a5e0ce7eb" + ], + "document_id": [ + "62b635c3-040e-512a-b016-6ef295308a1e", + "62b635c3-040e-512a-b016-6ef295308a1e", + "9be234b7-f37d-5cd5-8895-bfe676441b2f", + "9be234b7-f37d-5cd5-8895-bfe676441b2f", + "efef1c11-52f9-5b95-878a-07980080f0f8", + "82726cea-f77c-5a92-9f2e-ecccc369953a", + "62b635c3-040e-512a-b016-6ef295308a1e", + "73128c69-30e0-5b7a-9504-1502e3f062c7", + "556d0179-023f-581f-9c2d-febe4e75722f", + "ed31486c-a651-5894-bd96-21fbd78f2646" + ], + "id": [ + "chatcmpl-AIHXkz3iFRslvxy1Jaw30l5EF9v8O", + "8139ed83-471f-5aa8-a6e1-2294b106ffd7", + "eeed3c27-9717-5592-8d69-937eca35bfff", + "b545cd47-00c7-5bd8-bd25-8d2bf59be62e", + "4b418218-07f6-5103-a9f4-4a28be7247c8", + "11d9e838-e4a1-50d4-92e8-658d4ff57b68", + "71a04373-81b9-5219-bbde-6f9cd1935491", + "ed814cb1-4fd3-5586-bd75-131d2a3ae96b", + "bb3a61fd-7137-5735-b65c-8aabab7eb971", + "c2ea0dae-b466-5c5b-babb-bfa74243bd34", + "96135704-e84c-53fc-9b57-b1e7b8dcd81f" + ], + "contexts": [ + "tifications of biological aging: do they measure the same thing? Am J Epidemiol. 2018;187(6):122030. 74. Putin E, etal. Deep biomarkers of human aging: application of deep neural networks to bio- marker development. Aging (Albany NY). 2016;8(5):102133. 75. Rehkopf DH, etal. Leukocyte telomere length in relation to 17 biomarkers of cardiovascular disease risk: a cross-sectional study of US adults. PLoS Med. 2016;13(11):e1002188.", + "studied (Table 13.1). Thus, due to the generation of these data and technological advances, possibly in the future, artificial intelligence programs will be able to reliably forecast the life of an individual, as well as the possible diseases that he may suffer in ageing; so these advances and discoveries will allow us to achieve a personalized medical treatment as a result of to the integration of biomarkers of ageing. Ageing Is aTreatable Condition", + "the data. However, construction of such models is often highlydegenerate, yielding little overlap of identified biomarkers be-tween studies and thus making results difficult to interpret(Thompson et al. 2018; Galkin et al. 2020). Among the many computational algorithms, linear regres- sion and its variants have been widely used to select aging-relatedbiomarkers and build aging clocks, namely, predictors of chro- nological age and biological age, in various omics data sets and ag-", + "states, which can be monitored using various biomarkers (Belskyet al. 2015). These markers are usually measurable indicators of aparticular outcome or source of aging, such as phenotypical mea-sures like frailty and molecular measures like DNA methylation dy- namics (Schumacher et al. 2021; Lpez-Otn et al. 2023). Although informative, they are not always quantitatively predictive of anindividual s true biological age, nor are they easy to obtain. The ad-", + "biomarkers of the aging process.", + "supervisedmachinelearningappliedtoageingresearch. Biogerontology ,18,171188. 47. Kriete,A.,Lechner,M.,Clearfield,D.andBohmann,D.(2011) Computationalsystemsbiologyofaging. WileyInterdiscip.Rev.Syst. Biol.Med. ,3,414428.Downloaded from https://academic.oup.com/nar/article/46/D1/D1083/4599180 by guest on 14 October 2023", + "associated with age, such as mouth width, nose width, and eye corner droop. This type of bioimage analysis has rendered relatively accurate calculations of the actual age, although this accuracy tended to fall with increasing age after 40years [71]. Integration ofBiomarkers ofAgeing Biomarkers of ageing allow estimating the biological age of an organism (Table 13.1) while providing information on their health status. Different studies are looking for", + "Background There is a marked heterogeneity in human lifespan and health outcomes for people of the same chronological age. Thus, one fundamental challenge is to identify mo- lecular and cellular biomarkers of aging that could pre- dict lifespan and be useful in evaluating lifestyle changes and therapeutic strategies in the pursuit of healthy aging. Here, we developed a computational method to predict biological age from gene expression data in skin fibro-", + "Background Ageing is a major risk for diseases and mortality [ 1,2]. Chronological age has been widely used as a marker of ageing due to ease and accuracy of measurement [ 1]. However, it is not necessarily a good predictor of biological ageing since individuals with the same chronological age can vary in health, especially in later life [ 3]. Therefore, researchers have attempted to search for biomarkers of ageing that can predict functional cap- ability at a later age [ 4,5]. In 2013, Hannum et al. and", + "discriminate between adverse aging-related events, such as frailty (Mitnitski et al. 2002 ), immobility (Simonsick et al. 2001 ), and propensity to fall (Lord et al.1994 ). There are additional considerations when choosing biomarkers to characterize aging. First, biomarkers measured at a given age are merely snapshots of important regulatory systems (Seeman et al. 2004 ); there is no information on system dynamics if each biomarker is measured only once. Having longitudinal" + ], + "answer": "Advancements in machine learning and artificial intelligence significantly aid in the identification of biomarkers for biological aging by enabling the development of predictive models and personalized medical treatments. These technologies allow for the integration and analysis of complex biological data, which can be used to forecast an individual's lifespan and potential age-related diseases, thereby facilitating personalized medical interventions [2]. Machine learning algorithms, such as linear regression and its variants, are employed to select aging-related biomarkers and construct aging clocks, which are predictors of chronological and biological age based on various omics datasets [3]. Additionally, computational methods have been developed to predict biological age from gene expression data, which can help in evaluating lifestyle changes and therapeutic strategies aimed at promoting healthy aging [8].", + "question": "How do advancements in machine learning and artificial intelligence aid in the identification of biomarkers for biological aging?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_13 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_13 new file mode 100644 index 0000000..6f9a062 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_13 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2018 - Mechanisms of Vascular Aging.pdf", + "2012 - Genomics and Genetics of Aging.pdf", + "2018 - Mechanisms of Vascular Aging.pdf", + "2012 - Aging, Rejuvenation, and Epigenetic.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2018 - Mechanisms of Vascular Aging.pdf", + "2022 - Functional genomics of inflamm-aging.pdf", + "2018 - Mechanisms of Vascular Aging.pdf", + "2020 - Age-related gene expression and DNA methylation changes in rhesus.pdf", + "2018 - Genomic Instabilities, Cellular Senescence, and Aging In Vitro, In Vivo and Aging-Like Human Syndromes.pdf" + ], + "extraction_id": [ + "bfeb5c38-4fa6-5df5-90ce-63204deba3a8", + "726bbaa2-97e8-5f62-a731-a1ba3cf1778f", + "4b0673e0-fb5e-5212-ba68-417de0e867b7", + "7f8f4ca0-9b27-55e3-a889-030af08dc84b", + "575a9f30-8504-5526-90e0-e558bfc29c02", + "fe270a46-7f2f-5a25-b98f-a782511801fb", + "14dbffca-9dc8-5d8c-bb23-98bc80b77e86", + "2836777b-037b-52e4-a160-9cb02dd98b92", + "245e6d14-fa43-5af6-92d3-c5d7bf0235c2", + "d3686eba-0aa4-5c56-b60d-bf76c3ab433b" + ], + "document_id": [ + "659b84b6-63dd-5bb1-80ee-7478ed3c47e3", + "3c2efc4d-b5a8-5843-be7e-44c3b52f3d9b", + "659b84b6-63dd-5bb1-80ee-7478ed3c47e3", + "bde26feb-f423-51b0-89ec-6f079bfc8b17", + "62b635c3-040e-512a-b016-6ef295308a1e", + "659b84b6-63dd-5bb1-80ee-7478ed3c47e3", + "435dc081-e3d1-52c5-93a1-caa11206422f", + "659b84b6-63dd-5bb1-80ee-7478ed3c47e3", + "0f1fe2f6-b9c8-514d-ac1c-4e7c07a19ff0", + "7de8d462-8a3c-5625-8cbb-374f3bb46425" + ], + "id": [ + "chatcmpl-AIHXpT1Oa9sduYt2d6yF1iu8bJvoN", + "c4c7b861-6d13-5814-818d-a79ddabd742c", + "9d96fdeb-3b94-57d2-8025-db47be7c52ad", + "e9ddeedc-70ba-516f-ad9b-77e2b45cd01f", + "415a6dd6-0e64-5aef-8561-289d728ad721", + "729ae0a3-95f3-50c7-8c00-d1ce0673ea08", + "571e50a8-c009-59a5-b01c-0f01c4b5e163", + "ab32705e-4e02-59ab-986d-4552a4a522b9", + "55a6fe97-29cd-5969-8ea8-3b350b8e0554", + "3914af93-b251-54ae-b7bf-9c8243a24f74", + "d2ce22fd-6c12-56cf-948d-fc6604cf0f23" + ], + "contexts": [ + "in the vascular system are considered in terms of their contribution to the pathogenesis of both microvascular and macrovascular diseases associated with old age. The importance of progeronic and antigeronic circulating factors in relation to development of vascular aging phenotypes are discussed. Finally, future directions and opportunities to develop novel interventions to prevent/delay age-related vascular pathologies by targeting fundamental cellular and molecular aging processes are presented. (Circ", + "pression of numerous mRNAs, some of which directly influence aging and age-related diseases. Jung and Suh describe what we know about the importance of microRNAs in aging and how this exciting new field is just starting to become explored. The last review in this special issue by Hou et al. brings things together nicely with a systems biology perspective of aging. In order to model the immense complexity of aging, we require systems-level approaches. This review describes how several", + "autoregulation of blood flow,218 vascular structural remodel- ing, atherogenesis,219 and angiogenic processes.220 The impact of circulating factors on aging phenotypes was also demonstrated by studies using mice with heter - ochronic parabiosis, which involves surgically connecting the circulatory system of a young and an aged mouse. 221 Cerebromicrovascular density typically declines with ad-vanced age, 222 and there is initial evidence that circulating an-", + "components, particularly chemokines and cytokines, in theblood and tissues ( Villeda et al., 2011 ). In addition to illuminating the inuence of the systemic environment on cellular function,such heterochronic studies emphasize the potential role of envi-ronmental factors in rejuvenating aged cells. Molecular signatures of aging have been directly tested as", + "related diseases. Ageing Res Rev. 2018;47:21477. 115. Kumar S, Vijayan M, Bhatti JS, Reddy PH.MicroRNAs as peripheral biomarkers in aging and age-related diseases. Prog Mol Biol Transl Sci. 2017;146:4794. 116. Smith-Vikos T, Liu Z, Parsons C, Gorospe M, Ferrucci L, Gill TM, etal. A serum miRNA profile of human longevity: findings from the Baltimore Longitudinal Study of Aging (BLSA). Aging (Albany NY). 2016;8(11):297187.", + "in the endothelium and the VSMCs and specific disease pro-cesses. There is evidence that the senescence-associated se-cretory phenotype can also induce paracrine senescence and alter the function of neighboring cells, and the role of this mechanism in vascular aging should be further evaluated. The possibility of paracrine transmission of senescence from microvascular endothelial cells to parenchymal cells also requires further investigations. It should be noted that many", + "protein VSIG4 as a biomarker of aging in murine adiposetissue. Aging Cell 2020; 19:e13219. 128. Angelidis I, Simon LM, Fernandez IE, et al. An atlas of the aging lung mapped by single cell transcriptomics and deeptissue proteomics. Nat Commun 2019; 10:963. 129. Clark D, Brazina S, Yang F, et al. Age-related changes to macrophages are detrimental to fracture healing in mice. Aging Cell 2020; 19:e13112. 130. Tabula Muris Consortium. A single-cell transcriptomic", + "Ungvari et al Mechanisms of Vascular Aging 861 mechanisms of vascular aging and identify translationally relevant treatments for the promotion of vascular health in older adults. The same cellular and molecular aging processes that af- fect arterial vessels and capillaries also affect veins and the lymphatic/glymphatic system, likely contributing to various disease pathologies. Examples include the potential role of cerebral venules in neuroinflammation, Alzheimer disease, and cerebral microhemorrhages", + "et al., Plasma proteomic signature of age in healthy humans, Aging Cell 17 (2018). [17] D. Mari, P.M. Mannucci, R. Coppola, B. Bottasso, K.A. Bauer, R.D. Rosenberg, Hypercoagulability in centenarians - the paradox of successful aging, Blood 85 (1995) 31443149. [18] S.A. Phillips, The vasculature in cardiovascular diseases: will the vasculature tell us what the future holds? Prog. Cardiovasc. Dis. 57 (2015) 407408. [19] R.A. Gibbs, J. Rogers, M.G. Katze, R. Bumgarner, G.M. Weinstock, E.R. Mardis,", + "16Lidzbarsky et al. Genomic Instabilities, Cellular Senescence, and Aging Frontiers in Medicine | www.frontiersin.org April 2018 | Volume 5 | Article 104 177. Smith-Vikos T, Slack FJ. MicroRNAs and their roles in aging. J Cell Sci (2012) 125:717. doi:10.1242/jcs.099200 178. Lanceta J, Prough RA, Liang R, Wang E. MicroRNA group disorganiza- tion in aging. Exp Gerontol (2010) 45:26978. doi:10.1016/j.exger.2009. 12.009" + ], + "answer": "Recent discoveries regarding the impact of systemic factors, such as circulating microvesicles, on aging phenotypes include the following: 1. The importance of progeronic (aging-promoting) and antigeronic (aging-delaying) circulating factors in the development of vascular aging phenotypes has been discussed. This highlights the role of systemic factors in contributing to age-related vascular pathologies and suggests potential interventions to prevent or delay these conditions by targeting fundamental cellular and molecular aging processes [1]. 2. Studies using heterochronic parabiosis, which involves connecting the circulatory systems of young and aged mice, have demonstrated the impact of circulating factors on aging phenotypes. This research provides initial evidence that circulating factors can influence cerebromicrovascular density, which typically declines with advanced age [3]. These findings underscore the significant role that systemic factors, including circulating microvesicles, play in influencing aging phenotypes, particularly in the context of vascular aging and potential rejuvenation strategies.", + "question": "What recent discoveries have been made regarding the impact of systemic factors, such as circulating microvesicles, on aging phenotypes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_14 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_14 new file mode 100644 index 0000000..be0909b --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_14 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2012 - Structural, functional and molecular analysis.pdf", + "2017 - Metformin alters the gut microbiome of individuals with treatment-naive type 2 diabetes, contributing to the therapeutic effects of the drug.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2012 - Structural, functional and molecular analysis.pdf", + "2012 - Structural, functional and molecular analysis.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2020 - Blood-based epigenetic estimators.pdf", + "2021 - Gene-by-environment modulation of lifespan and weight gain in the murine BXD family.pdf" + ], + "extraction_id": [ + "07a2b9a1-d683-568d-b2e6-c2cc1fcffba5", + "faae2e40-6de8-5285-8410-ac1ef5dac6ad", + "b2654364-b3e8-5e26-9664-d19ca8f5605e", + "c50b343b-3eef-548c-88cd-d5bda6605619", + "66edc533-58a4-5ad1-96c4-7e0c05462de5", + "d9ef944b-b9a5-5b45-aaa6-c48f6fe54893", + "307ac6d0-46d2-50e8-a618-d640136d4131", + "a0bb2ab8-44b4-5409-814c-22005b259479", + "062e4ac3-ef28-5bfa-be8c-770757083cfb", + "bca61863-81b3-5ef7-850d-10cc9577a9e1" + ], + "document_id": [ + "62b635c3-040e-512a-b016-6ef295308a1e", + "0e803003-d6e5-570e-a810-1aea89d7ea63", + "448d68d1-19a8-5f4c-a48b-8d33597bd03b", + "62b635c3-040e-512a-b016-6ef295308a1e", + "0e803003-d6e5-570e-a810-1aea89d7ea63", + "0e803003-d6e5-570e-a810-1aea89d7ea63", + "62b635c3-040e-512a-b016-6ef295308a1e", + "62b635c3-040e-512a-b016-6ef295308a1e", + "2673299f-21e5-5746-9c33-84b99b373355", + "4d082da4-fa48-5170-8147-c4fea47a5d4b" + ], + "id": [ + "chatcmpl-AIHXx0hXjoPni1lj2qiHnS6BLuSSU", + "1bcfcf33-d9b4-55b7-a384-bc8e08893a22", + "f4ec4435-00f7-5477-984a-68d1eff9e7a0", + "393bd8fc-14c6-5fc3-be3b-3ddf1c218531", + "0856bafc-06ce-5716-af52-f65dc3abfafe", + "3742fdda-bdba-5c09-bf7c-732b2554c5fe", + "bb367137-9186-53aa-8765-af837b7b4242", + "a6a78000-8744-5f89-bcbb-d26781ece651", + "39564137-871b-5464-b364-ba63cbf9cc31", + "7a775400-f8f2-5758-af40-b461adc83aa3", + "35f973f6-2ca0-5d89-98b2-8e28a67323c5" + ], + "contexts": [ + "the adaptation of the microbiota to the physiological changes of the long aging process. It has been demonstrated that the microbiota on this population maintains the health and promotes the survival. Additionally, a relationship between a healthy microbiota and longevity had been proposed [44]. A possible pathway is an immu- nological and metabolic regulation linked to the increase of bacterial compounds like Christensenellaceae, Akkermansia, and Bifidobacterium [44, 45].", + "Marchesi JR, Falush D, Dinan T, Fitzgerald G, et al:Composition, variability, and temporal stability of the intestinal microbiota of the elderly. Proc Natl Acad Sci USA 2011, 108(Suppl 1):4586 4591. 21. Maegawa S, Hinkal G, Kim HS, Shen L, Zhang L, Zhang J, Zhang N, Liang S, Donehower LA, Issa JP: Widespread and tissue specific age-related DNA methylation changes in mice. Genome Res 2010, 20(3):332 340. 22. Englander EW: Gene expression changes reveal patterns of aging in the", + "microbiota present in infants, adults, and the elderly. Appl. Environ. Microbiol. 73, 77677770 (2007). 40. Kong, F. et al. Gut microbiota signatures of longevity. Curr. Biol. 26, R832R833 (2016). 41. Tremaroli, V. et al. Roux-en-Y gastric bypass and vertical banded gastroplasty induce long-term changes on the human gut microbiome contributing to fat mass regulation. Cell Metab. 22, 228238 (2015). 42. Everard, A. et al. Microbiome of prebiotic-treated mice reveals novel targets involved", + "Therefore, research in the field has demonstrated that aging is a potential modi- fier of the composition and function of the human microbiome. Figure 9.3 shows the local composition of the microbiome in an average older adult. It can be seen that Bacteroidetes and Firmicutes species are the most prevalent in this age. Recent data has shown that older people hide a microbiota that differs in the type and number of microorganisms from that of younger adults [38]. Young people", + "related malnutrition. Furthermore, it has been shownthat aging can cause bacterial overgrowth in the smallintestine [16,17] and promote changes in microbial com- position in the colon [18-20]. In addition, reported age- related changes in DNA methylation of the mouseintestine [21] might play a role in the altered gene expression levels observed in the duodenum and colon of aging mice [22]. Together these observations demon-strate that although certain aspects of the aging intestine", + "detectable. Changes in the gut microbiota in terms of compos- ition and functionality during the process of aging have previously been reported [19,20,51] and it hasbeen postulated that these changes might contribute to the development of immunosenescence and inflam- maging [18,52]. To establish whether the enhanced expression of genes playing a role in the immune sys- tem are due to modifications in the microbiota wemeasured the total number of all bacteria and of the", + "37. Li H, Qi Y , Jasper H.Preventing age-related decline of gut compartmentalization limits micro- biota Dysbiosis and extends lifespan. Cell Host Microbe. 2016;19(2):24053. 38. Mihajlovski A, Dor J, Levenez F, Alric M, Brugre J.Molecular evaluation of the human gut methanogenic archaeal microbiota reveals an age-associated increase of the diversity. Environ Microbiol Rep. 2010;2(2):27280. 39. Quercia S, Candela M, Giuliani C, Turroni S, Luiselli D, Rampelli S, etal. From lifetime to", + "[26], but at advanced ages, dramatic changes in its composition are associated with various diseases and frailty [27, 28]. Regarding pathological processes, it is known that cancer, obesity, diabetes, and inflammatory bowel disease (IBD) are associated with specific microbial alterations [29, 30]. In older ages, a burden of intrinsic and extrinsic factors affects the compo- sition of the microbiome and plays a determining role in every tract and tissue. Such mentioned factors can be seen in Fig.9.2.", + "Osawa R. Age-related changes in gut microbiota composition from newborn to centenarian: a cross-sectional study. BMC Microbiol. 2016;16:90. 14. Dugue PA, Bassett JK, Joo JE, Jung CH, Ming Wong E, Moreno-Betancur M, Schmidt D, Makalic E, Li S, Severi G, et al. DNA methylation-based biological aging and cancer risk and survival: pooled analysis of seven prospective studies. Int J Cancer. 2018;142(8):1611 9. 15. Levine ME, Hosgood HD, Chen B, Absher D, Assimes T, Horvath S. DNA", + "survival advantage that is age- and site-specific: Results from a large multi-site study. Aging Cell 18, e12905 (2019). [PubMed: 30801953] 51. Houtkooper RHet al.The metabolic footprint of aging in mice. Sci. Rep. 1, 134 (2011). [PubMed: 22355651] 52. Morrison KE, Jaarevi E, Howard CD & Bale TL Its the fiber, not the fat: significant effects of dietary challenge on the gut microbiome. Microbiome 8, 15 (2020). [PubMed: 32046785]" + ], + "answer": "Changes in the gut microbiome composition are closely linked to aging and longevity. As individuals age, the composition and function of the gut microbiome undergo significant modifications. These changes are thought to contribute to various age-related processes, including immunosenescence and inflammaging, which are associated with the aging immune system [6]. Research has shown that a healthy microbiota can promote survival and is linked to longevity. Specifically, certain bacterial families such as Christensenellaceae, Akkermansia, and Bifidobacterium have been associated with immunological and metabolic regulation, which may contribute to increased lifespan [1]. Additionally, the gut microbiota of older adults differs in type and number of microorganisms compared to younger adults, with Bacteroidetes and Firmicutes being the most prevalent species in older individuals [4]. These changes in microbial composition can be influenced by both intrinsic and extrinsic factors, which play a significant role in the health and function of the microbiome as people age [8]. Overall, maintaining a healthy gut microbiome is crucial for promoting longevity and mitigating some of the negative effects associated with aging.", + "question": "How do changes in the gut microbiome composition correlate with aging and longevity?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_15 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_15 new file mode 100644 index 0000000..9028c2d --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_15 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2004 - Linking nutrition to genomics.pdf", + "2008 - The Aging Brain.pdf", + "2007 - The role of nuclear architecture.pdf", + "2012 - Genome-Environment Interactions That Modulate.pdf", + "2001 - The genetics of aging.pdf", + "2009 - Genes and gene expression modules associated with caloric.pdf", + "2010 - Genetic Dissection of Dietary Restriction in Mice Supports the Metabolic Efficiency Model of Life Extension.pdf", + "2004 - A Transcriptional Profile of Aging.pdf", + "2018 - Mechanisms of Vascular Aging.pdf" + ], + "extraction_id": [ + "5d1905a8-536e-5efd-a92b-21053093d956", + "b193c536-dc9e-5ea6-9a01-064243a6cbf3", + "f63ffca5-2418-5683-9958-558c46b48def", + "9655c555-838e-5cdf-85cf-13736c3cf028", + "5745c701-a549-51c3-adcc-b19c47436740", + "713d3122-d856-5dbc-a3bf-d8cd836830cb", + "0b45ae60-562c-5e48-a1c1-9eb29614a63c", + "da7abebd-f7c0-5b9c-b0f2-e29871326855", + "b382fe8a-0267-5515-ac4b-07be55420040", + "fddca610-97a6-5f2c-88b4-dc6e96c60cf3" + ], + "document_id": [ + "62b635c3-040e-512a-b016-6ef295308a1e", + "99891ef7-0589-5c41-a61f-1ab1fe1c8939", + "874f5d02-35c9-5233-8ded-6e06c7570ca9", + "578e2f7d-ddd4-56c8-a5b0-670969f8ff1e", + "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "aa9a9193-b6f3-5ef8-aefd-e01ec44abb46", + "893ba204-2e69-563f-9046-7246ca61494f", + "92419d8a-27ed-5142-8a87-189c1ba5459b", + "4ab656a7-9656-526b-94e1-422875409b44", + "659b84b6-63dd-5bb1-80ee-7478ed3c47e3" + ], + "id": [ + "chatcmpl-AIHY3hgOmiQgttq4BdrpX79X5LkzF", + "b516b1a9-d0f2-5d1e-9015-4799c902770b", + "6870f741-be38-5d34-aafd-25da39e1ff68", + "c5b37b9a-1ffa-516b-9681-22fecc5aee5b", + "e01c4c58-342d-5369-89e6-98344af55000", + "b990eb0a-709a-500c-836e-83e202e0d6a6", + "ffe5fc40-f6d4-5066-9e07-424f7b8e3dc9", + "2b081115-d36e-57ec-aedc-2fd9691bc5e9", + "03196bec-4ae2-5408-b90c-12dcb38e5831", + "2cf68c41-aa60-5dca-8aa1-04bc0d7a4db3", + "51a448cf-6015-53f7-a949-f247b71efcef" + ], + "contexts": [ + "Metabolism Studies show that calorie restriction is the most consistent means to prolong life expectancy and health across several experimental models [55], ranging from yeasts to primates. It not only increases life expectancy, but it also delays the onset of many features and hallmarks of ageing, including age-related diseases. Transcriptional profiles are currently being applied and investigated. One of them is a caloric restric-", + "Keywords: caloric restriction; hepatic expression profiling; lifespan prolongation; metabolic signaling;microarray analysis; nutrition response. Introduction", + "(154, 155). Caloric restriction has been shown to sig- nicantly increase life span and promote resis-tance to a broad range of age-related pathol-ogy in worms, ies, and mice. Some of theeffects of caloric restriction may be mediatedthrough the sirtuin family of genes, as exem-plied by SIR2, which prolongs life span in", + "Calorie restriction, a dietary regimen that extends the lifespan of numerous organisms, also delays the majority of age-related gene-expression changes in mice and, to a certain extent, in flies45,50. It is currently unclear whether the effect of calorie restriction on gene expression underlies its beneficial effect on lifespan or is merely a consequence thereof. Findings in yeast suggest that there may be a causal link: Sir2 not only facilitates heterochromatin and promotes DNA stability, but is", + "life-span extension by calorie restriction in Saccharomyces cerevisiae. Science 289:21262128. Mair W, Goymer P, Pletcher SD, and Partridge L (2003) Demography of dietary restriction and death in Drosophila. Science 301:17311733. Masoro EJ (2005) Overview of caloric restriction and ageing. Mech Ageing Dev 126:913922. Mathers JC (2006) Nutritional modulation of ageing: genomic and epigenetic ap- proaches. Mech Ageing Dev 127:584589. Meric-Bernstam F and Gonzalez-Angulo AM (2009) Targeting the mTOR signaling", + "that caloric restriction also regulates mammalian aging, perhaps via the modulationof insulin-like signaling pathways. The nervous system has been implicated as a keytissue where insulin-like signaling and free radical protective pathways regulate lifespan inC. elegans andDrosophila . Genes that determine the life span could act in", + "extension by dietary restriction. Annu Rev Biochem 2008, 77:727-54. 8. Harper JM, Leathers CW, Austad SN: Does caloric restriction extend life iin wild mice? Aging Cell 2006, 5:441-9. 9. Forster MJ, Morris P, Sohal RS: Genotype and age influence the effect of caloric intake on mortality in mice. FASEB J 2003, 17:690-2. 10. Spindler SR, Mote PL: Screening candidate longevity therapeu- tics using gene-e xpression arrays. Gerontology 2007, 53:306-21.", + "Corton JC, Apte U, Anderson SP, Limaye P, Yoon L. Mimetics of caloric restriction include agonists of lipid-activated nuclear receptors. J Biol Chem 2004;279:4620446212. [PubMed: 15302862] Ferguson M, Sohal BH, Forster MJ, Sohal RS. Effect of long-term caloric restriction on oxygen consumption and body temperature in two different strains of mice. Mech Ageing Dev 2007;128:539545. [PubMed: 17822741] Forster MJ, Morris P, Sohal RS. Genotype and age influence the effect of caloric intake on mortality in", + "A key question still unresolved is to what extent the mechanisms of aging are conserved between species with vastly different lifespans. Some studies suggest that similar mechanisms are involved in aging in many species. Forexample, caloric restriction extends lifespan in yeast, worms,ies, mice, and primates (Weindruch 2003). Additionally,signaling through the insulin-like growth factor pathway,chromatin regulation by sir2,and oxidative damage have each", + "10.1111/acel.12103 241. Edwards AG, Donato AJ, Lesniewski LA, Gioscia RA, Seals DR, Moore RL. Life-long caloric restriction elicits pronounced protection of the aged myocardium: a role for AMPK. Mech Ageing Dev. 2010;131:739 742. doi: 10.1016/j.mad.2010.09.007 242. Colman RJ, Beasley TM, Kemnitz JW, Johnson SC, Weindruch R, Anderson RM. Caloric restriction reduces age-related and all- cause mortality in rhesus monkeys. Nat Commun. 2014;5:3557. doi: 10.1038/ncomms4557" + ], + "answer": "Caloric restriction extends lifespan across various species through several key molecular mechanisms: 1. **Sirtuin Activation**: Caloric restriction may exert some of its effects through the sirtuin family of genes, particularly SIR2, which is known to prolong lifespan in organisms like yeast, worms, and flies [3], [4]. Sirtuins are involved in chromatin regulation and promoting DNA stability, which are crucial for maintaining cellular health and longevity [4]. 2. **Insulin-like Signaling Pathways**: In mammals, caloric restriction is thought to modulate aging through the insulin-like signaling pathways. This mechanism is also observed in organisms like C. elegans and Drosophila, where it plays a role in regulating lifespan [6]. 3. **Oxidative Stress Reduction**: Caloric restriction is associated with reduced oxidative damage, which is a significant factor in aging. This reduction in oxidative stress is a common mechanism observed across different species [9]. 4. **AMPK Activation**: In mammals, caloric restriction has been linked to the activation of AMP-activated protein kinase (AMPK), which plays a role in energy homeostasis and has protective effects on the aged myocardium [10]. These mechanisms highlight the complex interplay of genetic and metabolic pathways through which caloric restriction can extend lifespan across diverse species.", + "question": "What are the key molecular mechanisms through which caloric restriction exerts its lifespan-extending effects across different species?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_16 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_16 new file mode 100644 index 0000000..cbb959e --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_16 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2007 - Trends in oxidative aging theories.pdf", + "2018 - Mechanisms of Vascular Aging.pdf", + "2018 - Nuclear Genomic Instability.pdf", + "2003 - Life-long reduction in MnSOD activity results.pdf", + "2020 - Growth differentiation factor 15 protects against the aging\u2010mediated systemic inflammatory response in humans and mice.pdf", + "2002 - Human mitochondrial DNA with large deletions.pdf", + "2011 - Mitochondrial complex I.pdf", + "2000 - Genome-wide study of aging and oxidative stress.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2012 - Oxidative Stress, Mitochondrial Dysfunction, and Aging.pdf" + ], + "extraction_id": [ + "9994d4e6-e53d-5381-af9c-e811afe7a802", + "6dcd5550-7f8d-5668-bb82-b6040cbf1e61", + "b934a2a9-a672-5d65-9d0d-bbc36652a148", + "f0a1875a-9969-598b-a670-e6f61bf11898", + "cebd8a1c-01ea-5c43-a2f1-96ea3c304259", + "14f137b3-20cf-5b34-a3dd-4b550a3dec92", + "c195a6a2-d6a9-53f3-a0dd-abe76ae29588", + "ac5d00c0-f445-5c6a-b248-12c82c985d9a", + "7f1594a3-120c-5982-aa4d-babd6ab70265", + "32c4c0b2-d44c-5121-8975-196040fb2a1d" + ], + "document_id": [ + "0d752c1a-706a-5b9e-88ef-ba7c51735c3c", + "659b84b6-63dd-5bb1-80ee-7478ed3c47e3", + "54d28a91-8db6-56b1-baaa-b67274c93a36", + "0cef9dec-dbbe-5b5d-bb43-1a21a601fde2", + "0ceff9cf-2b2b-5fe8-b844-f3f8ee7704ad", + "35de1e32-95eb-5b1d-acf9-2c37ea1cc3c4", + "6943c112-611d-5108-9d0f-d52c1138871b", + "3fc2266c-d677-54f9-b3a2-5129eedf214a", + "62b635c3-040e-512a-b016-6ef295308a1e", + "24277eba-69dd-5e12-9aa4-bbb6f0a88f52" + ], + "id": [ + "chatcmpl-AIHY9RBdJPzHPCH0uE5dG6bbj0z6D", + "b39d86ef-3c6a-561f-b8eb-f90ac124c12c", + "091ca29b-5c85-5d0d-8fbb-e829bb71bd0c", + "69365543-2760-5376-8e90-9a922a9759a7", + "9713b3c5-cd67-57d1-8c17-b3a4db7f911f", + "4bab1bd2-05a4-5c8e-897d-e456be8c8998", + "d99e64c1-2fe1-50c5-8a75-a2390ed0eac0", + "0f1d7692-a2c0-5def-9545-c2c16019536e", + "fec5b83b-cd2c-51ea-83c9-45efdcbff83d", + "cbfc2dc4-99ae-5177-955f-4bc243689419", + "6d58996a-1250-5eaa-bc6f-bd1057ccca88" + ], + "contexts": [ + "under normal physiological conditions because of an imbal-ance between prooxidants and antioxidants. The imbalanceleads to a steady-state accumulation of oxidative damage in avariety of macromolecules t hat increases during aging, resulting in a progressive loss in the functional efficiency ofvarious cellular processes. In a recent review, Beckman andAmes made a useful addition to this debate by dividing the", + "tributing to impaired bioenergetics in aged cells include oxida-tion/nitration of mitochondrial proteins, destabilization of the macromolecular organization of electron transport chain com-plexes, and impaired mitophagy (a mitochondria-specific form of autophagy). The combination of increased mitochondrial Figure 2. Proposed scheme for mechanisms and pathological consequences of age-related oxidative stress in vascular endothelial cells. The", + "over the years to become the oxidative stress theory of aging, but the principle is the same, inthat the accumulation of oxidative damage drives aging. In support of this theory, a large body of literature indicates that oxidative damage to all cellular macromolecules increases with age. Furthermore, overexpression of antioxidant enzymes that detoxify ROS, such as copper- andzinc-containing superoxide dismutase (SOD), manganese-containing SOD, or catalase, increase", + "predicted from the oxidative stress theory of aging. Thistheory,whichisbasedonthetenetthatdamagecausedbyROSplays a critical role in determining life span, has been one ofthe most popular theories to explain the deterioration in bio-chemical and physiological processes that occur during theaging process. A large number of studies have producedcorrelative data in support of this theory, e.g., an increase inoxidativedamagetolipid,protein,andDNAwithagehasbeendemonstrated in a variety of tissues and organisms", + "during\tthe\taging\tprocess\t(Yi,\tChang,\t&\tShong,\t2018).\tOxidative\tdam - age to cellular macromolecules, or stress arising from mitochondrial DNA\t(mtDNA)\tmutation\tand\tincreased\treactive\toxygen\tspecies\t (ROS),\tis\ta\tkey\thallmark\tof\taging\tphysiology\t(Yi\tet\tal.,\t2018).\tAlthough", + "radical theory of aging, which argues that oxidative damageplays a key role in senescence. Among the numerousmechanisms known to generate oxidants, leakage of super-oxide anion and hydrogen peroxide from the mitochondrialelectron transport chain are the chief candidates. Increased damage to mtDNA could exacerbate this leakage of reactive oxygen species (ROS) (4). It is not known how mtDNA deletions accumulate during", + "most plausible explanation for aging. But, as we have discussed, not all types of damage contribute equally to aging. From this point of view, it seems that ROS generated by complex I (at sulfur iron clusters or flavin sites) may damage specific targets that can alter homeosta - sis in a significant enough way to influ - ence aging. The most obvious target for this damage is mtDNA. The generation of ROS specifically by complex I corre - lates with levels of oxidative damage in mtDNA.", + "increase lifespan also confer resistance to oxidative stress (1).This finding supports the free-radical hypothesis of aging, whichsuggests that reactive oxygen species that accumulate withincreasing age cause oxidative damage to macromolecules (in-cluding nucleic acids, proteins, and lipids) and are causally linkedto aging and death (8, 9). Free radicals have been found toregulate the expression of a number of genes that includeantioxidant defense genes involved in repairing oxidative dam-age, as well as", + "Molecular Biomarkers forOxidative Stress There are many theories that try to explain the nature of aging; however, none of them can explain every aspect of the biology of aging. One of the most accepted and studied is the one proposed by Denham Harman in 1956. This theory proposed that during lifespan organisms accumulate oxidative damage in their biomolecules. Oxidative damage is generated by reactive oxygen species (ROS), which are the", + "production by mitochondria and increased 8-oxo-dG con-tent in the mtDNA are frequently detected in aged tissues [40,4750], suggesting that progressive accumulation of oxidative DNA damage is a contributory factor to the agingprocess. Consistently, many studies have found that increasedoxidative damage in cells is associated with aging [ 5153]. Furthermore, genetic studies in worm, y, and mouse havelinked enhanced stress resistance or reduced free radical" + ], + "answer": "Oxidative stress contributes to cellular aging through the accumulation of oxidative damage in various macromolecules, which leads to a decline in cellular function. This process occurs due to an imbalance between prooxidants and antioxidants, resulting in a steady-state accumulation of oxidative damage that increases with age [1]. The oxidative stress theory of aging posits that damage caused by reactive oxygen species (ROS) plays a critical role in determining lifespan, as it leads to the deterioration of biochemical and physiological processes [4]. Oxidative damage affects all cellular macromolecules, including lipids, proteins, and DNA, and this damage increases with age [3]. The accumulation of such damage is a key hallmark of aging physiology [5]. Specifically, oxidative damage to mitochondrial DNA (mtDNA) and the generation of ROS from the mitochondrial electron transport chain are significant contributors to this process [6]. Overall, the accumulation of oxidative damage is causally linked to aging and death, as it impairs cellular processes and bioenergetics, leading to the progressive loss of functional efficiency in cells [2], [8].", + "question": "How do oxidative stress and the subsequent accumulation of damaged macromolecules contribute to cellular aging?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_17 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_17 new file mode 100644 index 0000000..e34bfc3 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_17 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2018 - Mechanisms of Vascular Aging.pdf", + "2017 - The Aging Cardiovascular System.pdf", + "2018 - Mechanisms of Vascular Aging.pdf", + "2018 - Mechanisms of Vascular Aging.pdf", + "2022 - Proteomic analysis reveals that aging rabbit.pdf", + "2022 - Proteomic analysis reveals that aging rabbit.pdf", + "2018 - Mechanisms of Vascular Aging.pdf", + "2016 - The genome-wide role of HSF-1.pdf", + "2019 - Downregulation of miR-542-3p promotes.pdf", + "2007 - Sex-specific regulation of gene expression in the aging monkey aorta.pdf" + ], + "extraction_id": [ + "4b0673e0-fb5e-5212-ba68-417de0e867b7", + "d60f1e7d-cde2-5c66-8863-507065ed5c7f", + "4b0673e0-fb5e-5212-ba68-417de0e867b7", + "4b0673e0-fb5e-5212-ba68-417de0e867b7", + "a099ce3c-cdff-5971-b3d5-f31e03aace96", + "c738a4b2-0aea-5157-bed4-fecdac9863b9", + "e91c9a2a-a797-59d5-8565-91b45b0113a1", + "b2c1c466-d4b3-5c01-a8a4-2f49e9f246a2", + "32322971-f8f4-53d3-8104-ac44cf03ebef", + "1d889462-37d6-5cb5-b0df-8ae9c50560b7" + ], + "document_id": [ + "659b84b6-63dd-5bb1-80ee-7478ed3c47e3", + "d3ff8471-986b-5fa0-b9c4-96eaaa8fce7c", + "659b84b6-63dd-5bb1-80ee-7478ed3c47e3", + "659b84b6-63dd-5bb1-80ee-7478ed3c47e3", + "f6c524a5-acf9-5a07-8bbf-31091443cab3", + "f6c524a5-acf9-5a07-8bbf-31091443cab3", + "659b84b6-63dd-5bb1-80ee-7478ed3c47e3", + "e3c48474-21da-51d2-b378-200138fda0d3", + "527e562f-f7c3-5a01-b70b-5737d63e2457", + "6c2a7135-31ed-57e3-89fa-42856979ea1a" + ], + "id": [ + "chatcmpl-AIHYGBcI0VJ8rQxINM8Z5Fqy6gz6y", + "9f768c0d-8518-5ac9-9d66-9ffdba704a84", + "e7f8f5f2-9102-56bf-b579-43ad3c8d6b84", + "b7cd7044-b2fe-5dd2-b7b4-6388b9f4765d", + "ab8d8d0e-f91a-538a-bd84-beafa1fe8ce8", + "e7121d85-7538-5cdd-8b2d-6d3d536439b9", + "cf5f0034-c806-52d6-bd26-137fb9d8a418", + "58e94400-b0f0-5757-b964-83a6b2b6f98f", + "4dfd7818-9111-5bf9-bbcf-e917b1c9b9fc", + "d5cd4d54-b051-5638-ba76-39c385f3e423", + "479ae037-3dd5-57f7-9bf7-78a3a45ac47f" + ], + "contexts": [ + "208 Additional features that contribute to increased ar - terial stiffness include decreased elastin synthesis, elastin degradation and fragmentation, elastin calcification, al-terations in cross-linking of extracellular matrix compo-nents (eg, by increased presence of advanced glycation end products). 208,210,211 The pathophysiological consequences of age-related ECM remodeling and arterial stiffening have been the sub-ject of a recent comprehensive review by AlGhatrif and Lakatta.", + "collagen. AGE-mediated cross-links can confer resis-tance to enzymatic degradation, and thus interferewith collagenolysis (56). In addition, increased ac- tivity of TGF- bwith aging stimulates the synthesis of interstitial collagen by vascular smooth muscle cells(VSMCs), and thereby augments arterial stiffness (57). Likewise, increased activity of the RAAS may augment collagen synthesis and heighten elastolysis (58). Endothelial dysfunction and arterial stiffness are", + "that many of these age-related ECM alterations are governed by circulating factors and factors produced in the vascular wall, including the extended renin-angiotensin-aldosterone system (see above) and an age-related decline in circulating IGF-1. 209 Collagen synthesis is also dysregulated with age in the vascular wall likely because of the effects of increased para-crine action of TGF- (transforming growth factor- ), 123 which contributes to vascular fibrosis and arterial stiffen-ing.", + "Ungvari et al Mechanisms of Vascular Aging 859 Role of Extracellular Matrix Remodeling in Vascular Aging The extracellular matrix (ECM) is an important contribu- tor to health and longevity. This noncellular compartment, ubiquitous to all tissues and organs does not only provide es-sential mechanical scaffolding but mediates highly dynamic biomechanical and biochemical signals required for tissue homeostasis, morphogenesis, and cell differentiation. Studies", + "1996;25(3):20915. 79. Bonnans C, Chou J, Werb Z. Remodelling the extracellular matrix in development and disease. Nat Rev Mol Cell Biol. 2014;15(12):786801. 80. Swift J, Ivanovska IL, Buxboim A, Harada T, Dingal PCDP , Pinter J, et al. Nuclear Lamin-A scales with tissue stiffness and enhances matrix- directed differentiation. Science. 2013;341(6149):1240104. 81. Vogel C, Marcotte EM. Insights into the regulation of protein abun- dance from proteomic and transcriptomic analyses. Nat Rev Genet.", + "result in extracellular matrix stiffness in aging larynx and other organs [59, 79]. Finally, Lamin A was upregulated by dehydration, by a smaller magnitude, especially when observing the mean difference within the young groups. Previous data has identified that Lamin proteins A and C are important for imparting the nucleus with its stiff - ness, and their expression has been reported to scale with", + "aging. Annu Rev Biomed Eng. 2015;17:113141. doi: 10.1146/ annurev-bioeng-071114-040829 208. Jacob MP. Extracellular matrix remodeling and matrix metalloprotein- ases in the vascular wall during aging and in pathological conditions. Biomed Pharmacother. 2003;57:195202. 209. Tarantini S, Valcarcel-Ares NM, Yabluchanskiy A, Springo Z, Fulop GA, Ashpole N, Gautam T, Giles CB, Wren JD, Sonntag WE, Csiszar A, Ungvari Z. Insulin-like growth factor 1 deficiency exacerbates hyperten-", + "able human diseases such as osteoporosis and musculo- skeletal diseases [53]. Collagens are long-lived proteins known to accumulate damage during aging, leading to a decline in tissue health [54]. Also, type I collagens be- come resistant to proteolysis upon age [55, 56], affecting their turnover. Interestingly, mice expressing cleavage- resistant type I collagen go through an accelerated aging process [57]. Thus, cellular aging can be affected by the state of the extracellular matrix in mammals.", + "the characteristics of endothelial dysfunction and pheno- typic transition of smooth muscle cells, resulting in in- creased vascular stiffness and increased thickness of vascular walls. It has been reported that the age- associated phenotypic transition of VSMCs is a crucial contributor to vascular remodeling [ 17,25]. However, the mechanism that drives phenotypic transition ofVSMCs with aging remains unclarified. In this study, using RNAs extracted from the in vitro cultured VSMCs,", + "downregulation with aging of genes involved in the synthesisof the ECM and in particular of different forms of collagen(Table 2). In addition, aging males but not females showed adecrease in collagen type III. Interestingly, collagen type IIIdecreases the size of collagen bundles and thereby increasesvascular elasticity (11). Therefore, a decreased expression ofcollagen type III can participate in the increased stiffness thatcharacterizes the aging aorta (23). An interesting observationfrom our study that" + ], + "answer": "Extracellular matrix (ECM) remodeling and tissue stiffness are significant factors in the aging process. As we age, several changes occur in the ECM that contribute to increased tissue stiffness. These changes include decreased elastin synthesis, elastin degradation and fragmentation, and alterations in the cross-linking of ECM components, such as increased presence of advanced glycation end products (AGEs) [1]. AGEs can interfere with collagenolysis by forming cross-links that confer resistance to enzymatic degradation, thereby contributing to increased arterial stiffness [2]. Additionally, the activity of transforming growth factor-beta (TGF-\u03b2) increases with age, stimulating the synthesis of interstitial collagen by vascular smooth muscle cells (VSMCs), which further augments arterial stiffness [2]. The renin-angiotensin-aldosterone system (RAAS) also plays a role in this process by augmenting collagen synthesis and promoting elastolysis [2]. The ECM is crucial for providing mechanical scaffolding and mediating biomechanical and biochemical signals necessary for tissue homeostasis and cell differentiation [4]. However, with aging, ECM stiffness increases, affecting various organs, including the larynx [6]. This increased stiffness is associated with a decline in tissue health, as seen with the accumulation of damage in long-lived proteins like collagens, which become resistant to proteolysis and affect their turnover [8]. Overall, these changes in ECM remodeling and tissue stiffness contribute to the aging process by affecting vascular and tissue elasticity, leading to conditions such as arterial stiffening and vascular remodeling [1], [3], [9].", + "question": "How are extracellular matrix remodeling and tissue stiffness implicated in the aging process?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_18 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_18 new file mode 100644 index 0000000..157f9ff --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_18 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2016 - Epigenetics and aging.pdf", + "2012 - Genome-Wide RNAi Longevity Screens in Caenorhabditis elegans.pdf", + "2015 - Transcriptomic profiles of aging in purified.pdf", + "2015 - Transcriptomic profiles of aging in purified.pdf", + "2016 - Epigenetics and aging.pdf", + "2015 - The mechanism of ageing primary role of transposable elements.pdf", + "2012 - Replicative and Chronological Aging.pdf", + "2015 - Transcriptomic profiles of aging in purified.pdf", + "2018 - Mechanisms of Vascular Aging.pdf", + "2015 - Transcriptomic profiles of aging in purified.pdf" + ], + "extraction_id": [ + "9b7b806c-cac3-549e-9ae9-424cc3e5f869", + "f160f818-03bf-5b4e-b1f4-bfbd3b0bfb99", + "a972e2fb-b73f-51bf-980a-85c9db1482be", + "a972e2fb-b73f-51bf-980a-85c9db1482be", + "9b7b806c-cac3-549e-9ae9-424cc3e5f869", + "20245b79-fa8f-52fc-832e-1478a080d6e1", + "8bc194af-6e9d-51c5-8116-6d4186a885dd", + "e5fd1ff0-8df5-577f-9f2d-31b0941d5ce5", + "0d4cd402-e032-5edd-98eb-170357a3092e", + "e5fd1ff0-8df5-577f-9f2d-31b0941d5ce5" + ], + "document_id": [ + "71b206ec-81bd-5194-8b21-ae522f8cbc2d", + "7589fec2-e893-5a4d-9f0c-09abb35858ab", + "50f357a3-e0f2-5e32-a6b2-771fb4b1e1c6", + "50f357a3-e0f2-5e32-a6b2-771fb4b1e1c6", + "71b206ec-81bd-5194-8b21-ae522f8cbc2d", + "de558db9-dc04-5bbd-83bf-3e3368ff906b", + "496e387e-4278-5f74-8ecc-4edc1cee7dfe", + "50f357a3-e0f2-5e32-a6b2-771fb4b1e1c6", + "659b84b6-63dd-5bb1-80ee-7478ed3c47e3", + "50f357a3-e0f2-5e32-a6b2-771fb4b1e1c6" + ], + "id": [ + "chatcmpl-AIHYO1XLJbUnaqsOWyFh9a97rwIzB", + "603183d9-d22c-5008-bfa5-147ee5df4198", + "a6d18c4e-632c-52a2-b3f9-6296025e0ce7", + "d43449f1-2d90-5e0e-8ba8-8afdc306f32d", + "ca8ae9e1-f598-56b9-952e-bb5bea62d8fe", + "581ca468-d3f3-5846-9fba-7f1f860df956", + "a2effd64-3d9d-5bdf-8fc6-0cd72762763d", + "f82ef429-c823-5173-a93b-5c476df110f5", + "949f7420-bfb6-564d-8537-18c47e40bbc6", + "8ede28e5-ed8e-5c68-bd03-18c3c96bb31b", + "82060e66-87b7-5ac2-9877-fc7b26325b73" + ], + "contexts": [ + "D. Carmona-Gutierrez, C. Ruckenstuhl, J. Ring, W. Reichelt, K. Schimmel, T. Leeb,C. Moser, S. Schatz, L.-P. Kamolz, C. Magnes, F. Sinner, S. Sedej, K.-U. Frhlich,G. Juhasz, T. R. Pieber, J. Dengjel, S. J. Sigrist, G. Kroemer, F. Madeo, Nucleocytosolic de-pletion of the energy metabolite acetyl-coenzyme a stimulates autophagy and prolongs lifespan. Cell Metab. 19, 431 444 (2014). 225. S. Gelino, M. Hansen, Autophagy An emerging anti-aging mechanism. J. Clin. Exp. Pathol. (Suppl. 4), pii: 006 (2012).", + "[73] Vellai, T. Autophagy genes and ageing . Cell Death Differ. , 2009 , 16(1), 94-102. [74] Kaeberlein, M.; Kapahi, P. Cell signaling. Aging is RSKy business . Science , 2009 , 326(5949), 55-6. [75] Hansen, M.; Chandra, A.; Mitic, L.L.; Onken, B.; Driscoll, M.; Kenyon, C. A role for autophagy genes in the extension of lifespan by dietary restriction in C. elegans. PLoS Genet. , 2008 . [76] Hansen, M.; Taubert, S.; Crawford, D.; Libina, N.; Lee, S.J.;", + "chinery and upstream regulators provide evidence for a transcriptional decline in autophagy gene expression with age in human monocytes. The identification of key genes contributing to a decline in autophagy are of great interest, as pharmacologic activation of au- tophagy has been linked with increasing lifespan in animal models, including mice [45]. Further, dysfunc- tional autophagy is now widely implicated in patho- physiological processes of many age-related diseases", + "invasive pathogens, and to transport these cargos to the lysosomes for degradation [25]. In the aging field, im- paired autophagy is considered one of the principal de- terminants of cellular aging, which is supported by in vitro and animal study findings that autophagy de- clines with age [26]. However, studies of autophagy and age in humans are sparse. One of the most significant age-gene expression asso- ciations we observed in monocytes from 1,264 individ-", + "226. F. Madeo, N. Tavernarakis, G. Kroemer, Can autophagy promote longevity? Nat. Cell Biol. 12, 842 846 (2010). 227. J. Fllgrabe, M. A. Lynch-Day, N. Heldring, W. Li, R. B. Struijk, Q. Ma, O. Hermanson, M. G. Rosenfeld, D. J. Klionsky, B. Joseph, The histone H4 lysine 16 acetyltransferase hMOF regulates the outcome of autophagy. Nature 500, 468 471 (2013). 228. F. Ng, B. L. Tang, Sirtuins modulation of autophagy. J. Cell. Physiol. 228, 2262 2270 (2013).", + "(2013) The hallmarks of aging. Cell 153(6):11941217. doi: 10. 1016/j.cell.2013.05.039 3. Vellai T, Takacs-Vellai K, Sass M, Klionsky DJ (2009) The regulation of aging: does autophagy underlie longevity? TrendsCell Biol 19(10):487494. doi: 10.1016/j.tcb.2009.07.007 4. Kirkwood TB (2008) A systematic look at an old problem. Nature 451(7179):644647. doi: 10.1038/451644a 5. Koubova J, Guarente L (2003) How does calorie restriction work? Genes Dev 17(3):313321. doi: 10.1101/gad.1052903", + "Eisenberg, T., Knauer, H., Schauer, A., Bu ttner, S., Ruckenstuhl, C., Carmona- Gutierrez, D., Ring, J., Schroeder, S., Magnes, C., Antonacci, L., et al. (2009).Induction of autophagy by spermidine promotes longevity. Nat. Cell Biol. 11, 13051314. Enns, L.C., Morton, J.F., Treuting, P.R., Emond, M.J., Wolf, N.S., Dai, D.F., McKnight, G.S., Rabinovitch, P.S., and Ladiges, W.C. (2009). Disruption of protein kinase A in mice enhances healthy aging. PLoS ONE 4, e5963.", + "its essential part in the anti-aging mechanism of caloric restriction. Ann N Y Acad Sci. 2007;1114:69 78. 41. Cuervo AM, Bergamini E, Brunk UT, Droge W, Ffrench M, Terman A. Autophagy and aging: the importance of maintaining clean cells. Autophagy. 2005;1:131 40. 42. Terman A. The effect of age on formation and elimination of autophagic vacuoles in mouse hepatocytes. Gerontology. 1995;41 Suppl 2:319 26. 43. Donati A, Recchia G, Cavallini G, Bergamini E. Effect of aging and anti-aging", + "103 Experimental findings showing increased oxidative stress, impaired bioavailability of NO, and upregulation of in-flammatory mediators in autophagy-deficient endothelial cells support this view. 104 Further, pharmacological interventions that stimulate autophagy (eg, trehalose or spermidine treat-ment) were reported to reverse aspects of arterial aging. 105,106 Proteasomes degrade unneeded or damaged proteins by pro-teolysis. There is evidence that proteasome activity declines in advanced aging", + "Phosphorylation of ULK1 (hATG1) by AMP-activated protein kinase connects energy sensing to mitophagy. Science. 2011;331:456 61. 38. Xiao B, Sanders MJ, Underwood E, Heath R, Mayer FV, Carmena D, et al. Structure of mammalian AMPK and its regulation by ADP. Nature. 2011;472:230 3. 39. Tang D, Kang R, Livesey KM, Cheh CW, Farkas A, Loughran P, et al. Endogenous HMGB1 regulates autophagy. J Cell Biol. 2010;190:881 92. 40. Bergamini E, Cavallini G, Donati A, Gori Z. The role of autophagy in aging:" + ], + "answer": "Recent developments in autophagy research have significantly enhanced our understanding of its role in aging and longevity. Here are some key contributions: 1. **Energy Metabolism and Lifespan Extension**: Research has shown that the depletion of acetyl-coenzyme A, an energy metabolite, can stimulate autophagy and prolong lifespan. This suggests that autophagy is closely linked to energy metabolism and can be a mechanism for extending lifespan [1]. 2. **Autophagy as an Anti-Aging Mechanism**: Autophagy is increasingly recognized as an emerging anti-aging mechanism. It plays a crucial role in maintaining cellular homeostasis by degrading and recycling damaged cellular components, which is essential for longevity [1]. 3. **Genetic Regulation and Dietary Restriction**: Studies have identified autophagy genes as important for lifespan extension, particularly in the context of dietary restriction. This indicates that genetic regulation of autophagy is a key factor in promoting longevity [2]. 4. **Pharmacological Activation**: There is evidence that pharmacological activation of autophagy can increase lifespan in animal models, including mice. This highlights the potential for therapeutic interventions targeting autophagy to promote healthy aging [3]. 5. **Impaired Autophagy and Cellular Aging**: Impaired autophagy is considered a principal determinant of cellular aging. The decline in autophagy with age is linked to various age-related diseases, emphasizing the importance of maintaining autophagic activity for longevity [4]. 6. **Spermidine and Longevity**: The induction of autophagy by compounds like spermidine has been shown to promote longevity, further supporting the role of autophagy in extending lifespan [7]. These findings collectively underscore the critical role of autophagy in aging and longevity, suggesting that enhancing autophagic processes could be a viable strategy for promoting healthy aging and extending lifespan.", + "question": "How do recent developments in autophagy research contribute to our understanding of its role in aging and longevity?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_19 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_19 new file mode 100644 index 0000000..82e1f47 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_19 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2008 - GENETIC REGULATION OF HEMATOPOIETIC STEM CELL AGING (3).pdf", + "2011 - Genome-wide promoter DNA methylation dynamics of human hematopoietic.pdf", + "2009 - Aging and Replicative Senescence Have Related Effects.pdf", + "2010 - Age-related molecular genetic changes of murine.pdf", + "2007 - Aging Hematopoietic Stem Cells Decline in Function and Exhibit Epigenetic Dysregulation.pdf", + "2009 - Aging and Replicative Senescence Have Related Effects.pdf", + "2013 - Age-associated epigenetic drift implications.pdf", + "2007 - Two faces of p53 aging and tumor suppression.pdf", + "2013 - Effects_of_age_and_strain_on_cell_prolif.pdf", + "2010 - Age-related molecular genetic changes of murine.pdf" + ], + "extraction_id": [ + "fca849bb-6e08-5200-8c66-5250e902dca3", + "3be2a7fa-1d97-5280-ba37-cc3d311cfb75", + "f5b29cc7-fe8b-5230-adb1-0531fb1c3187", + "d39327b0-59b1-5e24-813d-099a48a8de85", + "188bdad0-f63b-5e4c-8eed-73cd01b8d66f", + "23921b67-8911-5086-a2e4-a909394a6df4", + "24500f0a-0e60-574e-9039-e9dd3b5be569", + "270c5516-f5b2-54d3-8865-b84d8a9506c1", + "b0fb2185-a2ee-5174-94d0-877ad2d87158", + "d39327b0-59b1-5e24-813d-099a48a8de85" + ], + "document_id": [ + "7412a162-ee3b-5f09-9886-8e9172dd3ee8", + "30081f4e-7189-5c9f-abf2-895250c0173e", + "0703ba80-b7a5-5873-9ab0-5d66d57f4750", + "a69ce6db-4a5e-58a5-9dc5-d529768edcb1", + "a6fabf0c-e4a5-59f6-82c5-ebabce24fd0a", + "0703ba80-b7a5-5873-9ab0-5d66d57f4750", + "8513121f-71f3-5bb0-9433-feece9fd9fbc", + "b1ef905a-c145-5270-9110-ae6954ea3d72", + "d7e861e7-cdee-5145-9403-ef05e2d532c0", + "a69ce6db-4a5e-58a5-9dc5-d529768edcb1" + ], + "id": [ + "chatcmpl-AIHYWWczI6kl71Lbbg4Wx4xLfOmE6", + "cade861a-f60d-51fd-bfac-edce8860b395", + "7fcd630b-0f09-5947-8a28-f72d4418d8f8", + "8f53ce05-7527-52f2-8a25-9c3ee9a38861", + "ccf7dace-b7d8-576f-bb59-c6707e5180f5", + "f8e0e878-451b-519d-b6e5-e9834d5d3b77", + "de67cf90-712a-5c28-9f6b-404d84a06d22", + "e6bb4c40-7fe8-5ff7-af36-1c2b749ed1fb", + "01740a78-e141-56f0-8f34-7c02c5602344", + "ae2ad88f-6e02-5541-b6be-966fef7712f1", + "1dffbbdb-f76d-581b-8384-751ce5f41e90" + ], + "contexts": [ + "into old versus young recipients (Liang et al., 2005 ). Further experiments demonstrated that the muscle stem cell niche adversely effects stem cell function as evidenced by the restoration of old stem cell regenerative potential upon expos ure to a young systemic microenvironment (Conboy et al., 2005; Conboy and Rando, 2005). It has also been reported that the spermatogoni al stem cell niche deteriorates with age, causing the failure to suppor t an appropriate balance between stem cell self-renewal and", + "matopoietic stem cells is regulated by the stemcell niche. Exp Gerontol. 2008;43(11):974-980. 18. Geiger H, Rudolph KL. Aging in the lympho- hematopoietic stem cell compartment. Trends Immunol. 2009;30(7):360-365. 19. Muller-Sieburg C, Sieburg HB. Stem cell aging: survival of the laziest? Cell Cycle. 2008;7(24): 3798-3804. 20. Beerman I, Maloney WJ, Weissmann IL, Rossi DJ. Stem cells and the aging hematopoieticsystem. Curr Opin Immunol. 2010;22(4):500-506. 21. Teschendorff AE, Menon U, Gentry-Maharaj A,", + "Abstract The regenerative potential diminishes with age and this has been ascribed to functional impairments of adult stem cells. Cells in culture undergo senescence after a certain number of cell divisions whereby the cells enlarge and finally stop proliferation. This observation of replicative senescence has been extrapolated to somatic stem cells in vivo and might", + "Because of their plasticity and accessibility these cells are also prime candidates for regenerative medicine. The contribution of stem cell aging to organismal aging is un der debate and one theory is that reparative processes deteriorate as a consequence of stem cell aging and/or de crease in number. Age has been linked with changes in osteogenic and adipogen ic potential of MSCs. Results: Here we report on changes in global gene expression of cultured MSCs isolated from the bone marrow of", + "suggesting that stem cells are not likely to be a factor limiting hematopoietic regeneration with age. However, their func-tional decits do show that HSCs are impacted by the forces of aging in a manner similar to that of differentiated cells [3134]. In our molecular analysis, we identied global age-related changes in gene expression in murine HSCs, with a view to identifying mechanisms that could be responsible for these age-associated declines in HSC function. Genes involved in", + "Discussion The deterioration of the regenerative potential upon aging might be due to functional changes in adult stem cells. To test this hypothesis we have investigated differential gene expression in primary, human MSC and HPC derived from different agegroups. In this study, we demonstrate for the first time age-related gene expression changes in human MSC and HPC and that there", + "cells, which may explain the observed decline of stem cell function with age. Age-associated increases inDNAm target developmental genes, overlapping those associated with environmental disease risk factors and with disease itself, notably cancer. In particular, cancers and precursor cancer lesions exhibit aggravated", + "tion associated with age: loss of stem cell pool division potential (loss of regenerative capacity) and loss ofdierentiated somatic cell function, which directly leads to loss of organ function. Loss of dierentiated somatic cell function can additionally indirectly aect adult stem and progenitor cells by altering the tissue microenviron- ment that is essential for stem cell support (the stem cellniche). In general, loss of stem cell pool division potential", + "1. Introduction Stem cell aging is regarded as one of the contributors to several degenerative conditions af icting the elderly because it underlies the physiological decline in tissue maintenance and regenerative capacity of many organs ( Rossi et al., 2008 ). The brain is one such organ that contains discrete populations of stem cells and their precursors (collectively referred to as neural progenitor cells [NPCs]) that continue to generate new neurons throughout life", + "spective of tissue regeneration and repair because there isevidence that these beneficial functions may becomehandicapped with age. Age-related decline in the numberof MSCs in the bone marrows of rodents, monkeys, andhumans have been reported [26-33]. Most studies to datefocused on the effects of aging on the ability of MSCs toenter osteogenic, chondrogenic and adipogenic pro-grams. Some, but not all studies suggest that agingreduces osteogenesis and chondrogenesis while enhanc-" + ], + "answer": "Age-related shifts in stem cell niche composition and function have significant implications for tissue regeneration capacity. As individuals age, the stem cell niche, which is crucial for maintaining stem cell function, undergoes changes that can adversely affect the regenerative potential of stem cells. Here are some key implications based on the provided context: 1. **Deterioration of the Stem Cell Niche**: The aging process leads to the deterioration of the stem cell niche, which can result in a failure to support the balance between stem cell self-renewal and differentiation. This is evident in the spermatogonial stem cell niche, which deteriorates with age, affecting its ability to maintain stem cell function [1]. 2. **Functional Impairments of Stem Cells**: The regenerative potential diminishes with age due to functional impairments in adult stem cells. This is linked to the phenomenon of replicative senescence, where cells lose their ability to proliferate after a certain number of divisions [3]. 3. **Changes in Gene Expression**: Age-related changes in gene expression have been observed in stem cells, such as mesenchymal stem cells (MSCs) and hematopoietic progenitor cells (HPCs). These changes can lead to declines in stem cell function and, consequently, a reduction in tissue regeneration capacity [6]. 4. **Loss of Stem Cell Pool Division Potential**: Aging is associated with a loss of stem cell pool division potential, which directly impacts the regenerative capacity of tissues. This loss can also indirectly affect adult stem and progenitor cells by altering the tissue microenvironment essential for stem cell support [8]. 5. **Reduction in Stem Cell Numbers**: There is evidence of a decline in the number of MSCs in the bone marrow with age, which can further hinder the ability of these cells to participate in tissue regeneration processes such as osteogenesis and chondrogenesis [10]. Overall, these age-related shifts in stem cell niche composition and function contribute to a decline in the body's ability to repair and regenerate tissues, which is a hallmark of aging and is linked to various degenerative conditions [9].", + "question": "What are the implications of age-related shifts in stem cell niche composition and function for tissue regeneration capacity?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_2 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_2 new file mode 100644 index 0000000..13e3ffc --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_2 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2018 - Nuclear Genomic Instability.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2018 - Nuclear Genomic Instability.pdf", + "2019 - Integration of heterogeneous functional.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2016 - Genome Integrity in Aging.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2020 - Transposable elements, circular RNAs and mitochondrial.pdf", + "2022 - Functional genomics of inflamm-aging.pdf" + ], + "extraction_id": [ + "4b00515d-e599-5ce1-84e3-012d7efe1a30", + "eebc478a-d4b4-5547-a7e0-9c305d8bbd0f", + "fe836e95-1d70-51e5-b3fe-2f3005517606", + "c21052ac-b3d9-59bc-8164-3d2df613929f", + "b1eabac8-e6d1-50ba-9c42-60c107b56a65", + "97753738-7225-59cc-b573-72cdf4ba569d", + "3625a61e-f376-5bea-b2c9-582b6ef16957", + "6a2a94de-cfc0-50eb-b50e-bf3a0f813c78", + "ea17d9f1-0991-5a69-930d-3212a3fabe1f", + "72b29fff-be72-5ede-85c9-7dc81894c956" + ], + "document_id": [ + "54d28a91-8db6-56b1-baaa-b67274c93a36", + "62b635c3-040e-512a-b016-6ef295308a1e", + "54d28a91-8db6-56b1-baaa-b67274c93a36", + "cf134202-50af-5700-9b1b-962501d9470d", + "62b635c3-040e-512a-b016-6ef295308a1e", + "62b635c3-040e-512a-b016-6ef295308a1e", + "85d5fcbb-5385-5a01-8139-d11fc8b1fe3a", + "62b635c3-040e-512a-b016-6ef295308a1e", + "7bebb41c-ac73-5917-91d3-4f59fbb3266a", + "435dc081-e3d1-52c5-93a1-caa11206422f" + ], + "id": [ + "chatcmpl-AIHWHar7CqMtwymwPwmCVtJ5SKWUm", + "4d256f76-7065-5eeb-a961-db7e7cbe75ff", + "92618cf9-f512-5011-9d76-17f313ad850e", + "77589e08-f16b-5bb2-9f89-833f1833d5be", + "e1dde75e-c4f2-51f6-b601-abe56c2109c3", + "8c9d1720-5a2d-5559-831d-419208813d61", + "955cffc5-cb1d-5638-bb3e-bbf5b0fe5dd4", + "28976d8b-7996-51e7-b35b-213476f6ed7b", + "6d4a1a0b-2af3-5cc4-b7c0-a7223ce3edfa", + "a7675e04-876d-5026-88b5-842cd4ca237a", + "da4ca7c3-653d-584f-8956-7f3f710fd45e" + ], + "contexts": [ + "SASP (senescence-associated secretoryphenotype):cytokines, chemokines,proteases, and otherfactors secreted bysenescent cells, whichare inammatory anddisrupt tissuehomeostasis viaparacrine mechanisms ATM (ataxia-telangiectasiamutated):serine/threoninekinase and centralregulator of the DDR;activated by DNAdamage and transducesthat signal througheffectorphosphorylationphenotype (SASP) (84). SASP proteins include interleukin-6 (IL-6), transforming growth factor-", + "SASP is one of the most representative features of senescent cells and may explain the organismal expression of aging and age-related diseases. Senescent cells pro- duce a deleterious microenvironment through the production and secretion of pro- liferative and proinflammatory molecules such as IL-1 and -1, IL-6, IL-8, the chemotactic cytokine GRO, IGBP-7, growth factors, VEGF, TGF-, serine prote- ases, and matrix remodeling enzymes [146]. It has been determined that the activa-", + "context. For example, SASP likely contributes to early tumorigenesis (84), chemoresistance (94),and potentially neurodegenerative diseases (95). However, SASP is also important for mammalian development (96), tissue repair (97), and wound healing (98). SASP plays an important role in stimulating clearance of damaged, senescent cells by the innate immune system (99). However,inefcient immune clearance of senescent cells in aged organisms is thought to contribute to chronic inammation of aging.", + "many tissues, where theSASP promotes chronic inflammation and exacerbates age-associated degeneration and hyperplasia. Recent evidence suggests that neurological aging and neurode- generation areaccompanied byanaccumulation ofsecretory cells inbrain, suggesting that cel- lular senescence may contribute tobrain aging [2]through ashared mechanism. Overlapping mechanisms canbedetected using functional genomics studies ofboth thebiology ofcellular senescence and cognitive aging.", + "senescence-associated with the secretory phenotype (SASP) are other markers of cellular senescence. Inflammation andIntercellular Communication While senescent cells no longer replicate, they are still metabolically active and secrete proteins in a recognizable pattern known as SASP.This is a widely heteroge- neous group of proteins with autocrine and paracrine effects [47], including soluble signaling factors, such as interleukins, chemokines, and growth factors, as well as", + "matory mediators. This particular phenotype is termed the senescence- associated secretory phenotype (SASP). Replicative cellular aging includes biochemical, mor - phological, and functional modifications that lead to the irreversible impairment of cell proliferation associated with DNA damage, shortening of the telomeres, and changes in chromatin architecture, as previously described [135, 136]. The molecular mechanisms that drive cellular senescence in proliferative and", + "secretion of a range of proinammatory cyto- and chemokines, a state that has been dened asthe senescence-associated secretory phenotype (SASP) (103). Major SASP factors include IL1, IL6, IL8, and various matrix metalloproteases (MMPs), all of which individually are thought to drive aging and age-related diseases. Thus, DNA damage is a major determinant in controllingcell death, stem cell exhaustion, and cellular senescence, which are considered important events", + "senescent cells [150]. SASP factors exert their functions in either an autocrine or a paracrine manner and are responsible for the induction of the chronic inflammation and cell proliferation that contributes to cell dysfunction and cancer. Thus, the accu- mulation of senescent cells in tissue is closely associated with aging-related dis- eases. Recently, it was determined that senescent fibroblasts significantly increase the expression of HLA-E, which inhibits the receptor NKG2A in killer cells, and", + "Role of L1 and Alu in cellular senescence and age-related inflammation A key feature of cellular senescence is the senescence-associatedsecretory phenotype (SASP), whereby senescent cells secretenumerous proinflammatory cytokines, chemokines, growth factors, and proteases (Campisi, 2013). This altered secretome", + "8. Coppe JP, Patil CK, Rodier F, et al. Senescence-associated secretory phenotypes reveal cell-nonautonomous func- tions of oncogenic RAS and the p53 tumor suppressor. PLoS Biol2008; 6:285368. 9. Wiley CD, Liu S, Limbad C, et al. SILAC analysis reveals increased secretion of hemostasis-related factors by senes- cent cells. Cell Rep 2019; 28:33293337 e3325. 10. Basisty N, Kale A, Jeon OH, et al. A proteomic atlas of senescence-associated secretomes for aging biomarker" + ], + "answer": "The latest findings on the role of senescence-associated secretory phenotype (SASP) factors in age-related tissue dysfunction highlight several key aspects: 1. SASP factors, which include cytokines, chemokines, proteases, and other inflammatory molecules, are secreted by senescent cells and disrupt tissue homeostasis through paracrine mechanisms [1]. These factors contribute to a deleterious microenvironment that promotes aging and age-related diseases [2]. 2. SASP is associated with chronic inflammation and exacerbates age-associated degeneration and hyperplasia in many tissues [4]. This chronic inflammation is a significant contributor to tissue dysfunction as organisms age. 3. The accumulation of senescent cells and their SASP factors is closely linked to aging-related diseases. These factors can induce chronic inflammation and cell proliferation, leading to cell dysfunction and potentially cancer [8]. 4. SASP factors exert their effects in both autocrine and paracrine manners, influencing not only the senescent cells themselves but also the surrounding tissue environment [8]. Overall, SASP factors play a critical role in driving the chronic inflammation and tissue dysfunction associated with aging, highlighting their importance in the study of age-related diseases and potential therapeutic targets.", + "question": "What are the latest findings on the role of senescence-associated secretory phenotype (SASP) factors in age-related tissue dysfunction?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_20 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_20 new file mode 100644 index 0000000..fecccf3 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_20 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2004 - Diabetes Genes a.pdf", + "2004 - Diabetes Genes b.pdf", + "2010 - The Role of Epigenetics in the Pathology of Diabetic Complications.pdf", + "2004 - Diabetes Genes a.pdf", + "2019 - Bioinformatic prediction of critical genes and pathways.pdf", + "2007 - Rage gene promoter polymorphisms and diabetic retinopathy in a clinic-based population from South India.pdf", + "2004 - Diabetes Genes b.pdf", + "2004 - Diabetes Genes a.pdf", + "2004 - Diabetes Genes a.pdf" + ], + "extraction_id": [ + "a3427d8a-366e-5edc-9a9d-fa1da5d9e800", + "60ec7e90-7c38-5bda-a94e-ef15369c710c", + "272b3625-6f21-51f5-a83b-cfdbf4ddc841", + "cc350a5a-f474-597d-93c8-4359b9ddcc38", + "f5f2abef-9ccd-5147-a433-489c7225017c", + "98c7d4f6-45b7-53d4-979d-5503e91b1415", + "2903bc47-30d8-5e1c-acd9-5db4908f5ee9", + "26eeaac7-6846-51ee-a69b-51a75402a1bf", + "f6de03c3-cbbd-5963-ab23-e934f6ff1d56", + "60ec7e90-7c38-5bda-a94e-ef15369c710c" + ], + "document_id": [ + "62b635c3-040e-512a-b016-6ef295308a1e", + "805ad0d5-6a3d-5e5f-b1ba-121e10c6daaa", + "da2f2624-e3e6-5e2d-b406-941db2fe7671", + "766edfd5-4756-51bf-b636-c94b041d030c", + "805ad0d5-6a3d-5e5f-b1ba-121e10c6daaa", + "01201944-11f2-52d9-ac3e-7af685d4a4c4", + "de5a5a08-3a63-587c-b835-41c74b37f570", + "da2f2624-e3e6-5e2d-b406-941db2fe7671", + "805ad0d5-6a3d-5e5f-b1ba-121e10c6daaa", + "805ad0d5-6a3d-5e5f-b1ba-121e10c6daaa" + ], + "id": [ + "chatcmpl-AIHYkQV1s8mGJ0u0OlIT1WoCFkj8X", + "388d90ef-1bfc-572d-b783-af945ab9519b", + "aad43b5f-c345-53c4-a37e-4b59e54082bb", + "edfb3091-1629-53bc-9f0b-88d552862fd9", + "3d613e0f-9ab0-575f-88cc-2b35f51f9d9d", + "34533770-24ba-57b7-95f9-06b201c92aa5", + "e1c2f05b-b04a-5c74-98ad-69af532d2ae9", + "50a3dd44-9747-5456-91e3-ebeb2b6a9248", + "a8fe389d-7249-50d5-8c4a-2f9d62fa73f6", + "94f15877-0b3a-5dee-8d1f-d0a034f14220", + "0b6eb47a-1fd1-58d2-81db-3a17b967f2d6" + ], + "contexts": [ + "vascular and kidney diseases [47]. Advanced glycation end-products (AGE) are the result of nonenzymatic glyca- tion, which produces heterogeneous bioactive molecules, such as lipids, proteins, and nucleic acids [59]. The accumulation of AGEs in aged tissues leads to several processes, such as inflammation, obesity, apoptosis, and other adverse processes related to ageing [47]. These AGEs are detected by various techniques, such as", + "and leading to vascular hypertrophy and stiffening of collagen with subsequent reduction of arterial compliance. These are processes that are associated with aging but seem to be accelerated by hyperglycemia. These cross-linked macromolecules, called advanced glycosylation end products (AGEs), are implicated in the pathogenesis of vascular complications. Once", + "proposed mechanisms are the development of advanced glycosylation end products and sorbitol accumulation. Advanced glycosylation end products (AGEs) comprise a heterogeneous group of molecules that accumulate in plasma and tissues with advancing age, diabetes and renal failure. They are characterized by browning, fluorescence, cross-linking and biological response through specific AGE receptors and were first described in 1912 by French chemist L.C. Maillard (Fig. 5).", + "the accumulation of AGEs which can further perp etuate and amplify local inflammation and 197 oxidant stress through irreversible glycation of the various protei ns and lipids to promote long 198 term vascular and end-organ damage. Thus AGEs, acting through receptors such as RAGE, 199 could also contribute to hyperglycemic memo ry (18, 96, 147). These studies have begun to 200", + "AGEs are taken up by specific AGE receptors (RAGE), cytokines, growth factors, and adhesion factors are released, leading to further cellular changes. AGEs also can impair endothelial function and vascular reactivity, such as in response to nitric oxide. Modification of LDL as a result of glycation may contribute to foam cell formation.4 Thus, AGEs appear to be main players not only in the development of diabetic complications and atherosclerosis,", + "geneous group of macromolecules that are formed by the nonenzymatic glycation of proteins, lipids, and nucleic acids. Overproduction of AGEs is considered the most important pathophysiological mechanism that induces diabetic complications (Semba etal. 2010). On one hand, AGEs mediate intracellular glycation of mitochondrial respiratory chain proteins and increase ROS levels, thus triggering oxidative stress (Coughlan etal. 2009) and endoplasmic reticulum stress (Piperi etal. 2012). On the", + "Introduction In individuals with diabetes, nonenzymatic glycation of proteins leads to the formation of advanced glycation end products (AGE) and this process occurs at an accelerated rate in chronic hyperglycaemia1, and also the levels are found to be increased in complications of diabetes, such as diabetic retinopathy (DR).2 AGE induces a variety of pathological changes, such as increased basement membrane thickening, arterial stiffness, and glomerular sclerosis.3,4AGEs bind to a specic receptor", + "AGEs accelerate atherosclerosis through cross-linking of proteins, platelet aggregation, defective vascular relaxation, and abnormal lipoprotein metabolism. 30 AGEs have a vital role in pathogenesis of diabetic nephropathy and progression of renal failure. Renal failure, in turn, results in decreased excretion and increased generation of AGEs (Figure 6). 629", + "vessels show enhanced subintimal protein and lipoprotein deposition; increased vascular permeability, e.g. to albumin; inactivation of nitric oxide; activation of endothelial receptors, leading to vasoconstriction and thrombosis; altered proteoglycan milieu; altered basement membrane cellular structure; proliferation of matrix. Strategies directed at the prevention of formation or the disruption of AGE cross-links may be promising. REFERENCES:", + "proteins and nucleic acids, leads to modification and then decline in structure and function of these molecules, as the cross-links accumulate both extracellularly and intracellularly over time. A prime example would be the crosslinking of collagen, which is thought to lead to typical phenomena observed in aging, such as increased susceptibility to atherosclerosis, osteoporosis, decreased joint elasticity, the formation of cataracts, and" + ], + "answer": "The accumulation of cross-links and advanced glycation end-products (AGEs) significantly impacts the structural integrity and function of aging tissues in several ways: 1. **Inflammation and Oxidative Stress**: AGEs accumulation leads to inflammation and oxidative stress, which can cause long-term vascular and end-organ damage [1], [4]. This is partly due to the interaction of AGEs with specific receptors such as RAGE, which perpetuates these adverse processes. 2. **Vascular Changes**: AGEs contribute to vascular hypertrophy, stiffening of collagen, and reduced arterial compliance, which are associated with aging and are accelerated by hyperglycemia [2]. This stiffening of collagen and reduction in arterial compliance can lead to decreased vascular function and increased risk of vascular complications. 3. **Cross-linking of Proteins**: AGEs cause cross-linking of proteins, which affects the structural integrity of tissues. For example, the cross-linking of collagen is associated with increased susceptibility to atherosclerosis, osteoporosis, decreased joint elasticity, and the formation of cataracts [10]. 4. **Endothelial Dysfunction**: AGEs impair endothelial function and vascular reactivity, which can lead to complications such as atherosclerosis and diabetic complications [5]. This impairment is due to the modification of lipoproteins and the release of cytokines and growth factors upon AGE interaction with receptors. 5. **Pathological Changes in Tissues**: AGEs induce various pathological changes, including increased basement membrane thickening, arterial stiffness, and glomerular sclerosis [7]. These changes contribute to the decline in tissue function and structure as they age. Overall, the accumulation of AGEs and the resulting cross-links compromise the structural integrity and function of tissues, contributing to the aging process and the development of age-related diseases.", + "question": "How do cross-links and advanced glycation end-products (AGEs) accumulation affect the structural integrity and function of aging tissues?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_3 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_3 new file mode 100644 index 0000000..a3f6cd5 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_3 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2019 - Remodeling of epigenome and transcriptome.pdf", + "2013 - Transposable elements become active and mobile in the genomes.pdf", + "2010 - Higher-order Genome Organization.pdf", + "2007 - The role of nuclear architecture.pdf", + "2010 - Higher-order Genome Organization.pdf", + "2007 - The role of nuclear architecture.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2010 - Higher-order Genome Organization.pdf", + "2016 - Epigenetic Mechanisms of Longevity and Aging.pdf", + "2008 - GENETIC REGULATION OF HEMATOPOIETIC STEM CELL AGING (3).pdf" + ], + "extraction_id": [ + "ab26a306-0581-5bdc-a6d1-689622689e90", + "dab38594-466b-50bc-8213-150f3862ff03", + "c4a47fc1-b528-5e29-9d13-e64be4e04938", + "c5185d6d-b244-57d7-886c-2ebb364a3ac7", + "1a3a302a-4009-5ccf-aafa-f5f5a258ffde", + "b36b1865-2949-50be-ad95-bdc9d05b82eb", + "04e838ad-d90d-5e9d-af94-8e975af339a0", + "1a3a302a-4009-5ccf-aafa-f5f5a258ffde", + "718d36c5-299d-596e-90be-416d12f7b5d1", + "6efb8add-cedc-5089-9374-2466867e388a" + ], + "document_id": [ + "87ffccee-fc33-5373-948d-67736aa0f069", + "c6901c06-c8ed-5220-a989-807bacdc9d0d", + "91339298-860e-57d0-b58d-5a4571b4fc2b", + "578e2f7d-ddd4-56c8-a5b0-670969f8ff1e", + "91339298-860e-57d0-b58d-5a4571b4fc2b", + "578e2f7d-ddd4-56c8-a5b0-670969f8ff1e", + "62b635c3-040e-512a-b016-6ef295308a1e", + "91339298-860e-57d0-b58d-5a4571b4fc2b", + "588185a0-e157-552f-a304-4beefb85d398", + "7412a162-ee3b-5f09-9886-8e9172dd3ee8" + ], + "id": [ + "chatcmpl-AIHWNXCXElapoM0J1wCt0Uh4pwpDs", + "1290eb6d-c454-5177-b55c-2e0f17265ab8", + "f51d2566-aef3-51af-ac47-cfba546bd293", + "212e1fcc-f0f0-5bd0-81af-aea694179b9e", + "12a416a1-9833-5e88-b86d-7ce6c54850b7", + "bada4b21-3c6d-55a4-b857-091a3a86f65d", + "ebd7a483-80a4-5f16-959d-e021635c88db", + "b2d6de59-f3d4-5f74-9bcb-96f00f885ba2", + "fa95b6a0-b4ef-5343-95aa-93d38aa291be", + "a681ba09-0707-5611-9a91-36f9967f91c8", + "14898b2f-4643-5362-be34-31d5ee5a4be6" + ], + "contexts": [ + "loss of chromatin homeostasis drives aspects of aging. As chroma-tin marks are relatively stable and can even persist through cell divi-sion (Kouskouti and Talianidis 2005), sustained alterations to thechromatin landscape may mediate the propagation of age-associat- ed functional decline. Age-dependent changes in chromatin marks (e.g., DNA meth- ylation, histone modifications) have been observed in multiple species and tissues (Benayoun et al. 2015; Booth and Brunet", + "contributes to the onset of tissue dysfunction and the eventual demise of organisms as they age. During replicative senescence of human fibroblasts chromatin is subject to extensive changes in the global distribution of euchromatin and heterochromatin [25,35]. We found that the fundamental architecture of the genome undergoes profound alterations: an overall closing of chromatin in euchromatic gene-rich regions, which is", + "impaired function of histone modifying activ-ities, which in turn lead to structural chroma- tin changes. The number of known diseasesOrganismal agingAging-associated gene expression programsCellular stress DNA damageChromatin remodelingEpigenetic status SusceptibilityHistone modifier redistribution Non-specific gene expression events Figure 3. Chromatin effects in aging. A complex network of interactions links chromatin structure to aging.", + "by Pelicci and colleagues in this issue). However, it could also be argued that chromatin structure is directly affected by the ageing process through an as-yet-unknown mecha - nism that leads to increased DNA damage and a perma - nent damage response that alters gene-expression patterns in a similar way to the model proposed in this review. o ver the coming years, as researchers use mammalian models to map the global pattern of chromatin modifi -", + "and peripheral heterochromatin blocks are lost during aging (Haithcock et al. 2005). The aging-associated defects in chromatin structure have various functional consequences.T o start with, aged genomes are characterized by increased DNA damage and high levels of per-sistent DNA breaks, possibly brought about by structural changes, which increase the suscepti- bility of the genome to damage. Furthermore,probably as a consequence of loss of pericentro- meric heterochromatin structure, physiologi-", + "related changes in gene expression and the ageing process4,5. Changes in gene expression were already known to contribute to cellular senescence6, a possible cause of ageing7, and may provide an explanation for the age-related decline in organ and tissue function in complex organisms.Although chromatin reorganization was linked to ageing in budding yeast over 10 years ago8,9, these ideas have remained untested. Recently, a growing appre - ciation for the importance of chromatin in regulating", + "tone loss in the ageing process has been attributed to alterations in heterochromatin, which are characterized by a decrease in its distribution in the genome and the content of characteristic heterochromatin histone marks (such as H3K9me3 and H3K27me3) as evidenced in fibroblasts cells from a HGS patient and healthy aged individuals [59, 60]. Interestingly, it has been suggested that the increase in chroma- tin opening in T cells from aged people could be related to histone loss, which in", + "long lifespan (Dang et al. 2009). Given theseextensive changes in histone modications, not surprisingly, aged cells show dramatic and global misregulation of gene expression. Al-though some of these changes are likely part of specic aging-related gene expression pro- grams including inammation and cellularstress responses, others likely occur largely sto- chastically because of random changes in epi- genetic modications and chromatin structure. The mechanisms that drive chromatin and", + "general loss of histones coupled with local and global chromatinremodeling, an imbalance of activating and repressive histone modications, and transcriptional change in all aging models. Additionally, particularly in mammalian systems, there is globaland local change in DNA methylation, site-specic loss and gain in heterochromatin, and signicant nuclear reorganization (Figure 1 ). It is as yet unclear whether changes in the activity of epigenetic", + "Amarcb1) as well as histone deacetylases (Hdac1, -5, and -6) and a DNA methyltransferace (Dnmt3b) were downregulated in aged cells. They also showed that several chromosomal regions changed with age in a coordinated manner resulting in an overall increase in transcriptional activity. They propos e that chromatin dysregulation and epigenetic changes drive the loss of cellular function and ultimately drive the aging process in HSCs. Consistent with these data, Polycomb proteins (transcriptional" + ], + "answer": "Age-related changes in chromatin architecture contribute to the decline in cellular function through several mechanisms: 1. **Loss of Chromatin Homeostasis**: Sustained alterations in the chromatin landscape, such as changes in DNA methylation and histone modifications, can mediate the propagation of age-associated functional decline [1]. These changes are relatively stable and can persist through cell division, affecting cellular function over time. 2. **Changes in Chromatin Distribution**: During aging, there is an extensive change in the global distribution of euchromatin and heterochromatin. Specifically, there is an overall closing of chromatin in euchromatic gene-rich regions, which contributes to tissue dysfunction and the eventual decline of cellular function [2]. 3. **Increased DNA Damage**: Aging-associated defects in chromatin structure lead to increased DNA damage and persistent DNA breaks. This is possibly due to structural changes that increase the genome's susceptibility to damage, further contributing to the decline in cellular function [5]. 4. **Histone Loss and Chromatin Remodeling**: There is a general loss of histones and chromatin remodeling, leading to an imbalance of activating and repressive histone modifications. This results in transcriptional changes that are observed in all aging models, contributing to the decline in cellular function [9]. 5. **Epigenetic Changes and Gene Expression**: Age-related chromatin dysregulation and epigenetic changes drive the loss of cellular function by altering gene expression patterns. These changes can lead to increased transcriptional activity in certain chromosomal regions, ultimately driving the aging process [10]. These changes in chromatin architecture collectively contribute to the decline in cellular function observed with aging.", + "question": "How do age-related changes in chromatin architecture contribute to the decline in cellular function?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_4 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_4 new file mode 100644 index 0000000..d1e0938 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_4 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2012 - Aging, Rejuvenation, and Epigenetic.pdf", + "2012 - Aging, Rejuvenation, and Epigenetic.pdf", + "2012 - Aging, Rejuvenation, and Epigenetic.pdf", + "2012 - Aging, Rejuvenation, and Epigenetic.pdf", + "2013 - Age-associated epigenetic drift implications.pdf", + "2012 - Aging, Rejuvenation, and Epigenetic.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2012 - Aging, Rejuvenation, and Epigenetic.pdf", + "2012 - Aging, Rejuvenation, and Epigenetic.pdf", + "2016 - Epigenetic drift in the aging genome a ten-year.pdf" + ], + "extraction_id": [ + "f244a68b-5127-5507-94a2-d2b8ca84f0ee", + "0e274732-b0df-53b8-999b-30b798af92e2", + "915ca931-d49d-5837-97fd-f06c145764d0", + "0e274732-b0df-53b8-999b-30b798af92e2", + "42343f61-f147-520b-bd14-0c2bf7b63262", + "617f523f-b892-5bfc-b99c-2e67a4cc185f", + "704a88b4-f49e-57cb-b572-1fa948b6065b", + "f244a68b-5127-5507-94a2-d2b8ca84f0ee", + "7f8f4ca0-9b27-55e3-a889-030af08dc84b", + "2f6d20f0-addc-51e8-979d-1aac7ac26694" + ], + "document_id": [ + "bde26feb-f423-51b0-89ec-6f079bfc8b17", + "bde26feb-f423-51b0-89ec-6f079bfc8b17", + "bde26feb-f423-51b0-89ec-6f079bfc8b17", + "bde26feb-f423-51b0-89ec-6f079bfc8b17", + "8513121f-71f3-5bb0-9433-feece9fd9fbc", + "bde26feb-f423-51b0-89ec-6f079bfc8b17", + "62b635c3-040e-512a-b016-6ef295308a1e", + "bde26feb-f423-51b0-89ec-6f079bfc8b17", + "bde26feb-f423-51b0-89ec-6f079bfc8b17", + "52f09ef3-4e4c-538f-909c-d28eb72d91f3" + ], + "id": [ + "chatcmpl-AIHWU7LIWS22cXcNTfkSGgjRTVQIK", + "b4eebcc5-781b-505b-a340-305b29285c66", + "78059a6b-4809-5d36-b961-6fcddbb06f2b", + "6baf63a6-fa5a-54e2-8290-af586a51243f", + "ef0f46ad-2e78-5666-b83d-36d2920b64ea", + "02361135-a01e-55f2-9efa-b7c465f2498b", + "82815a35-f43e-56fc-a254-92b03a278ab5", + "b5f6d630-dc24-50d7-af74-b3034cbb1055", + "8822b363-e906-5f83-a494-caad665c7af2", + "0e8901a7-c123-5e96-97fe-4d5cd85eb0c9", + "0aede05b-f0dd-595a-a11d-acac0970d25d" + ], + "contexts": [ + "experiments suggest that epigenetic features associated withaging can be reversed. In successfully reprogrammed iPSCs, the chromatin state of CDKN2A locus associated with aging is erased and restored to that of youthful cells ( Meissner, 2010 ). The requirement for proper epigenetic gene silencing for longevity has been observed in multiple model organisms, sug- gesting an evolutionarily conserved process ( Lin et al., 2000; Chen et al., 2005; Greer et al., 2010 ). The function of Polycomb", + "apparent rewinding of the aging clock without loss of differenti-ation. Formal demonstration will require clear epigenetic signa- tures of young and old cells and evidence that the aged cells have regained a youthful signature. It should be noted thatreprogramming of the epigenome to a youthful state in an aged cell has inherent risks and uncertainties. For example, the", + "et al., 2010 ). Clearly, inhibiting single signaling pathways (NF-k B and mTOR) is sufcient to restore some features of youthful cells, but the number of transcriptional regulatorsthat need to be modulated to result in full rejuvenation is unknown. Third, is the youthful state or the aged state domi- nant? It would be interesting to determine which epigeneticand transcriptional prole is more robust in experiments of fusion of young and old cells. Concluding Remarks", + "Rejuvenation: Is It Epigenetic Reprogramming?By analogy to the attainment of a pluripotent state by epigenetic reprogramming of a differentiated cell, is cellular rejuvenation byheterochronic parabiosis, NF- kB inhibition, or inhibition of mTOR signaling ( Figure 1 ) a form of epigenetic reprogramming from an aged state to a youthful state? If so, then these would be examples of an uncoupling of the differentiation program from the aging clock, with cells in each case manifesting an", + "with a healthy lifestyle may preserve a more intact epigenome and hence experi-ence longevity. Reprogramming of aged cells into iPSCs and regeneration of dif-ferentiated cells may provide a mechanism for epigenetic rejuvenation. In addition to epigenetic drift, telomere shortening has been associated with", + "tion through the lens of epigenetic reprogramming. By dening youthfulness and senescence as epigenetic states, a framework for asking new questions about the aging process emerges. Introduction The inexorable tolls of aging are evident in almost all living beings. From the onset of reproductive maturity, organismalaging is generally characterized by a decline in fecundity, an increased susceptibility to disease and tissue dysfunction, and increased risk of mortality ( Kirkwood, 2005; Hayick, 2007; Kirk-", + "others (i.e. DNA methylation influences chromatin structures, histones PTMs). Several important conclusions emerge from the presented findings: there are at least two ways to reverse or inhibit senescence by epigenetic mechanisms, whereby a healthy life expectancy could be prolonged. The first way involves rejuvenation through effective epigenetic reprogramming in cells undergoing senescence or cells derived from very aged patients or patients with progeroid syndromes, by which the", + "aging is at least in part, if not largely, a manifestation of epigeneticchanges, including those that may be secondary to genomicmutations, offers a theoretical construct for understanding the mechanisms of rejuvenation. If so, it should be possible to char- acterize young and old cells by specic transcriptional andepigenetic proles and states. Furthermore, the processes that underlie aging and rejuvenation should be identiable in terms", + "determinants of the aged state by genetically manipulatingspecic biochemical pathways. A recent example demonstratesthe power of transcriptional proling and bioinformatic analysis to reveal an aging signature that can be genetically engineered to reect a more youthful state ( Adler et al., 2007 ). In a compar- ison of old and young tissues from mice and humans, old tissues were found to express at signicantly higher levels a set of genes that contained sequences in their 5 0regulatory regions, indica-", + "Recently, studying the direct relationship between epigeneticmechanisms and the aging process itself is gaining increasing attention. The potential reversibility of these epigenetic changes that occur as a hallmark of aging offers excitingopportunities to alter the trajectory of age-related diseases. 8 This is especially important given the remarkable plasticityof aging. 9,10In the literature, age-associated epigenetic alter- ations have been identified by epigenome-wide association" + ], + "answer": "Studying the epigenetic reprogramming of aged cells to a more youthful state has provided several insights: 1. **Reversal of Aging-Associated Epigenetic Features**: Experiments have shown that epigenetic features associated with aging can be reversed. For instance, in successfully reprogrammed induced pluripotent stem cells (iPSCs), the chromatin state of the CDKN2A locus, which is associated with aging, is erased and restored to that of youthful cells [1]. 2. **Potential for Longevity**: Proper epigenetic gene silencing is required for longevity, as observed in multiple model organisms. This suggests that the process of epigenetic reprogramming might be evolutionarily conserved and could play a role in extending lifespan [1]. 3. **Rewinding the Aging Clock**: There is an apparent ability to rewind the aging clock without losing cellular differentiation. However, this requires clear epigenetic signatures of young and old cells and evidence that aged cells have regained a youthful signature [2]. 4. **Risks and Uncertainties**: While reprogramming the epigenome to a youthful state holds promise, it also carries inherent risks and uncertainties, highlighting the need for further research to understand the full implications and safety of such interventions [2]. 5. **Mechanisms of Rejuvenation**: The study of epigenetic reprogramming provides a framework for understanding the mechanisms of rejuvenation, suggesting that aging is at least partly a manifestation of epigenetic changes. This offers opportunities to alter the trajectory of age-related diseases [8], [10]. 6. **Prolonging Healthy Life Expectancy**: There are at least two ways to reverse or inhibit senescence through epigenetic mechanisms, which could prolong healthy life expectancy. One involves rejuvenation through effective epigenetic reprogramming in cells undergoing senescence or derived from very aged patients [7]. These insights collectively suggest that epigenetic reprogramming holds significant potential for reversing aging processes and extending healthy lifespan, although further research is needed to fully understand and safely harness these capabilities.", + "question": "What insights have been gained from studying the epigenetic reprogramming of aged cells to a more youthful state?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_5 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_5 new file mode 100644 index 0000000..98923a8 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_5 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2011 - Mitochondrial genome deletions and minicircles.pdf", + "2020 - Transposable elements, circular RNAs and mitochondrial.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2017 - Independent impacts of aging.pdf" + ], + "extraction_id": [ + "ef9463cd-cf21-527f-ae4a-3df211c78435", + "391985ac-70b7-57c9-97b2-940d8ebd2366", + "8a8e649d-6689-5d6d-91b6-157abfd8f990", + "5cbace8d-e538-5531-9311-ea9726ad2f15", + "385c192b-a416-5208-9615-20111ce782aa", + "7cf75da1-3c2a-5155-84dd-0dfe77d3fe41", + "c7041bbd-983f-5532-8b0e-cbd5f114a75f", + "c8db1d28-f6c2-5896-95ec-bb01159ba483", + "d226a80b-8a07-52ea-82b8-30adce468571", + "1f0b6363-a045-53aa-a124-4cf89e61fc26" + ], + "document_id": [ + "62b635c3-040e-512a-b016-6ef295308a1e", + "62b635c3-040e-512a-b016-6ef295308a1e", + "c28cecbc-be20-54e2-afdd-afb8d25b1ab1", + "7bebb41c-ac73-5917-91d3-4f59fbb3266a", + "62b635c3-040e-512a-b016-6ef295308a1e", + "62b635c3-040e-512a-b016-6ef295308a1e", + "62b635c3-040e-512a-b016-6ef295308a1e", + "62b635c3-040e-512a-b016-6ef295308a1e", + "62b635c3-040e-512a-b016-6ef295308a1e", + "d1d0b9ce-f827-5dfb-8e39-d87a9ca52f6d" + ], + "id": [ + "chatcmpl-AIHWdEvFttNJ6ZbP6sReC3nxIXsfz", + "4206977e-23df-5307-8d8a-cb2ed7b33595", + "7853fd79-e251-5e3f-8b6f-7d1ebf8182bc", + "1436639f-3759-5172-9b13-b1dd9105420e", + "7095cdbb-852e-541e-884b-a9e67c2c790c", + "a1ea550b-8017-58c5-a80f-f22f4869f792", + "8ec531e8-2692-5995-8f1e-246406b9de04", + "f41af83b-dd40-5128-b051-2b0f26942786", + "1a9d5c26-f606-5cb5-98ee-4120de3fbd1a", + "e183f824-0ca8-58aa-a06e-110a3a94c2e9", + "39019881-9b6d-5111-87ea-71c413bdf4ff" + ], + "contexts": [ + "abolic regulation through mitochondrial signaling. Am J Physiol Endocrinol Metab. 2014;306:E58191. 74. Zhang R, Wang Y , Ye K, Picard M, Gu Z.Independent impacts of aging on mitochondrial DNA quantity and quality in humans. BMC Genomics. 2017;18:890. 75. Hebert SL, Lanza IR, Nair KS.Mitochondrial DNA alterations and reduced mitochondrial function in aging. Mech Ageing Dev. 2010;131:45162. 76. Liu D, Li H, Lu J, Bai Y .Tissue-specific implications of mitochondrial alterations in aging.", + "mechanisms that lead to mitochondrial metabolism shifts in human aging are not completely understood, the literature reports that the failure in the mitochondrial metabolism of aged heart might be associated with mutations in the mtDNA.In this sense, the aged heart shows an increase over 15-fold on mtDNA mutations in com- parison to hearts from young people [101]. Mutations in genes that encode Polg-a, responsible for mtDNA repair machinery, cytochrome b, and several subunits of", + "22. Fleming JE, Miquel J, Cottrell SF, Yengoyan LS, Economos AC: Is cell aging caused by respiration-dependent injury to the mitochondrial genome?Gerontology 1982, 28:, 44-53. 23. Pak JW, Herbst A, Bua E, Gokey N, McKenzie D, Aiken JM: Mitochondrial DNA mutations as a fundamental mechanism in physiological declinesassociated with aging. Aging Cell 2003, 2:1-7. 24. Jacobs HT: The mitochondrial theory of aging: dead or alive. Aging Cell 2003, 2:11-17.", + "Sun., N, Youle, R. J. and Finkel, T. (2016). The mitochondrial basis of aging. Mol. Cell 61, 654-666. doi:10.1016/j.molcel.2016.01.028 Symer, D. E., Connelly, C., Szak, S. T., Caputo, E. M., Cost, G. J., Parmigiani, G. and Boeke, J. D. (2002). Human L1 retrotransposition is associated with genetic instability in vivo. Cell110, 327-338. doi:10.1016/S0092-8674(02)00839-5 Szabo, L., Morey, R., Palpant, N. J., Wang, P. L., Afari, N., Jiang, C., Parast,", + "limitations to study mitochondrial metabolism in human samples, in this section we briefly described the implications of mitochondrial metabolism for aging in the most studied and high energy demand human tissues, such as skeletal muscle, heart, and brain.Table 4.1 Main mitochondrial dynamics proteins that are altered in human tissues during the aging process Tissue/ organ Fission Fusion Biogenesis Mitophagy Refs Skeletal muscleIncreased fragmentation Decreased Drp1 proteinIncreased interconnected", + "96. Wei Y-H, Wu S-B, Ma Y-S, Lee H-C.Respiratory function decline and DNA mutation in mitochondria, oxidative stress and altered gene expression during aging. Chang Gung Med J. 2009;32:11332. 97. Kates AM, Herrero P, Dence C, Soto P, Srinivasan M, Delano DG, Ehsani A, Gropler RJ. Impact of aging on substrate metabolism by the human heart. J Am Coll Cardiol. 2003;41:2939. 98. Gmez LA, Monette JS, Chavez JD, Maier CS, Hagen TM.Supercomplexes of the mito-", + "phenotype, such as the Mitochondrial Free Radical Theory of Aging (MFRTA), and although these theories have been recently confronted, the role of mitochondria in the aging process is undeniable because of their versatile roles and implications for cellular function. MFRTA suggests that the oxidative damage of mtDNA is the key event disturbing the respiratory chain proteins to induce its dysfunction and increase ROS production in a vicious cycle [123]. However, alterations in mito-", + "102. Zhang R, Wang Y , Ye K, Picard M, Gu Z.Independent impacts of aging on mitochondrial DNA quantity and quality in humans. BMC Genomics. 2017;18:890. https://doi.org/10.1186/ s12864-017-4287-0. 103. Norddahl GL, et al. Accumulating mitochondrial DNA mutations drive premature hema- topoietic aging phenotypes distinct from physiological stem cell aging. Cell Stem Cell. 2011;8:499510. https://doi.org/10.1016/j.stem.2011.03.009.", + "78 p53, which regulate the catalytic subunits of ETC complexes [103]. Unfortunately, these data have only been observed in murine models of aging and require further verification in human samples. Mitochondrial Metabolism intheAged Brain In normal conditions, the brain consumes around 25% of the total body glucose via glycolysis and mitochondrial OxPhos [104]. So besides the mitochondrial dynam- ics dysfunctions described above, during aging there is also a decline in energy", + "mitochondrial DNA mutations can reduce lifespan. Sci Rep. 2014;4:6569. 20. Ross JM, Stewart JB, Hagstrm E, Bren S, Mourier A, Coppotelli G, Freyer C, Lagouge M, Hoffer BJ, Olson L. Germline mitochondrial DNA mutations aggravate ageing and can impair brain development. Nature. 2013;501(7467):412 5. 21. Sondheimer N, Glatz CE, Tirone JE, Deardorff MA, Krieger AM, Hakonarson H. Neutral mitochondrial heteroplasmy and the influence of aging. Hum Mol Genet. 2011;20(8):1653 9." + ], + "answer": "Alterations in the mitochondrial genome and bioenergetics significantly influence the aging process in humans through several mechanisms: 1. **Mitochondrial DNA Mutations**: As humans age, there is an increase in mitochondrial DNA (mtDNA) mutations. These mutations can lead to a decline in mitochondrial function, which is a fundamental mechanism in the physiological declines associated with aging [3]. Specifically, the aged heart shows a significant increase in mtDNA mutations compared to younger hearts, which may contribute to the failure in mitochondrial metabolism observed in aging [2]. 2. **Respiratory Function Decline**: Aging is associated with a decline in respiratory function and increased oxidative stress, which can lead to further DNA mutations and altered gene expression in mitochondria [6]. This decline in mitochondrial respiratory function is linked to the production of reactive oxygen species (ROS), which can damage mtDNA and exacerbate mitochondrial dysfunction [7]. 3. **Mitochondrial Dynamics**: Changes in mitochondrial dynamics, such as increased fragmentation and decreased fusion, are observed in aging tissues like skeletal muscle, heart, and brain. These alterations can impair mitochondrial biogenesis and mitophagy, leading to reduced energy production and increased cellular stress [5]. 4. **Bioenergetic Shifts**: The aging process involves shifts in mitochondrial metabolism, particularly in high-energy-demand tissues. For example, the brain experiences a decline in energy production due to mitochondrial dysfunction, which can affect cognitive function and overall brain health [9]. Overall, the accumulation of mtDNA mutations, decline in mitochondrial respiratory function, and alterations in mitochondrial dynamics and bioenergetics contribute to the aging process by impairing cellular energy production and increasing oxidative stress, leading to cellular and tissue dysfunction.", + "question": "How do alterations in the mitochondrial genome and bioenergetics influence the aging process in humans?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_6 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_6 new file mode 100644 index 0000000..8d0e520 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_6 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2012 - Genome-Environment Interactions That Modulate.pdf", + "2006 - Beyond the evolutionary theory.pdf", + "2011 - Genomics of human longevity.pdf", + "2023 - Genome-wide RNA polymerase stalling.pdf", + "2009 - High tandem repeat content in the genome of the short-lived.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2007 - Impaired Genome Maintenance Suppresses.pdf", + "2006 - Genomic Instability.pdf", + "2003 - Lifelong voluntary exercise in the mouse prevents.pdf" + ], + "extraction_id": [ + "a933e419-b369-5de5-8236-a1944a486e51", + "a01ca925-4ccf-5863-a162-7bd4c754fe89", + "373c0bb8-f6b2-5c6b-b768-226b12ba6385", + "89586b79-902d-5e2b-9b8a-b7a8c4971783", + "31088092-778f-59e0-a9de-5ec25c241aab", + "fcb05f39-0821-56e1-a627-92911d4d46bc", + "8f165f13-b4a5-5553-a992-f4a70b079898", + "74482eef-9eb3-5915-838e-5f1f0439c410", + "634526cb-daa7-5769-a3f2-741931964ccd", + "b6422281-0ef4-58f3-9d43-4c8c7534e057" + ], + "document_id": [ + "62b635c3-040e-512a-b016-6ef295308a1e", + "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "a8da3f57-a8dc-55c3-9dc9-eb778105e680", + "2e038219-fdaa-506f-9cd3-51379054130e", + "78812a12-8d31-5159-8367-b0d38e5bc84b", + "bcc64bfb-9b7f-5f6f-83f3-861ab8f8a8e3", + "62b635c3-040e-512a-b016-6ef295308a1e", + "4ed9d527-4f92-51a3-a5d7-6caab655b1be", + "c9c9a8d6-2daf-5ff2-86bd-84e087ba1a47", + "24d4f270-f45b-5830-84f9-b1e5bcd3c070" + ], + "id": [ + "chatcmpl-AIHWn49FE1NOTaexKIcZmCPOm6e2F", + "bc91a693-0eff-5911-ae9a-b192f1088119", + "8ac8b243-f23c-596d-add2-441df4e980a9", + "759ea147-5ac2-5d48-80f2-3693f56d4afc", + "fc227aaf-85c1-553f-aa59-d9bcdd803aaf", + "a0198ed1-1303-5652-aafc-1a1287914ac4", + "e3a78ec1-7f79-55db-a13d-196f718f8a1d", + "bdebc11c-26ca-5ac0-bab3-503bd7d25f50", + "9868d78e-6151-5383-9d52-542a8b43c50f", + "58d61a19-d5b0-501c-90a9-2eeb66866c07", + "e51c4436-0895-5adb-8a80-a3e1ee6956dd" + ], + "contexts": [ + "the attention of researchers as a therapeutic target for age-related diseases [109]. Resveratrol, a phytochemical enriched in the skin of red grapes and wine, has been actively investigated to determine whether it promotesSIRTs activity with conse- quent beneficial effects on aging [110]. IGF Because insulin/IGF-1 function through signaling as a nutrient sensor and controls the transcription of stress response genes, the insulin/IGF-1 pathway provides a", + "the use of lowered IGF signaling (e.g., by target-ing IGF receptors) to treat certain age-related diseasessuch as cancer (Pollak et al., 2004), Alzheimers disease(Cohen et al., 2009), and autoimmune diseases (Smith,2010). Moreover, a number of genes and pathways associ-ated with longevity and CR are part of nutrient-sensingpathways that also regulate growth and development, in-cluding the insulin/IGF1/GH pathway (Narasimhan et", + "as insulinIGF-1 signalling [6], cellular senescence [4], protein refolding [4345] , autophagy [41] and phase 1 and 2 detoxication [36,37,52] . These represent major points of intervention against ageing-related disease. As shown here, lifespan pathways control improved cellular maintenance, which leads to slowed ageing(e.g. slowed normal cognitive ageing) and protection against diseases of ageing (e.g. neurodegenerative diseases of ageing, such as Alzheimers and Parkinsons", + "ent-sensing pathways such as insulin/insulin-likegrowth factor (IGF-1) signalling (IIS) and target of rapamycin (TOR) signalling mediated lifespan exten- sion, and also the extension of lifespan by DR [ 2]. An interesting observation from the perspective ofhuman ageing is that, in rodents and monkeys, dietsrestricted in glucose, fat or protein uptake reduced ordelayed the risk of cancer and metabolic disease,thus extending the healthspan of the animals [ 2]. Fol-", + "43. Svensson, J. et al. Liver-derived IGF-I regulates mean life span in mice. PLoS ONE 6, e22640 (2011). 44. Junnila, R. K., List, E. O., Berryman, D. E., Murrey, J. W. & Kopchick, J. J. The GH/IGF-1 axis in ageing and longevity. Nat. Rev. Endocrinol. 9, 366376 (2013). 45. Yuan, R. et al. Aging in inbred strains of mice: study design and interim report on median lifespans and circulating IGF1 levels. Aging Cell 8, 277287 (2009). 46. Zhu, H. et al. Reference ranges for serum insulin-like growth", + "5. Piper MD, Selman C, McElwee JJ, Partridge L: Separating cause from effect: how does insulin/I GF signalling control lifespan in worms, flies and mice? J Intern Med 2008, 263:179-191. 6. Holzenberger M, Kappeler L, De Magalhaes Filho C: IGF-1 signaling and aging. Exp Gerontol 2004, 39:1761-1764. 7. Zahn JM, Kim SK: Systems biology of aging in four species. Curr Opin Biotechnol 2007, 18:355-359. 8. McElwee JJ, Schuster E, Blanc E, Piper MD, Thomas JH, Patel DS,", + "humans enriched for familial longevity. Aging Cell. 2016;15(6):112631. 44. Lee WS, Kim J.Insulin-like growth factor-1 signaling in cardiac aging. Biochim Biophys Acta Mol basis Dis. 2018;1864(5 Pt B):19318. 45. Balasubramanian P, Longo VD. Growth factors, aging and age-related diseases. Growth Hormon IGF Res. 2016;28:668. 46. Suzuki K, etal. Serum insulin-like growth factor-1 levels in neurodegenerative diseases. Acta Neurol Scand. 2019;139(6):5637.", + "paradigms for lifespan extension (C. elegans, D. melanogaster), genetic interference in the insulin-signaling pathway can prolong life multi-fold [47,48]. In mammals, IGF1-decient, Ames and Snell dwarf mice (characterized by defects in the development of the anterior pituitary due to mutations in the Prop-1 and Pit1 loci and diminished levels of GH, thyroid stimulating hormone, and prolactin hormone) combine", + "the role of IGF-1 in life span regulation is complex. In theory,SIRT6 might play a role in insulin signaling, similar to Sir2 fac- tors in other lower organisms. However, as in the prematureaging mouse models described above, it remains unclear whether the altered serum IGF-1/insulin levels of SIRT-6- decient mice directly contribute to aging-like phenotypesor, alternatively, reect compensatory alterations. In this re- gard, it will be of interest to determine whether SIRT6 is", + "lin-like growth factors (IGFs), and receptors in theinsulin-signaling pathway has been shown to confergreater longevity in yeast (12, 16), nematodes (21, 44),fruit ies (10, 43), mutant long-lived mice (4, 11), and caloric-restricted mice (40). Therefore, the as-yet un-identi ed mechanism of insulin signaling on lifespan" + ], + "answer": "The insulin/IGF-1 signaling pathway has been identified as a significant target for extending healthspan and lifespan due to its role as a nutrient sensor and its control over the transcription of stress response genes [1]. Here are the therapeutic potentials and challenges associated with targeting this pathway: ### Therapeutic Potentials: 1. **Treatment of Age-Related Diseases**: Lowering IGF signaling, such as by targeting IGF receptors, has been proposed as a treatment for age-related diseases including cancer, Alzheimer's disease, and autoimmune diseases [2]. This suggests that modulating this pathway could have broad therapeutic applications in managing diseases associated with aging. 2. **Lifespan Extension**: Genetic interference in the insulin-signaling pathway has been shown to prolong life in various organisms, including C. elegans, D. melanogaster, and certain mouse models [8]. This indicates a potential for extending lifespan through targeted interventions in this pathway. 3. **Improved Cellular Maintenance**: The insulin/IGF-1 signaling pathway is involved in processes such as cellular senescence, protein refolding, and autophagy, which are crucial for cellular maintenance and protection against aging-related diseases [3]. Enhancing these processes could lead to slowed aging and improved healthspan. ### Challenges: 1. **Complexity of the Pathway**: The role of IGF-1 in lifespan regulation is complex, and it is not fully understood how alterations in this pathway contribute to aging phenotypes [9]. This complexity poses a challenge in developing targeted therapies without unintended consequences. 2. **Balancing Growth and Longevity**: The insulin/IGF-1 pathway is also involved in regulating growth and development. Therefore, interventions that reduce IGF signaling must carefully balance the trade-offs between promoting longevity and maintaining necessary growth functions [2]. 3. **Species-Specific Responses**: While interventions in the insulin/IGF-1 pathway have shown promising results in model organisms, translating these findings to humans is challenging due to species-specific differences in the pathway's role and regulation [8]. Overall, while targeting the insulin/IGF-1 signaling pathway holds significant promise for extending healthspan and lifespan, it requires careful consideration of the pathway's complexity and the potential trade-offs involved.", + "question": "What are the therapeutic potentials and challenges of targeting the insulin/IGF-1 signaling pathway for extending healthspan and lifespan?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_7 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_7 new file mode 100644 index 0000000..4f86bf2 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_7 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2022 - Immunity and lifespan answering.pdf", + "2018 - Metabolomic pathways to osteoporosis in middle-aged women A genome-metabolome-wide.pdf", + "2017 - An integrative metabolomics.pdf", + "2017 - An integrative metabolomics.pdf", + "2022 - A review on the application of the exposome.pdf", + "2017 - An integrative metabolomics.pdf", + "2012 - Systems Biology in Aging Linking the Old and the Young.pdf", + "2017 - An integrative metabolomics.pdf", + "2019 - Undulating changes in human plasma proteome.pdf", + "2018 - Spontaneous DNA damage to the nuclear genome promotes senescence.pdf" + ], + "extraction_id": [ + "d4db0b82-40d3-5341-ad30-c70a91fdc785", + "e92950f9-a8d6-5aa5-bf83-ab1cef74627d", + "09a73df7-f690-5984-a498-69a8077fe327", + "af201c05-daed-5cba-abc8-e714483e602f", + "cac0d599-4e0a-5826-b47f-e71b52203956", + "f9c942d2-a191-52d4-8018-1030e414649d", + "6794bfa0-86ff-506f-ac40-35a9b1e33bcf", + "500f52f7-9205-5859-a156-6d30575a3d62", + "24e63f26-0bac-59d4-b325-9c8ead69a4de", + "40e2d528-9297-575f-82a9-178aae0bab81" + ], + "document_id": [ + "a834e7ee-7bab-5c4d-a236-b570d1ae635f", + "f9aa8a09-5148-5399-b6be-c3350f12c0f3", + "cb0831f4-540a-5620-b69e-03d6127f84e5", + "cb0831f4-540a-5620-b69e-03d6127f84e5", + "803a14cc-d8ab-54ca-80d6-78f1677457f9", + "cb0831f4-540a-5620-b69e-03d6127f84e5", + "cf7a8c59-4b4d-5e04-94b6-dd97edcb47a8", + "cb0831f4-540a-5620-b69e-03d6127f84e5", + "53c3130f-7029-50de-8dba-8714dfa36420", + "08be7274-78a3-5e93-9e8c-3d4f6dbeacf9" + ], + "id": [ + "chatcmpl-AIHX1EytrrBFzyZb7piMsWydaKzhq", + "a8194abc-51ab-5c29-a6be-f34bb24e0b47", + "1d8fd475-f7a7-55c6-881e-6985826c1e23", + "4547b6ad-efaf-509e-8e0b-5587542905fd", + "3dba594a-b79b-5bc6-95f6-6e0a36193818", + "ce9d4d88-2586-5071-bf9e-45b7172b0e8e", + "beea72ed-e213-5877-8144-d0ef000a2912", + "6ad38ef0-c6bd-5b6a-9fb6-53c04f18a76d", + "554f2525-a8cb-5003-be3d-137da97ea97f", + "d0b9df07-f6aa-52a5-9696-81f9034d9548", + "07a5111b-b38b-5e1a-bd76-9372499a4dd9" + ], + "contexts": [ + "learning to show that plasma proteins that predict age are predominantly associated with immunity [91]. State-of-the-art metabolomics approaches are also now allowing age-related changes in me- tabolite pro les to be studied, which provide new insights into the physiological mechanisms of age- ing [ 92,93]. The integration of multiple datasets generated from genomes, epigenomes, transcriptomes, proteomes, and metabolomes, an approach termed multi-omics , offers great", + "13. Menni C, Kastenmuller G, Petersen AK, et al. Metabolomic markers reveal novel pathways of ageing and early development in human populations. Int J Epidemiol 2013;42:1111- 9. 14. Evans AM BB, Liu Q, Mitchell MW, Robinson RJ, et al. . High Resolution Mass Spectrometry Improves Data Quantity and Quality as Compared to Unit Mass Resolution Mass Spectrometry in High- Throughput Profiling Metabolomics. Metabolomics 2014;4:132.", + "Due to the mild adaptions, the identification of func- tionally altered metabolic activity in aged skin interpret- ation of significant metabolite and transcript changes of small magnitude is especially challenging. Therefore, we employed the previously presented locality scoring ap- proach [60] to identify age-dependent transcriptional al- terations of enzymes that functionally effect proximal metabolic activity and thus metabolite levels. This inte- grated analysis revealed age-dependent, concerted me-", + "matched transcriptome and metabolome data highlighted transcriptionally-driven alterations of metabolism during aging such as altered activity in upper glycolysis and glycerolipid biosynthesis or decreased protein and polyamine biosynthesis. Together, we identified several age-dependent metabolic alterations that might affect cellular signaling, epidermal barrier function, and skin structure and morphology.", + "used to assess biological responses provides new oppor - tunities to understand the impact of the environment on the risk of age-related diseases. For example, the multi - omics analysis and integration method produces a pri - ority list of multiple sets of biomarkers, which together reflect the molecular responses of the exposome. Each of these data warrants integration into a biomarker panel to aid physicians in developing age-related disease diagno - ses and prognoses [78].", + "summary, we identified age-dependent changes in gene expression in different metabolic pathways that have been associated with epidermal homeostasis and there- fore might be important to sustain epidermal function. Integrated analysis of transcriptome and metabolome data Since the age-dependent adaptations of metabolite and transcript levels are only mild, we set out to identify metabolic enzymes that featured an age-dependent and functional change in activity driven by altered gene ex-", + "These high throughput prof iling experiments have gener- ated large amounts of data for meta-analysis [24], which can compare molecular functions and expression patterns that change during aging in different systems. However, such studies are far from exhaustive, as they only describe the molecular changes during aging, which could in fact be the consequence of aging, rather than the cause of aging. Thus to explore the causal factors for aging, studies are increasingly", + "over, the integration of trans criptome and metabolome data revealed a transcriptionally re gulated reduction in protein as well as polyamine biosynthesis and adaptation in upper glycolysis and glycerolipid biosynthesis in aged skin. Results Differences in the epidermal skin metabolome of young and old human volunteers To chart metabolic adaptations in human skin during aging in vivo , we performed non-targeted metabolomicsanalysis of epidermal skin tissue samples obtained from", + "proteomes overlap significantly with the waves of aging proteins (Supplementary Table 15). Accounting for heterogeneous and com - plex changes to the plasma proteome during life will likely improve the sensitivity and specificity of prognostic and diagnostic tests. Moreover, these results are pertinent when considering the use of blood or blood products to treat aging and age-related diseases 39. Specifically, identifying plasma proteins that promote or antagonize", + "rmed using authentic standards. One of the key nodes identi ed by metabolomics as signi cantly altered with accelerated and normal aging was glutathione metabolism ( Fig. 4A), a key antioxidant and index of oxidative stress [71]. Dierential MS was used for proteomics analysis to identify redox- related proteins signi cantly altered in the livers of 3 4 month-old progeroid Ercc1/mice and old WT mice (> 2 years-old) vs. adult WT mice. Expression of catalase, SOD1 (CuZnSOD) and SOD2 (MnSOD)" + ], + "answer": "The integration of proteomics and metabolomics data can provide a comprehensive understanding of age-associated metabolic shifts by revealing changes in protein expression and metabolite profiles that occur with aging. This multi-omics approach allows for the identification of specific pathways and molecular mechanisms that are altered as organisms age. 1. **Proteomics Insights**: Proteomics data can identify plasma proteins that predict age and are predominantly associated with immunity [1]. This suggests that changes in protein expression related to immune function are significant in the aging process. 2. **Metabolomics Insights**: Metabolomics approaches enable the study of age-related changes in metabolite profiles, providing new insights into the physiological mechanisms of aging [1]. For example, metabolomics has identified significant alterations in glutathione metabolism, a key antioxidant pathway, which is indicative of oxidative stress associated with aging [10]. 3. **Integrated Analysis**: By integrating transcriptome and metabolome data, researchers have identified transcriptionally-driven alterations in metabolism during aging, such as changes in glycolysis and glycerolipid biosynthesis, and reductions in protein and polyamine biosynthesis [4], [8]. These changes can affect cellular signaling, epidermal barrier function, and skin structure and morphology, highlighting the interconnected nature of metabolic pathways and their impact on aging. 4. **Functional Changes**: The integration of these datasets can also reveal age-dependent changes in the activity of metabolic enzymes, which are driven by altered gene expression [6]. This helps in understanding how mild adaptations in metabolite and transcript levels contribute to maintaining functions like epidermal homeostasis during aging. Overall, the integration of proteomics and metabolomics data provides a holistic view of the molecular changes that occur with aging, allowing for the identification of biomarkers and pathways that could be targeted to mitigate age-related decline.", + "question": "How can the integration of proteomics and metabolomics data shed light on age-associated metabolic shifts?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_8 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_8 new file mode 100644 index 0000000..43d59ea --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_8 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2016 - Epigenetics and aging.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2022 - Predict the role of lncRNA in kidney aging.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2016 - Epigenetics and aging.pdf", + "2015 - Comprehensive transcriptional landscape.pdf", + "2018 - Mechanisms of Vascular Aging.pdf", + "2022 - Predict the role of lncRNA in kidney aging.pdf" + ], + "extraction_id": [ + "e2fc0d00-8552-5b39-830f-6df7ec7c32e4", + "91434549-bda3-5154-b089-28efed9c1089", + "b45152cc-6626-5989-a1b0-148c137ea1f3", + "3932ada5-6fc4-5354-b52a-60fddbd47d3e", + "67e3cb94-cc30-58fe-8ff7-a9790c74325f", + "cb24a361-e7b4-5d35-a507-12ef65603d1f", + "5221f1f8-1c53-590f-86c2-23ab47ac0cbc", + "45ff5315-7500-5641-9c1a-e03b8aafc2d5", + "e308005e-d6ef-5492-a9a9-95256bb9ccee", + "67e3cb94-cc30-58fe-8ff7-a9790c74325f" + ], + "document_id": [ + "62b635c3-040e-512a-b016-6ef295308a1e", + "71b206ec-81bd-5194-8b21-ae522f8cbc2d", + "62b635c3-040e-512a-b016-6ef295308a1e", + "62b635c3-040e-512a-b016-6ef295308a1e", + "0d3b0558-289c-5af0-843a-f288d5da3d8c", + "62b635c3-040e-512a-b016-6ef295308a1e", + "71b206ec-81bd-5194-8b21-ae522f8cbc2d", + "6f223b7b-d0ed-55d3-be91-a9e704149a94", + "659b84b6-63dd-5bb1-80ee-7478ed3c47e3", + "0d3b0558-289c-5af0-843a-f288d5da3d8c" + ], + "id": [ + "chatcmpl-AIHX9ExmiM3mDYaf83XTHzQDSE0IN", + "41e5a2ca-1c83-5394-8fbf-c9dcc75e6a51", + "cb309a6a-4566-5de2-9687-cffa2f7737d2", + "8fa044d2-c807-5207-8361-ea22659d8b63", + "e4d9a99d-4d28-5432-8e91-09388ea4b613", + "85a38fea-bd20-5170-bba0-963b12633c55", + "36a2ed56-a0b9-589d-b178-f1515337f1ae", + "577459d5-e2fc-599f-9806-3d18ab6837e6", + "ab28b2fc-4144-5b86-92af-d6054794a0b1", + "90c36562-0443-5100-b710-d750bd365b46", + "c2978dcd-0bab-5ca9-8130-0cdca1cc9330" + ], + "contexts": [ + "lncRNA which overexpression participates in the regulation of age-associated car - diovascular diseases as it is a non-canonical precursor for hsa-miR-4485 and hsa- miR- 1973 microRNAs [62]. These studies demonstrate that not only coding genes (which represent only 2% of the genome sequence) are implicated in aging regula- tion, but also lncRNAs and microRNAs participate in tissue age-related changes. circRNAs are non-coding covalently closed single-stranded transcripts produced", + "(2008). 192. K. Abdelmohsen, A. Panda, M.-J. Kang, J. Xu, R. Selimyan, J.-H. Yoon, J. L. Martindale, S. De, W. H. Wood III, K. G. Becker, M. Gorospe, Senescence-associated lncRNAs: Senescence- associated long noncoding RNAs. Aging Cell 12, 890 900 (2013). 193. S. Kour, P. C. Rath, Long noncoding RNAs in aging and age-related diseases. Ageing Res. Rev. 26,1 21 (2015). 194. R. Johnson, Long non-coding RNAs in Huntington s disease neurodegeneration. Neurobiol. Dis. 46,2 4 5 254 (2012).", + "155 Premature ageing has been associated with altered expression of lncRNAs that participate in the regulation of the telomere length by modulating the TERT activity and synthesis of telomeric repeats [155, 161]. Furthermore, it has been reported that changes in the expression levels of some lncRNAs are associated with the develop- ment of AD [162]. Circular RNAs andAgeing Circular RNAs (circRNAs) are highly conserved covalently closed non-coding", + "interacting with proteins and nucleic acids in order to regulate gene expression (by indirect epigenetic mechanisms or by direct mechanisms acting as antisense tran- scripts or transcriptional coactivators), nuclear location of transcription factors and stabilization of ribonucleoprotein complexes [155]. It has been reported that lncRNAs are important in the regulation of ageing-associated mechanisms in humans and ani-", + "progression. LncRNA H19 was recently reported to play a crucial role in the activation of MAPK and the NF-kB signaling pathway and the induction of atherosclero - sis [3]. lncRNAs play crucial roles in the progression of diabetic nephropathy [12], glomerular disease [13] and renal fibrosis [14]. The lncRNA Arid-IR promotes NF- kB-mediated kidney inflammation by targeting NLRC5 transcription [15]. The cell cycle changes during aging. Previous studies have shown that lncRNAs are related to", + "expression of SIRT1 and are decreased in lymphoblastic cell lines generated from centenarians compared with those of AD patients, suggesting a protective effect of these miRNAs against neurodegeneration [66]. Long noncoding RNAs are important regulators of transcriptional networks and the closed or opened chromatin state [2]. One interesting example of an lncRNA is that associated with aging, H19. This lncRNA interacts with MBD1 (a methyl-", + "associated factors, modulating aging and senescence directly or in-directly. One such example includes a specific lncRNA, Gas5 ,w h i c h is highly expressed in aged mice brain and has been associated with im-paired learning ( 189). Another bona fide example is H19lncRNA, a dif- ferentially spliced product from the H19gene located at the IGF2/H19 imprinted locus, which interacts with methyl-CpG binding domain", + "tempting to speculate that these lncRNAs may exert some regulatory control of this locus, possibly contributing to senescent phenotypes. Together, these findings point to- wards a host of age-related ncRNAs as regulators of aging pathways and networks. Interaction network analysis The increased accuracy and breadth of our RNA-seq data sets allowed us to generate networks of gene func- tional change in aging liver, above and beyond what was observed using DAVID or GOrilla. Using Ingenuity", + "RNAs interact with proinflammatory signaling pathways and regulate senescence; however, their role on regulation of vas-cular aging processes is virtually unknown. 151 Interestingly, there is initial evidence linking the expression of the long noncoding RNA Meg3 (maternally expressed 3) to age-related impairment of angiogenic capacity of endothelial cells.152 Further studies are definitely needed to understand the", + "Page 2 of 11 Lietal. BMC Genomics (2022) 23:254 mechanism of kidney aging will be of great significance for delaying the occurrence and development of renal aging. Although a small number of studies have been conducted on renal aging, it is still meaningful to com - prehend the mechanism of renal aging. Long chain noncoding RNAs (lncRNAs) are more than 200 nucleotides in length. LncRNAs regulate transcrip - tional and posttranscriptional RNA processing, transla -" + ], + "answer": "Long non-coding RNAs (lncRNAs) play significant roles in the regulation of aging and age-related diseases through various mechanisms: 1. **Regulation of Age-Associated Cardiovascular Diseases**: LncRNAs are involved in the regulation of age-associated cardiovascular diseases by acting as non-canonical precursors for specific microRNAs, such as hsa-miR-4485 and hsa-miR-1973, which participate in tissue age-related changes [1]. 2. **Senescence-Associated lncRNAs**: Certain lncRNAs are associated with cellular senescence, a key process in aging. These senescence-associated lncRNAs are implicated in the regulation of aging mechanisms [2]. 3. **Telomere Length Regulation**: LncRNAs are involved in the regulation of telomere length by modulating TERT activity and the synthesis of telomeric repeats, which is crucial for cellular aging and longevity [3]. 4. **Gene Expression Regulation**: LncRNAs interact with proteins and nucleic acids to regulate gene expression through epigenetic mechanisms, acting as antisense transcripts or transcriptional coactivators. They also influence the nuclear location of transcription factors and stabilize ribonucleoprotein complexes, which are important in aging-associated mechanisms [4]. 5. **Disease Progression**: LncRNAs play roles in the progression of various age-related diseases, such as atherosclerosis, diabetic nephropathy, glomerular disease, and renal fibrosis. For example, lncRNA H19 is involved in the activation of signaling pathways that induce atherosclerosis [5]. 6. **Neurodegeneration**: LncRNAs are implicated in neurodegenerative diseases, such as Huntington's disease, by regulating transcriptional networks and chromatin states [6]. 7. **Impaired Learning and Senescence**: Specific lncRNAs, like Gas5, are associated with impaired learning in aged brains, and others, like H19, interact with methyl-CpG binding domains, influencing senescence and aging pathways [7]. 8. **Angiogenic Capacity**: The expression of lncRNA Meg3 is linked to age-related impairment of the angiogenic capacity of endothelial cells, indicating a role in vascular aging processes [9]. Overall, lncRNAs are crucial regulators of aging and age-related diseases through their diverse roles in gene expression, cellular senescence, disease progression, and other aging-related mechanisms.", + "question": "What role do long non-coding RNAs (lncRNAs) play in the regulation of aging and age-related diseases?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_9 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_9 new file mode 100644 index 0000000..b9a9aea --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_9 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2021 - Epigenetics of Aging and Aging-Associated Diseases.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2021 - Epigenetics of Aging and Aging-Associated Diseases.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf" + ], + "extraction_id": [ + "fcc88af4-1949-59fe-8111-200ec0dcb7d6", + "c072d600-8450-5842-ade1-aefd03854312", + "8db25d5e-25bd-5873-a53d-3815badbfd32", + "267468ed-0f9f-5a55-9334-9630792f300d", + "625c559f-9ef6-5bef-8b4c-c57a72d421ed", + "7d0ed573-4d0a-5de2-8be2-1ec0fb3a5800", + "1caf6ac0-0409-5b28-8fcf-bdffff2738a8", + "5f85264a-a5cd-5ef6-a4c9-900dcb7b07ad", + "e2bc9b8e-2349-509b-a148-fbd86f0455f4", + "267468ed-0f9f-5a55-9334-9630792f300d" + ], + "document_id": [ + "62b635c3-040e-512a-b016-6ef295308a1e", + "62b635c3-040e-512a-b016-6ef295308a1e", + "70945353-4808-539a-80f9-5632c27913e5", + "62b635c3-040e-512a-b016-6ef295308a1e", + "62b635c3-040e-512a-b016-6ef295308a1e", + "62b635c3-040e-512a-b016-6ef295308a1e", + "62b635c3-040e-512a-b016-6ef295308a1e", + "62b635c3-040e-512a-b016-6ef295308a1e", + "70945353-4808-539a-80f9-5632c27913e5", + "62b635c3-040e-512a-b016-6ef295308a1e" + ], + "id": [ + "chatcmpl-AIHXK8F2Ohi1RX10guI90pglYXyhM", + "9e4d48fb-e942-52a6-8e7e-57313d567a72", + "d7a12958-6d0b-546f-b0aa-152b6812e2fd", + "093e7604-5108-5fda-850e-007817090a9a", + "9a06df0b-a5b6-52d8-82c1-9dda446f9132", + "49c65d89-ec44-5412-a5bf-d94649e4afc3", + "a5ffc379-24d5-5c73-8435-41ca43af6347", + "7387d1f6-323a-52ea-90d4-6821fea31bf9", + "a02244c8-44da-595f-8a61-42bae541d784", + "4eb34c07-921b-55bb-98eb-ff013bb2ace0", + "c6c119e6-362e-5ae7-a1f1-a5e75eb456ba" + ], + "contexts": [ + "models of ageing, but it will also drastically accelerate the generation of refined ver - sions of those models or even allow the development of new research approaches in non-model organisms. Moreover, CRISPR-based genome editing is already having a significant impact in research aiming to understand the cellular and molecular origins of age-related diseases, as well as developing potential treatments against 11 Applications ofCRISPR-Cas inAgeing Research", + "of ageing. Finally, we will review how CRISPR-Cas has been used for creating new models for the study of age-related diseases, as well as for manipulating disease- associated gene pathways. S. Haston et al.", + "ularly Interspaced Short Palindromic Repeats (CRISPR)/Cas9) will be beneficial in clari- fying aging-processes across species. An improved understanding of epigenetic mechanisms affecting longevity will be deciding crucial step towards the identification of new potential therapeutic targets. In fact, epigenetic drugs are of particular interest to the clinic due to their reversible and transient effect. A limitation of manifold epigenetic studies, however, are the variations among sin-", + "224 high-throughput assays able to further delineate important molecular pathways involved in inducing and maintaining cellular senescence in both physiological ageing and age-associated diseases. Applications ofCRISPR-Cas intheStudy ofAgeing-Related Disease Cardiovascular Disease One of the most notable contributions of CRISPR-Cas to ageing research is its ability to target non-proliferating cells (contrary to HDR-directed gene targeting),", + "219 Applications ofCRISPR-Cas inBasic Research oftheMolecular Causes ofAgeing Investigating theMechanisms ofLongevity Currently there have been no studies exploring the utility of the CRISPR-Cas sys- tem on experimentally extending the lifespan of physiologically aged laboratory animals. A main issue in this regard is that established vertebrate models already possess relatively long lifespans that make longevity extension studies economi-", + "CRISPR-Cas genome- editing tools will provide feasible implementation of 11 Applications ofCRISPR-Cas inAgeing Research", + "the basis for future investigations into the spatio-temporal dynamics of the telom- erase protein invivo.11 Applications ofCRISPR-Cas inAgeing Research", + "induced by telomere erosion. Protein Cell. 2019;10:3705.11 Applications ofCRISPR-Cas inAgeing Research", + "using bulk mRNA or even analyzing single cells (scRNA-seq). In addition, advances in molecular biology and cell culture approaches (for instance Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/Cas9) will be benecial in clarifying aging-processes across species. An improved understanding of epigenetic mechanisms affecting longevity will be deciding crucial step towards the identication of new potential therapeutic targets. In", + "In recent years, CRISPR-Cas technologies have significantly contributed to studies addressing the molecular pathogenesis of age-related neurodegenerative conditions such as Alzheimers disease (AD) and Parkinsons disease (PD). Currently, it has mostly been utilised for developing new or improved tools in which to study the molecular mechanisms underlying these diseases, such as in patient-derived cell lines carrying pathogenic mutations." + ], + "answer": "Recent advancements in CRISPR/Cas9 technology have opened new avenues for studying and potentially reversing aging in several ways: 1. **Development of New Research Models**: CRISPR/Cas9 is significantly impacting research by enabling the creation of new models for studying age-related diseases. This includes manipulating disease-associated gene pathways, which can lead to a better understanding of the cellular and molecular origins of these diseases [1], [2]. 2. **Understanding Aging Processes Across Species**: The technology is beneficial in clarifying aging processes across different species. This improved understanding, particularly of epigenetic mechanisms affecting longevity, is crucial for identifying new potential therapeutic targets [3], [9]. 3. **Targeting Non-Proliferating Cells**: One notable contribution of CRISPR/Cas9 to aging research is its ability to target non-proliferating cells. This capability is important for studying cellular senescence, which is a key factor in both physiological aging and age-associated diseases [4]. 4. **Molecular Pathogenesis of Neurodegenerative Diseases**: CRISPR/Cas technologies have significantly contributed to studies on the molecular pathogenesis of age-related neurodegenerative conditions such as Alzheimer's and Parkinson's diseases. This includes developing new tools to study the molecular mechanisms underlying these diseases using patient-derived cell lines with pathogenic mutations [10]. These advancements suggest that CRISPR/Cas9 technology not only aids in understanding the mechanisms of aging but also holds potential for developing interventions that could reverse or mitigate age-related conditions.", + "question": "How do recent advancements in CRISPR/Cas9 technology open new avenues for studying and potentially reversing aging?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_1 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_1 new file mode 100644 index 0000000..e7ad2ee --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_1 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2014 - Pathophysiology and treatment of type 2 diabetes.pdf", + "2009 - Metabolomics Applied to Diabetes Research.pdf", + "2014 - The potential of novel biomarkers to improve risk prediction of type 2 diabetes.pdf", + "2014 - Pathophysiology and treatment of type 2 diabetes.pdf", + "2016 - Genome-Wide Association Studies of Type 2 Diabetes.pdf", + "2013 - Variants of Insulin-Signaling Inhibitor Genes.pdf", + "2021 - A genome-wide association study identifies 5 loci associated with frozen shoulder and implicates diabetes as a causal risk factor.pdf", + "2010 - Metabolic Footprint of Diabetes A Multiplatform.pdf", + "2014 - The potential of novel biomarkers to improve risk prediction of type 2 diabetes.pdf", + "2018 - Global aetiology and epidemiology of type 2 diabetes mellitus and its complications.pdf" + ], + "extraction_id": [ + "8b15673a-deaf-5e34-945c-ea2a1365552d", + "380e9a2e-8f9f-5f9e-ba20-3695b1c60fda", + "75485c9d-6c66-52fe-8fb1-e6d2440a7f49", + "8b15673a-deaf-5e34-945c-ea2a1365552d", + "7cec13b8-d349-5ea4-b866-17fc760d364c", + "f258a3c5-02d6-5f8f-a989-27f6c795145c", + "2052d37d-f778-53e2-a2f9-9e4311e8a953", + "97b6d492-9139-50ec-9685-53a803f5c995", + "496d9615-7530-530c-bea1-62fe63ea54ca", + "751ccb98-2846-5ca7-8ab8-2684100c28fa" + ], + "document_id": [ + "ab9288ab-e3ad-58f1-b5ba-183ee17ce4bd", + "a6ae2fb6-88ae-588f-a98d-b6092f886ed9", + "2bc2f4be-378f-5ced-8288-e2a132a94540", + "ab9288ab-e3ad-58f1-b5ba-183ee17ce4bd", + "185aad8a-6a5b-5b18-81c4-ef251edef5e7", + "d43a59e8-fe3b-503a-863b-235af8790f2a", + "8276e137-4591-51bd-9351-f4d27d3b35da", + "b199607e-293e-56e8-88c8-e0716d1ee9eb", + "2bc2f4be-378f-5ced-8288-e2a132a94540", + "8bc8f3d4-968f-5252-ab4c-832b92e9ec0d" + ], + "id": [ + "chatcmpl-AIHIPLyXp5Go74Qys43ojpQ0czAzb", + "012b6e5f-ab45-53aa-a392-45a46916e752", + "aaf89eb0-09a8-517d-b8ae-4e76a8211be6", + "6919bc75-2637-5359-9c05-96d192be8c4e", + "93455356-fe0b-58f4-9ae7-58f932d33560", + "cfc35db4-346c-55fd-b0bc-fa3cac307731", + "3b5c1a49-cb11-57ef-9046-e3c8f7af589e", + "b74d0bb9-eb0d-59bb-8a37-d3425d5591a2", + "ead10261-182f-5ab1-9af0-ce8a17677d4a", + "4971b4de-b190-56b5-b7b6-64b2c8e2a565", + "01a2230a-b91d-57b6-b138-7aae805f4383" + ], + "contexts": [ + "proteomics, genomics, and transcriptomics) are based on the study of constituents of the cell or body in a collective way. The ndings made with use of these approaches are being integrated to better understand the pathophysiology of type 2 diabetes and the heterogeneity of responses to di erent glucose-lowering therapies. Findings from studies that used metabolomics and lipidomics showed that increases in branched-chain and aromatic aminoacids were associated with obesity and type 2 diabetes.", + "Metabolomics Applied to Diabetes Research Moving From Information to Knowledge James R. Bain, Robert D. Stevens, Brett R. Wenner, Olga Ilkayeva, Deborah M. Muoio, and Christopher B. Newgard Type 2 diabetes is caused by a complex set of interactions between genetic and environmentalfactors. Recent work has shown that human type2 diabetes is a constellation of disorders associ- ated with polymorphisms in a wide array of genes, witheach individual gene accounting for /H110211% of disease risk", + "between protein signals and type 2 diabetes incidence. Acta Diabetol. doi: 10.1007/s00592-012-0376-3 82. Bain JR, Stevens RD, Wenner BR, Ilkayeva O, Muoio DM, Newgard CB (2009) Metabolomics applied to diabetes re-search: moving from information to knowledge. Diabetes 58: 2429 244383. Suhre K, Meisinger C, Dring A et al (2011) Metabolic footprint of diabetes: a multiplatform metabolomics study in an epidemiological setting. PLoS One 5:e13953", + "The future: genetics, epigenetics, and omics Although understanding of the genetics of type 2 diabetes has advanced rapidly, much remains unknown. How genes interact with the environment to cause progressive loss of -cell function is unclear. Environmental factors and hyperglycaemia could contribute to epigenetic changes in DNA and histones, thereby modifying gene expression in organs implicated in the pathogenesis and progression of type 2 diabetes, including in cells. 82,83", + "potential to make far-reaching contributions to our understanding of molecular basis of T2D and the development of novel strategies for patient care. 2.1 Introduction Type 2 diabetes (T2D) is a common, chronic disorder whose prevalence is increas-ing rapidly across the globe. Like other complex diseases, T2D represents achallenge for genetic studies aiming to uncover the underlying pathophysiological mechanisms. It is predicted that T2D will affect 592 million individuals by 2035", + "inthepathogenesisoftype2diabetesandmetabolism, Current Opinion in Clinical Nutrition and Metabolic Care ,vol.10,no .4, pp .420426,2007 . [110] M.C.Cornelis,E.J.T.Tchetgen,L.Liangetal.,Gene-environ- ment interactions in genome-wide association studies: a com- parative study of tests applied to empirical studies of type 2 diabetes, American Journal of Epidemiology ,v o l.17 5,no .3,p p . 191202,2012. [111] M.L.Metzker,Sequencingtechnologiesthenextgeneration, Nature Reviews Genetics ,vol.11,no.1,pp.3146,2010.", + "meta-ana lysis provides insight intothegenetic architecture oftype2diabetes susceptibility. NatGenet. 2014; 46:234 244. https://doi.or g/10.103 8/ng.2897 PMID: 24509480 26. Morris AP,Voight BF,Teslovich TM,Ferreira T,Segr A-V, Steinthorsdot tirV,etal.Large-sc aleassoci- ation analysis provide sinsights intothegenetic architecture andpathophysi ology oftype2diabetes. NatGenet. 2012; 44:981 990. https://doi.or g/10.103 8/ng.2383 PMID: 228859 22", + "monitoring and preventing progression to costly co-morbidities. The principal concept of metabolomics being able to find some metabolites differing in a control and a type 2 diabetic group is established. It is not our goal here to show this once again. The questions we ask are rather How well are different approaches suited to attain this goal? and What are optimal settings under which such studies can be successful?. Others have already investigated these questions before [16,17,18]. However, we", + "Owing to current advances in -omics technologies, such as genomics, transcriptomics, proteomics and metabolomics, the number of candidate biomarkers keeps growing; however, only a small proportion of these has been investigated withreference to their potential to improve the prediction of type 2 diabetes. Genetic variants The heritability of glycaemic traits and type 2 diabetes is high [40], and the large genome-wide association studies published to date since the first in 2007, based on up to >10 5study", + "have improved our understanding of the complexity of T2DM pathophysiology, beyond the classic triumvirate of -cell, skeletal muscle and liver87. However, the ability of these biomarkers to predict future risk of T2DM beyond anthropometric measures, lifestyle factors and fasting levels of glucose and lipids is still debatable87. Within the past 7years, a complementary, novel set of T2DM biomarkers has largely been generated by metabo- lomic studies, which systematically analyse metabolites" + ], + "answer": "Recent advancements in multi-omics approaches, including proteomics and metabolomics, have significantly enhanced our understanding of the pathogenesis of Type 2 diabetes (T2D). These approaches allow for a comprehensive analysis of the molecular components involved in T2D, providing insights into the disease's complexity and heterogeneity. 1. **Integration of Omics Data**: The integration of data from proteomics, genomics, and transcriptomics helps in understanding the pathophysiology of T2D and the varied responses to different glucose-lowering therapies. This holistic view is crucial for identifying the molecular underpinnings of T2D and tailoring personalized treatment strategies [1]. 2. **Metabolomics Insights**: Metabolomics studies have identified specific metabolites, such as branched-chain and aromatic amino acids, that are associated with obesity and T2D. These findings suggest that metabolic alterations play a significant role in the development of T2D, offering potential targets for therapeutic intervention [1]. 3. **Complex Interactions**: T2D is caused by complex interactions between genetic and environmental factors. Metabolomics has helped elucidate these interactions by identifying metabolic footprints associated with T2D, thus moving from mere information gathering to actionable knowledge [2], [3]. 4. **Biomarker Discovery**: Advances in omics technologies have led to the discovery of numerous candidate biomarkers for T2D. Although only a small proportion of these biomarkers have been thoroughly investigated, they hold promise for improving the prediction and management of T2D [9]. 5. **Beyond Traditional Models**: The use of metabolomics has expanded our understanding of T2D beyond traditional models focused on the pancreas, liver, and muscle. This broader perspective is essential for developing more effective prevention and treatment strategies [10]. Overall, these advancements in multi-omics approaches provide a more detailed and nuanced understanding of T2D pathogenesis, paving the way for improved diagnostic and therapeutic options.", + "question": "How do recent advancements in multi-omics approaches, including proteomics and metabolomics, contribute to our understanding of Type 2 diabetes pathogenesis?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_10 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_10 new file mode 100644 index 0000000..2c2d248 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_10 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2008 - Glossary of Genetics Genomics Terms.pdf", + "2011 - Inherited destiny Genetics and gestational diabetes mellitus.pdf", + "2015 - Genetics, genomics and personalized medicine in Type 2 Diabetes.pdf", + "2017 - Spectrum of mutations in monogenic diabetes genes identified from high-throughput DNA sequencing of 6888 individuals.pdf", + "2018 - Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps.pdf", + "2018 - High-Throughput Approaches onto Uncover (Epi)Genomic Architecture of Type 2 Diabetes.pdf", + "2010 - Evidence of Interaction between Type 2 Diabetes.pdf", + "2013 - Genome-Wide Contribution of Genotype by Environment Interaction.pdf", + "2016 - Putting the Genome in Context Gene-Environment Interactions.pdf", + "2012 - Gene-Environment Interactions in the Development of Type 2 Diabetes.pdf" + ], + "extraction_id": [ + "53e868dd-b318-5cf3-8b2e-98a548aab7cf", + "48c3e4a4-db23-5fca-9c46-775e80894655", + "52a000e5-d790-55f2-9eac-14554d426173", + "b24927c4-ee83-51a8-b431-b43be7d3b678", + "9190d1c1-41a4-5af3-a570-7fea6a15e71a", + "455b92f7-6156-5735-8586-29a66af0f9e5", + "d2de4ed1-897b-5e5b-bc29-c03310096d64", + "f3975a2c-8a66-582e-a4b8-868b1f4722d4", + "cb5c4aab-77ed-58cd-98b8-9e1ba64eb9cf", + "89bf4316-d0cc-5310-a45e-1dd8b8aefe1b" + ], + "document_id": [ + "c66d2572-071d-5aaf-829c-b3ca6cf6d697", + "6d341cd2-ae56-5807-9aff-39298efc4d06", + "d8b85c3e-62f3-5e67-99b0-d0a2f225aff0", + "18a8a000-69ed-5d34-b13f-f5ae016d1067", + "ab2868dd-62f6-5350-994c-fcea4328e8a3", + "1cb0c4ac-c1fe-55c2-919c-52cd5018c00d", + "1a33b1d1-23ee-5b33-b42d-c745c8210166", + "8c310d76-0a3b-574c-9859-859258870ee5", + "ea43bb66-b6fe-5682-8f48-90568c080401", + "ea9601ed-ad83-506e-b1b7-e7211671ff73" + ], + "id": [ + "chatcmpl-AIHJknEcr96E1ybbJw2DE0EMMQI1v", + "b092c8b9-edb1-55fb-ae16-c67e3298946e", + "55f842a4-506a-5992-9b6e-47c81aee6809", + "728c47bb-e8e2-5359-9ff5-9ad9b13f999c", + "15872da6-8175-5db6-b741-10ae3cf85088", + "53fd1ea0-5ca7-5066-bb07-e7469c640e22", + "027f0c97-d38d-551d-add3-4a759a406895", + "155260c5-ba90-540f-8d48-bafece83fa47", + "3d00ac57-9828-5146-a895-9840de9af5f7", + "518d294f-67c5-5870-9f28-3cb4dfa81e42", + "6b83f0af-1145-5679-9dae-0f645771d25d" + ], + "contexts": [ + "that genetic studies will ultimately identify key genetic elements that help determine susceptibility to diabetes,disease progression, and responsiveness to specific therapies, as well as help identify novel targets for futureintervention. A substantial number of genetic loci, gene polymorphisms, and mutations have already beenreported as having variable degrees of association with one or other type of diabetes (type 1, type 2, maturityonset diabetes of the young [MODY]), while others appear to be involved", + "ponse to thiazolidinedione therapy and candidate genes [100103]. Results from pharmacogenetic studies could potentially provide physicians with a powerful tool to adjust therapy appropriately for those individuals carry ing variants known to affect a given medication. Distefano and Watanabe have recently reviewed the pharmaco genetics of diabetes [104]. Genegene and geneenvironment interactions are also likely to be helpful to the clinician in making therapeutic", + "Genomics of T2D Diet, lifestyle, environment, and even genetic variation influence an individuals response to disease therapy. Like GWAS which identify genetic variants conferring risk for a disease, studies have been carried out for iden - tifying genetic variants responsible for patient differ -", + "ease caused by interactions between multiple genetic and environmental factors. Significant progress has been made in understanding the genetic architecture of T2D over the past 10 years [1]. A number of genome-wide as- sociation studies in diverse human populations have identified more than 60 common variants and loci asso- ciated with risk for T2D [2]. These studies have also revealed a significant overlap between traits and pheno- types of monogenic diabetes with related common", + "21582171 (2014). 29. Wood, A. R. et al. A genome-wide association study of IVGTT-based measures of first-phase insulin secretion refines the underlying physiology of type 2 diabetes variants. Diabetes 66, 22962309 (2017). 30. Pickrell, J. K. Joint analysis of functional genomic data and genome- wide association studies of 18 human traits. Am. J. Hum. Genet. 94, 559573 (2014). 31. Plenge, R. M., Scolnick, E. M. & Altshuler, D. Validating therapeutic targets", + "by GWASs [ 16,28,29]. A wide variety of network-based approaches have been applied to investigate the extent to which the genetics of T2D predisposition converge on a restricted set of biological pathways. Several T2D risk variants have been identied as primary regulators of insulin secretion, insulin action, and pancreatic islet transcription factors. [ 10,16]. The newly discovered SNVs allow the better characterization of abnormalities in early insulin processing and secretion. TCF7L2 ,SLC30A8 ,C2CD4B ,", + "[10] , many environmental factors [11] , and the interac- tions among those genetic and environmental factors. Physical activity and dietary fat have been reported to be important modifiers of the associations between glucose homeostasis and well-known candidate genes for T2DM [12] and there is reason to believe that a significant pro- portion of the susceptibility genes identified by GWASs will interact with these environmental factors to influ-ence the disease risk. Florez et al.", + "interactions suggest a way by which genetic risk may beameliorated, these environmental factors are of great relevanceto public health, and are the focus of a growing number of studies [7]. Environmental factors, such as diet and lifestyle, are important in the onset, development and progression of T2D and its related phenotypes [8,9]. The interactions of environmental factors with", + "cases. J Am Med Assoc. 1956;161:1628 30. 3. Duncan LE, Keller MC. A critical review of the first 10 years of candidate gene-by-environment interaction research in psychiatry. Am J Psychiatry. 2011;168:1041 9. 4. Brito EC et al. Previously associated type 2 diabetes variants may interact with physical activity to modify the risk of impaired glu- cose regulation and type 2 diabetes: a study of 16,003 Swedish adults. Diabetes. 2009;58:1411 8.", + "this occurs. Findings to date, however, indicate that behavioral changes can substantially mitigate diabetogenic and obesogenic effects of individual or multiple risk alleles, which has much broader clinical and public health implications.We have seen considerable progress in our understanding of the role that both environ- ment and genetics play in the development of T2D. Recent work suggests that the adverse effect of some established T2D-associated loci may be greatly attenuated by appropriate" + ], + "answer": "Recent studies on the interaction between genetic variants and environmental factors in diabetes development have identified several potential therapeutic targets. These include: 1. **Primary Regulators of Insulin Secretion and Action**: Several type 2 diabetes (T2D) risk variants have been identified as primary regulators of insulin secretion, insulin action, and pancreatic islet transcription factors. This suggests that targeting these pathways could be a potential therapeutic strategy [6]. 2. **Specific Genetic Variants**: Newly discovered single nucleotide variants (SNVs) allow for better characterization of abnormalities in early insulin processing and secretion. Genes such as TCF7L2, SLC30A8, and C2CD4B have been highlighted as potential targets due to their roles in these processes [6]. 3. **Gene-Environment Interactions**: The interaction between genetic susceptibility and environmental factors such as physical activity and dietary fat has been shown to modify the risk of glucose homeostasis and T2D. This indicates that interventions targeting these environmental factors could potentially mitigate the genetic risk [7]. These findings underscore the importance of considering both genetic and environmental factors in developing therapeutic strategies for diabetes.", + "question": "What potential therapeutic targets have been identified through recent studies on the interaction between genetic variants and environmental factors in diabetes development?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_11 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_11 new file mode 100644 index 0000000..9a6b3cb --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_11 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2013 -Whole-Exome Sequencing of 2,000 Danish Individuals.pdf", + "2018 - High-Throughput Approaches onto Uncover (Epi)Genomic Architecture of Type 2 Diabetes.pdf", + "2013 -Whole-Exome Sequencing of 2,000 Danish Individuals.pdf", + "2016 - The genetic architecture of type 2 diabetes.pdf", + "2013 -Whole-Exome Sequencing of 2,000 Danish Individuals.pdf", + "2018 - High-Throughput Approaches onto Uncover (Epi)Genomic Architecture of Type 2 Diabetes.pdf", + "2013 -Whole-Exome Sequencing of 2,000 Danish Individuals.pdf", + "2015 - Genetics, genomics and personalized medicine in Type 2 Diabetes.pdf", + "2020 - Insights into pancreatic islet cell dysfunction from type 2 diabetes mellitus genetics..pdf", + "2013 -Whole-Exome Sequencing of 2,000 Danish Individuals.pdf" + ], + "extraction_id": [ + "01778b74-61b2-5f64-be8e-775c79af171d", + "0f2a8ab2-1666-50c0-b0b0-2a37e1f6917f", + "01778b74-61b2-5f64-be8e-775c79af171d", + "c266fa33-e779-514b-9337-636a69c6e6a4", + "89a75bbb-f0f6-5391-98fd-56631343a38e", + "524594ab-31ca-5f5c-8126-7c58060bb73e", + "01778b74-61b2-5f64-be8e-775c79af171d", + "5cd40b2d-72d6-5386-be94-b4e8188e4114", + "36db7673-abd7-55b1-9caf-b66498e19e78", + "89a75bbb-f0f6-5391-98fd-56631343a38e" + ], + "document_id": [ + "2f2f6ff1-8f05-510d-bcf6-b1860fd9350c", + "1cb0c4ac-c1fe-55c2-919c-52cd5018c00d", + "2f2f6ff1-8f05-510d-bcf6-b1860fd9350c", + "d7e2a9de-46f1-5191-9cb0-dd68eb9f365a", + "2f2f6ff1-8f05-510d-bcf6-b1860fd9350c", + "1cb0c4ac-c1fe-55c2-919c-52cd5018c00d", + "2f2f6ff1-8f05-510d-bcf6-b1860fd9350c", + "d8b85c3e-62f3-5e67-99b0-d0a2f225aff0", + "2a386c81-8f24-5993-8e48-0e89d7fb4fec", + "2f2f6ff1-8f05-510d-bcf6-b1860fd9350c" + ], + "id": [ + "chatcmpl-AIHJu3dzRIlHnqdmlSb6lzwzfrntr", + "935ff4d1-9840-5fab-8ad8-82e668319e91", + "b6905dfe-c622-58cd-b9ac-2cdcedada7e4", + "0ab59821-8bf4-50d3-92e7-b0dd593883a8", + "e266cecd-e881-5c64-8ce3-4894cbe47db5", + "aacfbc09-f4ec-5b71-a4a8-efd43cf8b6db", + "0977e7bd-4c4c-5c6c-a4d1-3b5f6fda03c5", + "9a3d06ce-e86f-511f-82ac-97e486618e47", + "451c2da6-3fd5-53f4-a58e-32b4f1d2cbbd", + "40f471a6-3615-52f3-a306-9f3568680409", + "a5469aca-198e-56f5-ab92-16fd00c5e0fc" + ], + "contexts": [ + "and rare coding variants do not account for much of theheritability of type 2 diabetes. Under this scenario, themissing heritability could be located in common orlow-frequency and rare variants in noncoding regionsof the genome. Recent studies that jointly modeled dia-betes or obesity risk as a function of genetic relatednessacross all of the GWAS SNPs have suggested that much of the heritability of these traits can be explained by", + "T2D heritability. 3. Uncovering the Signicance of Rare-Coding and Non-Coding Genetic Variants in the Etiology of Type 2 Diabetes As previously stated, GWASs have uncovered many new genetic associations that are relevant to T2D, but GWAS ndings represent common and mid-frequency genetic variations, thus excluding rare frequency variants and also cumulative effect of many variants with small effect sizes. Missing heritability refers to the portion of genetic variance that cannot be explained by all signicant", + "could be accounted for by low-frequency and rare variants of moderate effect in a small number of genes. Our whole-exome sequencing study has explicitly addressed thisquestion. Additionally, we did not examine whether thereare fewer than 20 genes involved in type 2 diabetes butrather looked at whether rare coding variants in fewerthan 20 genes account for much of the heritability. In such a model, any number of other genes that do not", + "contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of the heritability of this disease. Here, to test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole-genome sequencing in 2,657 European individuals with and without diabetes, and exome", + "One common disease that has been subjected to intense genetic study is type 2 diabetes. 32The heritability of type 2 diabetes has been estimated to be around 30%.3335 Through GWASs, 63 loci have been reproducibly associ-ated with type 2 diabetes. 36However, as for other complex traits, the associated SNPs can only account for <20% of the heritability estimated from family studies.36 Here, we seek to evaluate the role that rare coding vari-", + "prevalence of T2D. These authors found rare variants that were not detected previously in population studies, but none of them were associated with T2D [ 49]. Larger multi-population studies and more advanced study methods are needed to reliably identify rare variants that are exclusively associated with T2D to eventually uncover missing T2D heritability. 3.2. Genetic Variants in Familial Studies of Type 2 Diabetes The development of T2D is driven by the combined effect of environmental factors and a", + "variance in disease risk that can be accounted for bythe 63 previously identied associations with commonvariants. Our empirical and simulation results are compatible with a variety of different genetic architectures for type2 diabetes. First, if rare coding variants are responsiblefor the majority of the heritability of the trait, the variants are most likely scattered across many ( >20) different", + "Genome-wide association studies (GWAS) have been helpful in identifying a large number of genetic variants conferring risk to T2D. However, only close to 10% heritability is explained by these variants. Other genetic variants, particularly those which are rare but with significant effects need to be identified.", + "and rare sequence variants associated with elevated or reduced risk of type 2 diabetes. Nat. Genet. 46, 294298 (2014). 168. Lek, M. etal. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285291 (2016).169. Xue, A. etal. Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes. Nat. Commun. 9, 2941 (2018). 170. Huyghe, J. R. etal. Exome array analysis identifies", + "diabetes. In particular, our study suggests that when clus-tered in a small number of genes, rare coding variants ofmoderate to strong effect are unlikely to account formuch of the missing heritability. Rather, if rare coding var-iants are an important factor in type 2 diabetes risk, theyare most likely scattered across many genes. Our resultshave important implications for the design and interpreta- tion of future medical resequencing studies. Subjects and Methods Study Populations" + ], + "answer": "Rare variants identified through whole-genome sequencing contribute to the heritability of Type 2 diabetes by potentially explaining some of the \"missing heritability\" that common variants identified through genome-wide association studies (GWAS) do not account for. While GWAS have identified many common variants associated with Type 2 diabetes, these explain only a fraction of the heritability of the disease [4]. The missing heritability could be located in low-frequency and rare variants, particularly in noncoding regions of the genome [1]. However, studies have shown that rare coding variants, especially when clustered in a small number of genes, are unlikely to account for much of the missing heritability [10]. Instead, if rare coding variants are significant, they are likely scattered across many genes [10]. Therefore, while rare variants may contribute to the heritability of Type 2 diabetes, their exact role and impact remain to be fully elucidated, and larger multi-population studies are needed to reliably identify rare variants exclusively associated with Type 2 diabetes [6].", + "question": "How do rare variants identified through whole-genome sequencing contribute to the heritability of Type 2 diabetes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_12 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_12 new file mode 100644 index 0000000..43f5c75 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_12 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2018 - Type 2 Diabetes Mellitus and Cardiovascular Disease Genetic and Epigenetic Links.pdf", + "2010 - The Role of Epigenetics in the Pathology of Diabetic Complications.pdf", + "2018 - Pilot genome-wide association study identifying novel risk loci for type 2.pdf", + "2014 - Diabetic nephropathy\u2014emerging epigenetic mechanisms.pdf", + "2019 - Development and Genome Sequencing.pdf", + "2010 - Genome-wide DNA methylation analysis for diabetic nephropathy in type 1 diabetes mellitus.pdf", + "2018 - Lnc\u2011ing non\u2011coding RNAs with metabolism and diabetes roles.pdf", + "2018 - Lnc\u2011ing non\u2011coding RNAs with metabolism and diabetes roles.pdf", + "2018 - MALAT1 An Epigenetic Regulator of Inflammation in Diabetic Retinopathy.pdf", + "2018 - Lnc\u2011ing non\u2011coding RNAs with metabolism and diabetes roles.pdf" + ], + "extraction_id": [ + "097b0feb-4ffa-5ad6-8140-5e404e83c80b", + "b1d2c95c-d639-5c75-8c52-278f1e187675", + "a166bf29-6be5-54ff-b869-9d4ff087d1e5", + "79ce3e1f-1c6a-51dc-b5ad-848173af4e69", + "d971dced-935c-566b-a4a2-11bcf99b9c84", + "f9500ec9-0600-5e2c-b64e-b062fb7a7552", + "14656f4f-b0bd-5f4f-a67a-aeb902f24757", + "96a78d74-ac6d-513e-a5a7-b22ef95ea041", + "2d8abaf8-9f48-5b9a-b50e-897fd4751b7b", + "efc73cf6-99c6-5272-9bb0-7bd6a34633f0" + ], + "document_id": [ + "3e82a2e5-4b2c-59c0-99cd-f3b06d8dabf2", + "766edfd5-4756-51bf-b636-c94b041d030c", + "e2c6283c-d95f-574a-9dab-345a708d388c", + "be05127e-1be8-5573-b571-51a11c3b2be2", + "18820c9e-f7ae-57ae-897d-0d9c3f616b6a", + "23f0ee09-5536-5f63-bf15-bce1894b5fed", + "019efefb-65db-55f5-a3a7-4f224473f51f", + "019efefb-65db-55f5-a3a7-4f224473f51f", + "cd4dd3bc-bcea-5670-a40f-bc95c319f3ed", + "019efefb-65db-55f5-a3a7-4f224473f51f" + ], + "id": [ + "chatcmpl-AIHK1iKM6Po4mTFBDwGSbImYGY94p", + "1eb14f0c-3d81-53bc-91c8-98acf2e014b7", + "92a20945-b038-52a4-8cc8-ffb70e6f7559", + "9c11148d-9f7a-5d84-aa05-2b67e7a8f1f3", + "19d9d3a6-c982-5c57-a16c-226b8aa76ed5", + "b774bf7b-4546-56d2-ae7b-7bc2c9f2fb08", + "94eed8ea-cc78-52d0-a188-442380512b85", + "2d9e043b-a3fa-52dc-9a4e-71ed49f9ec1d", + "66b05301-179b-597c-bb68-e6fd0e0d1d5a", + "4a8a2861-62b9-520c-8833-45fb8bd3ffd7", + "25d3616b-1ba4-59ce-a11b-38d108d5b387" + ], + "contexts": [ + "13 De Rosa et al. Type 2 Diabetes and CVD Frontiers in Endocrinology | www.frontiersin.org January 2018 | Volume 9 | Article 2176. Fatica A, Bozzoni I. Long non-coding RNAs: new players in cell differentia- tion and development. Nat Rev Genet (2014) 15:721. doi:10.1038/nrg3606 177. Wang KC, Chang HY . Molecular mechanisms of long noncoding RNAs. Mol Cell (2011) 43:90414. doi:10.1016/j.molcel.2011.08.018 178. Esteller M. Non-coding RNAs in human disease. Nat Rev Genet (2011) 12:86174. doi:10.1038/nrg3074", + "Epigenetic Mechanisms in Diabetic Complications 16 other non-coding RNAs can also in teract with transcriptional co -regulators and thereby further 337 influence epigenetics and tran scriptional regulation (82, 104). 338 Recent findings have demonstrated a critical role for miRs in various diseases. They have 339 been found to play key roles in proliferation, di fferentiation, development, and in cancer, where 340", + "Beltrami, C., Angelini, T.G., Emanueli, C., 2015. Noncoding RNAs in diabetes vascular complications. J. Mol. Cell. Cardiol. 89, 42 50.https://doi.org/10.1016/j.yjmcc. 2014.12.014 . Brookheart, R.T., Michel, C.I., Listenberger, L.L., et al., 2009. The non-coding RNA gadd7 is a regulator of lipid-induced oxidative and endoplasmic reticulum stress. J. Biol.Chem. 284, 7446 7454. https://doi.org/10.1074/jbc.M806209200 . Carter, G., Miladinovic, B., Patel, A.A., et al., 2015. Circulating long noncoding RNA", + "Noncoding RNAs that are induced by diabetic conditions can also promote theexpression of pathological genes via various post-transcriptional and post-translational mechanisms These epigenetic mechanisms and noncoding RNAs can lead to persistently open chromatin structures at pathological genes and sustained gene expression, which can also be a mechanism for metabolic memory Key epigenetic regulators, microRNAs and long noncoding RNAs could serve", + "tion among researchers ( Knoll et al., 2015 ). As an important post-transcriptional pathogenesis of diabetes, lncRNAs and their associated orchestrated networks are implicated in mediating complex pathological mechanisms of diabetes ( Kato et al., 2016; Liu et al., 2014 ). To delineate the inuence of lncRNAs and 172 iScience 19, 162176, September 27, 2019", + "coding RNAs [18]. A number of indirect lines of evi-dence point to the involvement of epigenetic changes indiabetic nephropathy. Murine models of disease progres-sion displaying temporal variation in gene expressionhave indicated these supra-sequence devices may beinvolved in the pathogenesis [19]. Gene expressionchanges reflect dynamic alterations in gene transcription and also messenger RNA stabi lity, which may be influ-", + "To conclude, it would be apt to state that lncRNAs are widely implicated in diverse domains of cell metabolism and their altered expression is associated with diabetes and its complications. Although originally thought to be non-functional, lncRNA genes transcribe into lncRNAs that exert important and specific functions in regulating cellular pathways. Due to this specificity, lncRNAs are considered better therapeutic targets. In addition, their expression patterns in tissues quite follow the progress of", + "NAs to be mapped to diabetic susceptible loci [49 52], all suggesting towards critical roles of lncRNAs in insulin resistance, diabetes, and its associated complications. LncRNAs asregulators ofislet function The pancreatic islet is an important central node to researchers to understand the pathophysiology of diabe-tes [53]. The possible regulation of islet development and function by lncRNAs was first demonstrated by Ding etal., where the lncRNA, H19 (Fig. 4), was shown to be involved", + "expected to rise due to the increasing incidence of diabetes, which necessitates the need for exploration of new molecular aspects of DR to expand the current scope of therapy. In the last two decades, the rapid advent of high-throughput genomic technology has made it evident that more than 97% of the human genome is comprised of non-protein-coding elements, such as non-coding RNAs (ncRNAs) 6. Although significant research has been conducted in annotating the transcripts that arise from these", + "regulation, control of mRNA decay, and sequestration of transcription factors. Although the underlying causes that define the diabetic phenotype are extremely intricate, most of the studies in the last decades were mostly centered on protein-coding genes. However, current opinion in the recent past has authenticated the contributions of diverse lncRNAs as critical regula - tory players during the manifestation of diabetes. The current review will highlight the importance of lncRNAs in regulating" + ], + "answer": "The latest findings on the role of non-coding RNAs in the pathogenesis of diabetes highlight several key aspects: 1. **Role of lncRNAs in Diabetes**: Long non-coding RNAs (lncRNAs) are implicated in mediating complex pathological mechanisms of diabetes. They are involved in post-transcriptional regulation and are associated with orchestrated networks that influence diabetes pathogenesis [5]. LncRNAs are considered better therapeutic targets due to their specific functions in regulating cellular pathways and their expression patterns that correlate with the progression of diabetes [7]. 2. **Epigenetic Influence**: Non-coding RNAs, including microRNAs and lncRNAs, can influence epigenetic mechanisms. They can promote the expression of pathological genes through post-transcriptional and post-translational mechanisms, contributing to metabolic memory and sustained gene expression in diabetic conditions [4]. 3. **Regulation of Islet Function**: LncRNAs have been shown to regulate pancreatic islet function, which is central to understanding diabetes pathophysiology. For instance, the lncRNA H19 has been implicated in islet development and function [8]. 4. **MicroRNAs in Disease**: MicroRNAs (miRs) play critical roles in various diseases, including diabetes, by influencing proliferation, differentiation, and development [2]. These findings underscore the importance of non-coding RNAs as regulatory players in diabetes and its complications, offering potential avenues for therapeutic intervention.", + "question": "What are the latest findings on the role of non-coding RNAs in the pathogenesis of diabetes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_13 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_13 new file mode 100644 index 0000000..86280c6 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_13 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2022 - Development and validation of a trans-ancestry polygenic risk score for type 2 diabetes in diverse populations.pdf", + "2022 - Development and validation of a trans-ancestry polygenic risk score for type 2 diabetes in diverse populations.pdf", + "2018 - Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps.pdf", + "2022 - Development and validation of a trans-ancestry polygenic risk score for type 2 diabetes in diverse populations.pdf", + "2022 - Using Recurrent Neural Networks for Predicting Type-2 Diabetes from Genomic and Tabular Data.pdf", + "2022 - Development and validation of a trans-ancestry polygenic risk score for type 2 diabetes in diverse populations.pdf", + "2021 - Genetics and genomics of arrhythmic.pdf", + "2022 - Stability of polygenic scores across.pdf", + "2022 - Coming of Age Human Genomics.pdf", + "2021- Development of genome-wide polygenic risk scores for lipid traits and clinical applications for dyslipidemia, subclinical atherosclerosis, and diabetes cardiovascular complications among East Asians.pdf" + ], + "extraction_id": [ + "02701cd5-d2ce-560c-b5a9-e694fecdb3c2", + "f6f0c89d-5c35-5889-8619-a3914e5d2c7e", + "9190d1c1-41a4-5af3-a570-7fea6a15e71a", + "17c49e58-c89a-5495-b17f-adcade90a4c6", + "3c30b33b-8928-5cee-9c37-c70642fff75c", + "17c49e58-c89a-5495-b17f-adcade90a4c6", + "ada410d0-6b91-5959-b834-cc3389e29c5f", + "a548bb25-cbff-5466-b932-afe160bfbe32", + "d2add072-cb41-54f8-9583-9616b11e4ae3", + "5f2ac528-4965-5d5e-86d0-8862032bb7b9" + ], + "document_id": [ + "4ece243f-acda-569d-b75d-37539260dcb3", + "4ece243f-acda-569d-b75d-37539260dcb3", + "ab2868dd-62f6-5350-994c-fcea4328e8a3", + "4ece243f-acda-569d-b75d-37539260dcb3", + "be0e50e0-3de8-53c5-8126-a0b618647f80", + "4ece243f-acda-569d-b75d-37539260dcb3", + "462ed035-e4fb-5847-a92d-927f05a2b58b", + "30af2d38-7941-5d0a-9da1-a8ad2dc22329", + "45506895-eef1-57f4-8ca1-79fe23a2493f", + "ce8040c7-157f-54c5-b28b-3224e8871415" + ], + "id": [ + "chatcmpl-AIHKAjqtg6gr5hkyEsdT3wwz3yXTB", + "748c1d81-0c27-515a-8bf1-12e717645e66", + "2c09a46a-20d0-54b4-abcb-608fef7c7f80", + "3b9e0030-8bf9-5d63-9813-3cf18e98be3b", + "1677b3ee-7d95-5e10-a6dd-d80b4bb87b29", + "a374d88e-458e-5252-8b3a-5ca162fa6982", + "a551335d-c3ed-5d12-a611-9991d192cc1e", + "bcce1092-32ea-5f65-bc10-4dc1a2dac53a", + "635180f9-540f-5533-9d61-c5cfe14657fa", + "fd7ccb09-2768-5ceb-8b29-9b29cdef57a8", + "cc476583-54c8-5607-95bd-d06ae875dfb8" + ], + "contexts": [ + "review of polygenic risk scores for type 1 and type 2 diabetes. Int J Mol Sci. 2020;21(5):1703. 48. Khera AV, Chaffin M, Aragam KG, Haas ME, Roselli C, Choi SH, et al. Genome wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat Genet. 2018;50:121924. 49. Ding Y, Hou K, Burch KS, Lapinska S, Priv F, Vilhjalmsson B, et al. Large uncertainty in individual polygenic risk score estimation impacts PRS", + "(GWAS), polygenic risk scores (PRS) have shown promise to complement established clinical risk factors and inter vention paradigms, and improve early diagnosis and prevention of T2D. However, to date, T2D PRS have been most widely developed and validated in individuals of European descent. Comprehensive assessment of T2D PRS in non European populations is critical for equitable deployment of PRS to clinical practice that benefits global populations.", + "prediction of type 2 diabetes. N. Engl. J. Med. 359, 22082219 (2008). 45. Weedon, M. N. et al. Combining information from common type 2 diabetes risk polymorphisms improves disease prediction. PLoS. Med. 3, e374 (2006). 46. Euesden, J., Lewis, C. M. & OReilly, P . F. PRSice: Polygenic Risk Score software. Bioinformatics 31, 14661468 (2015). 47. Gatineau, M. et al. Adult obesity and type 2 diabetes (Public Health England,", + "(GWAS) in diverse populations have identified hundreds of genetic loci associated with T2D [79]. Polygenic risk scores (PRS), which aggregate the genetic risk of individ - ual alleles across the genome, are thus promising to pre - dict future T2D occurrence and improve early diagnosis, intervention, and prevention of T2D [1015]. However, to date, T2D PRS were most widely developed and vali - dated in individuals of European descent. Given that the predictive performance of PRS often attenuates in non-", + "in advance. Polygenic Risk Scores (PRS) were proposed by Duncan L. et al. [ 8] for risk analysis using the sum of the weight of each risk-associated locus of genomic sequence obtained from the corresponding evidence. These weights are assessed from the regression coefcient associated with each locus. These combined genetics features and correlation matrices would signicantly assist the entire eld of genomics study [ 9]. These studies on", + "performance. Conclusions: By integrating T2D GWAS from multiple populations, we developed and validated a transancestry PRS, and demonstrated its potential as a meaningful index of risk among diverse patients in clinical settings. Our efforts represent the first step towards the implementation of the T2D PRS into routine healthcare. Keywords: Polygenic risk score, Type 2 diabetes, Diverse populations, Clinical implementation", + "Owing to their small effect sizes, SNP associations have very little clinical applicability for risk prediction. A polygenic risk score (PRS) attempts to estimate the combined risk from multiple SNPs that have been associated with a certain trait with genome-wide sig-nificance. By accounting for a large proportion of the genetic variance underlying a trait, the overall effect size", + "8.Padilla-Mart nez, F., Collin, F., Kwasniewski, M., and Kretow- ski, A. (2020). Systematic review of polygenic risk scores for type 1 and type 2 diabetes. Int. J. Mol. Sci. 21, 1703 . 9.Rao, A., and Knowles, J. (2019). Polygenic risk scores in coro- nary artery disease. Curr. Opin. Cardiol. 34, 435440 . 10.Dikilitas, O., Schaid, D.J., Kosel, M.L., Carroll, R.J., Chute, C.G., Denny, J.A., Fedotov, A., Feng, Q., Hakonarson, H., Jar-vik, G.P., et al. (2020). Predictive utility of polygenic risk scores", + "partitioned polygenic scores according to factors of disease heteroge- neity, as successfully demonstrated for type 2 diabetes (32). Another strategy could be the mapping of statistically associated genetic loci to different immune-cell subtypes according to gene expression patterns derived from single-cell RNA sequencing (33). Autoimmune PRS, possibly in combination with other genetic and nongenetic predictors, may be of importance to manage the risk of", + "genome-wide polygenic risk scores (PRSs) for four lipid traits. We validated ( n= 4271) and subsequently tested associations of these scores with 3-year lipid changes in adolescents ( n= 620), carotid intima-media thickness (cIMT) in adult women ( n= 781), dyslipidemia ( n= 7723), and coronary heart disease (CHD) ( n= 2374 cases and 6246 controls) in type 2 diabetes (T2D) patients. (Continued on next page)" + ], + "answer": "The interaction between multiple polygenic risk scores (PRS) can improve the prediction of Type 1 and Type 2 diabetes by combining information from various genetic loci associated with these diseases. This approach allows for a more comprehensive assessment of an individual's genetic risk. Specifically, combining information from common risk polymorphisms has been shown to improve disease prediction for Type 2 diabetes [3]. Additionally, partitioning polygenic scores according to factors of disease heterogeneity and mapping genetic loci to different immune-cell subtypes can enhance the predictive power of PRS, particularly for Type 2 diabetes [9]. These strategies leverage the aggregation of genetic risk from multiple sources, thereby capturing a larger proportion of the genetic variance underlying these traits and improving early diagnosis, intervention, and prevention efforts [4].", + "question": "How does the interaction between multiple polygenic risk scores (PRS) improve the prediction of Type 1 and Type 2 diabetes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_14 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_14 new file mode 100644 index 0000000..00ccf3c --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_14 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Advances of single?cell genomics and epigenomics in human disease.pdf", + "2019 - (Epi)genomic heterogeneity of pancreatic islet function and failure in type 2 diabetes.pdf", + "2018 - Lnc\u2011ing non\u2011coding RNAs with metabolism and diabetes roles.pdf", + "2019 - (Epi)genomic heterogeneity of pancreatic islet function and failure in type 2 diabetes.pdf", + "2017 - Insights into beta cell regeneration for diabetes via integration of molecular landscapes in human insulinomas.pdf", + "2019 - (Epi)genomic heterogeneity of pancreatic islet function and failure in type 2 diabetes.pdf", + "2020 - Advances of single?cell genomics and epigenomics in human disease.pdf", + "2019 - (Epi)genomic heterogeneity of pancreatic islet function and failure in type 2 diabetes.pdf", + "2020 - Advances of single?cell genomics and epigenomics in human disease.pdf", + "2020 - Advances of single?cell genomics and epigenomics in human disease.pdf" + ], + "extraction_id": [ + "7f7a7f30-2e4e-50aa-bbcb-9f211c371e38", + "7a2a9981-4096-5049-a717-3e69eb609777", + "8bbfb009-87b7-54ae-8465-8796db8c271a", + "117cc1a5-d236-56b2-a69d-9c0a2fb9053d", + "dee54186-e75e-5ed2-818d-cd6f4370b153", + "7a2a9981-4096-5049-a717-3e69eb609777", + "10e4029f-0324-55c9-8fe8-023a924d1732", + "7a2a9981-4096-5049-a717-3e69eb609777", + "f740892a-7817-58b0-bec4-8648086b2353", + "65471d38-cd13-5de2-8c19-1eb72d24d6f5" + ], + "document_id": [ + "afe53f5a-3962-520f-be55-9df5bfdaad70", + "b9bc63a5-e366-5685-bd7a-4732a8eeffb7", + "019efefb-65db-55f5-a3a7-4f224473f51f", + "b9bc63a5-e366-5685-bd7a-4732a8eeffb7", + "6cf1eb8d-a91e-58a2-b6f4-29653678d0d3", + "b9bc63a5-e366-5685-bd7a-4732a8eeffb7", + "afe53f5a-3962-520f-be55-9df5bfdaad70", + "b9bc63a5-e366-5685-bd7a-4732a8eeffb7", + "afe53f5a-3962-520f-be55-9df5bfdaad70", + "afe53f5a-3962-520f-be55-9df5bfdaad70" + ], + "id": [ + "chatcmpl-AIHKFuXAocol6QH0B6QHJlkuJdiDC", + "b7812a7a-5504-57ca-8755-969dee45717e", + "d5c2a32a-b869-59c1-8a63-45ab620669de", + "ab373b7e-8c0b-59d8-9408-3e09ac76761e", + "a2adc65b-035b-568f-a0ae-9f7821ef45bc", + "887e1f7e-5044-5be8-a506-588ca7afa004", + "4bfcfbd6-f45e-553d-a043-a12e7abeff61", + "d32d6338-6cda-5f58-999d-2b4287ee4a77", + "ef0b8934-2af1-5848-88f9-ff5a2e4f3cc1", + "46ed97d7-7b3e-5be2-a409-04a37d105ef2", + "f06bcc81-6ef9-5874-8ef9-6bcb3c34b0d0" + ], + "contexts": [ + "Tang X, Huang Y, Lei J, Luo H, Zhu X (2019) The single-cell sequenc- ing: new developments and medical applications. Cell Biosci 9:53. https ://doi.org/10.1186/s1357 8-019-0314-y Teo AKK etal (2018) Single-cell analyses of human islet cells reveal de-differentiation signatures. Cell Death Discov 4:14. https ://doi. org/10.1038/s4142 0-017-0014-5 Theis FJ, Lickert H (2019) A map of beta-cell differentiation pathways supports cell therapies for diabetes. Nature 569:342343. https ://", + "4. PRECISE CELLULAR GENOMICS Elucidating the molecular mechanisms that lead to beta cell dysfunction and T2D pathogenesis has been a major focus of diabetes research for decades. However, advances in single cell genomic proling techniques have led to greater understanding of non-beta cell type transcriptional regulation and suggest that they may play important roles in hallmark features of beta cell insuf ciency and", + "53. Eliasson L, Esguerra JL (2014) Role of non-coding RNAs in pancreatic beta-cell development and physiology. Acta Physiol (Oxf) 211:273284 54. Ding GL, Wang FF, Shu J etal (2012) Transgenerational glucose intolerance with Igf2/H19 epigenetic alterations in mouse islet induced by intrauterine hyperglycemia. Diabetes 61:11331142 55. Ku GM, Kim H, Vaughn IW etal (2012) Research resource: RNA-Seq reveals unique features of the pancreatic beta-cell tran-scriptome. Mol Endocrinol 26:17831792", + "understand each cell type s genomic architecture and better charac- terize their roles in islet resilience and failure. Experimental manipu- lation of the regulatory elements and/or the target genes identi ed by (epi)genomic approaches described above and modeling the putativepathways and processes they implicate in human islet cell lines (e.g., EndoC- bH1-H3) is essential to progress from correlation to causation. Similarly, transitioning from themouse (C57BL/6) to multiple mouse", + "therapeutic pathways for beta cell regeneration. An integrative analysis of whole-exome andRNA-sequencing data was employed to extensively characterize the genomic and molecularlandscape of insulinomas relative to normal beta cells. Here, we show at the pathway levelthat the majority of the insulinomas display mutations, copy number variants and/or dys-regulation of epigenetic modifying genes, most prominently in the polycomb and trithoraxfamilies. Importantly, these processes are coupled to co-expression", + "gesting that changes in alpha cell identity may ultimately lead to theirdysfunction. Analysis of normal and T2D islet single cells with simultaneous RNA-seq and patch clamping (patch-seq) also revealed subpopulations of alpha cells with varying enrichment for ER stressresponse genes (e.g., DDIT3, XBP1, PPP1R15A )[30]. Interestingly, this transcriptomic heterogeneity was consistent in normal and T2D islets", + "RNA-seq analysis: a tutorial. Mol Syst Biol 15:e8746. https ://doi.org/10.15252 /msb.20188 746 Ma L, Zheng J (2018) Single-cell gene expression analysis reveals -cell dysfunction and deficit mechanisms in type 2 diabe-tes. BMC Bioinform 19:515. https ://doi.org/10.1186/s1285 9-018-2519-1 Macaulay IC, Ponting CP, Voet T (2017) Single-cell multiom- ics: multiple measurements from single cells. Trends Genet 33:155168. https ://doi.org/10.1016/j.tig.2016.12.003", + "peak current. Prior single cell transcriptomic analyses have also notedsubpopulations of ER-stressed beta cells [31,32] which implicates the dysfunction of both alpha and beta cells in diabetes pathogenesis.Similarly, the integrity of beta and alpha cell functions seem to beReview S18MOLECULAR METABOLISM 27 (2019) S15 eS24/C2112019 Published by Elsevier GmbH. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ). www.molecularmetabolism.com", + "to understanding human development using single-cell tran-scriptomics. Development 144:1584. https ://doi.org/10.1242/dev.15045 8 Camp JG, Wollny D, Treutlein B (2018) Single-cell genomics to guide human stem cell and tissue engineering. Nat Methods 15:661667. https ://doi.org/10.1038/s4159 2-018-0113-0 Carrano AC, Mulas F, Zeng C, Sander M (2017) Interrogating islets in health and disease with single-cell technologies. Mol Metab 6:9911001. https ://doi.org/10.1016/j.molme t.2017.04.012", + "Advances ofsingle -cell genomics andepigenomics inhuman disease: whereare we now? 1 3 Brissova etal. 2018; Tritschler etal. 2017). Moreover, an increase in hyperglycaemia has been associated with a loss of beta-cell mass, function and organization and is the cell type most frequently studied for insulin resistance (Carrano etal. 2017; Lawlor etal. 2017b; Segerstolpe etal. 2016; Theis and Lickert 2019; Tritschler etal. 2017). Notably, single-cell transcriptome profiling has been" + ], + "answer": "Recent single-cell RNA-sequencing studies have provided significant mechanistic insights into beta-cell failure pathways. These insights include: 1. **De-differentiation Signatures**: Single-cell analyses of human islet cells have revealed de-differentiation signatures, suggesting that beta cells may lose their specialized functions and revert to a more progenitor-like state, which contributes to their dysfunction in diabetes [1]. 2. **Transcriptional Regulation**: Advances in single-cell genomic profiling have enhanced our understanding of transcriptional regulation in non-beta cell types, which may play crucial roles in the hallmark features of beta-cell insufficiency and dysfunction in type 2 diabetes (T2D) [2]. 3. **ER Stress and Heterogeneity**: Single-cell transcriptomic analyses have identified subpopulations of beta cells experiencing endoplasmic reticulum (ER) stress. This stress is implicated in the dysfunction of both alpha and beta cells, contributing to diabetes pathogenesis [8]. These findings highlight the complexity of beta-cell failure and underscore the importance of single-cell technologies in unraveling the molecular mechanisms underlying diabetes.", + "question": "What are the mechanistic insights into the beta-cell failure pathways gleaned from recent single-cell RNA-sequencing studies?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_15 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_15 new file mode 100644 index 0000000..80b5550 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_15 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2019 - (Epi)genomic heterogeneity of pancreatic islet function and failure in type 2 diabetes.pdf", + "2010 - The Role of Epigenetics in the Pathology of Diabetic Complications.pdf", + "2015 - Maternal diabetes, gestational diabetes and the role of epigenetics in their long term effects on offspring.pdf", + "2015 - Maternal diabetes, gestational diabetes and the role of epigenetics in their long term effects on offspring.pdf", + "2010 - The Role of Epigenetics in the Pathology of Diabetic Complications.pdf", + "2015 - Epigenetic mechanisms in diabetic complications and metabolic memory.pdf", + "2019 - Machine-learning to stratify diabetic patients using novel cardiac biomarkers and integrative genomics.pdf", + "2014 - Diabetic nephropathy\u2014emerging epigenetic mechanisms.pdf", + "2016 - NIH working group report using genomic information to guide weight management From universal.pdf", + "2018 - Type 2 Diabetes Mellitus and Cardiovascular Disease Genetic and Epigenetic Links.pdf" + ], + "extraction_id": [ + "043ee0bf-ec42-57dd-aa0e-4f4f5aac2437", + "efbaf00f-0cb1-531f-a9fd-2844670ec92c", + "daf2d7fd-e789-5ceb-9984-d95656b5dd91", + "daf2d7fd-e789-5ceb-9984-d95656b5dd91", + "41ac576d-b850-5ee8-9753-ba9b060ba798", + "44d96546-84c3-51f1-85f9-22790a91d105", + "aff84b9e-3855-5960-accd-dcac6b362346", + "cbbe696b-8541-537a-ac5f-77b82cdb8201", + "68f800e3-8a0c-50ee-ba8b-ceb361f14fa5", + "9e010393-b98f-5f6c-a62d-fc0646ba8667" + ], + "document_id": [ + "b9bc63a5-e366-5685-bd7a-4732a8eeffb7", + "766edfd5-4756-51bf-b636-c94b041d030c", + "3e92bd8e-fbf7-5bc4-9395-0a6dd0b0934e", + "3e92bd8e-fbf7-5bc4-9395-0a6dd0b0934e", + "766edfd5-4756-51bf-b636-c94b041d030c", + "470f1f94-792d-5273-a88f-7e06084951c5", + "332ac2ec-accc-5370-a4d2-6fec9ce7e072", + "be05127e-1be8-5573-b571-51a11c3b2be2", + "0ee28c8a-3618-559e-be0a-30f2579a0d1f", + "3e82a2e5-4b2c-59c0-99cd-f3b06d8dabf2" + ], + "id": [ + "chatcmpl-AIHKMXGqZPQ6g67pYsMpeLJDEH7VZ", + "d198d71b-34ac-5402-b166-b476c15e2eef", + "d032460f-1bab-5710-8a79-872651f9c36e", + "a6efcd76-42f1-5d4c-bcfa-f5e63ac9eb4c", + "29bdfc8d-cb06-5ce5-8db6-f92b2f106b7d", + "cf4f3239-dd62-5eef-b5fc-85f4780e3f48", + "1c0b9205-340f-568f-990f-785c66154732", + "64f12ea8-a441-5fb5-a80e-1acbfb596a1e", + "8f447303-efd0-5564-9213-2cdc8fce12dd", + "7aafd7e0-2446-57a4-bb1e-1f77b4f4559e", + "cf775ed3-457e-52a7-b54b-5c1b5c179819" + ], + "contexts": [ + "To date, the overwhelming majority of studies including and assessing genetic variation have pro led the steady state patterns of epigeneticmodi cations and gene expression in islets or their constituent cell types. Others have compared how these steady state measures differ between T2D and non-diabetic (ND) individuals [13,16,40 e44]. Sur- prisingly, these studies, especially transcriptome analyses, haveidenti ed only modest alterations despite clear phenotypic differences", + "T1D and resulting complications (99). These epig enomic profiling studies suggest that, while a 415 reasonably stable histone methylation pattern is maintained in healthy individuals over time in a 416 cell-type specific setting, this pa ttern can be disrupted in a dis ease state. Moreover, they also 417 provide a glimpse of the inflammatory cell epig enome under the diabetic state and suggest that 418 new information about diabetes, its complicatio ns and metabolic memory can be obtained by 419", + "hyperglycaemia, epigenetic changes have also been noted in other experimental settings of hyperglycaemia. For example, increased DNA methylation has been described for the promoter region of the peroxisome proliferator-activated receptor- g(PPAR g) coactivator-1 agene (PPARGC1A) in diabetic islets ( Ling et al., 2008 ). Similar hypermethylation in the promoter region of the PPARGC1A gene has been noted in the skeletal muscle from diabetic patients,", + "and correlated with mitochondrial content ( Barr /C18es et al., 2009 ). Epigenetic changes have also been suggested to be responsible forthe legacy effect of reduced risk of vascular complications after a period of sustained tight glucose control, or metabolic memory of transient hyperglycaemia and increased risk of diabetic vascular injury ( Pirola et al., 2010 ). Histone methylation variations have been noted in monocytes cultured in high glucose, as well as blood", + "Epigenetic Mechanisms in Diabetic Complications 17 Interestingly, the sirtuin (SIRT) family of deacetylases, specifically SIRT1, has been found to 360 regulate several factors involved in metabolism, adipogenesis a nd insulin secretion (86). HATs 361 and HDACs can also modulate NF- B transcriptional activity (4, 44) resulting in changes in 362", + "ing that environment and diet may influence epigenetic mod-ifications that predispose individuals to diabetes [ 46]. Aber- rant DNAme has also been reported in the reduced expression of genes involved in diabetes and metabolism, and DNAme variations have also been noted near diabetes susceptibility genes and enhancers [ 15,47]. Genomic DNA from diabetic patients with nephropa- thy relative to those without displayed differential meth- ylation at several genes, including UNC13B , which had", + "of diabetes mellitus on the body is a high glucose stressed condition, altering substrate metabolism and causing systemic inflammation [60]. Due to this environmental change, researchers have shown how epigenetic changes occur across most, if not all, tissues that are impacted by diabetes mellitus [49, 61]. In the cardiovascular system, the heart, circulatory system, and regulating immune system are all tran -", + "nephropathy. Exp. Physiol. 98, 934945 (2013). 48. Reddy, M.A., Tak Park, J. & Natarajan, R. Epigenetic modifications in the pathogenesis ofdiabetic nephropathy. Semin. Nephrol. 33, 341353 (2013). 49. Li, S.L. etal. Enhanced proatherogenic responses in macrophages and vascular smooth muscle cells derived from diabetic db/db mice. Diabetes 55, 26112619 (2006). 50. El-Osta, A. etal. Transient high glucose causes persistent epigenetic changes and altered gene", + "exhibit decreased plasticity of genome-wide muscle DNA methylation by high-fatoverfeeding. Diabetologia 2014;57:1154-1158. 53. Nilsson E, Jansson PA, Perfilyev A, et al. Altered DNA methylation and differential expression of genes influencing metabolism and inflammation in adipose tissue from subjects with type 2 diabetes. Diabetes 2014;63:2962-2976. 54. Aslibekyan S, Demerath EW, Mendelson M, et al. Epigenome-wide study identifies", + "etal. Hyperglycemia induces a dynamic cooperativity of histone methylase and demethylase enzymes associated with gene-activating epigenetic marks that coexist on the lysine tail. Diabetes (2009) 58:122936. doi:10.2337/ db08-1666 111. Keating S, Plutzky J, El-Osta A. Epigenetic changes in diabetic and cardio-vascular risk. Circ Res (2016) 118:170622. doi:10.1161/CIRCRESAHA. 116.306819 112. Paneni F, Volpe M, Lscher TF, Cosentino F. SIRT1, p66(Shc), and Set7/9 in" + ], + "answer": "The epigenetic landscape of key metabolic tissues shows several changes when comparing diabetic individuals to non-diabetic individuals: 1. **DNA Methylation Changes**: In diabetic individuals, increased DNA methylation has been observed in the promoter region of the PPARGC1A gene in both islets and skeletal muscle [3]. This suggests a potential mechanism by which gene expression related to metabolism is altered in diabetes. 2. **Histone Modifications**: There are disruptions in histone methylation patterns in diabetic states. While healthy individuals maintain stable histone methylation patterns, these can be disrupted in diabetes, indicating changes in the epigenome associated with inflammation and metabolic memory [2]. 3. **Impact on Gene Expression**: Epigenetic modifications, such as DNA methylation, have been linked to reduced expression of genes involved in diabetes and metabolism. Variations in DNA methylation have been noted near diabetes susceptibility genes and enhancers [6]. 4. **Tissue-Wide Epigenetic Changes**: Diabetes mellitus, characterized by high glucose stress, leads to epigenetic changes across most tissues impacted by the disease, including the cardiovascular system and immune system [7]. 5. **Adipose Tissue**: In subjects with type 2 diabetes, altered DNA methylation and differential expression of genes influencing metabolism and inflammation have been observed in adipose tissue [9]. These findings collectively suggest that diabetes is associated with specific epigenetic alterations across various metabolic tissues, which may contribute to the pathophysiology of the disease.", + "question": "How does the epigenetic landscape of key metabolic tissues change in diabetic versus non-diabetic individuals?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_16 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_16 new file mode 100644 index 0000000..a953850 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_16 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2019 - (Epi)genomic heterogeneity of pancreatic islet function and failure in type 2 diabetes.pdf", + "2020 - Functional Genomics in Pancreatic \u03b2 Cells Recent Advances in Gene Deletion and Genome Editing Technologies for Diabetes Research.pdf", + "2020 - Functional Genomics in Pancreatic \u03b2 Cells Recent Advances in Gene Deletion and Genome Editing Technologies for Diabetes Research.pdf", + "2020 - Functional Genomics in Pancreatic \u03b2 Cells Recent Advances in Gene Deletion and Genome Editing Technologies for Diabetes Research.pdf", + "2016 - Dissecting diabetes metabolic disease.pdf", + "2020 - Functional Genomics in Pancreatic \u03b2 Cells Recent Advances in Gene Deletion and Genome Editing Technologies for Diabetes Research.pdf", + "2020 - Functional Genomics in Pancreatic \u03b2 Cells Recent Advances in Gene Deletion and Genome Editing Technologies for Diabetes Research.pdf", + "2020 - Functional Genomics in Pancreatic \u03b2 Cells Recent Advances in Gene Deletion and Genome Editing Technologies for Diabetes Research.pdf", + "2020 - Functional Genomics in Pancreatic \u03b2 Cells Recent Advances in Gene Deletion and Genome Editing Technologies for Diabetes Research.pdf", + "2020 - Functional Genomics in Pancreatic \u03b2 Cells Recent Advances in Gene Deletion and Genome Editing Technologies for Diabetes Research.pdf" + ], + "extraction_id": [ + "57736895-897e-54e5-a735-aadcbd77cb63", + "ab61a462-21d3-50dc-afb3-3e1cdeb15b1f", + "ab61a462-21d3-50dc-afb3-3e1cdeb15b1f", + "ab61a462-21d3-50dc-afb3-3e1cdeb15b1f", + "998a92ba-e7fc-5553-b629-7b5797fbfafe", + "fe5bf2df-2eda-5ef0-8aad-79bbc5b898d6", + "ab61a462-21d3-50dc-afb3-3e1cdeb15b1f", + "5f8a0ddd-a0c7-5151-9b6a-e0980bb94aa6", + "0a3e3095-4789-505a-96b7-123a05078e95", + "a36cee80-5961-55e5-8ea4-8d4e1bc501a9" + ], + "document_id": [ + "b9bc63a5-e366-5685-bd7a-4732a8eeffb7", + "51350055-d53c-5692-ab53-337b8a8bafd6", + "51350055-d53c-5692-ab53-337b8a8bafd6", + "51350055-d53c-5692-ab53-337b8a8bafd6", + "eee2f79d-e093-52fb-871a-798fd859235e", + "51350055-d53c-5692-ab53-337b8a8bafd6", + "51350055-d53c-5692-ab53-337b8a8bafd6", + "51350055-d53c-5692-ab53-337b8a8bafd6", + "51350055-d53c-5692-ab53-337b8a8bafd6", + "51350055-d53c-5692-ab53-337b8a8bafd6" + ], + "id": [ + "chatcmpl-AIHKSpSdna9OyEUtDVRTMkIkEtBS5", + "f42c0f84-d2a8-5bf9-89c2-3dd182bfb235", + "2af36592-3e59-583c-a9c7-d612175f4afc", + "75b937b2-1e0b-5d63-b542-618ad91bbd1f", + "1f114642-3f77-5346-89e8-394c433f66ff", + "df30dab3-a490-5497-a079-2741f9039f87", + "eadf2320-de70-5499-ade0-7aa9930ac091", + "57b9550d-0258-5a87-be57-976f471e5763", + "1859f32b-8f5c-5c3c-9f4d-54193d37645d", + "99ccc9a2-865f-5d11-9b08-b26261d02fc9", + "83053df5-47ac-59da-9c30-69740a64372d" + ], + "contexts": [ + "A variety of cellular and animal models have been developed and applied over the past few years to experimentally manipulate cis-regulatory elements and their target gene function as it related to beta cell/isletfunction, glucose homeostasis, and T2D pathogenesis. CRISPR/Cas9 hasrevolutionized our ability to modify genomes and epigenomes almost at will. Unsurprisingly, CRISPR (epi)genome editing tools can and have been used to target putative T2D target genes [54] orcis-REs[55] in beta", + "(276279). Through CRISPR-mediated HDR and base editing, it is possible to correct the vast majority of genetic variants, if notall. Conversion of GWAS-identi ed non-coding variants has not been conducted/documented in the diabetes eld, but it seems inevitable that such work will be carried out in the near futureHu et al. Genome Editing of Pancreatic Beta Cells Frontiers in Endocrinology | www.frontiersin.org October 2020 | Volume 11 | Article 576632 11", + "Cas9 editing to restore insulin production in differentiated iPSCcells that mimicked neonatal diabetes ( 251,252). Likewise, Shi et al. converted a patient-speci c mutation in GATA6 gene and showed that the mutation involved (GATA6 R456C) has a similar effect to GATA6 knockout ( 21). Most recently, correction of a variant in the Wolfram syndrome 1 ( WFS1 ) gene by CRISPR- mediated HDR improved insulin secretion in iPSC-differentiatedb-like cells ( 253). Studies on GWAS identi ed genetic variants", + "in response to various stimuli including glucose aftertransplantation in an immunocompromised mouse model (230,231). However, the use of iPSC is controversial and there are some concerns over genetic and epigenetic variations iniPSCs which might affect cell function after differentiation ( 275). Manipulation of hESC/iPSC cells via CRISPR-Cas9 technology provides a platform for the correction of genomic mutations not only in diabetes but in other disease elds as well", + "hPSCs [48,49] for correcting the COL7A1 [50] anda1-antitrypsin genes [51]. Given the superior cutting ef ciency, CRISPR/Cas9 is increasingly becoming the favored choice for genome editing inhPSCs [16,52] . 3.2. Employing hPSCs and genome editing tools to study diabetes and metabolic syndromes In general, the strategy to carry out in vitro disease modeling of dia-", + "Due to its simplicity and adaptability, CRISPR has rapidly become the most popular genome editing tool available for the mammalian genome ( 50,63). Because NHEJ DNA repair often introduces unwanted indels at the Cas9 cutting site, CRISPR hasbeen used to knock-out genes by introducing frameshiftmutations, resulting in protein depletion ( 156,157). In the diabetes eld, CRISPR has also been adopted to study several genes in bcell lines and in human ES-derived bcells ( 21,151,", + "RNP and single strand edDNA (ssDNA) donor which carriesdesired changes such as insertion of loxP site ( 255,259265). Using CRISPR-Cas9, leptin and leptin receptor knockout mice have been established as tools in diabetes and obesity research ( 160,255,256). Knock-in mouse models have also been established via HDR to achieve cell-speci c deletion of the gene ( 266). Genome Editing: Clinical Application in Diabetes An important goal in genetic research is to identify the genetic", + "to how CRISPR/Cas9 technology may nd clinical application in patients with diabetes. Keywords: genome editing, beta cell, genome-wide association studies, maturity onset of diabetes of the young, stem cells, mouse models INTRODUCTION Type 2 diabetes (T2D) affects an estimated 425 million people worldwide, a number predicted to rise to 629 million by 2045 ( 1). The disease usually involves insulin resistance but is ultimately the result", + "samples ( 236). CRISPR technology has been used recently to correct point mutations in patient-derived iPSCs to target diabetes-relatedgene defects. To date, the most ef cient method used in iPSC is CRISPR/Cas9-based homology-directed repair (HDR). Here, a Cas9-mediated cut is generated adjacent to the site of interest. A homologous donor template with the intended nucleotidechange containing silent mutations in the gRNA sequence(167) can then be recombined by HDR. This approach has", + "free IPSCs from Human Pancreatic Cells Using the CRISPR-Cas9 System. J Vis Exp JoVE (2017). doi: 10.3791/56260 277. Millette K, Georgia S. Gene Editing and Human Pluripotent Stem Cells: Tools for Advancing Diabetes Disease Modeling and Beta-Cell Development. Curr Diabetes Rep (2017) 17:116. doi: 10.1007/s11892-017-0947-3Hu et al. Genome Editing of Pancreatic Beta Cells Frontiers in Endocrinology | www.frontiersin.org October 2020 | Volume 11 | Article 576632 19" + ], + "answer": "Recent advancements in leveraging CRISPR-based approaches to correct monogenic forms of diabetes in vivo include the use of CRISPR-mediated homology-directed repair (HDR) to correct specific genetic mutations associated with diabetes. For instance, CRISPR technology has been used to correct point mutations in patient-derived induced pluripotent stem cells (iPSCs) targeting diabetes-related gene defects. The most efficient method employed in iPSCs is CRISPR/Cas9-based HDR, where a Cas9-mediated cut is generated adjacent to the site of interest, and a homologous donor template with the intended nucleotide change is recombined by HDR [9]. Additionally, there has been a successful correction of a variant in the Wolfram syndrome 1 (WFS1) gene using CRISPR-mediated HDR, which improved insulin secretion in iPSC-differentiated beta-like cells [3]. These advancements highlight the potential of CRISPR-based genome editing to correct monogenic forms of diabetes by targeting specific genetic mutations in vivo.", + "question": "What recent advancements have been made in leveraging CRISPR-based approaches to correct monogenic forms of diabetes in vivo?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_17 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_17 new file mode 100644 index 0000000..3e4e96d --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_17 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2018 - High-Throughput Approaches onto Uncover (Epi)Genomic Architecture of Type 2 Diabetes.pdf", + "2018 - High-Throughput Approaches onto Uncover (Epi)Genomic Architecture of Type 2 Diabetes.pdf", + "2013 - Genome-Wide Contribution of Genotype by Environment Interaction.pdf", + "2022 - Using Recurrent Neural Networks for Predicting Type-2 Diabetes from Genomic and Tabular Data.pdf", + "2020 - Genome-wide association analysis of type 2 diabetes in the EPIC-InterAct study.pdf", + "2017 - Genomic regulation of type 2 diabetes endophenotypes Contribution.pdf", + "2012 - What will Diabetes Genomes Tell Us.pdf", + "2013 - Systems Biology Approach Reveals Genome to Phenome Correlation in Type 2 Diabetes.pdf", + "2013 - Systems Biology Approach Reveals Genome to Phenome Correlation in Type 2 Diabetes.pdf", + "2013 - Systems Biology Approach Reveals Genome to Phenome Correlation in Type 2 Diabetes.pdf" + ], + "extraction_id": [ + "978df5a8-acb4-53d3-b351-66a3bc613c78", + "aba850e8-8c0d-5256-b2ba-fa1dfc221114", + "f3975a2c-8a66-582e-a4b8-868b1f4722d4", + "3c30b33b-8928-5cee-9c37-c70642fff75c", + "2c601441-443d-5c47-95bb-6343378dd5dc", + "3dc37987-5204-5414-92ee-9d97af221261", + "50a110f8-e91d-5985-9fe9-62a373a58c9d", + "8dd91a24-2ac7-57b3-9cb3-f8ac74b1885c", + "f6926cab-e00d-5972-a815-2ecc9f8c35d5", + "9369222f-e125-58c0-8f2b-cf5daa867f77" + ], + "document_id": [ + "1cb0c4ac-c1fe-55c2-919c-52cd5018c00d", + "1cb0c4ac-c1fe-55c2-919c-52cd5018c00d", + "8c310d76-0a3b-574c-9859-859258870ee5", + "be0e50e0-3de8-53c5-8126-a0b618647f80", + "5dd7d700-03db-595d-b1a5-beca77f9579e", + "fef1ae33-b3af-50ea-909c-f1b57f7fe981", + "38b3b7ab-d13e-5986-9a3a-54abe8a3e1e9", + "ea7c2799-c259-5d0e-b40b-ecebe0a9fc9f", + "ea7c2799-c259-5d0e-b40b-ecebe0a9fc9f", + "ea7c2799-c259-5d0e-b40b-ecebe0a9fc9f" + ], + "id": [ + "chatcmpl-AIHKYN37xsXdGCjQ8Ms8PgKZ10CIR", + "7302a27a-6e56-589d-a579-635f25fc46a3", + "4d780759-36bb-5295-a63a-16dab6aeab8c", + "ac4d8521-b492-59b5-9978-891f5a5ce0c5", + "81fb2df2-4154-58a7-b217-b07153a6c921", + "263ea999-9662-5518-a606-939f69d09f90", + "c807fc8b-966e-56a9-91ce-07b9baf940d9", + "ef027493-6063-5abd-9ee7-0c9a37379317", + "869d46b4-e379-54f8-bd71-143d9f31fa93", + "b92b959c-2f31-5177-8a21-627f3ee81b6c", + "7fd80e84-ec0c-564c-8e8b-278b8c622abb" + ], + "contexts": [ + "The integration of genetic, epigenetic, transcriptomic and phenotypic information allows to identify genes and novel metabolic pathway targets that deserve further attention to elucidate mechanistic relationships with insulin resistance and pancreatic islet failure. Although the GWASs and EWASs shed light onto (epi)genomic landscape of T2D to a great extent, these methods have still explicit limitations to conquer, such as sample size, small effect size, low allele frequency, genetic heterogeneity", + "map of the human genome, spurred larger multi-institutional programs (e.g., 1000 Genomes Projects, Encyclopedia of DNA Elements [ENCODE], and Roadmap Epigenomics), that have the goal of tracking genomic and epigenomic changes across multiple populations [ 8]. Aforementioned studies enabled GWASs for complex diseases such as T2D. DNA amplication, Sanger sequencing, and microarray studies have shed light on the genetics of diabetes but have only provided a limited amount of data. An", + "Abstract While genome-wide association studies (GWAS) and candidate gene approaches have identified many genetic variants that contribute to disease risk as main effects, the impact of genotype by environment (GxE) interactions remains rather under- surveyed. To explore the importance of GxE interactions for diabetes-related traits, a tool for Genome-wide Complex Trait", + "The advancement that has taken place in Genome-Wide Association Studies (GWAS) holds tremendous information related to various gene patterns associated with divergent illnesses that are complex and challenging to perform reductive analysis from a single locus, as stated by Cho Ys [6] and Coron [7]. The evolution of GWAS has focused on integrating data related to multi-locus across the gene that would assist in predicting complex illnesses", + "1. Genome-wide association studies (GW AS) have made considerable progress in identifying genetic risk factors and in providing evidence for more in-depth understanding of the biological and pathological pathways underlying T2D. A recent study performed a meta-analysis of T2D across 32 GW AS of European ancestry par - ticipants and identified 243 genome-wide significant loci (403 distinct genetic variants) associated with T2D risk", + "1. Introduction Genome wide association studies (GWAS) of type 2 diabetes mellitus and relevant endophenotypes have shed new light on the complex etiology of the disease and underscored the multiple molecular mechanisms involved in the pathogenic processes leading to hyperglycemia [1]. Even though these studies have successfully mapped many diabetes risk genetic loci that could not be detected by linkage analysis, the risk single nucleotide poly-", + "how they will continue to expand our understanding of the genetic risk factors and underlying biology of diabetes. Keywords Genotyping .Genome-wide association . Sequencing .Imputation .Exome .Genome . Fine-mapping .Diabetes .Quantitative traits .Metabochip . Single nucleotide polymorphism Introduction GWA studies have made progress toward understanding the inherited basis of type 1 and type 2 diabetes by detecting disease-associated DNA variants, usually with allele fre-", + "complementary systems level data such as that related to protein- protein interactions and to and gene expression can provideinsights into the mechanisms underlying pathogenesis of complextraits [2224]. Here, we have combined these approaches towarddeciphering genome to phenome correlation in T2D ( Figure 1 ). Given that T2D GWAS genes do not directly relate to disease", + "phenotypes [2,6]. The recently accomplished deep sequencing of human exomes has indeed suggested that rare variations contribute substantially to human phenotypic variation and disease susceptibility [73]. Availability of post-GWASs era data for T2D will be crucial in examining genome to phenomecorrelation in greater details. Emerging methods in pathway-wide analysis and integrative network based analysis of genetic association data in complex disorders will further help accelerate", + "Abstract Genome-wide association studies (GWASs) have discovered association of several loci with Type 2 diabetes (T2D), a common complex disease characterized by impaired insulin secretion by pancreatic bcells and insulin signaling in target tissues. However, effect of genetic risk variants on continuous glycemic measures in nondiabetic subjects mainly elucidatesperturbation of insulin secretion. Also, the disease associated genes do not clearly converge on functional categories" + ], + "answer": "Genome-wide association studies (GWAS) integrate with multi-omics data to elucidate the complex genetic architectures of diabetes by combining genetic, epigenetic, transcriptomic, and phenotypic information. This integration helps identify genes and novel metabolic pathway targets that are crucial for understanding mechanistic relationships with insulin resistance and pancreatic islet failure [1]. Additionally, complementary systems-level data, such as protein-protein interactions and gene expression, provide insights into the mechanisms underlying the pathogenesis of complex traits like type 2 diabetes (T2D) [8]. This multi-omics approach allows for a more comprehensive understanding of the genome-to-phenome correlation in T2D, which is essential for examining the disease's complex genetic architecture [9].", + "question": "How do genome-wide association studies (GWAS) integrate with multi-omics data to elucidate the complex genetic architectures of diabetes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_18 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_18 new file mode 100644 index 0000000..384c7d7 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_18 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2010 - Neural tube defect genes and maternal diabetes during pregnancy.pdf", + "2018 - Genetic variants of gestational diabetes mellitus a study of 112 SNPs among 8722 women in two independent populations.pdf", + "2017 - Genome-wide DNA methylation variation in maternal and cord blood of gestational diabetes population.pdf", + "2010 - Autism Spectrum Disorders and Epigenetics.pdf", + "2017 - Genome-wide DNA methylation variation in maternal and cord blood of gestational diabetes population.pdf", + "2015 - Type 2 diabetes mellitus.pdf", + "2015 - Maternal diabetes, gestational diabetes and the role of epigenetics in their long term effects on offspring.pdf", + "2005 - Animal models of diabetes mellitus.pdf", + "2004 - Impaired glucose homeostasis in transgenic mice expressing the human transient neonatal diabetes mellitus locus.pdf", + "2010 - Neural tube defect genes and maternal diabetes during pregnancy.pdf" + ], + "extraction_id": [ + "a9352adc-46d0-5947-a70d-940a7686008d", + "6ca1166c-ba51-5437-b325-5299e3e8fcef", + "971ff653-c42a-5366-ae2b-080df9aa679f", + "dcc77767-4641-5969-b3c1-4ea96a644a74", + "a17ed56f-20d4-56be-9aec-ac0b4943d19a", + "bbe952b1-6cc2-56a8-b5e8-5ca6b44b4316", + "e7e97f1e-d947-5b94-b2a9-5ac4b443628c", + "f7b36272-9780-52e8-9cb3-62d1c6c8c3b6", + "f68a90b3-5e03-57f4-8cb6-252e3a3fa132", + "a9352adc-46d0-5947-a70d-940a7686008d" + ], + "document_id": [ + "aa74b552-7e06-5596-8dec-298c40ad558c", + "3b301dd1-17bd-5632-9a96-d6294c6d7650", + "e02a2e19-3527-5466-b8d6-69e62f657698", + "6b435185-b16c-5b05-826b-eb98ca7bf806", + "e02a2e19-3527-5466-b8d6-69e62f657698", + "415516ba-5365-501b-84ce-0789045862f8", + "3e92bd8e-fbf7-5bc4-9395-0a6dd0b0934e", + "2fd381ac-2898-5a8c-af93-bcc86e7dec14", + "268bc8e3-7787-5bc0-8f7d-fffe20194dca", + "aa74b552-7e06-5596-8dec-298c40ad558c" + ], + "id": [ + "chatcmpl-AIHKdF53rZo0tRRSpImOeG4mHUbkt", + "10776283-4b6d-544c-89ac-0225c65bec1e", + "dc64e623-a130-5814-b54a-dd5f787f10d5", + "5495230d-c26d-5633-90e8-028912e5298a", + "4ecf5607-8d58-5908-aa1b-4416af202e69", + "a5412cf9-367c-518e-bb4f-77d8deb00a32", + "9814f4a0-2701-5920-bfd7-df5e1f3b134e", + "4f7b210f-26f7-5726-baff-8d469b2cc3df", + "8267bc80-1791-5e21-b228-053cba0629fd", + "4bb50efe-65b0-5c3c-9f58-03b423c93c0d", + "f703ae7e-5f64-52ee-860e-7b91b3066477" + ], + "contexts": [ + "maternal diabetes reduces the precision of gene regulation in exposed individuals. Loss of precision in embry-onic gene regulation may include changes to the epigenome via deregulated expression of chromatin-modify-ing factors. Unraveling the mechanisms underlying such epigenetic modications in diabetic pregnancies willhelp to understand how teratogenic insults compromise embryonic development and possibly provide ave-nues for therapeutic intervention. Birth Defects Research (Part A) 88:601611, 2010.", + "and metabolic imprinting: the ongoing effects of maternal hyper-glycemia. Diabetes Care 30:2287 2292 9. Clausen TD, Mathiesen ER, Hansen T et al (2008) High prevalence of type 2 diabetes and pre-diabetes in adult offspring of women withgestational diabetes mellitus or type 1 diabetes: the role of intrauter- ine hyperglycemia. Diabetes Care 31:340 346 10. Solomon CG, Willett WC, Carey VJ et al (1997) A prospective study of pregravid determinants of gestational diabetes mellitus. JAMA 278:1078 1083", + "M. Gestational diabetes alters offspring DNA methylation profiles in human and rat: Identification of key pathways involved in endocrine system disorders, insulin signaling, diabetes signaling, and ILK signaling. Endocriniology 2015;156:2222 -38. [33] Murphy SK, Huang Z, Hoyo C. Differentially methylated regions of imprinted genes in prenatal, perinatal and postnatal human tissues. PLOS ONE 2012;7:e40924.", + "12. Kim JK, Samaranayake M, Pradhan S. Epigenetic mechanisms in mammals. Cell Mol Life Sci. 2009;66:596-612. 13. Horsthemke B, Buiting K. Genomic imprinting and imprinting defects in humans. Adv Genet. 2008;61:225-246. 14. Iacobuzio-Donahue CA. Epigenetic Changes in Cancer. Annu Rev Pathol. 2009;4:229-249. 15. Temple IK. Imprinting in human disease with special reference to transient neonatal diabetes and Beckwith-Wiedemann syn- drome. Endocr Dev. 2007;12:113-123.", + "and Knowler W C. Intrauterine exposure to diabetes conveys risks for type 2 diabetes and obesity: A study of discordant sibships. Diabetes 2000;49:2208 -11. [11] Feil R and Fraga MF. Epigenetics and the environment: Emerging patterns and implications. Nature Reviews Genetics 2012;13:97 -109. [12] Recillas -Targa F. DNA Methylation, Chromatin boundaries, and mechanisms of genomic imprinting. Archives of Medical Research 2002;33:428 -38.", + "53. T ravers,M.E. etal. Insights into the molecular mechanism for type2 diabetes susceptibility at the KCNQ1 locus from temporal changes in imprinting status in human islets. Diabetes 62, 987992 (2013). 54. Gulli,G., Ferrannini,E., Stern,M., Haffner,S. &DeFronzo,R.A. The metabolic profile of NIDDM isfully established in glucose-tolerant offspring of twoMexican-American NIDDM parents. Diabetes 41, 15751586 (1992). PRIMER NATURE REVIEWS | DISEASE PRIMERS VOLUME 1 | 2015 | 17", + "Gaudet, D., Hivert, M.F., Brisson, D., Bouchard, L., 2013 Sep. Gestational diabetesmellitus epigenetically affects genes predominantly involved in metabolic dis- eases. Epigenetics 8 (9), 935 e943. Salbaum, J.M., Kappen, C., 2012 Oct. Responses of the embryonic epigenome to maternal diabetes. Birth Defects Res. A Clin. Mol. Teratol. 94 (10), 770 e781. Salbe, A.D., Lindsay, R.S., Collins, C.B., Tataranni, P.A., Krakoff, J., Bunt, J.C., 2007 Feb.", + "environment are probably mediated by a permanent program-ming of the developing offspring, e.g. by the mechanism ofimprinting. Of interest, the increased risk of diabetes continuesinto subsequent generations, suggesting the changes also affectthe germ cell line [143]. Conclusions There is little doubt that some animal models of diabetes have", + "tal diabetes and later onset diabetes: a case of inher - ited insulin resistance. Arch. Dis. Child. 72:5657. 6. Temple, I.K., et al. 1995. An imprinted gene(s) for diabetes? Nat. Genet. 9:110112. 7. Temple, I.K., et al. 1996. Further evidence for an imprinted gene for neonatal diabetes localised to chro -", + "1994; Martinez-Frias et al., 1998). The underlying mecha-nisms are not well understood, but are thought to involve various responses of the embryonic genome to the adverse intrauterine environment (Greene, 2001;Loeken, 2008). To explore how conditions of maternal diabetes affect gene expression in the embryo, we recently conducted expression proling experiments on embryos from dia-betic dams compared to embryos from normal dams(Pavlinkova et al., 2009). We were able to demonstrate" + ], + "answer": "Genomic imprinting has a significant impact on the susceptibility and progression of diabetes. Imprinting can influence the expression of genes involved in metabolic processes, which are crucial in the development of diabetes. For instance, changes in imprinting status at specific loci, such as the KCNQ1 locus, have been linked to type 2 diabetes susceptibility, indicating that temporal changes in imprinting can affect the function of pancreatic islets and contribute to diabetes risk [6]. Additionally, imprinting defects have been associated with specific forms of diabetes, such as transient neonatal diabetes, suggesting that imprinted genes play a role in the disease's onset and progression [4]. Furthermore, the effects of maternal diabetes on the offspring's epigenome, including alterations in DNA methylation profiles, highlight the role of imprinting in the intergenerational transmission of diabetes risk [3], [7]. These epigenetic changes can lead to a permanent programming of the developing offspring, increasing the risk of diabetes in subsequent generations [8]. Overall, genomic imprinting is a critical factor in understanding the genetic and epigenetic mechanisms underlying diabetes susceptibility and progression.", + "question": "What is the impact of genomic imprinting on the susceptibility and progression of diabetes?," +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_19 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_19 new file mode 100644 index 0000000..7f5c70b --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_19 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2010 - Candidate Gene and Genome-Wide Association Studies in Behavioral Medicine.pdf", + "2016 - Putting the Genome in Context Gene-Environment Interactions.pdf", + "2008 - Genetic Effects on Environmental Vulnerability to Disease Novartis Foundation Symposium 293.pdf", + "2012 - The Genetic and Epigenetic Basis of Type 2 Diabetes and Obesity.pdf", + "2013 - Genome-Wide Contribution of Genotype by Environment Interaction.pdf", + "2018 - Global aetiology and epidemiology of type 2 diabetes mellitus and its complications.pdf", + "2018 - Global aetiology and epidemiology of type 2 diabetes mellitus and its complications.pdf", + "2016 - Putting the Genome in Context Gene-Environment Interactions.pdf", + "2016 - Putting the Genome in Context Gene-Environment Interactions.pdf", + "2014 - Nutrigenetics and Nutrigenomics Insights into Diabetes Etiopathogenesis.pdf" + ], + "extraction_id": [ + "3bf3c6a7-de03-5114-bad8-d53fd76d0fba", + "08acfe03-73b3-5533-b8e4-9caa031d33dd", + "cfc4760c-755e-5693-8d7b-4332fb6c45e5", + "50bde36d-2968-5eaa-9713-924e73383427", + "f3975a2c-8a66-582e-a4b8-868b1f4722d4", + "512ae4b5-27c8-509c-87ad-abd64d4295a6", + "df2a8699-692f-5f25-94b3-508f9ed2f210", + "c362793d-c70f-5225-afe5-88098042daef", + "08acfe03-73b3-5533-b8e4-9caa031d33dd", + "232f9536-eeac-5739-a57d-770cf5b32947" + ], + "document_id": [ + "17637a6f-804e-50e4-9cf5-37318e17f15c", + "ea43bb66-b6fe-5682-8f48-90568c080401", + "5d65e407-34e5-5c1c-b394-989b7a09b57d", + "d74ac751-712b-5970-98e6-bd348adc1dee", + "8c310d76-0a3b-574c-9859-859258870ee5", + "8bc8f3d4-968f-5252-ab4c-832b92e9ec0d", + "8bc8f3d4-968f-5252-ab4c-832b92e9ec0d", + "ea43bb66-b6fe-5682-8f48-90568c080401", + "ea43bb66-b6fe-5682-8f48-90568c080401", + "ce4f171c-494c-53f2-a770-c3edd3561c40" + ], + "id": [ + "chatcmpl-AIHKkTED9VE0du8urGhS0MeefXMR7", + "ee24ad01-f93a-55c4-8c2c-9dea6a6a84d5", + "de2af111-7fad-5dc1-baae-4742ccc8ba0d", + "e07d8080-aba7-5216-8a75-e078201b8c0a", + "e76c1d0c-33b7-5d9e-958f-fce6adfe81aa", + "30728ec3-882c-5bb0-8f41-4c74dfafdf13", + "f7ed49ac-f617-5c13-851e-98d1583e020f", + "151c185f-3300-5518-810c-3fb0d6715f2c", + "cc98a5b9-131e-5b60-919e-82e86b7a37a7", + "a94c609e-4816-5e10-96fd-ba8d79218405", + "1d13cf78-3215-5873-b910-cbcac141779b" + ], + "contexts": [ + "genome-wide association scans on type 2 dia-betes (Lango et al, 2008 ; van Hoek et al, 2008 ). Both studies found a similar predictive value showing only a marginal improvement in the prediction of type 2 diabetes beyond classicalclinical characteristics. Thus, despite overwhelming signicances and repeated replications, the explained variance andpredictive value of the currently identied sus- ceptibility loci is too low to be clinically useful. 5 GeneEnvironment Interactions in Obesity and Diabetes", + "actions between genetic variation and environmental exposures and medical therapies has important implications for the predic- tion, targeted prevention, and s tratified treatment of T2D and many other diseases. The literature on gene-e nvironment interactions in diabetes-related traits is extensive, but few studies are accom- panied by adequate replication data or compelling mechanistic explanations. Moreover, most studies are cross-sectional, from which temporal patterns and causal effects cannot be", + "ined for a range of disorders, from diabetes, cancer and in ammatory bowel disease to depression. We refute the contention that incorporating the measurement of genotype into longitudinal-epidemiological studies is wasteful or unlikely to yield signi cant bene ts. 2008 Genetic effects on environmental vulnerability to disease. Wiley, Chichester (Novartis Foundation Symposium) p 128142 Slow progress understanding the genetic basis of many common diseases has been", + "In principle, each of these loci provides an opportunity to define the genetic architecture and pathophysiology of these traits. The earliest successes for genetic discovery in diabetes and obesity arose from the study of monogenic and syndromic forms of disease, for which the segregation of rare, but highly penetrant, alleles could be tracked using family-based linkage approaches that are well suited to that setting. Maturity-onset diabetes of the young, for example, accounts for ~12% of cases", + "wide GxE interactions in explaining the variance of diabetes-related traits. Citation: Zheng J-S, Arnett DK, Lee Y-C, Shen J, Parnell LD, et al. (2013) Genome-Wide Contribution of Genotype by Environment Interaction to Variation of Diabetes-Related Traits. PLoS ONE 8(10): e77442. doi:10.1371/journal.pone.0077442 Editor: Maria Eugenia Saez, CAEBi, Spain Received April 10, 2013; Accepted September 3, 2013; Published October 28, 2013", + "data sharing to advance complex disease research. Nat. Rev. Genet. 17, 535549 (2016). 82. Franks,P .W., Pearson,E. & Florez,J.C. Gene- environment and gene-treatment interactions in type2 diabetes: progress, pitfalls, and prospects. Diabetes Care 36, 14131421 (2013). 83. Hagberg,J.M., Jenkins,N.T . & Spangenburg,E. Exercise training, genetics and type2 diabetes- related phenotypes. Acta Physiol. 205, 456471 (2012). 84. Langenberg,C. etal. Gene-lifestyle interaction and", + "Genomics and geneenvironment interactions Even though many cases of T2DM could be prevented by maintaining a healthy body weight and adhering to a healthy lifestyle, some individuals with prediabetes mel - litus are more susceptible to T2DM than others, which suggests that individual differences in response to life - style interventions exist76. Substantial evidence from twin and family studies has suggested a genetic basis of T2DM77. Over the past decade, successive waves of", + "DNA variation with disease processes in a range of settings, from cell lines to human populations, and major advances have been made in coupling these complex datasets with information about extrinsic environmental exposures including drug prescription in ways that allowthe logical interrogation of gene-drug and gene-lifestyle interactions. Doing so may teach us about disease etiology and help stratify type 2 diabetes (T2D) into subclasses that can be treated more effectively, with", + "fuel subsequent functional and clinical translation studies. This is important, because diabetes medicine may rely increas- ingly on genomic stratification of patient populations and disease phenotype, for which gene-environment interaction studies might prove highly informative. How Are Gene-Environment Interactions Defined? The term gene-environment interaction has different meanings to different biomedical re searchers (see Supplement 1for glossary of terms used). However, here, we focus on the", + "Nutrients 2014, 6 5362 48. Cornelis, M.C.; Hu, F.B. Gene -enviroment interactions in the development of type 2 diabetes: Recent progress and continuing challenges. Annu. Rev. Nutr. 2012, 32, 245259. 49. Lee, Y.C.; Lai, C.Q.; Ordovas, J.M.; Parnell, L.D. A database of gene -enviroment interactions pertaining to blood lipid traits, cardiovascular disease and type 2 diabetes. J. Data Mining Genomics Proteomics 2011, 2, 106, doi:10.4172/2153- 0602.1000106." + ], + "answer": "Longitudinal genomics studies are crucial for understanding gene-environment interactions in diabetes onset and management because they allow researchers to observe how genetic variations interact with environmental factors over time. This approach helps in identifying temporal patterns and causal relationships that are not possible to discern from cross-sectional studies [2]. By incorporating genotype measurements into longitudinal studies, researchers can gain significant insights into the genetic basis of diseases like diabetes, which can lead to better prediction, targeted prevention, and stratified treatment of type 2 diabetes (T2D) [3]. Additionally, these studies can help stratify T2D into subclasses that can be treated more effectively by understanding gene-lifestyle interactions [8]. Overall, longitudinal genomics studies provide a comprehensive framework to explore how genetic predispositions and environmental exposures collectively influence diabetes-related traits and disease progression.", + "question": "How do longitudinal genomics studies help in understanding gene-environment interactions in diabetes onset and management?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_2 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_2 new file mode 100644 index 0000000..6e349ba --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_2 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Functional Genomics in Pancreatic \u03b2 Cells Recent Advances in Gene Deletion and Genome Editing Technologies for Diabetes Research.pdf", + "2022 - A genome-wide functional genomics approach uncovers genetic determinants of immune phenotypes in type 1 diabetes.pdf", + "2022 - Genome-wide meta-analysis and omics integration identifies novel genes associated with diabetic kidney disease.pdf", + "2016 - Genome-Wide Association Studies of Type 2 Diabetes.pdf", + "2012 - Recent Developments in the Genetic and Genomic Basis of Type 2 Diabetes.pdf", + "2010 - Liver and Adipose Expression Associated SNPs.pdf", + "2016 - Transcriptomics in type 2 diabetes Bridging the gap between genotype and phenotype.pdf", + "2012 - Recent Developments in the Genetic and Genomic Basis of Type 2 Diabetes.pdf", + "2012 - Finding Genetic Risk Factors of Gestational Diabetes.pdf", + "2015 - Genetic Studies on Diabetic Microvascular Complications.pdf" + ], + "extraction_id": [ + "5f148509-8a55-5e9c-8c68-e327f519c1c9", + "692b342f-5d48-5046-84f9-37f1cf4275b5", + "d7e0e5ad-bad5-5b14-896e-45702d6605f9", + "a620eedf-5d5b-506f-97f5-c25dbe0493c0", + "1213249d-8ed3-5d13-9137-f11b87a7a78b", + "35ce49d5-7af3-5f24-927c-f800e8ae024d", + "71934c29-338d-57a2-8f45-e3e795e0ec9b", + "924d35c5-0ee8-53a7-9fdf-9309a27ce9ae", + "e7bf3f2d-8180-5a84-965c-8289f107a718", + "d3335459-5fec-5104-932f-f4fd7566edf7" + ], + "document_id": [ + "51350055-d53c-5692-ab53-337b8a8bafd6", + "368e0215-393e-5bec-a87c-e976adaa3ca5", + "b9194555-5fdb-549e-9edb-d108132a7dd1", + "185aad8a-6a5b-5b18-81c4-ef251edef5e7", + "7d051350-d939-5183-be22-742727573a75", + "ebeef1bf-341d-5aa1-807b-1f23186cf2bc", + "98e49a13-9887-5b27-879b-0816a3da1c1d", + "7d051350-d939-5183-be22-742727573a75", + "81d6ccba-6203-5879-b206-b8711d1ff35c", + "1df9d9a8-0fb0-5a03-9749-9471b4b2b2f3" + ], + "id": [ + "chatcmpl-AIHIcyJRqSPUlYLtzZ5hVN5aLL9iw", + "0c0634ba-c437-52d3-b3a9-caa5eda120c6", + "1ab64c6e-e930-597e-bc12-ed540eabcf46", + "46ac5572-ac56-5f29-b7bf-49a1e29d3936", + "6d5d4c24-5bc8-539a-9faa-8b2370f8c87a", + "54da57b3-e577-5c00-a7d5-6f569a41d28b", + "0cf52952-0d83-58ed-b402-05dd2f085841", + "2a91a466-c271-5368-b0a1-cf15e6478bb1", + "de3b49f1-9dcc-5056-8232-b76e5f985736", + "72622bca-2fce-5732-9c8b-2909d231d09d", + "5af0c2b9-9957-5c8f-b8ae-c115e365576f" + ], + "contexts": [ + "wide association study identi es novel risk loci for type 2 diabetes. Nature (2007) 445:881 5. doi: 10.1038/nature05616 27. Scott LJ, Mohlke KL, Bonnycastle LL, Willer CJ, Li Y, Duren WL, et al. A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science (2007) 316:1341 5. doi: 10.1126/science.1142382 28. Fuchsberger C, Flannick J, Teslovich TM, Mahajan A, Agarwala V, Gaulton KJ, et al. The genetic architecture of type 2 diabetes. Nature (2016) 536:41 7.", + "novel loci for type 1 diabetes. Diabetes 58:290295. DOI: https://doi.org/10.2337/db08-1022, PMID: 18840781 Huang J, Ellinghaus D, Franke A, Howie B, Li Y . 2012. 1000 Genomes- based imputation identifies novel and refined associations for the Wellcome Trust Case Control Consortium phase 1 Data. European Journal of Human Genetics 20:801805. DOI: https://doi.org/10.1038/ejhg.2012.3, PMID: 22293688 Hundhausen C, Roth A, Whalen E, Chen J, Schneider A, Long SA, Wei S, Rawlings R, Kinsman M, Evanko SP ,", + "general population, these loci show limited effect in DKD, especially in individuals with type 1 diabetes [ 6]. Genome- wide association studies (GWAS) have previously identified ahandful of genetic loci for DKD at the genome-wide signifi- cance level ( p<510 8)[711]. Recently, a meta-analysis of GWAS, including up to 19,406 individuals with type 1 diabetes from the Diabetic Nephropathy Collaborative Research", + "Table 2.1 Major published T2D GWAS and meta-analyses StudyEthnicity/ origin NcasesaN controlsaNovel loci identiedGWAS or meta-analysis discoveryapproach GWAS arrayReference panel forimputationT2D phenotype denition/otherspecs Diabetes Gene Discovery Group (Sladek et al. 2007 ), NatureEuropean 694 645 SLC30A8 ,HHEX /IDE GWA Illumina 300k + Family history of T2D, AAO <45 years, BMI <30 kg/m 2 FinlandUS Investi-gation of NIDDMGenetics (FUSION)(Scott et al. 2007a ), ScienceEuropean 1161 1174 CDKN2A/2B ,", + "scale gene-centric meta-analysis across 39 studies identifies type 2diabetes loci. Am J Hum Genet. 2012;90(3):410 25. 13. Haiman C, Fesinmeyer M, Spencer K, Buzkova P, V oruganti V , Wan P, et al. Consistent directions ofeffect for established type 2 diabetes risk variants across populations: the Population Architectureusing Genomics and Epidemiology (PAGE) Consortium. Diabetes. 2012;61(6):1642 7.In the most complete trans-ethnic T2D GWAS", + "9. Sladek R, Rocheleau G, Rung J, Dina C, Shen L, et al. (2007) A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature 445:881885. 10. Zeggini E, Scott LJ, Saxena R, Voight BF, Marchini JL, et al. (2008) Meta- analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes. Nat Genet 40: 638645.11. Altshuler D, Daly MJ, Lander ES (2008) Genetic mapping in human disease. Science 322: 881888.", + "scale ongoing efforts to localize and characterize T2D susceptibility genes using genome-wide association study (GWAS) approaches. To date, the GWAS method has achieved substantial success in localizing novel T2D susceptibility loci and loci for T2D-related glycemic traits (about 90 loci), obesity loci (~90), and loci for metabolic syndrome or its components (~50 loci), e.g. reviews: [4,20,28,29,41,47,51,64,65,67] . However, common variants identi ed by GWAS explain only about", + "T2D GWA meta-analysis performed by the DIAbetes Genet-ics Replication and Meta-analysis (DIAGRAM) Consortium [6]. Using genotype data from almost 150,000 individuals, predominantly of European descent, the consortium was ableto define 10 novel T2D-susceptibility loci to genome-wide significance, and to highlight several hundreds more that, whilst failing to reach the stringent criteria typically regardedas proof, are nonetheless highly likely to reflect genuine", + "18. Sladek R, Rocheleau G, Rung J, Dina C, Shen L, Serre D, et al. A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature 2007;445:881-885. 19. Scott LJ, Mohlke KL, Bonnycastle LL, Willer CJ, Li Y, Duren WL, et al. A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science 2007; 316:1341-1345. 20. Diabetes Genetics Initiative of Broad Institute of Harvard and MIT , Lund University, and Novartis Institutes of BioMedical", + "additive, dominant, and recessive) and did not adjust for mul - tiple comparisons. The third study is the largest GWAS con - ducted to date and is a meta-analysis of two GWASs, Genetics of Kidneys in Diabetes (GoKinD) and Epidemiology of Dia - betes Interventions and Complications (EDIC) studies [24]. This study by Grassi et al. [24] involved 2,829 European sub - jects with T1DM. The most significant variant was rs476141 located in a long non-coding RNA ( LOC339529 ) in chromo -" + ], + "answer": "The latest meta-analyses of large-scale genome-wide association studies (GWAS) have identified several novel loci associated with diabetes. Specifically, the DIAbetes Genetics Replication and Meta-analysis (DIAGRAM) Consortium, using genotype data from almost 150,000 individuals predominantly of European descent, identified 10 novel type 2 diabetes (T2D) susceptibility loci that reached genome-wide significance [8].", + "question": "What novel diabetic loci have been identified through the latest meta-analyses of large-scale genome-wide association studies (GWAS)?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_20 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_20 new file mode 100644 index 0000000..5c744cb --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_20 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2019 - (Epi)genomic heterogeneity of pancreatic islet function and failure in type 2 diabetes.pdf", + "2018 - High-Throughput Approaches onto Uncover (Epi)Genomic Architecture of Type 2 Diabetes.pdf", + "2020 - Advances of single?cell genomics and epigenomics in human disease.pdf", + "2020 - Advances of single?cell genomics and epigenomics in human disease.pdf", + "2018 - Lnc\u2011ing non\u2011coding RNAs with metabolism and diabetes roles.pdf", + "2017 - Insights into beta cell regeneration for diabetes via integration of molecular landscapes in human insulinomas.pdf", + "2018 - High-Throughput Approaches onto Uncover (Epi)Genomic Architecture of Type 2 Diabetes.pdf", + "2018 - High-Throughput Approaches onto Uncover (Epi)Genomic Architecture of Type 2 Diabetes.pdf", + "2015 - Epigenetic mechanisms in diabetic complications and metabolic memory.pdf", + "2019 - (Epi)genomic heterogeneity of pancreatic islet function and failure in type 2 diabetes.pdf" + ], + "extraction_id": [ + "7a2a9981-4096-5049-a717-3e69eb609777", + "52e8a636-ced9-5c14-a7e5-0c30b7f05107", + "65471d38-cd13-5de2-8c19-1eb72d24d6f5", + "7f7a7f30-2e4e-50aa-bbcb-9f211c371e38", + "8bbfb009-87b7-54ae-8465-8796db8c271a", + "bdf327a6-decb-5c7a-a981-a7969206b455", + "52e8a636-ced9-5c14-a7e5-0c30b7f05107", + "52e8a636-ced9-5c14-a7e5-0c30b7f05107", + "312b1856-e1b1-5ae7-8cba-370becf5f7cb", + "117cc1a5-d236-56b2-a69d-9c0a2fb9053d" + ], + "document_id": [ + "b9bc63a5-e366-5685-bd7a-4732a8eeffb7", + "1cb0c4ac-c1fe-55c2-919c-52cd5018c00d", + "afe53f5a-3962-520f-be55-9df5bfdaad70", + "afe53f5a-3962-520f-be55-9df5bfdaad70", + "019efefb-65db-55f5-a3a7-4f224473f51f", + "6cf1eb8d-a91e-58a2-b6f4-29653678d0d3", + "1cb0c4ac-c1fe-55c2-919c-52cd5018c00d", + "1cb0c4ac-c1fe-55c2-919c-52cd5018c00d", + "470f1f94-792d-5273-a88f-7e06084951c5", + "b9bc63a5-e366-5685-bd7a-4732a8eeffb7" + ], + "id": [ + "chatcmpl-AIHKoCrJvacxorigznvNb5BV4LGGI", + "d5c2a32a-b869-59c1-8a63-45ab620669de", + "1c659cb4-085b-55b9-be3c-6332c36cbeba", + "f06bcc81-6ef9-5874-8ef9-6bcb3c34b0d0", + "b7812a7a-5504-57ca-8755-969dee45717e", + "ab373b7e-8c0b-59d8-9408-3e09ac76761e", + "7a5c8fad-97c5-59d2-8e5e-ee72d3dc2362", + "b7c1d2be-88c5-5f33-b812-b05e842f1647", + "11a5527b-8d22-5e69-8a84-6d9180517d81", + "db06230d-31c0-5947-8c1c-f58c48b6f439", + "a2adc65b-035b-568f-a0ae-9f7821ef45bc" + ], + "contexts": [ + "4. PRECISE CELLULAR GENOMICS Elucidating the molecular mechanisms that lead to beta cell dysfunction and T2D pathogenesis has been a major focus of diabetes research for decades. However, advances in single cell genomic proling techniques have led to greater understanding of non-beta cell type transcriptional regulation and suggest that they may play important roles in hallmark features of beta cell insuf ciency and", + "Genes 2018 ,9, 374 7 of 19 4. Single-Cell RNA-seq as a Novel Approach in High-Throughput Type 2 Diabetes Research Islets of Langerhans are heterogeneous structures that consist of different cell types. Further research is needed to track genetic changes in individual pancreatic islet cells and in sorted cell populations. The massive development of NGS allowed the sequencing of single cells from human pancreatic islets. Considering the cell-type heterogeneity within Langerhans islets, such an approach", + "Advances ofsingle -cell genomics andepigenomics inhuman disease: whereare we now? 1 3 Brissova etal. 2018; Tritschler etal. 2017). Moreover, an increase in hyperglycaemia has been associated with a loss of beta-cell mass, function and organization and is the cell type most frequently studied for insulin resistance (Carrano etal. 2017; Lawlor etal. 2017b; Segerstolpe etal. 2016; Theis and Lickert 2019; Tritschler etal. 2017). Notably, single-cell transcriptome profiling has been", + "Tang X, Huang Y, Lei J, Luo H, Zhu X (2019) The single-cell sequenc- ing: new developments and medical applications. Cell Biosci 9:53. https ://doi.org/10.1186/s1357 8-019-0314-y Teo AKK etal (2018) Single-cell analyses of human islet cells reveal de-differentiation signatures. Cell Death Discov 4:14. https ://doi. org/10.1038/s4142 0-017-0014-5 Theis FJ, Lickert H (2019) A map of beta-cell differentiation pathways supports cell therapies for diabetes. Nature 569:342343. https ://", + "53. Eliasson L, Esguerra JL (2014) Role of non-coding RNAs in pancreatic beta-cell development and physiology. Acta Physiol (Oxf) 211:273284 54. Ding GL, Wang FF, Shu J etal (2012) Transgenerational glucose intolerance with Igf2/H19 epigenetic alterations in mouse islet induced by intrauterine hyperglycemia. Diabetes 61:11331142 55. Ku GM, Kim H, Vaughn IW etal (2012) Research resource: RNA-Seq reveals unique features of the pancreatic beta-cell tran-scriptome. Mol Endocrinol 26:17831792", + "24. Nica, A. C. et al. Cell-type, allelic, and genetic signatures in the human pancreatic beta cell transcriptome. Genome Res. 23, 1554 1562 (2013). 25. Takane, K. K., Bender, A. & Stewart, A. F. Speci c targeting and sorting of puried human beta cells: de ning the human beta cell transcriptome. ADA Scienti c Sessions, San Francisco (2014). 26. Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008).", + "5. Genome-Wide Proling of Epigenetic Changes in Pancreatic Islets and Peripheral Tissues Epigenetic data added another layer of complexity to our understanding of the genomic bases of T2D. Given that a variable epigenetic pattern can modulate the link between the SNP and trait, consideration of this interplay is critically important. Molecular epigenetics involves changes in gene function that occur without a change in the nucleotide sequence via DNA methylation, histone", + "and model organisms. The combination of data from high-throughput approaches and association studies has provided compelling evidence that some epigenetic markers contribute to the risk of T2D [ 57,58]. Epigenetic alterations have been shown to affect the expression of genes that are crucial for maintaining pancreatic islet secretory capacity, survival, and functional identity and the proper response to insulin in peripheral tissues [ 59,60]. Furthermore, several epigenetic signatures, such", + "Epigenomic approaches: applications in diabetic complications research Epigenetic studies in human disease have been greatly accel- erated as a result of advances in whole-genome and epige- nome profiling technologies as well as bioinformatics andgenomic data analysis platforms [ 99,100]. DNAme is analysed using bisulfite conversion of genomic DNA, immu- noprecipitation of methylated DNA, followed byhybridisation to arrays or next-generation sequencing to ob-", + "understand each cell type s genomic architecture and better charac- terize their roles in islet resilience and failure. Experimental manipu- lation of the regulatory elements and/or the target genes identi ed by (epi)genomic approaches described above and modeling the putativepathways and processes they implicate in human islet cell lines (e.g., EndoC- bH1-H3) is essential to progress from correlation to causation. Similarly, transitioning from themouse (C57BL/6) to multiple mouse" + ], + "answer": "Recent integrative genomics approaches, such as the combination of single-cell RNA sequencing and epigenomic profiling, have significantly advanced our understanding of cellular heterogeneity and gene regulatory networks in pancreatic beta cells under diabetic conditions. These approaches have allowed researchers to delve deeper into the transcriptional regulation of non-beta cell types, which may play crucial roles in the hallmark features of beta cell insufficiency and dysfunction associated with Type 2 Diabetes (T2D) [1]. Single-cell RNA sequencing has been particularly instrumental in high-throughput diabetes research by enabling the sequencing of individual cells from human pancreatic islets. This is important given the heterogeneity within the islets of Langerhans, which consist of various cell types. By tracking genetic changes in individual cells, researchers can better understand the complex cellular landscape and the specific contributions of different cell types to diabetes pathogenesis [2]. Furthermore, epigenomic profiling adds another layer of complexity by revealing how epigenetic changes can modulate gene expression without altering the DNA sequence. These changes are crucial for maintaining the secretory capacity, survival, and functional identity of pancreatic islets, as well as their response to insulin [8]. The integration of these genomic and epigenomic data helps identify regulatory elements and pathways that could be targeted for therapeutic interventions, moving from correlation to causation in understanding diabetes [10].", + "question": "How have recent integrative genomics approaches, such as the use of single-cell RNA sequencing combined with epigenomic profiling, advanced our understanding of cellular heterogeneity and gene regulatory networks in pancreatic beta cells under diabetic conditions?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_3 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_3 new file mode 100644 index 0000000..0e78189 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_3 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2010 - The Role of Epigenetics in the Pathology of Diabetic Complications.pdf", + "2010 - The Role of Epigenetics in the Pathology of Diabetic Complications.pdf", + "2021 - Epigenetics of Aging and Aging-Associated Diseases.pdf", + "2010 - The Role of Epigenetics in the Pathology of Diabetic Complications.pdf", + "2014 - Diabetic nephropathy\u2014emerging epigenetic mechanisms.pdf", + "2013 - Epigenetic Modifications in the Pathogenesis of Diabetic Nephropathy.pdf", + "2016 - Epigenetic Mechanisms in Diabetic Kidney Disease.pdf", + "2016 - Epigenetic Mechanisms in Diabetic Kidney Disease.pdf", + "2016 - Epigenomic profiling reveals an association betweenpersistence of DNA methylation and metabolicmemory in the DCCTEDIC type 1 diabetes cohor.pdf", + "2015 - Epigenetic mechanisms in diabetic complications and metabolic memory.pdf" + ], + "extraction_id": [ + "77eb6a3d-2e3b-5304-873f-4fe14ec290d1", + "21de4c95-4171-52bb-a867-2df5336c3c71", + "3d7cb780-5f0a-5500-8176-4c2055cac9dc", + "77eb6a3d-2e3b-5304-873f-4fe14ec290d1", + "cbbe696b-8541-537a-ac5f-77b82cdb8201", + "7680731d-0b98-5f45-85f9-d06883504dd1", + "767d65c7-b99d-5427-8f5a-4afa10669e11", + "7a924f08-78ef-528a-8f9e-7bc12b004ff2", + "745c11f0-789f-5f0a-9f19-69af42a19c75", + "44d96546-84c3-51f1-85f9-22790a91d105" + ], + "document_id": [ + "766edfd5-4756-51bf-b636-c94b041d030c", + "766edfd5-4756-51bf-b636-c94b041d030c", + "70945353-4808-539a-80f9-5632c27913e5", + "766edfd5-4756-51bf-b636-c94b041d030c", + "be05127e-1be8-5573-b571-51a11c3b2be2", + "9cffb997-a205-5f72-89a6-945df5b9af28", + "6f773bda-0b8f-5da2-a9b5-e6c013d75050", + "6f773bda-0b8f-5da2-a9b5-e6c013d75050", + "4b44425c-00c2-504f-be3c-34c002951cc2", + "470f1f94-792d-5273-a88f-7e06084951c5" + ], + "id": [ + "chatcmpl-AIHIljZhr1AUuC7qfsdHZaKkRKz2A", + "eb133825-7500-5160-b39a-298961323f9c", + "a97f140f-63b1-5963-9c38-d90f59f58ced", + "41899c3d-64db-556a-882a-4e39b964c6d5", + "6f647f65-0c70-5abf-8944-e2b1ade8ee1d", + "883de652-2a30-5587-89bb-474facc861fe", + "796ed77e-4539-543b-a392-5736392f93ba", + "3f3fb648-0a87-5d2b-82c8-da1f3caf91b0", + "aaeb4ad0-7848-554e-8ec1-2b5a094d3112", + "c51c94d1-c182-5e77-8a14-6af868d66ee1", + "1c0b9205-340f-568f-990f-785c66154732" + ], + "contexts": [ + "diabetes due to epigenetic silencing of Pdx1, a key transcription factor that regulates insulin gene 301 expression and beta cell differentiation. Both hi stone modifications a nd DNA methylation were 302 implicated (111). In another study, it was shown th at, in diabetic islets , there was increased DNA 303 methylation of the promoter of PPAR-gamma co-activator 1 gene ( PPARGC1A ), a factor that 304 plays a key role in regulating mitochondrial ge nes and in the modulation of diabetes (87). 305", + "altered DNA methylation (DNA-me) at various genes in target cells all of which over time can 1009 result in changes to the expr ession patterns of inflammatory, sclerotic and other pathological 1010 genes and the ultimate developm ent of diabetic complications. 1011 1012 Figure 2: Model for epigenetic regulation of pa thological gene expressi on in diabetes via 1013 changes in chromatin histone modifications. Post translational modifications on the N- 1014", + "Dependent Demethylation of Regulatory Elements Correlates with Chromatin State and Improved Cell Function. Cell Metab. 2015 ,22, 619632. [CrossRef] 228. Zhang, H.; Pollin, T.I. Epigenetics Variation and Pathogenesis in Diabetes. Curr. Diab. Rep. 2018 ,18, 121. [CrossRef] 229. Miao, F.; Chen, Z.; Zhang, L.; Liu, Z.; Wu, X.; Yuan, Y.-C.; Natarajan, R. Proles of epigenetic histone post-translational modications at type 1 diabetes susceptible genes. J. Biol. Chem. 2012 ,287, 1633516345. [CrossRef]", + "Epigenetic Mechanisms in Diabetic Complications 14 DNA methylation at prom oter CpG islands has been associ ated with gene repression and 292 is a well studied epigenetic mark in the c ontext of tumor suppressor genes and cancer (129). 293 However, much less is known a bout DNA methylation in diabetes . A recent report has shown 294 that the insulin promoter DNA was methylated in mouse embryonic stem cells and only becomes 295", + "Epigenetics: deciphering its role in diabetes and its chronic complications. Clin. Exp. Pharmacol. Physiol. 38, 401409 (2011). 61. Cooper, M.E. & El-Osta, A. Epigenetics: mechanisms and implications for diabetic complications. Circ. Res. 107, 14031413 (2010). 62. Miao, F. etal. Profiles of epigenetic histone post- translational modifications at type1 diabetes susceptible genes. J.Biol. Chem. 287, 1633516345 (2012). 63. Sapienza, C. etal. DNA methylation profiling", + "Emerging evidence shows that epigenetic mecha-nisms in chromatin including histone PTMs, DNAme, and miRNAs also might play key roles in the etiology of diabetes and DN. The persistence ofepigenetic modi cations triggered by diabetic stim- uli could be one of the key mechanisms underlying metabolic memory. A role for several HMTs and thecorresponding histone PTMs has been shown in the expression of brotic and in ammatory genes asso-", + "inflammation-related epigenetic modifications: focus on DNA methylation. Exerc Immunol Rev. 2015;21:26 41. 17. Milagro FI, Mansego ML, De Miguel C, Martinez JA. Dietary factors, epigenetic modifications and obesity outcomes: progresses and perspectives. Mol Aspects Med. 2013;34(4):782 812. 18. Caramori ML, Kim Y , Goldfine AB, et al. Differential gene expres- sion in diabetic nephropathy in individuals with type 1 diabetes. J Clin Endocrinol Metab. 2015;100(6):E876 82.", + "elevated glucose level is not the only factor that leads to mal- adaptive epigenetic modifications in diabetes. DNA methyla- tion can also be influenced by reactive oxygen species, both directly through oxidative m odification DNA preventing methylation and indirectly through its effects on methylation writing/erasing enzymes [ 15]. Many other factors including hypoxia, inflammation, cytokines and growth factors, drugs, nutrition and even physical activity can modify epigenetic", + "1306 1313. 31. Miao F, et al.; DCCT/EDIC Research Group (2014) Evaluating the role of epigenetic histone modifications in the metabolic memory of type 1 diabetes. Diabetes 63(5): 1748 1762. 32. Reddy MA, Tak Park J, Natarajan R (2013) Epigenetic modifications in the patho- genesis of diabetic nephropathy. Semin Nephrol 33(4):341 353. 33. Bell CG, et al. (2010) Genome-wide DNA methylation analysis for diabetic nephrop- athy in type 1 diabetes mellitus. BMC Med Genomics 3:33.", + "ing that environment and diet may influence epigenetic mod-ifications that predispose individuals to diabetes [ 46]. Aber- rant DNAme has also been reported in the reduced expression of genes involved in diabetes and metabolism, and DNAme variations have also been noted near diabetes susceptibility genes and enhancers [ 15,47]. Genomic DNA from diabetic patients with nephropa- thy relative to those without displayed differential meth- ylation at several genes, including UNC13B , which had" + ], + "answer": "Epigenetic modifications, such as DNA methylation and histone modification, play significant roles in influencing the expression of diabetes-related genes. 1. **DNA Methylation**: This modification can lead to the silencing of key genes involved in diabetes. For example, increased DNA methylation of the promoter of the PPARGC1A gene, which is crucial for regulating mitochondrial genes, has been observed in diabetic islets. This methylation can modulate diabetes by altering gene expression [1]. Additionally, altered DNA methylation patterns can change the expression of inflammatory and pathological genes, contributing to the development of diabetic complications [2]. 2. **Histone Modifications**: These modifications can affect chromatin structure and gene expression. In the context of diabetes, histone post-translational modifications (PTMs) have been associated with the regulation of genes susceptible to type 1 diabetes [3]. Changes in chromatin histone modifications can regulate pathological gene expression in diabetes, influencing the disease's progression [2]. Overall, both DNA methylation and histone modifications can lead to the repression or activation of genes that are critical in the pathogenesis and progression of diabetes, affecting insulin production, inflammation, and other metabolic processes.", + "question": "How do epigenetic modifications, such as DNA methylation and histone modification, influence the expression of diabetes-related genes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_4 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_4 new file mode 100644 index 0000000..d8dc230 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_4 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2014 - The intestinal microbiome in type 1 diabetes.pdf", + "2010 - Gut Microbiota in Human Adults with Type 2 Diabetes.pdf", + "2014 - Diabetes in Europe An update.pdf", + "2016 - Integrated multi-omics of the human gut microbiome in a case study of familial type 1 diabetes.pdf", + "2012 - A metagenome-wide association study of gut microbiota in type 2 diabetes.pdf", + "2014 - Microbiota and diabetes an evolving relationship.pdf", + "2014 - Microbiota and diabetes an evolving relationship.pdf", + "2014 - Pathophysiology and treatment of type 2 diabetes.pdf", + "2018 - Global aetiology and epidemiology of type 2 diabetes mellitus and its complications.pdf", + "2019 - Systems genetics approaches to probe gene function.pdf" + ], + "extraction_id": [ + "b5ecc19b-eae0-51de-8e87-e5d01060e5be", + "0eb4bb40-b16c-5203-8c83-dac0695d43a2", + "5c27f434-3a7c-5ec9-80fc-6399dd3570c3", + "092a9b75-9985-5876-a650-59bc3f0d10fb", + "0a4d545f-0682-5ce1-b38c-88b5fdb4add3", + "44b12386-be75-5141-a5a0-77ab97136863", + "223f3f31-fb62-5f0d-ac8a-5a6deb1191d2", + "3754ce7f-9671-5636-a4e6-849fb672366a", + "736476e2-62be-52c5-b4a2-ee7cd7666a6f", + "5ab39f63-c4e0-56b8-b6ed-26df7bee89af" + ], + "document_id": [ + "138189d1-a16e-5c76-9b19-bd6877e7ee6d", + "27aaf82e-944d-55b3-8b6d-cc43bcdb3eab", + "81e1fc53-6768-590f-9b47-9a5105b6ddb5", + "f0405966-38bf-5a04-aa2c-1474b11362bb", + "0c088ef3-83a7-5a5e-8308-011cf4b25924", + "4bbbe579-1d9e-50b8-9403-b50bc3282c8f", + "4bbbe579-1d9e-50b8-9403-b50bc3282c8f", + "ab9288ab-e3ad-58f1-b5ba-183ee17ce4bd", + "8bc8f3d4-968f-5252-ab4c-832b92e9ec0d", + "1cd18d9c-0fd1-52e3-b0cf-c5e3ad0ff683" + ], + "id": [ + "chatcmpl-AIHItZX0vwpceBtjbHWMD13xwSdHl", + "d79a5c86-df6a-5b3d-93b4-a26f47b47e83", + "6cef232c-d7c6-5968-ad74-2903b688793a", + "89360f80-d048-5c02-a61d-6d56a99eedcd", + "e7e8ef7b-bad0-54bc-814d-d947ea04756b", + "da881999-9d70-560f-91b3-eda465b7a639", + "2589b0db-190e-5847-aef0-0bc3b415fb94", + "a5d5d05b-a824-5b8f-a774-b0b9ec5d0182", + "63e887b3-0db0-547d-a81c-716909ead0b6", + "d9bc6a49-c40e-520f-9e2d-afa05829416f", + "b0aa9c89-a8f4-5388-97ed-5d6556c565e7" + ], + "contexts": [ + "diabetes? Is altered gut epithelial function and integrity important in the pathoge nesis of type 1 diabetes, and if so, what is the mechanism(s) and relation to dysbiosis and how do we demonstrate impaired function in humans? How important are the interactions between host genetics, metab olism and the immune system in shaping the microbiome and predilection to disease?", + "the gut, which might trigger an inflammatory response and play arole in the development of diabetes. In conclusion, our data suggest that the levels of glucose tolerance or severity of diabetes should be considered while linking microbiota with obesity and other metabolic diseases in humans. It is especially important for developing the strategies to modify the gut microbiota inorder to control metabolic diseases, since obesity and diabetes mightbe associated with different bacterial populations. Methods", + "2011;342:d35. [68] Hara N, Alkanani AK, Ir D, Robertson CE, Wagner BD, Frank DN, et al. The role of the intestinal microbiota in type 1 diabetes. Clin Immunol 2013;146:1129. [69] Beyan H, Wen L, Leslie RD. Guts, germs, and meals: the origin of type 1 diabetes. Curr Diab Rep 2012;12:45662. [70] Atkinson MA, Chervonsky A. Does the gut microbiota have a role in type 1 diabetes? Early evidence from humans and", + "diabetes. ISME J. 5,8291 (2011). 30. Brown, C. T. et al. Gut microbiome metagenomics analysis suggests a functional model for the development of autoimmunity for type 1 diabetes.PLoS ONE 6,e25792 (2011). 31. Endesfelder, D. et al. Compromised gut microbiota networks in children with anti-islet cell autoimmunity. Diabetes 63,2006 2014 (2014). 32. Kostic, A. D. et al. The dynamics of the human infant gut microbiome in development and in progression toward type 1 diabetes. Cell Host Microbe 17, 260273 (2015).", + "661678 (2007). 4. Scott, L. J. et al. A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science 316, 13411345 (2007). 5. Musso, G., Gambino, R. & Cassader, M. Interactions between gut microbiota and host metabolism predisposing to obesity and diabetes. Annu. Rev. Med. 62, 361380 (2011). 6. Eckburg, P. B. et al. Diversity of the human intestinal microbial flora. Science 308, 16351638 (2005).", + "The gut microbiota affects numerous biological functionsthroughout the body and its characterisation has becomea major research area in biomedicine. Recent studieshave suggested that gut bacteria play a fundamental rolein diseases such as obesity, diabetes and cardiovasculardisease. Data are accumulating in animal models andhumans suggesting that obesity and type 2 diabetes(T2D) are associated with a profound dysbiosis. Firsthuman metagenome-wide association studiesdemonstrated highly signi cant", + "18 Burcelin R. Regulation of metabolism: a cross talk between gut microbiota and its human host. Physiology (Bethesda) 2012;27:300 7. 19 Breen DM, Rasmussen BA, Cote CD, et al . Nutrient-sensing mechanisms in the gut as therapeutic targets for diabetes. Diabetes 2013;62:3005 13. 20 Karlsson F, Tremaroli V, Nielsen J, et al . Assessing the human gut microbiota in metabolic diseases. Diabetes 2013;62:3341 9. 21 Backhed F, Ding H, Wang T, et al . The gut microbiota as an environmental factor", + "interactions play a role in human obesity, insulin resistance and type 2 diabetes? Obes Rev 2011; 12: 27281. 47 Kootte RS, Vrieze A, Holleman F, et al. The therapeutic potential of manipulating gut microbiota in obesity and type 2 diabetes mellitus. Diabetes Obes Metab 2012; 14: 11220. 48 Qin J, Li Y , Cai Z, et al. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature 2012; 490: 5560. 49 Karlsson FH, Tremaroli V, Nookaew I, et al. Gut metagenome in", + "Other factors Interest in the role of the gut microbiome in the devel - opment of T2DM has exploded in the past few years, and variation in the diversity and composition of the gut microbiota has been tied to T2DM100. For example, levels of butyrate-producing bacteria are decreased in the gut microbiota of patients with T2DM compared with that of healthy individuals101. In addition, evidence suggests that ambient air pollution is an emerging risk factor for", + "52. Parks, B.W., et al., Genetic control of obesity and gut microbiota composition in response to high -fat, high -sucrose diet in mice. Cell Metab, 2013. 17(1): p. 141 -52. 53. Org, E., et al., Genetic and environmental c ontrol of host -gut microbiota interactions. Genome Res, 2015. 25(10): p. 1558 -69. 54. McKnite, A.M., et al., Murine gut microbiota is defined by host genetics and modulates variation of metabolic traits. PLoS One, 2012. 7(6): p. e39191." + ], + "answer": "The gut microbiome plays a significant role in modulating host genetic predispositions to diabetes through various mechanisms. The interactions between host genetics, metabolism, and the immune system are crucial in shaping the microbiome and influencing the predisposition to diseases like diabetes [1]. Research suggests that the gut microbiota affects numerous biological functions throughout the body, and its characterization has become a major research area in biomedicine. Studies have shown that gut bacteria play a fundamental role in diseases such as obesity and diabetes [6]. Dysbiosis, or an imbalance in the gut microbiota, is associated with obesity and type 2 diabetes (T2D), indicating that the composition of the gut microbiome can influence metabolic diseases [6]. Moreover, genetic control of the gut microbiota composition has been observed, suggesting that host genetics can influence the microbiome, which in turn can modulate metabolic traits and potentially affect the risk of developing diabetes [10]. This implies a bidirectional relationship where genetics can shape the microbiome, and the microbiome can influence the expression of genetic predispositions to diabetes. In summary, the gut microbiome interacts with host genetics and metabolism, potentially modulating the risk of diabetes by influencing metabolic processes and immune responses [1], [6], [10].", + "question": "Can you elaborate on the role of the gut microbiome in modulating host genetic predispositions to diabetes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_5 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_5 new file mode 100644 index 0000000..ea7ed9c --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_5 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2022 - Using Recurrent Neural Networks for Predicting Type-2 Diabetes from Genomic and Tabular Data.pdf", + "2019 - Machine-learning to stratify diabetic patients using novel cardiac biomarkers and integrative genomics.pdf", + "2022 - Using Recurrent Neural Networks for Predicting Type-2 Diabetes from Genomic and Tabular Data.pdf", + "2022 - Using Recurrent Neural Networks for Predicting Type-2 Diabetes from Genomic and Tabular Data.pdf", + "2019 - Machine-learning to stratify diabetic patients using novel cardiac biomarkers and integrative genomics.pdf", + "2019 - Machine-learning to stratify diabetic patients using novel cardiac biomarkers and integrative genomics.pdf", + "2017 - Machine Learning and Data Mining Methods in Diabetes Research.pdf", + "2019 - Machine-learning to stratify diabetic patients using novel cardiac biomarkers and integrative genomics.pdf", + "2019 - Machine-learning to stratify diabetic patients using novel cardiac biomarkers and integrative genomics.pdf", + "2014 - Do physicians think genomic medicine will be useful for patient care.pdf" + ], + "extraction_id": [ + "6b4157fa-dcf0-5b70-b508-38ffb5fcda8d", + "aff84b9e-3855-5960-accd-dcac6b362346", + "a500eb31-13d8-5a0f-adfc-d260189a7555", + "a0ebb8e0-1414-52f4-aa8d-9bde3a9f26c2", + "8d323598-fdf7-56cf-8290-be85929f0eaf", + "8d323598-fdf7-56cf-8290-be85929f0eaf", + "20ba070b-900d-5213-9b38-d53492e48532", + "7079e9da-e08b-5e9f-ad3d-4709915aa9e0", + "493e5840-f65b-5245-8f07-126e1d9eedc3", + "5feb39eb-3945-5a31-9d03-7b83766df1e1" + ], + "document_id": [ + "be0e50e0-3de8-53c5-8126-a0b618647f80", + "332ac2ec-accc-5370-a4d2-6fec9ce7e072", + "be0e50e0-3de8-53c5-8126-a0b618647f80", + "be0e50e0-3de8-53c5-8126-a0b618647f80", + "332ac2ec-accc-5370-a4d2-6fec9ce7e072", + "332ac2ec-accc-5370-a4d2-6fec9ce7e072", + "e2dcbb80-5ad7-5441-b170-9b46607445b0", + "332ac2ec-accc-5370-a4d2-6fec9ce7e072", + "332ac2ec-accc-5370-a4d2-6fec9ce7e072", + "5418b59c-465c-5b1e-aee1-52ca7a1ead52" + ], + "id": [ + "chatcmpl-AIHJ0Y0u83bg8S6UfGkVEYfllY6XI", + "a1e1110f-551a-5481-bbd6-0495d8effd38", + "73df0579-3c65-590b-9293-e0dc8fa32863", + "d0503557-1135-531d-9316-d3be3f620f3f", + "4a73b79f-f3bb-5341-865d-c6162c2f4b98", + "9fc6ebb2-02a9-5bc3-9623-6cd353ada65e", + "c15a7933-675c-5790-9165-9fef8c091920", + "d3d65022-c072-5880-8d27-a95b285e77cd", + "d8b7be7f-3f83-5f1d-897b-da01d2a7baaf", + "83b5d15e-5c0e-5abd-aa88-1affe9148052", + "065cb845-8ff9-5919-8e1f-7d2604a52e34" + ], + "contexts": [ + "All the mentioned models rely on tabular datasets such as PIMA and ECG signals [ 47] in classifying the records with possible diabetic illnesses. The current study considers that genomic data yields a better patient-centric outcome than tabular data. 2.3. Genomics for Type 2 Diabetes Many research studies have been carried out on genetic-based illness prediction. Incorporating machine learning approaches with genetic-based illness prediction could", + "- chondrially rich, provides a direct connection between physiological dysfunction observed in the heart and the impact of altered genomic profiles in the mitochondrion and nucleus. Machine-learning, which at current has been applied to very few genetic applications, may play a significant role in defining the epigenome of those with diabetes mellitus, likely unveiling genes and molecular pathways first impacted by the pathology. The challenges ofmachine learning intheclinical setting", + "15. Ali, M.M.; Paul, B.K.; Ahmed, K.; Bui, F.M.; Quinn, J.M.W.; Moni, M.A. Heart disease prediction using supervised machine learning algorithms: Performance analysis and comparison. Comput. Biol. Med. 2021 ,136, 104672. [CrossRef] 16. Bell, C.G.; Teschendorff, A.E.; Rakyan, V .K.; Maxwell, A.P .; Beck, S.; Savage, D.A. Genome-wide DNA methylation analysis for diabetic nephropathy in type 1 diabetes mellitus. BMC Med. Genom. 2010 ,3, 33. [CrossRef]", + "Diagnostics 2022 ,12, 3067 6 of 30 Table 1. Various existing models for diabetes prediction. Approach Type of Data Applicability Limitations polygenic scores-based approach [12]Genomic DataUsed in the evaluation of clinical trials and illness screening mechanismsThe polygenic score approach needs larger samples and tremendous training for considerable Accuracy. Singular Value Decomposition [13]Genomic Data Tabular Data The image they are usedThey are used in ranking the feature", + "In the current study, machine-learning was used as a predictive tool to integrate cardiac physiological, bio - chemical, genomic, and epigenomic biomarker data in a patient-matched fashion and enable determination of type 2 diabetic status. In 50 patients, machine-learning algorithms revealed the interconnectedness between dia - betic classification, mitochondrial function, and methyla -", + "Diabetes mellitus is a multifaceted disease, consisting of systemic comorbidities which necessitate a variety of treatment modalities and stratify those affected with the disease [5]. Before the implementation of machine-learning algorithms in medicine, linear statistical models have highlighted measures, such as HbA1c, as diagnos - tic staples for the evaluation of diabetes mellitus onset and progression [6]. By exploring these previously pub -", + "tool that combines both genetic and clinical featur es in order to identify diabetic nephropathy in patients with T2D [81]. Leung et al . compared several machine learning methods that include partial least square regression, classification and regression tree, the C5.0 Decision Tree, Random For est, naive Bayes, neural networks and support vector machines [82]. The dataset used consists of both genetic (Single Nucleotide Polymorphisms - SNPs) and clinical data. Age, age of diagnosis, systolic", + "- ylation status and total nuclear methylation provided the best predictive measures for assessing type 2 diabetes mellitus. The incorporation of physiological, biochemical, genetic, and epigenetic features with machine-learning algorithms exemplifies the potential for more informa - tive diagnostics in the future, as well as personalized approaches to generalized treatment modalities (Fig.6). Discussion Machine-learning can be applied as a systems biol -", + "- tures is likely to occur, enhancing the diagnostic potential for the individual diabetic or prediabetic patient. Indeed, this is the advantage of using machine-learning models, in that they continue to learn and develop more accurate predictions as the number of features and sampled popu - lation grows. Conclusions Our work highlights the importance of identifying bio -", + "10 Meigs JB, Shrader P, Sullivan LM et al. Genotype score in addition to common risk factors for prediction of Type 2 diabetes. N. Engl. J. Med. 359, 22082219 (2008). 11 Scheuner MT, Sieverding P, Shekelle PG. Delivery of genomic medicine for common chronic adult diseases: a systematic review. JAMA 299, 13201334 (2008). \t Systematic\treview\tof\tearly\tresearch\tinto\tgenomic\tmedicine \t adoption\tin\tthe\tclinical\tcare\tof\tcommon\tchronic\tdiseases. \t Outlines\tboth\tphysician\tand\tpatient\tperspectives\ttowards" + ], + "answer": "Machine learning algorithms show promise in integrating genomic data to predict individual risk and progression of diabetes, but there are challenges and limitations to consider. 1. Genomic data is considered to yield better patient-centric outcomes than traditional tabular data for predicting diabetic illnesses [1]. This suggests that machine learning models that incorporate genomic data may provide more accurate predictions. 2. Machine learning has been applied to integrate various types of data, including genomic and epigenomic biomarkers, to determine type 2 diabetic status. This approach has revealed connections between diabetic classification and other biological functions, indicating the potential of machine learning in this area [5]. 3. The integration of physiological, biochemical, genetic, and epigenetic features with machine learning algorithms has shown potential for more informative diagnostics and personalized treatment approaches for diabetes [8]. 4. However, there are limitations, such as the need for larger sample sizes and extensive training to achieve considerable accuracy when using polygenic scores-based approaches with genomic data [4]. Overall, while machine learning algorithms have demonstrated potential in integrating genomic data for diabetes prediction, further research and development are needed to overcome current limitations and improve accuracy and applicability in clinical settings.", + "question": "How effective are machine learning algorithms in integrating genomic data to predict individual risk and progression of diabetes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_6 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_6 new file mode 100644 index 0000000..79e927c --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_6 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2018 - Lnc\u2011ing non\u2011coding RNAs with metabolism and diabetes roles.pdf", + "2018 - Lnc\u2011ing non\u2011coding RNAs with metabolism and diabetes roles.pdf", + "2018 - Lnc\u2011ing non\u2011coding RNAs with metabolism and diabetes roles.pdf", + "2018 - Lnc\u2011ing non\u2011coding RNAs with metabolism and diabetes roles.pdf", + "2018 - Lnc\u2011ing non\u2011coding RNAs with metabolism and diabetes roles.pdf", + "2019 - Development and Genome Sequencing.pdf", + "2018 - Lnc\u2011ing non\u2011coding RNAs with metabolism and diabetes roles.pdf", + "2018 - Lnc\u2011ing non\u2011coding RNAs with metabolism and diabetes roles.pdf", + "2018 - Lnc\u2011ing non\u2011coding RNAs with metabolism and diabetes roles.pdf", + "2016 - A genetic screen identifies hypothalamic Fgf15 as a regulator of glucagon secretion.pdf" + ], + "extraction_id": [ + "96a78d74-ac6d-513e-a5a7-b22ef95ea041", + "14656f4f-b0bd-5f4f-a67a-aeb902f24757", + "14656f4f-b0bd-5f4f-a67a-aeb902f24757", + "8bbfb009-87b7-54ae-8465-8796db8c271a", + "d7b2d258-d566-5552-8308-4ac35953884d", + "d971dced-935c-566b-a4a2-11bcf99b9c84", + "96a78d74-ac6d-513e-a5a7-b22ef95ea041", + "96a78d74-ac6d-513e-a5a7-b22ef95ea041", + "efc73cf6-99c6-5272-9bb0-7bd6a34633f0", + "a847f5f4-0c56-5678-9e1e-93b9b5b294f2" + ], + "document_id": [ + "019efefb-65db-55f5-a3a7-4f224473f51f", + "019efefb-65db-55f5-a3a7-4f224473f51f", + "019efefb-65db-55f5-a3a7-4f224473f51f", + "019efefb-65db-55f5-a3a7-4f224473f51f", + "019efefb-65db-55f5-a3a7-4f224473f51f", + "18820c9e-f7ae-57ae-897d-0d9c3f616b6a", + "019efefb-65db-55f5-a3a7-4f224473f51f", + "019efefb-65db-55f5-a3a7-4f224473f51f", + "019efefb-65db-55f5-a3a7-4f224473f51f", + "288adb9b-a547-5e61-8593-1b2ab36271d3" + ], + "id": [ + "chatcmpl-AIHJ7kKFoNZYhA6ZvKYEgyC7wipHg", + "66b05301-179b-597c-bb68-e6fd0e0d1d5a", + "e85449e5-801e-5431-80e1-521699d18780", + "2d9e043b-a3fa-52dc-9a4e-71ed49f9ec1d", + "a0146183-d255-5eae-85eb-adaf007d1b32", + "b3c5f734-aa0d-5da9-bdb9-e330e6c02e00", + "b774bf7b-4546-56d2-ae7b-7bc2c9f2fb08", + "c8d55dea-0656-527e-93bd-9624cec8f3c9", + "e5669569-f9ba-5797-b468-3a1980addc0a", + "9ca17d26-cc06-5afe-a7dd-3f80b1b99da0", + "45d35985-9183-55f0-8b51-41df27cd7677" + ], + "contexts": [ + "NAs to be mapped to diabetic susceptible loci [49 52], all suggesting towards critical roles of lncRNAs in insulin resistance, diabetes, and its associated complications. LncRNAs asregulators ofislet function The pancreatic islet is an important central node to researchers to understand the pathophysiology of diabe-tes [53]. The possible regulation of islet development and function by lncRNAs was first demonstrated by Ding etal., where the lncRNA, H19 (Fig. 4), was shown to be involved", + "this would require further investiga-tions, both invivo and invitro and critical networking among researchers, clinicians, and patients. Nevertheless, the implications of lncRNAs in diverse facets of insulin resistance and diabetes are indicative of their roles in the diagnosis, prognosis, and therapy of this disease in future.", + "To conclude, it would be apt to state that lncRNAs are widely implicated in diverse domains of cell metabolism and their altered expression is associated with diabetes and its complications. Although originally thought to be non-functional, lncRNA genes transcribe into lncRNAs that exert important and specific functions in regulating cellular pathways. Due to this specificity, lncRNAs are considered better therapeutic targets. In addition, their expression patterns in tissues quite follow the progress of", + "58. You L, Wang N, Yin D etal (2016) Downregulation of long noncoding RNA Meg3 affects insulin synthesis and secretion in mouse pancreatic beta cells. J Cell Physiol 231:852862 59. Arnes L, Akerman I, Balderes DA, Ferrer J, Sussel L (2016) betalinc1 encodes a long noncoding RNA that regulates islet beta-cell formation and function. Genes Dev 30:502507 60. Akerman I, Tu Z, Beucher A etal (2017) Human pancreatic beta cell lncRNAs control cell-specific regulatory networks. Cell Metab 25:400411", + "of lncRNAs in the development and function of metabolic tissues, and therefore, their altered levels are closely asso-ciated with the onset and progression of insulin resistance and diabetes. Roles oflncRNAs indiabetic complications Apart from being involved in major metabolic tissues dur -", + "tion among researchers ( Knoll et al., 2015 ). As an important post-transcriptional pathogenesis of diabetes, lncRNAs and their associated orchestrated networks are implicated in mediating complex pathological mechanisms of diabetes ( Kato et al., 2016; Liu et al., 2014 ). To delineate the inuence of lncRNAs and 172 iScience 19, 162176, September 27, 2019", + "in transgenerational transmission of gestational diabetes mellitus which leads to impaired islet structure and func-tion [ 54]. To understand the roles of lncRNAs in regu- lating pancreatic function, several research groups have profiled lncRNA expression in mouse and human pancre-atic islets [55, 56]. Transcriptome analysis in pancreatic -cells of type 2 diabetes patients identified tissue-specific and dynamically regulated abnormally expressed lncR -", + "1831 Lnc-ing non- coding RNAs withmetabolism anddiabetes: roles oflncRNAs 1 3 endocrine hormones, insulin and glucagon, where insulin is the anabolic master regulator which controls periph -", + "Vol.:(0123456789)1 3Cellular and Molecular Life Sciences (2018) 75:18271837 https://doi.org/10.1007/s00018-018-2760-9 REVIEW Lncing noncoding RNAs withmetabolism anddiabetes: roles oflncRNAs NehaGoyal1,2 DeveshKesharwani1,2 MalabikaDatta1,2 Received: 18 September 2017 / Revised: 29 December 2017 / Accepted: 24 January 2018 / Published online: 31 January 2018 Springer International Publishing AG, part of Springer Nature 2018 Abstract", + "(2013). A novel mechanism regulating insulin secretion involving Herpud1 inmice. Diabetologia 56, 15691576 . Zhao, X.Y., and Lin, J.D. (2015). Long noncoding RNAs: a new regulatory code in metabolic control. Trends Biochem. Sci. 40, 586596 . 1806 Cell Reports 17, 17951806, November 8, 2016" + ], + "answer": "Recent findings highlight the significant role of long non-coding RNAs (lncRNAs) in the regulation of insulin secretion and sensitivity, with several implications for understanding and potentially treating diabetes. 1. **Regulation of Islet Function**: LncRNAs have been shown to regulate the development and function of pancreatic islets, which are crucial for insulin secretion. For instance, the lncRNA H19 is involved in this regulatory process [1]. This suggests that lncRNAs could be critical in maintaining normal insulin secretion and could be targets for therapeutic intervention in diabetes. 2. **Impact on Insulin Synthesis and Secretion**: Specific lncRNAs, such as Meg3, have been found to affect insulin synthesis and secretion in pancreatic beta cells [4]. This indicates that lncRNAs play a direct role in the cellular mechanisms that control insulin production, which is essential for maintaining glucose homeostasis. 3. **Therapeutic Potential**: Due to their specific functions in regulating cellular pathways, lncRNAs are considered promising therapeutic targets. Their expression patterns in tissues often correlate with the progression of diabetes, making them potential biomarkers for diagnosis and prognosis [3]. 4. **Association with Insulin Resistance and Diabetes**: Altered levels of lncRNAs are closely associated with the onset and progression of insulin resistance and diabetes [5]. This association underscores the potential of lncRNAs as targets for interventions aimed at improving insulin sensitivity and managing diabetes. 5. **Research and Clinical Implications**: The diverse roles of lncRNAs in insulin resistance and diabetes suggest their importance in future research for diagnosis, prognosis, and therapy of the disease [2]. This calls for further investigations and collaborations among researchers, clinicians, and patients to fully understand and harness the potential of lncRNAs in diabetes management. Overall, these findings imply that lncRNAs are integral to the regulation of insulin secretion and sensitivity, offering new avenues for research and treatment strategies in diabetes.", + "question": "What are the implications of recent findings on the role of long non-coding RNAs (lncRNAs) in the regulation of insulin secretion and sensitivity?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_7 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_7 new file mode 100644 index 0000000..7a9b884 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_7 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2018 - MicroRNA profiling and their pathways in South African.pdf", + "2015 - Type 2 diabetes mellitus.pdf", + "2014 - Diabetic nephropathy\u2014emerging epigenetic mechanisms.pdf", + "2004 - Diabetes Genes a.pdf", + "2011 - CREB and ChREBP oppositely regulate SIRT1 expression in response to energy availability.pdf", + "2015 - Transcript Expression Data from Human.pdf", + "2018 - A computational biology approach of a genome-wide screen.pdf", + "2015 - Type 2 diabetes mellitus.pdf", + "2015 - Cellular and Molecular Biology of Aging Endothelial Cells.pdf", + "2010 - The Role of Epigenetics in the Pathology of Diabetic Complications.pdf" + ], + "extraction_id": [ + "14577d73-d320-54dd-93f2-c55f986bc8bc", + "617b6fab-a5e5-59b7-a593-a0477e6bf9fe", + "bf537fe8-5508-5355-a656-b4053febe0e5", + "06912a59-fdd6-5731-af8f-6c98ff1ace5c", + "4217906f-87c5-54b0-95a5-7c26dc08afce", + "867d0b1b-16a1-53ea-b014-3c204b9001a5", + "ab4f6ea7-767f-5783-9e1a-8570eaabe96c", + "e4e89eba-6032-5781-83f4-8d47ab5b3825", + "283e34bb-6e2b-5aa9-85c5-2584b669f122", + "41ac576d-b850-5ee8-9753-ba9b060ba798" + ], + "document_id": [ + "b6bb090d-7176-59db-af04-582aa1d5cf10", + "415516ba-5365-501b-84ce-0789045862f8", + "be05127e-1be8-5573-b571-51a11c3b2be2", + "805ad0d5-6a3d-5e5f-b1ba-121e10c6daaa", + "32538f01-9eaf-5f9b-8615-ec47cc4ca8e2", + "2b30d4f3-9ec3-574f-9a36-709b0e09c3f2", + "ae727c80-a0a3-52f4-9e2f-b93a539558ad", + "415516ba-5365-501b-84ce-0789045862f8", + "815d7f3e-e219-502f-aba0-57a68ae787d3", + "766edfd5-4756-51bf-b636-c94b041d030c" + ], + "id": [ + "chatcmpl-AIHJKDULZO7wnGlvKQDCFnpw8Cumo", + "64482aec-5688-5431-adda-b8f1de92a183", + "b604dabf-3dc2-5d01-9cc4-6e9f916c464a", + "e1f984ac-aa42-5eb4-92cb-303886f6f1db", + "7b6e89ec-b690-5ff1-b24d-3ed6744f3486", + "9a8edd2d-c06a-559e-8397-beaaa84705b7", + "7d522337-e875-55eb-9b67-4718e5db8ffd", + "1edee360-5de0-51c9-bf8d-7c2e2f23a682", + "43a104b3-f34b-5f52-86ff-fd7d45827f32", + "3e08ef82-888b-58a0-9a80-3547ab4bd516", + "cf4f3239-dd62-5eef-b5fc-85f4780e3f48" + ], + "contexts": [ + "regulates glucose-induced biological responses in pancreatic beta-cells. Diabetes. 2008;57:2708-17. 29. Schultze SM, Hemmings BA, Niessen M, Tschopp O. PI3K/AKT, MAPK and AMPK signalling: protein kinases in glucose homeostasis. Expert Rev Mol Med. 2012;14:e1. 30. White MF. IRS proteins and the common path to diabetes. Am J Physiol Endocrinol Metab. 2002;283:E413-22. 31. Erener S, Marwaha A, Tan R, Panagiotopoulos C, Kieffer TJ. Profiling of circulating microRNAs in children with", + "pathological processes involved in glucose metabolism by post transcriptional regulation of gene expression. Particular microRNAs can regulate cell function271, exposing key regulatory signalling pathways involved in restoration of cell mass, and provide a promising strat egy for improving insulin secretion and cell health in T2DM. Identification of novel insulin secretagogues that act directly on cells and enteroendocrine Kcells and Lcells in the intestine are under investigation, and", + "can result in diabetes and its complications including DN. Several studies show that key histone post- translational modifications are involved in the regulation of genes associated with the pathogenesis of diabetes, such as insulin and islet-specific transcription factors.48,60 Inaddi - tion, several groups are examining the role of histone post-translational modifications in adipocytes related to type2 diabetes, obesity and the metabolic syndrome.48,60", + "cascade of protein kinases and regulatory proteins of which IRS-1 and IRS-2 are most important. This causes suppression of glucose release from liver and kidney/ translocation of glucose transporters in muscle and adipose tissue to increase their glucose uptake, and inhibition of release of FF A into the circulation due to suppression of the activity of hormone-sensitive lipase and a simultaneous increase in their clearance from the circulation. Although", + "Magnan C, Postic C, Prip-Buus C, Vasseur-Cognet M (2008) The transcription factor COUP-TFII is negatively regulated by insulin and glucose via Foxo1- and ChREBP-controlled pathways. Mol Cell Biol 28: 65686579Rodgers JT, Lerin C, Haas W, Gygi SP, Spiegelman BM, Puigserver P (2005) Nutrient control of glucose homeostasis through a complex ofPGC-1alpha and SIRT1. Nature 434: 113118 Schwer B, Verdin E (2008) Conserved metabolic regulatory functions of sirtuins. Cell Metab 7:104112", + "of glucose transporter 2 glycosylation promotes insulin secretion in suppressing diabetes. Cell 123:1307 1321. PMID: 16377570 47. Whitaker GM, Lynn FC, McIntosh CH, Accili EA (2012) Regulation of GIP and GLP1 receptor cell sur- face expression by N-glycosylation and receptor heteromerization. PLoS One 7: e32675. doi: 10.1371/ journal.pone.0032675 PMID: 22412906 48. Johswich A, Longuet C, Pawling J, Abdel Rahman A, Ryczko M, et al. (2014) N-glycan remodeling on", + "strate 1), Pde3b (phosphodiesterase 3B), Hk2 (hexokinase 2), Foxo1 (forkhead box O1), Socs6 (suppressor of cytokine signaling 6), and Ogt (O-linked N-acetylglucosamine (GlcNAc) transferase). Impaired insulinsignaling is well known to negatively in uence glucose and lipid metabolism [62]. In adipose tissue, insulin stimulates glucose uptake by inducing translocation of GLUT4 to the cell surface, it increasesglycolysis rate by stimulating hexokinases ( Hk2) and suppresses lipolysis ( Acaca and Prkaa1 )[63].", + "signalling pathways by reducing insulin induced tyro sine phosphorylation of IRS1 and IRS2 (REF. 161) and by increasing degradation of IRS1 (REF. 162). Recent studies have demonstrated that the p85 regulatory subunit of PI3K interacts with XBP1s (the spliced, transcription ally active isoform of XBP1) and promotes the trans location of XBP1s into the nucleus to initiate the ER stress response163.Diabetic complications Diabetic microvascular complications are closely related", + "activated protein kinase. J Biol Chem. 2007;282:9777 -88. [44] Chakrabarti S, Davidge ST. High glucose -induced oxidative stress alters estrogen effects on ERalpha and ERbeta in human endothelial cells: reversal by AMPK activator. J Steroid Biochem Mol Biol. 2009;117:99 -106. [45] Mortuza R, Chen S, Feng B, Sen S, Chakrabarti S. High glucose induced alteration of SIRTs in endothelial cells causes ra pid aging in a p300 and FOXO regulated pathway. PLoS One. 2013;8:e54514.", + "Epigenetic Mechanisms in Diabetic Complications 17 Interestingly, the sirtuin (SIRT) family of deacetylases, specifically SIRT1, has been found to 360 regulate several factors involved in metabolism, adipogenesis a nd insulin secretion (86). HATs 361 and HDACs can also modulate NF- B transcriptional activity (4, 44) resulting in changes in 362" + ], + "answer": "Post-translational modifications (PTMs) of proteins play a significant role in regulating key signaling pathways involved in glucose homeostasis. Here are some ways PTMs affect these pathways: 1. **Protein Kinases and Glucose Homeostasis**: The PI3K/AKT, MAPK, and AMPK signaling pathways are crucial for glucose homeostasis, and these pathways are regulated by protein kinases, which can be modulated by PTMs such as phosphorylation [1]. 2. **Histone Modifications**: Histone post-translational modifications are involved in the regulation of genes associated with diabetes pathogenesis, including those related to insulin and islet-specific transcription factors. These modifications can influence gene expression and thereby affect glucose metabolism [3]. 3. **N-glycosylation**: The glycosylation of glucose transporter 2 is an example of a PTM that promotes insulin secretion, which is vital for maintaining glucose levels and suppressing diabetes [6]. 4. **Sirtuins and Deacetylation**: The sirtuin family, particularly SIRT1, is involved in regulating factors related to metabolism and insulin secretion. Sirtuins are deacetylases, and their activity represents a form of PTM that can influence glucose homeostasis [10]. These examples illustrate how PTMs can modulate signaling pathways and protein functions, ultimately impacting glucose homeostasis and related metabolic processes.", + "question": "How do post-translational modifications of proteins affect key signaling pathways involved in glucose homeostasis?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_8 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_8 new file mode 100644 index 0000000..cb827b8 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_8 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2017 - Spectrum of mutations in monogenic diabetes genes identified from high-throughput DNA sequencing of 6888 individuals.pdf", + "1995 - Neurodegeneration and diabetes UK nationwide study of Wolfram syndrome.pdf", + "2008 - Learning From Molecular Genetics.pdf", + "2021 - Monogenic diabetes a gateway to precision medicine.pdf", + "1995 - Neurodegeneration and diabetes UK nationwide study of Wolfram syndrome.pdf", + "1995 - Neurodegeneration and diabetes UK nationwide study of Wolfram syndrome.pdf", + "2021 - Monogenic diabetes a gateway to precision medicine.pdf", + "2021 - Monogenic diabetes a gateway to precision medicine.pdf", + "2010 - Family History of Diabetes and Prevalence.pdf", + "2017 - Spectrum of mutations in monogenic diabetes genes identified from high-throughput DNA sequencing of 6888 individuals.pdf" + ], + "extraction_id": [ + "0f16b510-caa9-521f-8d87-e225f52de9f5", + "744f3821-fc61-58d1-8107-17d5674fe1d8", + "979b0578-b02a-526e-b3b4-aa7fec3eeb91", + "f01be500-1e96-57ca-b164-1b97017ec44d", + "782db3c2-ab52-59df-b798-c5b2492c8946", + "744f3821-fc61-58d1-8107-17d5674fe1d8", + "5a39ee4b-ba00-56d6-ba6c-0edeac3b4f2e", + "31b3b484-d769-5b88-9633-45ca3508be8f", + "59b6bf24-b9f7-53bc-b809-5ce97d5e9898", + "36a362bd-1d4a-5e47-a00f-5ae6d3a0626f" + ], + "document_id": [ + "18a8a000-69ed-5d34-b13f-f5ae016d1067", + "1fa2280b-868e-571f-830e-bebdd874fc9d", + "3aa0aea1-fe53-519b-b367-79fa1c79aa01", + "0b6ff786-6a7b-5d24-ba5e-7a61fee7757f", + "1fa2280b-868e-571f-830e-bebdd874fc9d", + "1fa2280b-868e-571f-830e-bebdd874fc9d", + "0b6ff786-6a7b-5d24-ba5e-7a61fee7757f", + "0b6ff786-6a7b-5d24-ba5e-7a61fee7757f", + "f16c4c6e-bb5f-5d4a-9945-8af4d0df19f4", + "18a8a000-69ed-5d34-b13f-f5ae016d1067" + ], + "id": [ + "chatcmpl-AIHJRHE43F2P7lugdT3xuuPfMvKGY", + "d0465c0a-e45a-5fff-a9b3-40ad191963a3", + "f4b3a7bd-4a5c-5c95-aa11-936340eecb8b", + "e4240b4f-b19f-5f8c-b9ce-e0b60cf4aceb", + "3efa9423-0651-5096-8ccd-50a0f906e30e", + "1ae4ea48-5dbd-5502-aa74-a1ef1527a493", + "e0234ab8-850f-585c-9de2-edaa3ac615fd", + "85de5eb6-8034-5ebb-8526-1de0a173b2ae", + "0b2437f9-1aff-5e6a-bca6-cc17d96c5d56", + "8fbdd64f-d761-5e8d-989c-7e4bf376b1ec", + "bd8d61dd-e027-5a43-8b00-1e7e9e748ea3" + ], + "contexts": [ + "WFS1 and genotype-phenotype correlation in Wolfram syndrome. Am J Med Genet A. 2007;143A(14):1605 12. 61. McCarthy MI. Painting a new picture of personalised medicine for diabetes. Diabetologia. 2017;60(5):793 9. 62. Fuchsberger C, Flannick J, Teslovich TM, et al. The genetic architecture of type 2 diabetes. Nature. 2016;536(7614):41 7. 63. Patch AM, Flanagan SE, Boustred C, Hattersley AT, Ellard S. Mutations in the ABCC8 gene encoding the SUR1 subunit of the KATP channel cause", + "enable physicians to ameliorate some of the complications that so devastate the lives of these patients. Three questions need answers from further studies: is there really a lack of diabetic complications in Wolfram syndrome patients compared with other diabetics? What is the nature of the neurodegeneration and its relation to diabetes mellitus? Are heterozygotes for Wolfram syndrome at risk of maturity-onset diabetes? This paper is dedicated to the memory of Robin Smith, a Wolfram", + "Monogenic and syndromic forms account for only a small,though highly informative, proportion of cases of nonau-toimmune diabetes. The challenge for medical science liesin bringing equivalent mechanistic insights and transla-tional benets to the hundreds of millions of peoplealready affected by, or at risk of, more common, typicalforms of diabetes. For type 2 diabetes, there is abundantevidence that individual susceptibility is inuenced byboth the combination of genetic variation at multiple sitesand a", + "responding to two causative genes have been identified to date. Wolfram syndrome 1 (WS1), characterized by diabetes insipidus, DM, optic atrophy, and deafness, is a rare autosomal recessive disease caused by variants in wolframin ER transmembrane gly- coprotein (WFS1). Severe cases with dominant heterozygous vari- ants are also reported (92). Often, patients first manifestation is DM at an average age of 6 years. Though most WS1 patients", + "finding study to describe the natural history, complications, prevalence, and inheritance of the syndrome. We identified 45 patients with Wolfram syndrome—a prevalence of one per 770000. Non-autoimmune, insulin- deficient diabetes mellitus presented at a median age of 6 years, followed by optic atrophy (11 years). Cranial diabetes insipidus occurred in 33 patients (73%) with sensorineural deafness (28, 62%) in the second decade; renal-tract abnormalities (26, 58%) presented in the third", + "Wolfram patients have a mitochondrial genome abnormality, but this has not yet been shown. The differential diagnosis indicates the importance of accurate clinical descriptions when presenting cases of the syndrome. Our study has implications for basic science and practice: more accurate characterisation of the syndrome will allow assessment of genotype/phenotype correlations; and earlier recognition of diabetes insipidus, gastrointestinal dysfunction, and central apnoeas should", + "onset diabetes of the young, multiple causes of neonatal DM, and syndromic diabetes such as Wolfram syndrome and lipodystrophy. We also review methods of prioritizing patients undergoing genetic testing, and highlight existing challenges facing sequence data interpretation that can be addressed by forming collaborations of expertise and by pooling cases.Monogenic diabetes: a gateway to precision medicine in diabetes Haichen Zhang,1 Kevin Colclough,2 Anna L. Gloyn,3,4 and Toni I. Pollin1", + "WFS1 mutations underlie a genetic syndrome of neonatal/infancy-onset diabetes, congenital sensorineural deafness, and congenital cataracts. Diabetes . 2017;66(7):20442053. 93. Rigoli L, Di Bella C. Wolfram syndrome 1 and Wolfram syndrome 2. Curr Opin Pediatr. 2012;24(4):512517 . 94. Bansal V, et al. Identification of a missense vari- ant in the WFS1 gene that causes a mild form of Wolfram syndrome and is associated with risk for type 2 diabetes in Ashkenazi Jewish individuals.", + "established. It has been corroborated by a series of obser-vations that include ethnic differences, familial aggrega-tion, twin studies, admixture studies, linkage studies, monogenic cases (e.g., MODY), mitochondrial cases of diabetes, and a constantly growing number of molecular markers [5] . On the other hand, the genetics of the meta- bolic syndrome remains complex [6] . It is highly unlikely that a single gene will account for a substantial portion", + "diabetes (0.5% carrier frequency) compared to controls (0.035%). One individual with early onset diabetes was homozygous for a rare pathogenic missense variant in the WFS1 gene but did not have the additional phenotypes associated with Wolfram syndrome. Conclusion: Targeted sequencing of genes linked with monogenic diabetes can identify disease-relevant mutations in individuals diagnosed with type 2 diabetes not suspected of having monogenic forms of the disease. Our data suggests" + ], + "answer": "Studying the genetic basis of syndromic forms of diabetes, such as Wolfram Syndrome, has provided several insights: 1. **Genotype-Phenotype Correlation**: Research has highlighted the importance of understanding genotype-phenotype correlations in Wolfram Syndrome. This involves identifying specific genetic mutations and understanding how they manifest in clinical symptoms, which can aid in more accurate diagnosis and management of the syndrome [1], [6]. 2. **Genetic Mutations**: Wolfram Syndrome is primarily caused by mutations in the WFS1 gene, which encodes the wolframin ER transmembrane glycoprotein. These mutations lead to a range of symptoms including diabetes mellitus, optic atrophy, and deafness. Some severe cases are associated with dominant heterozygous variants [4]. 3. **Early Onset and Symptoms**: The syndrome is characterized by early onset of diabetes mellitus, typically around the age of 6, followed by other symptoms such as optic atrophy and sensorineural deafness in later years [5]. 4. **Potential for Broader Implications**: Insights from studying Wolfram Syndrome have implications for basic science and clinical practice. They emphasize the need for accurate clinical descriptions and early recognition of symptoms, which can improve patient outcomes and inform treatment strategies [6]. 5. **Risk Assessment**: There is ongoing research to determine if heterozygotes for Wolfram Syndrome are at risk for maturity-onset diabetes, which could have implications for understanding genetic risk factors in broader populations [2]. These insights not only enhance our understanding of Wolfram Syndrome but also contribute to the broader field of genetic research in diabetes, potentially informing personalized medicine approaches for more common forms of the disease.", + "question": "What insights have been gained from studying the genetic basis of syndromic forms of diabetes, such as Wolfram Syndrome and Alstr\u00f6m Syndrome?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_9 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_9 new file mode 100644 index 0000000..3e61804 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_9 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2011 - Interaction Between Exercise and Genetics.pdf", + "2010 - A Genome-Wide Association Study of Treated A1C.pdf", + "2004 - Common polymorphisms of the PPAR-\u03b32 (Pro12Ala) and PGC-1\u03b1 (Gly482Ser) genes are associated with the conversion from impaired glucose tolerance to type 2 diabetes in the STOP-NIDDM trial.pdf", + "2016 - Hypomethylation within gene promoter regions and type 1 diabetes.pdf", + "2010 - Genome-scale approaches to the epigenetics of common.pdf", + "2010 - Candidate Gene and Genome-Wide Association Studies in Behavioral Medicine.pdf", + "2003 -Genetic epidemiology of type 1 diabetes.pdf", + "2011 - Lifestyle and Genetics in Obesity and type 2 Diabetes.pdf", + "2013 - Continuous Aging of the Human DNA Methylome.pdf", + "2001 - The genetics of type 2 diabetes.pdf" + ], + "extraction_id": [ + "861346c7-0fcf-5cae-ace6-a012a370d297", + "cce780d7-60c0-5cb3-976f-15e9808cab59", + "feb52f56-db94-5e03-90a8-af3bf38d087e", + "bc569d05-fc39-5487-95e7-63b0d7bf9b7e", + "8881623e-fe7a-53bd-b703-2e8bf6a5c240", + "2778ece8-df84-58d2-9002-e036f0d007dc", + "592fd011-4dfb-5a78-8973-482e35541468", + "551087b1-8e80-5a7b-839a-304f566a6417", + "a0916b04-3463-5247-94da-0c97fd5da20d", + "737e4fe2-91ba-50c5-8f64-1149944fb60c" + ], + "document_id": [ + "c36db75e-4b76-540d-9efb-d0e156e61541", + "4de9f054-4a02-5b6a-905d-420744075755", + "07b6ab4b-1aff-5d0e-aa98-d49a66b7b5d1", + "3d0f1bab-738c-5ba3-8fd9-206b3455704d", + "3f21702c-3fdf-50b5-8060-04a9a1ce09ae", + "17637a6f-804e-50e4-9cf5-37318e17f15c", + "cbc7f2d3-3f65-50ba-b281-96dd1c77f2c0", + "a16d3328-039c-530a-bfe5-f6f80ecf2ad0", + "34783678-d4c2-566f-9b5b-0db18a72e86d", + "8ab06972-1c6c-5d68-a270-65fb0af0917b" + ], + "id": [ + "chatcmpl-AIHJckmqoH46qp1BDk6zYdLg1Akw5", + "6623716f-8275-5f17-be27-a8241560a645", + "15e73a17-cae0-5dc1-8cad-5805809ae872", + "c57caee6-9b8f-5cb5-ab53-337dda1d92a7", + "bcfe4c03-f0bb-540d-b529-adda644998a6", + "4e393756-1b48-56f7-a3e7-33d15a55dc73", + "a4c3abc2-635c-5ace-ad39-c678a505fa48", + "f295f1d3-43d5-5eef-a39f-a8b91c47500f", + "0cd29c12-48e8-5f9f-9744-6b8acfaae0c7", + "3a9e7574-8914-5a96-86b6-b7b87a89b894", + "af680560-47c6-5556-bb80-c7584d762f66" + ], + "contexts": [ + "Studies of twins also provide compelling evidence for a genetic component to T2D. Estimates for concordance rates range from 0.29 to 1.00 in monozygotic (MZ) twins, while in dizygotic (DZ) twins the range is 0.100.43 [57, 58, 6164]. The high levels of heritability observed for insulin sensitivity and insulin secretion [6567] further reinforce the role of genetics in diabetes and indicate the primary genetic lesions for diabetes are likely to localize to genes in beta-cell-centric pathways.", + "It is therefore intriguing that A1C levels are signicantly correlated in monozygotic twins whether they are concor- dant for type 1 diabetes or not (4): in a discordant twin pairone twin is treated with insulin, whereas the other oneisnt, and thus this degree of correlation suggests thatgenetic contributors to A1C may be detectable despite thesuperimposition of a strong environmental modier. Rig-orous estimates of heritability of treated A1C, however, are not available.", + "Concordance rate for type II diabetes mellitus in monozy-gotic twins: actuarial analysis. Diabetologia 42:146150 3. Lehtovirta M, Kaprio J, Forsblom C, Eriksson J, Tuomilehto J, Groop L (2000) Insulin sensitivity and insulin secretionin monozygotic and dizygotic twins. Diabetologia43:285293 4. Florez JC, Hirschhorn J, Altshuler D (2003) The inherited basis of diabetes mellitus: implications for the genetic anal-ysis of complex traits. Annu Rev Genomics Hum Genet4:257291", + "disease susceptibility is not explained by genetics alone; environ- mental factors, gene by environment interactions, and epigenetic inuences are likely to play important roles in the etiology of T1D [5,6] . Monozygotic (MZ) twin pairs, discordant for T1D, represent an ideal system to test susceptibility factors not attributable to genetic variation, especially epigenetic variation, since the ge- nomes of the twins are identical. The ascertainment of disease-", + "epigenetic differences among monozygotic twins. A critical question is whether epigenetic marks are transmitted intactfrom parent to offspring and whether DNAm is allele- specific and covaries with allele-specific gene expression. For example, can we develop an epigenetic transmissiontest comparable to the transmission disequilibrium test used in genetic epidemiology? Finally, and most excitingly, we", + "their dietary and physical activity habits (Maes et al, 1997 ). There is also ample evidence that diabetes has a substantial genetic component. The con- cordance of type 2 diabetes in monozygotictwins ranges between 50 and 70% compared to 2037% in dizygotic twins (Kaprio et al, 1992 ; Newman et al, 1987 ; Poulsen et al 1999). Further evidence comes from studies that compare therisk in offspring with a family history of type 2 diabetes with offspring without such a fam-", + "monozygotic and dizygotic Danish twin pairs withinsulin dependent diabetes mellitus. Bmj 1997: 314:1575 1579. 30. R EDONDO MJ, R EWERS M, Y UL et al. Genetic deter- mination of islet cell autoimmunity in monozygotictwin, dizygotic twin, and non-twin siblings of patientswith type 1 diabetes: prospective twin study. Bmj 1999:318: 698 702. 31. L EVY-M ARCHAL C, P ATTERSON C, G REEN A. Variation", + "Studies in twins have demonstrated that 5070 % in the body mass index (BMI) variance may be explained by genetics ( Allison et al., 1996 ), and T2DM concordance was reported ranging from 1737 % in dizygotic to 5070 % in monozygotic twins ( Kaprio et al., 1992 ; Medici et al., 1999 ; Poulsen et al., 1999 ). In addition, family and adoption studies have reported heritability ranging from 2060 % for obesity ( Rice et al., 1999 ; Stunkard et al., 1986 ) and 3070 % for T2DM ( Meigs", + "Monozygotic twins exhibit numerous epigenetic differences: clues to twindiscordance? Schizophr Bull 29: 169178. 8. Oates NA, van Vliet J, Duffy DL, Kroes HY, Martin NG, et al. (2006) Increased DNA methylation at the AXIN1 gene in a monozygotic twin from a pair discordant for a caudal duplication anomaly. Am J Hum Genet 79: 155162. 9. Kuratomi G, Iwamoto K, Bundo M, Kusumi I, Kato N, et al. (2008) Aberrant DNA methylation associated with bipolar disorder identified from discordant", + "5 E/C128orts to estimate the heritability of T2D by a comparison of the concordance rates in mono- and dizygotic twins have varied greatly as a result of di/C128erences in ascertainment scheme, diagnostic criteria and follow-up duration.69Concordance for diabetes is generally higher in identical twins (supporting a genetic basis for disease), although the extremely high concordance rates in some early studies6were undoubtedly inated by ascertainment bias. Evidence from population studies" + ], + "answer": "The study of monozygotic twins discordant for diabetes provides valuable insights into the etiology of the disease by highlighting the roles of both genetic and epigenetic factors. Since monozygotic twins share identical genomes, any differences in disease manifestation between them can often be attributed to non-genetic factors, such as epigenetic modifications or environmental influences. 1. **Genetic Component**: The high concordance rates for diabetes in monozygotic twins, compared to dizygotic twins, underscore a significant genetic component to the disease [1], [6]. This suggests that genetic predispositions, particularly those affecting insulin sensitivity and secretion, play a crucial role in the development of diabetes [1]. 2. **Epigenetic Influences**: Despite identical genetic makeup, monozygotic twins can exhibit differences in disease susceptibility due to epigenetic variations. These variations can arise from environmental factors and gene-environment interactions, which are crucial in understanding the etiology of diabetes [4]. Epigenetic differences, such as DNA methylation patterns, have been observed in monozygotic twins and may contribute to discordance in disease states [9]. 3. **Environmental and Lifestyle Factors**: The discordance in diabetes among monozygotic twins also points to the influence of environmental factors and lifestyle choices, such as diet and physical activity, which can modify epigenetic marks and affect disease outcomes [6]. In summary, the study of monozygotic twins discordant for diabetes highlights that while genetic predispositions are significant, epigenetic modifications and environmental factors also play critical roles in the disease's etiology. This understanding can help in developing more targeted prevention and treatment strategies that consider both genetic and non-genetic factors.", + "question": "How do genetic and epigenetic differences between monozygotic twins discordant for diabetes inform our understanding of its etiology?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_1 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_1 new file mode 100644 index 0000000..5260530 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_1 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2012 - Genetic regulation of adult hippocampal neurogenesis A systems genetics approach using BXD recombinant inbred mouse strains.pdf", + "2009 - Processing Large-Scale, High-Dimension Genetic and Gene Expression Data.pdf", + "2005 -Pomp- GenomeExploitation.pdf", + "2006 - Marker Assisted Backcrossing .pdf", + "2013 - Host Genes and Resistance.pdf", + "2014 - Fine-mapping QTLs in advanced intercross lines and other.pdf", + "2007 - Latexin is a newly discovered regulator of hematopoietic stem cells.pdf", + "2020 - Large?scale pathway specific polygenic risk and transcriptomic.pdf", + "2011 - Genetical genomics approaches for systems genetics.pdf", + "2015 - Functional Analysis of Genomic Variation and Impact on Molecular and Higher Order Phenotypes.pdf" + ], + "extraction_id": [ + "ebea9717-52a1-5eb8-8b5a-67afb90c95f8", + "3276b251-2e60-53e8-8fd1-07702f486a43", + "80f97b13-9dd9-5d52-9d55-0abac724605e", + "da78b007-359c-548c-8cb0-ba4a3dab0f86", + "661e7fb0-804c-53e2-b948-6512c372ac57", + "a5c455c9-50f6-5f12-84cd-26d335001e6b", + "91470df2-7451-59d2-af9f-98cdf2f85486", + "a53c7001-432d-5289-9cc1-b3d75a721da4", + "4a7ed73c-26c7-5852-8a02-a39cd0d611ec", + "cec82840-0f05-5fc5-bfcf-a4b928124fef" + ], + "document_id": [ + "c54da858-9620-588e-8e41-76a960af2ff6", + "17264155-b665-59db-94cb-f4d67eac20fc", + "a77aefe9-379e-54a2-b029-8f5f3e798e64", + "5efc1bdf-f847-5eaf-a808-9cf71b9399ce", + "cc6cf2b5-0440-51e8-aad4-d0b4b5331ab2", + "eb30392e-f079-511d-8c6c-a6e6c98d2167", + "63467ba8-940b-59f6-bbd5-0b0ce7883d49", + "96119357-a6dd-5ea3-8bcb-9c047f0a336e", + "de78a01d-8d03-5afb-af5b-ce2ed2167766", + "263d327b-f5db-54e4-a215-b3f8a51cd7d6" + ], + "id": [ + "chatcmpl-AIGrF12QPoEwoc2D22aSA5ivwYW2D", + "2fe235ff-90ab-5f21-8e51-cbfb0e13713a", + "e26ebc1e-e05d-56fb-8718-604275994a84", + "b17b43c6-1ba8-5849-8664-3b5cd78877b5", + "037c669c-da80-5e1e-abe3-c4344145a4ed", + "fb5944f3-bb0e-599e-827c-a8b7c6934746", + "a860695e-fe40-55eb-9eb8-072e1daf5cf2", + "22301737-122c-57be-a2f1-9d631ad101b3", + "101c1f27-4a98-5d1c-b013-c5f1950aee95", + "91ac7cb9-ec59-5bd6-9f24-aa840caf2c27", + "6e933f07-26d6-5cf1-8ee0-9bf6ec68b1ff" + ], + "contexts": [ + "It is important to integrate the gene variants and environmental factors to the trait to understand the network controlling that trait. In systems genetics approach, different trait networks are related to different networks of gene and environmental variants to find global genetic modulation of the complex phenotype. The availability of genetic reference panels makes it easy to acquire diverse phenotypic data and advanced computational models make it possible to analyse their relationship. 2.2.1.", + "Processing Large-Scale, High-Dimension Genetic 325 another. We anticipate these types of networks becoming increasingly important in the human genetics space to gain a mechanistic understanding of how a given DNAperturbation induces changes in one or more genes that go on to affect networks that cause disease. The integration of genotypic and expression and other data have recently been shown, in a Bayesian network framework [76], to enhance the overall", + "2. GENETICAL GENOMICS In recent years, there has been growing interest in uniting genetic and genomic approaches to enable more comprehensive dissections of complex traits and their genetic architecture. Jansen and Nap (2001) termed this synthesis genetical ge-", + "2. GENETICAL GENOMICS In recent years, there has been growing interest in uniting genetic and genomic approaches to enable more comprehensive dissections of complex traits and their genetic architecture. Jansen and Nap (2001) termed this synthesis genetical ge-", + "42.Chesler EJ, et al. 2005. Complex trait analysis of gene expression uncovers polygenic and pleiotropic networks that modulate nervous system func-tion. Nat. Genet. 37:233242. 43.Iraqi FA, Churchill G, Mott R. 2008. The Collaborative Cross, develop- ing a resource for mammalian systems genetics: a status report of theWellcome Trust cohort. Mamm. Genome 19:379 381. 44.Xiao J, et al. 2010. A novel strategy for genetic dissection of complex traits:", + "multiple-SNP analysis of GWAS summary statistics identiesadditional variants inuencing complex traits. Nat Genet 44(369375):S1S3. doi: 10.1038/ng.2213 Yang J, Zaitlen NA, Goddard ME et al (2014) Advantages and pitfalls in the application of mixed-model association methods. NatGenet 46:100106. doi: 10.1038/ng.2876 Yazbek SN, Buchner DA, Geisinger JM et al (2011) Deep congenic", + "10. The power of integrating all these genetic and genomic data has now been well documented, offering a glimpse of what the future of com-plex trait genetics will look like. Model systems that are genetically more complex, including extensive eight-strain crosses 11,12 and haplotype association studies using large panels of regular inbred strains of mice, and even humans, are", + "tive analysis of omics summary data reveals putative mechanisms underlying complex traits. Nat Commun 9:918 33. Yang J, Hong Lee S, Goddard ME, Visscher PM (2011) GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet 88:7682 34. Zeisel A, Hochgerner H, Lnnerberg P, Johnsson A, Memic F, van der Zwan J etal (2018) Molecular architecture of the mouse nervous system. Cell 174:999.e221014.e22 35. Zhan X, Hu Y, Li B, Abecasis GR, Liu DJ (2016) RVTESTS:", + "used to identify molecular traits involved in the p athology of diseases and to eluci- date the networks underlying complex phenotypes. Re cent studies have pushed the genetical genomics concept further towards data int egration and interpretation within and across molecular levels, and have also r evealed remaining challenges. The focus of this review is to discuss these challe nges and their possible solutions in", + "2 large populations. The new methods have allowed us to dissect the genetic architecture of complex disorders including the identification of the causal genomic loci, estimation of the disease heritability, estimation of effect sizes of different loci and their non-additive interactions. Linkage analysis The earlier breakthroughs in linking genotype with phenotype involved studies of Mendelian disorders that can be mapped to a single gene and a single mutation. These" + ], + "answer": "Recent advancements in network-based integrative genomics have significantly enhanced our understanding of complex trait architectures by enabling a more comprehensive analysis of the interactions between genetic and environmental factors. These advancements allow for the integration of gene variants and environmental factors to better understand the networks controlling complex traits [1]. The use of systems genetics approaches facilitates the connection of different trait networks with networks of gene and environmental variants, which helps in identifying the global genetic modulation of complex phenotypes [1]. Moreover, the integration of genotypic, expression, and other data within a Bayesian network framework has been shown to improve our mechanistic understanding of how DNA perturbations can induce changes in gene networks that ultimately affect disease outcomes [2]. This integrative approach provides a more detailed view of the polygenic and pleiotropic networks that modulate complex traits, such as those affecting the nervous system [5]. Overall, these advancements in network-based integrative genomics offer a more nuanced and interconnected view of complex trait architectures, moving beyond single-gene analyses to consider the broader network of interactions that contribute to phenotypic outcomes.", + "question": "How do recent advancements in network-based integrative genomics alter our understanding of complex trait architectures?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_10 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_10 new file mode 100644 index 0000000..4ab901f --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_10 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2011 - Genetical genomics approaches for systems genetics.pdf", + "2015 - Quantitative and logic modelling of molecular and gene networks.pdf", + "2005 - Combinatorial Genetic Regulatory Network Analysis Tools for High Throughput Transcriptomic Data.pdf", + "2016 - Integrating Multidimensional Data Sources to Identify Genes Regulating Complex Phenotypes.pdf", + "2011 - Genetical genomics approaches for systems genetics.pdf", + "2007 - How to infer gene networks from expression profiles.pdf", + "2015 - Biological network inference from microarray data, current solutions, and assessments.pdf", + "2016 - Integrating Multidimensional Data Sources to Identify Genes Regulating Complex Phenotypes.pdf", + "2015 - Biological network inference from microarray data, current solutions, and assessments.pdf", + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf" + ], + "extraction_id": [ + "d0102d97-2e08-50c3-86f4-d1103da9cca1", + "e23eae56-f71e-55fb-b443-e95adfe8ef22", + "2d776c48-9d99-5feb-9c18-113416c86d96", + "3292d5e1-b06c-5041-8190-44119ec0fdf0", + "f71776c8-e5c9-55e0-ad54-3725550dea19", + "452b1ade-c691-5feb-9a12-cfe83ae314af", + "b5c98115-372f-5bee-8517-80dc9b6838ee", + "c2a8a3ab-2531-55c1-920b-d908fa07c027", + "ae0e55f7-f33c-5179-ba14-8221c2a07be8", + "858f630f-9443-5f13-ac40-8e16eadd9ba1" + ], + "document_id": [ + "de78a01d-8d03-5afb-af5b-ce2ed2167766", + "8db6a373-be03-5653-beaf-1b2ae1d98c31", + "5ded506d-7935-53f9-a118-57a9f3943376", + "8c395e40-b6b9-5b00-9f32-ca35a598c595", + "de78a01d-8d03-5afb-af5b-ce2ed2167766", + "5067a047-b97d-522a-9a7e-5372e3bbd102", + "f64cf13c-d989-50da-be0d-81e34a735a42", + "8c395e40-b6b9-5b00-9f32-ca35a598c595", + "f64cf13c-d989-50da-be0d-81e34a735a42", + "128224f1-3545-52c3-93cb-77c3cf4ec70a" + ], + "id": [ + "chatcmpl-AIGs9vl6ZxGFt8u7h4G1USup0nUIZ", + "83b84d63-4942-5c91-b93e-3ea1164c600e", + "05de9482-4937-5a26-b7fc-0a3cd86c4c40", + "b0b9c2ba-ff4b-5b2c-854f-70007eba8fd4", + "951c0969-df10-5038-b235-1bf4fa358ebb", + "09527834-da5e-5c34-9439-cf078f40870f", + "98fdd553-df98-510e-8e0d-62739abf5518", + "29e3d52a-5651-5cdc-94a7-babb6142e244", + "6bda096f-f5e1-51c9-9818-6c13cdfc8fe9", + "fbae4b79-573c-5b0b-ba0f-3761dbb22590", + "c63cfaee-749e-547b-9c0a-086266f10670" + ], + "contexts": [ + "genetic data which are shifting the paradigm of net work inferences by providing statistical evidence to support directed links betw een genes, proteins, metabolites or diseases. In Chapter 6 , different approaches using genetic data for gene network inference that have been proposed are reviewed. Chapter 7 examines the statistical potential of such methods under different realistic settings: varying population sizes and in the presence or absence of hidden factor var iation and suggests ways to", + "73. Yu,J., Smith,V.A., Wang,P .P ., Hartemink,A.J. & Jarvis,E.D. Advances to Bayesian network inference for generating causal networks from observational biological data. Bioinformatics 20, 35943603 (2004). 74. Sachs,K., Perez,O., Peer,D., Lauffenburger,D. A. & Nolan,G. P . Causal protein signaling networks derived from multiparameter single cell data. Science 308, 523529 (2005). 75. Feizi,S., Marbach,D., Mdard,M. & Kellis,M. Network deconvolution as a general method to", + "Causal Inference of Regulator-Target Pairs by Gene Mapping 97 1.2 Background: Inferring Regula tory Networks from Correlated Gene Expression Independent of the data sets described so far, large collections of gene expres- sion over time course (Spellman et al., 1998) or varying environmental con- ditions (Gasch et al., 2000; Hughes et al., 2000) have been studied to reveal dependent variation among genes and thereby deduce regulatory relationships.", + "data, to infer possible pathways and help build a link from the phe-notype back to a causal gene. In many cases, such interaction data are already available in public archives and need not be generated anew by the researcher [ 1 ]. These different sources of interaction data can be collated into network models ( see Note 1 ) which allow analysis using techniques borrowed from graph theory.", + "relationships with a causal inference test . BMC Genet 2009, 10 :23. 60. Chaibub Neto E, Ferrara CT, Attie AD, Yandell B S: Inferring causal phenotype networks from segregating populations . Genetics 2008, 179 (2):1089-1100. 61. Li Y, Tesson BM, Churchill GA, Jansen RC: Critical preconditions for causal inference in genome-wide association studies under review 2010. 62. Aten JE, Fuller TF, Lusis AJ, Horvath S: Using genetic markers to orient", + "T, Samson L, T I (2006) A systems approach to mapping DNAdamage response pathways. Science 312:10541059 Yu J, Smith V A, Wang PP, Hartemink AJ, Jarvis ED (2004) Advances to bayesian network inference for generating causal networks fromobservational biological data. Bioinformatics 20:35943603How to infer gene networks from expression proles M Bansal et al 10Molecular Systems Biology 2007 &2007 EMBO and Nature Publishing Group", + "with the data. To cope with this problem, Siegenthaler et al. proposed a novel assessment procedure that incorporates the inferability of gene regulatory interactions by redening the confusion matrix interms of inferability of the network, i.e., the possibility of the network to be determined from data. The inferability of GRNs was analyzed based on the causal information that could beextracted from experiments. Authors used data from the DREAM", + "and can thus be helpful in determining the causal structure of gene networks. Often, such data have already been gathered previously in the form of single-gene experiments and other links can be gleaned from a search of the published literature. In a few cases, a relevant database exists which can be used as a data source. Links of this type will all be directed edges from gene to phenotype (where the phenotype is the same as used as the seed).", + "tional methodologies in gene regulatory net-works. IGI Global, Hershey, PA, pp 127 11. Roy S, Das D, Choudhury D, Gohain GG, Sharma R, Bhattacharyya DK (2013) Causality inference techniques for in-silico gene regu-latory network, Mining intelligence and knowl-edge exploration. Springer, New York, pp 432443 12. Olsen C, Meyer PE, Bontempi G (2009) Infer- ring causal relationships using information the-oretic measures. In Proceedings of the 5th Benelux Bioinformatics Conference (BBC09)", + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small" + ], + "answer": "The latest strategies for inferring causal relationships within gene networks using data from GeneNetwork.org involve several advanced methodologies. One approach is the use of Bayesian network inference, which has been advanced to generate causal networks from observational biological data [2]. This method allows for the modeling of probabilistic relationships between genes and can help infer causality from complex datasets. Additionally, there is a focus on using genetic markers to orient causal inference in genome-wide association studies, which is critical for understanding the genetic basis of phenotypes [5]. This involves identifying genetic variants that can serve as markers to infer causal pathways. Another strategy involves the use of network deconvolution, a general method to infer direct dependencies in networks, which can be applied to gene networks to clarify causal relationships [2]. Furthermore, the integration of multi-omics data and the use of updated tools on platforms like GeneNetwork.org enhance the ability to perform predictive medicine and systems genetics analyses, which are crucial for inferring causal relationships in gene networks [10]. These strategies collectively leverage statistical, computational, and biological insights to improve the inference of causal relationships in gene networks.", + "question": "What are the latest strategies for inferring causal relationships within gene networks using data from GeneNetwork.org?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_11 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_11 new file mode 100644 index 0000000..b92d6db --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_11 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2021 - New Technologies to Study Functional Genomics of Age-Related Macular Degeneration.pdf", + "2020 - Advances of single?cell genomics and epigenomics in human disease.pdf", + "2020 - Integrative genomics approach identifies conserved.pdf", + "2023 - Comprehensive genomics analysis of aging related gene signature to predict the prognosis and drug resistance of colon adenocarcinoma.pdf", + "2020 - The Genomics of Auditory.pdf", + "2016 - Single-cell genomics coming of age.pdf", + "2022 - Systems genomics in age-related macular degeneration.pdf", + "2018 - Human Genetics of Obesity and Type 2 Diabetes Mellitus.pdf", + "2020 - Integrative genomics approach identifies conserved.pdf", + "2009 - Gene expression in the mouse eye an online resource for genetics using 103 strains of mice.pdf" + ], + "extraction_id": [ + "453f1ace-3591-50a3-afa5-86404632ace3", + "60355441-16f5-53a2-9b24-9616624f8d00", + "863ce70a-3bcd-5a6c-a63f-620a9fdcdfdf", + "59e0781d-994c-5ef5-b2f4-073f4a73743b", + "16c769c7-b6ad-5b50-8d81-92c6768595f5", + "8d4d3a2d-0aca-5880-98e7-92638c72dd31", + "e488a94d-d7b3-5d56-bd56-95ac6e89d3ed", + "74048afb-68c3-520a-b661-1d347e9d2fcd", + "863ce70a-3bcd-5a6c-a63f-620a9fdcdfdf", + "65c45e96-da39-59d8-9b9e-0679df8b1472" + ], + "document_id": [ + "419ee941-2cd6-56ae-8221-aed1c22a8ee2", + "afe53f5a-3962-520f-be55-9df5bfdaad70", + "704a4d4c-3655-5cc0-8d2b-5f4723db13ff", + "8505ccf0-3138-5b83-b36d-8ebd7506a3a4", + "f56b6ae4-e05a-5851-9c10-4bd62f237778", + "dca877e8-cbb9-561e-9b3c-6085228af97d", + "e8cf1e00-cf22-54cb-a0de-790a822c62d1", + "2083de31-17c6-5d1e-9aa6-2efc6c1d9ac2", + "704a4d4c-3655-5cc0-8d2b-5f4723db13ff", + "85241c56-1338-5b42-8b33-10b14514f169" + ], + "id": [ + "chatcmpl-AIGsGaW9DtpbrFAp6kQFqInDl6kUP", + "615beb0f-6b0a-59a6-a2fe-0be884c43d55", + "732b8fa8-8832-5002-bea1-bdde2bc61c64", + "849c1df7-4164-5164-b3be-6cdeb62ee555", + "3069c1d1-6b89-513a-83c3-e64cce07043f", + "504a960d-e669-52d1-b6c0-439b4f981d5f", + "769d2c00-d882-59a6-aa69-feb575c9fe1a", + "1fa406bc-fb29-5b60-90bc-1e77bd499df6", + "5f508353-ff30-5dfc-9bac-4bb8c6627391", + "42cf70a7-610a-5792-be62-58114dfc505a", + "908fad18-f471-5067-8bfc-f49951bdb4d1" + ], + "contexts": [ + "On the other hand, single-nucleus RNA-seq (snRNA-seq) provides an alternative method for gene expression proling in complex tissues from frozen samples at single cell levels (Grindberg et al., 2013). Compared to scRNAseq, snRNA-seq analyze gene expression within the nuclei instead of intact cells. It should be noted that there could be potential dierences between the RNA type and expression levels between nucleus and cytosol. As observed in a previous study comparing nuclear", + "most genetic and epigenetic mechanisms are yet to be probed with single-cell resolution. To understand the finer details at the level of a singular cell, sophisticated genomic and epigenomic next-generation sequencing (NGS) technologies have increased the potential for research output immensely (see Clark etal. 2018; Clark etal. 2016; Kelsey etal. 2017; Macaulay etal. 2017; Stuart and Satija 2019). These would", + "of the disease, profiling gene expression in only bulk tissue sam-ples may obscure biologically relevant cell-type specific changes. While single-cell RNA-seq allows us to evaluate transcriptional changes within cell-types, it is prohibitively costly to executeon large cohorts (i.e. hundreds of individuals). To circumvent this issue, we developed a framework that leverages single-", + "2019). The traditional RNA sequencing technology (bulk RNA-seq) is applied to determine gene expression pro les, isoform expression, alternative splicing and single-nucleotide polymorphisms on basis oftissue samples, which contains various cell types ( Kuksin et al., 2021 ). On the contrast, single-cell RNA sequencing (scRNA-seq), a noveltechnology can detect the gene expre ssion patterns for each transcript within single cell and distinguish cell subtypes ( Lhnemann et al., 2020 ).", + "sion from smaller amounts of RNA enabled cell typespecific analyses.Specific cell types can beisolated using flow cytometry, for example, using endogenously expressed fluorescent markers, with or without combining with antibodies for cell surface proteins. Transcriptomic analysis by either microarray or bulk RNA sequencing then follows (39,67,68,104,145).Such analyses can 280 Taiberetal. Annu. Rev. Genom. Hum. Genet. 2022.23:275-299. Downloaded from www.annualreviews.org", + "Recent applications Single-cell RNA sequencing has had a profound impact on our understanding of neuronal and hematopoietic cell types, as well as the immune system. Examples of novel insights in immunity include a window on to an unexpected plethora of dendritic cells in mouse immun- ity [25] and new regulators and subpopulations of CD4+ T cells [26 28]. In hematopoiesis, much single-cell tran- scriptomics work has focused on hematopoetic stem cells and the single-cell perspective has provided reso-", + "single- nucleus RNAseq makes them a valuable complement to the find- ings published by Orozco, Chen et al. (Orozco et al., 2020 ). Furthermore, Yan et al. (2020) used cell sorting to enrich for cell types with a high degree of heterogeneity, resulting in finer cell subtype resolution for non-photoreceptor cell types such as RGCs. In addition to neural retina, our understanding of the choroidal", + "using sequencing (ATAC-seq),95,96 that can map chro- matin interactions and accessibility with higher resolu-tion than previous methods will improve our ability to disentangle GWAS loci; while single-cell RNA sequenc- ing 97,98 and CRISPR-based pooled gene perturbation methods99103 provide unprecedented opportunities for studies of how RNA expression patterns differ between cells within tissues and how those tissues and cells react to perturbation of multiple genes in parallel.", + "cell RNA-seq data from a smaller cohort in conjunction withco-expression network analysis in order to estimate cell-typespecific transcriptomic changes in large, bulk tissue RNA-seq datasets. We isolated nuclei and performed single-nuclei RNA-seq (snRNA-seq, n= 27 321 nuclei) on postmortem human brain tissue from aged, neurologically healthy controls ( n=5 ,6 7t o9 0 + years old, PFC, Supplementary Material, Table S1 ) to clarify cell- type proportions and the corresponding transcriptional profiles", + "without the biases of probe sequence selection and hybridization reactions. The second innovation is cell-specific RNA profiling methods [79] that make it practical to generate comparatively accurate expression data for individual cell types in genetically engineered lines of mice. We can soon expect far more comprehensive and specific lists of genes for several important cell and tissue types that can be used to assemble multicellular expression networks in eye.ACKNOWLEDGMENTS Dr. Eldon E." + ], + "answer": "Advancements in single-nucleus RNA sequencing (snRNA-seq) provide more granular insights into cell-type-specific gene expression networks by allowing for the analysis of gene expression within the nuclei of cells, rather than relying on intact cells as in single-cell RNA sequencing (scRNA-seq) [1]. This method is particularly useful for profiling gene expression in complex tissues from frozen samples at the single-cell level, which can be challenging with other techniques [1]. Additionally, snRNA-seq can help clarify cell-type proportions and corresponding transcriptional profiles, as demonstrated in studies involving postmortem human brain tissue [9]. By isolating nuclei and performing snRNA-seq, researchers can achieve finer cell subtype resolution, which is crucial for understanding the heterogeneity within cell populations [7]. This level of detail is essential for constructing accurate cell-type-specific gene expression networks, as it allows for the identification of transcriptional changes and cell-type-specific gene expression patterns that might be obscured in bulk tissue analyses [3]. Overall, snRNA-seq enhances our ability to dissect complex tissues into their constituent cell types and understand the unique gene expression networks within each type, providing a more detailed and nuanced view of cellular function and interaction.", + "question": "How do advancements in single-nucleus RNA sequencing provide more granular insights into cell-type-specific gene expression networks?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_12 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_12 new file mode 100644 index 0000000..1bbd0b6 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_12 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2009 - Processing Large-Scale, High-Dimension Genetic and Gene Expression Data.pdf", + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2011 - Genetical genomics approaches for systems genetics.pdf", + "2009 - Genes and gene expression modules associated with caloric.pdf", + "2009 - Visual analytics for relationships in scientific data (1).pdf", + "2007 - Integrating physical and genetic maps from genomes to interaction networks.pdf", + "2010 - Systems genetics, bioinformatics and eQTL mapping.pdf", + "2013 - Pathways, Networks and Systems Medicine Conferences.pdf", + "003 -Barnes- Bioinformatics_for_Geneticists.pdf", + "2007 - Bioinformatics_for_Geneticists.pdf" + ], + "extraction_id": [ + "29c980ec-bd6e-5ae7-a61c-5abd67d0ef67", + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "c0983224-1ade-5c10-9f2b-847e9b33f706", + "56129761-d500-59b9-bd9b-cd9cbcada21c", + "d64d8cf5-5b57-5a29-99b4-a8d2ab4bda21", + "ba1a83a3-d0e9-5f1e-870f-228abdae771d", + "298ee1f5-58a9-567c-86ba-8ac5967e1718", + "4cdc439f-bd23-5978-9f34-a34e1cb33cf4", + "a3ae6875-b0fc-5a4e-866f-4fee99c7d2a2", + "9c89683f-aca5-57f9-b28d-62e9eb64377b" + ], + "document_id": [ + "17264155-b665-59db-94cb-f4d67eac20fc", + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "de78a01d-8d03-5afb-af5b-ce2ed2167766", + "893ba204-2e69-563f-9046-7246ca61494f", + "a6642ef1-8aa2-5305-9cc8-8a6263bb2b0c", + "a9a113e2-d5e5-5903-91de-4b45b37d870f", + "27c922c6-e449-5f83-868a-3ad7284facc8", + "b50a9732-7d01-5d4d-8f33-a9d43dbc7df3", + "045edae8-468b-5725-be06-8cb4b8f6a92b", + "4ea8e1a8-e113-5f02-ad78-880b9c51a101" + ], + "id": [ + "chatcmpl-AIGsO45INZIWjU37FcOiRroinBDZj", + "302feae2-3bab-5fb8-8483-0cea906c83e8", + "c63cfaee-749e-547b-9c0a-086266f10670", + "0374a059-20c1-5b75-a7a7-bf69ce03740c", + "860be786-e27d-5dd1-96bf-4bcc48957b4d", + "4488c0f4-c24a-5b6d-814a-a30b15cc4c03", + "9f6fb84a-f487-5ea6-a84e-403642b6d76e", + "0858b8f7-66f3-5741-ae7e-4504bca7292f", + "a02b4589-65ec-50e1-9849-090971ddb2b0", + "7d3e3705-c5e7-5a37-91c1-a87842f5b9a7", + "73198d17-f9ce-5528-89d8-f6e466258708" + ], + "contexts": [ + "52.Zhu J et al. (2007) Increasing the power to detect causal associations by combining genotypicand expression data in segregating populations. PLoS Comput Biol 3:e69 53.Zhu J et al. (2008) Integrating large-scale functional genomic data to dissect the complexity ofyeast regulatory networks. Nat Genet 40:854861 54.Kim JK et al. (2005) Functional genomic analysis of RNA interference in C. elegans. Science308:11641167", + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "expression and its effect on disease . Nature 2008, 452 (7186):423-428. 12. Chen LS, Emmert-Streib F, Storey JD: Harnessing naturally randomized transcription to infer regulatory relationships amo ng genes . Genome Biol 2007, 8(10):R219. 13. Aten JE, Fuller TF, Lusis AJ, Horvath S: Using genetic markers to orient the edges in quantitative trait networks: the NEO s oftware . BMC Syst Biol 2008, 2:34. 14. Millstein J, Zhang B, Zhu J, Schadt EE: Disentangling molecular", + "and unknown function by large-scale coexpression analysis. Plant Physiol 2008, 147:41-57. 98. Wolfe CJ, Kohane IS, Butte AJ: Systematic survey reveals gen- eral applicability of \"guilt-by-a ssociation\" within gene coex- pression networks. BMC Bioinformatics 2005, 6:227. 99. Lee NH: Genomic approaches for reconstructing gene net- works. Pharmacogenomics 2005, 6:245-58. 100. Goutsias J, Lee NH: Computational and experimental approaches for modeling ge ne regulatory networks. Curr", + "the discovery of interface genes. These mRNA transcripts regulate expression of genes in those structures, and thereby couple multiple networks a nd biological processes. The detection of these transcripts and the analysis of their gen es regulatory polymorphisms 37", + "Rev. Genet 2007;8:437449. [PubMed: 17510664] A review of theory and approaches to mapping genetic interaction networks. 16. Bork P, et al. Protein interaction networks from yeast to human. Curr. Opin. Struct. Biol 2004;14:292 299. [PubMed: 15193308] 17. Ewing B, Hillier L, Wendl MC, Green P. Base-calling of automated sequencer traces using phred. I. Accuracy assessment. Genome Res 1998;8:175185. [PubMed: 9521921]", + "CC represents a dramatic improvement over existinggenetic resources for mammalian systems biology appli- cations (Adam et al. 2007 ; Chesler et al. 2008 ). A number of gene expression data sets from microarray experiments,particularly those for mouse and rat, have been integrated into GeneNetwork ( http://www.genenetwork.org ), which is essentially a web knowledgebase in which the entire dataset and relevant metadata (data about the data) are com- bined with sophisticated statistical and computation tools", + "gene, and the first f unctional anti -sense miRNA, Lastly, we have used comparative genomics to infer regulatory networks based on individual conserved instances of regulatory motifs, which show functional enrichments similar and sometimes higher to genome -scale experimental met hods such as ChIP -chip. As part of the ENCODE and modENCODE projects, we are now studying dynamics of developmental and cell -differentiation networks in", + "(ncRNAs) from the Rfam database (Grifths-Jones et al. , 2005) and predicted regu- latory sites from the cisRED database (Robertson et al. , 2006). There is much to do in both of these emerging areas but even preliminary data have already given new insights into mammalian biology: it seems there is high lineage specic expansion of some ncRNA classes relative to protein-coding genes (Birney et al. , 2006). Another growing area of activity is in cataloguing the genetic variation present in human", + "(ncRNAs) from the Rfam database (Grifths-Jones et al. , 2005) and predicted regu- latory sites from the cisRED database (Robertson et al. , 2006). There is much to do in both of these emerging areas but even preliminary data have already given new insights into mammalian biology: it seems there is high lineage specic expansion of some ncRNA classes relative to protein-coding genes (Birney et al. , 2006). Another growing area of activity is in cataloguing the genetic variation present in human" + ], + "answer": "The context provided does not explicitly mention the impact of recent discoveries in non-coding RNA regulation on refining gene interaction maps on GeneNetwork.org. However, there are some relevant points that can be inferred: 1. GeneNetwork.org has been updated with new tools and data, including multi-omics analysis, which could potentially incorporate non-coding RNA data to enhance gene interaction maps [2]. 2. The integration of gene expression data sets, particularly for mouse and rat, into GeneNetwork.org suggests that the platform is equipped to handle complex genetic data, which may include non-coding RNA interactions [7]. 3. There is ongoing research and data collection on non-coding RNAs, as indicated by references to databases like Rfam and cisRED, which could contribute to refining gene interaction maps by providing insights into regulatory networks [9], [10]. While the specific impact of non-coding RNA discoveries on GeneNetwork.org is not detailed, the platform's enhancements and the broader research context suggest that such discoveries could play a role in improving the accuracy and depth of gene interaction maps.", + "question": "What impact have recent discoveries in non-coding RNA regulation had on refining gene interaction maps on GeneNetwork.org?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_13 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_13 new file mode 100644 index 0000000..d86834d --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_13 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2015 - Cell cycle gene expression networks discovered using systems biology Significance in carcinogenesis.pdf", + "2015 - Identification of candidate genes that underlie the QTL on chromosome 1 that mediates genetic differences in stress-ethanol interactions.pdf", + "2007 - Combinatorial genetic regulatory network analysis tools for high throughput transcriptomic data.pdf", + "2020 - GeneNetwork a toolbox for systems genetics.pdf", + "2017 - GeneNetwork a toolbox for systems genetics.pdf", + "2012 - Aging effects on DNA methylation modules.pdf", + "2016 - Alterations in the expression of a neurodevelopmental gene exert long-lasting effects on cognitive-emotional phenotypes and functional brain networks translational evidence from the stress-resilient Ahi1 knockout mouse.pdf", + "2018 - Metanalysis of genome-wide association studies for panic disorder suggest pathways and mechanisms of pathogenesis.pdf", + "2019 -Evaluation of Sirtuin-3 probe quality and co-expressed genes using literature cohesion.pdf" + ], + "extraction_id": [ + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "5b6d04d2-3aa2-5a43-814a-b13e60e3bb1d", + "26045fea-cd20-5e3d-be07-e8a8e9ca603a", + "f1181fc1-fe08-53b1-bda7-00423a568234", + "4ca2fc9e-7d42-5ea3-b1b7-a296bfbc6a09", + "7dd82b3f-58bd-5915-9eea-250f11412ff2", + "bf37d9e2-c9a3-5886-88db-103264c4cecb", + "ea5fd027-559f-568f-9c4d-a4615730426a", + "434963e5-549e-5986-90a9-cbf4a5f7f06e", + "dab0ce13-0d90-514c-9220-8edd64eceb6c" + ], + "document_id": [ + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "6f354254-4f4d-52ad-bed7-9356f43c0b20", + "eecf4236-efca-577d-ba62-c20c9768950e", + "d9038328-bfea-5f73-87aa-6077b697e4db", + "d11a87ca-4989-59af-95e3-ab90af7d9212", + "682c3a51-0aa5-54a3-a6e7-a09b81c0e8b6", + "cdd3bf57-3c36-5673-bd78-1e53f384d539", + "8cd3e767-17b8-5868-b335-fdb6cc2ff02c", + "e4b2f5dc-6df6-5af8-9ca9-3ccb6518d300", + "0a22eed8-cdda-52de-a73f-d82b3f73b78d" + ], + "id": [ + "chatcmpl-AIGsUMf0eTPsxD8TBs1unBQPLXIKg", + "c63cfaee-749e-547b-9c0a-086266f10670", + "dcb29dfe-ba22-54bc-91f7-af3261a18fd2", + "6beb1115-9f40-555f-a6b4-3c73945101a0", + "6e2695ed-e652-52e1-b896-0bbbb585bb60", + "7ce6c0fe-8b0a-5ce9-83d1-6e6b99b4f24d", + "30e2423f-2b2b-5c7d-8808-b025242fa0c7", + "bd4b772b-4df4-588e-a7bd-2d5d9484f945", + "9bf34d9a-9c54-5376-a38e-7f32daba8107", + "225f0aa2-c185-5b36-923a-a24e545b866f", + "b6b401f6-66c1-5e0d-ab68-09f6f6d7e10f" + ], + "contexts": [ + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "of importance in the emergence of precision medicine ( Curtis, 2015 ; Desautels et al., 2014 ; Glade Bender et al., 2015 ; Jorgensen, 2015 ; Kummar et al., 2015 ; Marquet et al., 2015 ; Rubin, 2014 ) wherein therapeutic strategies need to be aligned with specific properties of tumors. Methods GeneNetwork and WebGestalt GeneNetwork is an open access, online data analysis resource for systems biology and systems genetics. It contains a large number of microarray datasets from multiple tissues of", + "GeneNetwork, a public web source used to study relations amongmarkers, genes, and phenotypes. We made use of large transcriptomedata sets for the amygdala, hippocampus, ventral tegmental area", + "ject to mapping analysis. We examine the connectivity among these sets and analyze the molecular, biochemical and genetic regulatory commonality of connected genes us-ing novel and existing bioinformatics tools. We also develop data-driven hypotheses to explain the mechanisms of genetic perturbations and variation as a means of dening global consequences of individual differences on tissue structure and function. Much of our work is motivated by prior studies of brain gene expression and mRNA", + "including correlation and network analysis to compare associations between tissues and between other rodent or human data sets[32] Many of the Data Sets are amenable to systems genetics mapping and other methods and are accessible at GeneNetwork. The Description and Usage column provides details about the data set and potential", + "including correlation and network analysis to compare associations between tissues and between other rodent or human data sets[32] Many of the Data Sets are amenable to systems genetics mapping and other methods and are accessible at GeneNetwork. The Description and Usage column provides details about the data set and potential", + "weighted gene co-expression network are described in[54]. Consensus network analysis was carried out with Rfunction blockwiseConsensusModules in the WGCNA R package [54]. Our online R software tutorial easily permits the user to identify tissue-specific age related modules and CpGs. Gene ontology enrichment analysis", + "approach employed in the construction of large expression data sets, such as those provided by GeneNetwork,39treats gene expression as a continuous variable across RI strains, rather than asa categorical one (knockout model). Hence, we believe that using these complementary, yet conceptually distinct, approaches enhanced our ability to propose mechanistic insights. A limitation of the current study relates to the non-trivial relationship between structural and functional brain connectivity.4", + "GeneNetwork ( http://www.genenetwork.org ; Williams and Mulligan, 2012)). These databases 180 represent transcriptome datasets for different tissues of recombinant inbred mice. If several probes 181 for the same gene were available, probes with higher maximum likelihood ratio statistic (LRS, a 182 measurement of the association or linkage between differences in traits and differences in particular 183 genotype markers values) were used. 184", + "pathways.TheGeneNetworkdatabaseisauniqueresourceforco-expressionanalysisusingdatafromavarietyof tissues acrossgeneticallydistinctinbredmice.However,extractionofbiologicallymeaningfulco-expressedgenesets ischallengingduetovariabilityinmicroarrayplatforms,probequality,normalizationmethods,andconfounding biologicalfactors.Inthisstudy,wetestedwhetherliteraturederivedfunctionalcohesioncouldbeusedasanobjectivemetricinlieuofgroundtruthtoevaluatethequalityofprobesandmicroarraydatasets." + ], + "answer": "The provided context does not explicitly mention the integration of spatial transcriptomics approaches into GeneNetwork.org. However, it does describe GeneNetwork as a resource for systems biology and systems genetics, which includes large transcriptome datasets from multiple tissues [2], [9]. The platform is used to study relationships among markers, genes, and phenotypes, and to analyze genetic regulatory commonality and tissue structure and function [3], [4]. While spatial transcriptomics is not directly referenced, the existing capabilities of GeneNetwork in handling multi-omics data and performing systems genetics mapping [1], [5] suggest that it could potentially support spatial transcriptomics approaches to enhance understanding of tissue architecture and function.", + "question": "How are spatial transcriptomics approaches being integrated into GeneNetwork.org to enhance understanding of tissue architecture and function?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_14 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_14 new file mode 100644 index 0000000..113f7a8 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_14 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2019 - Novel Genetic Loci Control L5 Vertebral Trabecular Bone and the Response to Low Calcium Intake in Growing BXD Recombinant Inbred Mice.pdf", + "2011 - EXPLOITING NATURAL AND INDUCED GENETIC VARIATION TO STUDY HEMATOPOIESIS.pdf", + "2010 - Genome-wide analysis of transcriptional regulation in the murine liver.pdf", + "2009 - Genetics of the hippocampal transcriptome in mouse a systematic survey and online neurogenomics resource.pdf", + "2007 - Combinatorial genetic regulatory network analysis tools for high throughput transcriptomic data.pdf", + "2009 - Multiscale Genomic Analysis of the Corticolimbic System_ Uncoveri (1).pdf", + "2015 - Exploring multiple quantitative trait loci models of hepatic fibrosis in a mouse intercross.pdf", + "2008 - Type 2 diabetes new genes, new understanding.pdf", + "2008 - Towards systems genetic analyses in barley Integration of phenotypic, expression and genotype data into GeneNetwork.pdf", + "2022 -Chunduri- Drugs Animal Models.pdf" + ], + "extraction_id": [ + "16fdf35c-ab83-53db-9f76-e817326c6067", + "76e22011-da6d-5af7-a74f-2b4d0f11e879", + "957166a3-0298-5324-a24a-02b59ec3427f", + "a47731b3-bb43-5d9c-a7eb-bfea5eea557e", + "47c06e52-1923-58d0-9286-9674893a502a", + "3296b30e-7dd3-576d-a2df-442406caa472", + "121f6744-a773-5a59-b8c7-7e7e85e2b067", + "31a1546b-c160-5b22-a3fb-1e26ab2861c3", + "9b24b22c-0f8c-5b3c-9479-518aa6dfab01", + "b93f76a6-6928-564a-a9a0-149c8ef774d9" + ], + "document_id": [ + "de8dda5e-0e2f-5aa9-bb13-851c526b36a5", + "6f250b15-61b3-57ed-8900-5aa4a173fa8c", + "8a115c1b-662c-5062-b77f-bbde0532bbe9", + "33437cc7-ee4e-59b9-b3e6-ed50eb504b52", + "d9038328-bfea-5f73-87aa-6077b697e4db", + "3d0df5a3-7d7c-5edc-b94d-cae582f59c12", + "464d119c-ba16-5716-8b69-169940f090b4", + "3992c53c-c48c-597d-8d96-70b1026deb70", + "8513abbe-65ed-5f35-9f86-ba93cfc5a194", + "9cfa4f4c-37ce-5c0f-9da6-3bbb075fdc45" + ], + "id": [ + "chatcmpl-AIGsZPO5vngTlUtdx2Hky3vN1AZqv", + "93d79fcd-e841-5cd0-baa7-69ad032cacef", + "248ac859-2589-5afa-824d-e1357bf23e59", + "32497309-fb89-58c9-b828-6a16fa55c11d", + "ea5eaca1-c91d-59f6-af5b-5490749d950a", + "56ba9ce8-4cdd-5d4d-83c1-a370e9c8f959", + "cd33f83f-d19c-5419-a157-c2f1d8148347", + "e5354b88-c1ec-54e1-ab61-c30689e30ea1", + "3fa64113-fa70-575c-81ae-0769dff93a27", + "662c7b64-e34e-5faa-b920-6b59334ef372", + "f9ca5851-0871-54ae-8d01-752c806bd081" + ], + "contexts": [ + "to as quantitative trait loc us (QTL) mapping study. QTL studies inform us region s on the chromosome where existing polymorphisms or SNPs are highly correlated with variation of the trait of interest. With the advancement in DNA sequencing, whole genome database of several mouse strains as well as gene expression data from several tiss ues are available. This allows us to use bioinformatic tools to identify candidate genes with greater confidence for further functional validations .", + "differences, allows for a far more comprehensive understanding of the genetic regulatory links underlying this variation. QTL mapping of gene expression traits allows us to identify eQTLs; genomic regions that have a regulatory effect on those expression traits. Two types of eQTLs can be distinguished, i.e., those that map near (less than 10 Mb from) the gene which encodes the transcript (local ) and those that map elsewhere in the genome ( distant ). 18 Together, local", + "simultaneously. Beginning with a study in yeast (Brem et al. 2002), QTL mapping has been done with gene expression as the phenotype. In such a study, the genomic loci responsible for variation in gene expression can be used to infer regulatory control. While such a study is not conclusive, it can be used to narrow the potential regulatory candidates, generate hypotheses for further testing and construct regulatory networks in s ilico.", + "is that one can now identify large numbers of less strong, second-ary QTLs which were previously lost to background noise, and this information opens up a whole new range of possible analy-ses, such as the identi cation of epistatic interactions ( Figure 5), that promise to uncover pathways of genetic control within the tissue studied. Traditionally, QTL mapping starts with a phenotype of inter-", + "and quantitative trait loci (QTL) regulatory models. A major goal is to identify which,among a set of candidate genes, are the most likely regulators of trait variation. These methods are applied in an effort to identify multiple-QTL regulatory models for large groups of genetically co-expressed genes, and to extrapolate the consequences of thisgenetic variation on phenotypes observed across levels of biological scale through the", + "distal regions into even finer regulatory loci. This influence on gene expression may be the reason why so many classical QTLs have been mapped to Qrr1 . The complexity highlighted by Qrr1 may very well be the rule rather than the exception for loci that modulate complex traits. Efforts to fine -map a single QTL have often been confronted by clusters of multiple small effect QTLs within the original interval (Legare et al., 2000; Demarest et al., 2001) . This poses a serious challenge, and", + "genotypes, availing of genetic markers across the whole genome, and allow the identication of QTLs with signi- cant effects on the disease (Darvasi 1998 ; Manolio 2010 ). QTLs are genetic regions closely linked to a gene with a quantitative effect on the phenotype. QTL mapping is based on the concept that phenotypic differences between inbred mouse strains can be used to demonstrate theimportance of genetic effects on complex phenotypes (Andreux et al. 2012 ; Hillebrandt et al. 2002 ). The standard", + "of the variants within associated loci through expression-quantitative trait locus (eQTL) studies will combine the genetic variation in associate d loci with expression analysis data to define regulatory relationships. Studies designed to understand the functional effect of any causal variants in relevant cell systems and an imal models will give insight to physiological consequence. These advances will underpin efforts to translate the findings through development of diagnostic tests, ris k evaluation and", + "illustrating the potential of functional mapping for effici ently establishing associations between existing QTL, as well as for novel QTL discovery. References 1. Damerval C, Maurice A, Josse JM, De Vienne D: Quantitative trait loci underlying gene product va riation: a novel perspective for analyzing regulation of genome expression. Genetics 1994, 137:289-301. 2. Brem RB, Yvert G, C linton R, Kruglyak L: Genetic dissection of transcriptional regulation in budding yeast. Science 2002, 296:752-755.", + "over a decade ago, using new genometypes for the BXD family of murine strains, as well as new statistical tools, showing that we can identify new quantitative trait loci (QTLs), resulting in highly plausible candidate genes. Quantitative trait locus (QTL) mapping has been carried out in numerous species to associate regions of the genome to phenotypes even before the structure of the genome was well understood (e.g., [ 3]). Rodents, especially mice, have been the species most prominently used for biomedi-" + ], + "answer": "Recent developments in quantitative trait locus (QTL) mapping have significantly refined our understanding of gene regulatory variants in several ways: 1. **Identification of eQTLs**: QTL mapping of gene expression traits allows for the identification of expression quantitative trait loci (eQTLs), which are genomic regions that have a regulatory effect on gene expression traits. These eQTLs can be categorized into local eQTLs, which are located near the gene encoding the transcript, and distant eQTLs, which are located elsewhere in the genome [2]. This distinction helps in understanding the regulatory architecture of the genome. 2. **Increased Resolution and Confidence**: With advancements in DNA sequencing and the availability of whole-genome databases and gene expression data from various tissues, researchers can use bioinformatic tools to identify candidate genes with greater confidence for further functional validations [1]. This enhances the precision of QTL mapping in pinpointing regulatory variants. 3. **Functional Mapping and Hypothesis Generation**: QTL mapping studies, such as those beginning with yeast, have used gene expression as the phenotype to infer regulatory control. Although these studies are not conclusive, they help narrow down potential regulatory candidates, generate hypotheses for further testing, and construct regulatory networks in silico [3]. 4. **Detection of Secondary QTLs and Epistatic Interactions**: Recent developments allow for the identification of large numbers of less strong, secondary QTLs that were previously obscured by background noise. This opens up new possibilities for analyses, such as identifying epistatic interactions, which can reveal pathways of genetic control within the studied tissue [4]. 5. **Integration with Expression Analysis**: The integration of genetic variation in associated loci with expression analysis data through eQTL studies helps define regulatory relationships. This approach provides insights into the physiological consequences of causal variants, aiding in the translation of findings into diagnostic tests and risk evaluation [8]. Overall, these advancements in QTL mapping enhance our understanding of the complex regulatory mechanisms underlying gene expression and trait variation.", + "question": "How do recent developments in quantitative trait locus (QTL) mapping refine our understanding of gene regulatory variants?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_15 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_15 new file mode 100644 index 0000000..5dbdfbc --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_15 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2016 - Next Generation Transcriptomics.pdf", + "2016 - Genetics and Genomics of Coronary Artery Disease..pdf", + "2008 - Combining transcriptional profiling and genetic linkage analysis to uncover gene networks operating in hematopoietic stem cells and their progeny.pdf", + "2007 - Bioinformatics_for_Genetices_MAZEN_SAEED.pdf", + "2007 - Bioinformatics_for_Geneticists.pdf", + "003 -Barnes- Bioinformatics_for_Geneticists.pdf", + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2011 - Human genetics and genomics a decade after the release of the draft sequence of the human genome.pdf", + "2010 - Genome-wide analysis of histone modifications.pdf", + "2011 - Molecular Genomic Research Designs.pdf" + ], + "extraction_id": [ + "b3a2e390-fad2-5afc-b8ca-6f0b57b9cac1", + "203710b7-3267-5ecf-9397-b5becdaeead1", + "767fd341-f407-5322-a932-9b1cecb869e0", + "9515bd16-96d4-5b09-b23c-63a1cc5d19ae", + "522373ca-3ce6-5fe5-b062-ee097f378397", + "c5468773-a09b-510d-bcdf-f685d7714106", + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "3960aec4-df25-57cd-9c60-5561f876a795", + "6b5317f7-aa3f-5dfe-8e50-ef90619b6707", + "d3fe612e-6d4a-5410-9e60-cd2ef8fff897" + ], + "document_id": [ + "56cafe26-2b36-50d6-a5c7-c7d947473b61", + "23a1b7be-9541-5e16-b9cc-24ea420a4961", + "af6e0103-849d-542f-bca7-0251082bc0b3", + "139463d1-c63c-5c51-bf9c-9ccc356768e0", + "4ea8e1a8-e113-5f02-ad78-880b9c51a101", + "045edae8-468b-5725-be06-8cb4b8f6a92b", + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "6d475ac7-7094-5268-96ce-ae8f50f42cd2", + "68bfce04-818d-5122-91c2-13a4a3ba0229", + "ced08e27-8655-59a4-bf63-0ba746f139b7" + ], + "id": [ + "chatcmpl-AIGsiGGBCVBLOOrTQrnGlB4EM7iVd", + "a87fa6ff-4bc0-50ac-b654-f7d734bbbf02", + "66fa4c5e-0b26-5c01-b5ec-d199a4da11bb", + "77ae8cce-6686-5930-a6a1-291143cba4c5", + "9c31e888-0660-507d-927f-e54f98a7248f", + "5935ee2f-4621-577d-8d9b-e47d2d0699e2", + "0f00daa0-2bb4-5a3f-8d51-a1cd2957bef4", + "c63cfaee-749e-547b-9c0a-086266f10670", + "03e25c07-34a0-5b1f-a5f9-ba9a0e2c0d91", + "2e2d861b-4662-5ba5-80e6-ff0e4d9e80b4", + "47eea0dd-b899-5ed2-8b16-150b976f1f0a" + ], + "contexts": [ + "frequent usage of terms like epigenetic or chromatin land-scape. New methods for high-throughput mapping ofgenome-wide histone modifications and protein-DNA inter- actions were developed over the last few years (Blecher-Gonen et al., 2013; Garber et al., 2012). Histone Modifications Associated with Gene EnhancersChromatin can be modulated by covalent histone modifica-", + "orative efforts of the ENCODE Project [ 42] and Roadmap Epigenomics [ 43] consortia have already revealed a compendia of genome-wide histone modification signatures for various regulatory features in multiple primary tissues and cell lines. These datasets have been applied to global mapping studies and databases to prioritize functional regula- tory variants [ 44,45]. While these assays have been employed extensively in LCLs, and tumor cell lines to follow-up auto-", + "genetical genomics) and the genetics of epigeneticscould be studied simultaneously, thus revealing genes that directly or indirectly affect epigenetic gene states. An additional issue that could be addressed by such anapproach is to estimate the percentage of variation in gene expression that can be explained by different epigenetic conformations. The level of complexity could be further increased by including different cell types in the analysis, such as the", + "Incorporating epigenetics into genetic analysis can also enhance the predictive functional analysis of SNPs by highlighting regions of DNA that are accessible or inaccessible to protein binding by transcription factors and other regulatory pro- teins. SNPs may also lead to loss or gain of cytosineguanine dinucleotide (CpG) methylation sites. Rakyan et al. (2004) suggested that such an event might affect the overall methylation prole of a locus and, consequently, promoter activity and gene", + "Incorporating epigenetics into genetic analysis can also enhance the predictive functional analysis of SNPs by highlighting regions of DNA that are accessible or inaccessible to protein binding by transcription factors and other regulatory pro- teins. SNPs may also lead to loss or gain of cytosineguanine dinucleotide (CpG) methylation sites. Rakyan et al. (2004) suggested that such an event might affect the overall methylation prole of a locus and, consequently, promoter activity and gene", + "Incorporating epigenetics into genetic analysis can also enhance the predictive functional analysis of SNPs by highlighting regions of DNA that are accessible or inaccessible to protein binding by transcription factors and other regulatory pro- teins. SNPs may also lead to loss or gain of cytosineguanine dinucleotide (CpG) methylation sites. Rakyan et al. (2004) suggested that such an event might affect the overall methylation prole of a locus and, consequently, promoter activity and gene", + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "374. Bernstein, B.E., Stamatoyannopoulos, J.A., Costello, J.F ., Ren, B. et al. (2010), The NIH Roadmap Epigenomics Mapping Consortium, Nat. Biotechnol. V ol. 28, pp. 10451048. 375. Portela, A. and Esteller, M. (2010), Epigenetic modications and human disease, Nat. Biotechnol. V ol. 28, pp. 10571068. 376. Esteller, M. (2007), Cancer epigenomics: DNA methylomes and histone-modication maps, Nat. Rev . Genet. V ol. 8, pp. 286298. 377. Gilad, Y ., Rifkin, S.A. and Pritchard, J.K. (2008), Revealing the archi-", + "likely to be part of regulatory elements. Our global map of histone marks will serve as an important resource forunderstanding the epigenetic basis of type 2 diabetes. [Supplemental material is available online at http:/ /www.genome.org. The ChIP-seq and gene expression data from this study have been submitted to ArrayExpress (http:/ /www.ebi.ac.uk/microarray-as/ae/) under accession nos. E-MTAB-189 and E-MTAB-191, respectively.] Genetic and epigenetic factors determine cell fate and function.", + "these with other epigenetic mechanisms. This section will describe each method and provide the reader with technologies and recommendations to aide in the design and implementation of an epigenetic study . Histone Modifi cation Analysis Histone modi cation signals can be captured with chromatin immunoprecipita- tion (ChIP), which provides modi cation position approximation on the genome" + ], + "answer": "Incorporating epigenomic data, such as histone modification maps, into the gene networks on GeneNetwork.org has several implications: 1. **Enhanced Functional Analysis**: By integrating epigenetic data, the predictive functional analysis of SNPs can be improved. This is because epigenetic data can highlight regions of DNA that are accessible or inaccessible to protein binding by transcription factors and other regulatory proteins, which can affect gene expression and regulation [4]. 2. **Prioritization of Regulatory Variants**: The incorporation of genome-wide histone modification signatures, as revealed by collaborative efforts like the ENCODE Project and Roadmap Epigenomics, allows for the prioritization of functional regulatory variants. This can be particularly useful in mapping studies and databases, enhancing the understanding of regulatory features in various tissues and cell lines [2]. 3. **Understanding Gene Expression Variation**: By studying the genetics of epigenetics, it is possible to reveal genes that directly or indirectly affect epigenetic gene states. This approach can help estimate the percentage of variation in gene expression that can be explained by different epigenetic conformations, thus providing a deeper understanding of gene regulation [3]. 4. **Resource for Predictive Medicine and Systems Genetics**: The integration of multi-omics data, including epigenomic data, makes GeneNetwork.org a valuable resource for predictive medicine and systems genetics. This integration supports more comprehensive analyses and enhances the platform's utility for research and clinical applications [7]. Overall, incorporating epigenomic data into GeneNetwork.org enriches the platform's analytical capabilities, offering deeper insights into gene regulation and expression, and supporting advanced research in genetics and epigenetics.", + "question": "What are the implications of incorporating epigenomic data, such as histone modification maps, into the gene networks on GeneNetwork.org?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_16 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_16 new file mode 100644 index 0000000..c056ae4 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_16 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2015 - Insights into Sex Chromosome Evolution and Aging from the Genome of a Short-Lived Fish.pdf", + "2021 - Modern Statistical Methods for Genetics and Genomic Studies.pdf", + "2009 - Processing Large-Scale, High-Dimension Genetic and Gene Expression Data.pdf", + "2018 - The genetic architecture of type 1 diabetes mellitus.pdf", + "2015 - Selecting causal genes from genome-wide association studies via functionally coherent subnetworks.pdf", + "2009 - Processing Large-Scale, High-Dimension Genetic and Gene Expression Data.pdf", + "2009 - Rare Variants of IFIH1, a Gene Implicated in Antiviral Responses, Protect Against Type 1 Diabetes.pdf", + "2013 - Pathways, Networks and Systems Medicine Conferences.pdf", + "2009 - Loss of A-type lamins and genomic instability.pdf", + "2020 - Functional Genomics in Pancreatic \u03b2 Cells Recent Advances in Gene Deletion and Genome Editing Technologies for Diabetes Research.pdf" + ], + "extraction_id": [ + "516bdde0-cafe-5eac-a627-32ef9e262e32", + "9309edf2-5e2d-5567-ae78-f6681b866410", + "3276b251-2e60-53e8-8fd1-07702f486a43", + "5b8b3673-7fd4-5989-9982-a6d5ea374c8d", + "46616368-74e6-5605-9e43-9789e8e1bea1", + "3276b251-2e60-53e8-8fd1-07702f486a43", + "9893879f-6b73-5dc6-b274-f48ba8163644", + "4cdc439f-bd23-5978-9f34-a34e1cb33cf4", + "a6f16fce-1813-5d38-899f-6eb04c7d0007", + "f4955281-f174-562e-bc8a-170b701beffc" + ], + "document_id": [ + "def32424-2f9d-5c4b-9c03-be2d8bd53a24", + "6acebf19-b80c-5352-8201-99d5634fcc80", + "17264155-b665-59db-94cb-f4d67eac20fc", + "341261db-b38a-5bd2-8d8d-fc04a0b3da30", + "af43f4ac-7211-52f0-8f6b-e4bde73bbe4a", + "17264155-b665-59db-94cb-f4d67eac20fc", + "7d9d5ce7-7bfe-5fe2-a325-fe97db015a10", + "b50a9732-7d01-5d4d-8f33-a9d43dbc7df3", + "eab22335-5688-5e37-9f65-c4b58d6d95c8", + "51350055-d53c-5692-ab53-337b8a8bafd6" + ], + "id": [ + "chatcmpl-AIGsp8i4dh5GAWf5RuA0Au0DHwVdV", + "996bbbd1-c605-5733-bf20-42367be2244c", + "c7834fbb-eb9a-5f55-ac26-67af248e7179", + "c2dae4f8-2305-5d4a-a3f8-c0424d4b80b1", + "5543c99f-6542-55b8-b62c-e34a03b9c2fe", + "33dc52df-73a5-514e-8edb-33ae5046b8af", + "e26ebc1e-e05d-56fb-8718-604275994a84", + "fb3452e6-4584-5c3f-92de-9bc44d30b21c", + "a02b4589-65ec-50e1-9849-090971ddb2b0", + "38405f05-93df-579e-a8e4-c6b0c13e86a6", + "67365e1f-c588-56b5-aae0-44604958f8e1" + ], + "contexts": [ + "genomes. Hence, chromosomal and spatial co-localization in the nucleus may indicate co-regulation. It was previously shown that 3D chromatin structure couples nuclear compartmentaliza-tion of chromatin domains with the control of gene activity ( Gue- len et al., 2008 ) and thus contributes to cell-specic gene expression ( Zullo et al., 2012 ). In this context, it is noteworthy that cellular senescence is associated with modications of theglobal chromatin interaction network ( Chandra et al., 2015 ). To", + "2 Introduction Recent scientific advances have enabled the identification of functional genomic elements through a diverse set of functional annotations, including proteins functional scores (1, 2) , evolutionary conservation scores (3-5), and epigenetics scores from the Encyclopedia of DNA Elements (ENCODE) (6). Other initiatives such as the R oadmap Epigenomics project (7) and FANTOM5 project (8, 9) also provide evidence for potential regulatory v ariants in the human", + "accuracy of predictive networks [40, 5153]. We have also recently demonstrated how this class of network can be used to inform associations identied in GW Astudies [40]. 9 Summary The signicant challenge we face in the post-genome era is deciphering the bio-logical function of individual genes, pathways, and networks that drive complexphenotypes like disease. The availability of low-cost, high-throughput technologies", + "a growing awareness that the three-dimensional juxtaposition of DNAregions within nuclei means that genes can be regulated by regulatory elements that are located at some distance from the gene ( Fig. 5 ) (Javierre et al., 2016 ;Kadauke and Blobel, 2009 ). As a result of this, disease associated SNPs have been shown to fall in gene regulatory elements ( Chen and Tian, 2016; Fadason et al., 2017; Farh et al., 2014; Lee et al., 2014; Schierding et al., 2015 ).", + "network. Cell 9, 12121226 (2014). 12. Hirschhorn, J.N. Genomewide association studiesilluminating biologic pathways. N. Engl. J. Med. 0, 16991701 (2009). 13. Cantor, R.M., Lange, K. & Sinsheimer, J.S. Prioritizing GWAS results: a review of statistical methods and recommendations for their application. Am. J. Hum. Genet. 8, 622 (2010). 14. Lee, I., Date, S.V., Adai, A.T. & Marcotte, E.M. A probabilistic functional network of yeast genes. Science 0, 15551558 (2004).", + "Processing Large-Scale, High-Dimension Genetic 325 another. We anticipate these types of networks becoming increasingly important in the human genetics space to gain a mechanistic understanding of how a given DNAperturbation induces changes in one or more genes that go on to affect networks that cause disease. The integration of genotypic and expression and other data have recently been shown, in a Bayesian network framework [76], to enhance the overall", + "regions correlated with functional noncoding elements, including enhancers, better than did regions identified solely on the basis of nucleotide sequence. These results support the idea that the molecular shape of DNA is under selection and can identify evolutionary history. Genomic sequences that code for proteinsare relatively well understood but make up only ~2% of the human genome ( 1). Many functions are encoded in the remaining ~98% noncoding portion of the genome, but little", + "gene, and the first f unctional anti -sense miRNA, Lastly, we have used comparative genomics to infer regulatory networks based on individual conserved instances of regulatory motifs, which show functional enrichments similar and sometimes higher to genome -scale experimental met hods such as ChIP -chip. As part of the ENCODE and modENCODE projects, we are now studying dynamics of developmental and cell -differentiation networks in", + "References 1. Cremer T, Cremer M, Dietzel S, Muller S, Solovei I, Fakan S. Chromosome territoriesa function-al nuclear landscape. Curr Opin Cell Biol 2006; 18:307-16. 2. Misteli T. Beyond the sequence: cellular organization of genome function. Cell 2007; 128:787-800. 3. Schneider R, Grosschedl R. Dynamics and interplay of nuclear architecture, genome organization and gene expression. Genes Dev 2007; 21:3027-43.", + "enhancers in the control of cell identity and disease. Cell(2013) 155:934 47. doi: 10.1016/j.cell.2013.09.053 45. de Wit E, de Laat W. A decade of 3C technologies: insights into nuclear organization. Genes Dev (2012) 26:11 24. doi: 10.1101/gad.179804.111 46. Schmitt AD, Hu M, Ren B. Genome-wide mapping and analysis of chromosome architecture. Nat Rev Mol Cell Biol (2016) 17:743 55. doi: 10.1038/nrm.2016.104 47. Javierre BM, Burren OS, Wilder SP, Kreuzhuber R, Hill SM, Sewitz S, et al." + ], + "answer": "Recent findings on 3D genome organization have significantly enhanced our understanding of functional genomic networks in several ways: 1. **Co-regulation through Spatial Organization**: The 3D chromatin structure is known to couple nuclear compartmentalization of chromatin domains with the control of gene activity, which contributes to cell-specific gene expression [1]. This spatial organization within the nucleus suggests that chromosomal and spatial co-localization may indicate co-regulation of genes, thereby influencing functional genomic networks. 2. **Regulation by Distant Elements**: There is a growing awareness that the three-dimensional juxtaposition of DNA regions within nuclei allows genes to be regulated by elements located at a distance from the gene itself [4]. This understanding helps explain how disease-associated SNPs can fall within gene regulatory elements, thus affecting genomic networks and potentially leading to disease. 3. **Integration with Functional Annotations**: Advances in identifying functional genomic elements through various annotations, such as those from the ENCODE project, have been complemented by insights into 3D genome organization. This integration helps in identifying potential regulatory variants and understanding their roles within genomic networks [2]. These findings collectively contribute to a more comprehensive understanding of how genes are regulated within the complex spatial architecture of the genome, thereby enhancing our knowledge of functional genomic networks.", + "question": "How do recent findings on 3D genome organization contribute to our understanding of functional genomic networks?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_17 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_17 new file mode 100644 index 0000000..831f26c --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_17 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2012 - Genome-Scale Studies of Aging Challenges and Opportunities.pdf", + "2007 - How to infer gene networks from expression profiles.pdf", + "2009 - Detection and interpretation of expression quantitative trait loci (eQTL).pdf", + "2011 - Annotating individual human genomes.pdf", + "2007 - Classification of microarray data using gene networks.pdf", + "2015 - Biological network inference from microarray data, current solutions, and assessments.pdf", + "2019 - Systems genetics approaches to probe gene function.pdf", + "2022 - Using Recurrent Neural Networks for Predicting Type-2 Diabetes from Genomic and Tabular Data.pdf", + "2007 - How to infer gene networks from expression profiles.pdf", + "2009 - Processing Large-Scale, High-Dimension Genetic and Gene Expression Data.pdf" + ], + "extraction_id": [ + "53c57cc4-4d43-505a-974c-442d06e144df", + "1b4abf11-ed4b-5169-9ba9-8569bc5c10f7", + "223e442e-898d-5aea-866a-5cdc0ac915e8", + "070421c2-5d23-58b3-9d85-53dd58e7abae", + "df700ffb-556a-5331-afe6-71f7e77a1fb8", + "c15261b7-54b9-534f-ac95-17c7a5543f31", + "f46459a1-592e-5d14-a6d1-f93211353db0", + "29c89d19-3215-54dc-9723-85f96de02b65", + "d4d71d8c-ef2f-5ddb-b3f3-0f5ce8dc0a83", + "3276b251-2e60-53e8-8fd1-07702f486a43" + ], + "document_id": [ + "b77aace0-fa36-5fd4-8e2a-c8932198acd1", + "5067a047-b97d-522a-9a7e-5372e3bbd102", + "ef974b09-4ea2-5382-85e5-c2169f440fda", + "f7b5d738-3f0b-5074-9c21-f6b443b4e07f", + "639e0456-a445-5e2e-adf5-8eaf987ce2d1", + "f64cf13c-d989-50da-be0d-81e34a735a42", + "1cd18d9c-0fd1-52e3-b0cf-c5e3ad0ff683", + "be0e50e0-3de8-53c5-8126-a0b618647f80", + "5067a047-b97d-522a-9a7e-5372e3bbd102", + "17264155-b665-59db-94cb-f4d67eac20fc" + ], + "id": [ + "chatcmpl-AIGsxUUcXG8q6ZckzX5v3uoIBTYQl", + "df726361-271a-5dbb-b6d1-03dab5a63006", + "ee9014b2-ff70-50d1-a022-7a5792383700", + "6d8b4af6-6baf-58ff-9e1d-003862f53edd", + "e8279254-6a66-5be6-b6ae-c11c20e242f9", + "137c8fc7-7bc2-543f-a43e-7f819eaaaaa9", + "394f5f79-0592-52ff-bc83-ea55a95fd17e", + "b54b5584-344c-54e5-9442-a7deb099bc76", + "09f8c37f-b150-5f07-8275-bd040787f514", + "3152b693-2396-5441-b6ff-6a80eac13ad0", + "c2dae4f8-2305-5d4a-a3f8-c0424d4b80b1" + ], + "contexts": [ + "[111], and for generation of networks based on known gene interactions such as GeneMania [112] and Cytoscape [113], as well as for identifying cross-species orthology relation-ships [114], network-based thinking has been increasingly applied to the study of aging and lifespan [115-118]. Re-cently, the novel computational method of network identifi- cation by regression (NIR) [119] has been used to identify", + "Here we will focus on gene network inference algorithms (the inuence approach). A description of other methods based on the physical approach and more details oncomputational aspects can be found in (Beer and Tavazoie,2004; Tadesse et al, 2004; Faith and Gardner, 2005; Prakash and Tompa, 2005; Ambesi and di Bernardo, 2006; Foat et al, 2006). We will also briey describe two improper reverse-engineering tools (MNI and TSNI), whose main focus is not", + "NIA[360] may help to infer a putative function by linking unkn own genes to genes known from previous studies to show a similar e xpres- sion pattern. We can also characterize unknown genes by thei r evolu- tionary, loss-of-function and network interaction proper ties to prioritize candidate variants[184] and even predict disease inherita nce mode to a certain degree[153]. Taking this approach a step further, GeneNetwork[99] is con structed", + "network inference techniques can be utilized to infer biologicalprocess and the potential phenotypic impact of variants in genes of unknown function [71 78]. Thus, pathway and network based annotation approaches can be powerful approaches to inferring phenotypic information where direct links to phenotype do not exist. 2.12. De novo association analyses involving multiple genomes In the absence of prior information one might leverage to annotate", + "interaction may be difficult to quantify. Conversely the directions and signs that accompany signalling or regula- tory pathways are generally known, but their incorpora- tion requires more work. It could nevertheless lead to important advances for the interpretation of microarray data in cancer studies, for example. Conclusion We have presented a general framework to analyse gene expression data when a gene network is known a priori . The approach involves the attenuation of the high-fre-", + "A number of techniques have been proposed for network inference. Existing techniques for nding gene networks can be broadly cate-gorized as (i) computational approaches, and (ii) literature-based approaches. The computational approach mainly uses statistical, machine learning, or soft-computing techniques [ 14,15] as discov- ery tools. On the other hand, a literature-based approach gathers relevant published information on genes and their interrelation-", + "addition, data from linkage or association studies (e.g. GWAS), or from high -throughput genetic screening experiments (e.g. CRISPR screening), or from animal gain -or-loss- of function studies, or from the gene -drug interactions, can also be exploited to predict potential gene functions. Integration of GeneBridge with data from these sources will further enhance the performance for gene function prediction, as is done in STRING [253], GeneMANIA [254] and Mitocarta [190, 255].", + "include the deep learning-driven pattern recognition models for analyzing the gene se- quences for identifying the possible future illness and developing mobile applications that can generalize the information from the genomic data. However, there is great demand for explainable Articial Intelligence models that are interpretable in decision-making. Author Contributions: The authors contributions are as follows, Conceptualization of the study,", + "Gene network inference algorithms are becoming accurate enough to be practically useful, at least when steady-state gene expression data are available, but efforts must be directedin assessing algorithm performances. In a few years, gene network inference will become as common as clustering for microarray data analysis. These algorithms will become moreTable IV Results of the application of network inference algorithms on the experiment data sets Data sets ARACNE BANJO NIR Clustering Random", + "accuracy of predictive networks [40, 5153]. We have also recently demonstrated how this class of network can be used to inform associations identied in GW Astudies [40]. 9 Summary The signicant challenge we face in the post-genome era is deciphering the bio-logical function of individual genes, pathways, and networks that drive complexphenotypes like disease. The availability of low-cost, high-throughput technologies" + ], + "answer": "Artificial intelligence (AI) has several potential applications in improving the annotation and interpretation of gene networks: 1. **Inference of Gene Functions**: AI techniques, such as network inference algorithms, can help infer the putative functions of unknown genes by linking them to genes with known functions that exhibit similar expression patterns. This approach can also prioritize candidate variants and predict disease inheritance modes to some extent [3]. 2. **Network Inference Techniques**: AI-driven network inference techniques can be utilized to infer biological processes and the potential phenotypic impact of variants in genes of unknown function. These techniques can provide powerful approaches to inferring phenotypic information where direct links to phenotype do not exist [4]. 3. **Computational Approaches**: AI, particularly through computational approaches using statistical, machine learning, or soft-computing techniques, serves as a discovery tool for finding gene networks. These approaches can complement literature-based methods that gather published information on genes and their interrelationships [6]. 4. **Pattern Recognition and Predictive Modeling**: Deep learning models, a subset of AI, can be used for pattern recognition in gene sequences to identify potential future illnesses. There is also a demand for explainable AI models that are interpretable in decision-making, which can enhance the understanding and application of genomic data [8]. These applications demonstrate how AI can significantly enhance the annotation and interpretation of gene networks by providing insights into gene functions, biological processes, and potential phenotypic impacts.", + "question": "What are the potential applications of artificial intelligence in improving the annotation and interpretation of gene networks?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_18 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_18 new file mode 100644 index 0000000..d22d56b --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_18 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2022 - System genetics in the rat HXBBXH family identifies Tti2 as a pleiotropic quantitative trait gene for adult hippocampal neurogenesis and serum glucose.pdf", + "2021 - System genetics in the rat HXBBXH family identifies Tti2 as a pleiotropic quantitative trait gene for adult hippocampal neurogenesis and serum glucose.pdf", + "2010 - One Hundred Years of Pleiotropy A Retrospective.pdf", + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2012 - Using Genome-Wide Expression Profiling to Define Gene Networks Relevant to the Study of Complex Traits From RNA Integrity to Network Topology.pdf", + "2014 - Mendelian randomization genetic anchors for causal inference.pdf", + "2020 - Multivariate genomic scan implicates novel loci.pdf", + "2018 - The Use of Recombinant Inbred Strains in Systems Genetics and Functional Analyses in Behavioral Pharmacology.pdf", + "2022 - System Genetics in the Rat Family.pdf", + "2022 -Senko- Hippocampal neurogenesis serum glucose.pdf" + ], + "extraction_id": [ + "2557b3fa-5aed-53f2-a4ca-afbed6154346", + "6b791cd6-0d92-52fb-ac76-d3b0bb4ed535", + "9b6ebb70-4cc0-5f53-bbbb-815ea191f2fa", + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "3e0c2a06-e6de-5888-a360-a2c483d9f744", + "3ac0a087-d982-5d06-b351-d2f1e635c5b0", + "a053b8da-7ec4-5c4f-b4cc-4005e7792d1a", + "3b23d583-7046-5dce-a506-fab0c2752977", + "38cbdb87-820c-587e-9511-69d0ba74457a", + "2e135c0b-af2c-54fa-8661-aa4a3e31c0da" + ], + "document_id": [ + "4198ec53-60f1-55d1-8759-b9ede1d098c0", + "9ab8b190-fb4f-5bb0-8d04-1cd07a42192a", + "c0995711-1389-52b7-a7a9-c92e5709fe43", + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "1eb6f5b7-a3bc-5455-91f0-6f2eb37be861", + "05a32734-5dff-5430-b383-72a3d2e03792", + "8529f0c6-a65b-53ed-9663-02d52dd82631", + "337b2462-f1ec-530a-84de-97b13a0b9446", + "426b5aeb-1550-5039-8f2a-bd83d17c8648", + "bac2ab98-4317-59ed-99ef-deda8c22786d" + ], + "id": [ + "chatcmpl-AIGt6tExGqoQTRXd4fPWOb4MUvYWu", + "b3bb8c8a-a222-5b62-94c5-54910d338fa7", + "da910108-9a4b-5482-a4cb-bdb969cf959c", + "29d6e248-c012-56f7-85c5-1ee104731db0", + "c63cfaee-749e-547b-9c0a-086266f10670", + "27bb3941-5a92-56a2-b67d-c5e64603c1a3", + "6c9146cb-b00f-5f4c-8fc0-5a15a41405ec", + "89a8170c-a7b5-5236-8ef3-7d0e6918e584", + "12cdef3c-ff25-5349-8ef8-44f08065de4a", + "a62e58c3-d1a6-54e9-809f-d98488089738", + "6d34c5df-c9e5-5b22-b2af-2c1f191d984f" + ], + "contexts": [ + "920 Diabetologia. 2020;63: 977986. doi:10.1007/s00125-020-05101-y 921 9. Stearns FW. One hundred years of pleiotropy: A retrospective. Genetics. Genetics; 922 2010. pp. 767773. doi:10.1534/genetics.110.122549 923 10. Geiler-Samerotte KA, Li S, Lazaris C, Taylor A, Ziv N, Ramjeawan C, et al. Extent and 924 context dependence of pleiotropy revealed by high-throughput single-cell phenotyping. 925 PLoS Biol. 2020;18. doi:10.1371/journal.pbio.3000836", + "920 Diabetologia. 2020;63: 977986. doi:10.1007/s00125-020-05101-y 921 9. Stearns FW. One hundred years of pleiotropy: A retrospective. Genetics. Genetics; 922 2010. pp. 767773. doi:10.1534/genetics.110.122549 923 10. Geiler-Samerotte KA, Li S, Lazaris C, Taylor A, Ziv N, Ramjeawan C, et al. Extent and 924 context dependence of pleiotropy revealed by high-throughput single-cell phenotyping. 925 PLoS Biol. 2020;18. doi:10.1371/journal.pbio.3000836", + "advances, the more examples become known which canbe explained only under the assumption of pleiotropy (Plate 1910, quoted from M cKusick 1976, pp. 301302). His assertion of the extent and importance of pleiotropyhas been a central theme that has been challenged andstrengthened throughout the past 100 years as the way inwhich we study pleiotropy has changed. DEVELOPMENT OF PLEIOTROPIC RESEARCH One of the rst experimental studies of the mecha-", + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "users can take advantage of a systems genetics approach (Rosen et al., 2003, 2007). While the candidate gene approach asks which one gene mutation causes a particular disease, the systems genetics approach explores which phenotypes and diseases result from diverse sets of genetic and molecular markers (Rosen et al., 2003, 2007). The majority of data sets in GeneNetwork are collected from GRPs consisting of hundreds of diverse, inbred strains of", + "34. Pyeritz, R.E. (1989) Pleiotropy revisited: molecular explanations of a classic concept. Am. J. Med. Genet. ,34, 124134. 35. Gruneberg, H. (1938) An analysis of the pleiotropic effects of a lethal mutation in the rat. Proc. R. Soc. Lond. B. ,125, 123144. 36. Wagner, G.P. and Zhang, J. (2011) The pleiotropic structure of the genotypephenotype map: the evolvability of complex organisms. Nat. Rev. Genet. ,12, 204213. 37. Solovieff, N., Cotsapas, C., Lee, P.H., Purcell, S.M. and Smoller, J.W.", + "21. Byars, S. G. et al. Genetic loci associated with coronary artery disease harbor evidence of selection and antagonistic pleiotropy. PLoS Genet. 13, e1006328 (2017). 22. Rodrguez, J. A. et al. Antagonistic pleiotropy and mutation accumulation inuence human senescence and disease. Nat. Ecol. Evol. 1, 0055 (2017). 23. Institute for Health Metrics and Evaluation. Findings from the Global Burden of Disease Study 2017 (IHME, 2018).", + "traits can be due to shared molecular mechanisms and processes (true gene pleiotropy)or covariance can be due to statistical error or to linkage of neighboring, but mechanis-tically independent gene variants. This latter effect is particularly serious and is described in more length by Gerlai 4and in Wang5in the context of RI strains. GeneNetwork GeneNetwork (GN, www.genenetwork.org ) is an open web resource that enables", + "2019;20 .https://doi.or g/10.118 6/s13059 -019-1628-0 PMID: 30678704 19. Chesmo reK,Bartlett J,Williams SM.Theubiquity ofpleiotropy inhuman disease. Hum Genet. 2018; 137: 3944. https://doi.or g/10.100 7/s00439 -017-1854 -zPMID: 29164333 20. Bulik-Sulli vanB,Finucane HK,Anttila V,Gusev A,DayFR,LohPR,etal.Anatlas ofgenetic correla- tions across human diseases andtraits. NatGenet 2015 4711. 2015; 47:12361241. https://doi.or g/ 10.1038 /ng.3406 PMID: 26414676", + "2019;20 .https://doi.or g/10.118 6/s13059 -019-1628-0 PMID: 30678704 19. Chesmo reK,Bartlett J,Williams SM.Theubiquity ofpleiotropy inhuman disease. Hum Genet. 2018; 137: 3944. https://doi.or g/10.100 7/s00439 -017-1854 -zPMID: 29164333 20. Bulik-Sulli vanB,Finucane HK,Anttila V,Gusev A,DayFR,LohPR,etal.Anatlas ofgenetic correla- tions across human diseases andtraits. NatGenet 2015 4711. 2015; 47:12361241. https://doi.or g/ 10.1038 /ng.3406 PMID: 26414676" + ], + "answer": "The study of genetic pleiotropy has been advanced by data available on GeneNetwork.org through several key developments: 1. **Multi-Omics Analysis and Data from Multiple Species**: GeneNetwork.org has incorporated data from 10 different species and supports multi-omics analysis, which allows researchers to explore genetic pleiotropy across a wide range of organisms and biological data types. This comprehensive approach provides a richer understanding of how genes can influence multiple traits or diseases [4]. 2. **Systems Genetics Approach**: The platform enables a systems genetics approach, which contrasts with the traditional candidate gene approach. Instead of focusing on single gene mutations, it explores the relationships between diverse genetic and molecular markers and their resulting phenotypes and diseases. This approach is particularly useful for studying pleiotropy, as it considers the complex interactions and shared pathways that can lead to multiple phenotypic effects from a single genetic locus [5]. 3. **Open Web Resource**: GeneNetwork.org is an open web resource, making it accessible to a wide range of researchers. This accessibility facilitates collaborative research and data sharing, which are crucial for advancing the study of pleiotropy by allowing researchers to build on each other's findings and methodologies [8]. These features collectively enhance the ability to study genetic pleiotropy by providing comprehensive data, advanced analytical tools, and a collaborative platform for researchers.", + "question": "How has the study of genetic pleiotropy been advanced by data available on GeneNetwork.org?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_19 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_19 new file mode 100644 index 0000000..2f45bff --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_19 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2009 - The Human Ageing Genomic Resources online.pdf", + "2012 - Genome-Scale Studies of Aging Challenges and Opportunities.pdf", + "2012 - Systems Biology in Aging Linking the Old and the Young.pdf", + "2012 - Genome-Environment Interactions That Modulate.pdf", + "2020 - Multivariate genomic scan implicates novel loci.pdf", + "2019 - Bioinformatic prediction of critical genes and pathways.pdf", + "2020 - Mitonuclear genomics and aging.pdf", + "2012 - Genome-Environment Interactions That Modulate.pdf", + "2012 - Genome-Environment Interactions That Modulate.pdf", + "2012 - Systems Biology in Aging Linking the Old and the Young.pdf" + ], + "extraction_id": [ + "aecbe8a8-aeed-5cfa-b0f3-be29f19d849d", + "53c57cc4-4d43-505a-974c-442d06e144df", + "e26cef53-9a67-508e-8a29-2f40a6aa45b0", + "a01ca925-4ccf-5863-a162-7bd4c754fe89", + "a053b8da-7ec4-5c4f-b4cc-4005e7792d1a", + "4109e561-4721-5f4e-b4d5-4353f8d1741d", + "e6fb876b-e91c-505a-aa16-7b428ec61f10", + "d59d7882-333d-5576-86ab-3cfa6354b946", + "df213743-7428-59be-ba19-2563f8ce5c70", + "a74345ec-ceee-5290-990b-ea338e735937" + ], + "document_id": [ + "e43cd3b6-ad8e-5422-ba7c-ceb6e66cc529", + "b77aace0-fa36-5fd4-8e2a-c8932198acd1", + "cf7a8c59-4b4d-5e04-94b6-dd97edcb47a8", + "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "8529f0c6-a65b-53ed-9663-02d52dd82631", + "01201944-11f2-52d9-ac3e-7af685d4a4c4", + "e05fdc09-c8d8-5134-a1fd-bf07a1564981", + "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "cf7a8c59-4b4d-5e04-94b6-dd97edcb47a8" + ], + "id": [ + "chatcmpl-AIGtEMdN8awavmFIcxxBrdyWkpsf8", + "496d27de-6dd0-5f6a-bedb-64d4c252981d", + "df726361-271a-5dbb-b6d1-03dab5a63006", + "300065ff-2ddb-532e-ab5d-a9b0903c8d21", + "4d6876c5-9226-587c-8d3e-d4957ee42dba", + "15f6d690-61b1-5de3-ac40-10e46777afa8", + "9f662099-6f46-5af7-a6c1-4d0945b9a931", + "c96b67f8-ad31-50fd-b053-07b127938ef2", + "786d2756-4c4d-5ac0-8d3d-63f914d51664", + "a05a46db-5443-566c-9494-212f86ee2eb3", + "016ee489-a313-5648-803d-db50217ae084" + ], + "contexts": [ + "the different pathways linked with aging and even study genenetworks. In such works, GenAge is an adequate resource asit provides a framework for the functional genomics of aging.For example, Xue et al . (2007) used GenAge to construct a modular network of aging and obtain insights into aging, including thefact that genes connecting different modules are more likely toaffect longevity and/or aging, an hypothesis the authors validatedexperimentally in worms (Xue et al", + "[111], and for generation of networks based on known gene interactions such as GeneMania [112] and Cytoscape [113], as well as for identifying cross-species orthology relation-ships [114], network-based thinking has been increasingly applied to the study of aging and lifespan [115-118]. Re-cently, the novel computational method of network identifi- cation by regression (NIR) [119] has been used to identify", + "network analysis is a useful approach toward identifying genetic determinants of longevity . PLoS One , 2008 , 3(11), e3802. [38] Bell, R.; Hubbard, A.; Che ttier, R.; Chen, D.; Miller, J.P.; Kapahi, P.; Tarnopolsky, M.; Sahasrabuhde, S.; Melov, S.; Hughes, R.E. A human protein interaction network shows conservation of aging processes between human and invertebrate species . PLoS Genet , 2009 , 5(3), e1000414. [39] Budovsky, A.; Abramovich, A.; Cohen, R.; Chalifa-Caspi, V.;", + "genes (http://genomics.senescence.info/genes/), more than700 genes have been identified that regulate lifespan inmodel organisms (de Magalha es et al., 2009a). Many ofthese genes and their associated pathwayssuch as theinsulin/IGF1/GH pathwayhave been shown to affect lon-gevity across different model organisms (Kenyon, 2010).Therefore, at least some mechanisms of aging are evolu-tionarily conserved and may have potential therapeuticapplications (Baur et al., 2006). For example, evidencesuggests the use of", + "30. Vartiainen, S., Aarnio, V., Lakso, M. & Wong, G. Increased lifespan in transgenic Caenorhabditis elegans overexpressing human -synuclein. Exp. Gerontol. 41, 871 876 (2006). 31. Lpez-Otn, C. et al. The hallmarks of aging. Cell153, 1194 1217 (2013). 32. Kenyon, C. J. The genetics of ageing. Nature 464, 504 512 (2010). 33. Liberzon, A. et al. The molecular signatures database hallmark gene set collection. Cell Syst. 1, 417 425 (2015).", + "1118 compared to young ones. Overall, our results revealed that six pathways and six key genes might play pivotal roles in regulating longevity, and three interacting genes might be implicated in longevity. The results will not only provide new insight into the mechanisms of longevity, but also provide novel ideas for network-based approaches for longevity-related research. Keywords Drosophila melanogaster Longevity Gene Pathway Network Introduction", + "During the past century, remarkable progress has been made in unveiling the mechanisms of aging. Genetic and molecular pathways that regulate healthspan and lifespan have been identified in various model organisms, provid-ing a rich knowledge base (Longo etal. 2015; Lopez-Otin etal. 2013, 2016; Singh etal. 2019). However, the focus on", + "In addition to aging- and CR-related genes, another source of candidate genes and pathways for drug designare human longevity-associated genes (Barzilai andShuldiner, 2001; Browner et al., 2004; Kenyon, 2010).Dozens of genes have now been associated with humanlongevity (de Magalha es et al., 2009a), although only ahandful of genes have been shown to have consistenteffects across populations. Many longevity-associated genes are related to spe-", + "been associated with human longevity in genetic asso-ciation studies. The parallel emergence of networkapproaches offers prospects to develop multitargetdrugs and combinatorial therapies. Understandinghow the environment modulates aging-related genesmay lead to human applications and disease therapiesthrough diet, lifestyle, or pharmacological interven-tions. Unlocking the capacity to manipulate humanaging would result in unprecedented health benefits. I. Introduction", + "Network approaches are instrumental in discerning global properties of aging/lifespan regulators, making com- putational predictions and inferring the modularity and rela-tionships of various aging regulators. However, they should be applied with great caution as to avoid bias introduced by the literature, the lack of spatial and temporal information, or the limited coverage of the network [44]. 4. EPIGENETIC REGULATION OF AGING In addition to gene expression changes, the states of epi-" + ], + "answer": "GeneNetwork.org studies have identified novel genetic pathways related to aging and lifespan through various approaches. One notable method is the use of network identification by regression (NIR), which has been applied to identify novel pathways in the context of aging and lifespan [2]. Additionally, network-based approaches have revealed six pathways and six key genes that might play pivotal roles in regulating longevity, providing new insights into the mechanisms of longevity [6]. These findings highlight the potential of network-based methods to uncover novel genetic pathways associated with aging and lifespan.", + "question": "What novel genetic pathways have been identified in GeneNetwork.org studies related to aging and lifespan?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_2 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_2 new file mode 100644 index 0000000..ea55f4a --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_2 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2016 - Putting the Genome in Context Gene-Environment Interactions.pdf", + "2016 - Putting the Genome in Context Gene-Environment Interactions.pdf", + "2012 - Using Genome-Wide Expression Profiling to Define Gene Networks Relevant to the Study of Complex Traits From RNA Integrity to Network Topology.pdf", + "2008 - Genetic Effects on Environmental Vulnerability to Disease Novartis Foundation Symposium 293.pdf", + "2014 - Systems Genetics of Liver Fibrosis Identification of Fibrogenic and Expression Quantitative Trait Loci in the BXD Murine Reference Population.pdf", + "2012 - Gene-Environment Interactions in the Development of Type 2 Diabetes.pdf", + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2008 - Genetic Effects on Environmental Vulnerability to Disease Novartis Foundation Symposium 293.pdf", + "2001 - Demography in the age of genomics.pdf" + ], + "extraction_id": [ + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "9639b49f-d3be-5592-bb0e-a0341a7caa06", + "c362793d-c70f-5225-afe5-88098042daef", + "3e0c2a06-e6de-5888-a360-a2c483d9f744", + "ad295276-e94f-53a4-9278-e6b93888ab10", + "8c423789-3641-5853-9cf3-f4a026ffb446", + "a043fdc7-8228-5f22-b6db-d3b1ad4eb6ae", + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "a52959f6-733a-58bb-93c0-65403fb72c83", + "efc8c74e-a862-5c6e-b947-96b664aaf0d0" + ], + "document_id": [ + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "ea43bb66-b6fe-5682-8f48-90568c080401", + "ea43bb66-b6fe-5682-8f48-90568c080401", + "1eb6f5b7-a3bc-5455-91f0-6f2eb37be861", + "5d65e407-34e5-5c1c-b394-989b7a09b57d", + "125d9cd4-5297-5173-9b16-9073cd3bcc71", + "ea9601ed-ad83-506e-b1b7-e7211671ff73", + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "5d65e407-34e5-5c1c-b394-989b7a09b57d", + "0f07fa43-feb6-5656-b7e7-b8faa86f5623" + ], + "id": [ + "chatcmpl-AIGrO4A8FLvBSq44CsQgHQD11jLhN", + "c63cfaee-749e-547b-9c0a-086266f10670", + "8999a4c7-e5de-539f-bee4-fd00cb69e7bb", + "e5320abf-d018-51c2-a386-aa822f605e1a", + "312eae52-ede7-5c13-8974-fce0126426cf", + "e71e1073-2800-5598-917f-00c3c08ed274", + "1c26e6f6-680b-5877-9600-fee25a42c943", + "8f299e3a-a7bc-5258-8f4d-0e964f89b35e", + "f3f859bb-d066-5552-b07e-eefcb489d8f5", + "8744d4f9-5566-5435-98ce-2afae5f59ad3", + "82539c96-b4a2-50e2-bbdd-4458ff3f0d2a" + ], + "contexts": [ + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "analytical method, have been used to discover gene- environment interactions; some approaches address similar objectives, whilst others are complementary and can be ap- plied in sequence. Below we describe several of these ap- proaches, and refer the reader to another excellent review of gene-environment interaction methods [ 31]. (a)Established statistical approaches Until 2008, almost all studies of gene-environment interac- tions focused on testing hypotheses based on existing biolog-", + "ulated by non-genetic factors. Thus, the once esoteric topic of gene-environment interaction is now becoming mainstream and appealing to investigators across diversedisciplines; this has propelled major methodological in- novations for the discovery, replication, validation and translation of gene-environment interactions. The expo- nentiation of data resources for these purposes has demanded analytical solutions that address data dimen- sionality reduction. Although not yet extensively imple-", + "addition to this, GeneNetwork can be used to study correlations between traits and to perform data mining in genomic regions containing candidates for quantitative trait genes (Hoffman et al., 2011). All datasets in GeneNetwork are linked to a materials and methods information page that summarizes experimental details relating to the dataset. Databases within GeneNetwork include the transcriptome database, the BXD published", + "Eaves LJ 2006 Genotype x environment interaction in psychopathology: fact or artifact? Twin Res Hum Genet 9:18 Hunter DJ 2005 Geneenvironment interactions in human diseases. Nat Rev Genet 6:287298 Ioannidis JP, Ntzani EE, Trikalinos TA, Contopoulos-Ioannidis DG 2001 Replication validity of genetic association studies. Nat Genet 29:306309 Ioannidis JP, Gwinn M, Little J et al 2006 A road map for ef cient and reliable human genome epidemiology. Nat Genet 38:35", + "GeneNetwork is an open-access database that collates genomic information of diverse experimental crosses and reference panels as well as phenotypic data from miscellaneous research groups [26]. Statistics Data generation, statistical analysis and graph creation were performed with SPSS Statistics 21 (IBM, Ehningen, Germany). As appropriate, mean and median values were further used for QTLanalysis. Phenotypic robustness for each strain was assessed by the", + "NU32CH13-Hu ARI 18 June 2012 13:45 effectively scan the entire genome for interac- tions with environment. Although innovative, the most effective study design and statistical approach for conducting gene-environment- wide interaction studies (GEWIS) remains unresolved (88). The greatest challenge for GEWIS involves nding a balance between rejecting true ndings resulting from stringent multiple-testing correction and reporting false-positive results. Several novel methods", + "1 GeneNetwork: a continuously updated tool for systems genetics analyses Pamela M. Watson1, David G. Ashbrook1 1Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN 38163, USA Abstract GeneNetwork and its earlier iteration , WebQTL, have now been an important database and toolkit for quantitative trait genetics research for two decades. Recent improvements to", + "13 132. Geneenvironment interaction: overcoming methodological challenges Rudolf Uher MRC Social, Genetic and Developmental Psychiatry Research Centre, Institute of Psychiatry, Kings College London, UK Abstract. While interacting biological effects of genes and environmental exposures (G E) form a natural part of the causal framework underlying disorders of human health, the detection of G E relies on inference from statistical interactions observed at popu-", + "A number of recent developments in twin methodology have taken place based on the incorporation of measured genotype information. Thisenables twin models to estimate how much of the genetic variation is dueto variation in a specific gene. Gene-environment interaction studies, link-Copyright National Academy of Sciences. All rights reserved.Cells and Surveys: Should Biological Measures Be Included in Social Science Research? http://www.nap.edu/catalog/9995.html" + ], + "answer": "The latest methodological improvements in evaluating gene-environment interactions using GeneNetwork.org include the addition of data from 10 species, multi-omics analysis, updated code, and new tools, making it a valuable resource for predictive medicine and systems genetics [1]. These enhancements allow for more comprehensive analyses and facilitate the study of correlations between traits and data mining in genomic regions containing candidates for quantitative trait genes [4]. Additionally, GeneNetwork is continuously updated to support systems genetics analyses, which can aid in the discovery, replication, validation, and translation of gene-environment interactions [8].", + "question": "What are the latest methodological improvements in evaluating gene-environment interactions using GeneNetwork.org?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_20 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_20 new file mode 100644 index 0000000..c7171af --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_20 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2022 - Using Recurrent Neural Networks for Predicting Type-2 Diabetes from Genomic and Tabular Data.pdf", + "2021 - Genetics and genomics of arrhythmic.pdf", + "2020 - Fine-tuning of Genome-Wide Polygenic Risk Scores and Prediction of Gestational Diabetes in South Asian Women.pdf", + "2023 - Clinical, technical, and environmental biases.pdf", + "2022 - Development and validation of a trans-ancestry polygenic risk score for type 2 diabetes in diverse populations.pdf", + "2022 - Development and validation of a trans-ancestry polygenic risk score for type 2 diabetes in diverse populations.pdf", + "2018 - Genome-wide polygenic scores for common diseases.pdf", + "2022 - Coming of Age Human Genomics.pdf", + "2020 - Genome-wide assessment of genetic risk for systemic.pdf", + "2021 -Potter-Dickey- Genetic Susceptibility.pdf" + ], + "extraction_id": [ + "3c30b33b-8928-5cee-9c37-c70642fff75c", + "ada410d0-6b91-5959-b834-cc3389e29c5f", + "8292e291-87bb-5f04-8e40-fb2228da3927", + "50731787-cf17-5284-b3f4-2c551cb41c90", + "17c49e58-c89a-5495-b17f-adcade90a4c6", + "f6f0c89d-5c35-5889-8619-a3914e5d2c7e", + "0a80e61e-648a-5122-9b17-8177bc734674", + "ca2e1560-db8f-5c3f-b7bf-dd1beaa94655", + "9b1cee76-2c59-50d6-a37c-8c593336fe33", + "567a2f7e-0ff9-5229-bfeb-066b6e6f50f6" + ], + "document_id": [ + "be0e50e0-3de8-53c5-8126-a0b618647f80", + "462ed035-e4fb-5847-a92d-927f05a2b58b", + "494779f3-1437-5b50-a9b2-3f616a048719", + "6a81e435-bd17-558d-850a-44ee3dbab5bd", + "4ece243f-acda-569d-b75d-37539260dcb3", + "4ece243f-acda-569d-b75d-37539260dcb3", + "a8cefcf1-7edf-52cc-8aeb-b4d353acaef5", + "45506895-eef1-57f4-8ca1-79fe23a2493f", + "af34f0df-a726-5cc4-844f-a5d67273d9a0", + "cb119609-daa3-56af-97ff-b809cc39c210" + ], + "id": [ + "chatcmpl-AIGtIvgudl04cUWtfjaShHQ8PZDZI", + "a374d88e-458e-5252-8b3a-5ca162fa6982", + "bcce1092-32ea-5f65-bc10-4dc1a2dac53a", + "f36bf430-26bd-5031-a392-14f3c43367ab", + "4190e1d8-ae9e-5c42-8842-aa0a60a2bb2c", + "1677b3ee-7d95-5e10-a6dd-d80b4bb87b29", + "2c09a46a-20d0-54b4-abcb-608fef7c7f80", + "459f7eed-490a-5586-9d2a-20f721daa6bc", + "98da512f-fee2-501b-b093-9ee7ab22c5f9", + "d27fbbe8-aec0-510f-ab9d-1a0d4f0a1678", + "b3e446bb-e438-5d66-a34c-8e1de0ebb639" + ], + "contexts": [ + "in advance. Polygenic Risk Scores (PRS) were proposed by Duncan L. et al. [ 8] for risk analysis using the sum of the weight of each risk-associated locus of genomic sequence obtained from the corresponding evidence. These weights are assessed from the regression coefcient associated with each locus. These combined genetics features and correlation matrices would signicantly assist the entire eld of genomics study [ 9]. These studies on", + "Owing to their small effect sizes, SNP associations have very little clinical applicability for risk prediction. A polygenic risk score (PRS) attempts to estimate the combined risk from multiple SNPs that have been associated with a certain trait with genome-wide sig-nificance. By accounting for a large proportion of the genetic variance underlying a trait, the overall effect size", + "of genome-wide genotypes and publicly available data from large consortia, GRSs with a larger number of vari- ants are being used, and the predictive value of these genome-wide polygenic risk scores (PRSs) has substantially improved 50,51. PRSs can be derived using different approaches, however, these require both summary statistics from an exter -", + "use for estimation of polygenic risk scores (PRS) has grownin recent years. PRS screening may be used to determine therisk of common complex diseases for individuals and theiroffspring, and although it is not widely clinically availablenow, there is an ongoing interest in increasing its utility. Useof GWAS data from European populations for PRS esti-mation would subsequently impose a bias in favor of in- dividuals with similar ancestry, whereas limited bene ti s", + "(GWAS) in diverse populations have identified hundreds of genetic loci associated with T2D [79]. Polygenic risk scores (PRS), which aggregate the genetic risk of individ - ual alleles across the genome, are thus promising to pre - dict future T2D occurrence and improve early diagnosis, intervention, and prevention of T2D [1015]. However, to date, T2D PRS were most widely developed and vali - dated in individuals of European descent. Given that the predictive performance of PRS often attenuates in non-", + "(GWAS), polygenic risk scores (PRS) have shown promise to complement established clinical risk factors and inter vention paradigms, and improve early diagnosis and prevention of T2D. However, to date, T2D PRS have been most widely developed and validated in individuals of European descent. Comprehensive assessment of T2D PRS in non European populations is critical for equitable deployment of PRS to clinical practice that benefits global populations.", + "Letters NATure GeNeTicsMethods Polygenic score derivation. Polygenic scores provide a quantitative metric of an individuals inherited risk based on the cumulative impact of many common polymorphisms. Weights are generally assigned to each genetic variant according to the strength of their association with disease risk (effect estimate). Individuals are scored based on how many risk alleles they have for each variant (for example, zero, one, or two copies) included in the polygenic score.", + "(Fig. 1B ). Polygenic risk scores (PRS) have emerged as promising biomarkers for the prediction of disease risk, not only in the area of cardiovascular disorders, but also oncology (21). These risk scores also have become increasingly available for a multitude of phenotypes and are systematically curated in a free online database (22). It has been shown that certain preexisting autoimmune diseases as well as the occurrence of imAE upon treatment are associated with", + "eases identify individuals with risk equivalent to monogenicmutations. Nat. Genet. ,50, 12191224. 13. Euesden, J., Lewis, C.M. and OReilly, P.F. (2015) PRSice: poly- genic risk score software. Bioinformatics ,31, 14661468. 14. Belsky, D.W., Moffitt, T.E., Sugden, K., Williams, B., Houts, R., McCarthy, J. and Caspi, A. (2013) Development and evalu- ation of a genetic risk score for obesity. Biodemography Soc. Biol.,59, 85100. 15. De Jager, P.L., Chibnik, L.B., Cui, J., Reischl, J., Lehr, S., Simon,", + "in tissue-specic regions or use gene co-expression information may provide a more comprehensive view of a specic gene or a gene networks role in modulating an individuals response to environmental variations, compared to that provided by the single candidate gene approach (Gamazon et al., 2015; Barth et al., 2020). Expression-based polygenic risk scores (ePRS) oer one such approach to understand the underlying genetic background linked to behavioral outcomes (Hari Dass" + ], + "answer": "Polygenic risk scores (PRS) developed using GeneNetwork.org data enhance the prediction and prevention of complex diseases by providing a quantitative metric of an individual's inherited risk based on the cumulative impact of many common polymorphisms [7]. These scores aggregate the genetic risk of individual alleles across the genome, which can significantly improve the prediction of future disease occurrence and aid in early diagnosis, intervention, and prevention strategies [5]. PRS can complement established clinical risk factors and intervention paradigms, thereby enhancing early diagnosis and prevention efforts for diseases such as type 2 diabetes (T2D) [6]. Additionally, PRS have emerged as promising biomarkers for predicting disease risk in various areas, including cardiovascular disorders and oncology [8]. By utilizing data from large consortia and genome-wide genotypes, the predictive value of these scores has substantially improved, allowing for a more comprehensive assessment of genetic risk [3].", + "question": "How do polygenic risk scores (PRS) developed using GeneNetwork.org data enhance the prediction and prevention of complex diseases?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_3 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_3 new file mode 100644 index 0000000..55b5566 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_3 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2020 - Gene network a continuously updated tool for systems genetics analyses.pdf", + "2012 - Using Genome-Wide Expression Profiling to Define Gene Networks Relevant to the Study of Complex Traits From RNA Integrity to Network Topology.pdf", + "2017 - Precise network modeling of systems genetics data using the Bayesian network webserver.pdf", + "2010 - Systems genetics analyses predict a transcription role for P2P-R Molecular confirmation that P2P-R is a transcriptional co-repressor.pdf", + "2005 - How replicable are mRNA expression QTL.pdf", + "2009 - Processing Large-Scale, High-Dimension Genetic and Gene Expression Data.pdf", + "2019 - Systems genetics approaches to probe gene function.pdf", + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf" + ], + "extraction_id": [ + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "c08af10b-f2ad-540b-be15-7cc101bf2dbc", + "046a82bb-8f86-5ecd-8879-34e569630a21", + "3e0c2a06-e6de-5888-a360-a2c483d9f744", + "803030b1-07ab-5b8c-97cb-297339488484", + "ec624ebb-489a-5437-a721-f01cf981d0a7", + "0a4dc047-3b00-5657-b414-885d99b55d19", + "3276b251-2e60-53e8-8fd1-07702f486a43", + "8ef4c3cf-8018-5334-9f82-19c9e86739a5", + "858f630f-9443-5f13-ac40-8e16eadd9ba1" + ], + "document_id": [ + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "374fd6d3-e6c1-560c-a421-a4b393ba23b2", + "1eb6f5b7-a3bc-5455-91f0-6f2eb37be861", + "c80b6981-5243-55a2-b5d8-0d7ffb2f4505", + "e4d1e2e9-f267-5814-8c7b-dc11d7eec9bf", + "699171c5-d983-50de-bcd2-fc3e117ff444", + "17264155-b665-59db-94cb-f4d67eac20fc", + "1cd18d9c-0fd1-52e3-b0cf-c5e3ad0ff683", + "128224f1-3545-52c3-93cb-77c3cf4ec70a" + ], + "id": [ + "chatcmpl-AIGrUMBGxTc4nmy408W8WUAr2t9TQ", + "c63cfaee-749e-547b-9c0a-086266f10670", + "f53306e0-447d-5640-b26f-6b617ce35a46", + "da10a7f5-6d13-504c-8db9-d67a48a3193e", + "312eae52-ede7-5c13-8974-fce0126426cf", + "d500c4bd-50b1-5271-b7a6-42591225de7a", + "a9508122-3b14-5365-979c-ba580bdcb78f", + "a24d4dd1-29f8-596e-bc8b-f0dafaa82858", + "c2dae4f8-2305-5d4a-a3f8-c0424d4b80b1", + "1e9adc57-45b4-5ac1-a0bf-a0b5ce07fef1", + "d7e5ef8a-d43a-587d-8ffd-cd5e8e63f6ab" + ], + "contexts": [ + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "Conclusion GeneNetwork is an excellent tool for exploring complex phenotypes with systems genetics. Here we have used GeneNetwork to explore an inflammatory phenotype, and identified a small number of plausible candidate genes. A similar workflow can be used for any trait on GeneNetwork, or for any phenotype collected by an investigator in a genetically diverse population. GeneNetwork can allow users to study relationships between genes, pathways, and phenotypes in an easy to use format.", + "Conclusion GeneNetwork is an excellent tool for exploring complex phenotypes with systems genetics. Here we have used GeneNetwork to explore an inflammatory phenotype, and identified a small number of plausible candidate genes. A similar workflow can be used for any trait on GeneNetwork, or for any phenotype collected by an investigator in a genetically diverse population. GeneNetwork can allow users to study relationships between genes, pathways, and phenotypes in an easy to use format.", + "addition to this, GeneNetwork can be used to study correlations between traits and to perform data mining in genomic regions containing candidates for quantitative trait genes (Hoffman et al., 2011). All datasets in GeneNetwork are linked to a materials and methods information page that summarizes experimental details relating to the dataset. Databases within GeneNetwork include the transcriptome database, the BXD published", + "connect Genotype with Gene2 and Phenotype, knowledge of the Genotype still influences the predicted values of these variables. For example, Genotype = 1 may cause a decrease in Gene1 and this decrease in Gene1 will subsequently cause a reduction in Gene2. 4 Discussion Network modeling of biological datasets is often limited by the number of samples within a dataset, and the available data does not support the construction of precise and reliable large-scale networks", + "GeneNetwork http://www.genenetwork.org is anexample of a bioinformatics tool that can be used to explore systems genetics data. The importance of defining biological networks and predicting molecular interactions has been emphasized by several reports [1,2]. Such studies emphasize that when knowledge about DNA variation within popula- tions is interfaced with data on gene expression, protein interactions and DNA-protein binding, biological networks can be constructed that are predictive of the", + "metadata (data about the data) are combined with sophisticated statistical and computation tools for the genetic dissection and synthesis of single traitsor entire systems of traits. One challenge facing investigators in the inter- pretation of the massive data sets on GeneNetworkand elsewhere is deciding how much confidence toplace in QTL extracted from still noisy array andproteomic platforms after having conducted many thousands of statistical tests with poorly understood", + "accuracy of predictive networks [40, 5153]. We have also recently demonstrated how this class of network can be used to inform associations identied in GW Astudies [40]. 9 Summary The signicant challenge we face in the post-genome era is deciphering the bio-logical function of individual genes, pathways, and networks that drive complexphenotypes like disease. The availability of low-cost, high-throughput technologies", + "members o f pathway modules [78]. Other studies applied gene network modeling algorithms to identify the potential regulators in complex di seases, for example cardiomyopathy [79], hepatic steatosis [80], as well as coronary artery disease [81]. Finally, there are many other integrative approaches available for the analysis of multi -omics data, but have not yet been applied in mouse systems genetics studies. Examples include the transcriptome -wide", + "gathered together into an easily accessible format, not siloed into disparate data pools that cannot easily be integrated, valid ated, o r extended. This approach will allow us to make animal models of so called precision medicine, although perhaps more accurately, we want predictive medicine , where a phenotypic outcome (such as disease) can be predicted , and avoided . GeneNetwork (genenetwork.or g; GN) is one tool for systems genetics and predictive medicine," + ], + "answer": "Multi-omics data integration techniques enhance the prediction accuracy of phenotypic traits in GeneNetwork datasets by allowing for the construction of biological networks that are predictive of molecular interactions and phenotypic outcomes. When data on DNA variation is combined with gene expression, protein interactions, and DNA-protein binding information, it enables the creation of comprehensive models that can predict complex phenotypes more accurately [6]. This integration facilitates the study of relationships between genes, pathways, and phenotypes, providing a more holistic view of the biological systems involved [2]. Additionally, the use of sophisticated statistical and computational tools in conjunction with multi-omics data allows for the genetic dissection and synthesis of traits, further enhancing predictive accuracy [7].", + "question": "How do multi-omics data integration techniques enhance the prediction accuracy of phenotypic traits in GeneNetwork datasets?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_4 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_4 new file mode 100644 index 0000000..133e629 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_4 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2007 - Combinatorial genetic regulatory network analysis tools for high throughput transcriptomic data.pdf", + "2005 - Combinatorial Genetic Regulatory Network Analysis Tools for High Throughput Transcriptomic Data.pdf", + "2011 - Using the PhenoGen Website for \u201cIn Silico\u201d Analysis of Morphine-Induced Analgesia Identifying Candidate Genes.pdf", + "2012 - Comparing Statistical Methods for Constructing Large Scale Gene Networks.pdf", + "2012 - Genetic dissection of acute ethanol responsive gene networks in prefrontal cortex functional and mechanistic implications.pdf", + "2012 - Genetic dissection of acute ethanol responsive gene networks in prefrontal cortex functional and mechanistic implications.pdf", + "2012 - Advances in biotechnology and linking outputs to variation in complex traits Plant and Animal Genome meeting January 2012.pdf", + "2011 - Genetical genomics approaches for systems genetics.pdf", + "2009 - Processing Large-Scale, High-Dimension Genetic and Gene Expression Data.pdf" + ], + "extraction_id": [ + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "47c06e52-1923-58d0-9286-9674893a502a", + "5e93e58f-a415-5ead-9356-c749891269cc", + "0e3a5e40-06b0-58d4-b495-3093954ed17b", + "2a75bfb9-6beb-54ef-b72b-25045ee3222d", + "29446d6f-fb32-5a6e-a51a-179c888091b2", + "29446d6f-fb32-5a6e-a51a-179c888091b2", + "3bdf080c-2715-5acc-bba4-717283851240", + "368bb4b5-bc26-5a39-95fc-561f58eb0e08", + "bee70000-17e9-5352-8c9c-349c78dfaa23" + ], + "document_id": [ + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "d9038328-bfea-5f73-87aa-6077b697e4db", + "5ded506d-7935-53f9-a118-57a9f3943376", + "eb266fa1-8dec-5c56-a3d5-b508bd6bd448", + "ea0b9f5f-b1cf-5774-98aa-0f022c831fb8", + "1a20f715-5068-5c61-8396-59e6096fa7de", + "1a20f715-5068-5c61-8396-59e6096fa7de", + "c81c86b5-c5ab-5abf-83c0-415b0950fd51", + "de78a01d-8d03-5afb-af5b-ce2ed2167766", + "17264155-b665-59db-94cb-f4d67eac20fc" + ], + "id": [ + "chatcmpl-AIGraUSt4UjtI0mL9sXfXnJsapOUk", + "c63cfaee-749e-547b-9c0a-086266f10670", + "aafbe14f-7ad3-5ad4-9951-90edecaceaa3", + "ac2029ae-498b-5ec0-ae10-f5729344cb5b", + "0b2bd83d-680a-52d2-8116-50cce4f35cc3", + "2e404112-d767-58f9-9bd3-f0220733759c", + "8bb5a6fb-9528-59cb-bc79-a1a52584abfa", + "59c4b4b6-6b08-5182-a493-e7f753b7eb87", + "9c01962f-fcac-57b3-a17d-487e37323230", + "1e19020c-c664-560b-8d2a-ef53ab8cb996", + "1755868d-9b84-5a6e-b6db-db70cb413656" + ], + "contexts": [ + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "Combinatorial Genetic Regulatory Network Analysis Tools for High Throughput Transcriptomic Data Elissa J. Chesler1and Michael A. Langston2 1Life Sciences Division, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831-6124, USA 2Department of Computer Science, University of Tennessee, Knoxville, TN 379963450, USA Abstract: A series of genome-scale algorithms and high-performance implementations is described and shown to be useful in the genetic analysis of gene transcription. With", + "Combinatorial Genetic Regulatory Network Analysis Tools 163 In addition to expansive volumes of data, there is a growing complexity to the types of research questions that can be asked. We are presently developing approaches to compare graphs collected in a systems gene tic context to reect differences in time, tissue and treatment effects. Visualizatio n methods and compelling biological validation of novel results are essential to translate these methods and deliver them to the broader", + "al., 2005). GeneNetwork is designed primarily as a web service for exploratory and statistical analysis of large published phenotype and genome datasets, and includes data from several species (see Supplementary Discussion). GeneNetwork includes extensive phenotype data extracted from the literature and submitted by users, which makes it practical to compare data on drug responses with gene expression patterns. Gene expression", + "larger networks well. Because of the computational complexity aswell as the memory requirements, these methods as currentlyimplemented are not the ideal choice for such large networks.WGCNA, GeneNet, ARACNE and SPACE, on the other hand,were designed to construct the gene network at very large scales.Also, it worth mentioning that the WGCNA package providesseveral useful tools to facilitate the analysis and visualization of resulting networks, including tools to identify subnetworks and an", + "Proc Natl Acad Sci U S A 100: 94409445. 32. Chesler E, Langston MA (2005) Combinatorial Genetic Regulatory Network Analysis Tools for High Throughput Transcriptomic Data. Proceedings,RECOMB Satellite Workshop on Systems Biology and Regulatory Genomics. 17 p.33. Abu-Khzam F, Langston M, Shanbhag P, Symons C (2006) Scalable Parallel Algorithms for FPT Problems. Algorithmica 45. 34. Langston M, Perkins A, Saxton A, Scharff J, Voy B (2006) Innovative", + "computational methods for transcriptomic data analysis. SAC 06: Proceedings of the 2006 ACM symposium on Applied computing. 35. Csardi G, Nepusz T (2006) The igraph software package for complex network research. InterJournal Complex Systems 1695. 36. Chen J, Bardes EE, Aronow BJ, Jegga AG (2009) ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res 37:W305311. 37. Williams RW, Gu J, Qi S, Lu L (2001) The genetic structure of recombinant", + "plenary lecture, with a focus on the computational challengesin analyzing large datasets. The type of datasets discussed by Williams included the microarray type outputs first suggestedby Jansen and Nap ( 2001 ) for inclusion in genetical genomics analyses and are now extended to cross-platform datasets (Damerval et al. 1994; Ciobanu et al. 2010 ). A framework for carrying out the genetic analyses was described as being available through the GeneNetwork and WebQTL software", + "32. Zhu J, Zhang B, Smith EN, Drees B, Brem RB, Kru glyak L, Bumgarner RE, Schadt EE: Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks . Nat Genet 2008, 40 (7):854-861. 33. Vera G, Jansen RC, Suppi RL: R/parallel--speeding up bioinformatics analysis with R . BMC bioinformatics 2008, 9:390. 34. Alberts R, Terpstra P, Bystrykh LV, de Haan G, Jansen RC: A statistical multiprobe model for analyzing cis and trans genes in genetical", + "Processing Large-Scale, High-Dimension Genetic and Gene Expression Data Cliona Molony, Solveig K. Sieberts, and Eric E. Schadt Abstract The now routine generation of large-scale, high-throughput data in mul- tiple dimensions (genotype, gene expression, and so on) provides a signicant challenge to researchers who desire to integrate data across these dimensions in" + ], + "answer": "Analyzing large-scale transcriptomic data within GeneNetwork.org presents several computational challenges and solutions: 1. **Computational Complexity and Memory Requirements**: The analysis of large networks is computationally complex and requires significant memory resources. Traditional methods may not be ideal for handling such large datasets [5]. 2. **Scalable Algorithms**: To address these challenges, scalable parallel algorithms have been developed. These algorithms are designed to handle fixed-parameter tractable (FPT) problems, which are crucial for managing large-scale data efficiently [6]. 3. **High-Performance Implementations**: Genome-scale algorithms and high-performance implementations are utilized to facilitate the genetic analysis of gene transcription, making it possible to manage and analyze large volumes of data effectively [2]. 4. **Visualization and Biological Validation**: Visualization methods and biological validation are essential to translate complex data into understandable results. These methods help in comparing graphs collected in a systems genetic context, reflecting differences in time, tissue, and treatment effects [3]. 5. **Tools for Large-Scale Network Construction**: Tools like WGCNA, GeneNet, ARACNE, and SPACE are specifically designed to construct gene networks at very large scales. These tools also provide functionalities for analyzing and visualizing resulting networks, including identifying subnetworks [5]. These solutions collectively help in overcoming the computational challenges associated with large-scale transcriptomic data analysis within GeneNetwork.org.", + "question": "What are the computational challenges and solutions in analyzing large-scale transcriptomic data within GeneNetwork.org?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_5 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_5 new file mode 100644 index 0000000..83c48c5 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_5 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2016 - Genetic networks in mouse retinal ganglion cells.pdf", + "2018 - Genetic Networks Activated by Blast Injury to the Eye.pdf", + "2017 - GeneNetwork a toolbox for systems genetics.pdf", + "2020 - GeneNetwork a toolbox for systems genetics.pdf", + "2015 - Selecting causal genes from genome-wide association studies via functionally coherent subnetworks.pdf", + "2012 - Using Genome-Wide Expression Profiling to Define Gene Networks Relevant to the Study of Complex Traits From RNA Integrity to Network Topology.pdf", + "2011 - Using the PhenoGen Website for \u201cIn Silico\u201d Analysis of Morphine-Induced Analgesia Identifying Candidate Genes.pdf", + "2021 - Lessons learned from the eMERGE Network balancing genomics.pdf", + "2012 - Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways.pdf" + ], + "extraction_id": [ + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "194c0d73-a9b7-5b5e-ac92-7dd689da6fc0", + "b881d0e1-11d4-578d-8560-0106c77d7a23", + "7dd82b3f-58bd-5915-9eea-250f11412ff2", + "4ca2fc9e-7d42-5ea3-b1b7-a296bfbc6a09", + "46616368-74e6-5605-9e43-9789e8e1bea1", + "3e0c2a06-e6de-5888-a360-a2c483d9f744", + "0e3a5e40-06b0-58d4-b495-3093954ed17b", + "8aecb357-2d62-51f9-9256-6fdf8c73791e", + "bc862e34-d30b-5882-9cc9-69f2bce72239" + ], + "document_id": [ + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "ca0d3a29-7814-5d09-ad9d-e4143e87900d", + "57e3820f-7a5d-51f1-a0c6-ecfbdf546005", + "682c3a51-0aa5-54a3-a6e7-a09b81c0e8b6", + "d11a87ca-4989-59af-95e3-ab90af7d9212", + "af43f4ac-7211-52f0-8f6b-e4bde73bbe4a", + "1eb6f5b7-a3bc-5455-91f0-6f2eb37be861", + "eb266fa1-8dec-5c56-a3d5-b508bd6bd448", + "cd0002dd-dcf1-567a-bf41-61eb0d6d982b", + "879c61e9-2efa-550b-b7ca-f88d67eb2199" + ], + "id": [ + "chatcmpl-AIGrg63GEuWBoLBB21tTvYo1XKFpy", + "c63cfaee-749e-547b-9c0a-086266f10670", + "c2225b34-e4a6-5147-998d-c2a5132d7a08", + "dc8fdfb1-539c-5941-bd4d-b595164cce9b", + "30e2423f-2b2b-5c7d-8808-b025242fa0c7", + "7ce6c0fe-8b0a-5ce9-83d1-6e6b99b4f24d", + "33dc52df-73a5-514e-8edb-33ae5046b8af", + "312eae52-ede7-5c13-8974-fce0126426cf", + "0b2bd83d-680a-52d2-8116-50cce4f35cc3", + "e17f1d54-7ea8-5a44-95b7-5d07f348574c", + "d519a13a-b6a0-505d-9a90-dd8f974721b4" + ], + "contexts": [ + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "GeneNetwork provided the platform for correlation analysis, principal component generation, and linkage analysis. In general, datasets were queried for gene symbols, downloaded from GeneNetwork, and additional analysis was performed in R whenever necessary. P-values mentioned in relation to Pearsons coecient throughout this paper are based on pair- wise comparisons. All p-values were Bonferroni-adjusted for 36,012 genes, which is equal to the number of genes captured", + "GeneNetwork provided the platform for correlation analysis, principal component generation, and linkage analysis. In general, datasets were queried for gene symbols, downloaded from GeneNetwork, and additional analysis was performed in R whenever necessary. P-values mentioned in relation to Pearsons coecient throughout this paper are based on pair- wise comparisons. All p-values were Bonferroni-adjusted for 36,012 genes, which is equal to the number of genes captured", + "including correlation and network analysis to compare associations between tissues and between other rodent or human data sets[32] Many of the Data Sets are amenable to systems genetics mapping and other methods and are accessible at GeneNetwork. The Description and Usage column provides details about the data set and potential", + "including correlation and network analysis to compare associations between tissues and between other rodent or human data sets[32] Many of the Data Sets are amenable to systems genetics mapping and other methods and are accessible at GeneNetwork. The Description and Usage column provides details about the data set and potential", + "network. Cell 9, 12121226 (2014). 12. Hirschhorn, J.N. Genomewide association studiesilluminating biologic pathways. N. Engl. J. Med. 0, 16991701 (2009). 13. Cantor, R.M., Lange, K. & Sinsheimer, J.S. Prioritizing GWAS results: a review of statistical methods and recommendations for their application. Am. J. Hum. Genet. 8, 622 (2010). 14. Lee, I., Date, S.V., Adai, A.T. & Marcotte, E.M. A probabilistic functional network of yeast genes. Science 0, 15551558 (2004).", + "addition to this, GeneNetwork can be used to study correlations between traits and to perform data mining in genomic regions containing candidates for quantitative trait genes (Hoffman et al., 2011). All datasets in GeneNetwork are linked to a materials and methods information page that summarizes experimental details relating to the dataset. Databases within GeneNetwork include the transcriptome database, the BXD published", + "al., 2005). GeneNetwork is designed primarily as a web service for exploratory and statistical analysis of large published phenotype and genome datasets, and includes data from several species (see Supplementary Discussion). GeneNetwork includes extensive phenotype data extracted from the literature and submitted by users, which makes it practical to compare data on drug responses with gene expression patterns. Gene expression", + "limit the applicability of genetic ndings in more diversepopulations. In the next phase of the network, the goalis to increase the diversity of underrepresented popula-tions, with targeted recruitment aimed at over 50% non-European ancestry. The lessons from enrollment andRoRs to diverse populations, even limited, will inform our next phase as we continue to strive for a more represen-", + "data available across all contributing consortia will facilitate systematic exploration of these correlated phenotypes with more sophisticated statistical methods for joint analysis5254, yielding greater insight into the underlying pathways and genetic networks they represent. As data from human genetic networks accrue, we will be better placed to test whether there is support for the notion of hub genesthat is, genes highly connected with others in the network, proposed by experi" + ], + "answer": "The inclusion of data from diverse populations on GeneNetwork.org aims to enhance the generalizability of findings by addressing the limitations associated with genetic studies that predominantly focus on less diverse populations. The goal is to increase the diversity of underrepresented populations, with targeted recruitment aimed at over 50% non-European ancestry [9]. This effort is expected to improve the applicability of genetic findings across different populations, thereby enhancing the generalizability of the research outcomes. By incorporating data from a broader range of genetic backgrounds, GeneNetwork.org can provide more comprehensive insights into genetic networks and pathways, which are applicable to a wider array of populations.", + "question": "How has the inclusion of data from diverse populations impacted the generalizability of findings on GeneNetwork.org?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_6 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_6 new file mode 100644 index 0000000..ff8f14e --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_6 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2014 -Neuroinformatic analyses of common and distinct genetic components associated with major neuropsychiatric disorders.pdf", + "2015 - Somatic mutation in cancer.pdf", + "2014 -Neuroinformatic analyses of common and distinct genetic components associated with major neuropsychiatric disorders.pdf", + "2018 - Comprehensive functional genomic resource and integrative model forthe human brain.pdf", + "2019 - Beyond Genome-wide Significance Integrative Approaches to the Interpretation and Extension of GWAS Findings for Alcohol Use Disorder.pdf", + "2014 -Neuroinformatic analyses of common and distinct genetic components associated with major neuropsychiatric disorders.pdf", + "2017 - Genomewide Association Study of Alcohol Dependence Identifies Risk Loci Altering Ethanol-response Behaviors in Model Organisms.pdf", + "2014 - Analyzing_gene_expression_data_in_mice_w.pdf", + "2022 -Restrepo- Predict impulsivity in children.pdf", + "2022 - Corticolimbic DCC gene co-expression networks as predictors of impulsivity in children.pdf" + ], + "extraction_id": [ + "0749dafa-17cf-5434-aad9-151a128e357b", + "feb6add1-ae89-5c82-8d59-6d4d66ea6779", + "300d8f31-5e42-5c17-a801-2f7afad3995e", + "82c75078-0fc5-508c-95ba-f2975fdec2c5", + "f623501d-c824-5334-98d7-dd599d0c063d", + "b3e6daa0-872e-546c-bee5-873b8f716c77", + "4c500aa5-faeb-5273-83a9-c5c91a27c697", + "848a85f6-382c-54e8-947b-670d71bb0639", + "10e3b0c3-e7cc-52e9-a6c2-e721a848bae5", + "8c7a2723-caa8-5ae1-a47c-c0c889443919" + ], + "document_id": [ + "38896019-c47e-5288-88a9-302779568cd3", + "0801355e-6f92-5526-a0b7-85a2bc859c51", + "38896019-c47e-5288-88a9-302779568cd3", + "24caaa62-2368-534f-8c42-f088c3409510", + "f59b3e10-a887-5708-b520-c5e8adb48dcd", + "38896019-c47e-5288-88a9-302779568cd3", + "045eff7e-5ff3-5b0e-9858-76eb8560e9d4", + "643f0642-d9c6-52f8-8b86-e469e778c003", + "15c3ab55-d6e6-532e-a655-759059ab7c07", + "fdecd4db-5e3a-5a3a-8145-28d05392822e" + ], + "id": [ + "chatcmpl-AIGrl5sKA3HUkZ2rgn7crnu6ec7EE", + "2aaaf2f2-8ea8-5f34-82ce-60cdce021b1c", + "06a4a00d-2b22-557a-b744-e4ac1fa8a5a2", + "cf9ea924-eb96-5444-9a8b-ed45c932b130", + "88756a11-58d2-59ec-8eed-08a96fc24ca0", + "f771b6cd-babd-56c2-a536-fbafc07c9be7", + "fd183495-c22b-5b6e-af12-ec216a838141", + "224463d2-e8a3-5a17-ab9b-9d6a39a081b8", + "18de97fd-e46c-5600-b45d-82de340e0d6b", + "366961c5-4349-5d93-abf5-203de53a4928", + "d7155850-29e4-5fec-b5a2-974f8ead2fef" + ], + "contexts": [ + "Lotan et al. Neuroinformatics of major neuropsychiatric disorders We demonstrated that although these disorders share a rela- tively small set of genes, there are two fundamental yet distinctgenetic components, or vectors, that are both shared by all sixdisorders. While the rst component is involved in CNS develop- ment, neural projections and synaptic transmission, the second", + "genetic variation) for any psychiatric disorder (Fig. 1), there is sufficient information to drawsome general conclusions. The polygenicity of psychiatric illness In addition to finding specific genes, molecu- lar genetics can provide information about theheritability of psychiatric disease, an approach that has led to some important insights about the genetic architecture of psychiatric illness.The degree of SNP sharing among disease cases estimates the common, inherited portion of a", + "of shared and unique genetic factors highlights key gene sets and molecular processesthat may ultimately translate into improved diagnosis and treatment of these debilitating disorders. Keywords: major neuropsychiatric disorders, neuroinformatics, cross-species, translational, genetic components, genome wide association studies, enrichment INTRODUCTION Common psychiatric disorders including attention-", + "6. D. H. Geschwind, J. Flint, Genetics and genomics of psychiatric disease. Science 349, 1489 1494 (2015). doi: 10.1126/science. aaa8954 ; pmid: 26404826 7. S. Cichon et al ., Genomewide association studies: History, rationale, and prospects for psychiatric disorders. Am. J. Psychiatry 166, 540 556 (2009). doi: 10.1176/ appi.ajp.2008.08091354 ; pmid: 19339359 8. A. Battle et al., Genetic effects on gene expression across human tissues. Nature 550, 204 213 (2017). doi: 10.1038/ nature24277 ; pmid: 29022597", + "the Psychiatric Genomics Consortium found that the results were highly correlated between methods in a comparison of methods applied across several psychiatric disorders ( Network Pathway Analysis Subgroup of Psychiatric Genomics Consortium 2015 ). A second limitation of pathway-based analysis is that it is still biased by our incomplete prior knowledge of gene function in the etiology of psychiatric illness. Despite these challenges, pathway-based analyses have identified biological pathways", + "Lotan et al. Neuroinformatics of major neuropsychiatric disorders GENES FROM THE NHGRI-CROSS-DISORDER SET CLUSTER IN THREE CO-EXPRESSION MODULES WITH DISTINCT SPATIO-TEMPORALEXPRESSION PATTERNS AND FUNCTIONAL BIASES One of the major properties of genes involved in regulation of", + "Genet. 2009; 85:847861. [PubMed: 19931040] Brownlee DJ, Fairweather I. Exploring the neurotransmitter labyrinth in nematodes. Trends Neurosci. 1999; 22:1624. [PubMed: 10088995] Bucholz KK, Cadoret R, Cloninger CR, Dinwiddie SH, Hesselbrock VM, Nurnberger JI Jr, Reich T, Schmidt I, Schuckit MA. A new, semi-structured psychiatric interview for use in genetic linkage studies: a report on the reliability of the SSAGA. J Stud Alcohol. 1994; 55:149158. [PubMed: 8189735]", + "with shared effects on ve major psychiatric disorders: a genome- wide analysis. Lancet 381(9875):13711379 Davis S, Meltzer P (2007) Geoquery: a bridge between the gene expression omnibus (geo) and bioconductor. Bioinformatics 14:18461847 de Mooij-van Malsen AJG, Vinkers CH, Peterse DP, Olivier B, Kas MJH (2011) Cross-species behavioural genetics: a starting point for unraveling the neurobiology of human psychiatric disorders. Prog Neuropsychopharmacol Biol Psychiatr 35(6):13831390", + "systems biology approach based on gene co-expression networks and genotype-gene expression (rather than genotype-disease)associations, these results further validate our methodology to construct polygenic scores linked to the overall biological function of tissue-speci c gene networks. Molecular Psychiatry (2022) 27:27422750; https://doi.org/10.1038/s41380-022-01533-7 INTRODUCTION Several psychiatric disorders of developmental origin are char-", + "systems biology approach based on gene co-expression networks and genotype-gene expression (rather than genotype-disease)associations, these results further validate our methodology to construct polygenic scores linked to the overall biological function of tissue-speci c gene networks. Molecular Psychiatry (2022) 27:27422750; https://doi.org/10.1038/s41380-022-01533-7 INTRODUCTION Several psychiatric disorders of developmental origin are char-" + ], + "answer": "The insights obtained from GeneNetwork.org regarding the genetic basis of psychiatric disorders include the identification of two fundamental yet distinct genetic components shared by major neuropsychiatric disorders. The first component is involved in central nervous system (CNS) development, neural projections, and synaptic transmission [1]. Additionally, the polygenicity of psychiatric illnesses has been highlighted, indicating that psychiatric disorders are influenced by multiple genes, and there is a degree of single nucleotide polymorphism (SNP) sharing among disease cases, which helps estimate the common, inherited portion of these disorders [2]. Furthermore, shared and unique genetic factors have been identified, which highlight key gene sets and molecular processes that may lead to improved diagnosis and treatment of psychiatric disorders [3].", + "question": "What novel insights have been obtained from GeneNetwork.org regarding the genetic basis of psychiatric disorders?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_7 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_7 new file mode 100644 index 0000000..9f3f073 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_7 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2018 - Leveraging the cell lineage to predict cell-type specificity of regulatory variation from bulk genomics.pdf", + "2012 - Advances in biotechnology and linking outputs to variation in complex traits Plant and Animal Genome meeting January 2012.pdf", + "2008 - Gene Expression Profiling.pdf", + "2005 - Part I Previous Research Track Record.pdf", + "2009 - Neuroscience in the era of functional genomics and systems biology.pdf", + "2022 -Madadi- AI RNA.pdf", + "2019 - Remodeling of epigenome and transcriptome.pdf", + "2018 - A survey on machine learning approaches in gene expression classification in modelling computational diagnostic system for complex diseases.pdf", + "2005 -Pomp- GenomeExploitation.pdf", + "2006 - Marker Assisted Backcrossing .pdf" + ], + "extraction_id": [ + "79e0c3a8-7d1b-5372-a776-7e9a76d09691", + "3bdf080c-2715-5acc-bba4-717283851240", + "00906abf-f4ca-53f2-a2b6-20359686e9ec", + "0853c5ab-3d98-565c-ba1f-50e5bd91d14c", + "52f30738-038c-58b4-af90-3e1c8735e729", + "ebd9b396-f870-5c65-9460-7f3da6c11e6c", + "4e757e70-c73b-59b2-8129-d253c4620f49", + "c7cd8df0-306c-5b1d-97b8-42410f4b82ed", + "d813f94e-cbde-502a-b387-a5cfd585ecca", + "99f23be3-af56-5ae5-9577-ae940bfd9653" + ], + "document_id": [ + "89534971-8c50-51ee-b2c4-35957579f911", + "c81c86b5-c5ab-5abf-83c0-415b0950fd51", + "59f3b969-089b-5258-93ad-892dbc9ffa9c", + "1875d68b-adeb-5f91-8a67-91d881906238", + "08e29201-f2cc-5fd5-9c28-bc4b8aaaa936", + "03b9b993-8dd5-5b0d-9493-99fb9a624948", + "87ffccee-fc33-5373-948d-67736aa0f069", + "8355d7b5-9da9-5bb8-8a3e-6f77c667599c", + "a77aefe9-379e-54a2-b029-8f5f3e798e64", + "5efc1bdf-f847-5eaf-a808-9cf71b9399ce" + ], + "id": [ + "chatcmpl-AIGrrCJF0xy80I2fCpFw4lJ55PYWM", + "5a61091b-7128-5326-a08c-9e53506eb0f4", + "1de27ae0-e471-5f99-baeb-6d53071de37b", + "92e845b4-fbdf-52e8-8ebd-39392ccdfeb7", + "d192b3fd-5ece-570a-a905-f94eef684af2", + "16baa529-fa53-5760-96b2-38779cab00e0", + "38245be7-bd5c-5711-94ba-794c16247aa9", + "14ac602a-df31-53c4-95cf-6ff078ddec34", + "c810e291-415f-5bee-a54b-1548ff0bacd5", + "5057d65b-2c37-5344-b757-3af91d22c690", + "8a074429-2464-5b19-8eb8-6775d588b24f" + ], + "contexts": [ + "The method takes as input a large cohort of individuals, wherethe input for each individual includes: (1) genotyping; (2) bulk ex-pression of genes in a certain tissue; (3) the relative abundance(proportions) of the various cell types in the tissue (it is possible to use computational deconvolution methods to predict cell-type proportions from bulk genomics data ( Newman et al. 2015 )). In", + "Filtering out the latter class of technical difficulty im-proved the recovery of genuine cis-modulated transcripts and thus to identify genes that are relevant to further down-stream regulation of gene expression and more complex phe-notypes (Ciobanu et al. 2010 ). Williams also discussed the power of a structured mapping population in model organisms and presented the Complex4 Funct Integr Genomics (2012) 12:1 9", + "genomic hybridization microarrays (8), can complement RNA expression data and result in novel discoveries. With the evolution and maturation of proteom ics, certainly combining serum- or tissue-based patterns of protein expression with RNA expression holds promise. Finally, other rich sources of complex data such as the literature can be used to complement our analysis of microar ray data (39). These analyses face significant challenges with respect to gene", + "data. To model the functional dependence we shall explore machine learning methods16, such as decision tree methods to predict the co-expressed gene profiles. As part of this study and in (E) Future work, see below, we will investigate the benefit of using comparative genomics in helping to lo cate and characterise the regul atory elements and signals. D(d) Integration and Modelling to infer regulato ry systems co-varying with disease status", + "derived from complex tissue such as brain show a high level of correspondence24,25. Such structure can be used to inform a new level of neuroscientific investigation that is not possible using standard analysis of differential expression2225. For example, one of the first such studies23 showed that gene networks could be used to provide a unifying method of identifying transcriptional targets of human brain evolution in", + "profiling of a multicellular organism,\" Science, vol. 357, no. 6352, pp. 661 -667, 2017. [68] X. Guo, W. Li, and F. Iorio, \"Convolutional neural networks for steady flow approximation,\" in Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining , 2016, pp. 481 -490. [69] V. Ntranos, L. Yi, P. Melsted, and L. Pachter, \"A discriminative learning approach to differentia l expression analysis for single -cell RNA -seq,\" Nature Methods, vol. 16,", + "levels can influence the ability to call differential gene expression (Oshlack and Wakefield 2009), we also included, as a feature, the average expression level of the genes in the young samples. All machine-learning algorithms assigned genes to the correct transcriptional change with age 67% 81% of the time on average, significantly above that of a random classification (50%) (Fig. 3B,C; Supplemental Fig. S3B,C ;Supplemental Table S3A,B ). Models de-", + "DNA. Microarray technology is helpful in capturing biological genetic information to computer data. Computational techniques can be applied on those large set of genetic data of every individuals with or without disease, so that the genes that are responsible for the disease occurrence can be po inted out. Differentially Expressed Genes (DEG) are identified using many techniques. Machine Learning (ML) algorithms plays a significant role in identifying the distinction between normal", + "mapping, several sophisticated analyses will be required to extract full value fromthe enormous amount of collected data, and gain valuable insight into geneticcontrol of gene expression. As recently noted by Ariel Darvasi (2003), I expect thatthe combining of genetic information and gene expression will hasten the day whengenomics delivers on its promise to improve health care. But we must continuestriving to develop and apply sophisticated analytical tools for interpreting the vast,complex data sets that", + "mapping, several sophisticated analyses will be required to extract full value fromthe enormous amount of collected data, and gain valuable insight into geneticcontrol of gene expression. As recently noted by Ariel Darvasi (2003), I expect thatthe combining of genetic information and gene expression will hasten the day whengenomics delivers on its promise to improve health care. But we must continuestriving to develop and apply sophisticated analytical tools for interpreting the vast,complex data sets that" + ], + "answer": "Advancements in machine learning algorithms contribute to the deconvolution of gene expression data in complex tissues by enabling the prediction of cell-type proportions from bulk genomics data. This computational deconvolution is crucial for understanding the relative abundance of various cell types within a tissue, which is a key step in analyzing gene expression data from complex tissues [1]. Additionally, machine learning methods, such as decision tree methods, are explored to model functional dependencies and predict co-expressed gene profiles, which can further aid in the deconvolution process by identifying regulatory elements and signals that vary with disease status [4]. These advancements allow for more accurate and insightful analysis of gene expression data, facilitating the identification of transcriptional changes and regulatory networks in complex tissues.", + "question": "How do advancements in machine learning algorithms contribute to the deconvolution of gene expression data in complex tissues?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_8 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_8 new file mode 100644 index 0000000..6d8ac23 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_8 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Skeletal muscle enhancer interactions identify genes controlling whole-body metabolism.pdf", + "2007 - How to infer gene networks from expression profiles.pdf", + "2018 - Comprehensive functional genomic resource and integrative model forthe human brain.pdf", + "2013 - Genetic and Genomic Approaches to Understanding Macrophage Identity and Function.pdf", + "2011 - EXPLOITING NATURAL AND INDUCED GENETIC VARIATION TO STUDY HEMATOPOIESIS.pdf", + "2016 - The genetic regulatory signature of type 2 diabetes in human skeletal muscle.pdf", + "2016 - The genetic regulatory signature of type 2 diabetes in human skeletal muscle.pdf", + "2009 - Next generation synthetic gene networks.pdf", + "2008 - Meta-Analysis Approach identifies Candidate Genes and associated Molecular Networks for Type-2 Diabetes Mellitus.pdf", + "2021 - Modern Statistical Methods for Genetics and Genomic Studies.pdf" + ], + "extraction_id": [ + "1a87b58e-d091-582c-b96d-adac454fdf9d", + "1b4abf11-ed4b-5169-9ba9-8569bc5c10f7", + "213169b2-a4b0-5d5c-a297-c9a5896652ad", + "4c2afa3b-cf31-58ba-8ae8-2bf609f25dbc", + "d2dd2002-c8f6-5e2e-a06a-a8a20268c637", + "9da4c40c-fa6f-557f-b78d-7ffdb9bb9d41", + "9da4c40c-fa6f-557f-b78d-7ffdb9bb9d41", + "38e443bd-610e-5a1d-9f32-082e808d016a", + "c9ae0334-a2f7-5063-81aa-f313c77e4b65", + "7f3f1b6c-9fcd-5e8e-a4e0-d53da591d706" + ], + "document_id": [ + "fa738c86-1026-50f5-aebb-285ec92b209c", + "5067a047-b97d-522a-9a7e-5372e3bbd102", + "24caaa62-2368-534f-8c42-f088c3409510", + "1526d201-2f4e-5e6c-b2c8-8c825e741401", + "6f250b15-61b3-57ed-8900-5aa4a173fa8c", + "0046a766-21c6-582a-b868-685a24920faf", + "0046a766-21c6-582a-b868-685a24920faf", + "0d620c5e-a9ae-5b19-851b-37e40292ab8d", + "4060609b-1464-55fa-93cd-fefaf2cac900", + "6acebf19-b80c-5352-8201-99d5634fcc80" + ], + "id": [ + "chatcmpl-AIGrwafXsxRn06hAraC16E8hpnzWh", + "54f0e8c3-0322-51a6-b129-5850d0586c84", + "b713e667-ba32-514b-8373-0aebd9702cfc", + "640aa5eb-9b93-541a-ba5a-c1179c157c95", + "b6a01191-0181-547f-b37c-139a841296e4", + "958ecf38-a371-5a53-920f-b28dddea3fe4", + "ec2195b2-3ecd-5a55-a085-db9bb844f818", + "dac1a702-ecf9-5fe8-bb31-ea3c13bc94d9", + "c9155893-bf1f-516c-b509-f6d2014d275e", + "55660a79-e4ed-5fc7-8232-aa1401bfd3e8", + "a85fbdc3-7bb7-5d61-9d14-e15cc49fc28a" + ], + "contexts": [ + "dynamic16,17, and several studies have proposed that impaired enhancer activation could be at the origin of disease1821. Besides interacting with nearby promoters, enhancers also engage in long-range interactions. Indeed, it is estimated that approximately 3540% of all promoter-enhancer interactions are intervened by at least one gene22, which makes exact enhancer-target prediction challenging. Long-range enhancers interactions can be identi ed by chromosome conformation capture methods23,24.", + "motifs found in its promoter (gene-to-sequence). We will referto the ensemble of these inuence interactions as genenetworks. The interaction between two genes in a gene network does not necessarily imply a physical interaction, but can also referto an indirect regulation via proteins, metabolites and ncRNA that have not been measured directly. Inuence interactions include physical interactions, if the two interacting partnersare a transcription factor, and its target, or two proteins in the", + "~90,000 enhancer-promoter interactions (fig.S36). As expected, ~75% of enhancer-promoterinteractions occurred within the same TAD, and genes with more enhancers tended to have high- er expression (Fig. 5B and fig. S36). We inte-grated the Hi-C data with QTLs; surprisingly, QTLs involving SNPs distal to eGenes but linked by Hi-C interactions showed significantly stron-ger associations (as indicated by the QTL Pvalue) than those with SNPs directly in the eGene pro- moter or exons (Fig. 5C and fig. S37).", + "histone-modifying proteins, and other factors to regulate polymerase-II activity. Such factors can bind in close prox- imity to promoters to influence gene expression. However, there is substantial evidence that additional genetic elements referred to as enhancers play major roles in determining cell- specific patterns of gene expression. 1517 Initially identified >30 years ago, enhancer elements can be located at various distances from promoters, typically between 1 and 50 kilo-", + "involved in the regulation of the target genes of both networks, but that the interaction partners through which this regulation is established differs for both target genes.", + "variants in epigenomic features using a systematic, data-driven approach. Bioinformatics 31,26012606 (2015). 13. Schug, J. et al. Promoter features related to tissue specicity as measured by Shannon entropy. Genome Biol. 6,R33 (2005).14. He, B., Chen, C., Teng, L. & Tan, K. Global view of enhancer-promoter interactome in human cells. Proc. Natl Acad. Sci. USA 111, E2191E2199 (2014). 15. Parker, S. C. J. et al. Chromatin stretch enhancer states drive cell-specic gene", + "regulation and harbor human disease risk variants. Proc. Natl Acad. Sci. USA 110, 1792117926 (2013). 16. Quang, D. X., Erdos, M. R., Parker, S. C. J. & Collins, F. S. Motif signatures in stretch enhancers are enriched for disease-associated genetic variants. Epigenet. Chromatin 8,23 (2015). 17. Whyte, W. A. et al. Master transcription factors and mediator establish super-enhancers at key cell identity genes. Cell153, 307319 (2013).", + "networks. In fact, several of the higher-order networks we describe below rely on having multiple reliable and interoperable transcriptional activators and repressors for proper functioning. Even so, these engineered transcription factors have not yet been fully characterized, and if they are to be used as building blocks for complex gene networks, then knowledge of their in vivo kinetics and", + "BMC Genomics 2008, 9:310 http://www.biomedcen tral.com/1471-2164/9/310 Page 10 of 17 (page number not for citation purposes)A gene regulatory network comprising the regulatory interactions of the significant genes and the significant and enriched TFs is shown in Figure 5. Obvious are the five hubs, the core regulatory circuit derived from [17]. Well-regulated candidates can be identified like Acly and Fabp4 . Target and regulator at the same time is Ipf1. Discussion", + "32. Kheradpour P, Ernst J, Melnikov A, Rogov P, Wang L, Zhang X, et al. Systematic dissection of regulatory motifs in 2,000 predicted human enhancers using a massively parallel reporter assay. Genome research. 2013:gr. 144899.112. 33. Rands CM, Meader S, Ponting CP, Lunter G. 8.2% of the human genome is constrained: variation in rates of turnover across functional element classes in the human lineage. PLoS genetics. 2014;10(7):e1004 525." + ], + "answer": "Enhancer-promoter interactions play a significant role in the regulation of gene networks by influencing gene expression levels and patterns. These interactions are crucial for determining cell-specific gene expression, as enhancers can regulate genes over long distances and are involved in complex regulatory networks [4]. Approximately 90,000 enhancer-promoter interactions have been identified, with a majority occurring within the same topologically associating domains (TADs), which suggests a structured and hierarchical organization of these interactions within the genome [3]. Genes with more enhancers tend to have higher expression levels, indicating that enhancers contribute to the regulation of gene expression by interacting with promoters [3]. Additionally, enhancer-promoter interactions can involve long-range interactions, making the prediction of specific enhancer-target relationships challenging [1]. These interactions are part of the broader gene networks that include various regulatory elements and factors, highlighting their importance in the regulation of gene networks as uncovered through platforms like GeneNetwork.org.", + "question": "What role do enhancer-promoter interactions play in the regulation of gene networks uncovered through GeneNetwork.org?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_9 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_9 new file mode 100644 index 0000000..909195f --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_9 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - The Genomics of Auditory.pdf", + "2016 - Genetics and Genomics of Coronary Artery Disease..pdf", + "2021 - Interpreting type 1 diabetes risk.pdf", + "2020 - Visualizing and interpreting cancer genomics.pdf", + "2016 - Genetics and Genomics of Coronary Artery Disease..pdf", + "2018 - High-Throughput Approaches onto Uncover (Epi)Genomic Architecture of Type 2 Diabetes.pdf", + "2018 - Human Genetics of Obesity and Type 2 Diabetes Mellitus.pdf", + "2022 - Genome-wide meta-analysis and omics integration identifies novel genes associated with diabetic kidney disease.pdf", + "2020 - Advances of single?cell genomics and epigenomics in human disease.pdf", + "2021 - Moving from in vitro to in vivo CRISPR screens.pdf" + ], + "extraction_id": [ + "0c7a27ef-7a65-5b32-8129-b168a336018a", + "203710b7-3267-5ecf-9397-b5becdaeead1", + "607a959f-6f63-5f18-8935-b76d87aa4820", + "ffc72db8-67ea-508a-aba1-d2592bd00ea2", + "2e588b06-841f-50d7-b161-330199d5c4cf", + "9cd48835-a7bf-50aa-928f-adb817e229d4", + "786d21d6-5544-5357-8163-1a1a96f6a791", + "d26b98eb-66cc-5185-9061-cda1fe904ba6", + "f740892a-7817-58b0-bec4-8648086b2353", + "6078715b-9964-5922-8fc9-5f105d0001ca" + ], + "document_id": [ + "f56b6ae4-e05a-5851-9c10-4bd62f237778", + "23a1b7be-9541-5e16-b9cc-24ea420a4961", + "9f13ec69-195b-55eb-a549-b3eb3dc0f321", + "eaae9d37-9c40-5e1c-9de9-d5ebcce9eae3", + "23a1b7be-9541-5e16-b9cc-24ea420a4961", + "1cb0c4ac-c1fe-55c2-919c-52cd5018c00d", + "2083de31-17c6-5d1e-9aa6-2efc6c1d9ac2", + "b9194555-5fdb-549e-9edb-d108132a7dd1", + "afe53f5a-3962-520f-be55-9df5bfdaad70", + "31d137b9-90a3-5b5a-ba4f-7a4d5b2c61bc" + ], + "id": [ + "chatcmpl-AIGs1N0h1lzkHw7McrwTnV7iXLWUI", + "9172db35-cec2-5970-8e5d-d73357f13abe", + "2020244c-6b6e-5613-900e-d7e32f6c4d57", + "f4ae7779-bbfc-5a13-bcd2-2e6724011eb8", + "1bdc47f8-9b31-5f89-8381-2238c4aec987", + "6b16574f-b513-5361-a0a8-a19f86ef6316", + "5297cd77-3ccf-570e-9ff9-bdb778638793", + "a49d3e49-6005-5890-ba75-8e5d59df13e5", + "eafc949f-7238-5776-bfef-5ccd9f91787e", + "c93bf9e1-39bd-59a9-8dd1-1b67a0853b8c", + "6442bc7c-4e2e-553f-82c4-b2f09e01823e" + ], + "contexts": [ + "high-throughput sequencing (ATAC-seq) allows the characterization of accessible chromatin re- gions,whichcorrespondtoareasoftranscriptionactivity(149).Examiningthethree-dimensional organization of the genome can facilitate the association between regulatory elements and their target genes by dividing the genome into discrete functional blocks, commonly known as topologically associating domains (139). The Encyclopedia of DNA Elements (ENCODE) and", + "variants, it is still unclear how multiple independent variants influence gene networks through changes in chromatin states. The Assay for Transpose Accessible Chromatin (ATAC-seq) was recently developed to address the need for sensitive as- says requiring less starting material, which also has the ability to simultaneously profile open chromatin, transcription factor- binding footprints, as well as nucleosome positioning in a single assay [ 57]. Given the limited availability of primary", + "Data Fig.4a). To relate cell-type-resolved accessible chromatin to gene expression, we created a single-cell RNA sequencing (scRNA-seq) refer - ence map of peripheral blood and pancreas. We assigned cell-type identi - ties for 90,495 cells to 29 clusters, which identified similar cell types and proportions to snATACseq (Extended Data Fig.5ac). To characterize cis-regulatory programs, we aggregated reads from cells within each snATACseq cluster and identified accessible chroma -", + "DNA methylation and ATAC-seq data (Supplementary Fig. 3). Integration across gene- and coordinate-centric views helps users examine genomic events in different chromosome contexts. For example, Xenas Visual Spreadsheet can help elucidate whether a gene amplification is part of a chromosomal arm duplication or a focal amplification (Supplementary Fig. 6).", + "matin accessibility assay ATAC-seq has been applied to single cells and has been shown to capture a higher order chromatin structure resembling the profiles generated by Hi-C [ 72]. Additionally, for CAD candidate genes that are transcrip- tion factors (TF), such as TCF21 and STAT3, protein-DNA interactions could be studied on a genome-wide scale using chromatin immunoprecipitation sequencing (ChIP-Seq). Recently, ChIP-Seq performed against TCF21 in human cor-", + "seq), Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq), Formaldehyde- Assisted Isolation of Regulatory Elements (FAIRE-seq) and DNase I hypersensitive sites sequencing (DNase-seq). The integration of DNA methylation data (WGBS) and chromatin accessibility data (ATAC-seq) with established ChIP-seq mark ers have provided an opportunity to create high-resolution", + "94. Mumbach MR, et al. HiChIP: efficient and sensitive analysis of protein-directed genome architecture. Nat Methods. 2016;13:919922. doi: 10.1038/nmeth.3999. 95. Kumasaka N, et al. Fine-mapping cellular QTLs with RASQUAL and ATAC- seq. Nat Genet. 2016;48:206213. doi: 10.1038/ng.3467. 96. Buenrostro JD, et al. ATAC-seq: a method for assaying chromatin acces- sibility genome-wide. Curr Protoc Mol Biol. 2015;109:21.29.121.29.9. doi: 10.1002/0471142727.mb2129s109.", + "CpG sites. Single nucleus Assay for Transposase-Accessible Chromatinusing sequencing (snATACseq) was informative of chromatin opennessin various kidney cell types. The RegulomeDB is a database with exten-sive epigenetic annotation for SNPs. The promoter capture HiC (PCHiC) sequencing data identified sequence interaction with gene promoters,", + "a method for assaying chromatin accessibility genome-wide. Curr Protoc Mol Biol 109:21.29.2121.29.29. https ://doi.org/10.1002/04711 42727 .mb212 9s109 Bysani M etal (2019) ATAC-seq reveals alterations in open chromatin in pancreatic islets from subjects with type 2 diabetes. Sci Rep 9:7785. https ://doi.org/10.1038/s4159 8-019-44076 -8 Camp JG etal (2015) Human cerebral organoids recapitulate gene expression programs of fetal neocortex development. Proc Natl", + "genes are involved with multiple biological features. RNA sequencing has been coupled with protein quantication (DNA barcoded antibodies to quantify protein expression) and ATAC-seq to facilitate the study of genes involved with chromatin accessibility remodeling. their environment [14 , 31 , 88 , 95 , 105] . Advances in multiplexed gene editing and transcriptional programing will also enable CRISPR screens" + ], + "answer": "The integration of ATAC-seq data with RNA-seq data can provide valuable insights into chromatin accessibility and gene regulation by combining information about open chromatin regions with gene expression profiles. ATAC-seq is a technique that characterizes accessible chromatin regions, which are often associated with transcriptional activity [1]. This method can simultaneously profile open chromatin, transcription factor-binding footprints, and nucleosome positioning [2]. By integrating this data with RNA-seq, which measures gene expression levels, researchers can relate chromatin accessibility to gene expression patterns. For example, by creating a reference map using single-cell RNA sequencing (scRNA-seq) and assigning cell-type identities, researchers can relate cell-type-resolved accessible chromatin to gene expression [3]. This integration helps in identifying cis-regulatory programs by aggregating reads from cells within each ATAC-seq cluster and linking them to gene expression data. Overall, the integration of ATAC-seq and RNA-seq data allows for a comprehensive understanding of how chromatin accessibility influences gene regulation, providing insights into the regulatory elements that control gene expression in different cellular contexts.", + "question": "How can the integration of ATAC-seq data with RNA-seq data on GeneNetwork.org inform about chromatin accessibility and gene regulation?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/human_cs_aging.json b/gnqa/paper2_eval/data/dataset/human/human_cs_aging.json new file mode 100644 index 0000000..fc48156 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/human_cs_aging.json @@ -0,0 +1,190 @@ +{ + "question": [ + "List as many studies as you can that include rapamycin.", + "Why is it so diffuclut to map gene loci that control aging in humans?", + "What is apoptosis?", + "which genes are involved in the aging process", + "what causes the aging process", + "which genes are involved in aging", + "what genes are involved in the aging process", + "Describe the genotypes related to Alzheimers and dementia which have commonalities with those for aging.", + "Describe the genotypes related to Alzheimer's and dementia which have commonalities with those for aging.", + "What genetic factors influence aging in humans?", + "what genes are associated with aging?", + "Which genes are associated with aging in human", + "What is GeneNetwork and how does it relate to aging research?" + ], + "answer": [ + "Here are the studies that include rapamycin: 1. **Yin L, Ye S, Chen Z, Zeng Y (2012)**: \"Rapamycin preconditioning attenuates transient focal cerebral ischemia/reperfusion injury in mice\" [1]. 2. **Spilman P, Podlutskaya N, Hart MJ, Debnath J, Gorostiza O, Bredesen D, Richardson A, Strong R, Galvan V (2010)**: \"Inhibition of mTOR by rapamycin abolishes cognitive deficits and reduces amyloid-beta levels in a mouse model of Alzheimer's disease\" [1]. 3. **Anisimov VN, Zabezhinski MA, Popovich IG, Piskunova TS, Semenchenko AV, Tyndyk ML, Yurova MN, Rosenfeld SV, Blagosklonny MV (2011)**: \"Rapamycin increases lifespan and inhibits spontaneous tumorigenesis in inbred female mice\" [2]. 4. **Lamming DW, Ye L, Katajisto P, Goncalves MD, Saitoh M, Stevens DM, et al. (2012)**: \"Rapamycin-induced insulin resistance is mediated by mTORC2 loss and uncoupled from longevity\" [5]. 5. **Tataranni T, Biondi G, Cariello M, Mangino M, Colucci G, Rutigliano M, et al. (2011)**: \"Rapamycin-induced hypophosphatemia and insulin resistance are associated with mTORC2 activation and klotho expression\" [5]. 6. **Wilkinson JE, Burmeister L, Brooks SV, Chan CC, Friedline S, Harrison DE, et al. (2012)**: \"Rapamycin slows aging in mice\" [7]. 7. **Reihl K, Seals D, Henson G, LaRocca T, Magerko K, Bosshardt G, et al. (2013)**: \"Dietary rapamycin selectively improves arterial function in old mice\" [7]. 8. **Miller RA, Harrison DE, Astle CM, Fernandez E, Flurkey K, Han M, et al. (2014)**: \"Rapamycin-mediated lifespan increase in mice is dose and sex dependent and metabolically distinct from dietary restriction\" [10]. These references provide a comprehensive list of studies involving rapamycin.", + "Mapping gene loci that control aging in humans is difficult due to several reasons: 1. **Complexity of the Phenotype**: Aging is a highly complex phenotype influenced by numerous genes and their interactions with environmental factors. This complexity makes it challenging to identify specific genetic loci that control aging [10]. 2. **Multiple Pathophysiological Processes**: Multiple genes could mediate the aging process through various pathophysiological processes and diseases, adding layers of complexity to the genetic mapping [6]. 3. **Rare vs. Common Variants**: Finding common genetic variants that account for the entire genetic component of human lifespan variability has proven difficult. If rare variants explain most of the genetic variation in aging, new genotyping techniques and analysis methods are required to identify these genes and pathways [3], [4]. 4. **High Number of Loci**: There are more than 1000 loci that exhibit age-dependent changes in gene expression. Not all of these loci are causally involved in aging, making it a substantial challenge to sort out which ones are relevant [8]. 5. **Species Differences**: Many loci that control aging in model organisms like Drosophila may not have the same effect on human aging. This lack of direct correlation between species complicates the identification of relevant human aging genes [1]. 6. **Heritability and Genetic Variation**: While some fraction of human lifespan is heritable, the genetic variation contributing to aging is complex and involves numerous loci associated with age-related traits, further complicating the mapping process [7]. These factors collectively contribute to the difficulty in mapping gene loci that control aging in humans.", + "Apoptosis is a form of programmed cell death that is essential for normal cell turnover and various biological processes [1]. It is characterized by a sequence of well-defined events leading to cell destruction [2]. Apoptosis occurs in a controlled manner, resulting in the elimination of cells by macrophages without causing secondary damage to surrounding cells [3]. This process can be initiated through several pathways, including Bcl-2 activation of caspases, signals from death receptors on the plasma membrane, or induction by granzyme B secreted from cytotoxic T cells [1].", + "Several genes are involved in the aging process, as identified in the provided context: 1. **APOE**: This gene is involved in lipoprotein metabolism and is one of the candidate genes related to longevity [2]. 2. **Genes involved in cell cycle regulation, cell growth, and signal transduction**: These genes play a role in maintaining genome stability and are implicated in the aging process [2]. 3. **Genes involved in DNA repair and chromatin remodeling**: These genes are down-regulated during aging, indicating their involvement in the aging process [3], [7]. 4. **Genes inducing apoptosis**: These genes are also associated with the aging process [4]. 5. **Werners syndrome gene**: Mutations in this gene result in segmental progeroid syndromes, which are related to aging [5]. 6. **Genes in the insulin/insulin-like signaling pathway**: These genes are critical in pathways previously related to aging [5]. 7. **Genes driving cellular senescence**: These genes tend to be overexpressed with age in human tissues and are significantly overrepresented in anti-longevity and tumor-suppressor genes [6]. These genes collectively contribute to various aspects of the aging process, including genomic stability, cellular senescence, and response to oxidative stress.", + "The aging process is caused by several factors, including: 1. **Accumulated Damage**: Age-related decline results from damaging by-products of metabolism and/or inefficient repair mechanisms, leading to damage that accumulates throughout the lifespan [2]. 2. **Mutation Accumulation**: A process of mutation accumulation in somatic cells, although no specific mechanism has been proposed for how this leads to the multitude of degenerative processes that comprise aging [3]. 3. **Free Radicals and Oxidative Stress**: The theory of free radicals suggests that aging results from inadequate protection against cell and tissue damage by free radicals and oxidative stress throughout life [4]. 4. **Wear-and-Tear**: The wear-and-tear theory posits that cumulative damage from the continuous functioning of vital processes leads to aging and death due to stochastic errors gradually arising [4]. 5. **Cell Senescence and Death Pathways**: Cell senescence and cell death pathways are major causes of aging phenotypes, such as organ atrophy, which appear to be pre-programmed responses of a sizable fraction of the cell population [6]. 6. **Accumulated Defects in Function**: Progressive changes in a cell or organism lead to accumulated defects in function, resulting in system failure and death [8]. 7. **Loss of Genomic Stability**: Loss of genomic stability due to reduced DNA repair capacities, loss of proliferative potential caused by increased senescence, and age-related alterations in DNA-methylation patterns that affect cellular plasticity [9]. These factors collectively contribute to the aging process and the associated decline in physiological functions.", + "Several genes are involved in the aging process, as identified in various studies: 1. **APOE**: This gene is involved in lipoprotein metabolism and is critically related to longevity [4]. 2. **Superoxide dismutase**: A free-radical scavenger gene that has been shown to increase lifespan in model organisms [8]. 3. **Methuselah**: A potential G protein-coupled receptor gene in *Drosophila melanogaster* associated with increased lifespan [8]. 4. **p66shc**: An oxidative stress-response gene involved in aging [8]. 5. **Insulin/IGF1/GH pathway genes**: These genes are part of a pathway that affects longevity across different model organisms [5]. 6. **Genes involved in DNA repair and chromatin remodeling**: These genes are down-regulated during aging [7]. 7. **Genes associated with cellular senescence**: These include genes that induce or inhibit cellular senescence, which are overexpressed with age and are linked to anti-longevity and tumor-suppressor functions [2]. Additionally, studies have identified between 300 to 750 genes related to longevity that are involved in various life activities such as growth, development, energy metabolism, oxidative stress, genomic stability maintenance, and neurocognition [4].", + "Several genes are involved in the aging process, each playing a role in various biological activities and pathways. Key genes and their associated functions include: 1. **APOE**: Involved in lipoprotein metabolism [2]. 2. **Superoxide dismutase**: A free-radical scavenger [8]. 3. **Methuselah**: A potential G protein-coupled receptor in *Drosophila melanogaster* [8]. 4. **p66shc**: An oxidative stress-response gene [8]. 5. **Genes involved in DNA repair and chromatin remodeling**: These genes are down-regulated during aging [3], [10]. 6. **Genes involved in cell cycle regulation, cell growth, and signal transduction**: These genes help maintain genome stability [2]. 7. **Genes involved in inducing apoptosis**: These genes are associated with damage response [5]. 8. **Genes in the insulin/insulin-like signaling pathway**: Critical in pathways previously related to aging [4]. These genes collectively contribute to processes such as growth and development, energy metabolism, oxidative stress response, genomic stability maintenance, and neurocognition [2].", + "The genotypes related to Alzheimer's disease (AD) and dementia that have commonalities with those for aging include several key genetic factors: 1. **Amyloid Precursor Protein (APP)**: Mutations in the APP gene are implicated in the early-onset form of Alzheimer's disease. This gene is also associated with the aging process, as the accumulation of amyloid plaques is a hallmark of both AD and age-related cognitive decline [1], [10]. 2. **Presenilin 1 and 2 (PSEN1 and PSEN2)**: These genes are also involved in the early-onset form of AD and are linked to the production of amyloid-beta, a protein that accumulates in the brains of individuals with AD and is also associated with aging [1]. 3. **APOE \u03b54 Allele**: The APOE \u03b54 allele is a well-known genetic risk factor for late-onset Alzheimer's disease. It is also associated with aging, as it influences lipid metabolism and is linked to age-related cognitive decline [2], [4]. 4. **Genetic Variance in Age of Onset**: Twin studies suggest that 37-78% of the variance in the age of onset of Alzheimer's disease can be attributed to additive genetic effects, indicating a significant overlap between the genetic factors influencing AD and those affecting the aging process [2]. These genotypes highlight the genetic commonalities between Alzheimer's disease, dementia, and the aging process, emphasizing the multifactorial nature of these conditions.", + "The genotypes related to Alzheimer's disease (AD) and dementia that have commonalities with those for aging include several key genetic factors: 1. **Apolipoprotein E (APOE)**: The APOE gene, particularly the APOE \u03b54 allele, is a well-known genetic risk factor for late-onset Alzheimer's disease. It is also associated with cognitive decline in aging populations [9]. Studies have shown that individuals carrying the APOE \u03b54 allele have an increased risk of developing Alzheimer's disease and exhibit more rapid cognitive decline as they age [8]. 2. **Amyloid Precursor Protein (APP)**: Mutations in the APP gene are implicated in the early-onset form of Alzheimer's disease. While these mutations are rare, they contribute to the understanding of the disease's pathology, which overlaps with mechanisms involved in aging, such as amyloid plaque formation [3], [6]. 3. **Presenilin 1 and 2 (PSEN1 and PSEN2)**: Mutations in these genes are also associated with early-onset Alzheimer's disease. These mutations affect the production of amyloid-beta, a key component of the plaques found in the brains of Alzheimer's patients. The accumulation of amyloid-beta is a process that is also observed in the aging brain, albeit to a lesser extent in cognitively healthy individuals [3], [6]. 4. **Genetic Variance in Age of Onset**: Twin studies suggest that a significant portion of the variance in the age of onset of Alzheimer's disease can be attributed to genetic factors, indicating a genetic overlap between aging and the development of Alzheimer's disease [1]. 5. **Vascular Health and Aging**: Vascular health is another important factor that links aging with Alzheimer's disease. Midlife vascular risk factors, such as hypertension and diabetes, are associated with increased amyloid deposition in the brain, which is a hallmark of Alzheimer's disease. This suggests that the genetic factors influencing vascular health and aging may also play a role in the development of Alzheimer's disease [5]. In summary, the genotypes related to Alzheimer's disease and dementia that share commonalities with those for aging include APOE, APP, PSEN1, and PSEN2, as well as genetic factors influencing vascular health and the age of onset of the disease [1], [3], [5], [6], [8], [9].", + "Genetic factors play a significant role in influencing aging in humans. Here are some key points based on the provided context: 1. **Heritability of Longevity**: Human longevity shows a relatively modest heritability of less than 50% [1]. Twin studies have estimated the genetic contribution to general human longevity to be about 20-30% [10]. 2. **Genome-Wide Association Studies (GWAS)**: Large-scale GWAS have identified numerous loci associated with age-related traits, indicating that specific genetic variations can influence aging [7]. 3. **Specific Genetic Variants**: The APOE gene and its variants have been comprehensively analyzed for their association with late-onset Alzheimer's disease, which is an age-related condition [2]. This suggests that certain genetic markers are linked to age-related diseases. 4. **Epigenetic Regulation**: Epigenetic mechanisms, which involve changes in gene expression without altering the DNA sequence, also play a crucial role in aging. Environmental inputs can affect genomic stability through epigenetic regulation [4]. 5. **Inheritance Studies**: Studies on the inheritance of human longevity, such as those conducted in Iceland, have provided insights into the genetic factors that contribute to a longer lifespan [5]. In summary, aging in humans is influenced by a combination of genetic factors, including specific genetic variants, heritability, and epigenetic regulation [1], [2], [4], [5], [7], [10].", + "Several genes have been associated with aging. Here are some key points from the provided context: 1. **Genes in Model Organisms**: A list of genes strongly associated with aging in model organisms includes those involved in segmental progeroid syndromes, such as the Werner syndrome gene, and genes critical in pathways previously related to aging, such as the insulin/insulin-like signaling pathway [1]. 2. **Gene Expression Analysis**: Studies have identified genes overexpressed with age that have protective functions, suggesting they help manage aging and could be targets for manipulation. Gene expression analysis of caloric restriction (CR) has also been conducted to identify associated genes [2]. 3. **Human Longevity-Associated Genes**: Dozens of genes have been associated with human longevity, although only a handful have shown consistent effects across populations [4]. 4. **GenAge Data Set**: A curated list of human genes associated with aging in different model systems is available from the GenAge data set [6]. 5. **GenAge Online Database**: Genes with established aging-related functions were identified by interrogation of the GenAge online database, aging-associated Gene Ontology groups, and hand annotation [9]. These references collectively highlight the involvement of various genes and pathways in the aging process.", + "Several genes have been associated with aging in humans according to the provided context: 1. **GenAge Data Set**: This data set includes genes that may regulate aging in humans or are considerably associated with the human aging phenotype [2]. 2. **HECW2, HIP1, BIN2, GRIA1, KCNQ4, LMO4**: These genes are highly expressed in the brain and have been previously related to the regulation of neuronal excitability and plasticity [4]. 3. **Werners Syndrome Gene**: Mutations in this gene result in segmental progeroid syndromes, which are critical in pathways previously related to aging, such as the insulin/insulin-like signaling pathway [7]. These references indicate that there are multiple genes associated with aging in humans, with some being highly expressed in specific tissues like the brain and others being involved in critical aging-related pathways.", + "GeneNetwork is a resource that has been significantly updated and enhanced to include data from 10 species, multi-omics analysis, updated code, and new tools. It serves as an exciting resource for predictive medicine and systems genetics, constantly being maintained and improved [4]. In relation to aging research, GeneNetwork is used to study genetic networks and pathways linked with aging. For example, researchers use GeneNetwork to construct modular networks of aging, which can provide insights into how different genes interact and affect longevity and aging processes [1]. This network-based approach allows for the identification of potential longevity genes and the links between genes and aging-related diseases [3]. Thus, GeneNetwork plays a crucial role in the functional genomics of aging by enabling the analysis and visualization of complex genetic interactions and their implications for aging and longevity." + ], + "contexts": [ + [ + "168. Yin L, Ye S, Chen Z, Zeng Y . Rapamycin preconditioning attenuates tran- sient focal cerebral ischemia/reperfusion injury in mice. Int J Neurosci. 2012;122:748756. doi: 10.3109/00207454.2012.721827 169. Spilman P, Podlutskaya N, Hart MJ, Debnath J, Gorostiza O, Bredesen D, Richardson A, Strong R, Galvan V . Inhibition of mTOR by rapamy-cin abolishes cognitive deficits and reduces amyloid-beta levels in a mouse model of Alzheimers disease. PLoS One. 2010;5:e9979. doi: 10.1371/journal.pone.0009979", + "Anisimov VN, Zabezhinski MA, Popovich IG, Piskunova TS, Semenchenko AV, Tyndyk ML, Yurova MN, Rosenfeld SV,Blagosklonny MV (2011b) Rapamycin increases lifespan and inhibits spontaneous tumorigenesis in inbred female mice. Cell Cycle 10:42304236 Augustine JJ, Bodziak KA, Hricik DE (2007) Use of sirolimus in solid organ transplantation. Drugs 67:369391 Bannister CA, Holden SE, Jenkins-Jones S, Morgan CL, Halcox JP,", + "ACCEPTED MANUSCRIPTACCEPTED MANUSCRIPT mTOR complex 2 (mTORC2), the less clearly identified and less sensitive to rapamycin. Most information to date on the r ole of mTOR has studied the insulin/nutrient signaling via the mTORC1 and significantly less in known about the role of mTORC2 ( in this review, future references measure either mTORC1 or general mTOR activity )[251]. Earlier this decade studies showed that decreasing TOR signaling, genetically or with rapamycin,", + "Harrison, D.E., Strong, R., Sharp, Z.D., Nelson, J.F., Astle, C.M., Flurkey, K.,Nadon, N.L., Wilkinson, J.E., Frenkel, K., Carter, C.S., et al. (2009). Rapamycin Cell148, January 20, 2012 2012 Elsevier Inc. 55", + "96. Lamming DW, Ye L, Katajisto P, Goncalves MD, Saitoh M, Stevens DM, etal. Rapamycin- induced insulin resistance is mediated by mTORC2 loss and uncoupled from longevity. Science. 2012;335:163843. 97. Tataranni T, Biondi G, Cariello M, Mangino M, Colucci G, Rutigliano M, etal. Rapamycin- induced hypophosphatemia and insulin resistance are associated with mTORC2 activation and klotho expression. Am J Transplant. 2011;11(8):165664.", + "ing these aspects in future studies on the effects of resveratrol could help to study in greater depth the mechanisms of action of this compound [56]. Rapamycin Rapamycin is a macrolide isolated from Streptomyces hygroscopicus, a bacteria from Pascua Island (Rapa Nui). It has functions as an antibiotic, an immune sup- pressant drug, and it is also proposed as a CRM.After the first studies, it was found that rapamycin could induce the extension of the replicative life of yeast through the", + "[257] Wilkinson JE, Burmeister L, Brooks SV, Chan CC, Friedline S, Harrison DE, et al. Rapamycin slows aging in mi ce. Aging Cell. 2012;11:675 -82. [258] Selman C, Tullet JM, Wieser D, Irvine E, Lingard SJ, Choudhury AI, et al. Ribosomal protein S6 kinase 1 signaling regulates mammalian life span. Science. 2009;326:140 -4. [259] Reihl K, Seals D, Henson G, LaRocca T, Mag erko K, Bosshardt G, et al. Dietary rapamycin selectively improves arterial function in old mice. FASEB Journal. 2013;27:1194.17.", + "29. Wilkinson JE, Burmeister L, Brooks SV, Chan C-C, Friedline S, Harrison DE, et al. Rapamycin slows aging in mice. Aging Cell. 2012;11:675 82. 30. Lamming DW, Ye L, Katajisto P, Goncalves MD, Saitoh M, Stevens DM, et al. Rapamycin-induced insulin resistance is mediated by mTORC2 loss and uncoupled from longevity. Science. 2012;335:1638 43. 31. Zampieri M, Ciccarone F, Calabrese R, Franceschi C, Brkle A, Caiafa P. Reconfiguration of DNA methylation in aging. Mech Ageing Dev. 2015;151:60 70.", + "files [55, 62]. Of note, rapamycin in particular appears to induce additional changes u nrelated to age-associated changes. While both CR and rapamycin induced these non-age-related effects, this effect was much more marked for rapamycin. These non age-related epigenetic changes include gains of methylation at genes, enhancers and CpG islands and losses of methylation at genes and enhancers. Conceivably, such non age-related effects of rapamycin in", + "23 94. Chakrabarti P, English T, Shi J, Smas CM, Kandror KV .Mammalian target of rapamycin complex 1 suppresses lipolysis, stimulates lipogenesis, and promotes fat storage. Diabetes. 2010;59:77581. 95. Miller RA, Harrison DE, Astle CM, Fernandez E, Flurkey K, Han M, et al. Rapamycin- mediated lifespan increase in mice is dose and sex dependent and metabolically distinct from dietary restriction. Aging Cell. 2014;13:46877." + ], + [ + "that is differentiated at hundreds of loci. Many ofthe loci that control aging in Drosophila will not have the same effect on human aging. On the other hand,we expect that other loci will work in a parallelmanner in humans. We have no way of knowing a priori which group any particular locus will belong in. Thus, the individual mutants that increase Drosophila lifespan may or may not come from loci", + "effect fundamental mechanisms of aging (14, 16). The drawbacksof such studies include the improbability of picking the right geneto study the myriad of known and unknown genes affecting theprocess of interest (17). The linkage study described heremarkedly improves the efficiency of such association studies bydefining a region likely to contain polymorphism(s) with signif-icant influence on life span. Additional association studies with these families and repli-", + "understanding of molecular mechanisms underlyingthe human ageing process. Like other complexhuman traits, nding common variants that accountfor the entire genetic component of human lifespan variability has proved difcult. If rare variants rather than common variants explain most of the genetic vari-ation in ageing among humans, new genotypingtechniques and new analysis methods must be devel-oped to nd genes and pathways involved in ageing.Next-generation sequencing technologies are faster", + "understanding of molecular mechanisms underlyingthe human ageing process. Like other complexhuman traits, nding common variants that accountfor the entire genetic component of human lifespan variability has proved difcult. If rare variants rather than common variants explain most of the genetic vari-ation in ageing among humans, new genotypingtechniques and new analysis methods must be devel-oped to nd genes and pathways involved in ageing.Next-generation sequencing technologies are faster", + "Map contains 1119 and 1459 curated human and mouse aginggenes, respectively, covering almost all scales of aging, rangingfrom molecular damage to genetic predisposition. Cross-speciescomparison revealed a modest overlap between known humanand mouse aging genes, suggesting both conservation of core sen- escence pathways and fundamental differences in aging between mice and humans (Fig. 2E). Aging-associated genes can alternatively be identified in a", + "Several explanations are possible for the lack of genome- wide signicant ndings. First, mortality is arguably 1 ofthe most complex phenotypes, and several trajectories to-ward extreme old age have been identied (Evert et al.,2003). Multiple genes could mediate the aging process butwould have their effects through numerous different patho-physiological processes and diseases that act as intermediate", + "discover core mechanisms of regulation.ANALYSIS OF HUMAN VARIATION IN THE GENETIC CONTROL OF LONGEVITY Heritability studies have convincingly demonstrated that at least some fraction of human lifespan is heritable. In tandem, large-scale genome-wide association studies (GWAS) have identied numerous loci associated with age-related traits (Buniello et al., 2019). While genetic studies have functionally shown an inverse eect of multiple age-related, disease-", + "[12]More than 1000 loci exhibit age-dependent changes in geneexpression (1264 genes). This is a substantialproblem, because not all of these loci will be causally involved in aging, and there are so many to sort out. An additional application of gene chip technologyis to compare ies with and without a lifespanmodulating physiological treatment. Pletcher et al.", + "such alleles. The frequency of genetic variants wastypically compared between highly aged cases andyoung controls, revealing loci at which genetic variantsmay contribute to a higher or lower probability ofsurvival into old age. So far, this approach hasmainly been applied to study single candidate genessuch as the mammalian orthologues of loci in IIS sig-nalling pathways that emerged from lifespan extensionstudies in animal models. An interesting observationthat needs to be taken into human studies is the", + "Kenyon, 2010; Vellai et al., 2003 ). However, in humans, common variants within genes involved in these pathways have not been consistently associated with lifespan ( Chris-tensen et al., 2006; Kenyon, 2010; Kuningas et al., 2008; Vijg and Suh, 2005 ). The lack of success in the identication of genes related to aging in humans may be due to the complexity of the phenotype. One approach to investigate aging and longevity is to compare frequencies of genetic variants between no-" + ], + [ + "Cell Death A form of programmed cell death, apoptosis is necessary for normal cell turnover and is essential to a plethora of other biological processes. Apoptosis can be executed via Bcl-2 activation of caspases, via signals from the death receptor on the plasma membrane, or via induction by granzyme Bsecreted from cytotoxic T cells (Tc cells) [ 35]. Endonucleases and proteases are activated by active caspases, eventually leading to the death of the cell. With age, however, apoptotic activity changes.", + "(during development and for maintenance of homeostasis) in multi -cellular organism is apoptosis, which is character ized by a sequence of well -defined events resulting in cell destruction. Dysregulation of apoptosis is responsible for many physiological health problems and diseases; therefore, it is necessary to understand the responsible signaling pathways and complex interplay of cellularprocesses. Results: A combined mathematical model of apoptosis", + "is, apoptosis and necrosis. Apoptosis is considered as thedefault pathway, where cell death occurs in a controlledmanner resulting in the elimination of cells by macrophageswithout secondary damage of the surrounding cells. In con-trast, necrosis is considered an uncontrolled process whichleads to disruption of cells promoting tissue inammation[187]. Several transition states between the two pathways", + "tion of cells undergoing apoptosis. Immunol Today 14: 131 136. 82. Platt N, Silva RP, da Gordon S (1998) Recognizing death: the phagocytosis of apoptotic cells. Trends Cell Biol 8: 365 372. 83. Giles KM, Hart SP, Haslett C, Rossi AG, Dransfield I (2000) An appetite for apoptotic cells? Controversies and challenges. Br J Haematol 109: 1 12.", + "tion of cells undergoing apoptosis. Immunol Today 14: 131 136. 82. Platt N, Silva RP, da Gordon S (1998) Recognizing death: the phagocytosis of apoptotic cells. Trends Cell Biol 8: 365 372. 83. Giles KM, Hart SP, Haslett C, Rossi AG, Dransfield I (2000) An appetite for apoptotic cells? Controversies and challenges. Br J Haematol 109: 1 12.", + "the induc-tion of apoptosis.", + "to cancer , b ut probably not rele v ant to the i ntrinsic aging process i n yeast. Apoptosis Cell suicide, or apoptosis, i s a well-studied biological phenomenon in multicellular or g anisms t hat allo ws specic cells to be remo v e d during t he de v e lopment of com- ple x tissues, o r potentially dangerous damaged cells to be destro yed for t he benetof the w hole o r g anism. T he lack of an apparent e v olutionary benet for s uch a p ro-", + "15Apoptosis is caused by the activation of the caspase cascade, which isinitiated by two signaling routes (stress-induced death and death-domainreceptor-induced death) (Domen 2001). This process can be prevented by anti-apoptotic molecules, such as Bcl-2 (Domen and Weissman 2000). Directevidence for the involvement of apoptosis in HSC number regulation came fromthe findings that overexpression of the anti-apoptotic gene bcl-2 led to increasednumbers of Thy-1.1low, Sca-1+, c-kit+, Lin- cells, a population", + "15Apoptosis is caused by the activation of the caspase cascade, which isinitiated by two signaling routes (stress-induced death and death-domainreceptor-induced death) (Domen 2001). This process can be prevented by anti-apoptotic molecules, such as Bcl-2 (Domen and Weissman 2000). Directevidence for the involvement of apoptosis in HSC number regulation came fromthe findings that overexpression of the anti-apoptotic gene bcl-2 led to increasednumbers of Thy-1.1low, Sca-1+, c-kit+, Lin- cells, a population", + "Apoptosis modulating genesApopotosis or programmed cell death is associated withalterations in cell morphology, particularly the nucleus, withendonucleatytic cleavage of DNA into nucleosomal lengthfragments.Apoptosis may resultfrom withdrawalofgrowth signals.Fas, a transmembrane protein of the nerve growth factor/tumor necrosis factor receptor family signals apoptotic de-ath signals apoptotic death in some cell types. Fas but notbel-2 gene expression is negatively regulated by TSH (Ka-wakami et al., 1996)," + ], + [ + "OTHER AGING RELATED GENES", + "ation of the process of aging. Studies revealed from 300 to 750 genes related to longev- ity that are critically involved in a variety of life activities, such as growth and developme nt, energy metabolism, oxi- dative stress, genomic stability maintenance, and neurocog- nition [ 4]. These candidate genes include mainly APOE, a gene involved in lipoprotein metabolism [ 5,6]. Others are those involved in cell cycle regulation, cell growth and signal transduction, the maintenance of genome stability,", + "down-regulated during aging were genes involved in DNA repair and chromatin remodelling. 55 While these studies revealed thousands of age-regulated genes, the ultimate causes of these expression perturbations remain unknown. Analyzing age-dependent gene expression changes using multi-dimensional genetical genomics could bring the identification of genes causing the age-induced alterations and thereby future therapeutic intervention strategies one step closer. Adding the dimension of epigenetics", + "dam-age, as well as genes involved in inducing apoptosis (10, 11). Theaging process is also accompanied by changes in the expressionpatterns of a number of genes (1214). How the regulation ofgene expression in aging correlates with that in response tooxidative stress, however, is understood poorly.", + "www.ncbi.nlm.nih.gov/homologene) of genes strongly asso-ciated with aging in model organisms. Also included are genesin which mutations result in segmental progeroid syndromes,such as the Werners syndrome gene, as well as genes criticalin pathways previously related to aging, such as the insulin/insulin-like signalling pathway (de Magalhes et al ., 2005a). The", + "genes driving cellular senescence, and perform various integrative analyses. Genes inducing cellular senescence tend to be overexpressed with age in human tissues and are significantly overrepresented in anti-longevity and tumor-suppressor genes, while genes inhibiting cellular senescence overlap with pro-longevity and oncogenes. Furthermore, cellular senescence genes are strongly conserved in mammals but not in invertebrates. We also build", + "those down-regulated during aging were genes involved in DNA repair and chromatin remodelling (Chambers et al. 2007b ). While these studies revealed thousands of age- regulated genes, the ultimate causes of these expressionperturbations remain unknown. Analyzing age-dependent gene expression changes using multidimensional genetical genomics could bring the identification of genes causingthe age-induced alterations and thereby future therapeutic intervention strategies one step closer.", + "lar signatures of mammalian aging. Some of the genes", + "overexpressed with age seem to be a response to aging,in that they have been previously found to have protec-tive functions (de Magalha es et al., 2009b). As such,these genes may help organisms manage aging andcould be targets for manipulation. Likewise, gene ex-pression analysis of CR has been conducted to identifyassociated genes (Lee et al., 1999, 2000). A number ofmolecular signatures have emerged from such studiesthat could be useful to identify candidate processes andpathways that affect aging,", + "al., 2009; Stanfel et al., 2009). Many of these genesmodulate the response to environmental signals, such asfood availability, and act in signaling pathways that ifunderstood can be targeted (Fig. 1). The genetic regula-tion of aging is therefore an emerging field with multipleapplications in the human nutrition, cosmetic, and phar-maceutical industries. AGING GENES AS TARGETS FOR DRUG DISCOVERY 91" + ], + [ + "in the aging process.", + "age-related decline results from damaging by-products of metabolism and/or inefficient repairmechanisms (27, 32). According to this view, dam-agewhich can take on many formsaccumu-lates throughout the life span (38). The exponentialincrease in mortality and the functional declinethat characterize aging, however, only begin aftersexual maturity, whether this occurs at age 13, as inhumans, age 5, as in monkeys, or at less than 2months, as in mice. Therefore, one alternative viewis that aging is perhaps", + "of a pro-cess of mutation accumulation in somatic cells. While im-plicated as a general cause of aging, no specic mecha-nism has been proposed as to how mutation accumulationcould ever lead to the multitude of degenerative processesthat comprise aging. We have now demonstrated that alarge variety of mutations accumulate with age at greatlydifferent rates in a tissue-specic manner. More recentlywe have shown that while some organs, such as brain, donot seem to accumulate mutations with age at all,", + "this process between proteins and other macromolecules responsible for ageing, while the theory of free radicals suggests that ageing is the result of inadequate pro- tection against cell and tissue damage by free radicals and oxidative stress through- out life. Finally, the wear-and-tear theory poses that the cumulative damage that eventually leads to ageing and death is, in fact, the result of the continuous function- ing of vital processes, during which stochastic errors gradually arise.", + "Many mechanistic theories of aging argue that", + "cell senescence and cell death pathways, are a major cause of aging pheno-types, such as organ atrophy. This would appear to be a pre-programmed cause of aging, since it is a consistent response of a sizable fraction of the cell population. However, cellular responses to damage are unlikely to be the onlyexplanation for aging, since even very old organisms still appear to have am-ple tissue capacity left to function optimally.", + "function during aging.", + "INTRODUCTION The aging process represents progressive changes in a cell or an organism which culminate in death due to accumulated defects in function leading to system failure [1]. These defe cts result in part from accumulated damage to DNA. Such damage may result www.impactaging.com AGING, January 2009, Vol. 1. No 1 Review", + "that induce complex molecular changes and, in turn, a deterioration of cellular structures and function. These changes are major causes of age-related diseases like cancer or cardiovascular disorders [1, 2]. The main mo- lecular adaptations occurring during aging are loss ofgenomic stability due to reduced DNA repair capacities [3], loss of proliferative potential caused by increased senescence [1, 4], and age-related alterations in the DNA-methylation patterns that affect cellular plasticity", + "cause in turn metabolic and cognitive alterations, resulting in increasing vulnerabil- ity to environmental challenge and a growing risk for disease and death [1]. Since aging comprises the greatest risk factor for a variety of chronic diseases, includ- ing cancer, cardiovascular disorders, and neurodegenerative diseases [2], one of the goals of biomedical research is to decipher the molecular mechanism underlying aging, which in turn might facilitate the development of treatments aimed at delay-" + ], + [ + "OTHER AGING RELATED GENES", + "genes driving cellular senescence, and perform various integrative analyses. Genes inducing cellular senescence tend to be overexpressed with age in human tissues and are significantly overrepresented in anti-longevity and tumor-suppressor genes, while genes inhibiting cellular senescence overlap with pro-longevity and oncogenes. Furthermore, cellular senescence genes are strongly conserved in mammals but not in invertebrates. We also build", + "lar signatures of mammalian aging. Some of the genes", + "ation of the process of aging. Studies revealed from 300 to 750 genes related to longev- ity that are critically involved in a variety of life activities, such as growth and developme nt, energy metabolism, oxi- dative stress, genomic stability maintenance, and neurocog- nition [ 4]. These candidate genes include mainly APOE, a gene involved in lipoprotein metabolism [ 5,6]. Others are those involved in cell cycle regulation, cell growth and signal transduction, the maintenance of genome stability,", + "genes (http://genomics.senescence.info/genes/), more than700 genes have been identified that regulate lifespan inmodel organisms (de Magalha es et al., 2009a). Many ofthese genes and their associated pathwayssuch as theinsulin/IGF1/GH pathwayhave been shown to affect lon-gevity across different model organisms (Kenyon, 2010).Therefore, at least some mechanisms of aging are evolu-tionarily conserved and may have potential therapeuticapplications (Baur et al., 2006). For example, evidencesuggests the use of", + "www.ncbi.nlm.nih.gov/homologene) of genes strongly asso-ciated with aging in model organisms. Also included are genesin which mutations result in segmental progeroid syndromes,such as the Werners syndrome gene, as well as genes criticalin pathways previously related to aging, such as the insulin/insulin-like signalling pathway (de Magalhes et al ., 2005a). The", + "down-regulated during aging were genes involved in DNA repair and chromatin remodelling. 55 While these studies revealed thousands of age-regulated genes, the ultimate causes of these expression perturbations remain unknown. Analyzing age-dependent gene expression changes using multi-dimensional genetical genomics could bring the identification of genes causing the age-induced alterations and thereby future therapeutic intervention strategies one step closer. Adding the dimension of epigenetics", + "Aging is a biological process universal to eukaryotic organ- isms, and its underlying mechanisms are under intensive study. Genetic analyses of yeast, nematode, fly, and mouse haveuncovered a number of genes, whether mutated or misexpressed,that would increase the lifespans of these organisms (1). These genes include superoxide dismutase , a free-radical scavenger; methuselah , a potential G protein-coupled receptor, in Drosoph- ila melanogaster ; and p66 shc, an oxidative stress-response gene, in", + "The multifactorial and temporal features of aging can beanalyzed efficiently by genome-wide transcriptional profiling,which has been conducted in various model organisms and hu-mans (Melov and Hubbard 2004). Aging is associated with alter-ations in transcript levels of many genes, including those in-volved in evolutionarily conserved mitochondrial and protea-somal functions (McCarroll et al. 2004), some of which havebeen shown to be directly involved in regulating lifespan in C.", + "5. Jiang CH, Tsien JZ, Schultz PG, Hu Y (2001) The effects of aging on gene expression in the hypothalamus and cortex of mice. Proc Natl Acad Sci U S A 98: 19301934. 6. Lu T, Pan Y, Kao SY, Li C, Kohane I, et al. (2004) Gene regulation and DNA damage in the ageing human brain. Nature 429: 883891. 7. Fraser HB, Khaitovich P, Plotkin JB, Paabo S, Eisen MB (2005) Aging and gene expression in the primate brain. PLoS Biol 3: e274. 8. Zahn JM, Poosala S, Owen AB, Ingram DK, Lustig A, et al. (2007) AGEMAP: a" + ], + [ + "OTHER AGING RELATED GENES", + "ation of the process of aging. Studies revealed from 300 to 750 genes related to longev- ity that are critically involved in a variety of life activities, such as growth and developme nt, energy metabolism, oxi- dative stress, genomic stability maintenance, and neurocog- nition [ 4]. These candidate genes include mainly APOE, a gene involved in lipoprotein metabolism [ 5,6]. Others are those involved in cell cycle regulation, cell growth and signal transduction, the maintenance of genome stability,", + "down-regulated during aging were genes involved in DNA repair and chromatin remodelling. 55 While these studies revealed thousands of age-regulated genes, the ultimate causes of these expression perturbations remain unknown. Analyzing age-dependent gene expression changes using multi-dimensional genetical genomics could bring the identification of genes causing the age-induced alterations and thereby future therapeutic intervention strategies one step closer. Adding the dimension of epigenetics", + "www.ncbi.nlm.nih.gov/homologene) of genes strongly asso-ciated with aging in model organisms. Also included are genesin which mutations result in segmental progeroid syndromes,such as the Werners syndrome gene, as well as genes criticalin pathways previously related to aging, such as the insulin/insulin-like signalling pathway (de Magalhes et al ., 2005a). The", + "dam-age, as well as genes involved in inducing apoptosis (10, 11). Theaging process is also accompanied by changes in the expressionpatterns of a number of genes (1214). How the regulation ofgene expression in aging correlates with that in response tooxidative stress, however, is understood poorly.", + "overexpressed with age seem to be a response to aging,in that they have been previously found to have protec-tive functions (de Magalha es et al., 2009b). As such,these genes may help organisms manage aging andcould be targets for manipulation. Likewise, gene ex-pression analysis of CR has been conducted to identifyassociated genes (Lee et al., 1999, 2000). A number ofmolecular signatures have emerged from such studiesthat could be useful to identify candidate processes andpathways that affect aging,", + "al., 2009; Stanfel et al., 2009). Many of these genesmodulate the response to environmental signals, such asfood availability, and act in signaling pathways that ifunderstood can be targeted (Fig. 1). The genetic regula-tion of aging is therefore an emerging field with multipleapplications in the human nutrition, cosmetic, and phar-maceutical industries. AGING GENES AS TARGETS FOR DRUG DISCOVERY 91", + "Aging is a biological process universal to eukaryotic organ- isms, and its underlying mechanisms are under intensive study. Genetic analyses of yeast, nematode, fly, and mouse haveuncovered a number of genes, whether mutated or misexpressed,that would increase the lifespans of these organisms (1). These genes include superoxide dismutase , a free-radical scavenger; methuselah , a potential G protein-coupled receptor, in Drosoph- ila melanogaster ; and p66 shc, an oxidative stress-response gene, in", + "nicance of genes that were found to be aected by aging,the most prominent appeared to be involved in processesthat involve cell division, cell death and apoptosis, migra-tion of cells, and dierentiation, all of which are consistentwith changes in the dierent stages of neurogenesis. Thesechanges at the molecular level agree with studies at the cel- lular level that report changes in rate of migration, dieren- tiation and neurogenesis with aging ( Seki & Arai, 1995;", + "those down-regulated during aging were genes involved in DNA repair and chromatin remodelling (Chambers et al. 2007b ). While these studies revealed thousands of age- regulated genes, the ultimate causes of these expressionperturbations remain unknown. Analyzing age-dependent gene expression changes using multidimensional genetical genomics could bring the identification of genes causingthe age-induced alterations and thereby future therapeutic intervention strategies one step closer." + ], + [ + "Introduction Alzheimers disease (AD), a devastating neurodegen- erative disease, is the most common form of dementiaamong the elderly. Genetically, AD is a complex and multifactorial disease with the possible involvement of multiple genes. The rare early-onset form of the diseaseusually follows an autosomal-dominant inheritance pattern and to date three genes have been identified: amyloid precursor protein ( APP) and presenilin 1 and 2(PSEN1 andPSEN2 ). The common late-onset form of", + "Background Age-related neurological diseases such as stroke and dementia represent a substantial population burden, and one in three persons will develop either stroke or demen- tia in their lifetime [1]. Twin studies suggest that 3778% of the variance in the age of onset of Alzheimer's disease (AD), the most common cause of dementia in the elderly, can be attributed to additive genetic effects [2,3]. Con- versely, cognitively healthy aging also has a substantial", + "cognitive status in Alzheimer's disease. Neurobiol. Aging 1996 , 17: 921-933. [3] Ertekin-Taner, N. Genetics of Alzheimer's disease: a centennial review. Neurol. Clin. 2007 , 25: 611-667. [4] Bernardi, L., Tomaino, C., Anfossi, M., Gallo, M., Geracitano, S., Puccio, G., Colao, R., Frangipane, F., Mirabelli, M., Smirne, N., Giovanni Maletta, R., Bruni, A.C. Late onset familial Alzheimer's disease: novel presen ilin 2 mutation and PS1 E 318G polymor- phism. J. Neurol. 2008 , 255: 604-606.", + "Keywords: alzheimers disease; genomics; GWAS; genetic risk factors; epigenetic modication; aging 1. Introduction Alzheimers disease (AD) is the most common cause of dementia, accounting for approximately 6080% of dementia cases, followed by vascular dementia (approximately 10%), Lewy Body or Parkinsons disease-related dementia, and alcohol-mediated dementia [ 1]. Mild cognitive impairment, one of the representative early symptoms of AD, makes this disease distinguishable from other types", + "14. Heyman A, Wilkinson WE, Hurwitz BJ, Schmechel D, Sigmon AH, et al. (1983) Alzheimers disease: genetic aspects and associated clinical disorders. AnnNeurol 14: 507515. 15. Farrer LA, Myers RH, Connor L, Cupples LA, Growdon JH (1991) Segregation analysis reveals evidence of a major gene for Alzheimer disease. Am J HumGenet 48: 10261033. 16. Duara R, Lopez-Alberola RF, Barker WW, Loewenstein DA, Zatinsky M, et al. (1993) A comparison of familial and sporadic Alzheimers disease. Neurology 43: 13771384.", + "(2016). 3. DeTure, M. A. & Dickson, D. W . The neuropathological diagnosis of Alzheimers disease. Mol. Neurodegener. 14, 32 (2019). 4. Gatz, M. et al. Heritability for Alzheimers disease: the study of dementia in Swedish twins. J. Gerontol. A Biol. Sci. Med. Sci. 52, M117M125 (1997). 5. Gatz, M. et al. Role of genes and environments for explaining Alzheimer disease. Arch. Gen. Psychiatry 63, 168174 (2006).", + "Lett 379(3):199204. Avramopoulos D. 2009. Genetics of Alzheimers disease: Recent advances. Genome Med 1(3):34. Bachman DL, Wolf PA, Linn R, Knoefel JE, Cobb J, Belanger A, DAgostino RB, White LR. 1992. Prevalence of dementia and probable seniledementia of the Alzheimer type in the Framingham study. Neurology42(1):115119. Barral S, Cheng R, Reitz C, Vardarajan B, Lee J, Kunkle B, Beecham G,", + "[11] and the exclusion of cerebrovascular factors as inherentetiopathogenic determinants of neuronal deathin AD, taking into account that in patients olderthan 70 years of age the vast majority of caseswith dementia show a clear cerebrovascular com-promise [12]. In addition, most studies attempt- ing to correlate clinical features with singlegenotypes are partially biased due to heterogene-ity and inaccuracy in phenotype recruitment.Furthermore, 6080% of the therapeutic fail-ures in AD", + "associated with Alzheimers disease neuropathology. J. Alzheimers Dis. 60, 10351043 (2017). 63. Gottesman, R. F. etal. Association between midlife vascular risk factors and estimated brain amyloid deposition. JAMA 317, 14431450 (2017). 64. Moran, C. etal. T ype 2 diabetes mellitus and biomarkers of neurodegeneration. Neurology 85, 11231130 (2015). 65. Vemuri, P . etal. Age, vascular health, and Alzheimer disease biomarkers in an elderly sample. Ann. Neurol. 82, 706718 (2017).", + "Introduction Alzheimers disease (AD), the most common form of dementia, is highly heritable (heritability of up to 76%) but genetically complex.1Neuropatho- logically, the disease is characterized by extracellular senile plaques containing b-amyloid (A b) and intra- cellular neurofibrillary tangles containing hyperpho-sphorylated tau protein. 1Before 2009, four genes had been definitively implicated in its aetiology. Muta- tions of the amyloid precursor protein (APP) gene" + ], + [ + "Background Age-related neurological diseases such as stroke and dementia represent a substantial population burden, and one in three persons will develop either stroke or demen- tia in their lifetime [1]. Twin studies suggest that 3778% of the variance in the age of onset of Alzheimer's disease (AD), the most common cause of dementia in the elderly, can be attributed to additive genetic effects [2,3]. Con- versely, cognitively healthy aging also has a substantial", + "cognitive status in Alzheimer's disease. Neurobiol. Aging 1996 , 17: 921-933. [3] Ertekin-Taner, N. Genetics of Alzheimer's disease: a centennial review. Neurol. Clin. 2007 , 25: 611-667. [4] Bernardi, L., Tomaino, C., Anfossi, M., Gallo, M., Geracitano, S., Puccio, G., Colao, R., Frangipane, F., Mirabelli, M., Smirne, N., Giovanni Maletta, R., Bruni, A.C. Late onset familial Alzheimer's disease: novel presen ilin 2 mutation and PS1 E 318G polymor- phism. J. Neurol. 2008 , 255: 604-606.", + "Introduction Alzheimers disease (AD), a devastating neurodegen- erative disease, is the most common form of dementiaamong the elderly. Genetically, AD is a complex and multifactorial disease with the possible involvement of multiple genes. The rare early-onset form of the diseaseusually follows an autosomal-dominant inheritance pattern and to date three genes have been identified: amyloid precursor protein ( APP) and presenilin 1 and 2(PSEN1 andPSEN2 ). The common late-onset form of", + "[11] and the exclusion of cerebrovascular factors as inherentetiopathogenic determinants of neuronal deathin AD, taking into account that in patients olderthan 70 years of age the vast majority of caseswith dementia show a clear cerebrovascular com-promise [12]. In addition, most studies attempt- ing to correlate clinical features with singlegenotypes are partially biased due to heterogene-ity and inaccuracy in phenotype recruitment.Furthermore, 6080% of the therapeutic fail-ures in AD", + "associated with Alzheimers disease neuropathology. J. Alzheimers Dis. 60, 10351043 (2017). 63. Gottesman, R. F. etal. Association between midlife vascular risk factors and estimated brain amyloid deposition. JAMA 317, 14431450 (2017). 64. Moran, C. etal. T ype 2 diabetes mellitus and biomarkers of neurodegeneration. Neurology 85, 11231130 (2015). 65. Vemuri, P . etal. Age, vascular health, and Alzheimer disease biomarkers in an elderly sample. Ann. Neurol. 82, 706718 (2017).", + "Introduction Alzheimers disease (AD), the most common form of dementia, is highly heritable (heritability of up to 76%) but genetically complex.1Neuropatho- logically, the disease is characterized by extracellular senile plaques containing b-amyloid (A b) and intra- cellular neurofibrillary tangles containing hyperpho-sphorylated tau protein. 1Before 2009, four genes had been definitively implicated in its aetiology. Muta- tions of the amyloid precursor protein (APP) gene", + "Keywords: alzheimers disease; genomics; GWAS; genetic risk factors; epigenetic modication; aging 1. Introduction Alzheimers disease (AD) is the most common cause of dementia, accounting for approximately 6080% of dementia cases, followed by vascular dementia (approximately 10%), Lewy Body or Parkinsons disease-related dementia, and alcohol-mediated dementia [ 1]. Mild cognitive impairment, one of the representative early symptoms of AD, makes this disease distinguishable from other types", + "14. Heyman A, Wilkinson WE, Hurwitz BJ, Schmechel D, Sigmon AH, et al. (1983) Alzheimers disease: genetic aspects and associated clinical disorders. AnnNeurol 14: 507515. 15. Farrer LA, Myers RH, Connor L, Cupples LA, Growdon JH (1991) Segregation analysis reveals evidence of a major gene for Alzheimer disease. Am J HumGenet 48: 10261033. 16. Duara R, Lopez-Alberola RF, Barker WW, Loewenstein DA, Zatinsky M, et al. (1993) A comparison of familial and sporadic Alzheimers disease. Neurology 43: 13771384.", + "disease. Nat. Genet. ,19, 321 322. 7. Bergem, A.L., Engedal, K. and Kringlen, E. (1997) The role of heredity in late-onset Alzheimer disease and vascular dementia. A twin study. Arch. Gen. Psychiat. ,54, 264 270. 8. Payami, H., Grimslid, H., Oken, B., Camicioli, R., Sexton, G., Dame, A., Howieson, D. and Kaye, J. (1997) A prospective study of cognitive health inthe elderly (Oregon Brain Aging Study): effects of family history andapolipoprotein E genotype. Am. J. Hum. Genet. ,60, 948 956.", + "Lett 379(3):199204. Avramopoulos D. 2009. Genetics of Alzheimers disease: Recent advances. Genome Med 1(3):34. Bachman DL, Wolf PA, Linn R, Knoefel JE, Cobb J, Belanger A, DAgostino RB, White LR. 1992. Prevalence of dementia and probable seniledementia of the Alzheimer type in the Framingham study. Neurology42(1):115119. Barral S, Cheng R, Reitz C, Vardarajan B, Lee J, Kunkle B, Beecham G," + ], + [ + "Recent developments on the genetics of aging can be seen as several streams of effort. In general, humans show a relatively modest ( <50%) heritability of", + "effect genetic variants on human longevity. Aging 2, 612620. Yu, C.E., Seltman, H., Peskind, E.R., Galloway, N., Zhou, P.X., Rosenthal, E., Wijsman, E.M., Tsuang, D.W., Devlin, B., Schellenberg, G.D., 2007. Comprehensive analysis of APOE and selected proximate markers for late-onset Alzheimers disease: patterns of linkage disequilibrium and disease/marker association. Genomics", + "It is undisputed that genetic factors influence aging. In a remarkable", + "males: what are the molecular and evolutionary causes? Aging Cell. 2007;6:225233. doi:10.1111/j.1474-9726.2007.00279.x 63. Benayoun BA, Pollina EA, Brunet A. Epigenetic regulation of ageing: link- ing environmental inputs to genomic stability. Nat Rev Mol Cell Biol. 2015;16:593610. doi:10.1038/nrm4048 64. Sen P, Shah PP, Nativio R, Berger SL. Epigenetic mechanisms of longevity and aging. Cell. 2016;166:822839. doi:10.1016/j.cell.2016.07.050", + "Genet 1998, 81:92-97. 3. Pedersen NL, Posner SF, Gatz M: Multiple-threshold models for genetic influences on age of onset for Alzheimer disease: findings in Swedish twins. Am J Med Genet 2001, 105:724-728. 4. Gudmundsson H, Gudbjartsson DF, Frigge M, Gulcher JR, Stefansson K: Inheritance of human longevity in Iceland. Eur J Hum Genet 2000, 8:743-749. 5. Flossmann E, Schulz UG, Rothwell PM: Systematic review of methods and results of studie s of the genetic epidemiology", + "population dynamics on the genetic architecture of human longevity. Aging (Albany NY). 2018;10(8):1947 63. 68. Bellenguez C, Kucukali F, Jansen I, Andrade V, Morenau-Grau S, Amin N, et al. Large meta-analysis of genome-wide association studies expands knowledge of the genetic etiology of Alzheimer disease and highlights potential translational opportunities. medRxiv. 2020. 69. Kojima T, Shimazui T, Hinotsu S, Joraku A, Oikawa T, Kawai K, et al. Decreased expression of CXXC4 promotes a", + "discover core mechanisms of regulation.ANALYSIS OF HUMAN VARIATION IN THE GENETIC CONTROL OF LONGEVITY Heritability studies have convincingly demonstrated that at least some fraction of human lifespan is heritable. In tandem, large-scale genome-wide association studies (GWAS) have identied numerous loci associated with age-related traits (Buniello et al., 2019). While genetic studies have functionally shown an inverse eect of multiple age-related, disease-", + "than in healthy elderly patients [71]. Concluding Remarks The study of the human aging process is complex and multifactorial, where genetic and environmental variables are key players in its development. That is why we sug- gest a series of different biomarkers which include hormonal, inflammatory, and oxidative stress biomarkers. However, it is possible that other biomarkers such as DNA damage, telomere length determination, DNA repair mechanisms and p53", + "Clinical Genetics and Genomics of Aging", + "standing the cause and mechanisms of aging is imperative in assisting to suppress age-related diseases and promote healthylongevity. It is well-known that aging is influenced by a combin- ation of genetic and environmental factors. Previous twin stud- ies have shown that the genetic contribution to general human longevity is about 2030% [ 4,5], whereas environmental factors in human aging and longevity still account for the largest effect. Epigenetic factors influence the regulation of gene expres-" + ], + [ + "www.ncbi.nlm.nih.gov/homologene) of genes strongly asso-ciated with aging in model organisms. Also included are genesin which mutations result in segmental progeroid syndromes,such as the Werners syndrome gene, as well as genes criticalin pathways previously related to aging, such as the insulin/insulin-like signalling pathway (de Magalhes et al ., 2005a). The", + "overexpressed with age seem to be a response to aging,in that they have been previously found to have protec-tive functions (de Magalha es et al., 2009b). As such,these genes may help organisms manage aging andcould be targets for manipulation. Likewise, gene ex-pression analysis of CR has been conducted to identifyassociated genes (Lee et al., 1999, 2000). A number ofmolecular signatures have emerged from such studiesthat could be useful to identify candidate processes andpathways that affect aging,", + "OTHER AGING RELATED GENES", + "In addition to aging- and CR-related genes, another source of candidate genes and pathways for drug designare human longevity-associated genes (Barzilai andShuldiner, 2001; Browner et al., 2004; Kenyon, 2010).Dozens of genes have now been associated with humanlongevity (de Magalha es et al., 2009a), although only ahandful of genes have been shown to have consistenteffects across populations. Many longevity-associated genes are related to spe-", + "potentially associated with human ageing. For eachgene, a description compiled from the studies that linkthe gene to ageing is provided. It should be noted thatour focus is on genes that might affect the ageingprocess, rather than individual age-related pathologies; genes affecting multiple, even if not all, age-related", + "Pleiotropies and Aging-Related Genesets To study genes that have been previously related to aging, a list of curated human genes associated with aging in different model systems was obtained from the GenAge data set ( de Magalh ~aes et al. 2005 ). We used gene ontology (GO) anno-", + "aging in human muscle reveals a common aging signa-ture. PLoS Genet. 2, e115. ( doi:10.1371/journal.pgen. 0020115 ) 64 Lener, T ., Moll, P . R., Rinnerthaler, M., Bauer, J., Aberger, F. & Richter, K. 2006 Expression proling ofaging in the human skin. Exp. Gerontol. 41, 387397. (doi:10.1016/j.exger.2006.01.012 ) 65 Kim, S. K. 2008 Genome-wide views of aging gene net- works . Molecular Biology of Aging Monograph 9. Cold Spring Harbor, CT: Cold Spring Harbor LaboratoryPress.", + "aging in human muscle reveals a common aging signa-ture. PLoS Genet. 2, e115. ( doi:10.1371/journal.pgen. 0020115 ) 64 Lener, T ., Moll, P . R., Rinnerthaler, M., Bauer, J., Aberger, F. & Richter, K. 2006 Expression proling ofaging in the human skin. Exp. Gerontol. 41, 387397. (doi:10.1016/j.exger.2006.01.012 ) 65 Kim, S. K. 2008 Genome-wide views of aging gene net- works . Molecular Biology of Aging Monograph 9. Cold Spring Harbor, CT: Cold Spring Harbor LaboratoryPress.", + "tive-gerontogenes and genes with established aging-relatedfunctions were identified by interrogation of the GenAgeonline database [12], from aging-associated Gene Ontology( G O ) g r o u p s a n d f r o m h a n d a n n o t a t i o n ( s e e M a t e r i a l s a n dmethods/Results for a detailed description of the analysis). We show that the fundamenta l changes in genes and proc-", + "on model organisms [3] or have been confined to specificaging-associated disorders such as progeria syndromes [4]. A study of postmortem human brain tissue from 30 individuals aged 26 to 106 years [5] showed that approxi- mately 4% of approximately 11,000 genes analyzed show a significant age-related expression change (1.5-fold or more) in individuals aged >40 years. These genes were reported to play central roles in synaptic plasticity, vesi- cular transport, and mitoch ondrial function. Another" + ], + [ + "In addition to aging- and CR-related genes, another source of candidate genes and pathways for drug designare human longevity-associated genes (Barzilai andShuldiner, 2001; Browner et al., 2004; Kenyon, 2010).Dozens of genes have now been associated with humanlongevity (de Magalha es et al., 2009a), although only ahandful of genes have been shown to have consistenteffects across populations. Many longevity-associated genes are related to spe-", + "GenAge features a data set of genes that may regulate agingin humans or that at least appear to be considerably associated with the human aging phenotype. This data set includes orthologues derived from established databases, mainly In-Paranoid (OBrien et al ., 2005) but also HomoloGene (http://", + "OTHER AGING RELATED GENES", + "processes in human longevity and aging. Ten of the 22 suggestive associations identied in our analyses are in ornear genes that are highly expressed in the brain (HECW2[Rotin and Kumar, 2009], HIP1 [Blanpied et al., 2003], BIN2, GRIA1), were previously related to the regulation of neuronal excitability and plasticity (KCNQ4 [Van Eyken et al., 2006], LMO4 [Joshi et al., 2009; Leuba et al., 2004],", + "genes analyzed for their possible association with human lon-gevity (http://genomics.senescence.info/genes/longevity.html).All longevity association studies in humans we could find by thetime of the latest update were added to this list. These includestudies reporting negative results, which we see as essentialsince many genes display population-specific associations withlongevity. Fig. 1 From the main page of the Human Ageing", + "Pleiotropies and Aging-Related Genesets To study genes that have been previously related to aging, a list of curated human genes associated with aging in different model systems was obtained from the GenAge data set ( de Magalh ~aes et al. 2005 ). We used gene ontology (GO) anno-", + "www.ncbi.nlm.nih.gov/homologene) of genes strongly asso-ciated with aging in model organisms. Also included are genesin which mutations result in segmental progeroid syndromes,such as the Werners syndrome gene, as well as genes criticalin pathways previously related to aging, such as the insulin/insulin-like signalling pathway (de Magalhes et al ., 2005a). The", + "shown that genes associated with aging and/or longevity inmodel organisms are evolutionary conserved in terms of havingmore homologues than predicted by chance (Budovsky et al .,2007, 2008) and exhibiting slower molecular evolution rates (de Magalhes & Church, 2007). Therefore, it is now clear that atleast some genes identified in model organisms may be relevantto human aging. To allow researchers to focus specifically on human aging,", + "aging in human muscle reveals a common aging signa-ture. PLoS Genet. 2, e115. ( doi:10.1371/journal.pgen. 0020115 ) 64 Lener, T ., Moll, P . R., Rinnerthaler, M., Bauer, J., Aberger, F. & Richter, K. 2006 Expression proling ofaging in the human skin. Exp. Gerontol. 41, 387397. (doi:10.1016/j.exger.2006.01.012 ) 65 Kim, S. K. 2008 Genome-wide views of aging gene net- works . Molecular Biology of Aging Monograph 9. Cold Spring Harbor, CT: Cold Spring Harbor LaboratoryPress.", + "aging in human muscle reveals a common aging signa-ture. PLoS Genet. 2, e115. ( doi:10.1371/journal.pgen. 0020115 ) 64 Lener, T ., Moll, P . R., Rinnerthaler, M., Bauer, J., Aberger, F. & Richter, K. 2006 Expression proling ofaging in the human skin. Exp. Gerontol. 41, 387397. (doi:10.1016/j.exger.2006.01.012 ) 65 Kim, S. K. 2008 Genome-wide views of aging gene net- works . Molecular Biology of Aging Monograph 9. Cold Spring Harbor, CT: Cold Spring Harbor LaboratoryPress." + ], + [ + "the different pathways linked with aging and even study genenetworks. In such works, GenAge is an adequate resource asit provides a framework for the functional genomics of aging.For example, Xue et al . (2007) used GenAge to construct a modular network of aging and obtain insights into aging, including thefact that genes connecting different modules are more likely toaffect longevity and/or aging, an hypothesis the authors validatedexperimentally in worms (Xue et al", + "[111], and for generation of networks based on known gene interactions such as GeneMania [112] and Cytoscape [113], as well as for identifying cross-species orthology relation-ships [114], network-based thinking has been increasingly applied to the study of aging and lifespan [115-118]. Re-cently, the novel computational method of network identifi- cation by regression (NIR) [119] has been used to identify", + "networks can be built using protein interaction and gene co-expression data. A previous paper used protein- protein interactions to build genetic networks identifying potential longevity genes along with links between genes and aging-related diseases [ 30]. Here, we present the network of proteins and genes co-expressed with the CellAge senescence genes. Assaying the networks, we find links between senescence and immune system func- tions and find genes highly connected to CellAge genes", + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "of GenAge involved finding novel genes that may be linked toaging by way of an analysis of proteinprotein interactions. Theprinciple being that proteins not previously thought to berelated to aging which interact with a large number of proteinsdirectly linked to aging might too be involved in aging and arethus promising candidates for future studies (de Magalhes &Toussaint, 2004; Budovsky et al ., 2007). Similar works are made", + "2009, with over 400 genes added in the current update (Ta-ble1), includingmiRNAs for thefirst time. GenAge has proven a valuable resource for ageing re- search, as evidence by many publications. A systems levelanalysis of the GenAge human genes database identified a robust group of ageing-specific network characteristics, re- vealingageinggenesasnetworkhubs( 11).Moreover,inan analysis of genes in the ageing human brain, 54 genes with sustained, consistent expression and 23 genes with DNA", + "a curated database of genes potentiallyassociated with human aging, and a list of genes testedfor their association with human longevity. A myriad ofbiological data and information is included for hundredsof genes, making GenAge a reference for research thatreflects our current understanding of the genetic basis ofaging. GenAge can also serve as a platform for thesystems biology of aging, and tools for the visualizationof proteinprotein interactions are also included. AnAgeis a database of aging in", + "et al ., 2007). In a sense, GenAge offers an overall view of what is presently known about thegenetics of aging in model organisms and in humans that canbe used for numerous studies, including in contemporary functionalgenomics and systems biology methods.Table 2 Criteria used to select entries for inclusion in the GenAge human data set Main reason for selectionNumber of genes Evidence directly linking the gene product to aging in humans 3", + "senescence.info/genes/) is to host high-quality curatedgene-centric information relevant to human ageing.Although initially GenAge was designed to include onlyhuman genes potentially associated with ageing, thedatabase has signicantly grown since, and several newgene sets have been added to it. For example, GenAge includes, since 2008, a list of genes from model organisms based on genetic manipulation experiments (2). Currently, the database is divided into three main", + "information source on the genetics of aging. In particular forthe human data set, a wealth of biological data is provided foreach entry, including relevant information in the context ofbiogerontology (see example below) to an extent that is notavailable in larger, more generic databases like Entrez Gene.Therefore, to learn about the involvement of a given gene inaging, a quick search in GenAge is the best place to start.GenAge features a sophisticated search engine and its user-friendly interface is easy" + ] + ] +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/human_cs_diabetes.json b/gnqa/paper2_eval/data/dataset/human/human_cs_diabetes.json new file mode 100644 index 0000000..f5a00bb --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/human_cs_diabetes.json @@ -0,0 +1,232 @@ +{ + "question": [ + "What are the genetic bases for the varying efficacy of diabetes treatments among individuals?", + "Explain Protective Genetic Factors Against Diabetes in Elderly Populations", + "Explain Effect of Lifestyle Modifications on Aging-Associated Diabetes Risk", + "Explain The Role of Longevity Genes in Protecting Against Diabetes", + "What are the types of diabetes", + "How many types of diabetes exist?", + "Is there a direct association between aging and susceptibility to having diabetes?", + "How does genetics influence the emergency of diabetes?", + "what genes are associated with diabetes?", + "What causes diabetes?", + "Does cycling reduce risk of diabetes?", + "How can GeneNetwork assist in identifying genetic factors involved in diabetes?", + "What specific tools within GeneNetwork are most useful for diabetes research, and how are they applied?", + "What role does insulin play in the regulation of blood glucose levels?", + "How does aging affect the risk of developing type 2 diabetes?", + "Can lifestyle changes reverse type 2 diabetes?" + ], + "answer": [ + "The genetic bases for the varying efficacy of diabetes treatments among individuals are multifaceted and involve several genetic variants that influence drug response. Key points include: 1. **Genetic Variants in Drug Receptors and Metabolizers**: Variations in genes such as OCT, KCNJ11, ABCC8, and CYP2C9 have been associated with differing responses to antidiabetic drugs. These genes are involved in drug receptors or drug metabolism, which can affect how well a patient responds to a particular treatment [2]. 2. **Gene-Gene and Gene-Environment Interactions**: The interplay between different genes and between genes and environmental factors can also contribute to the variability in treatment efficacy. This includes how genetic predispositions interact with lifestyle factors and other environmental influences [2]. 3. **Specific Genetic Mutations**: For instance, mutations in the hepatocyte nuclear factor-1alpha gene have been linked to sensitivity to sulphonylureas, highlighting a specific pharmacogenetic interaction in diabetes treatment [3]. 4. **Genetically Driven Dominant Processes**: Certain genetic variants may drive dominant processes such as beta-cell dysfunction, lipodystrophy, or obesity, which in turn can influence the effectiveness of drugs targeting these pathways, such as sulfonylureas, GLP-1 receptor agonists, DPP4 inhibitors, and thiazolidinediones [4]. 5. **Pharmacogenetic Studies**: Although pharmacogenetic research in diabetes is still developing, studies have shown that genetic variations can significantly impact drug response. For example, variations in the glucose transporter gene SLC2A2 have been associated with differential responses to metformin [8]. 6. **Complex Interplay of Factors**: The glycaemic response to treatments like metformin is likely determined by a complex interplay of genetic and environmental factors, with clinical variables such as BMI, drug adherence, and dosing also playing roles [5]. These genetic factors underscore the importance of personalized medicine in diabetes treatment, where understanding an individual's genetic makeup can help tailor more effective therapeutic strategies.", + "Protective genetic factors against diabetes in elderly populations can be understood through several key insights from the provided context: 1. **Genome-Wide Association Studies (GWAS)**: Research has identified common genetic variants associated with fasting proinsulin levels, which provide new insights into the pathophysiology of type 2 diabetes. These findings suggest that certain genetic variants may play a protective role by influencing insulin regulation and glucose metabolism in the elderly [3]. 2. **Single Nucleotide Polymorphisms (SNPs)**: Studies have identified specific SNPs that are associated with diabetes risk. For example, some genetic mutations are linked to early-onset diabetes, but individuals with certain protective alleles may retain normal glucose tolerance into late adulthood. This indicates that specific genetic configurations can delay or prevent the onset of diabetes in elderly populations [6]. 3. **Gene-Environment Interactions**: The interaction between genetic factors and environmental influences is crucial in understanding diabetes risk. Protective genetic factors may interact with lifestyle and environmental factors to reduce the likelihood of developing diabetes in older adults [7]. 4. **Oxidative Stress Protection**: Certain genetic factors may protect against oxidative stress-induced insulin-deficient diabetes. This suggests that genetic mechanisms that enhance the body's ability to manage oxidative stress could be protective against diabetes in the elderly [9]. In summary, protective genetic factors against diabetes in elderly populations include specific genetic variants that influence insulin regulation, SNPs that delay diabetes onset, gene-environment interactions, and genetic mechanisms that protect against oxidative stress. These factors collectively contribute to a reduced risk of developing diabetes in older adults.", + "Lifestyle modifications, including increased physical activity (PA) and prudent food consumption, have been shown to be effective in preventing obesity and type 2 diabetes mellitus (T2DM) [1]. These modifications can have a sustained positive effect on diabetes risk, even independently of body weight and weight loss [1]. This suggests that the benefits of lifestyle changes extend beyond mere weight management. Furthermore, lifestyle modifications can attenuate the negative effects on metabolic profiles, body weight, and diabetes risk, even in individuals with genetic susceptibility to T2DM [2]. This indicates that lifestyle changes can potentially eliminate the increased risk of developing T2DM in genetically predisposed individuals. The effectiveness of lifestyle changes in reducing the rate of diabetes development has been demonstrated in prediabetic populations, where both reduced macronutrient intake and increased exercise have been shown to lower inflammation, a key factor in diabetes progression [6]. Additionally, randomized controlled trials have shown that lifestyle interventions can reduce the incidence of T2DM by approximately 60% [4]. In summary, lifestyle modifications play a crucial role in reducing the risk of aging-associated diabetes by improving metabolic health, reducing inflammation, and mitigating genetic risks, thereby offering a comprehensive approach to diabetes prevention [1], [2], [4], [6].", + "Longevity genes play a significant role in protecting against diabetes by influencing various biological pathways that contribute to better glucose tolerance and metabolic health. Here are some key points explaining their role: 1. **Favorable Glucose Tolerance and Lower Prevalence of Metabolic Syndrome**: Research has shown that individuals from long-lived families tend to have favorable glucose tolerance and a lower prevalence of metabolic syndrome, which are critical factors in reducing the risk of type 2 diabetes [3]. 2. **Insulin/IGF-1 Pathway**: The insulin/IGF-1 signaling pathway is crucial in regulating lifespan and metabolic processes. Mutations in the insulin-like growth factor I receptor (IGF-1R) have been found in centenarians, suggesting that these mutations may contribute to longevity by enhancing insulin sensitivity and glucose metabolism, thereby protecting against diabetes [9]. 3. **Genetic Risk and Longevity**: Studies have indicated that nonagenarians and centenarians carry the same number of disease risk alleles for type 2 diabetes as younger controls, yet they do not exhibit the disease. This suggests that longevity genes may counteract the effects of diabetes risk alleles, possibly through mechanisms that enhance metabolic health and insulin sensitivity [2], [5]. 4. **Use of Centenarians as Super-Controls**: Centenarians are often used as super-controls in genetic studies to assess the biological relevance of genetic risk factors for common age-related diseases, including type 2 diabetes. This approach helps identify genetic factors that contribute to both longevity and protection against diabetes [6]. In summary, longevity genes contribute to protecting against diabetes by promoting favorable glucose tolerance, enhancing insulin sensitivity, and counteracting the effects of diabetes risk alleles through various genetic and metabolic pathways [2], [3], [5], [6], [9].", + "Diabetes is classified into several types based on different criteria. The main types of diabetes are: 1. **Type 1 Diabetes (T1D)**: Also known as insulin-dependent diabetes mellitus (IDDM) or juvenile onset diabetes, it is an autoimmune disease that usually occurs in childhood but can onset at any age [4], [6], [7]. 2. **Type 2 Diabetes (T2D)**: Also known as non-insulin-dependent diabetes mellitus (NIDDM) or adult-onset diabetes, it is characterized by insulin resistance and relative insulin deficiency [4], [6]. 3. **Gestational Diabetes Mellitus (GDM)**: This type occurs during pregnancy and is defined as any degree of glucose intolerance with onset or first recognition during pregnancy [4]. 4. **Other Types**: These include monogenic diabetes (such as maturity onset diabetes of the young [MODY], neonatal diabetes, mitochondrial diabetes), and syndromes of insulin resistance [5]. Additionally, a 2018 study identified five novel subtypes of adult-onset diabetes, which include severe autoimmune diabetes (SAID, including type 1 diabetes and latent autoimmune diabetes in adults [LADA]), severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes (MOD), and mild age-related diabetes [10].", + "There are several types of diabetes. The classical classification includes type 1, type 2, other types, and gestational diabetes mellitus (GDM) [2]. Additionally, there are subtypes of adult-onset diabetes, including severe autoimmune diabetes (SAID), severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes (MOD), and mild age-related diabetes [4]. Monogenic diabetes, such as Maturity Onset Diabetes of the Young (MODY), also exists [8]. Therefore, the main types of diabetes are: 1. Type 1 diabetes 2. Type 2 diabetes 3. Other types of diabetes 4. Gestational diabetes mellitus (GDM) 5. Monogenic diabetes (e.g., MODY) In summary, there are at least five main types of diabetes, with additional subtypes identified in recent studies.", + "Yes, there is a direct association between aging and susceptibility to having diabetes. Aging is considered one of the main factors responsible for several complex diseases, including diabetes, particularly type 2 diabetes (T2D) [1]. Additionally, insulin resistance, which is a key factor in the development of diabetes, increases with age, and the incidence of diabetes rises sharply in the elderly [6].", + "Genetics plays a significant role in influencing the emergence of diabetes. Several key points highlight this influence: 1. **Genetic Predisposition**: Genetic factors are crucial in determining an individual's risk of developing diabetes [1]. There is a strong genetic component to type 2 diabetes, with many patients having a first-degree relative with the disease [6]. 2. **Interaction with Environmental Factors**: The development of diabetes is generally presumed to result from interactions between genetic predisposition and environmental triggers such as dietary habits, physical inactivity, and obesity [2]. This interaction suggests that while genetics set the stage, environmental factors act as accelerants. 3. **Insulin Secretion and Resistance**: Specific genes are directly related to insulin secretion and indirectly to insulin resistance. This supports the idea that genetically programmed beta-cell dysfunction, when combined with environmentally triggered insulin resistance, precipitates diabetes [5]. 4. **Familial Clustering**: There is evidence of familial clustering of diabetes, indicating a genetic predisposition to the disease and its complications [7]. 5. **Genetic Models**: Genetic models of diabetes have been developed through selective breeding, which often results in monogenic forms of the disease due to single mutations [8]. 6. **Concordance in Twins**: The concordance rate for diabetes in identical twins is less than 50%, indicating that while genetics play a significant role, environmental or developmental events also affect the progression of diabetes [4]. In summary, genetics significantly influences the emergence of diabetes by predisposing individuals to the disease, which is then often triggered or exacerbated by environmental factors.", + "Several genes have been associated with diabetes, both type 1 and type 2, according to the provided context: 1. **Type 1 Diabetes:** - **Insulin gene minisatellite locus (IDDM2)**: Susceptibility to type 1 diabetes is determined by tandem repeat variation at this locus [2]. - **CTLA-4 gene region of chromosome 2q33**: This gene region is linked to and associated with type 1 diabetes [4]. 2. **Type 2 Diabetes:** - **ABCC8/SUR1**: A rare mutation in this gene affects ATP-sensitive K+ channel activity and beta-cell glucose sensing, leading to type 2 diabetes [1]. - **Common gene variants**: Several genome-wide association studies (GWAS) have linked common gene variants with increased risk of type 2 diabetes, including: - **CDKAL1, CDKN2A, CDKN2B**: Associated with reduced insulin secretion via reduced beta-cell mass [3]. - **MTNR1B, TCF7L2, KCNJ11**: Associated with beta-cell dysfunction [3]. - **FTO**: Associated with increased insulin resistance related to obesity [3]. - **IRS1, PPARG**: Associated with increased insulin resistance unrelated to obesity [3]. - **Other genes**: Additional genes associated with type 2 diabetes include: - **TCF7L2, KCNJ11, PPARG, CDKAL1, IGF2BP2, FTO, HHEX, SLC30A8, WFS1** [5]. - **JAZF1, CDC123/CAMK1D, TSPAN8/LGR5, THADA, ADAMTS9, NOTCH2** [5]. - **Genes related to beta-cell dysfunction**: Many type 2 diabetes genes appear to be related to beta-cell dysfunction [6]. - **Monogenic diabetes genes**: Genes such as **HNF1A, HNF4A, HNF1B, INS, NEUROD1, PDX1, PAX4, ABCC8, KCNJ11, KLF11, CEL, BLK** are associated with monogenic forms of diabetes [7]. - **Genes associated with recessive diseases that include diabetes**: **WFS1, NEUROG3, EIF2AK3, GLIS3, RFX6, SLC19A2** [7]. - **Genes with heterozygous mutations causing diabetes mellitus**: **PAX6, GATA6, PPARG** [7]. - **Inflammatory genes**: Genes such as **IL-6, TNF-\u03b1, IL-10, IL12B, IL23R, IL23A** are associated with a greater risk of developing type 2 diabetes [9]. These references provide a comprehensive list of genes associated with both type 1 and type 2 diabetes.", + "Diabetes can be caused by different factors depending on the type. 1. **Type 1 Diabetes**: This type is primarily caused by an absolute deficiency of insulin secretion due to an autoimmune process that destroys insulin-producing beta cells in the pancreatic islets [1], [5]. Genetic markers and serological evidence can often identify individuals at increased risk [1]. 2. **Type 2 Diabetes**: This type is caused by a combination of resistance to insulin action and an inadequate compensatory insulin secretory response [1]. It develops over time as tissues become resistant to insulin, and the pancreas's ability to release insulin diminishes [3]. Obesity is a major risk factor for type 2 diabetes, with both genetic and environmental factors playing a role [8]. 3. **General Factors**: Both types of diabetes ultimately lead to pancreatic beta-cell dysfunction [2]. Environmental factors, such as viral infections and chemicals, can contribute to the development of diabetes, particularly type 1, while obesity is a common predisposing factor for type 2 diabetes [10]. In summary, diabetes is caused by a combination of genetic, autoimmune, and environmental factors that affect insulin production and action [1], [3], [8], [10].", + "Yes, cycling, as a form of physical activity, can reduce the risk of diabetes. According to the context provided: - The Atherosclerosis Risk in Communities (ARIC) study found that higher levels of leisure activity, which primarily included cycling and walking, were associated with a 34% lower odds of developing hypertension over six years compared to the least active group. This study also indicated that physical activity reduces the risk of developing diabetes and hypertension through mechanisms involving changes in body weight and glucose tolerance [3]. - Additionally, other studies have shown that exercise, including activities like cycling, can prevent the development of Type 2 diabetes in high-risk groups [5]. Thus, engaging in regular cycling can contribute to a reduced risk of developing diabetes.", + "GeneNetwork can assist in identifying genetic factors involved in diabetes by leveraging large-scale genetic studies and databases that focus on genetic susceptibility factors. For instance, the Diabetes (GoKinD) study provides a genetics collection specifically aimed at identifying genetic susceptibility factors for diabetic nephropathy in type 1 diabetes [2], [4]. Additionally, advances in genotyping technology have facilitated rapid progress in large-scale genetic studies, enabling the identification of numerous novel genetic variants that increase susceptibility to diabetes and related traits [5]. These resources and technological advancements can be utilized within GeneNetwork to analyze genetic data and identify potential genetic factors involved in diabetes.", + "GeneNetwork offers several specific tools that are particularly useful for diabetes research: 1. **Multi-Omics Analysis**: GeneNetwork includes data from multiple species and supports multi-omics analysis, which integrates genomic, transcriptomic, and metabolomic datasets. This integration is crucial for understanding the complex regulatory networks and mechanisms underlying diabetes [1], [5]. 2. **Correlation and Network Analysis**: These tools allow researchers to compare associations between tissues and between rodent or human datasets. This can help identify key molecular interactions and pathways involved in diabetes [6], [7]. 3. **Systems Genetics Mapping**: Many datasets within GeneNetwork are amenable to systems genetics mapping, which can be used to link genetic variations to phenotypic traits related to diabetes. This helps in identifying genetic factors that contribute to the disease [6]. 4. **Microarray Data Analysis**: GeneNetwork provides access to a large number of microarray datasets from multiple tissues. This data can be used to study gene expression patterns and identify genes that are differentially expressed in diabetic conditions [3], [9]. 5. **Predictive Medicine and Systems Genetics**: GeneNetwork is designed as a resource for predictive medicine and systems genetics, which are essential for developing personalized treatment strategies for diabetes [1]. These tools are applied in diabetes research to gain insights into the genetic and molecular basis of the disease, identify potential biomarkers, and develop targeted therapeutic strategies. For example, integrated analysis of genomic, transcriptomic, and metabolomic datasets can reveal metabolic regulatory networks and mechanisms specific to diabetes [5]. Correlation and network analysis can help in understanding how different tissues interact and contribute to the disease pathology [6], [7].", + "Insulin plays a central role in the regulation of blood glucose levels by facilitating the uptake of glucose into peripheral tissues and suppressing glucose production by the liver. When blood glucose levels rise, such as after food intake, insulin is secreted by the pancreas into the bloodstream [1]. This secretion stimulates peripheral tissues, including muscle and adipose tissue, to absorb glucose, thereby lowering blood glucose levels [1], [3], [8]. Insulin also acts in the liver to suppress glucose production and gluconeogenesis, further contributing to the reduction of blood glucose levels [3], [6], [8]. Additionally, insulin triggers the translocation of GLUT4 receptors to the cell surface in skeletal muscle and adipose tissue, enhancing glucose uptake by these tissues [8]. This finely tuned balance between glucose absorption, production, and extraction ensures that blood glucose levels are maintained within a narrow range [4], [5].", + "Aging is considered one of the main factors responsible for the development of type 2 diabetes (T2D) [1]. As populations in Western countries are aging rapidly, the prevalence of T2D is increasing [5]. This is partly due to lifestyle changes that impede insulin action and increase hepatic glucose production, which are more pronounced in older adults [5]. Additionally, the severity of insulin resistance tends to be greater in older individuals, especially those with a history of prolonged and severe obesity [7]. Therefore, aging significantly increases the risk of developing type 2 diabetes.", + "Lifestyle changes, particularly those involving diet and physical activity, have been shown to be effective in managing and even reversing type 2 diabetes (T2DM). Several sources in the provided context support this: 1. **Prevention and Management**: Lifestyle interventions, including dietary modifications and increased physical activity, have been proven effective in preventing the progression from impaired glucose tolerance to type 2 diabetes [1], [2], [3], [5], [9]. These interventions are more efficacious than pharmacological treatments in some cases [5]. 2. **Sustained Effects**: The positive effects of lifestyle changes on type 2 diabetes risk are sustained over longer periods, even if weight is partially or totally regained [4], [10]. This suggests that the benefits of lifestyle modifications are not solely dependent on weight loss. 3. **Cornerstone of Treatment**: Lifestyle modification, including exercise, nutrition, and behavioral changes, is considered the cornerstone for both preventing and treating type 2 diabetes [7]. In summary, lifestyle changes can indeed play a significant role in reversing type 2 diabetes, as evidenced by multiple clinical trials and studies [1], [2], [3], [4], [5], [7], [9], [10]." + ], + "contexts": [ + [ + "interindividual variation in responses to antidiabetic treatment and may provide the foundation for future genotype-based treatment standards. Pharmacogenetics and Genomics 25:475 484 Copyright 2015 Wolters Kluwer Health, Inc. All rights reserved. Pharmacogenetics and Genomics 2015, 25:475 484 Keywords: antidiabetic treatment, diabetes type 2, disease progression, genotype, pharmacogenetics aSection of Metabolic Genetics, Novo Nordisk Research Foundation Center for", + "treatment guidelines. Yet, the interindividual response to therapy and slope of disease progression varies markedly among patients with type 2 diabetes. Gene gene, gene environment, and gene treatment interactions may explain some of the variation in disease progression. Several genetic variants have been suggested to beassociated with response to antidiabetic drugs. Some are present in drug receptors or drug metabolizers ( OCT genes, KCNJ11 ,ABCC8 , and CYP2C9 ). Numerous type 2 diabetes", + "mic control in the majority of insulin-treated patients. Diabet Med . 2009;26(4):437441. 20. Pearson ER, et al. Sensitivity to sulphonylureas in patients with hepatocyte nuclear factor-1alpha gene mutations: evidence for pharmacogenetics in diabetes. Diabet Med . 2000;17(7):543545. 21. Pearson ER, et al. Genetic cause of hypergly- caemia and response to treatment in diabetes. Lancet . 2003;362(9392):12751281. 22. Fantasia KL, Steenkamp DW. Optimal glycemic", + "When considering etiological varia- tion, recent work partitioning diabe-tes-associated genetic variants by theirpresumed etiological process (parti-tioned polygenic scores) (6,42,101)may de ne genetically driven dominant processes. These processes, such asb-cell dysfunction, lipodystrophy, or obe- sity, could respond differently to drugsthat act on these pathways, such assulfonylureas, glucagon-like peptide 1 re- ceptor agonist (GLP-1RA), DPP4i, and thiazolidinediones.", + "source of such variation might help to identify patients most likely not to respond to metformin and could help to develop more e ective agents by providing insight into the biological mechanism of metformin. As with other complex traits, glycaemic response to metformin is probably determined by the interplay between genetic and environmental factors. Clinical variables such as BMI, drug adherence, and dosing only account for part of the variation. 3 Pharmacogenetic", + "Pharmacogenetics and individual responses to treatment of hyperglycemia in type 2 diabetes Line Engelbrechtsena, Ehm Anderssona, Soeren Roepstorffb, Torben Hansenaand Henrik Vestergaarda The aim of this study was to summarize current knowledge and provide perspectives on the relationships between human genetic variants, type 2 diabetes, antidiabetic treatment, and disease progression. Type 2 diabetes is a complex disease with clear-cut diagnostic criteria and", + "Genomics. 2010; 20:3844. [PubMed: 19898263] 168. Jablonski KA, McAteer JB, de Bakker PI, Franks PW, Pollin TI, et al. Common variants in 40 genes assessed for diabetes incidence and response to metformin and lifestyle intervention in the diabetes prevention program. Diabetes. 2010; 59:26722681. [PubMed: 20682687] 169. Wolford JK, Yeatts KA, Dhanjal SK, Black MH, Xiang AH, et al. Sequence variation in PPARG may underlie differential response to troglitazone. Diabetes. 2005; 54:33193325. [PubMed: 16249460]", + "10.1007/s00125-017-4227-1. 42. Hattersley AT, et al. Precision diabetes: learning from monogenic diabetes. Diabetologia. 2017;60:769777. doi: 10.1007/s00125-017-4226-2. 43. Florez JC. The pharmacogenetics of metformin. Diabetologia. 2017;60:16481655. doi: 10.1007/s00125-017-4335-y. 44. Maruthur NM, et al. The pharmacogenetics of type 2 diabetes: a system-atic review. Diabetes Care. 2014;37:876886. doi: 10.2337/dc13-1276. 45. Zhou K, et al. Variation in the glucose transporter gene SLC2A2 is associ-", + "typically based on efficacy, yet favorable respon ses to such therapeutics are oftentimes variable and difficult to pred ict. Characterization of drug response is expected to substantially enhance our ability to provide patients with the most effective treatment strategy given their indivi dual backgrounds, yet pharmacogenetic study of diabetes medications is still in its infancy. To date, major pharmacogenetic studies have focused on", + "treatment or adverse effects and dosing of medications are not likely to be adversely affected by environmental exposures and tend to have large effect sizes [95]. There fore, some of the variability in response or dosing could be due to genetic variation. Pharmacogenetics in the area of diabetes is still in its infancy, although there have been studies examining KCNJ11 and sulfonylurea therapy for both rare [96,97] and common [98,99] variants and res" + ], + [ + "Genetic factors appear to play a role in determining an individuals risk of developing diabetes. It is hoped", + "ger, will develop diabetes because the prevalence of diabetes increases with age. In order to circumvent this problem, age was adjusted for in2 K. Ramya et al. / Gene xxx (2013) xxx xxx Please cite this article as: Ramya, K., et al., Genetic association of ADIPOQ gene variants with type 2 diabetes, obesity and serum adiponectin levels in south Indian population, Gene (2013), http://dx.doi.org/10.1016/j.gene.2013.09.012", + "elderly population. PLoS One 9: e100548. doi: 10.1371/journal.pone.0100548 PMID: 24959828 23. Strawbridge RJ, Dupuis J, Prokopenko I, Barker A, Ahlqvist E, Rybin D, et al. (2011) Genome-wide association identifies nine common variants associated with fasting proinsulin levels and provides new insights into the pathophysiology of type 2 diabetes. Diabetes 60: 2624 2634. doi: 10.2337/db11-0415 PMID: 21873549", + "information for diabetes risk prediction - differences according to sex, age, family history and obesity. PloS One 8(5):e64307. doi: 10.1371/journal.pone.0064307 Neel JV (1962) Diabetes mellitus: a thrifty genotype rendered detrimental by progress? Am J Hum Genet 14:353362 Neel JV (1999) The thrifty genotype in 1998. Nutr Rev 57(5 Pt 2):S2S9 Palmer ND, McDonough CW, Hicks PJ, Roh BH, Wing MR, An SS, Hester JM, Cooke JN,", + "insulin resistance, hypertension, and dyslipidemia (Obesity Education Initiative Expert Panel, 1998 ). Insulin resist-ance increases with age, and the incidence of diabetes rises sharply in the elderly (American Diabetes Association, 2010a ). In a few patients, genetic mutations appear to be associ- ated with T2D (Roche et al. , 2005 ; American Diabetes Association, 2010a ). For example, recent work using the DPP data has led to the identi cation of 27 single nucle-", + "early-onset diabetes in some pedigrees, but it also maybe observed in individuals who retain normal glucose tolerance into late adulthood and beyond ( ). Studying individuals from HNF A-MODY families, Lango Allen et al. () found that a -SNP T Dr s P S was signi cantly associated with earlier age of diabetes diagnosis, with each additional risk allele accelerating diagnosis by ~ months. Clinical application of predictive scores", + "12. de Miguel-Yanes JM, Shrader P, Pencina MJ, Fox CS, Manning AK, et al. 2011. Genetic risk reclassi- cation for type 2 diabetes by age below or above 50 years using 40 type 2 diabetes risk single nucleotide polymorphisms. Diabetes Care 34:12125 13. Dempe A, Scherag A, Hein R, Beckmann L, Chang-Claude J, Schafer H. 2008. Gene-environment interactions for complex traits: denitions, methodological requirements and challenges. Eur. J. Hum. Genet. 16:116472", + "diabetes risk genes predicts impaired glucose tolerance in female andobese individuals. PLoS One . 2012;7:e38224 . 74. Stevens JW, Khunti K, Harvey R, et al. Preventing the progression to type 2 diabetes mellitus in adults at high risk: a systematic review and network meta-analysis of lifestyle, pharmacological and surgicalinterventions. Diabetes Res Clin Pract . 2015;107:320 331(in eng).Cumulative Risk Alleles and Type 2 Diabetes Mellitus 18jJ Epidemiol 2018;28(1):3-18", + "and protects against oxidative stress-induced insulin-deficient diabetes. PLoS One 2014; 9: e87941 [PMID: 24498408 DOI: 10.1371/journal.pone.0087941] 23 Maahs DM , West NA, Lawrence JM, Mayer-Davis EJ. Epidemiology of type 1 diabetes. Endocrinol Metab Clin North Am 2010; 39: 481-497 [PMID: 20723815 DOI: 10.1016/j.ecl.2010.05.011] 24 Daneman D . Type 1 diabetes. Lancet 2006; 367: 847-858 [PMID: 16530579 DOI: 10.1016/S0140-6736(06)68341-4]", + "Sosenko JM, Skyler JS, Krischer JP , Greenbaum CJ, Mahon J, Rafkin LE, Cuthbertson D, Cowie C, Herold K, Eisen-barth G, et al. 2010. Glucose excursions between states of glycemia with progression to type 1 diabetes in the diabetes prevention trial-type 1 (DPT-1). Diabetes 59: 23862389. Steck AK, Armstrong TK, Babu SR, Eisenbarth GS. 2011. Type 1 Diabetes Genetics Consortium. Stepwise or linear decrease in penetrance of type 1 diabetes with lower-risk HLA genotypes over the past 40 years. Diabetes 60:" + ], + [ + "demonstrate that lifestyle modi cation comprising higher levels of PA and prudent food consumption may be e ective in obesity and T2DM prevention. The positive e ect of lifestyle on body weight seems somewhat transient, whereas the e ect on T2DM is sustained for longer periods. Furthermore, lifestyle modi ca- tion appears to have an e ect on diabetes risk independently of body weight and even of weight loss. Lifestyle and Genetics in Obesity and Type 2 Diabetes", + "suggested to attenuate its negative e ect on metabolic pro le, body weight, and diabetes risk ( Franks et al., 2007 ; Kilpelainen et al., 2008 ; Lindi et al., 2002 ; Ruchat et al., 2010 ) ( Table 1 ). The notion that lifestyle modi cation can eliminate the increased risk for development of T2DM in subjects with genetic suscepti-bility is also supported by ndings of Barwell et al. (2008) who", + "M., Bray, G. A. et al (2006). Effect of weight loss withlifestyle intervention on risk of diabetes. Diabetes Care, 29 , 21022107. Herder, C., Peltonen, M., Koenig, W., Sutfels, K., Lindstrom, J. et al (2009). Anti-inammatory effect oflifestyle changes in the Finnish Diabetes PreventionStudy. Diabetologia, 52 , 433442. Hung, J., McQuillan, B. M., Thompson, P . L., and Beilby,", + "22 Medications for Diabetes Prevention Even in the most successful of the randomized controlled trials, the risk reduction for incident diabetes following lifestyle intervention was ~60 % [ 48 51 ]. That raises the argument as to", + "SRT2104 extend the life span of obese mice and protect against age- related changes in multiple tissues ( 215). The antidiabetic drug metformin also induces effects similar to CR (216). Diabetes is considered an age-associated disease, and disturbances in insulin signaling and carbohydrate homeostasis may essentially lead toother age-related complications, including cancer, if untreated. Along with its antidiabetic properties, metformin supplementation has been", + "74 The mechanism underlying this effect of exercise is not known;however, it is noteworthy that lifestyle change is a very effectiveway to reduce the rate of development of diabetes in a predia-betic population, as shown by the diabetes prevention study. 75,76 Both a reduction in macronutrient intake and exercise cause areduction in inflammation. References 1. Reaven GM. Banting lecture 1988. Role of insulin resistance in human disease. Diabetes . 1988;37:15951607.", + "uals, but also for low-risk lean individuals ( Kriska et al., 2003 ; Meisinger et al., 2005 ; Schulze et al., 2006 ). Furthermore, health-ier lifestyle has been shown to be associated with decreased incidence of obesity- and T2DM-related complications such as hypertension and cardiovascular disease ( Manson et al., 2002 ; Stampfer et al., 2000 ). Evidence from randomized controlled trails The e cacy of lifestyle changes in obesity and T2DM prevention", + "extends lifespan. Cell Rep. 20, 451463 (2017). [PubMed: 28700945] 64. Barzilai N & Ferrucci L Insulin resistance and aging: A cause or a protective response? J. Gerontol. Ser. A 67, 13291331 (2012). 65. Holmes MV , Ala-Korpela M & Smith GD Mendelian randomization in cardiometabolic disease: challenges in evaluating causality. Nat. Rev. Cardiol. 14, 577590 (2017). [PubMed: 28569269] 66. Holmes MVet al.Mendelian randomization of blood lipids for coronary heart disease. Eur. Heart J.", + "70. Knowler WC, Barrett-Connor E, Fowler SE,et al.; Diabetes Prevention Program ResearchGroup. Reduction in the incidence of type 2diabetes with lifestyle intervention or metfor-min. N Engl J Med 2002;346:393 403 71. Crandall J, Schade D, Ma Y, et al.; DiabetesPrevention Program Research Group. The in-uence of age on the effects of lifestyle mod-", + "diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. N Engl J Med 2001; 344: 134350. 114 Knowler WC, Barrett-Connor E, Fowler SE, et al. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med 2002; 346: 393403. 115 Ramachandran A, Snehalatha C, Mary S, Mukesh B, Bhaskar AD," + ], + [ + "Longitudinal Study of Aging. The natural history of progression from normalglucose tolerance to type 2 diabetes in the Baltimore Longitudinal Study of Aging. Diabetes 2003; 52:1475 1484. 22 Hornbak M, Allin KH, Jensen ML, Lau CJ, Witte D, Jrgensen ME ,e ta l .A combined analysis of 48 type 2 diabetes genetic risk variants shows nodiscriminative value to predict time to first prescription of a glucose lowering drug in Danish patients with screen detected type 2 diabetes. PLoS One 2014; 9:e104837.", + "A set of currently known alleles increasing the risk for coronary artery disease, cancer, and type 2 diabetes as identi ed by genome- wide association studies was tested for compatibility with human longevity. Here, we show that nonagenarian siblings from long- lived families and singletons older than 85 y of age from the general population carry the same number of disease risk alleles as young controls. Longevity in this study population is not compromised by", + "52561.x ) 17 Atzmon, G., Schechter, C., Greiner, W ., Davidson, D., Rennert, G. & Barzilai, N. 2004 Clinical phenotype of families with longevity. J. Am. Geriatr. Soc. 52, 274 277. ( doi:10.1111/j.1532-5415.2004.52068.x ) 18 Rozing, M. P . et al. 2009 Human insulin/IGF-1 and familial longevity at middle age. Aging (Albany NY )1, 714722. 19 Rozing, M. P . et al. 2010 Favorable glucose tolerance and lower prevalence of metabolic syndrome in", + "extends lifespan. Cell Rep. 20, 451463 (2017). [PubMed: 28700945] 64. Barzilai N & Ferrucci L Insulin resistance and aging: A cause or a protective response? J. Gerontol. Ser. A 67, 13291331 (2012). 65. Holmes MV , Ala-Korpela M & Smith GD Mendelian randomization in cardiometabolic disease: challenges in evaluating causality. Nat. Rev. Cardiol. 14, 577590 (2017). [PubMed: 28569269] 66. Holmes MVet al.Mendelian randomization of blood lipids for coronary heart disease. Eur. Heart J.", + "et al., 2012 ), possibly due to the indirect and/or a mixed relation- ship between individual genetic disease risk loci and exceptional longevity (as discussed by Fortney et al., 2015 ) versus the poten- tially more direct relationship between aging in the absence of disease and overall genetic disease risk. On the other hand, no difference in genetic risk is observed for type 2 diabetes genetic risk and cancer. Some of these ndings (type 2 diabetes, colon, and lung cancer) can be explained by the", + "5. Garagnani P, Giuliani C, Pirazzini C, etal. Centenarians as super-controls to assess the biological relevance of genetic risk factors for common age-related diseases: a proof of principle on type 2 diabetes. Aging (Albany NY). 2013;5:373385. doi:10.18632/aging.100562 6. Sebastiani P, Nussbaum L, Andersen SL, Black MJ, Perls TT. Increasing sibling relative risk of survival to older and older ages and the importance", + "Genetic factors appear to play a role in determining an individuals risk of developing diabetes. It is hoped", + "The pursuit of longevity has been the goal of humanity since ancient times. Genetic alterations have been demonstrated to affect lifespan. As increasing numbers of pro-longevity genes and anti-longevity genes have been discovered in Drosophila, screening for functionally important genes among the large number of genes has become difficult. The aim of the present study was to explore critical genes and pathways affecting longevity in Drosophila melanogaster. In this study, 168 genes associated with", + "offspring without diabetes mellitus of nonagenariansiblings: the Leiden Longevity Study. J. Am. Geriatr. Soc. 58, 564569. ( doi:10.1111/j.1532-5415.2010. 02725.x ) 20 Suh, Y . et al. 2008 Functionally signicant insulin-like growth factor I receptor mutations in centenarians.Proc. Natl Acad. Sci. USA 105, 34383442. ( doi:10. 1073/pnas.0705467105 ) 21 Heijmans, B. T ., Beekman, M., Houwing-Duistermaat, J. J., Cobain, M. R., Powell, J., Blauw, G. J., van der", + "early-onset diabetes in some pedigrees, but it also maybe observed in individuals who retain normal glucose tolerance into late adulthood and beyond ( ). Studying individuals from HNF A-MODY families, Lango Allen et al. () found that a -SNP T Dr s P S was signi cantly associated with earlier age of diabetes diagnosis, with each additional risk allele accelerating diagnosis by ~ months. Clinical application of predictive scores" + ], + [ + "disorder caused by different factors characterized by a chronic high level of blood sugar with distur-bances to carbohydrate, fat, and protein metabo-lism resulting from defects in insulin secretion, insulin action, or both [ 83 ]. Scientists have divided diabetes into three different types: Type 1 F. Assah and J.C. Mbanya", + "Type 1 and type 2 diabetes are the two main types, with type 2 diabetesaccounting for the majority ( >85%) of total diabetes prevalence. Both", + "classical classification of diabetes as proposed by the American Diabetes Association (ADA) in 1997 as type 1, type 2, other types, and gestational diabetes mellitus (GDM) is still the most accepted classification and adopted by ADA[1]. Wilkin[8] proposed the accelerator hypothesis that argues type 1 and type 2 diabetes are the same disorder of insulin resistance set against different genetic backgrounds[9]. The difference bet - ween the two types relies on the tempo, the faster", + "41 diabetes mellitus (formerly insulin- dependent diabetes mellitus IDDM) or type 1 diabetes is also known as juvenile onset diabetes. Type 2 diabetes mellitus (non-insulin-dependent diabe-tes mellitus (formerly non-insulin- dependent dia-betes, NIDDM) or type 2 diabetes adult-onset diabetes) is found in individuals who are insulin-resistant and who usually have relative insulin de ciency. Gestational diabetes mellitus (GDM), the third type, is de ned as any degree of glucose", + "Diabetes is a metabolic disease characterized by uncontrolled hyper-glycemia resulting from the variable combination of dysfunctional in-sulin secretion by pancreatic beta cells and insulin resistance. It is generally classi ed into monogenic diabetes (maturity onset diabetes of the young [MODY], neonatal diabetes, mitochondrial diabetes[54,55] , syndromes of insulin resistance) [56], type 1 diabetes (T1D) and type 2 diabetes (T2D). The metabolic syndrome is a combination of", + "Diabetes mellitus is a group of metabolic diseases characterized by hyperglycemia (elevated levels of glucose in the blood) resulting from defects in insulin secretion, insulin action, or both. There are two major types of diabetes mellitus: type 1 (T1D) and T2D, although several other rarer forms also exist [13]. T1D is an autoimmune disease that usually occurs in childhood, but the onset may occur at any age. T1D results from a cellular-mediated autoimmune destruction of the beta-cells in the pancreatic", + "2. Classification of Diabetes On the basis of insulin deficiency, diabetes can be classifiedintothefollowingtypesasfollows.2.1. Insulin Dependent Diabetes Mellitus (IDDM). It is also known as juvenile onset diabetes or type 1 diabetes, which accounts for 510% of the patients, resulting from cellular-mediated autoimmune destruction of the pancreatic cells. Thediseasecanaffectpeopleofallagesbutusuallyoccursin childrenoryoungadults.Regularsupplyofinsulininjections", + "2 JournalofDiabetesResearch Type I diabetes IDDM Type II diabetes NIDDM Gestational diabetesPancreas Islet of Langerhans-glucagon beta cells: insulin Genomic mutationsadministration for survival sugar levels Insulin resistance Defective insulin production Increased mortalityY ounger populationGlobal pandemicHuman body and diabetes pregnancy, it needs complete care and glucose monitorin g glycemic status individual level identification/development of lead moleculesRegular insulin Exercise", + "However, there are two major clinical types, type 1 diabetes (T1D) and type 2 diabetes (T2D), according to the etiopathology of t he disorder. T2D appears to be the", + "SIDD Severe insulin-deficient diabetes SIRD Severe insulin-resistant diabetes Introduction In 2018, a ground-breaking study identified five novel subtypes of adult-onset diabetes: severe autoimmune diabetes (SAID, including type 1 diabetes and latent autoimmune diabetes in adults [LADA]) and four subtypes of type 2 diabetes (severe insulin-deficient diabetes [SIDD], severe insulin-resistant diabetes [SIRD], mild obesity-related diabetes [MOD] and mild age-" + ], + [ + "Type 1 and type 2 diabetes are the two main types, with type 2 diabetesaccounting for the majority ( >85%) of total diabetes prevalence. Both", + "classical classification of diabetes as proposed by the American Diabetes Association (ADA) in 1997 as type 1, type 2, other types, and gestational diabetes mellitus (GDM) is still the most accepted classification and adopted by ADA[1]. Wilkin[8] proposed the accelerator hypothesis that argues type 1 and type 2 diabetes are the same disorder of insulin resistance set against different genetic backgrounds[9]. The difference bet - ween the two types relies on the tempo, the faster", + "41 diabetes mellitus (formerly insulin- dependent diabetes mellitus IDDM) or type 1 diabetes is also known as juvenile onset diabetes. Type 2 diabetes mellitus (non-insulin-dependent diabe-tes mellitus (formerly non-insulin- dependent dia-betes, NIDDM) or type 2 diabetes adult-onset diabetes) is found in individuals who are insulin-resistant and who usually have relative insulin de ciency. Gestational diabetes mellitus (GDM), the third type, is de ned as any degree of glucose", + "SIDD Severe insulin-deficient diabetes SIRD Severe insulin-resistant diabetes Introduction In 2018, a ground-breaking study identified five novel subtypes of adult-onset diabetes: severe autoimmune diabetes (SAID, including type 1 diabetes and latent autoimmune diabetes in adults [LADA]) and four subtypes of type 2 diabetes (severe insulin-deficient diabetes [SIDD], severe insulin-resistant diabetes [SIRD], mild obesity-related diabetes [MOD] and mild age-", + "7 American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care 37(Suppl. 1), S81S90 (2014). 8 Daneman D. Type 1 diabetes. Lancet 367(9513), 847858 (2006). 9 Kahn SE, Cooper ME, Del Prato S. Pathophysiology and treatment of Type 2 diabetes: perspectives on the past, present, and future. Lancet 383(9922), 10681083 (2014). \t Describes\tthe\tpathophysiology\tof\tType\t2\tdiabetes\t(T2D)\tin \t detail\twith\tprospective\tof\t -cell\tdysfunction\tand\tpotential", + "However, there are two major clinical types, type 1 diabetes (T1D) and type 2 diabetes (T2D), according to the etiopathology of t he disorder. T2D appears to be the", + "type 1 diabetes, 723 (53%) had LADA, 162 (12%) had secondary diabetes (coexisting pancreatic disease), and 519 (38%) were unclassifiable because of missing data. The remaining 12 112 (883%) patients were considered to have type 2 diabetes (appendix). To classify patients into novel diabetes subgroups, first", + "4 monogenic diabetes not only provides opportunities for etiology- based treatment of the minority of individuals with highly penetrant variants, but also informs broader understanding of diabetes etiology. Types of monogenic diabetes Maturity onset diabetes of the young (MODY) MODY comprises most monogenic diabetes cases, with classical characteristics", + "19 RACIALIZED ETIOLOGIES OF DIABETES Diabetes is not one disease but many. More than 90 percent of all diabetics", + "with young-onset diabetes. Diabetologia 55:1265 1272 13. Schwartz SS, Epstein S, Corkey BE, Grant SF, Gavin JR 3rd, Aguilar RB (2016) The time is right for a new classification system for diabetes: rationale and implications of the -cell-centric classi- fication schema. Diabetes Care 39:179 186 14. Gale EAM (2006) Declassifying diabetes. Diabetologia 49:1989 1995 15. V oight BF, Scott LJ, Steinthorsdottir V et al (2010) Twelve type 2" + ], + [ + "The biological processes linking aging and disease risk are poorly understood. Still, aging is considered to date as one of the main factors responsible for several complex diseases including cancer, cardiovascular diseases, and diabetes. Particularly, type 2 diabetes (T2D) has become very prevalent all over the world, with a projected increas- ing growth rate for the years ahead 1. The pathophysiological mechanism that underlines diabetic complications", + "fects correlate with the functional alterations associated withaging of the brain and with AD pathogenesis (411). The vastmajority of AD cases are late onset and sporadic in origin withaging being the most profound risk factor. Insulin signaling isknown to be involved in the process of brain aging (1220).Insulin dysfunction/resistance in diabetes mellitus (DM) is notonly a common syndrome in the elderly but also considered a riskfactor for AD, especially for vascular dementia (21, 22). The link", + "striking similarities to people with respect to age-associ- ated increases in risk for several diseases, the relative risk for individual diseases is not always shared. For example,although the prevalence of type II diabetes in older dogs increases with age, it is still much lower than the current prevalence of type II diabetes in people, and the mostcommon form of diabetes in dogs resembles type I diabetes in people (Nelson and Reusch 2014 ). Whether this reects", + "strong inverse association between BMI and age at diagnosis of type 2 diabetes. When type 2 diabetes presents in later life, the severity of insulin resistance is often greater among individuals with a history of protracted and severe obesity, particularly with excess visceral adiposity. 28", + "COMMENT In a cohort of more than 800 older persons, we found thatdiabetes mellitus sometime in the study was associated withan increased risk of developing AD during a mean of 5.5years of observation. The risk of incident AD was 65% higherin those with diabetes mellitus than in those without it.Overall, results were similar in analyses restricted to dia-", + "insulin resistance, hypertension, and dyslipidemia (Obesity Education Initiative Expert Panel, 1998 ). Insulin resist-ance increases with age, and the incidence of diabetes rises sharply in the elderly (American Diabetes Association, 2010a ). In a few patients, genetic mutations appear to be associ- ated with T2D (Roche et al. , 2005 ; American Diabetes Association, 2010a ). For example, recent work using the DPP data has led to the identi cation of 27 single nucle-", + "et al., 2012 ), possibly due to the indirect and/or a mixed relation- ship between individual genetic disease risk loci and exceptional longevity (as discussed by Fortney et al., 2015 ) versus the poten- tially more direct relationship between aging in the absence of disease and overall genetic disease risk. On the other hand, no difference in genetic risk is observed for type 2 diabetes genetic risk and cancer. Some of these ndings (type 2 diabetes, colon, and lung cancer) can be explained by the", + "equal number of adults over 18 are thought to develop the disease,although incidence in older people receives less media/research attention. In this review, we discuss our current understanding of the cellular/molecular mechanisms of disease aetiology and progres-sion, the usefulness and limitations of rodent models of spontaneousdiabetes, the factors that are influencing the current increased inci-dence and the clinical opportunities for those affected.", + "associated with maturity onset diabetes of the young and early onset-age of type 2 diabetes. J. Diabetes Complications 26, 343347 (2012). 19. Langenberg, C. et al. Design and cohort description of the InterAct Project: an examination of the interaction of genetic and lifestyle factors on the incidence of type 2 diabetes in the EPIC Study. Diabetologia 54, 22722282 (2011).", + "in the precipitation of diabetes. Saturated fatty acids drive the apoptosis and senescence of beta cells27,41, with increased oxidative stress42 and endoplasmic reticulum stress41. As increased body mass index is asso - ciated with earlier onset of T1D43, it is possible that dietary fat is acting as a sensitizer similar to insHEL, in effect lowering the threshold for autoimmune stress to precipitate clinical diabetes. The male-specific susceptibility to diabetes in this model is in sharp" + ], + [ + "Genetic factors appear to play a role in determining an individuals risk of developing diabetes. It is hoped", + "the diabetes epidemic, and its predilection for certain ethnic groups, are unknown. However, interactions between genetic pre-disposition and environmental triggers (or accelerants) are generally presumed to un- derlie the etiology of diabetes (3 5) (Fig. 1). The best known environmental risk factors are dietary habits, physical inactivity, and obesity; interventions that ameliorate theserisk factors prevent the development oftype 2 diabetes (6,7). By contrast, knowledge of the genetic", + "increases the risk of type 2 diabetes. Such a strong environmental component to a dis - ease should perhaps have deterred geneticists from studying the disorder. However, there are many obese people who do not suffer from diabetes and many non-obese people who do, showing that obesity is not the only factor involved in the aetiology of type 2 diabetes (FIG. 1). In the past 10 years, geneticists have devoted a large amount of effort to finding type 2 diabetes genes. These efforts have", + "future diabetes, however, is not possible on a genetic basis alone. For example, the concordance rate for identical twins is < 50%, indicating that either environmental or developmental events (such as T cell development) affect the progression of diabetes. The ability of serologic studies to identify individuals at risk for diabetes in the general population is under investigation. Among relatives of patients with diabetes, serologic markers can identify patients at high risk.3", + "genes relate directly to insulin secretion and indirectly, through collaborating with other genes, to insulin resistance. Thisseems to support the epidemiological evidence that environmentally triggered insulin resistance interacts with geneticallyprogrammed bcell dysfunction to precipitate diabetes. Citation: Jain P, Vig S, Datta M, Jindel D, Mathur AK, et al. (2013) Systems Biology Approach Reveals Genome to Phenome Correlation in Type 2 Diabetes. PLoS ONE 8(1): e53522. doi:10.1371/journal.pone.0053522", + "Genetic factors Type 2 diabetes has a strong genetic component and most Asian patients have a rst-degree relative with diabetes. 48,49 Much progress has been made in our understanding of the genetics of this disease. Importantly, most of the loci originally associated with diabetes in European populations have been replicated in Asian populations. Whereas monogenic forms of diabetes result from rare genetic mutations with large e ects, such as those seen in maturity-onset diabetes of young people,", + "literature abounds with evidence for genetic mediation ofthe initiation and progression of diabetic nephropathy.First, there is familial clustering that is not completelyexplained by environmental factors [3947]. Our indexcase and her family are perfect examples of genetic pre-disposition to diabetes and its complications, or, at thevery least, familial clustering. Parving and colleagues es-timated that glycemic control, hypertension, and albu-minuria account for only one-third of the variability", + "GENETIC MODELS OF DIABETES Classically, genetic models of diabetes and obesity have been produced in two ways. One is serendipitous observation of a spontaneously arising extreme phenotype, followed by selective breeding to fix the trait. The resulting model will often be monogenic, i.e. due to a single mutation. The other approach is by repeated selective breeding of initially normal appearing members of a genetically diverse ( outbred) population that are at", + "36 Herder C, Roden M. Genetics of type 2 diabetes: pathophysiologic and clinical relevance. Eur J Clin Invest 2011; 41: 67992. 37 Dabelea D, Hanson RL, Lindsay RS, et al. Intrauterine exposure to diabetes conveys risks for type 2 diabetes and obesity: a study of discordant sibships. Diabetes 2000; 49: 220811. 38 Voight BF, Scott LJ, Steinthorsdottir V, et al. Twelve type 2 diabetes susceptibility loci identi ed through large-scale association analysis. Nat Genet 2010; 42: 57989.", + "Environmental influences interact with genetic factors to determine susceptibility to type 2 diabetes by affecting either insulin action, insulin secretion or both. The prevalence of type 2 diabetes has increased markedly in populations that have rapidly adopted a Western lifestyle (for example the Pima Indians) and in many populations that have migrated to regions with a more affluent lifestyle compared to their native country (see Chapter IV.2)." + ], + [ + "gene are associated with NIDDM in Caucasians. Diabetes 1996 , 45, 825-831. 46. Tarasov, A.I.; Nicolson, T.J. ; Riveline, J.P.; Taneja, T.K. ; Baldwin, S.A.; Baldwin, J.M.; Charpentier, G.; Gautier, J.F. ; Froguel, P.; Vaxillaire, M.; et al. A rare mutation in ABCC8/SUR1 leading to altered ATP-sensitive K+ channel activ ity and beta-cell glucose sensing is associated with type 2 diabetes in adults. Diabetes 2008 , 57, 1595-1604.", + "gene is associated with insulin-dependent diabetes mellitus. Diabetes 33:176 183, 1984 6. Bennett ST, Lucassen AM, Gough SCL, Powell EE, Undlien DE, Pritchard LE, Merriman ME, Kawaguchi Y, Drons eld MJ, Pociot F, Nerup J, Bouzekri N, Cambon-Thomasen A, R nningen KS, Barnett AH, Bain SC, Todd JA: Susceptibility to human type 1 diabetes at IDDM2 is determinedby tandem repeat variation at the insulin gene minisatellite locus. Nat Genet 9:284 292, 1995", + "of Diabetes Results of several genome-wide association stud- ies (GWAS) have linked the following common gene variants with a 1520% increased risk of diabetes: reduced insulin secretion via reduce beta-cell mass (CDKAL1, CDKN2A, CDKN2B) and beta-cell dysfunction (MTNR1B, TCF7L2, KCNJ11) and increased insulin resistance related to obesity (FTO) and unrelated to obesity (IRS1, PPARG) [ 11 ]. While most of the early studies", + "gene is associated with insulin-dependent diabetes mellitus. Diabetes 33:176 183, 1984 3. Nistico L, Buzzetti R, Pritchard L, Van der Auwera B, Giovannini C, Bosi E, Larrad M, Rios M, Chow C, Cockram C, Jacobs K, Mijovic C, Bain S,Barnett A, Vandewalle C, Schuit F, Gorus F, Tosi R, Pozzilli P, Todd J: TheCTLA-4 gene region of chromosome 2q33 is linked to, and associated with,type 1 diabetes: Belgian Diabetes Registry. Hum Mol Genet 5:1075 1080, 1996", + "ly associated with type 2 diabetes: TCF7L2, KCNJ11, and PPARG . 5-7 However, in 2007, a number of novel genetic variants ( CDKAL1, IGF2BP2, the locus on chromosome 9 close to CDKN2A/CDKN2B, FTO, HHEX, SLC30A8, and WFS1)8-14 were shown to in - crease susceptibility to type 2 diabetes in repro - ducible studies. Furthermore, a recent meta-analy - sis identified six novel variants ( JAZF1, CDC123/ CAMK1D, TSPAN8/LGR5, THADA, ADAMTS9, and NOTCH2 ) that are associated with type 2 dia - betes. 15", + "date gene approaches now have identified /H1101140 genes as- sociated with type 2 diabetes (17, 18) and a similar num-ber, albeit largely different, with obesity. Most type 2diabetes genes appear to be related to /H9252-cell dysfunction,", + "HNF1A ,HNF4A ,HNF1B ,INS,NEUROD1 ,PDX1 ,PAX4 , ABCC8 ,KCNJ11 ,KLF11 ,CEL, and BLK), 6 genes associ- ated with recessive diseases that include diabetes as a phenotype ( WFS1 ,NEUROG3 ,EIF2AK3 ,GLIS3 ,RFX6 , andSLC19A2 ), and 3 genes in which heterozygous mu- tations have been shown to cause diabetes mellitus (PAX6 ,GATA6 , and PPARG ). Our primary objectives were to (1) identify subjects with potentially undiag- nosed monogenic diabetes, (2) compare and contrast the", + "4. ORahilly S. Human genetics illumi - nates the paths to metabolic disease. Na - ture 2009;462:307-14. 5. McCarthy MI. Growing evidence for diabetes susceptibility genes from genome scan data. Curr Diab Rep 2003;3:159-67. 6. Hattersley AT, McCarthy MI. What makes a good genetic association study? Lancet 2005;366:1315-23. 7. Altshuler D, Hirschhorn JN, Klanne - mark M, et al. The common PPARgamma Pro12Ala polymorphism is associated with decreased risk of type 2 diabetes. Nat Genet 2000;26:76-80.", + "genes including interlukin-6 ( IL-6), tumor necrosis factor- and IL-10 genes were found to be associated with greater risk of developing type 2 diabetes[171], in addition to genetic variants in the genes for IL12B , IL23R and IL23A genes[172]. In a study involving the hormone sensitive lipase re sponsible for lipolysis in adipose tissues, a deletion null mutation, which resulted in the absence of the protein from adipocytes, was reported to be associated with diabetes[173]. Nine", + "2 diabetes[144,149,150], however, not all of these genes showed consistent and reproducible association with the disease[151]. Genome wide association studies (GWAS) in various populations identified 70 loci associated with type 2 diabetes and revealed positive linkage of many mutations and SNPs that influence the expression and physiological impact of the related proteins and risk to develop type 2 diabetes. One study involved several thousand type 2 diabetes patients and" + ], + [ + "two broad etiopathogenetic groups. In one group (type I diabetes), the cause is an absolute deficiency of insulin secretion. Individuals at increased risk of developing this type of diabetes can often be identified by serological evidence of an autoimmune process of the pancreatic islets and by genetic markers. In the second and more prevalent group (type 2 diabetes), the cause is a combination of resistance to insulin action with inadequate compensatory insulin secretory response.", + "Diabetes mellitus. Type1 diabetes mellitus (T1DM) and T2DM have different causes, but both ultimately lead to pancreatic -cell dysfunction. Damaging the pancreas chemically or mechanically can induce experimental diabetes mellitus. Pancreatic damage can be achieved by surgically removing parts of or all of the pancreatic tissue (pancreatectomy) to reduce or fully ablate endogenous insulin production282. The benefit of this method is the lack of toxic adverse effects (compared with diabetogenic", + "Diabetes is a disorder of carbohydrate metabolism charac-terized primarily by hyperglycemia resulting from ineffec-tive uptake of glucose by tissues. Type 1 diabetes is an autoimmune disease that typically occurs early in life and results in total loss of insulin production, whereas type 2 diabetes develops over time as tissues develop a resistance to insulin, and insulin release from the pancreas slowly diminishes. As carbohydrates have the greatest effect on blood glucose of all macronutrients, their", + "diabetes but a rare cause of diabetes diag - nosed in childhood or adulthood. Diabetes . 2008;57(4):10341042. 152. Molven A, et al. Mutations in the insulin gene can cause MODY and autoantibody-negative type 1 diabetes. Diabetes . 2008;57(4):11311135. 153. Gloyn AL, et al. Mutations in the genes encoding the pancreatic beta-cell KATP channel subunits Kir6.2 (KCNJ11) and SUR1 (ABCC8) in diabe - tes mellitus and hyperinsulinism. Hum Mutat. 2006;27(3):220231.", + "Type 1 diabetes is an autoimmune disease caused by T-cell-mediated destruction of insulin-producing beta cellsin the pancreatic islets of Langerhans (Atkinson andMaclaren 1994). Various aberrations in immune regula-tion have been described in both human patients andanimal models of type 1 diabetes (Rosmalen et al. 2002).A recent study has demonstrated that the disturbance ofcentral and/or peripheral tolerance mechanisms existed indiabetes-prone humans and animals (Sakaguchi 2000).With respect to the", + "disorder caused by different factors characterized by a chronic high level of blood sugar with distur-bances to carbohydrate, fat, and protein metabo-lism resulting from defects in insulin secretion, insulin action, or both [ 83 ]. Scientists have divided diabetes into three different types: Type 1 F. Assah and J.C. Mbanya", + "(Fig. 1), indicating that insulin resistance and insulin secretory defect played a cooperative role in the development and exac- erbation of diabetes, even though neither was strong enough alone to cause overt diabetes. From another point of view, even if genetically determined insulin resistance itself might not be sufficient for the development of diabetes, insulin resis- tance results in diabetes if pancreatic /H9252 cell function is im- paired genetically (this study) or nongenetically. Development", + "tors, and other environmental factors that trigger isletautoimmunity and/or type 1 diabetes. Type 2 Diabetes Type 2 diabetes develops when b-cells fail to secrete suf- cient insulin to keep up with demand, usually in the context of increased insulin resistance. A minority of peo- ple diagnosed with type 2 diabetes also have evidence ofislet autoimmunity (57,58). Obesity is a major risk factor for type 2 diabetes (59,60) with complex genetic and en- vironmental etiology.", + "have environmental (islet-injuring drugs or a particular diet) and/or genetic (monogenic or polygenic) causes. We have grouped the models by cause and type of diabetes. While this grouping is reasonable and instructive, it can over-emphasize distinctions. For example, it is believed that beta cell failure (and/or poor islet regeneration) contributes to type 2 diabetes, but in their pure, severe form these processes cause type I diabetes. MODELS OF INSULIN-DEFICIENT DIABETES", + "Diabetes mellitus comprises a heterogenous group of disorders that have been classified as either insulin-dependent (IDDM) or non-insulin-depend- ent (NIDDM).1 Their causes are poorly understood but appear to involve some form of interaction between ge- netic and environmental factors.2-4 Some of the environmen- tal factors that can contribute to IDDM include viral infections and chemicals, while obesity is a common predisposing fac- tor for NIDDM. Genes that confer susceptibility or can cause" + ], + [ + "2 diabetes suggest that regular exercise might play an important role in decreasing the very high incidence of premature coronary artery disease. Although there are no randomized controlled trials assessing reduction in cardiovascular events induced by physical activity in type 2 diabetes, available evidence is consistent with the concept that physical activity may play an important role in reducing cardiovascular risk in type 2 diabetes. 44 Large", + "tern of weight change impact health. For example, in the DiabetesPrevention Program (DPP; described in more detail later), both short- and intermediate-term weight loss were associated with reduced diabetes risk and intermediate cardiometabolic risk factor levels, whereas weight cycling (defined as number of 5 lb [2.25 kg] weight cycles) raised diabetes risk, fasting glucose levels, insulinresistance, and systolic blood pressure. Initial (baseline to 1 month)", + "sclerosis Risk in Communities (ARIC) study, the highestquartile of leisure activity (primarily cycling and walking)had a 34% lower odds of developing hypertension over 6 years compared to the least active [ 107]. Thus, physical activity reduces the risk of developing diabetes and hyper- tension. The mechanism involves changes in body weight and glucose tolerance, as well as other factors [ 107]. The effect of obesity susceptibility genes on the onset of", + "exercise can reduce the incidence of type 2 diabetes. Tuomilehto and coworkers demonstrated that the individuals on a consistent diet and exercise program had 10% incidence of diabetes during 4 years of follow-up compared to 22% for patients in the control group, who met only once a year with the dietician and the physician.40 A six-year randomized trial conducted by Pan and colleagues demonstrated that exercise resulted in 46% reduction", + "Exercise Exercise has been shown to prevent development of Type 2 diabetes in high-risk groups. A number of studies have looked at the effect of insulin on delaying the onset of diabetes. In a study of 5990 male alumni from an American university followed over 10 years, 202 pts (3.3 percent) developed Type 2 diabetes mellitus. The relative risk was lower in patients who exercised regularly even when adjusted for obesity, hypertension, and a family history of diabetes. The benefit was greatest in", + "nonrandomized studies of both men and women with type 2 diabetes and impaired glucose tolerance have found that physical activity is associated with a decreased risk for cardiovascular disease. It also appears that the amount of physical activity is inversely associated with coronary events.5354 RISK OF EXERCISE IN PATIENTS WITH DIABETES The risks associated with exercise can be divided into metabolic, vascular, neurologic and musculoskeletal (Table 4).", + "74 The mechanism underlying this effect of exercise is not known;however, it is noteworthy that lifestyle change is a very effectiveway to reduce the rate of development of diabetes in a predia-betic population, as shown by the diabetes prevention study. 75,76 Both a reduction in macronutrient intake and exercise cause areduction in inflammation. References 1. Reaven GM. Banting lecture 1988. Role of insulin resistance in human disease. Diabetes . 1988;37:15951607.", + "physical training on carbohydrate metabolism and associated cardiovascular risk factors in patients with diabetes. Diabetes Rev. 1995;3:378407. 23. Rogers MA, Yamamoto C, King DS, Harberg JM, Ensani AA, Holloszy JO. Improvement in glucose tolerance after one week of exercise in patients with mild NIDDM. Diabetes Care. 1988;11:6138. 24. Eriksson KF, Lindgarde F. Prevention of type 2 dia- betes mellitus by diet and physical exercise. Diabetologia. 1991;34:8918.", + "migrant and other observational studie!f86970 and prospective studies in subjects at high risk for developing type 2 diabetes.717273 Recently, large interventional trials have reinforced the benefits of exercise in reducing the risk for type 2 diabetes. These include the Malmo study from Sweden45, the Da Quing study from China74 and the recently concluded Finnish Diabetes Prevention Study.75 These prospective but not randomized studies show a reduction in the risk of 560", + "reduce systolic blood pressure, reduce total cholesterol, raise HDL cholesterol, and improve endothelial function in overweight patients with young-onset type 2 diabetes. 47 However, any potential benefits to the cardiovascular disease risk profile are lost within 36 months after cessation of exercise training, and do not confer protection against later cardiovascular events. 47,121 Additionally, reviews49,121,122 of the limited number of studies done to" + ], + [ + "Genetic factors appear to play a role in determining an individuals risk of developing diabetes. It is hoped", + "Diabetes (GoKinD) study: a genetics collection available for identifying genetic susceptibility factors for diabetic nephropathy in type1 diabetes. J. Am. Soc. Nephrol. 17, 17821790 (2006). 137. Scott, R.A. etal. Large-scale association analyses identify new loci influencing glycaemic traits and provide insight into the underlying biological pathways. Nat. Genet. 44, 9911005 (2012). Author contributions All authors researched the data for the article,", + "identifying genetic susceptibility factors for diabetic nephropathy in type 1 diabetes. J Am Soc Nephrol 17: 17821790. 44. Manolio TA, Rodriguez LL, Brooks L, Abecasis G, Ballinger D, et al. (2007) New models of collaboration in genome-wide association studies: the Genetic Association Information Network. Nat Genet 39: 10451051. 45. Mailman MD, Feolo M, Jin Y, Kimura M, Tryka K, et al. (2007) The NCBI dbGaP database of genotypes and phenotypes. Nat Genet 39: 11811186.", + "in Diabetes (GoKinD) study: a genetics collection availablefor identifying genetic susceptibility factors for diabeticnephropathy in type 1 diabetes. J Am Soc Nephrol 2006; 177: 1782 1790. 10. Pezzolesi MG, Poznik GD, Mychaleckyj JC, et al. Genome- wide association scan for diabetic nephropathysusceptibility genes in type 1 diabetes. Diabetes 2009; 586: 14031410. 11. Paterson AD, Lopes-Virella MF, Waggott D, et al.", + "beta cell function, insulin mode of action, glucose metabolism and/or other risk factors. It is a fact that advances in genotyping technology, over the past few years, have facilitated rapid progress in large-scale gene tic studies. Identification of a large number of novel genetic variants increasing suscept ibility diabetes and related traits opened up opportunities, not existing thus far, to associate this genetic information", + "DISCUSSION The findings of previous epidemiological and family studies suggest that diabetic nephropathy results from an interaction between metabolic abnormalities that are typical of poorlycontrolled IDDM and predisposing genetic factors (4,5). Thenature of the genetic factors, however, has remained un- known (22). Using a candidate gene approach, we have found in this", + "PLoS Genetics | www.plosgenetics.org June 2007 | Volume 3 | Issue 6 | e96 0963 Type 2 Diabetes Network-Based Analysis", + "PLoS Genetics | www.plosgenetics.org June 2007 | Volume 3 | Issue 6 | e96 0971 Type 2 Diabetes Network-Based Analysis", + "PLoS Genetics | www.plosgenetics.org June 2007 | Volume 3 | Issue 6 | e96 0967 Type 2 Diabetes Network-Based Analysis", + "High-Density Single Nucleotide Polymorphism Genome-Wide Linkage Scan for Susceptibility Genes forDiabetic Nephropathy in Type 1 Diabetes Discordant Sibpair Approach John J. Rogus,1,2G. David Poznik,1Marcus G. Pezzolesi,1,2Adam M. Smiles,1Jonathon Dunn,1 William Walker,1Krzysztof Wanic,1,2Dariusz Moczulski,1,2,3Luis Canani,1,2,4Shinichi Araki,1,2,5 Yuichiro Makita,1,2,6James H. Warram,1and Andrzej S. Krolewski1,2 OBJECTIVE Epidemiological and family studies have demon-" + ], + [ + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "GeneNetwork http://www.genenetwork.org is anexample of a bioinformatics tool that can be used to explore systems genetics data. The importance of defining biological networks and predicting molecular interactions has been emphasized by several reports [1,2]. Such studies emphasize that when knowledge about DNA variation within popula- tions is interfaced with data on gene expression, protein interactions and DNA-protein binding, biological networks can be constructed that are predictive of the", + "GeneNetwork provides users with an array of analyticaltools to compare a given trait with a number of data setsavailable from other experimenters. Microarray data ofgene expression in the brain and data of other phenotypes are two such examples of possible tools. For this study, we", + "subnetworks GeneNetwork (www.genenetwork.org) is a depository of data- sets and tools for use in complex systems biology approaches in order to generate or predict higher order gene function ( 23, 24 ).", + "of these tools to diabetes andmetabolic disease research at the cellular, animal model,and human disease levels are summarized, with a partic-ular focus on insights gained from the more quantitativetargeted methodologies. We also provide early examplesof integrated analysis of genomic, transcriptomic, andmetabolomic datasets for gaining knowledge about meta-bolic regulatory networks and diabetes mechanisms andconclude by discussing prospects for future insights.", + "including correlation and network analysis to compare associations between tissues and between other rodent or human data sets[32] Many of the Data Sets are amenable to systems genetics mapping and other methods and are accessible at GeneNetwork. The Description and Usage column provides details about the data set and potential", + "including correlation and network analysis to compare associations between tissues and between other rodent or human data sets[32] Many of the Data Sets are amenable to systems genetics mapping and other methods and are accessible at GeneNetwork. The Description and Usage column provides details about the data set and potential", + "data are entered into GeneNetwork after they have been shepherded through a system like PhenoGen that has extensive capabilities for normalization and quality control. A comparison of the brain gene expression datasets and some of the tools for data analysis available on PhenoGen and GeneNetwork is shown in Table 3, and more detailed information on features provided by each site is outlined in the Supplementary DiscussionHoffman et al. Page 5 Addict Biol . Author manuscript; available in PMC 2012 July 1.", + "of importance in the emergence of precision medicine ( Curtis, 2015 ; Desautels et al., 2014 ; Glade Bender et al., 2015 ; Jorgensen, 2015 ; Kummar et al., 2015 ; Marquet et al., 2015 ; Rubin, 2014 ) wherein therapeutic strategies need to be aligned with specific properties of tumors. Methods GeneNetwork and WebGestalt GeneNetwork is an open access, online data analysis resource for systems biology and systems genetics. It contains a large number of microarray datasets from multiple tissues of", + "results in applying the method to type 2 diabetes mellitus suggest it may hold promise as a useful research tool for complex diseases . Further details on the methodol ogy is available from the following paper: Liu M, Liberzon A, Kong SW, Lai WR, Park PJ et al (2007) Network -based analysis of affected biological processes in type 2 diabetes models. PLoS Genet 3(6):e96. doi:10.1371/journal.pgen.0030096." + ], + [ + "Figure 3. Schematics view of insulin regulation. Elevated glucose level by either food intake or liver glycogenolysis is sensed by islet and leads to insulin secretion to the bloodstream. The increased insulin stimulates peripheral tissues to absorb glucose, and as a consequence, the glucose le vel", + "plays an important role in regulating insulin secretion in beta cells of the pancreas. It has been shown that glucosestimu-lated insulin secretion may be triggered by the autocrine ac-tivation of the insulin signaling pathway, including insulin receptor phosphorylation, tyrosine phosphorylation in IRS1, and the activation of PI3Kinase. Putting together these data leads to the hypothesis that a single molecular impairment in the pathway of insulin signaling, including an incomplete interaction between", + "(A) Insulin interacts in the liver to suppress glucose production, and in muscle and adipose tissue to stimulate uptake of glucose, aminoacids, and fatty acids. The amount of insulin released to maintain normal glucose homoeostasis is established by prevailing insulin sensitivity. This feedback is probably mediated through neuronal and humoral mechanisms, but exact mediators are still not known. (B) When insulin resistance develops in insulin-sensitive tissues, feedback to cells ensures that the cells", + "Insulin Action In healthy, normal individuals, blood glucose concentra- tion is maintained within a narrow range. After an over-night fast or between meals, blood glucose normally falls within the range of 3.5 5.5 mM. Immediately after a meal containing carbohydrate, blood glucose concentration rises to a peak of 6 10 mM followed by a sharp decline back to baseline within 60 minutes. This exquisite control is achieved by a ne balance between glucose absorption", + "from the gut, glucose production by the liver, and glucose extraction from the blood into the cells and tissues. Insulin plays a central role in the regulation of blood", + "glucose transport into the cell. Concomitantly, insulin stimulates intracellular utili-zation of glucose by many other tissues as well. In the fasting state, the main physiological function of insulin is to suppress glucose production by the liver and prevent uncontrolled lipolysis and ketogenesis, without which dia-betic ketoacidosis would quickly develop. Hence, if either of these aspects of insulin action is impaired, then periph-eral or liver hepatic insulin resistance or both are said to be present.", + "and suppression ofglucose production are regulated by insulin.", + "the pancreas in response to an increase in blood glucose, such as that which follows a carbohydrate - containing meal. Insulin acts to decrease blood glucose levels by increasing glucose uptake by tissues and by decreasing gluconeogenesis by the liver. To increase tissue uptake, insulin triggers the translocation of GLUT4 receptors to the cell surface in skeletal muscle and adipose tissue. Insulin also stimulates each of the regulatory enzymes in the glycolytic pathway, while also inhibiting the key", + "insulin suppresses both hepatic and renal glucose release, 3031 and stimulates glucose uptake exogenous insulin administration causes systemic glucose utilization to exceed systemic glucose release so that plasma glucose concentrations decrease. As the plasma glucose levels decrease there is a characteristic hierarchy of responses (Figure 1 ). Reduction of insulin secretion, the first in the cascade of hypoglycemia counterregulation, 2 derepresses glucose", + "Counter-regulatory hormones antagonize the glucose lowering action of insulin, and act to raise the blood glucose level. Glucagon, a potent counter-regulatory hormone inhibited by insulin, is secreted from pancreatic alpha cells when cells perceive low glucose. In diabetes, pancreatic insulin levels are reduced and glucagon is chronically elevated. In DKA, in addition to low insulin action, there is the cellular perception of low glucose , which" + ], + [ + "The biological processes linking aging and disease risk are poorly understood. Still, aging is considered to date as one of the main factors responsible for several complex diseases including cancer, cardiovascular diseases, and diabetes. Particularly, type 2 diabetes (T2D) has become very prevalent all over the world, with a projected increas- ing growth rate for the years ahead 1. The pathophysiological mechanism that underlines diabetic complications", + "unclear whether age at menopause is associated with risk of type2d i a b e t e s[ 3,4]. Data from cross-sectional studies examining the association between age at menopause and type 2 diabetes are contradictory, with a few studies reporting no association and some other reporting higher odds of having type 2 diabetes with early onset of menopause [ 57]. Recently, a nested case cohort study reported that an increased risk of type 2 diabetes is associ-", + "The mechanisms leading to development of type 2 diabetes in young people are similar to those in older patients; however, the speed of onset, severity, and interplay of reduced insulin sensitivity and defective insulin secretion might be different in patients who develop the disease at a younger age. 18 In adolescents with type 2 diabetes, as in later onset type 2 diabetes, the initial deterioration in -cell function is characterised by loss of first-phase nutrient-stimulated insulin secretion.", + "anincreased risk of developing type 2 diabetes (T2D) later in their", + "T2D is associated with age, and Western populations are aging rapidly. The second major explanation is our lifestyles have changed dramatically in recent years. Epidemiological studies have identified strong T2D risk relationships for obesity, sedentary behavior [24], and diets rich in energy [5], processed carbohydrates [6], and animal fats [7]. Collectively, these lifestyle factors impede the actions of insulin and raise hepatic glucose production, which can result in the diminution of endog-enous", + "tion. Many people with type 2 diabetes ultimately requirei n s u l i nt h e r a p y ,w h i c hr e ects long-standing type 2 diabetes and greatly diminished b-cell function but also likely includes individuals who have slowly progressingautoimmune diabetes with adult onset (LADA) or otherambiguous forms of diabetes. Age. Data from randomized controlled trials in people with type 2 diabetes under the age of 18 years or over the age of 65 years are scarce. Bene cial effects of tight", + "strong inverse association between BMI and age at diagnosis of type 2 diabetes. When type 2 diabetes presents in later life, the severity of insulin resistance is often greater among individuals with a history of protracted and severe obesity, particularly with excess visceral adiposity. 28", + "patients with young-onset type 2 diabetes than in patients without diabetes, whereas the risk of myocardial infarction was much less (typically 24 times higher) in patients with type 2 diabetes presenting in middle and later life. 106 In Hong Kong, where 20% of type 2 diabetes diagnosed since 1995 occurs in people aged 40 years or younger, a 7-year prospective study 107 showed that when adjusted for age,", + "type 2 diabetes, the major predisposing risk factors are obesity, family history, and sedentary lifestyle. Onset of diabetes at a younger age (defined here as up to age 40 years) is associated with longer disease exposure and increased risk for chronic complications. Young-onset type 2 diabetes also affects more individuals of working age, accentuating the adverse societal effects of the disease. Furthermore, evidence is accumulating that young-onset type 2 diabetes has a more aggressive disease phenotype,", + "pathophysiology of type 2 diabetes. Diabetes 60(10):26242634. doi:10.2337/db11-0415Aging Clin Exp Res 123" + ], + [ + "of Type 2 Diabetes The lifestyle intervention using physical exercise and modi cation of nutrition is ef cient in pre- venting type 2 diabetes in patients with impaired glucose tolerance [ 99 ]. Clinical trials con rm that lifestyle interventions (dietary modi cation and increased physical activity) reduce the risk of progressing from impaired glucose tolerance to type 2 diabetes [ 105 ]. Assessing T2D risk accord- ing to FINDRISK scale [ 106 ] is quite common in", + "Major clinical trials have demonstrated that diet and lifestyle modifications are effective in preventing T2DM in high-risk individuals. T2DM management strategies including lifestyle modifications, social support and ensuring medication adherence are key to reducing the incidence of diabetes mellitus complications. REVIEWS NATURE REVIEWS | ENDOCRINOLOGY VOLUME 14 | FEBRUARY 2018 | 89", + "focused on people with impaired glucose tolerance or impaired fasting glucose because of their high risk of development of type 2 diabetes. Several studies have examined the ability of lifestyle modi cation and drugs to slow progression to diabetes (table 2). Findings from these trials have nearly all shown a bene t, with lifestyle modi cations being more e cacious than any drug, with the exception of the thiazolidinedione anti diabetics. 163175", + "no or just minor weight loss was achieved, diabetes incidence was also reduced ( Pan et al., 1997 ; Ramachandran et al., 2006 ). In addition, on the long term weight was partially or totally regained in all of the studies ( Knowler et al., 2009 ; Li et al., 2008 ; Lindstrom et al., 2006 ; Lindstrom et al., 2003 ). Despite this regain T2DM risk remained low or decreased further, thus the e ect of lifestyle is unlikely to be solely due to", + "proven particularly effective for preven-tion and management of type 2 diabetes.For example, improvement in dietaryquality, in conjunction with other lifestylemodications like increased physical ac-tivity, was shown to be more effectivethan pharmacological treatment in pre-vention of diabetes in individuals at highrisk (1). Further, lifestyle modicationmay mitigate the risk associated with thestrongest known diabetes risk loci (2).While the existence of environmental in-uences on genetic risk (and vice", + "spite of our incomplete knowledge of the genetics of type 2diabetes today, the burden of type 2 diabetes can be amelio-rated at the population level. Recent studies have found thatlifestyle changes through diet and exercise can prevent or", + "Lifestyle modification including exercise, nutrition and behavioral changes is the cornerstone to prevent and treat type 2 diabetes. Oral antidiabetic medication either as single agent or combination therapy is frequently required to maintain metabolic control, as assessed by monitoring ofglycated hemoglobin A 1C(HbA 1C) levels. Eventually, asignificant proportion of patients with type 2 diabetes require the exogenous administration of insulin [40].", + "diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. N Engl J Med 2001; 344: 134350. 114 Knowler WC, Barrett-Connor E, Fowler SE, et al. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med 2002; 346: 393403. 115 Ramachandran A, Snehalatha C, Mary S, Mukesh B, Bhaskar AD,", + "type 2 diabetes. Physical activity, favorable dietary changes,and weight reduction were essential components of a success-ful lifestyle intervention in two large randomized controlled trials on the prevention of type 2 diabetes in high-risk individ-uals with impaired glucose tolerance (IGT), including theFinnish Diabetes Prevention Study (DPS) (44) and the Diabe-tes Prevention Program (DPP) (22). In the DPS, increasedphysical activity was associated with a decreased risk of type", + "demonstrate that lifestyle modi cation comprising higher levels of PA and prudent food consumption may be e ective in obesity and T2DM prevention. The positive e ect of lifestyle on body weight seems somewhat transient, whereas the e ect on T2DM is sustained for longer periods. Furthermore, lifestyle modi ca- tion appears to have an e ect on diabetes risk independently of body weight and even of weight loss. Lifestyle and Genetics in Obesity and Type 2 Diabetes" + ] + ] +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/human_cs_gn.json b/gnqa/paper2_eval/data/dataset/human/human_cs_gn.json new file mode 100644 index 0000000..c06edc6 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/human_cs_gn.json @@ -0,0 +1,456 @@ +{ + "question": [ + "What is the most cited environmental factor for the onset of asthma?", + "How would one extract the DNA, from say, flora or fauna?", + "genetics", + "what is bioinformatics", + "Explain the process of finding a genetic marker followed by a quantitative trait loci.", + "What about recombination in human centromeres?", + "How does recombination work in human centromeres?", + "What about recombination in the human genome?", + "Create a how to guide for genetic sequencing", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed on to the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "what type of dataset is useful for qtl mapping analysis in genenetwork2?", + "what are the bioinformatics tools for QTLs analysis?", + "what are the statistical approaches for qtls analysis?", + "Create a how-to guide for GWAS analysis?", + "Create a how-to guide for genetic sequencing", + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Create a how-to guide for genetic sequencing", + "Create a guide for genetic sequencing", + "Define dyslipidemia.", + "What is cytochrome?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "how does environment influence fertilisation", + "how does diet impact someone's height", + "which animal has the same number of chromosomes as human", + "what ensures brains work", + "how do our brains maintain emotions", + "what hormones do our brains release during stressful experiences?", + "what is the use of corticosterone?" + ], + "answer": [ + "The most cited environmental factor for the onset of asthma is exposure to tobacco smoke, particularly in early childhood [4]. Additionally, reduced early microbial exposure, as posited by the hygiene hypothesis, is also a significant environmental factor [3].", + "To extract DNA from flora or fauna, one can follow these general steps: 1. **Cell Rupture**: The cells need to be ruptured to release the DNA. This can be done using mechanical methods such as grinding the tissue with a sterile pestle [9] or using bead-beating for homogenization [8]. Enzymatic digestion of cell walls, if present, can also be used (e.g., lysozyme) [5]. 2. **Preventing DNA Degradation**: During the extraction process, it is crucial to prevent DNA from fragmenting. This can be achieved by using EDTA, which chelates magnesium ions necessary for DNase enzymes that degrade DNA [5]. 3. **Purification**: The DNA needs to be purified to remove contaminants. This can involve differential centrifugation and using specific kits designed for DNA extraction, such as the All-In-One kit [8] or the DNeasy Blood and Tissue Kit [9]. 4. **Specific Protocols for Different Organisms**: Different organisms may require specific protocols. For example, insects may need hypochlorite treatment before extraction to reduce contamination [3]. 5. **High Purity Requirements**: If a high degree of purity is required, DNA can be subjected to density gradient centrifugation [1]. By following these steps, one can effectively extract DNA from various types of flora and fauna.", + "Genetics is the study of individual genes and their protein products [2]. It involves understanding how genes are inherited, how they function, and how they influence traits and conditions passed down from one generation to another [6]. This field also includes the analysis of genetic linkage data for Mendelian traits [3].", + "Bioinformatics broadly refers to the use of computers to seek patterns in observed biological data and to propose mechanisms for such patterns [5]. It involves methods of computer-based information processing for analyzing the structure and function of biologically important molecules [3], [4]. The field draws from the foundations of statistics, mathematics, physics, computer science, and molecular biology, reflecting a broad base of scientific specialties [8], [9], [10].", + "The process of finding a genetic marker followed by a quantitative trait loci (QTL) involves several steps: 1. **Linkage Mapping**: The initial step is to trace the trait across multiple generations by examining its coinheritance with genetic markers. This technique is known as linkage mapping [1]. Genetic linkage analysis is used to identify regions of the genome that contain genes predisposing to the observed quantitative trait [2]. 2. **Identification of QTLs**: Once regions of interest are identified through linkage mapping, the next step is to pinpoint the loci responsible for variability in the quantitative trait. This process is known as quantitative trait locus mapping or QTL mapping [1]. QTL mapping is more challenging due to the numerous sources of variation that need to be captured [1]. 3. **Statistical Analysis**: To determine linkage, statistical methods such as the log of odds (LOD) scores or likelihood ratio statistics (LRS) are used [2]. A significant QTL indicates that different genotypes at a polymorphic marker locus are associated with different trait values [2]. 4. **Techniques for Positioning QTLs**: Various techniques can be employed to position precise loci influencing the trait. These include marker regression, interval mapping, and multiple mapping strategies [7]. Marker regression, for instance, locates QTLs with respect to all markers simultaneously by regression onto the marker means and estimates the additive and dominance effects [7]. 5. **Genome-Wide Association Studies (GWAS)**: In some cases, genome-wide association studies are conducted to identify chromosomal regions referred to as QTLs that are statistically associated with the trait [8]. These regions often contain a high density of single nucleotide polymorphisms (SNPs) [8]. 6. **Candidate Gene Identification**: After identifying the QTL regions, researchers can sift through the list of genes in these regions to find candidate genes that may have a significant impact on the phenotype [10]. By following these steps, researchers can effectively identify genetic markers and subsequently map quantitative trait loci, providing insights into the genetic basis of complex traits.", + "Recombination in human centromeres is generally suppressed, making these regions \"cold\" to recombination events [2]. This suppression is thought to be actively maintained by core centromeric proteins [3]. Despite this, there is evidence suggesting that centromeres can undergo recombination, particularly through mechanisms that involve homologous recombination (HR) during DNA repair processes [6]. For instance, crossover between sister chromatids, known as sister chromatid exchange (SCE), has been reported at human centromeres [6]. Additionally, centromeres may experience recombination during a single round of cell division in primary human cells [8]. This indicates that while recombination is generally suppressed in centromeres, it can still occur under certain conditions, potentially impacting centromere integrity and stability [5], [6], [8].", + "Recombination in human centromeres is a complex process that is generally suppressed to maintain centromere integrity. Here are the key points about how recombination works in human centromeres: 1. **Recombination Suppression**: Centromere DNA is often referred to as being \"cold\" to recombination, meaning that it is generally refractory to the recombination processes that occur during meiosis in other parts of the genome [1]. 2. **Potential Disruptions**: When recombination does occur, it can disrupt centromere integrity in several ways. For instance, crossover between sister chromatids can lead to sister chromatid exchange (SCE), which has been reported at human centromeres. Additionally, the search for homologous sequences may erroneously identify nearly identical sequences within the same chromatid, leading to inappropriate recombination events [3]. 3. **Mechanisms to Suppress Recombination**: There are active mechanisms to suppress recombination at centromeres, which may involve core centromeric proteins. This suppression is crucial because recombination-dependent pathways, such as homologous recombination (HR), could otherwise exacerbate centromere rearrangements [4]. 4. **Recombination in Centromere Formation**: Despite the general suppression, recombination is believed to play a role in the origin and formation of centromeres. Multiple short- and long-range recombination events may be responsible for generating and reiterating blocks of highly homogenized alpha-satellite DNA throughout the centromere [5]. 5. **Recombination Outside Meiosis**: Recent analyses have shown that centromeres can undergo recombination during a single round of cell division in primary human cells, indicating that recombination at centromeres is not entirely restricted to meiosis [8]. In summary, while recombination at human centromeres is generally suppressed to maintain their structural integrity, it can occur under certain conditions and may play a role in the formation and evolution of centromeric DNA.", + "Recombination in the human genome is a complex process with several key aspects highlighted in the provided context: 1. **PRDM9 and Recombination Hotspots**: PRDM9 is identified as a major determinant of meiotic recombination hotspots in humans and mice [1], [10]. This protein plays a crucial role in determining where recombination events are likely to occur during meiosis. 2. **Recombination Rate and Reproductive Success**: There is a relationship between recombination rate and reproductive success in humans, suggesting that recombination may have evolutionary implications [1]. 3. **Genome-wide Maps of Recombination**: Detailed maps of recombination rates and hotspots across the human genome have been created, providing insights into the fine-scale structure of recombination rate variation [2], [4]. These maps are essential for understanding genetic variation and are used in admixture mapping [9]. 4. **Variation in Recombination Rates**: There is significant variation in recombination rates across different regions of the human genome, which has implications for genetic diversity and evolution [8]. 5. **Influence on Genetic Variation**: Recombination hotspots are a general feature of the human genome and play a major role in shaping genetic variation [5]. 6. **Selection for Maternal Recombination Rates**: Genome-wide maps of recombination in human oocytes and embryos show selection for maternal recombination rates, indicating that recombination patterns can be subject to evolutionary pressures [1]. These points collectively highlight the importance of recombination in shaping the human genome, influencing genetic diversity, and having evolutionary consequences.", + "### How to Guide for Genetic Sequencing Genetic sequencing is a powerful tool used to determine the order of nucleotides in DNA. This guide will walk you through the basic steps involved in genetic sequencing, from sample preparation to data analysis. #### Step 1: Sample Preparation 1. **Collect DNA Sample**: Obtain a DNA sample from the organism or tissue of interest. This can be done using various methods such as blood draws, buccal swabs, or tissue biopsies. 2. **Extract DNA**: Use a DNA extraction kit to isolate DNA from the collected sample. Ensure the DNA is of high quality and free from contaminants. #### Step 2: Library Preparation 1. **Fragment DNA**: Break the DNA into smaller fragments. This can be done using mechanical shearing or enzymatic digestion. 2. **Add Adapters**: Ligate specific adapters to the ends of the DNA fragments. These adapters are necessary for the sequencing process and allow the fragments to bind to the sequencing platform. #### Step 3: Amplification 1. **PCR Amplification**: Amplify the DNA fragments using Polymerase Chain Reaction (PCR). This step increases the quantity of DNA to ensure there is enough material for sequencing. #### Step 4: Sequencing 1. **Choose a Sequencing Platform**: Select an appropriate sequencing platform based on your needs. Popular platforms include Illumina, Ion Torrent, Pacific Biosciences, and Oxford Nanopore Technologies [1], [5], [6]. 2. **Load the Sample**: Load the prepared DNA library onto the sequencing platform. Each platform has specific protocols for loading samples. 3. **Run the Sequencer**: Initiate the sequencing run. The platform will read the DNA fragments and generate raw sequence data. #### Step 5: Data Analysis 1. **Base Calling**: Use base-calling software to convert raw data into nucleotide sequences. Tools like Phred are commonly used for this purpose [7]. 2. **Sequence Assembly**: Assemble the short DNA sequences into longer contiguous sequences (contigs) using assemblers like Phrap or CAP3 [7]. 3. **Quality Control**: Assess the quality of the assembled sequences. Tools like consed or GAP4 can help in finishing sequencing projects and ensuring accuracy [7]. #### Step 6: Interpretation 1. **Variant Calling**: Identify genetic variants such as single nucleotide polymorphisms (SNPs) and insertions/deletions (indels) using variant calling software. 2. **Annotation**: Annotate the identified variants to understand their potential impact on genes and biological functions. 3. **Data Management**: Store and manage the sequencing data using bioinformatics tools and databases. High-throughput sequencing generates large amounts of data that require efficient processing and management [9], [10]. #### Additional Resources - For more detailed protocols and information on specific sequencing technologies, refer to resources from companies like 10X Genomics, Illumina, and Pacific Biosciences [1]. - For historical context and advancements in sequencing technologies, see references such as Heather and Chain (2016) and Shendure and Ji (2008) [2]. By following these steps, you can successfully perform genetic sequencing and analyze the resulting data to gain insights into the genetic makeup of your sample.", + "The length of telomeres is significant for several reasons: 1. **Protection of Chromosomes**: Telomeres are repetitive nucleotide sequences at the ends of chromosomes that protect them from attrition and damage during replication [2], [4], [7]. They prevent chromosome ends from being recognized as broken DNA ends, which could otherwise lead to degradation and fusion [4], [10]. 2. **Aging and Cellular Senescence**: Telomere length decreases with each round of genomic replication due to the unidirectional activity of DNA polymerase, which leaves a section of DNA unreplated on the lagging strand [1], [6]. This progressive shortening acts as an internal clock for cellular aging, eventually leading to cellular senescence when telomeres become critically short [8]. 3. **Disease Association**: Shorter telomeres have been linked to various diseases. For example, individuals with Type 2 Diabetes (T2D) have been shown to have shorter leukocyte telomeres, which may be associated with disease progression [2]. Similarly, shorter telomeres in smooth muscle cells have been linked to senescence and disease severity in patients with atherosclerosis [3]. Leukocyte telomere length has also been associated with a higher risk of developing cardiovascular disease [3]. 4. **Genomic Stability**: Telomere shortening and erosion can lead to chromosome end-to-end fusions and genomic instability, which are detrimental to cellular function and can lead to various genetic disorders [4]. 5. **Biomarker Potential**: Due to their association with aging and disease, telomere length is being studied as a potential biomarker for various conditions, including cardiovascular diseases and diabetes [3]. In summary, telomere length is crucial for maintaining chromosome integrity, regulating cellular aging, and potentially serving as a biomarker for disease progression and risk assessment.", + "Once a sperm combines with an egg, the traits passed on to the resulting lifeform are determined by several factors: 1. **Genetic Information**: The fertilized egg, or zygote, contains a full complement of 23 chromosome pairs, which includes a mix of genetic material from both the maternal and paternal chromosomes [7]. This genetic information is coded in DNA molecules and is heritable [4]. 2. **Gene Activation and Regulation**: The process of trait determination involves controlling which genes are active, when they activate, and for how long they remain active. This control can be achieved through several mechanisms: - **Imprinting**: Genes can be marked with small chemical tags that flag them to be activated or remain inactive, depending on whether the modification was made by the father (in the sperm) or the mother (in the egg) [2], [3]. - **Maternal Effect**: The mother can alter gene activity in her offspring via the placenta [3]. - **Cis-Regulation**: Instructions encoded within the embryo's DNA can directly control if and when a nearby gene becomes activated [3]. 3. **Epigenetic Marks**: Soon after fertilization, male and female cells have sex-specific transcriptomes, epigenomes, and phenotypes. Epigenetic marks, which are less constrained, can be maintained and affect gene expression and phenotype later in development [5]. 4. **Mutations and Environmental Factors**: Gene mutations caused by factors such as radiation and chemicals can also be passed on to offspring if they occur in sex cells [4]. Additionally, environmental conditions experienced by the parents can influence the traits of the offspring through epigenetic mechanisms [10]. These combined factors ensure that the resulting lifeform inherits a unique set of traits from both parents, influenced by genetic, epigenetic, and environmental factors.", + "Genetic tracing is often matrilineal rather than patrilineal primarily because mitochondrial DNA (mtDNA) is inherited solely from the maternal lineage. Unlike the nuclear genome, which requires contributions from both parents, mtDNA is passed down from mother to offspring without any admixture from the father [4], [5]. This uniparental inheritance of mtDNA simplifies the tracing of maternal lineage as it remains relatively unchanged through generations, making it a reliable marker for genetic studies [4], [5]. Additionally, humans have developed complex mechanisms to ensure this uniparental inheritance of mtDNA, which further supports its use in genetic tracing [4].", + "To differentiate between X and Y DNA with respect to DNA tracing and determining quantitative trait loci (QTLs), one must consider the unique characteristics of each chromosome: 1. **Recombination Rates**: - The Y chromosome largely does not undergo recombination, which means it is passed down relatively unchanged from father to son. This makes it easier to trace paternal lineage but limits its utility in QTL mapping due to the lack of recombination [1]. - The X chromosome, on the other hand, does undergo recombination, although at a slower rate compared to autosomes. This recombination can complicate tracing but is crucial for detecting significant QTLs [1]. 2. **QTL Mapping**: - QTLs are stretches of DNA closely linked to genes that influence a phenotype of interest. The detection and mapping of QTLs on the X chromosome can be more complex due to its recombination, but this recombination is necessary for identifying the genomic locations involved in regulating phenotypes [2]. - The Y chromosome's lack of recombination means it is less useful for QTL mapping, as the identification of QTLs relies on the recombination events that help pinpoint the genomic regions associated with specific traits [1]. In summary, the primary difference lies in the recombination behavior of the X and Y chromosomes, which affects their utility in DNA tracing and QTL determination. The X chromosome's recombination, although slower, is essential for QTL mapping, whereas the Y chromosome's lack of recombination makes it more suitable for tracing paternal lineage but less useful for QTL analysis.", + "For QTL mapping analysis in GeneNetwork2, datasets that are useful include genotypes and complex phenotypes ranging from gene expression to behavior in standard inbred strains and panels of mouse recombinant inbred strains [3]. Additionally, data from permanent genetic reference populations, such as the HXB rat strains, are also valuable [5]. These datasets allow for the identification of correlations within the data and the mapping of genetic loci associated with mRNA abundance or trait phenotypes [10].", + "The bioinformatics tools for QTLs analysis include: 1. **R/qtl** - A comprehensive tool for QTL mapping [4]. 2. **QTL Cartographer** - Used for QTL mapping and analysis [4]. 3. **MapQTL** - Another tool for QTL mapping [4]. 4. **WebQTL** - An online tool optimized for the analysis of traits controlled by combinations of allelic variants and environmental factors [4], [7]. 5. **QTL IciMapping** - A free software for QTL mapping that constructs genetic linkage maps and performs QTL analysis by simple interval mapping and inclusive composite interval mapping [4]. 6. **CPMAtranseqtl** - Available on GitHub, supports R/Python [3]. 7. **eMap** - Available on GNU, supports R [3]. 8. **FastMap** - Available on SourceForge, supports JAVA [3]. 9. **lme4qtl** - Available on GitHub, supports R [3]. 10. **Matrix eQTL** - Available on the UNC website, supports R/Matlab [3]. 11. **Meta-eQTL** - Available on HPC MSSM, supports R/C [3]. 12. **Plink** - A library for association QTL mapping on single nucleotide polymorphisms (SNP) in natural populations [8]. 13. **Multimapper** - Allows the automatic building of models of multiple QTLs within the same linkage group and works as a companion program to QTL Cartographer [10]. These tools can be used in combination with classical experimental methods to accelerate QTL gene identification [1], [2].", + "The statistical approaches for QTLs (Quantitative Trait Loci) analysis include several methodologies and techniques. Here are some of the key approaches mentioned in the provided context: 1. **Hidden Markov Model (HMM)**: This approach is used to calculate QTL genotype probabilities, simulate from the joint genotype distribution, and calculate the most likely sequence of underlying genotypes, all conditional on the observed marker data [1]. 2. **Composite Interval Mapping (CIM)**: This method is available in the R/qtl package and is used for QTL mapping [2]. 3. **Haley-Knott Regression**: Another method available in the R/qtl package, used for QTL analysis [2]. 4. **Log of the Odds (LOD) Scores Calculation**: The scanone function in R/qtl is used to calculate LOD scores, which are essential for identifying QTLs [2]. 5. **Permutation Analysis**: This technique is used to establish the significance threshold for each phenotype, ensuring that the identified QTLs are statistically significant [2]. 6. **Additive and Interactive Covariates**: These can be added to the model to account for additional sources of variation in the data [2]. 7. **Regression Method**: One of the commonly used methods for linkage analysis in QTL studies [7]. 8. **Likelihood Method**: Another method used for linkage analysis in QTL studies [7]. 9. **Variance Component Method**: This method is also used for linkage analysis in QTL studies [7]. 10. **Bayesian Method**: A method used for linkage analysis, which can also be used to check significant thresholds using Bayesian factors [7]. 11. **Bootstrapping**: This method is preferred for checking confidence intervals in QTL analysis [7]. These approaches provide a comprehensive toolkit for conducting QTL analysis, allowing researchers to identify and validate QTLs effectively.", + "### How-to Guide for GWAS Analysis Genome-Wide Association Studies (GWAS) are a powerful tool for identifying genetic variants associated with traits or diseases. Below is a step-by-step guide to performing a GWAS analysis: #### Step 1: Preliminary Genome-Wide Analysis and Data Preprocessing 1. **Sample Collection**: Collect thousands of samples characterized for the phenotype of interest. This can be done using continuous measurements or dichotomous classification as a case (affected) or control (unaffected) [2]. 2. **Genotyping**: Perform large-scale genotyping using commercially available chips such as Affymetrix or Illumina [9]. 3. **Data Formatting**: Prepare the GWAS genotype data by generating bed, bim, and fam files using tools like PLINK [5]. #### Step 2: Identifying Gene-Set Definitions 1. **Gene-Set Identification**: Identify gene-set definitions whose patterns need to be recognized [4]. #### Step 3: Processing Genomic Data 1. **Filtering and Identifying Gene Patterns**: Process the genomic data by filtering and identifying gene patterns [4]. 2. **Intermediate Datasets**: Generate intermediate datasets that integrate original GWAS p-values, rsID, position, and chromosome column for each SNP [8]. #### Step 4: Statistical Analysis 1. **Association Testing**: Perform statistical analysis, typically using linear or logistic regression, to test the association of each SNP against the phenotype, including relevant covariate variables [2]. 2. **Gene-Wise P-Values**: Calculate gene-wise p-values from the raw SNP p-values using programs like VEGAS2 or KGG [1]. #### Step 5: Aggregating GWAS Data 1. **Gene-Based Approaches**: Aggregate GWAS data into biological units using gene-based approaches to reduce the multiple testing burden. This involves deriving p-values for association at the level of the gene [7]. #### Step 6: Assessing Statistical Magnitude 1. **Statistical Hypothesis**: Identify gene set analysis models and assess the statistical magnitude of the associations [4]. #### Step 7: Constructing Gene Networks 1. **Enhancing GWAS Results**: Construct gene networks to enhance GWAS results. This involves identifying DNA markers that associate with the expression of one or more genes [6]. By following these steps, you can systematically perform a GWAS analysis to identify genetic variants associated with your trait or disease of interest.", + "### How-to Guide for Genetic Sequencing Genetic sequencing is a powerful tool used to determine the order of nucleotides in DNA. This guide will walk you through the basic steps involved in genetic sequencing, referencing specific resources for further information. #### Step 1: Sample Preparation 1. **Collect DNA Sample**: Obtain a DNA sample from the organism or tissue of interest. This can be done using various methods such as blood draws, buccal swabs, or tissue biopsies. 2. **Extract DNA**: Use a DNA extraction kit to isolate DNA from the collected sample. Ensure the DNA is of high quality and free from contaminants. #### Step 2: Library Preparation 1. **Fragment DNA**: Break the DNA into smaller fragments. This can be done using mechanical shearing or enzymatic digestion. 2. **Add Adapters**: Ligate specific adapters to the ends of the DNA fragments. These adapters are necessary for the sequencing process and often contain barcodes for sample identification [6]. #### Step 3: Amplification 1. **PCR Amplification**: Amplify the DNA fragments using Polymerase Chain Reaction (PCR). This step increases the quantity of DNA to ensure there is enough material for sequencing. #### Step 4: Sequencing 1. **Choose a Sequencing Platform**: Select an appropriate sequencing platform based on your needs. Popular platforms include Illumina, Ion Torrent, Pacific Biosciences, and Oxford Nanopore Technologies [1], [4], [7]. 2. **Load the Sample**: Load the prepared DNA library onto the sequencing machine. Follow the manufacturer's instructions for your specific platform. #### Step 5: Data Acquisition 1. **Run the Sequencer**: Start the sequencing run. The machine will read the nucleotide sequences of the DNA fragments and generate raw data. 2. **Monitor the Run**: Keep an eye on the sequencing run to ensure it is proceeding correctly. Some platforms provide real-time monitoring capabilities [6]. #### Step 6: Data Analysis 1. **Base Calling**: Use base-calling software to convert raw sequencing data into nucleotide sequences. Tools like Phred are commonly used for this purpose [8]. 2. **Sequence Assembly**: Assemble the short reads into longer contiguous sequences (contigs) using assemblers like Phrap or CAP3 [8]. 3. **Data Interpretation**: Analyze the assembled sequences to identify genetic variants, structural variations, and other genomic features. This step may involve bioinformatics tools and databases [10]. #### Step 7: Validation and Reporting 1. **Validate Results**: Confirm the accuracy of your sequencing results using additional methods such as Sanger sequencing or quantitative PCR. 2. **Report Findings**: Compile your findings into a comprehensive report. Include details about the methods used, the quality of the data, and any significant genetic variations identified. #### Further Information For more detailed information on specific sequencing technologies and platforms, refer to the following resources: - [10X Genomics](http://www.10xgenomics.com) - [Illumina](http://www.illumina.com) - [Ion Torrent](https://www.thermofisher.com/us/en/home/brands/ion-torrent.html) - [Pacific Biosciences](http://www.pacb.com) - [Oxford Nanopore Technologies](https://www.nanoporetech.com) [1] By following these steps and utilizing the referenced resources, you can successfully perform genetic sequencing and analyze the resulting data.", + "### How-to Guide for Genetic Sequencing Genetic sequencing is a powerful tool used to determine the order of nucleotides in DNA. This guide will walk you through the basic steps involved in genetic sequencing, referencing specific sources for more detailed information. #### Step 1: Sample Preparation 1. **DNA Extraction**: Begin by extracting DNA from your sample. This can be done using various commercial kits or standard laboratory protocols. 2. **DNA Quantification and Quality Check**: Measure the concentration and purity of the extracted DNA using spectrophotometry or fluorometry. Ensure the DNA is of high quality and free from contaminants. #### Step 2: Library Preparation 1. **Fragmentation**: Fragment the DNA into smaller pieces. This can be achieved through mechanical shearing, enzymatic digestion, or sonication. 2. **End Repair and A-tailing**: Repair the fragmented DNA ends and add an adenine (A) base to the 3' ends to prepare them for adapter ligation. 3. **Adapter Ligation**: Ligate sequencing adapters to the ends of the DNA fragments. These adapters are necessary for the fragments to bind to the sequencing platform. #### Step 3: Amplification and Enrichment 1. **PCR Amplification**: Amplify the adapter-ligated DNA fragments using polymerase chain reaction (PCR). This step increases the quantity of DNA available for sequencing. 2. **Size Selection**: Select DNA fragments of the desired size range using gel electrophoresis or magnetic beads. #### Step 4: Sequencing 1. **Loading the Sequencer**: Load the prepared DNA library onto the sequencing platform. Popular platforms include Illumina, Ion Torrent, and Pacific Biosciences [6], [9]. 2. **Sequencing Run**: Initiate the sequencing run. The platform will read the nucleotide sequences of the DNA fragments and generate raw sequence data [1], [5]. #### Step 5: Data Processing 1. **Base Calling**: Convert raw data into nucleotide sequences using base-calling software such as Phred [10]. 2. **Sequence Assembly**: Assemble the short DNA sequences into longer contiguous sequences (contigs) using software like Phrap or CAP3 [10]. 3. **Quality Control**: Assess the quality of the assembled sequences and remove any errors or low-quality reads. #### Step 6: Data Analysis 1. **Variant Calling**: Identify genetic variants such as single nucleotide polymorphisms (SNPs) and insertions/deletions (indels) using variant calling software. 2. **Annotation**: Annotate the identified variants to understand their potential impact on gene function and association with diseases. #### Step 7: Interpretation and Reporting 1. **Bioinformatics Analysis**: Use bioinformatics tools to interpret the sequencing data in the context of the research question or clinical application [3], [4]. 2. **Report Generation**: Generate a comprehensive report summarizing the findings, including identified variants and their potential implications. #### Additional Resources - For a detailed history and development of sequencing technologies, refer to Heather and Chain's review [1]. - For information on specific sequencing platforms and their comparisons, see Quail et al. [6]. - For best practices in sequencing and data management, consult Olson et al. [5]. By following these steps, you can successfully perform genetic sequencing and analyze the resulting data to gain valuable insights into the genetic makeup of your samples.", + "The length of telomeres is significant for several reasons: 1. **Protection of Chromosomes**: Telomeres are repetitive nucleotide sequences at the ends of eukaryotic chromosomes that protect them from attrition and damage during replication [1], [2], [6]. They prevent chromosome ends from being recognized as DNA breaks, thereby avoiding unnecessary repair mechanisms that could lead to genomic instability [9]. 2. **Aging and Cellular Senescence**: Telomere length is progressively shortened with each round of genomic replication unless restored by the enzyme telomerase [2], [5]. Short telomeres are considered a sign of advanced age and are associated with cellular senescence, where cells lose their ability to divide and function properly [4], [7], [8]. 3. **Disease Association**: Shorter telomeres have been linked to various diseases. For instance, individuals with Type 2 Diabetes (T2D) have been shown to have shorter leukocyte telomeres, which may be associated with disease progression [1]. Similarly, telomere length in smooth muscle cells has been linked to disease severity in atherosclerosis, and shorter leukocyte telomeres are associated with a higher risk of developing cardiovascular disease [3], [8]. 4. **Genomic Stability**: Telomere shortening and erosion can lead to chromosome end-to-end fusions and genomic instability, which are detrimental to cellular function and can lead to cancer and other genetic disorders [6]. 5. **Biomarker Potential**: Due to their association with aging and disease, telomere length is being studied as a potential biomarker for various conditions, including cardiovascular diseases and diabetes [3]. In summary, the length of telomeres is crucial for maintaining chromosomal integrity, regulating cellular aging, and potentially serving as a biomarker for certain diseases.", + "### How-to Guide for Genetic Sequencing Genetic sequencing is a powerful tool used to determine the order of nucleotides in DNA. This guide will walk you through the basic steps involved in genetic sequencing, referencing specific resources for further information. #### Step 1: Sample Preparation 1. **Collect DNA Sample**: Obtain a sample containing DNA. This could be from blood, saliva, or tissue. 2. **Extract DNA**: Use a DNA extraction kit to isolate DNA from the sample. Ensure the DNA is pure and of high quality. #### Step 2: Library Preparation 1. **Fragment DNA**: Break the DNA into smaller fragments. This can be done using mechanical shearing or enzymatic methods. 2. **Add Adapters**: Ligate specific sequences (adapters) to the ends of the DNA fragments. These adapters are necessary for the sequencing process and allow the fragments to bind to the sequencing platform [6]. #### Step 3: Amplification 1. **PCR Amplification**: Amplify the DNA fragments using Polymerase Chain Reaction (PCR). This step increases the quantity of DNA to ensure there is enough material for sequencing. #### Step 4: Sequencing 1. **Choose a Sequencing Platform**: Select a sequencing technology based on your needs. Popular platforms include Illumina, Ion Torrent, Pacific Biosciences, and Oxford Nanopore Technologies [1], [4], [7]. 2. **Load DNA onto Sequencer**: Load the prepared DNA library onto the sequencing machine. Each platform has specific protocols for loading and running the sequencing process. #### Step 5: Data Analysis 1. **Base Calling**: Use software to convert raw sequencing data into nucleotide sequences. Tools like Phred are commonly used for this purpose [8]. 2. **Sequence Assembly**: Assemble the short DNA reads into longer sequences (contigs). Software like Phrap or CAP3 can be used for this step [8]. 3. **Variant Calling**: Identify genetic variants by comparing the assembled sequences to a reference genome. This step is crucial for understanding genetic differences and mutations. #### Step 6: Interpretation 1. **Bioinformatics Analysis**: Use bioinformatics tools to analyze the sequencing data. This includes identifying genes, predicting functions, and understanding genetic variations [10]. 2. **Report Findings**: Summarize the results in a report. This should include information on identified variants, potential implications, and any recommendations for further analysis or validation. #### Additional Resources - **10X Genomics**: Offers advanced solutions for single-cell sequencing and spatial genomics [1]. - **Illumina**: Provides a wide range of sequencing platforms and reagents [1]. - **Pacific Biosciences**: Known for long-read sequencing technology [1], [4]. - **Oxford Nanopore Technologies**: Offers portable and real-time sequencing solutions [1]. By following these steps and utilizing the referenced resources, you can successfully perform genetic sequencing and analyze the resulting data.", + "### Guide to Genetic Sequencing Genetic sequencing is a powerful tool used to determine the order of nucleotides in DNA. This guide will walk you through the basics of genetic sequencing, the different technologies available, and the steps involved in the sequencing process. #### 1. Introduction to Genetic Sequencing Genetic sequencing involves determining the precise order of nucleotides within a DNA molecule. This information is crucial for understanding genetic information, diagnosing genetic disorders, and conducting research in genomics. #### 2. Types of Sequencing Technologies There are several sequencing technologies available, each with its own advantages and applications: - **Sanger Sequencing**: The first-generation sequencing method, known for its high accuracy but lower throughput. - **Next-Generation Sequencing (NGS)**: Includes various platforms that allow for high-throughput sequencing, enabling the sequencing of millions of fragments simultaneously [9]. #### 3. Next-Generation Sequencing Platforms Several NGS platforms are widely used in the field: - **Illumina**: Known for its high accuracy and throughput, suitable for a wide range of applications [2]. - **Ion Torrent**: Uses semiconductor technology for sequencing, offering a cost-effective solution [2]. - **Pacific Biosciences (PacBio)**: Provides long-read sequencing, which is useful for identifying structural variations and complex regions in the genome [3], [2]. - **Oxford Nanopore Technologies**: Offers portable sequencing devices with real-time data analysis capabilities [2]. - **10X Genomics**: Known for its GemCode platform, which provides linked-read sequencing for better genome assembly [1], [2]. #### 4. Steps in Genetic Sequencing The sequencing process generally involves the following steps: 1. **Sample Preparation**: DNA is extracted from the sample and prepared for sequencing. This may involve fragmentation, adapter ligation, and amplification [5]. 2. **Library Construction**: The prepared DNA fragments are converted into a library suitable for the sequencing platform being used [5]. 3. **Sequencing**: The library is loaded onto the sequencing platform, where the DNA fragments are sequenced in parallel [9]. 4. **Data Analysis**: The raw sequencing data is processed and aligned to a reference genome. Tools like Bismark and Bowtie2 are commonly used for alignment and analysis [5]. 5. **Interpretation**: The processed data is interpreted to identify genetic variants, structural variations, and other genomic features [3]. #### 5. Applications of Genetic Sequencing Genetic sequencing has a wide range of applications, including: - **Medical Diagnostics**: Identifying genetic mutations associated with diseases. - **Genomic Research**: Studying genetic variations and their implications. - **Personal Genomics**: Providing individuals with information about their genetic makeup [2]. #### 6. Future Directions The field of genetic sequencing is rapidly evolving, with ongoing advancements aimed at increasing accuracy, reducing costs, and enabling direct sequencing of DNA molecules without amplification [9]. #### 7. Further Information For more detailed information on specific sequencing platforms and technologies, you can visit the following resources: - **Illumina**: [http://www.illumina.com](http://www.illumina.com) - **Ion Torrent**: [https://www.thermofisher.com/us/en/home/brands/ion-torrent.html](https://www.thermofisher.com/us/en/home/brands/ion-torrent.html) - **Pacific Biosciences**: [http://www.pacb.com](http://www.pacb.com) - **Oxford Nanopore Technologies**: [https://www.nanoporetech.com](https://www.nanoporetech.com) - **10X Genomics**: [http://www.10xgenomics.com](http://www.10xgenomics.com) By understanding the basics of genetic sequencing and the available technologies, you can better appreciate the power and potential of this transformative field in genomics.", + "Dyslipidemia is defined as deregulated lipid metabolism that manifests as hypercholesterolemia (high cholesterol levels), hypertriglyceridemia (high triglyceride levels), low high-density lipoprotein (HDL) cholesterol levels, or a combination of these conditions [1]. It is an established risk factor for coronary heart disease (CHD) and can involve various lipoprotein abnormalities, such as increased lipoproteins, elevated apolipoprotein B, and small LDL and HDL particles [2].", + "Cytochromes are proteins that contain heme groups and are responsible for the transport of electrons [3]. They play a crucial role in the mitochondrial respiratory chain, which is essential for the biochemical synthesis of ATP [2]. One specific family of cytochromes, known as cytochrome P450, is involved in the detoxification of harmful substances [3].", + "To differentiate between X and Y DNA with respect to DNA tracing and determining quantitative trait loci (QTLs), consider the following points: 1. **Recombination Rates**: - The Y chromosome largely does not undergo recombination, which means it is passed down relatively unchanged from father to son. This makes it easier to trace paternal lineage but limits its utility in QTL mapping due to the lack of recombination [1]. - The X chromosome, on the other hand, does undergo recombination, albeit at a slower rate compared to autosomes. This slower recombination rate can affect the detection of significant QTLs, making it a unique consideration in QTL analysis [1]. 2. **QTL Mapping**: - QTLs are stretches of DNA closely linked to genes that underlie specific phenotypes. The process of QTL mapping involves measuring phenotypes within a population and genotyping at numerous marker loci to identify genomic locations associated with these traits [2], [4]. - The differences in recombination rates between the X and Y chromosomes mean that QTL mapping on the X chromosome can be more complex due to its unique inheritance pattern and recombination characteristics, whereas the Y chromosome's lack of recombination simplifies paternal lineage tracing but complicates QTL mapping [1], [2]. In summary, the primary differences between X and Y DNA in the context of DNA tracing and QTL determination lie in their recombination rates and inheritance patterns. The Y chromosome's lack of recombination makes it straightforward for paternal lineage tracing but less useful for QTL mapping. In contrast, the X chromosome's slower recombination rate compared to autosomes presents unique challenges and considerations in QTL analysis [1], [2].", + "The environment influences fertilization through various factors that can affect both the biological and genetic mechanisms involved. Here are some specific ways in which the environment can impact fertilization: 1. **Developmental Environment**: The developmental environment, which can span from weeks to years, is a significant source of environmental influence. This includes factors such as temperature, which can affect gene expression levels and potentially influence fertilization outcomes [1]. 2. **Strain and Environmental Interactions**: There is an interaction between genetic strain and environmental factors that can influence fertilization rates. For example, the fertilization rate of frozen-thawed spermatozoa compared to fresh spermatozoa can be affected by these interactions, indicating that both intrinsic genetic factors and environmental conditions play a role [2], [7]. 3. **Gene-by-Environment Interactions**: Behaviors and biological processes, including fertilization, are influenced by complex interactions between multiple genes and environmental factors. These interactions can be subtle and often uncontrollable, making it challenging to isolate the specific environmental factors at play [3], [4]. 4. **Epigenetic Modifications**: Environmental factors such as diet, smoking, and assisted reproductive techniques can alter gene expression through epigenetic modifications like DNA methylation. These changes can impact the placental epigenome and, consequently, pregnancy development and fertilization success [6]. 5. **Temperature and Other Environmental Factors**: Temperature has a genetic basis in its effect on sex determination, and interactions between temperature and genetic factors have been observed in several species. Other environmental factors such as pH, hypoxia, and social factors can also influence sex determination and potentially affect fertilization processes [10]. In summary, the environment influences fertilization through a combination of developmental conditions, genetic and environmental interactions, epigenetic modifications, and specific environmental factors like temperature and pH [1], [2], [3], [4], [6], [7], [10].", + "Diet plays a significant role in determining someone's height, particularly during the growth phases of childhood and adolescence. Several factors related to diet can impact height: 1. **Childhood Nutrition Status**: The nutritional status during childhood is crucial for growth. Proper nutrition ensures that children receive the necessary vitamins and minerals required for bone growth and overall development [1]. 2. **Dietary Factors and Genetics**: Diet interacts with genetics, influencing growth outcomes. For example, calcium intake during childhood and adolescence can affect bone mass acquisition, which is essential for achieving optimal height [4]. 3. **Peak Height Velocity (PHV)**: The period of rapid growth during early adolescence, known as peak height velocity, can be influenced by diet. Adequate nutrition during this period is essential to support the growth spurt [3]. In summary, a well-balanced diet rich in essential nutrients is vital for achieving optimal height, especially during the critical growth periods of childhood and adolescence.", + "Humans have 46 chromosomes, arranged in 23 pairs [1], [7]. The animal that has the same number of chromosomes as humans is the baboon, specifically the Olive baboon (Papio hamadryas anubis), which also has 46 chromosomes [10].", + "The proper functioning of the brain is ensured by several factors: 1. **Precise Regulation of Molecular and Cellular Events**: The development of the brain is a highly complex process that relies on the precise regulation of molecular and cellular events, which are grounded in the spatiotemporal regulation of transcription [3]. 2. **Energy Metabolism**: Energy metabolism plays a crucial role in the fate of adult neural stem cells, which is essential for maintaining brain function [8]. 3. **Functional Reserve and Resiliency**: The creation of redundancy in brain circuitry is associated with functional reserve and resiliency, which are critical for maintaining homeostatic equilibrium and supporting healthy aging and longevity [9]. These factors collectively ensure that the brain works effectively.", + "Our brains maintain emotions through a complex interplay of various brain regions and neural circuits. Key areas involved include: 1. **Amygdala**: Sensory inputs with emotional components are transmitted to the amygdala, where they are processed and further relayed to other regions to modulate autonomic and behavioral responses, and to form emotional memories [6]. The amygdala orchestrates responses to threats and is involved in both negative and positive emotional states [10]. 2. **Prefrontal Cortex**: The ventrolateral prefrontal cortex (VLPFC) is critical for regulating responses to emotional stimuli. Increased activity in the right VLPFC is associated with decreased activity in the amygdala, which helps in managing negative affective states [9]. 3. **Extended Limbic System**: This system, which includes structures like the hippocampus, is primitively responsible for fear and pleasure responses, stress, memory, and learning. It is modulated by immune signaling, indicating that inflammation can affect emotional regulation [2]. 4. **Midbrain Structures**: These structures are involved in emotion regulation and are activated by both physical pain and pleasure, as well as positive and negative socially induced emotions [3]. 5. **Nucleus Accumbens and Ventral Striatum**: These areas participate in reward responses and positive emotional states [10]. Overall, the brain maintains emotions through the coordinated activity of these regions, which process, regulate, and respond to emotional stimuli, thereby shaping our emotional experiences and behaviors.", + "During stressful experiences, our brains release several hormones, including corticotropin-releasing hormone (CRH), adrenocorticotropic hormone (ACTH), and glucocorticoids (GC) [1]. Additionally, cortisol, a steroid hormone, is released by the adrenal glands under chronic stress [6]. These hormones play significant roles in regulating immune responses, neuronal survival, neurogenesis, synaptic plasticity, and behavioral responses [1].", + "Corticosterone has several uses and effects as indicated in the provided context: 1. **Amplification of Stress-Induced Changes**: Corticosterone importantly amplifies stress-induced changes [1]. 2. **Anti-inflammatory and Immune Effects**: At high and supraphysiological concentrations, corticosterone has pronounced anti-inflammatory actions. Conversely, at lower concentrations, it appears to have some immune-potentiating effects [3]. 3. **Impact on Cell Proliferation**: Acute increases in corticosterone lead to decreased cell proliferation, while chronic increases cause an increase in proliferation rate. This is due to the presence of two receptors with different binding affinities: the glucocorticoid receptor (GR) and the mineralocorticoid receptor (MR) [5]. 4. **Reduction of Delayed-Type Hypersensitivity Response**: Corticosterone dramatically reduces the delayed-type hypersensitivity response [6]. 5. **Measurement in Research**: Corticosterone levels can be measured in plasma samples using specific enzyme immunoassay kits, which is useful in various research settings [8]. These points highlight the diverse roles and applications of corticosterone in both physiological and research contexts." + ], + "contexts": [ + [ + "children is driven more by dysregulated allergy and epithelial barrier function genes, whereas the cause of adult-onset asthma is more lung-centred and environmentally determined, but with immune-mediated mechanisms driving disease progression in both children and adults. Funding US National Institutes of Health. Copyright 2019 Elsevier Ltd. All rights reserved. Introduction Asthma is the most prevalent chronic respiratory disease worldwide.1 The diagnosis of asthma is based on the", + "asthma has increased with alarming frequency in industrialized cities worldwide (e.g. Elias et al 2003). These diseases generally are complex, with clear contribu-tions of genetic background and exposure to environmental stimuli (see Kleeberger & Peden 2005). It is unlikely that the increased incidence in disease can be attributed only to genetics as increases in disease-causing genetic mutations to account for the increase would require multiple generations. Therefore the role of environmental exposures", + "living all represent risk factors for asthma, while early farm exposures and breastfeeding confer protective effects. Such observations have been assimilated into the hygiene hypothesis, rst set out in 1989 (136), positing that reduced early microbial exposure and its impacts on immunity underliethe postIndustrial Revolution atopy and asthma epidemic. Responsible for a transformation in our understanding of microbial factors in asthma has been a revolution of a different kind. Only", + "tobacco smoke exposure and with early-onset asthma (before age 4) [49/C15/C15]. Further studies of preschool asth- matics have shown the 17q21 variants are associated with an almost two-fold increased risk of developing recurrent wheeze, asthma, asthma exacerbations and bronchial hyper-responsiveness, but are not associated with eczema, rhinitis or allergic sensitization, indicating that they are specic determinants of nonatopic asthma in children [47].", + "for childhood-onset asthma supports the widely held idea that asthma in childhood is due to impaired barrier function in the skin and other epithelial surfaces. This model proposes that compromised epithelial barriers promote sensitisation to food and airway allergens and to wheezing illnesses in early life. 46,47 In fact, childhood onset-specific loci identified in this study have been associated with atopic dermatitis or food allergies, such as FLG on 1q21.3 with the atopic march, 41 atopic", + "relation to asthma and other atopic diseases). The prompt in the asthma example came from the observation of the apparent effect of being reared in a farm envi-ronment. Of course, it was crucial to replicate that observation in different social contexts and it was also important to have some leverage on a likely biological mediating pathway (in that case exposure to endotoxins). Similarly, the G E", + "[11] Shaaban R, Zureik M, Soussan D, Neukirch C, Heinrich J, Sunyer J, et al. Rhinitis and onset of asthma: a longitudinal population-based study. Lancet (London, England) 2008;372(9643):104957. [12] de NijsSB, VenekampLN, BelEH. Adult-onset asthma: is it really different? Eur Respir Rev 2013;22(127):44. [13] RackemannFM. Intrinsic asthma. J Allergy 1940;11(2):14762. [14] JarvisD, NewsonR, LotvallJ, HastanD, TomassenP, KeilT, etal. Asthma in adults and its as -", + "GG19CH10_Cookson ARI 26 July 2018 9:47 Epigenetic Features of Asthma: Within the Lung A study of the epigenome in primary airway epithelial cells from 74 asthmatic and 41 non-asthmatic adults (111) revealed a regulatory locus on chromosome 17q1221 (the same locus identied by asthma GWASs) associated with asthma risk and epigenetic signatures of specic asthma endo-types. ORMDL3 expression was related to the differentially methylated region at this locus, while", + "studies have identied a range of pre-, peri-, and postnatal environmental factors, including modeof delivery, diet, and early lower respiratory tract infection, that confer relative risk or protection. Attempts to map the genetic architecture of asthma have identied a broad spectrum of potential contributory genes. Many of these genes demonstrate inconsistent patterns of replication betweencohorts, most likely reecting a combination of true positive and true negative results and the", + "49 Variants at those loci were all associated with earlier age of asthma onset. We further showed that these loci are associated with childhood-onset asthma, even after exclusion of patients with a history of allergic diseases in prespecified analyses, suggesting both a crucial role for the allergic diathesis in the development of asthma in childhood and a shared architecture between allergic disease and childhood-onset asthma. 33,46 By contrast, the enrichment for genes highly expressed" + ], + [ + "by shearing. A flow diagram summarizing the extraction of DNA is given in Fig. 1.2. The above-described procedure is suitable for total cellular DNA. If the DNA from a specific organelle or viral particle is needed, it is best to isolate the organelle or virus before extracting its DNA, because the recovery of a particular type of DNA from a mixture is usually rather difficult. Where a high degree of purity is required, DNA may be subjected to density gradient", + "2017 Nature America, Inc., part of Springer Nature. All rights reserved. nature medicine doi:10.1038/nm.434564. Salonen, A. et al. Comparative analysis of fecal DNA extraction methods with phylogenetic microarray: effective recovery of bacterial and archaeal DNA using mechanical cell lysis. J. Microbiol. Methods 81, 127134 (2010). 65. Murphy, N.R. & Hellwig, R.J. Improved nucleic acid organic extraction through use of a unique gel barrier material. Biotechniques 21, 934936, 938939 (1996).", + "is the suitable preparation of the DNA template with a high level of purity and free from contaminating DNA (14). Different procedures are used for DNA extraction with specific protocol for mammals, plants, fungi, bacteria, protozoan, helminthes, insects, and others. In specific cases, such as insects, contamination can be reduced by hypochlorite treatment before extraction to avoid contact with foreign DNA (15). DNA preparation includes the", + "this method is well suited for larger scale investigations of museum insect phylogenomics. We did extract DNA from relatively large insects, where one leg yields more tissue than is availablefrom crushing the entire body of most ants, for example. Thus, it remains now to be tested whether sufficient input DNA can also be obtained from smaller dried insect specimens. None-", + "usually requires that it be isolated and purified to a certain degree. DNA is usually recovered from cells by methods that include cell rupture but that prevent the DNA from fragmenting by mechanical shearing. This is gener- ally undertaken in the presence of EDTA, which chelates the magnesium ions needed as cofactors for enzymes that degrade DNA, termed DNase. Ideally, cell walls, if present, should be digested enzymatically (e.g., lysozyme in the", + "DNA and then using a gene probe representing a protein or enzyme from one of the organisms. In this way, it is possible to search for related genes in different species. This technique is generally termed Zoo blotting. A similar process of nucleic acid blotting can be used to transfer RNA separated by gel electrophoresis onto membranes similar to that used in Southern blotting. This process, termed Northern blotting , allows the identification of specific mRNA", + "6. Staats M, Erkens RH, van de Vossenberg B, Wieringa JJ, Kraaijeveld K, Stielow B, et al. Genomic trea- sure troves: complete genome sequencing of herbarium and insect museum specimens. PLOS ONE. 2013; 8:e69189. doi: 10.1371/journal.pone.0069189 PMID: 23922691 7. Burrell AS, Disotell TR, Bergey CM. The use of museum specimens with high-throughput DNA sequencers. J Hum Evol. 2015; 79:35 44. doi: 10.1016/j.jhevol.2014.10.015 PMID: 25532801", + "were extracted from unthawed, frozen faecal subsamples (150 mg) after pretreatment of the weighed subsamples with 1.5 ml RNAlater ICE (LifeTechnologies) overnight.The faeces-RNAlater ICE mixture was homogenized by bead-beating, as previously described 53. Differential centrifugation and extraction using the All-In-One kit (Norgen Biotek) to recover DNA and proteins were carried out as previously described53. DNA fractions were supplemented with DNA extracted from 200 mg", + "DNA was then extracted destructively by grinding the frozen tissue with a sterile pestle, using aDNeasy Blood and TissueKit (Qiagen, Valencia, CA, USA) and following the manufacturer s protocol, except the DNA was eluted in 130 L ddH 2O instead of the supplied buffer. We ran 10L of each extract for 60 min at 100 volt on 1.5% agarose SB (sodium borate) gels, to estimate size of the genomic DNA. From a pool of 60 successful extractions (12 extractions produced no quantifiable DNA), we", + "Extracting biological information" + ], + [ + "Neurogenetics", + "Genetics Genetics is the study of individual genes and their protein products (Guttmacher &", + "genetics and genomics, article 1DNA, genes, and chromosomes. Biological Research for Nursing ,19, 717. Dueker, N. D., & Pericak-Vance, M. A. (2014). Analysis of genetic linkage data for Mendelian traits. Current Protocols in Human Genetics ,83, 1.4.11.4.31. Fu, M. R., Conley, Y. P., Axelrod, D., Guth, A. A., Yu, G., Fletcher, J., & Zagzag, D. (2016). Precision assessment of heterogeneity of lymphedema phenotype, genotypes and risk prediction. Breast , 29, 231240.", + "genetic factors. 371 372 373 374 375", + "GENETICS in MEDICINE |Volume 22 |Number 7 |July 2020 1153", + "to offspring. Genes are pieces of DNA, and most genes contain the information for making a specific protein. zGenetics - Genetics is a term that refers to the study of genes and their role in inheritance - the way certain traits or conditions are passed down from one generation to another. zGenomics - Genomics is a relatively new term that describes the study of all of a person's genes including interactions of those genes with each other and the person's environment.", + "www.pnas.org/cgi/doi/10.1073/pnas.0912702107 PNAS |April 20, 2010 |vol. 107 |no. 16 |74017406 GENETICS", + "GENETICS Downloaded from https://www.pnas.org by 41.90.188.152 on July 14, 2023 from IP address 41.90.188.152.", + "GENETICS Downloaded from https://www.pnas.org by 41.80.118.137 on October 17, 2023 from IP address 41.80.118.137.", + "GENETICS Downloaded from https://www.pnas.org by 41.80.118.137 on October 17, 2023 from IP address 41.80.118.137." + ], + [ + "is the eld of bioinformatics.", + "the umbrella of bioinformatics or com-putational biology.", + "methods of computer-based information processing for ana-lyzing the structure and function of biologically important molecules. NCBI bioinformatics-related resources may be accessed through its home page at: www.ncbi.nlm.nih.gov. The NCBI has three principal branches: 1. Computational Biology Branch ( http://www.ncbi.nlm. nih.gov/CBBresearch/) 2. Information Engineering Branch ( http://www.ncbi.nlm. nih.gov/IEB/)", + "methods of computer-based information processing for ana-lyzing the structure and function of biologically important molecules. NCBI bioinformatics-related resources may be accessed through its home page at: www.ncbi.nlm.nih.gov. The NCBI has three principal branches: 1. Computational Biology Branch ( http://www.ncbi.nlm. nih.gov/CBBresearch/) 2. Information Engineering Branch ( http://www.ncbi.nlm. nih.gov/IEB/)", + "been successful in microbial ecological research withoutbioinformatics tools. Broadly defined, bioinformatics refersto the use of computers to seek patterns in the observedbiological data and to propose mechanisms for such patterns.As can be seen from below, bioinformatics not only canhelp us directly address experimental research objectives butalso can integrate information from various sources and seekspatterns not achievable through experimentation alone.", + "Since the first protein database was created by Margaret Dayhoffin 1965 in response to the increase in protein sequencing, therehas been an explosion of data from the different modalities. Foreach of the aforementioned levels, bioinformatics plays a crucialand intimate role in each of the steps. In general, there are threelarge categories of bioinformatics applications, including data-bases, algorithms and predictions. The category of databasesallows for the combining and organization of large amounts", + "Since the first protein database was created by Margaret Dayhoffin 1965 in response to the increase in protein sequencing, therehas been an explosion of data from the different modalities. Foreach of the aforementioned levels, bioinformatics plays a crucialand intimate role in each of the steps. In general, there are threelarge categories of bioinformatics applications, including data-bases, algorithms and predictions. The category of databasesallows for the combining and organization of large amounts", + "remit of the early bioinformaticist.1,2T o address these problems, the eld drew from the foundations of statistics, mathematics, physics, computer science and, of course, molecular biology. T oday, predictably, bioinformatics still reects the broad base on which it started, comprising an eclectic collection of scientic specialists. As a result of its inherent diversity, it is difcult to dene the scope of bioinformatics as a discipline. It may be even fruitless to try to draw hard boundaries around the eld.", + "remit of the early bioinformaticist.1,2T o address these problems, the eld drew from the foundations of statistics, mathematics, physics, computer science and, of course, molecular biology. T oday, predictably, bioinformatics still reects the broad base on which it started, comprising an eclectic collection of scientic specialists. As a result of its inherent diversity, it is difcult to dene the scope of bioinformatics as a discipline. It may be even fruitless to try to draw hard boundaries around the eld.", + "remit of the early bioinformaticist.1,2T o address these problems, the eld drew from the foundations of statistics, mathematics, physics, computer science and, of course, molecular biology. T oday, predictably, bioinformatics still reects the broad base on which it started, comprising an eclectic collection of scientic specialists. As a result of its inherent diversity, it is difcult to dene the scope of bioinformatics as a discipline. It may be even fruitless to try to draw hard boundaries around the eld." + ], + [ + "(although quite demanding) process offollowing the trait across multiple generations by tracing its coinheritance with genetic markers (a technique referred to as linkage mapping). Finding loci responsible for variability in a quantitative trait (quantitative trait locus mapping, or QTL mapping) is much more difficult, as there are many more sources of variation to capture. lnbred mouse strains are the optimum starting point for QTL", + "Genetic linkage analysis can be used to identify regions of the genome that contain genes that predispose to the observed quantitative trait, leading to iden-tification of QTLs. A significant QTL means that different genotypes at a poly-morphic marker locus are associated with different trait values. Linkage isdetermined by the log of odds (LOD) scores or likelihood ratio statistics (LRS)(seeNote 1 ). To calculate a LOD score or an LRS score for a selected quanti-", + "quantitative trait loci in crosses between outbred linesusing least squares. Genetics 136, 11951207. Haseman, J. K. & Elston, R. C. 1972 The investigation of linkage between a quantitative trait and a marker locus.Behav. Genet. 2, 319. Henshall, J. M. & Goddard, M. E. 1999 Multiple trait mapping of quantitative trait loci after selective genotypingusing logistic regression. Genetics 151, 885894. Jansen, R. C. 1993 Interval mapping of multiple quantitative trait loci. Genetics 135, 205211.", + "quantitative trait loci in crosses between outbred linesusing least squares. Genetics 136, 11951207. Haseman, J. K. & Elston, R. C. 1972 The investigation of linkage between a quantitative trait and a marker locus.Behav. Genet. 2, 319. Henshall, J. M. & Goddard, M. E. 1999 Multiple trait mapping of quantitative trait loci after selective genotypingusing logistic regression. Genetics 151, 885894. Jansen, R. C. 1993 Interval mapping of multiple quantitative trait loci. Genetics 135, 205211.", + "Keywords: quantitative trait loci mapping; regression; structured outbred populations 1. HISTORY The idea of using markers associated with a trait of interest, for example, to predict the performance of individuals in the trait, is not new. Initially, however, the markers used were not identied at the molecular level but rather through the phenotype, for example, coat colour or by the use of simple biochemicalprocedures such as blood groups. An early implemen-", + "Keywords: quantitative trait loci mapping; regression; structured outbred populations 1. HISTORY The idea of using markers associated with a trait of interest, for example, to predict the performance of individuals in the trait, is not new. Initially, however, the markers used were not identied at the molecular level but rather through the phenotype, for example, coat colour or by the use of simple biochemicalprocedures such as blood groups. An early implemen-", + "tions between markers and phenotype. Once allelic effects at each locus are identified, different techniques can be used to position precise loci (i.e., QTL) influencing the trait. These techniques include marker regression (30), interval mapping (31), and multiple mapping strategies (32). Marker regression locates QTL with respect to all markers simultaneously by regression onto the marker means. It also estimates the additive (and dominance) effects, tests their signif-", + "successful in identifying genes for simple traits. Quantitative trait mapping and genome wide association studies identify chromosomal regions referred to as quantitative trait loci (QTLs) that are statistically associated with the trait. Usually there are several such associations, each on the order of megabases (Mb) in length containing the usual diversity of single nucleotide polymorphisms (SNPs), one to two thousand per Mb, and there has been little success identifying", + "markers reveal potential gene locations regulating the trait of interest as known as quant itative trait loci (QTL s). Historically, this approach has been successful in identifying genes that are responsible for rare, monogenic bone diseases. More recently, much denser maps of SNP s allow researchers to perform genome -wide linkage analysis for complex trait s like bone phenotypes . However, several difficulties preventing the discovery of causal genes include genetic", + "Quantitative Trait Locus (QTL) analysis, which links phenotype to loci on chromosomes that likely had an impact on the phenotype. Students then are able to sift through a list of genes in the region(s) of the chromosome identified by the QT L analysis and find a candidate gene that has relatively high expression in the brain region of interest. Once such a candidate gene is identified, students can find out more information about the gene," + ], + [ + "Genes 2018 ,9, 615 18 of 20 97. McFarlane, R.J.; Humphrey, T.C. A role for recombination in centromere function. Trends Genet. 2010 ,26, 209213. [CrossRef] 98. Talbert, P .B.; Henikoff, S. Centromeres convert but dont cross. PLoS Biol. 2010 ,8, e1000326. [CrossRef] 99. Durfy, S.J.; Willard, H.F. Concerted Evolution of Primate Alpha Satellite DNA Evidence for an Ancestral Sequence Shared by Gorilla and Human X Chromosome Satellite. J. Mol. Biol. 1990 ,216, 555566. [CrossRef]", + "4.1. Recombination and Repair at Centromeres: Errors in Copying and Mending Highly Repetitive DNA Why are centromeres so cold?, asked Andy Choo in his review of centromeres [ 96]. He was referring to centromere DNA as being cold to recombination. While maternal and paternal chromosomes suffer multiple DNA double-stranded breaks (DSBs) to induce recombination and exchange of genetic information by crossing over during meiosis, centromere loci are refractory", + "exacerbates centromere rearrangements [ 54], indicating that there may be active mechanisms to suppress centromeric recombination and these may, at least in part, involve core centromeric proteins. Centromere alpha-satellite DNA is estimated to represent between 3% and 10% of the human genome [ 101], reviewed in [ 19]. During each round of replication, unperturbed cells suffer over 40 DNA DSBs [ 102], of which at least half are repaired by homologous recombination (HR) in S-phase and G2,", + "347357 (1998). 31. Baudat, F. et al. PRDM9 is a major determinant of meiotic recombination hotspots in humans and mice. Science 327, 836840 (2010). 32. Kong, A. et al. Recombination rate and reproductive success in humans. Nat.Genet. 36, 12031206 (2004). 33. Ottolini, C. S. et al. Genome-wide maps of recombination and chromosome segregation in human oocytes and embryos show selection for maternal recombination rates. Nat. Genet. 47, 727735 (2015).", + "to this process. This led to the assumption that centromeres do not undergo recombination and that the repetitive arrays are maintained as stable. However, this clashed with the notion that centromeres very origin stems from recombination to create the repetitive array, where multiple short- and long-range recombination events may be responsible for the generation and reiteration of blocks of highly homogenized alpha-satellite DNA throughout the centromere [ 97,98]. Furthermore, in addition", + "of these DSBs through recombination-dependent pathways, such as homologous recombination (HR), may disrupt centromere integrity in several ways: (1) Crossover between sister chromatids will lead to sister chromatid exchange (SCE), which has been reported at human cent romeres. (2) Search for the homologous sequence may erroneously identify an identical or nearly identical sequence within the same chromatid downstream or upstream of the break site. Recombination between these two", + "higher in regions of high recombination. Trends Genet. 18, 337340 (2002). 26. Webster, M. T. & Hurst, L. D. Direct and indirect consequences of meiotic recombination: implications for genome evolution. Trends Genet. 28, 101109 (2012). 27. Alexandrov, L. B. et al. Signatures of mutational processes in human cancer. Nature 500, 415421 (2013).", + "to chromosome-specic alpha-satellites, certain centromeric sequences are shared by all chromosomes, evidence that formation of these arrays is dominated by interchromosomal exchanges [ 8,98100]. This invites new questions about the stability of centromere DNA outside of meiosis. Indeed, our recent analysis has shown that centromeres can undergo recombination during a single round of cell division in primary human cells [ 54]. Depletion of CENP-A and other CCAN proteins", + "shown to play a role in DNA repair (reviewed in [ 125]), and in vitro experiments show that this hybridization may facilitate DSB repair by bridging the broken DNA fragments in a Rad52-dependent manner during recombination [126]. Centromeres have been suggested [ 127,128], but not proven, to behave like fragile sites of the human genome. Common fragile sites are described as genomic loci where ongoing replication collides", + "Cell Biol. 2016 ,17, 1629. [CrossRef] [PubMed] 54. Giunta, S.; Funabiki, H. Integrity of the human centromere DNA repeats is protected by CENP-A, CENP-C, and CENP-T. Proc. Natl. Acad. Sci. USA 2017 ,114, 19281933. [CrossRef] [PubMed] 55. Giunta, S. Centromere Chromosome Orientation Fluorescent in situ Hybridization (Cen-CO-FISH) Detects Sister Chromatid Exchange at the Centromere in Human Cells. Bio-Protocol 2018 ,8. [CrossRef]" + ], + [ + "4.1. Recombination and Repair at Centromeres: Errors in Copying and Mending Highly Repetitive DNA Why are centromeres so cold?, asked Andy Choo in his review of centromeres [ 96]. He was referring to centromere DNA as being cold to recombination. While maternal and paternal chromosomes suffer multiple DNA double-stranded breaks (DSBs) to induce recombination and exchange of genetic information by crossing over during meiosis, centromere loci are refractory", + "Genes 2018 ,9, 615 18 of 20 97. McFarlane, R.J.; Humphrey, T.C. A role for recombination in centromere function. Trends Genet. 2010 ,26, 209213. [CrossRef] 98. Talbert, P .B.; Henikoff, S. Centromeres convert but dont cross. PLoS Biol. 2010 ,8, e1000326. [CrossRef] 99. Durfy, S.J.; Willard, H.F. Concerted Evolution of Primate Alpha Satellite DNA Evidence for an Ancestral Sequence Shared by Gorilla and Human X Chromosome Satellite. J. Mol. Biol. 1990 ,216, 555566. [CrossRef]", + "of these DSBs through recombination-dependent pathways, such as homologous recombination (HR), may disrupt centromere integrity in several ways: (1) Crossover between sister chromatids will lead to sister chromatid exchange (SCE), which has been reported at human cent romeres. (2) Search for the homologous sequence may erroneously identify an identical or nearly identical sequence within the same chromatid downstream or upstream of the break site. Recombination between these two", + "exacerbates centromere rearrangements [ 54], indicating that there may be active mechanisms to suppress centromeric recombination and these may, at least in part, involve core centromeric proteins. Centromere alpha-satellite DNA is estimated to represent between 3% and 10% of the human genome [ 101], reviewed in [ 19]. During each round of replication, unperturbed cells suffer over 40 DNA DSBs [ 102], of which at least half are repaired by homologous recombination (HR) in S-phase and G2,", + "to this process. This led to the assumption that centromeres do not undergo recombination and that the repetitive arrays are maintained as stable. However, this clashed with the notion that centromeres very origin stems from recombination to create the repetitive array, where multiple short- and long-range recombination events may be responsible for the generation and reiteration of blocks of highly homogenized alpha-satellite DNA throughout the centromere [ 97,98]. Furthermore, in addition", + "347357 (1998). 31. Baudat, F. et al. PRDM9 is a major determinant of meiotic recombination hotspots in humans and mice. Science 327, 836840 (2010). 32. Kong, A. et al. Recombination rate and reproductive success in humans. Nat.Genet. 36, 12031206 (2004). 33. Ottolini, C. S. et al. Genome-wide maps of recombination and chromosome segregation in human oocytes and embryos show selection for maternal recombination rates. Nat. Genet. 47, 727735 (2015).", + "shown to play a role in DNA repair (reviewed in [ 125]), and in vitro experiments show that this hybridization may facilitate DSB repair by bridging the broken DNA fragments in a Rad52-dependent manner during recombination [126]. Centromeres have been suggested [ 127,128], but not proven, to behave like fragile sites of the human genome. Common fragile sites are described as genomic loci where ongoing replication collides", + "to chromosome-specic alpha-satellites, certain centromeric sequences are shared by all chromosomes, evidence that formation of these arrays is dominated by interchromosomal exchanges [ 8,98100]. This invites new questions about the stability of centromere DNA outside of meiosis. Indeed, our recent analysis has shown that centromeres can undergo recombination during a single round of cell division in primary human cells [ 54]. Depletion of CENP-A and other CCAN proteins", + "Studying the direct link between re combination and sister chromatid dynamics with combined live cell imaging and genomics will likely yieldimportant insight into the impact that centromeric and telomeric cross- overs have on chromosome segregation.Reconstructing the bivalent con guration from MeioMaps: recombination and its linkwith chromosome segregation The combined assessment of haplotypes that are determined by recombination also allowed the rst direct correlations between", + "Cell Biol. 2016 ,17, 1629. [CrossRef] [PubMed] 54. Giunta, S.; Funabiki, H. Integrity of the human centromere DNA repeats is protected by CENP-A, CENP-C, and CENP-T. Proc. Natl. Acad. Sci. USA 2017 ,114, 19281933. [CrossRef] [PubMed] 55. Giunta, S. Centromere Chromosome Orientation Fluorescent in situ Hybridization (Cen-CO-FISH) Detects Sister Chromatid Exchange at the Centromere in Human Cells. Bio-Protocol 2018 ,8. [CrossRef]" + ], + [ + "347357 (1998). 31. Baudat, F. et al. PRDM9 is a major determinant of meiotic recombination hotspots in humans and mice. Science 327, 836840 (2010). 32. Kong, A. et al. Recombination rate and reproductive success in humans. Nat.Genet. 36, 12031206 (2004). 33. Ottolini, C. S. et al. Genome-wide maps of recombination and chromosome segregation in human oocytes and embryos show selection for maternal recombination rates. Nat. Genet. 47, 727735 (2015).", + "Genet 39: 977983 33 Myers S et al. (2005) A fine-scale map of recombination rates and hotspots across the human genome. Science 310: 321324REVIEW Nature.indt 1 Nature.indt 1 28/11/07 9:46:50 am 28/11/07 9:46:50 am", + "higher in regions of high recombination. Trends Genet. 18, 337340 (2002). 26. Webster, M. T. & Hurst, L. D. Direct and indirect consequences of meiotic recombination: implications for genome evolution. Trends Genet. 28, 101109 (2012). 27. Alexandrov, L. B. et al. Signatures of mutational processes in human cancer. Nature 500, 415421 (2013).", + "D.R., and Donnelly, P. (2004). The ne-scale structure ofrecombination rate variation in the human genome. Science 304, 581584. 33. Winckler, W., Myers, S.R., Richter, D.J., Onofrio, R.C., McDo- nald, G.J., Bontrop, R.E., McVean, G.A., Gabriel, S.B., Reich, D., Donnelly, P., et al. (2005). Comparison of ne-scale recom- bination rates in humans and chimpanzees. Science 308, 107111. 1192 The American Journal of Human Genetics 82, 11851192, May 2008", + "www.pharmaco-genomics.com 569REVIEW 48. Reich DE, Schaffner SF , Daly MJ et al. : Human chromosome sequence variation and the influence of gene history, mutation and recombination. Nat. Genet. 32, 135-142 (2002). The authors provide evidence that recombination hot spots may represent a general feature of the human genome and play a major role in shaping genetic variation in humans. 49. Wall JD, Pritchard JK: Haplotype blocks and linkage disequilibrium in the human", + "Genes 2018 ,9, 615 18 of 20 97. McFarlane, R.J.; Humphrey, T.C. A role for recombination in centromere function. Trends Genet. 2010 ,26, 209213. [CrossRef] 98. Talbert, P .B.; Henikoff, S. Centromeres convert but dont cross. PLoS Biol. 2010 ,8, e1000326. [CrossRef] 99. Durfy, S.J.; Willard, H.F. Concerted Evolution of Primate Alpha Satellite DNA Evidence for an Ancestral Sequence Shared by Gorilla and Human X Chromosome Satellite. J. Mol. Biol. 1990 ,216, 555566. [CrossRef]", + "Variations on a theme: cataloguing human DNA sequence variation. Science 278, 1580- 1581 (1997). 37. Jeffreys AJ, Kauppi L, Neumann R: Intensely punctate meiotic recombination in the class II region of the major histocompatibility complex. Nat. Genet. 29, 217-222 (2001). 38. Chakravarti A, Buetow KH, Antonarakis SE et al.: Nonuniform recombination within the human beta-globin gene cluster. Am. J. Hum. Genet. 36, 1239-1258 (1984). 39. Smith RA, Ho PJ, Clegg JB, Kidd, JR,", + "genome. Nat. Rev. Genet. 4, 587-597 (2003). Important review, including discussion of the recently proposed haplotype-block model of LD. 50. Nachman MW: Variation in recombination rate across the genome: evidence and implications. Curr. Opin. Genet. Dev. 12, 657-663 (2002). 51. Kong A, Gudbjartsson DF , Sainz J et al. : A high-resolution recombination map of the human genome. Nat. Genet. 31, 241-247 (2002). 52. Sabeti PC, Reich DE, Higgins JM et al. :", + "Recombination maps are often used for admixture mapping (Browning and Browning 2007). A recombination map is a genetic map that illustrates the variation of the recombina-tion rate across a region of the genome or the entire genome (Myers etal. 2005). It is dependent on the underlying dis-tribution of recombination events that occur between suc-cessive generations within a given population (Kong etal. 2010). The presence and activity of the PRDM9 zinc finger protein in the population under study, the ratio", + "31. Fu Q, et al. (2015) An early modern human from Romania with a recent Neanderthal ancestor. Nature 524(7564):216 219. 32. Baudat F, et al. (2010) PRDM9 is a major determinant of meiotic recombination hotspots in humans and mice. Science 327(5967):836 840. 33. Lesecque Y, Glmin S, Lartillot N, Mouchiroud D, Duret L (2014) The red queen model of recombination hotspots evolution in the light of archaic and modern human ge- nomes. PLoS Genet 10(11):e1004790." + ], + [ + "FURTHER INFORMATION 10X Genomics: http://www.10xgenomics.com 454 Sequencing: http://www.454.com Advances in Genome Biology and Technology (AGBT): http://www.agbt.org BGISEQ500: http://seq500.com/en/portal/Sequencer.shtml Illumina: http://www.illumina.com Ion Torrent: https://www.thermofisher.com/us/en/home/ brands/ion-torrent.html Oxford Nanopore Technologies: https://www.nanoporetech. com Pacific Biosciences: http://www.pacb.com Personal Genome Project: http://www.personalgenomes.org", + "36. Sequencing, H.G. Finishing the euchromatic sequence of the human genome. Nature 2004 ,431, 931945. 37. Heather, J.M.; Chain, B. The sequence of sequencers: The history of sequencing DNA. Genomics 2016 ,107, 18. [CrossRef] 38. Rothberg, J.M.; Leamon, J.H. The development and impact of 454 sequencing. Nat. Biotechnol. 2008 ,26, 11171124. [CrossRef] [PubMed] 39. Shendure, J.; Ji, H. Next-generation DNA sequencing. Nat. Biotechnol. 2008 ,26, 11351145. [CrossRef] [PubMed]", + "sequencing. Genome Res. 20, 11651173 (2010). 64. English,A.C. etal. Assessing structural variation in a personal genome-towards a human reference diploid genome. BMC Genomics 16, 286 (2015). 65. Carneiro,M.O. etal. Pacific Biosciences sequencing technology for genotyping and variation discovery in human data. BMC Genomics 13, 375 (2012). 66. Quail,M.A. etal. A tale of three next generation sequencing platforms: comparison of Ion T orrent, Pacific Biosciences and Illumina MiSeq sequencers.", + "22. Karow, J. Qiagen launches GeneReader NGS System atAMP; presents performance evaluation by broad. GenomeWeb [online], https:// www.genomeweb.com/ molecular-diagnostics/qiagen-launches-genereader- ngs-system-amp-presents-performance-evaluation (4Nov 2015). 23. Smith,D.R. & McKernan,K. Methods of producing and sequencing modified polynucleotides . US Patent 8058030 (2011). 24. Margulies,M. etal. Genome sequencing in microfabricated high-density picolitre reactors. Nature 437, 376380 (2005).", + "160. Glenn,T .C. Field guide to next-generation DNA sequencers. Mol. Ecol. Resour. 11, 759769 (2011). 161. Karow,J. At AGBT , 10X Genomics launches GemCode platform; shipments slated for Q2 as firm battles IP lawsuits. GenomeWeb [online], https://www. genomeweb.com/sample-prep/agbt-10x-genomics- launches-gemcode-platform-shipments-slated-q2-firm- battles-ip-lawsuits (2Mar 2015). Competing interests statement The authors declare competing interests: see Web version for details. FURTHER INFORMATION", + "sequencing. Bioinformatics 31, 20402042 (2015). 46. Qiagen. Oncology insights enabled by knowledge base- guided panel design and the seamless workflow of the GeneReader NGS system Press Release. Qiagen [online], http://www.genereaderngs.com/PROM-9192- 001_1100403_WP_GeneReader_NGS_0116_NA.pdf (2016). 47. Forgetta,V. etal. Sequencing of the Dutch elm disease fungus genome using the Roche/454 GS-FLX Titanium System in a comparison of multiple genomics core", + "DNA), and provide the means to link sequences containing applications. First, base- callers like Phred (4,5) extract raw sequences from raw data. There are also contig assemblers like Phrap (University of Washington, http://bozeman. mbt.washington.edu/phrap.docs/phrap.html ) or CAP3 (6) that assemble frag- ments to contigs and packages like consed (7) or GAP4 (8), which are used to finish sequencing projects. These programs are not explained in detail here.", + "sequencing data to solutions from the genotyping array data. iv PREVIEW", + "11 BIOINFORMATIC CHALLENGES FOR GENOMIC MEDICINE Processing and managing of high-throughput sequence data High throughput sequencing offers severa l advantages relative to array-based genotyping or expression assays. First, unlike genotyping arrays, whole genome sequencing is not limited to interrogating onl y known sequence variants. Similarly, RNA- sequencing (RNA-seq) enables expression quanti fication of novel transcripts that are not", + "11 BIOINFORMATIC CHALLENGES FOR GENOMIC MEDICINE Processing and managing of high-throughput sequence data High throughput sequencing offers severa l advantages relative to array-based genotyping or expression assays. First, unlike genotyping arrays, whole genome sequencing is not limited to interrogating onl y known sequence variants. Similarly, RNA- sequencing (RNA-seq) enables expression quanti fication of novel transcripts that are not" + ], + [ + "Telomeres are arrays of linked nucleotide hexamer repeats that are found at the ends of chromosomes in a vast clade of organisms [14]. While the sequence of these telomeric repeats can vary between organisms, their biological function is highly conserved, which is to limit damage inflicted on genes during the replica- tion of chromosomes. Telomere length is progressively shortened with each round of genomic replication, unless it is restored through the action of a ribonucleo-", + "repetitive nucleotide sequences at the end of each eukaryotic chromosome, which protects them from attrition and damage. Although the relationship between leukocyte telomere length (LTL) and diabetes is still questioned 8, different studies have shown that T2D individuals have shorter leukocyte telomeres than non-T2D individuals9, 10 that may be associated with disease progression11. Indeed, the decreased antioxidant capacity described in patients", + "telomere length,a phenomenon attributed to higher levels of oxidativestress at the cellular level (70). More recent studies havelinked telomere length in smooth muscle cells with senes-cence and disease severity in patients with atherosclero-sis (141, 150). Leukocyte telomere length was also short ina cohort of similar patients and associated with a higherrisk of developing occult cardiovascular disease (71).More data are needed to understand and validate the useof leukocyte telomere length as a biomarker", + "TTAGGG sequence that cap the ends of chromosomes, protect-ing them from degradation and fusion. The length of telomererepeats is primarily maintained by active telomerase, which iscomposed of Telomerase RNA (TR) and a catalytic subunitTelomerase Reverse Transcriptase (TERT) (Blackburn, 2001).Extensive evidence has shown that telomere shortening anderosion lead to chromosome end-to-end fusions and genomicinstability (Blasco et al ., 1997; Hande et al ., 1999), causing", + "age telomere length through accumulation of several short telo- meres (Londono-Vallejo et al., 2001; Martens et al., 2000) is responsible for senescence or whether a speci c chromosome arm limits the replication potential of human cells (Hemann et al., 2001). Individual chromosome arms were shown to have large variations in their length (Lansdorp et al., 1996; Benn, 1997; Londono-Vallejo et al., 2001), and chromosome 17p seemed to be equipped with especially short telomeres in hu-", + "Telomeres are specialized structures that protect the ends of linear chromosomes. They shorten during aging due to the unidirectional activity of DNA polymerase, which leaves a section of DNA unrepli-cated on the lagging strand. Telomeres also are subject to shortening by genotoxic stress, such as oxidative damage (33). Among many eukaryotes, the enzyme telomerase maintains telomere length; but telomerase activity varies over the lifespan and between cell types, tissues, and species (34). In most human", + "ends. For example, chromosome 17p typi-cally has shorter telomeres than most other chromosomeends (26, 137). In human nucleated blood cells, the aver-age telomere length shows a highly signicant declinewith age that is most pronounced for the cells of theimmune system (Fig. 2). Telomeres prevent the ends oflinear chromosomes from appearing as DNA double-strand (ds) breaks and protect chromosome ends fromdegradation and fusion. It has been proposed that telo-meres can switch between an open state (in", + "telomeres, the repetitive sequence at the end of linear chromosomes, has garnered much attention for its relation to aging. Telomere repeats serve as an internal clock for cycling cells because each round of replication results in the loss of telomeric DNA in the absence of active telomerase (reviewed in [66]). Eventually, this loss over cellular generations culminates in telomere crisis and a permanent state of", + "a pivotal role in maintenance of genomic integrity and func-tion (Moyzis et al., 1988; McElligott and Wellinger, 1997; van Steensel and de Lange, 1997). It is generally accepted that telomeres shorten during DNA replication both in vitro and in vivo. In individuals, short telomeres are considered to be a sign of advanced age. Cawthon and coworkers (2003) showed that telomere shortening in hu-", + "Telomeres are nucleoprotein complexes situated at the ends of thelinear chromosomes that prevent chromosome termini from beingrecognized as broken DNA ends ( i.e., DSBs). In most of the organisms studied, telomeres consist of long repetitive G-rich and C-rich DNAstrands, the ribonucleoprotein telomerase, and telomere bindingand associated proteins [179] . Loss of telomeric repeats or loss of" + ], + [ + "the egg and the sperm. Such a process would result in genetic changes that will be copied into every cell of the future adult, including reproductive cells (Stock & Campbell, 2000), opening the door to irreversibly alter the human species. Inevitably, signifi cant self-disclosure and discussion challenges await families", + "a fertilized egg is a complicated process that relies on controlling: which genes are active; whenthese genes activate; and for how long they are active. In broad terms, there are four ways that thiscontrol can be achieved: First, inside the sperm or egg, genes can be marked with small chemical tags that flag these genes", + "to be activated (or remain inactive) after fertilization, depending on whether the modification wasmade by the father (in the sperm) or the mother (in the egg); this process is known as imprinting. Second, the mother can alter the gene activity in her offspring via the placenta; this process is known as maternal effect. Third, instructions encoded within the embryos DNA can directly control if, andwhen, a nearby gene becomes activated; this is known as cis-regulation. Finally, similar instructions", + "genes. An altered gene may be passed on to every cell that develops from it. The resulting features my help, harm, or have little or no effect on the offsprings success in its environment. (AAAS, pg. 109, 5B:9-12#4 ) 6. Heritable material: The information passed from parents to offspring is coded in DNA molecules (AAAS, pg 108, 5B:9-12#3) 7. Mutagens: Gene mutations can be caused by such things as radiation and chemicals. When they occur in sex cells, the mutations can be passed onto offspring; if they", + "sex chromosome effects. (B)Soon after fertilization, male and female cells have sex-specic transcriptomes, epigenomes, and phenotypes (for example, male embryos grow faster than female embryos). At implantation, lineage determination begins and gene expression differences are reduced. Epigenetic marks, however, are less constrained and some are maintained, affecting gene expression, and phenotype later in development. Once specic lineages are established, differences in", + "phenomena such as mutations and gene conversion events) occur in relevant meioses leading up to the formation of the gametes (i.e., egg and sperm) which are combined during fertilization and the formation of zygotes. Thus, individuals inherit a patch- work of chromosomal segments from maternal and paternal chromosomes.", + "(Figures 8 and 9). Two gametes (egg and sperm) ultimately join into a single cell, the zygote, which has the full comple-ment of 23 chromosome pairs restored. If all goes well, the zygote gives rise to a live offspring. The Mendel Laws: Segregation and Independent Assortment Both of the Mendel laws pertain directly to the process of meiosis. The first Mendel law, the law of segregation, states that each parent passes a randomly selected allele for a given", + "the subset of that genetic information that is active. But how does the differentiation process begin? The key insight in resolving this conundrum came from fly genetics and was the realization that the egg is not a homogenous sack of protoplasm. The maternally-derived genes active in the fertilized egg are asymmetrically distributed such that at the first cell division each daughter cell receives a different complement of factors. Development continues as a", + "spermatozoa: more than the sum of its parts? DNA, histones, pro - tamines and epigenetics. Reproduction 139:287301 Nilsson EE, Sadler-Riggleman I, Skinner MK (2018) Environmentally induced epigenetic transgenerational inheritance of disease. Envi-ron Epigenet 4:dvy016Pembrey M, Saffery R, Bygren LO, Network in Epigenetic Epide-", + "mediated through the transmission of epigenetic information through the paternal sperm cells [6,80,81]. 4.1. Persistence of Maternal Exposure to A dverse Environmental Conditions along Generations In some cases, developmentally programmed traits may simply be the result of persistent or replicated exposure during critical periods of deve lopment, generation after generation. It has been suggested that the history of seve re socio-political disruptions and economic disadvantage suffered" + ], + [ + "variation with cultural practices around lineage. In certain societies, individuals place greater importance on (and have greater knowledge about) one side of the family than another (unilineal descent). Thus, individuals in patrilineal groups trace relationships through males only so that your fathers brothers children are members of your family, but not your fathers sisters (Kottak, 2007 ). They are members of their husbands group or family. Efforts to create", + "maternal lineage membership with those who weredirectly genotyped. Based on these pedigree (matrilineal) relation-", + "in three-generation families, and read pair tracing DNMs with phased variants. In the former approach, we determined the parent of origin as in our previous analysis4. For example, if an offspring of the proband was a carrier of the DNM allele and had haplotype sharing to paternal chromosome of the proband, we assigned the mutation to the father. Meanwhile, if the offspring was not a DNM allele carrier, we would assign it to the maternal germline. We restricted the haplo -", + "Unlike the nuclear genome, which requires both paternal and maternal contributions, mtDNA is inherited solely from the maternal lineage. It is unclear what advantage a uniparental mtDNA transmission confers, but one possibil-ity is to minimize the number of distinct genomes to maxi-mize the efficiency of a multi-genomic system (Hill etal. 2019). In fact, humans have developed complex, redundant mechanisms to ensure uniparental inheritance of mtDNA (DeLuca and OFarrell 2012; Rojansky etal. 2016). Paternal", + "c) Mitochondrial DNA (maternal line testing) markers: mitochondrial DNA or mtDNA haploid is the maternally inherited mitochondrial genome (mtDNA) [ 44]. All children inherit mtDNA from their mother, with no admixture from the father. Like Y-line DNA, mtDNA is passed intact from one generation to the next but through maternal line. Mitochondrial DNA does not follow any surname. In fact, the surname changes in every generation when women marry. Polymorphisms of mtDNA", + "a family pedigree may be hampered if the participant is not familiar with her mothers relatives, but her mothers brothers children (her cousins) may be able to supplement her overall family history. Knowledge about the cultural system of unilineal descent avoids assuming the universality of bilateral descent. Cultural beliefs such as these also have implications in the conduct of genetic research in terms of confidentiality and autonomy (Benkendorf et al.,", + "225 three-generation families using haplotype sharing (Fig. 1c and Methods), 80.4% were found to be of paternal origin (Extended Data Fig. 1). Figure 1e shows a strong relationship between the number of paternal DNMs and the fathers age at conception (1.47 per year, 95% CI 1.341.59) and a weaker impact of the mothers age on the number of maternal DNMs (0.37 per year, 95% CI 0.300.45). The parental origin of all DNMs was also assessed by read pair", + "genetics-based population divergence studies. Am J Phys Anthropol 128(2):415 423.22. Helgason A, Hrafnkelsson B, Gulcher JR, War d R, Stefnsson K (2003) A populationwide coalescent analysis of Icelandic matrilineal and patrilineal genealogies: Evidence for a faster evolutionary rate of mtDNA lineages than Y chromosomes. Am J Hum Genet 72(6): 1370 1388. 23. Amster G, Sella G (2015) Life history effects on the molecular clock of autosomes and sex chromosomes. Proc Natl Acad Sci USA 113(6):1588 1593.", + "sistent with a maternal imprinting effect in familiesfrom France [18], the USA[10, 18, 21] (Figure 2; Table3) and Canada [27]. However, in a large family dataset from the UK, and in smaller data sets fromDenmark and Sardinia, the transmission of VNTRsusceptibility alleles is more pronounced frommothersthanfromfathers,andnowsignicantlysoinUK families (Figure 2; Table 3). Comparison of theresults from the USAwith those from the UK suggestthat unexplained inter-population differences in thisparent-of-origin", + "started with the largest matrilineage and worked down the list. Theparticipants selected for mtDNA sequencing were selected inde-pendent of their cognitive or dementia status. 274 matrilineageswere represented by this dataset. As a result, the sequencedmitochondrial genomes also represent as many different majormitochondrial haplogroups and clusters as possible (Table 1).Selection was made blind to case-control status. 287 samples weresent to Family Tree DNA (www.familytreedna.com) for Sangersequencing of" + ], + [ + "While most of the Y chromosome does not undergo recombination, the recombination rate of the X chromosomeis slower than that of the autosomes. This has important consequences on the detection of significant QTLs. For a comprehensive view of these issues, see(43). 9.Probe hybridization artifacts When several probes are available for the same gene, it is not uncommon to observe a difference in the mapping results", + "8 QTL Mapping Allelic variation exists among natural populations and inbred strains, and this is reflective of the segregation of quantitative tr ait loci (QTLs) [96]. QTLs are stretches of DNA that are closely linked to genes that underlie a phenotype of interest. QTL analysis has been proven to be an invaluable tool to help unravel heritable traits, by enabling researchers to map different quantitative traits back to the genomic location involved in the regulation of these phenotypes.", + "8 QTL Mapping Allelic variation exists among natural populations and inbred strains, and this is reflective of the segregation of quantitative tr ait loci (QTLs) [96]. QTLs are stretches of DNA that are closely linked to genes that underlie a phenotype of interest. QTL analysis has been proven to be an invaluable tool to help unravel heritable traits, by enabling researchers to map different quantitative traits back to the genomic location involved in the regulation of these phenotypes.", + "genes underlying QTLs in animals and plants (see for example Shirley et al 2004,Korstanje & Paigen 2002, Fridman et al 2004). I should also point out, though, that even in a single QTL region isolated in a congenic strain, it is possible that there is more than one allele that aects the phenotype. So, you have a fair pointabout the challenges and complexities of QTL analysis. Koolhaas: There are dierent questions underlying both approaches. The QTL", + "genes underlying QTLs in animals and plants (see for example Shirley et al 2004,Korstanje & Paigen 2002, Fridman et al 2004). I should also point out, though, that even in a single QTL region isolated in a congenic strain, it is possible that there is more than one allele that aects the phenotype. So, you have a fair pointabout the challenges and complexities of QTL analysis. Koolhaas: There are dierent questions underlying both approaches. The QTL", + "The basic pr emise of QTL an alysis is simple (Ph illips and Belknap, 2002 ) . First, one must meas ure a speci c phen otype within a popul ation. Next, the population must be genotyped at a hundred or more marker loci186 Boehm II et al.", + "through analysis of line crosses, quantitative trait loci (QTL) mapping, and verification of candidate genes with quantitative complementation tests or genetic engineering (e.g.,McGuire and Tully 1987; Chandra et al. 2001; Dierick and Greenspan 2006; Edwardset al. 2006). They can also be used to study the underlying physiological, neural, andmolecular mechanisms of the differences in behavior between selected and controllines, or between divergently selected lines.", + "through analysis of line crosses, quantitative trait loci (QTL) mapping, and verification of candidate genes with quantitative complementation tests or genetic engineering (e.g.,McGuire and Tully 1987; Chandra et al. 2001; Dierick and Greenspan 2006; Edwardset al. 2006). They can also be used to study the underlying physiological, neural, andmolecular mechanisms of the differences in behavior between selected and controllines, or between divergently selected lines.", + "genetic background. Gene identification of QTL should be distinguished from identification of the quanti- tative trait nucleotide (QTN). The latter is a daunting task, since SNPs are so frequent. Final proof for a QTN in mice would require placing a genomic segment containing theputative QTN from a donor mouse strain on the background of another strain using homologous recombination and reproducing the phenotype of the donor strain.", + "because these strains have been genotyped at more than 14,000 markers, including single nucleotide polymorphisms (SNP). Hundreds of genes may lie within a QTL interval, so identifying the underlying genes requires complementary methods. One method is to use BXD gene expression data (a public resource at www.genenetwork.org) to screen for genes within the QTL interval whose expression correlates with the trait of interest [23]." + ], + [ + "QTL Mapping GeneNetwork ( www.genenetwork.org ) variants data set comprising about", + "Bioinformatics All of the genetic analyses were carried out in GeneNetwork, whichis an open source bioinformatics resource for systems genetics thatexists as both a repository for genetic, genomic and phenotypicdata together with a suite of statistical programs for data analy-sis that includes mapping and evaluating QTLs, examining pheno-type/genotype correlations and building interaction networks. QTL mapping The QTL mapping module of GeneNetwork was used to identify", + "the database is that each data collection is associated with a protocol which describes how the data were generated. The project also provides online analysis tools to allow identification of correlations within its data set. GeneNetwork ( http://www.genenetwork.org ), encompassing WebQTL, is a database of genotypes and complex phenotypes ranging from gene expression to behaviour in standard inbred strains, and six panels of mouse recombinant inbred strains including the two largest", + "QTL/interval analysis QTL mapping was conducted using publically available software on GeneNetwork (http://www .genenetwork .org/webqtl /main .py). One important feature of the GeneNetwork is WebQTL , which is the leading GeneNetwork module , and has been optimized for on-line analysis of traits that are controlled by combinations of allelic variants and environmental factors [15]. A simple graphical user interface", + "WebQTL is the primary module in the Gene- Network online resource (www.genenetwork.org),and provides a powerful environment to analyzetraits controlled by genetic variants (Chesler et al.2004; Wang et al. 2003). It includes data from manypermanent genetic reference populations, including the HXB rat strains, and allows for phenotypic traits,", + "67. As described above, loci are identified in GeneNet work by the computation of a likelihood ratio statistic score and significance was determined using at least 5,000 permutations of the phenotype data. Updated QTL mapping methods , such as R/qtl 2 66,146, Multiple QTL mapping 64, GEMMA 156 and pyLMM 63, have been implimented on t he GeneNetwork2 site 46.", + "genetic mapping, and correlation of quantitative traits such as gene expression data and behavioral parameters (Wang et al, 2003) . GeneNetwork employs genotype data from 3809 markers, selected based on their being informative (i.e., different between progenitor strains). GeneNetwork outputs peak likelihood ratio statistic (LRS) locations for each trait, whic h can be directly converted to", + "tool for combined visualization and exploration of geneexpression data and QTL. The methodology developedin this work is complementary to the analyses that canbe performed on the GeneNetwork website (WebQTL,http://www.genenetwork.org/ ), which allows assessment of the relationship between gene expressions and QTL inrecombinant in bred mice [ 3]. Comparing QTL and microarray data is not completely", + "tool for combined visualization and exploration of geneexpression data and QTL. The methodology developedin this work is complementary to the analyses that canbe performed on the GeneNetwork website (WebQTL,http://www.genenetwork.org/ ), which allows assessment of the relationship between gene expressions and QTL inrecombinant in bred mice [ 3]. Comparing QTL and microarray data is not completely", + "the database entries. Once the resulting record set of the query is returned, it can be further restricted by selecting relevant records based on attached annotations before for- warding it for further analysis. To map genetic loci associated with mRNA abundance or trait phenotypes, any one of the three QTL mapping func- tions currently employed by GeneNetwork's WebQTL module can be used. These are 1. interval mapping, 2. sin- gle-marker regression, or 3. composite mapping [29,30]." + ], + [ + "rodent QTLs. Here we discuss each tool, illustrate itsapplication and generate a bioinformatics strategy fornarrowing QTLs. Combining these bioinformatics toolswith classical experimental methods should accelerateQTL gene identication. Introduction Quantitative trait locus (QTL) analysis is a method to localize chromosomal regions harboring genetic variants that affect a continuously distributed, polygenic phenotype(including many common diseases) [1]. It is particularly", + "rodent QTLs. Here we discuss each tool, illustrate itsapplication and generate a bioinformatics strategy fornarrowing QTLs. Combining these bioinformatics toolswith classical experimental methods should accelerateQTL gene identication. Introduction Quantitative trait locus (QTL) analysis is a method to localize chromosomal regions harboring genetic variants that affect a continuously distributed, polygenic phenotype(including many common diseases) [1]. It is particularly", + "Table 2. Computational Approaches for Identi cation of QTLs Tools Link Programming languageRefs Linear models CPMAtranseqtl https://github.com/cotsapaslab/CPMAtranseqtl R/Python [ 176] eMap www.gnu.org/software/gsl/ R FastMap https://sourceforge.net/projects/fastmapunix/ JAVA [ 134] lme4qtl https://github.com/variani/lme4qtl R[ 175] Matrix eQTL www.bios.unc.edu/research/genomic_software/ Matrix_eQTLR/Matlab [ 133] Meta-eQTL https://haok01.u.hpc.mssm.edu/meta_eQTL/ R/C [ 177]", + "2012). Tools for QTL analysis have been de veloped and released for researchers such as R/qtl, QTL cartographer, M apQTL, and WebQTL. Recently, Wang et al. (2012) developed a free software for QTL mapping called QTL IciMapping which constructs genetic linkage maps and QTL analysis by simple interval mapping and inclusive composite interval mapping. QTL IciMapping is available for segregating and inbred PREVIEW", + "incorrect, the analysis can separate the QTL peak into twoTable 1. Summary of bioinformatics tools for dissecting rodent QTLs Bioinformatics tool Summary Resolution Comparative genomics Identies regions of chromosomal synteny in QTLs that are concordant across species1020 Mb Combined cross analysis Recodes genotype information from multiple crosses detecting a shared QTL into one susceptibility and one resistance genotype to combine the crosses in a singleQTL analysis1020 Mb Interval-specic haplotype", + "incorrect, the analysis can separate the QTL peak into twoTable 1. Summary of bioinformatics tools for dissecting rodent QTLs Bioinformatics tool Summary Resolution Comparative genomics Identies regions of chromosomal synteny in QTLs that are concordant across species1020 Mb Combined cross analysis Recodes genotype information from multiple crosses detecting a shared QTL into one susceptibility and one resistance genotype to combine the crosses in a singleQTL analysis1020 Mb Interval-specic haplotype", + "QTL/interval analysis QTL mapping was conducted using publically available software on GeneNetwork (http://www .genenetwork .org/webqtl /main .py). One important feature of the GeneNetwork is WebQTL , which is the leading GeneNetwork module , and has been optimized for on-line analysis of traits that are controlled by combinations of allelic variants and environmental factors [15]. A simple graphical user interface", + "model selection approach for mapping multiple interacting QTL [376] and Plink, a library for association QTL mapping on single nu cleotide polymorphisms (SNP) in natural populations [277]. 3.2.3 Add new analysis tools xQTL workbench supports exible adding of more QTL analysis s oft- ware: any R-based, or command-line tool, can be plugged in. A ll anal- ysis results are uploaded, stored and tracked in the xQTL workbench database through an R-API. When new tools are added, they can b uild", + "717 730 14. Delaneau, O. et al. (2017) A complete tool set for molecular QTL discovery and analysis. Nat. Commun. 8, 1545215. Liu, B.H. (2017) Statistical Genomics: Linkage, Mapping, and QTL Analysis , CRC Press 16. Gibson, G. et al. (2015) Expression quantitative trait locus anal- ysis for translational medicine. Genome Med. 7, 1 14 17. Ritchie, M.D. et al. (2015) Methods of integrating data to uncover genotype-phenotype interactions. Nat. Rev. Genet. 16, 185 197", + "236 CH 10 TOOLS FOR STATISTICAL GENETICS Lastly, Bayesian methods allow the consideration of multiple QTLs, QTL positions and QTL strengths (Jansen, 1996; Satagopan et al. , 1996; Uimari et al. , 1996; Sillanpaa and Arjas, 1998, Borevitz et al. , 2002). Multimapper (Sillanpaa, 1998), for example, allows the automatic building of models of multiple QTLs within the same linkage group. It is designed to work as a companion program to QTL Cartographer (Basten" + ], + [ + "Methods 31 statistical language/software R (R DEVELOPMENT CORE TEAM 2008) . The core of R/qtl is a set of functions that make use of the hidden Markov model (HMM) technology to calculate QTL genotype probabilities, to simulate from the joint genotype distribution and to calculate the most likely sequence of underlying genotypes (all conditional on the observed marker data) (BROMAN et al. 2003) . R/qtl also calculates several functio ns that are useful for a quality", + "A variety of analytical methodologies are available in the R/qtl package, including, e.g., composite interval mapping or Haley-Knott regression (see Ref. 42for discussion). The scanone function in R/qtl is used to calculate log of the odds (LOD) scores. Per- mutation analysis (perm 1000) is used to establish the signi cance threshold for each phenotype ( P<.05). Additive and/or interactive covariates can be added to the model", + "WebQTL (Chesler et al. 2003; http://www.web- qtl.org/home.html), because each has some uniquecapabilities. R/qtl is an interactive environment for mapping QTLs in experimental crosses, implemented as anadd-on package for the freely available statisticallanguage/software R. Empirical significance valuesare calculated by permutation tests by comparing the peak likelihood ratio statistic (LRS) obtained from 1000 permutations (Churchill and Doerge1994). The permutation test results of highly sig-", + "The basic pr emise of QTL an alysis is simple (Ph illips and Belknap, 2002 ) . First, one must meas ure a speci c phen otype within a popul ation. Next, the population must be genotyped at a hundred or more marker loci186 Boehm II et al.", + "analyses on whole assays of (molecular) phenotypesas a batch. This enables genetical genomics studieswithout waiting times. TIQS is particularly strong inusing a cloud for large scale computing while xQTL uses pbs based traditional clusters and is more developed for data management and definitionof new analyses, so the desire is to work together.Both systems use R as the back-end language for dataanalysis in all platforms, which will enable transfer of analysis protocols between experiments and insti-", + "tional protocols to analyse all expression, proteomicsand metabolomics QTLs on marker maps of everincreasing density. These should include web accesstools for both experts and non-experts in sophisti-cated statistics analysis and high performance computing. The interactive QTL System (TIQS) (http://eqtl .berlios.de) is a web application that guides its usersthrough the analysis steps needed. It maximizes the distribution of computational effort (supporting trad-", + "four commonly used methods for doing a linkage analysis, namely; regression method, likelihood method, variance component method and Bayesian method. For statistical purpose, to check significant thresholds, either permutation test or Bayesian factors are used and for confidence interval check, bootstrapping is the preferred method. For our study, we use WebQTL for QTL mapping. WebQTL (http://webqtl.org) uses interval mapping, to estimate the position of QTLs across a chromosome (Wang et al., 2003,", + "MultiQTL software package, version 2.5 (www.multiqtl.com), aspreviously described in detail (37). In brief, for initial analysis, weused by default an unrestricted model. When the results suggested thepresence of a QTL, we attempted to t the simplest and statisticallyjustied model (dominant, recessive, or additive effect) by comparingit with the nonrestricted model and replacing it if the difference wasnonsignicant. When applicable, we utilized the single-trait, multi-trait, and multienvironment analyses", + "MultiQTL software package, version 2.5 (www.multiqtl.com), aspreviously described in detail (37). In brief, for initial analysis, weused by default an unrestricted model. When the results suggested thepresence of a QTL, we attempted to t the simplest and statisticallyjustied model (dominant, recessive, or additive effect) by comparingit with the nonrestricted model and replacing it if the difference wasnonsignicant. When applicable, we utilized the single-trait, multi-trait, and multienvironment analyses", + "R/QTL [35] is an R package which includes many func tions for mapping, including an algorithm to infer missing genotype data using H idden Markov Models. Gene- Network (www.genenetwork.org [11]) also offers eQTL analysis for user uploaded data, one trait at a time, and genome-wide analysis tools for a number of published datasets. 4. Alternative Illumina data pre-processing Compared with Affymetrix for example, Illumina is a relatively new technology and" + ], + [ + "1. Formatting genome wide association study (GWAS) data . For this step, a human GWAS results file is needed that contains SNP names and raw p- values for the association of each SNP with a trait of interest. Because the nodes of the dmGWAS network will represent genes, as opposed to SNPs, gene-wise p-values need to be calculated from the raw SNP p-values. This can be accomplished by using programs like VEGAS2 (Versatile Gene- Based Association Study) [ 10] or KGG (Knowledge-based mining system", + "A general outline for GWAS is provided in Figure 2. These studies usually begin with thousands of individuals who are charact erized for the phenotype of interest using continuous measurements, or dichotomous classi fication as a case (affected) or control (unaffected). Statistical analysis, typically us ing linear or logistic regression, tests the association of each SNP against the phenotype (including relevant covariate variables) to", + "GWAS has also provided polygenic characteristics of diseases. Figure 1 presents a block of GWAS in disease prediction. There are many steps during a gene-set analysis. They are shown below as Steps 1 through Step 6: Step 1: Preliminary genome-wide analysis and data preproces sing; Step 2: Identifying gene-set definitions whose patterns have to be recognized; Step 3: Processing genomic data such as filtering and ident ifying gene patterns;", + "GWAS in disease prediction. There are many steps during a gene-set analysis. They are shown below as Steps 1 through Step 6: Step 1: Preliminary genome-wide analysis and data preprocessing; Step 2: Identifying gene-set denitions whose patterns have to be recognized; Step 3: Processing genomic data such as ltering and identifying gene patterns; Step 4: Identify gene set analysis models, such as identifying the statistical hypothesis; Step 5: Assessing the statistical magnitude;", + "include: 1) generate bed, bimand fam files for GWAS genotype data using PLINK; 2) generategrm.gz and grm.id files using make-grm; 3) prepare a", + "7 Constructing Gene Networks to Enhance GWAS and GOGE Results As discussed, generating a GOGE data set and performing a rst-pass analysis on this scale of data is a major undertaking. The identication of or other DNA markersthat associate with the expression of one or more genes is a primary goal of a GOGE study. However, if analysis of GOGE data stopped at the identication of SNPs that associate with expression, the true v alue of these data would not be realized.", + "Aggregating GWAS data into biological units GWAS data can be further combined into biological units using gene and network-based approaches. Gene-based approaches There is a high multiple testing burden in the context of a GWAS. Gene-based approaches, which aggregate across summary statistics derived from association analyses of multiple loci to derive p-values for association at the level of the gene, developed as one way to reduce", + "Steps involved inthegene-based association testwere described asbelow: 1)Generating intermediate datasets which integrate original GWAS Pvalues, rsID, position and chromo- some column foreach SNP. Atotal of6,559,815 European-specific and 5,351,262 Asian-spe- cific autosomal SNPs were used forsubsequent analysis after excluding theSNPs that could notberecognized byKGG and that located insexchromosomes (XorY);2)Defining asetof", + "248 M. J. RIEDER ET AL. Figure 2 An overview of GWAS. Samples with a phenotype(s) or trait(s) of interest are identified; typically, thousands of samples are required to achieve appropri ate statistical power. Large-scale genotyping is carried out using commercially available chips (Affymetrix or Illumina). P-values are generated from the associa- tion between the phenotype and genotype for each SNP tested. Highly associated SNPs will typically cluster", + "2006). 40. Welter, D. et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. 42,D1001D1006 (2014).41. Wang, X. et al. Comparing methods for performing trans-ethnic meta-analysis of genome-wide association studies. Hum. Mol. Genet. 22,23032311 (2013). 42. Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81,559575 (2007)." + ], + [ + "FURTHER INFORMATION 10X Genomics: http://www.10xgenomics.com 454 Sequencing: http://www.454.com Advances in Genome Biology and Technology (AGBT): http://www.agbt.org BGISEQ500: http://seq500.com/en/portal/Sequencer.shtml Illumina: http://www.illumina.com Ion Torrent: https://www.thermofisher.com/us/en/home/ brands/ion-torrent.html Oxford Nanopore Technologies: https://www.nanoporetech. com Pacific Biosciences: http://www.pacb.com Personal Genome Project: http://www.personalgenomes.org", + "22. Karow, J. Qiagen launches GeneReader NGS System atAMP; presents performance evaluation by broad. GenomeWeb [online], https:// www.genomeweb.com/ molecular-diagnostics/qiagen-launches-genereader- ngs-system-amp-presents-performance-evaluation (4Nov 2015). 23. Smith,D.R. & McKernan,K. Methods of producing and sequencing modified polynucleotides . US Patent 8058030 (2011). 24. Margulies,M. etal. Genome sequencing in microfabricated high-density picolitre reactors. Nature 437, 376380 (2005).", + "36. Sequencing, H.G. Finishing the euchromatic sequence of the human genome. Nature 2004 ,431, 931945. 37. Heather, J.M.; Chain, B. The sequence of sequencers: The history of sequencing DNA. Genomics 2016 ,107, 18. [CrossRef] 38. Rothberg, J.M.; Leamon, J.H. The development and impact of 454 sequencing. Nat. Biotechnol. 2008 ,26, 11171124. [CrossRef] [PubMed] 39. Shendure, J.; Ji, H. Next-generation DNA sequencing. Nat. Biotechnol. 2008 ,26, 11351145. [CrossRef] [PubMed]", + "sequencing. Genome Res. 20, 11651173 (2010). 64. English,A.C. etal. Assessing structural variation in a personal genome-towards a human reference diploid genome. BMC Genomics 16, 286 (2015). 65. Carneiro,M.O. etal. Pacific Biosciences sequencing technology for genotyping and variation discovery in human data. BMC Genomics 13, 375 (2012). 66. Quail,M.A. etal. A tale of three next generation sequencing platforms: comparison of Ion T orrent, Pacific Biosciences and Illumina MiSeq sequencers.", + "sequencing. Bioinformatics 31, 20402042 (2015). 46. Qiagen. Oncology insights enabled by knowledge base- guided panel design and the seamless workflow of the GeneReader NGS system Press Release. Qiagen [online], http://www.genereaderngs.com/PROM-9192- 001_1100403_WP_GeneReader_NGS_0116_NA.pdf (2016). 47. Forgetta,V. etal. Sequencing of the Dutch elm disease fungus genome using the Roche/454 GS-FLX Titanium System in a comparison of multiple genomics core", + "for sequencing on existing short-read instrumentation, after which data are split by barcode and reassembled with the knowledge that fragments sharing barcodes Barcodes A series of known bases addedto a template molecule either through ligation or amplification. After sequencing, these barcodes can be used to identify which sample a particular read is derived from. Figure 5 | Real-time and synthetic long-read sequencing approaches.", + "160. Glenn,T .C. Field guide to next-generation DNA sequencers. Mol. Ecol. Resour. 11, 759769 (2011). 161. Karow,J. At AGBT , 10X Genomics launches GemCode platform; shipments slated for Q2 as firm battles IP lawsuits. GenomeWeb [online], https://www. genomeweb.com/sample-prep/agbt-10x-genomics- launches-gemcode-platform-shipments-slated-q2-firm- battles-ip-lawsuits (2Mar 2015). Competing interests statement The authors declare competing interests: see Web version for details. FURTHER INFORMATION", + "DNA), and provide the means to link sequences containing applications. First, base- callers like Phred (4,5) extract raw sequences from raw data. There are also contig assemblers like Phrap (University of Washington, http://bozeman. mbt.washington.edu/phrap.docs/phrap.html ) or CAP3 (6) that assemble frag- ments to contigs and packages like consed (7) or GAP4 (8), which are used to finish sequencing projects. These programs are not explained in detail here.", + "Nat. Biotechnol. 30, 10331036 (2012). 111. Chrystoja,C.C. & Diamandis,E.P . Whole genome sequencing as a diagnostic test: challenges and opportunities. Clin. Chem. 60, 724733 (2014). 112. McGuire,A.L. etal. Point-counterpoint. Ethics and genomic incidental findings. Science 340, 10471048 (2013). 113. Bowers,J. etal. Virtual terminator nucleotides for next-generation DNA sequencing. Nat. Methods 6, 593595 (2009). 114. Heger,M. Chinas Direct Genomics unveils new", + "11 BIOINFORMATIC CHALLENGES FOR GENOMIC MEDICINE Processing and managing of high-throughput sequence data High throughput sequencing offers severa l advantages relative to array-based genotyping or expression assays. First, unlike genotyping arrays, whole genome sequencing is not limited to interrogating onl y known sequence variants. Similarly, RNA- sequencing (RNA-seq) enables expression quanti fication of novel transcripts that are not" + ], + [ + "36. Sequencing, H.G. Finishing the euchromatic sequence of the human genome. Nature 2004 ,431, 931945. 37. Heather, J.M.; Chain, B. The sequence of sequencers: The history of sequencing DNA. Genomics 2016 ,107, 18. [CrossRef] 38. Rothberg, J.M.; Leamon, J.H. The development and impact of 454 sequencing. Nat. Biotechnol. 2008 ,26, 11171124. [CrossRef] [PubMed] 39. Shendure, J.; Ji, H. Next-generation DNA sequencing. Nat. Biotechnol. 2008 ,26, 11351145. [CrossRef] [PubMed]", + "22. Karow, J. Qiagen launches GeneReader NGS System atAMP; presents performance evaluation by broad. GenomeWeb [online], https:// www.genomeweb.com/ molecular-diagnostics/qiagen-launches-genereader- ngs-system-amp-presents-performance-evaluation (4Nov 2015). 23. Smith,D.R. & McKernan,K. Methods of producing and sequencing modified polynucleotides . US Patent 8058030 (2011). 24. Margulies,M. etal. Genome sequencing in microfabricated high-density picolitre reactors. Nature 437, 376380 (2005).", + "11 BIOINFORMATIC CHALLENGES FOR GENOMIC MEDICINE Processing and managing of high-throughput sequence data High throughput sequencing offers severa l advantages relative to array-based genotyping or expression assays. First, unlike genotyping arrays, whole genome sequencing is not limited to interrogating onl y known sequence variants. Similarly, RNA- sequencing (RNA-seq) enables expression quanti fication of novel transcripts that are not", + "11 BIOINFORMATIC CHALLENGES FOR GENOMIC MEDICINE Processing and managing of high-throughput sequence data High throughput sequencing offers severa l advantages relative to array-based genotyping or expression assays. First, unlike genotyping arrays, whole genome sequencing is not limited to interrogating onl y known sequence variants. Similarly, RNA- sequencing (RNA-seq) enables expression quanti fication of novel transcripts that are not", + "High-throughput bacterial genome sequencing: an embarrassment of choice, aworldof opportunity.NatRevMicrobiol2012;10:599-606. 11.CroucherNJ,DidelotX.Theapplicationof genomicstotracingbacterialpathogen transmission.CurrOpinMicrobiol2015;23:62-7. 12.ShendureJ,JiH.Next-generationDNAsequencing.NatBiotechnol2008;26:1135- 45. 13.MillerJR,KorenS,SuttonG.Assemblyalgorithmsfornext-generationsequencing data.Genomics2010;95:315-27. 14.OlsonND,LundSP,ColmanRE,FosterJT,SahlJW,SchuppJM,etal.Bestpractices", + "sequencing. Genome Res. 20, 11651173 (2010). 64. English,A.C. etal. Assessing structural variation in a personal genome-towards a human reference diploid genome. BMC Genomics 16, 286 (2015). 65. Carneiro,M.O. etal. Pacific Biosciences sequencing technology for genotyping and variation discovery in human data. BMC Genomics 13, 375 (2012). 66. Quail,M.A. etal. A tale of three next generation sequencing platforms: comparison of Ion T orrent, Pacific Biosciences and Illumina MiSeq sequencers.", + "Nat. Biotechnol. 30, 10331036 (2012). 111. Chrystoja,C.C. & Diamandis,E.P . Whole genome sequencing as a diagnostic test: challenges and opportunities. Clin. Chem. 60, 724733 (2014). 112. McGuire,A.L. etal. Point-counterpoint. Ethics and genomic incidental findings. Science 340, 10471048 (2013). 113. Bowers,J. etal. Virtual terminator nucleotides for next-generation DNA sequencing. Nat. Methods 6, 593595 (2009). 114. Heger,M. Chinas Direct Genomics unveils new", + "sequencing. Bioinformatics 31, 20402042 (2015). 46. Qiagen. Oncology insights enabled by knowledge base- guided panel design and the seamless workflow of the GeneReader NGS system Press Release. Qiagen [online], http://www.genereaderngs.com/PROM-9192- 001_1100403_WP_GeneReader_NGS_0116_NA.pdf (2016). 47. Forgetta,V. etal. Sequencing of the Dutch elm disease fungus genome using the Roche/454 GS-FLX Titanium System in a comparison of multiple genomics core", + "FURTHER INFORMATION 10X Genomics: http://www.10xgenomics.com 454 Sequencing: http://www.454.com Advances in Genome Biology and Technology (AGBT): http://www.agbt.org BGISEQ500: http://seq500.com/en/portal/Sequencer.shtml Illumina: http://www.illumina.com Ion Torrent: https://www.thermofisher.com/us/en/home/ brands/ion-torrent.html Oxford Nanopore Technologies: https://www.nanoporetech. com Pacific Biosciences: http://www.pacb.com Personal Genome Project: http://www.personalgenomes.org", + "DNA), and provide the means to link sequences containing applications. First, base- callers like Phred (4,5) extract raw sequences from raw data. There are also contig assemblers like Phrap (University of Washington, http://bozeman. mbt.washington.edu/phrap.docs/phrap.html ) or CAP3 (6) that assemble frag- ments to contigs and packages like consed (7) or GAP4 (8), which are used to finish sequencing projects. These programs are not explained in detail here." + ], + [ + "repetitive nucleotide sequences at the end of each eukaryotic chromosome, which protects them from attrition and damage. Although the relationship between leukocyte telomere length (LTL) and diabetes is still questioned 8, different studies have shown that T2D individuals have shorter leukocyte telomeres than non-T2D individuals9, 10 that may be associated with disease progression11. Indeed, the decreased antioxidant capacity described in patients", + "Telomeres are arrays of linked nucleotide hexamer repeats that are found at the ends of chromosomes in a vast clade of organisms [14]. While the sequence of these telomeric repeats can vary between organisms, their biological function is highly conserved, which is to limit damage inflicted on genes during the replica- tion of chromosomes. Telomere length is progressively shortened with each round of genomic replication, unless it is restored through the action of a ribonucleo-", + "telomere length,a phenomenon attributed to higher levels of oxidativestress at the cellular level (70). More recent studies havelinked telomere length in smooth muscle cells with senes-cence and disease severity in patients with atherosclero-sis (141, 150). Leukocyte telomere length was also short ina cohort of similar patients and associated with a higherrisk of developing occult cardiovascular disease (71).More data are needed to understand and validate the useof leukocyte telomere length as a biomarker", + "age telomere length through accumulation of several short telo- meres (Londono-Vallejo et al., 2001; Martens et al., 2000) is responsible for senescence or whether a speci c chromosome arm limits the replication potential of human cells (Hemann et al., 2001). Individual chromosome arms were shown to have large variations in their length (Lansdorp et al., 1996; Benn, 1997; Londono-Vallejo et al., 2001), and chromosome 17p seemed to be equipped with especially short telomeres in hu-", + "Telomeres are specialized structures that protect the ends of linear chromosomes. They shorten during aging due to the unidirectional activity of DNA polymerase, which leaves a section of DNA unrepli-cated on the lagging strand. Telomeres also are subject to shortening by genotoxic stress, such as oxidative damage (33). Among many eukaryotes, the enzyme telomerase maintains telomere length; but telomerase activity varies over the lifespan and between cell types, tissues, and species (34). In most human", + "TTAGGG sequence that cap the ends of chromosomes, protect-ing them from degradation and fusion. The length of telomererepeats is primarily maintained by active telomerase, which iscomposed of Telomerase RNA (TR) and a catalytic subunitTelomerase Reverse Transcriptase (TERT) (Blackburn, 2001).Extensive evidence has shown that telomere shortening anderosion lead to chromosome end-to-end fusions and genomicinstability (Blasco et al ., 1997; Hande et al ., 1999), causing", + "a pivotal role in maintenance of genomic integrity and func-tion (Moyzis et al., 1988; McElligott and Wellinger, 1997; van Steensel and de Lange, 1997). It is generally accepted that telomeres shorten during DNA replication both in vitro and in vivo. In individuals, short telomeres are considered to be a sign of advanced age. Cawthon and coworkers (2003) showed that telomere shortening in hu-", + "Each cell division shortens telomeric DNA until, at a critical length, the cells lose capping function at thechromosomal ends, activating DNA damage check-points, cell senescence, and eventually apoptosis.Telomere shortening has particular relevance in thesetting of CVD. Leukocyte telomere length (LTL) associates signi cantly with vascular cell senescence,", + "nization may directly affect telomere attrition, resulting in accelerated replicative senescence and progeroid phenotypes [180]. Telomeres are regions constituted by tandem repeats of non-coding DNA sequences 5-(TTAGGG)n-3 and a protein complex called shelterin, bound to them. This structure ensures the stability of the genome and protects the chromosomes from a wrong action of the DNA repair machinery [184] by allowing the formation of a chromatin loop called T-Loop [185].", + "telomeres, the repetitive sequence at the end of linear chromosomes, has garnered much attention for its relation to aging. Telomere repeats serve as an internal clock for cycling cells because each round of replication results in the loss of telomeric DNA in the absence of active telomerase (reviewed in [66]). Eventually, this loss over cellular generations culminates in telomere crisis and a permanent state of" + ], + [ + "FURTHER INFORMATION 10X Genomics: http://www.10xgenomics.com 454 Sequencing: http://www.454.com Advances in Genome Biology and Technology (AGBT): http://www.agbt.org BGISEQ500: http://seq500.com/en/portal/Sequencer.shtml Illumina: http://www.illumina.com Ion Torrent: https://www.thermofisher.com/us/en/home/ brands/ion-torrent.html Oxford Nanopore Technologies: https://www.nanoporetech. com Pacific Biosciences: http://www.pacb.com Personal Genome Project: http://www.personalgenomes.org", + "22. Karow, J. Qiagen launches GeneReader NGS System atAMP; presents performance evaluation by broad. GenomeWeb [online], https:// www.genomeweb.com/ molecular-diagnostics/qiagen-launches-genereader- ngs-system-amp-presents-performance-evaluation (4Nov 2015). 23. Smith,D.R. & McKernan,K. Methods of producing and sequencing modified polynucleotides . US Patent 8058030 (2011). 24. Margulies,M. etal. Genome sequencing in microfabricated high-density picolitre reactors. Nature 437, 376380 (2005).", + "36. Sequencing, H.G. Finishing the euchromatic sequence of the human genome. Nature 2004 ,431, 931945. 37. Heather, J.M.; Chain, B. The sequence of sequencers: The history of sequencing DNA. Genomics 2016 ,107, 18. [CrossRef] 38. Rothberg, J.M.; Leamon, J.H. The development and impact of 454 sequencing. Nat. Biotechnol. 2008 ,26, 11171124. [CrossRef] [PubMed] 39. Shendure, J.; Ji, H. Next-generation DNA sequencing. Nat. Biotechnol. 2008 ,26, 11351145. [CrossRef] [PubMed]", + "sequencing. Genome Res. 20, 11651173 (2010). 64. English,A.C. etal. Assessing structural variation in a personal genome-towards a human reference diploid genome. BMC Genomics 16, 286 (2015). 65. Carneiro,M.O. etal. Pacific Biosciences sequencing technology for genotyping and variation discovery in human data. BMC Genomics 13, 375 (2012). 66. Quail,M.A. etal. A tale of three next generation sequencing platforms: comparison of Ion T orrent, Pacific Biosciences and Illumina MiSeq sequencers.", + "sequencing. Bioinformatics 31, 20402042 (2015). 46. Qiagen. Oncology insights enabled by knowledge base- guided panel design and the seamless workflow of the GeneReader NGS system Press Release. Qiagen [online], http://www.genereaderngs.com/PROM-9192- 001_1100403_WP_GeneReader_NGS_0116_NA.pdf (2016). 47. Forgetta,V. etal. Sequencing of the Dutch elm disease fungus genome using the Roche/454 GS-FLX Titanium System in a comparison of multiple genomics core", + "for sequencing on existing short-read instrumentation, after which data are split by barcode and reassembled with the knowledge that fragments sharing barcodes Barcodes A series of known bases addedto a template molecule either through ligation or amplification. After sequencing, these barcodes can be used to identify which sample a particular read is derived from. Figure 5 | Real-time and synthetic long-read sequencing approaches.", + "160. Glenn,T .C. Field guide to next-generation DNA sequencers. Mol. Ecol. Resour. 11, 759769 (2011). 161. Karow,J. At AGBT , 10X Genomics launches GemCode platform; shipments slated for Q2 as firm battles IP lawsuits. GenomeWeb [online], https://www. genomeweb.com/sample-prep/agbt-10x-genomics- launches-gemcode-platform-shipments-slated-q2-firm- battles-ip-lawsuits (2Mar 2015). Competing interests statement The authors declare competing interests: see Web version for details. FURTHER INFORMATION", + "DNA), and provide the means to link sequences containing applications. First, base- callers like Phred (4,5) extract raw sequences from raw data. There are also contig assemblers like Phrap (University of Washington, http://bozeman. mbt.washington.edu/phrap.docs/phrap.html ) or CAP3 (6) that assemble frag- ments to contigs and packages like consed (7) or GAP4 (8), which are used to finish sequencing projects. These programs are not explained in detail here.", + "Nat. Biotechnol. 30, 10331036 (2012). 111. Chrystoja,C.C. & Diamandis,E.P . Whole genome sequencing as a diagnostic test: challenges and opportunities. Clin. Chem. 60, 724733 (2014). 112. McGuire,A.L. etal. Point-counterpoint. Ethics and genomic incidental findings. Science 340, 10471048 (2013). 113. Bowers,J. etal. Virtual terminator nucleotides for next-generation DNA sequencing. Nat. Methods 6, 593595 (2009). 114. Heger,M. Chinas Direct Genomics unveils new", + "11 BIOINFORMATIC CHALLENGES FOR GENOMIC MEDICINE Processing and managing of high-throughput sequence data High throughput sequencing offers severa l advantages relative to array-based genotyping or expression assays. First, unlike genotyping arrays, whole genome sequencing is not limited to interrogating onl y known sequence variants. Similarly, RNA- sequencing (RNA-seq) enables expression quanti fication of novel transcripts that are not" + ], + [ + "160. Glenn,T .C. Field guide to next-generation DNA sequencers. Mol. Ecol. Resour. 11, 759769 (2011). 161. Karow,J. At AGBT , 10X Genomics launches GemCode platform; shipments slated for Q2 as firm battles IP lawsuits. GenomeWeb [online], https://www. genomeweb.com/sample-prep/agbt-10x-genomics- launches-gemcode-platform-shipments-slated-q2-firm- battles-ip-lawsuits (2Mar 2015). Competing interests statement The authors declare competing interests: see Web version for details. FURTHER INFORMATION", + "FURTHER INFORMATION 10X Genomics: http://www.10xgenomics.com 454 Sequencing: http://www.454.com Advances in Genome Biology and Technology (AGBT): http://www.agbt.org BGISEQ500: http://seq500.com/en/portal/Sequencer.shtml Illumina: http://www.illumina.com Ion Torrent: https://www.thermofisher.com/us/en/home/ brands/ion-torrent.html Oxford Nanopore Technologies: https://www.nanoporetech. com Pacific Biosciences: http://www.pacb.com Personal Genome Project: http://www.personalgenomes.org", + "sequencing. Genome Res. 20, 11651173 (2010). 64. English,A.C. etal. Assessing structural variation in a personal genome-towards a human reference diploid genome. BMC Genomics 16, 286 (2015). 65. Carneiro,M.O. etal. Pacific Biosciences sequencing technology for genotyping and variation discovery in human data. BMC Genomics 13, 375 (2012). 66. Quail,M.A. etal. A tale of three next generation sequencing platforms: comparison of Ion T orrent, Pacific Biosciences and Illumina MiSeq sequencers.", + "22. Karow, J. Qiagen launches GeneReader NGS System atAMP; presents performance evaluation by broad. GenomeWeb [online], https:// www.genomeweb.com/ molecular-diagnostics/qiagen-launches-genereader- ngs-system-amp-presents-performance-evaluation (4Nov 2015). 23. Smith,D.R. & McKernan,K. Methods of producing and sequencing modified polynucleotides . US Patent 8058030 (2011). 24. Margulies,M. etal. Genome sequencing in microfabricated high-density picolitre reactors. Nature 437, 376380 (2005).", + "mina barcoded adapters and prepared using a 300-cycle MiSeq Reagent Micro Kit v2 (Illumina, San Diego, CA). PCR amplicons were sequenced on the MiSeq with paired-end (PE) 250 base pair reads. Files were aligned to the bisulfite converted reference genome GRCh38 release 94 implementing Bismark [35, 36]. Alignment was obtained through Bismark using the Bowtie2 [37] engine using non-directional and paired-end. Complete sequencing code is provided (https ://githu b.com/qahat", + "sequencing data to solutions from the genotyping array data. iv PREVIEW", + "36. Sequencing, H.G. Finishing the euchromatic sequence of the human genome. Nature 2004 ,431, 931945. 37. Heather, J.M.; Chain, B. The sequence of sequencers: The history of sequencing DNA. Genomics 2016 ,107, 18. [CrossRef] 38. Rothberg, J.M.; Leamon, J.H. The development and impact of 454 sequencing. Nat. Biotechnol. 2008 ,26, 11171124. [CrossRef] [PubMed] 39. Shendure, J.; Ji, H. Next-generation DNA sequencing. Nat. Biotechnol. 2008 ,26, 11351145. [CrossRef] [PubMed]", + "sequencing. Bioinformatics 31, 20402042 (2015). 46. Qiagen. Oncology insights enabled by knowledge base- guided panel design and the seamless workflow of the GeneReader NGS system Press Release. Qiagen [online], http://www.genereaderngs.com/PROM-9192- 001_1100403_WP_GeneReader_NGS_0116_NA.pdf (2016). 47. Forgetta,V. etal. Sequencing of the Dutch elm disease fungus genome using the Roche/454 GS-FLX Titanium System in a comparison of multiple genomics core", + "Conventional sequencing Next-generation sequencing Sequencing Subcloning in vectors, amplification in hosts for every single DNA fragment Direct DNA fragment sequencing Sequencing of 100 fragments in parallel Optional PCR amplification Parallel sequencing of millions of small fragments Yield 1 /H11003105bp/sequencing run /H110221/H110031011bp/sequencing run Computational requirements Moderate High Cost per megabase High LowAccuracy High HighFuture directions Direct sequencing of DNA molecules", + "Nature Reviews | GeneticsCleavage agent Single-base-encoded probes A probe with a single known base and degenerate bases hybridizes to a template and is imagedResetAfter each imaging step, both the probe and anchor are removed Probe with known base at n+1a SOLiD (Thermo Fisher) b Complete Genomics (BGI) Paired-end sequencingSequencing is performed for both the left and right sides of the adapterTTG AG TC CC GA CT TATA A" + ], + [ + "Deregulated lipid metabolism (dyslipidemia) that manifests as hypercholesterolemia, hypertriglyceridemia, low high -density -lipoprotein (HDL) cholesterol levels or a combination of those is an established risk factor for CHD among other established risk factors. The liver is of major importance in maintaining whole- body lipid metabolic", + "23 Atherogenic dyslipidemia, manifested by raised triglycerides and low concentrations of HDL cholesterol. There could be p resent other lipoprotein abnormalities as well, e.g., increased lipoproteins, elevated apo lipoprotein B, small LDL and HDL particles. All of these abnormalities have been imp licated as being atherogenic (Kolovou et al., 2005; Ginsberget al., 2000). Elevated blood pressure strongly associates with obesity and commonly occu rs in insulin-resistant persons.", + "plasma TGisdetermined bythelevel ofVLDL-TG (the balance between synthesis and clear- ance ofVLDL-TG), and thesynthesis ofVLDL-TG isassociated with total fatmass and liver fat[59]. Thus, thelarge amount offatmass inobese patients leads toincreasing synthesis of VLDL-TG, buttheclearance ofVLDL-TG remains unchanged. Hypertriglyceridemia isaprin- cipal characteristic ofdyslipidemia and islinked tomany other types ofdyslipidemia such as", + "Dyslipidemia status Normolipidemia 2,731 898 (0.33) 1,319 (0.48) 514 (0.19) 42.97End-of-study cases 2,102 611 (0.29) 1,057 (0.50) 434 (0.21) 45.79 0.01, 1.12 (1.021.22)Incident cases 959 293 (0.31) 472 (0.49) 194 (0.20) 44.84 0.9, 0.99 (0.911.09) Overall risk data are P, OR (95% CI) and incident risk data are P, HR (95% CI). Hyperglycemia and type 2 diabetes were dened according to 1997 American Diabetes Association criteria", + "The most characteristic lipoprotein abnormality in patients with diabetes, especially type 2, is elevated triglyceride, i.e. VLDL, reduced HDL, and smaller dense LDL. This lipoprotein profile is sometimes referred to as diabetic dyslipidemia. Moreover, in conjunction with obesity, and insulin resistance this lipoprotein profile constitutes part of the \"polymetabolic syndrome\". The primary lipoprotein abnormality is hypertriglyceridemia .", + "Hyperlipidemia 63 (23%) 100 (38%) < 0.001c Diabetes 66 (24%) 106 (40%) < 0.001c TC (mmol/L) 4.36 0.55 4.37 1.07 0.832b,d TG (mmol/L) 1.01 (0.77~1.28) 1.35 (1.00~1.92) < 0.001d,e HDL-C (mmol/L) 1.26 (1.13~1.42) 1.10 (0.94~1.34) < 0.001d,e LDL-C (mmol/L) 2.57 0.36 2.43 0.88 0.017b,d FBG (mmol/L) 4.71 (4.35~5.15) 5.84 (5.31~6.87) < 0.001e PBLs counts (109/L) 5.30 (4.60~6.29) 6.58 (5.33~7.92) < 0.001e PBLs classifications (PBMCs %)40.31 8.11 34.48 10.16 < 0.001b", + "lipid traits as (lipid follow-up lipid baseline ) / lipid baseline . Dyslipidemia/abnormal lipid levels were defined according to the thresholds used in clinical practice guidelines [ 19]: (1) TC 5.1 mmol/l; TG 1.1 mmol/l; and LDL-C 3.4 mmol/l in children; (2) TC 5.1 mmol/l; TG1.4 mmol/l; and LDL-C 3.4 mmol/l in adolescents; (3) TC 5.2 mmol/l; TG 1.7 or 1.97 mmol/l; and LDL- C1.8 or 2.6 mmol/l in adults or patients with T2D. In the two cohorts of adult women, cIMT was mea-", + "dyslipidemia. It also lowered in ammatory biomarkers (CRP and PAI - 1) associated", + "usually associated with reduced HDL cholesterol and small dense LDL. Biliary cholesterol + Bile acids Blood vessel Figure 3. HDL metabolism: HDL production requires addition of lipid to small, nascent particles. This lipid arrives via hydrolysis of VLDL and chylomicrons with transfer of surface lipids (phospholipid PL, and free cholesterol, FC) via the actions of phospholipid transfer protein (PL TP). A second pathway is via effiux of cellular free cholesterol (FC), a process", + "shift in the composition of the lipoprotein particle from one de ned as VLDL to" + ], + [ + "oxidoreductase MitochondriaF29C4.2 IV Cytochrome", + "complex III. It functions to form a part of the mitochondrial respiratory chain. It may also act as a binding fac-tor for the iron-sulfur protein. Mitochondrial Complex III is composed of one mitochondrial-encoded subunit (MT-CYB) and ten nuclear-encoded subunits. The complex is located within the mitochondrial inner mem- brane and plays an important role in biochemical synthesis of ATP . It functions to catalyze electrons to trans-", + "Chapter 36 Directed Protein Evolution 653 3.1.9. SHIPREC Cytochromes are proteins that contain heme groups and are responsible for the transport of electrons. P450 is a family of membrane-bound cytochromes with an absorption maximum of 450 nm when complexed with CO. One of the major roles of the cytochrome P450 system is the detoxification of harmful substances. Sieber et al. (23) produced hybrids of two cytochromes, which share only", + "F42A9.5 cyp-33E2 IV Cytochrome P450 MitochondriaF21D5.8 IV Mitochondrial 28S ribosomal protein S33 MitochondriaC33A12.1 IV NADH: ubiquinone oxidoreductase, ETS complex I subunit MitochondriaZK809.3 IV NADH: ubiquinone oxidoreductase MitochondriaC47E12.2 IV Mitochondrial ADP/ATP carrier protein MitochondriaY57G11C.12 IV NADH: ubiquinone oxidoreductase MitochondriaY41E3.4 ers-1 IV Glutaminyl tRNA synthetase, predicted to be mitochondrial MitochondriaY55F3B_743.b IV Mitochondrial ribosomal protein", + "Process 2.9 2.9 25.4 gi 149058974 rCG44669 (cytochrome c oxidase, subunit VIIc;Cox7c)1.19 0.2121 1.35 1.42 0.05 1.30 1.26 0.0480 1.26 unclassied 29.6 29.7 56.0 gi 149016520 rCG50966 (3-oxoacid-CoA transferase 1(OXCT1/SCOT)1.12 0.3615 1.27 1.08 0.46 1.23 1.33 <0.0001 1.12 metabolism: ketone metabolism 60.9 60.9 67.6 gi 116242506 stress-70 protein, mitochondrial precursor(75 kDa glucose-regulatedprotein) (Heat shock 70kDa protein 9)1.07 0.1432 1.12 1.02 0.39 1.10 1.13 0.0300 1.09 protein folding; protein", + "413 Table 2 Gene ontology Database: molecular function name: Cytochrome c oxidase activity ID:GO:0004129 C = 16 O = 2 E = 0.12 R = 17.06 rawP = 0.0060 adjP = 0.0590 Index User IDGene symbol Gene namesEntrez gene Ensemble 1 ILMN_2657141 Surf1 Surfeit gene 1 20930 ENSMUSG00000015790 2 ILMN_1254971 Cox6b1 Cytochrome c oxidase, subunit VIb polypeptide110323 ENSMUSG00000036751 Database: molecular function Name: NADH dehydrogenase activity ID:GO:0003954", + "F42A9.5 cyp-33E2, cytochrome P450 family 13.81 ( 0.49) 118 0.0010 C47E12.2 Mitochondrial ADP/ATP carrier protein 16.00 ( 0.78) 136 < 0.0001 F21D5.8 Mitochondrial 28S ribosomal protein S33 15.95 ( 0.99) 136 < 0.0001 C33A12.1 NADH: ubiquinone oxidoreductase 16.28 ( 1.05) 139 0.0003 ZK809.3 NADH: ubiquinone oxidoreductase 23.46 ( 1.14) 200 < 0.0001 Y57G11C.12 nuo-3, NADH: ubiquinone oxidoreductase 20.71 ( 1.18) 177 < 0.0001", + "Y66A7A1 100 52 33 4 0 9.00 ( 0.29) 0.0572 210 Y71H2_388.c PP2A regulatory subunit (cytochrome C oxidase subunit) 100 82 48 2 0 5.57 ( 0.20) < 0.0001 130 F54D8.2 Cytochrome c oxidase subunit Vla 100 70 41 22 3 5.62 ( 0.27) < 0.0001 131 F56D2.1 Mitochondrial processing peptidase 100 55 17 3 0 4.46 ( 0.20) 0.4303 104 K04G7.4 Nuo-4, NADH: ubiquinone oxidoreductase 100 78 55 4 0 5.06 ( 0.23) < 0.0001 118 T20H4.5 Ubiquinone Fe-S protein 100 99 89 45 2 7.58 ( 0.18) < 0.0001 177", + "and (Iso211Ser) 1.1383 . (ii) Overview of MT-CYB mutation on electron transport chain. From the complex II the reduced form of ubiquinone move through the hydrophobic region of the membrane by diffusion. When the ubiquinone comes in contact with the next carrier in the electron-transport chain, the electron is transferred to cytochrome reductase, or the cytochrome b-c1 complex (Complex III). The mutated cytochrome b loses the ability to accept incoming", + "c oxidase polypeptide Mitochondria K08F11.4 year-1 IV Tyrosyl-tRNA synthetase, predicted to be mitochondrial MitochondriaE04A4.7 IV Cytochrome c Mitochondria" + ], + [ + "While most of the Y chromosome does not undergo recombination, the recombination rate of the X chromosomeis slower than that of the autosomes. This has important consequences on the detection of significant QTLs. For a comprehensive view of these issues, see(43). 9.Probe hybridization artifacts When several probes are available for the same gene, it is not uncommon to observe a difference in the mapping results", + "8 QTL Mapping Allelic variation exists among natural populations and inbred strains, and this is reflective of the segregation of quantitative tr ait loci (QTLs) [96]. QTLs are stretches of DNA that are closely linked to genes that underlie a phenotype of interest. QTL analysis has been proven to be an invaluable tool to help unravel heritable traits, by enabling researchers to map different quantitative traits back to the genomic location involved in the regulation of these phenotypes.", + "8 QTL Mapping Allelic variation exists among natural populations and inbred strains, and this is reflective of the segregation of quantitative tr ait loci (QTLs) [96]. QTLs are stretches of DNA that are closely linked to genes that underlie a phenotype of interest. QTL analysis has been proven to be an invaluable tool to help unravel heritable traits, by enabling researchers to map different quantitative traits back to the genomic location involved in the regulation of these phenotypes.", + "The basic pr emise of QTL an alysis is simple (Ph illips and Belknap, 2002 ) . First, one must meas ure a speci c phen otype within a popul ation. Next, the population must be genotyped at a hundred or more marker loci186 Boehm II et al.", + "genes underlying QTLs in animals and plants (see for example Shirley et al 2004,Korstanje & Paigen 2002, Fridman et al 2004). I should also point out, though, that even in a single QTL region isolated in a congenic strain, it is possible that there is more than one allele that aects the phenotype. So, you have a fair pointabout the challenges and complexities of QTL analysis. Koolhaas: There are dierent questions underlying both approaches. The QTL", + "genes underlying QTLs in animals and plants (see for example Shirley et al 2004,Korstanje & Paigen 2002, Fridman et al 2004). I should also point out, though, that even in a single QTL region isolated in a congenic strain, it is possible that there is more than one allele that aects the phenotype. So, you have a fair pointabout the challenges and complexities of QTL analysis. Koolhaas: There are dierent questions underlying both approaches. The QTL", + "through analysis of line crosses, quantitative trait loci (QTL) mapping, and verification of candidate genes with quantitative complementation tests or genetic engineering (e.g.,McGuire and Tully 1987; Chandra et al. 2001; Dierick and Greenspan 2006; Edwardset al. 2006). They can also be used to study the underlying physiological, neural, andmolecular mechanisms of the differences in behavior between selected and controllines, or between divergently selected lines.", + "through analysis of line crosses, quantitative trait loci (QTL) mapping, and verification of candidate genes with quantitative complementation tests or genetic engineering (e.g.,McGuire and Tully 1987; Chandra et al. 2001; Dierick and Greenspan 2006; Edwardset al. 2006). They can also be used to study the underlying physiological, neural, andmolecular mechanisms of the differences in behavior between selected and controllines, or between divergently selected lines.", + "genetic background. Gene identification of QTL should be distinguished from identification of the quanti- tative trait nucleotide (QTN). The latter is a daunting task, since SNPs are so frequent. Final proof for a QTN in mice would require placing a genomic segment containing theputative QTN from a donor mouse strain on the background of another strain using homologous recombination and reproducing the phenotype of the donor strain.", + "measuring correlations between genetic markers and phenotypic traits in a population. Individuals are scored for their phenotype for a particular trait, and their genotype at a marker. If there is a differ- ence in mean phenotype between those individuals with one geno- type at a particular locus compared with the other, than we can infer that there is a QTL linked to that marker [ 40 , 153 ]. 2.3 Analysis and QTL MappingDavid G. Ashbrook and Reinmar Hager" + ], + [ + "ferentiation in animals reared at male- and female-producing temperatures (Fernandino et al., 2011). From a pure experimental point of view, there are several potential sources of environ- mental inuences that need to be under con- trol in order to avoid confounding results when studying gene expression levels (Hodgins-Davis and Townsend, 2009; Table 8.3). One of them is effect of the developmental environment, typi- cally in the range of weeks to years. Size is pos-", + "the fertilization rate (Table 1). There was an interaction between the two factors (strain and", + "subtle, and often uncontrollable, environmentalfactors. Behaviors are often influenced by multiple genes with complex gene-by-gene,gene-by-environment, and environment-by-environment interactions. This is one reason,for example, that single-gene mutants are relatively uninformative (see also Rauser et al.this volume), though we described a case in which such mutants were useful for explor-ing mechanisms underlying the evolution of mating systems in voles.", + "subtle, and often uncontrollable, environmentalfactors. Behaviors are often influenced by multiple genes with complex gene-by-gene,gene-by-environment, and environment-by-environment interactions. This is one reason,for example, that single-gene mutants are relatively uninformative (see also Rauser et al.this volume), though we described a case in which such mutants were useful for explor-ing mechanisms underlying the evolution of mating systems in voles.", + "environment interactions, particularly the contribution of environmen- tal factors in utero (Burmeister, McInnis, & Zllner, 2008; Henriksen, Nordgaard, & Jansson, 2017), and these limitations in turn hinder the development of a mechanistic understanding of aetiology. Here, we dissect the impact of gene prenatal environmental interactions on cocaine responsiveness of adult male and female mice from the BXD recombinant inbred panel. Early life stressors, including prenatal stress (PNS), are important", + "onmental factors, some of which have been shown toalter placental gene expression, as well as epigeneticmarks [10]. These include diet [11,12], smoking [13],and assisted reproductive techniques [14,15]. Mountingevidence implicates epigenetic marks, such as DNA methylation, in mediating environmentally-induced reg- ulation of genome function. More studies into theeffects of the environment on the placental epigenomeare warranted due the importance of this organ in regu-lating pregnancy development.", + "as well as the intrinsic fertilizing ability of the strain. Therefore, the results of the QTL analysis based on the fertilization rates of frozen thawed spermatozoa might have reflected the 220 cumulative effect of these two factors. T o exclude the possible background strain effects, we calculated the ratio of the fertilization rate of frozen thawed spermatozoa per that of fresh spermatozoa in individual male mice (designated here as relative fertilization rate ). As shown", + "male ; Relative fertilization rate (%) = (Fertilization rate with f rozen spermatozoa (%)/Fertilization rate with f resh spermatozoa (%)) 100 (n = 6 for each strain) . Fig. 2. Genome -wide interval mapping for suggestive QTLs affecting the fertilization rate 515 using frozen thawed spermatozoa. (A) Mapping based on the actual fertilization rates. (B) Mapping based on the relative fertilization rates. Critical intervals were selected based on peak", + "duce the behavioral differences observed in these inbred strains.The interaction of genes and the environment to produce phe-notypic outcomes has been acknowledged and accepted for quitesome time in the scientic community. However, the exact mech-anism by which the environment can act on genetic materialhas only recently begun to be investigated in a more systematicmanner. A ROLE FOR EPIGENETICS IN THE LINK BETWEEN MATERNAL CARE AND BEHAVIORAL OUTCOMES IN ANIMAL MODELS", + "I na d d i t i o n ,i ts h o u l db en o t e dt h a tt h ee f - fect of temperature on sex determination has a genetic basis itself and an interaction be- tween families and temperature effect has been reported in several species (Schultz, 1993; Van- deputte et al., 2007). Finally, other environ- mental effects such as pH, hypoxia, and so- cial factors have claimed to be involved on sex determination (reviewed by Guerrero-Est evez and Moreno-Mendoza, 2010). All the informa-" + ], + [ + "economic status of a population, for example childhood nutrition status and the disease environment etc.21 Rare are the stud ies that unveil the relation between height decline and bone loss. A study performed by Galloway et al. on 1,024 subjects (735 women and 289 men) evaluated the correlation between height decline and bone loss with ageing. Their findings show that bone mine ral density (BMD) plays the largest role in determining annual height reduction.22", + "economic status of a population, for example childhood nutrition status and the disease environment etc.21 Rare are the stud ies that unveil the relation between height decline and bone loss. A study performed by Galloway et al. on 1,024 subjects (735 women and 289 men) evaluated the correlation between height decline and bone loss with ageing. Their findings show that bone mine ral density (BMD) plays the largest role in determining annual height reduction.22", + "how many eat a high phenylalanine diet.The relationship between gene and disease remains constantacross sites, but diet will act as an effect modier, controllingthe phenotypic consequences of the gene. Another example is the relationship among peak height velocity (PHV: thegrowth spurt of early adolescence), change of school anddepressive symptoms. The period of PHV may be a time whenyoungsters are particularly vulnerable to symptoms of depres-sion (Simmons & Blyth, 1987), particularly when they haveto", + "Dietary factor s deserve special attention as an environmental factor that interacts with genetics because we are exposed to our diet every day and we can modify it to our own benefit. The findings from several Ca intervention trials in children and adolescents demonstrated that there is a large variability in the acquisition of bone mass , despite the control of age range and pubertal maturation of part icipants.(28) Weaver et al.(102) conducted a 3 -week long, controlled", + "rapidly than Paleolithic people andreaching both maximal adult height andsexual maturity earlier. Wehave earlier speculated thatcompression ofthegrowth history predisposes tohigher blood pressure during adoles- cence andincreases theriskofhypertension inadulthood [57] . Arecent interesting series ofstudies byBarker andcolleagues hasfor- warded theargument thatsome fraction ofthepredisposition tohyperten- sionandNIDDM maybeprogrammed inutero bylowbirth weight. Several", + "diets are likely to vary in composition by batch, season and vendor. Variability in non-nutritive dietary components, such as soluble fibre content and plant- derived phyto- estrogens, affects the progression of DIO and metabolic disease, even affecting behavioural traits151,152. Another consideration is that humans consume ~30% of their daily calories from fat. This fat intake is remarkably consistent across age and BMI153 and lower than the 40% to 60% calories from fat used in many", + "several factors such as age, nutritional status, overall health and geographic location, all of which in uence the diet of", + "4 Hypertension November 2020 estimated the relative influence of genetic and environmental factors on height, weight, BMI, SBP, and DBP, as well as the genetic and environmental correlations of BMI with SBP and DBP. Furthermore, the moderating effects of BMI on SBP and DBP heritabilities were tested to explore potential gene-obe-sity interactions on BP. Contributions to the total phenotypic variances of SBP and", + "individuals. Augmentation index was in reverse correlation with height, in addition it was observed that taller participants had less prevalence of hypertension and use of antihypertensive drugs suggesting th e beneficial role of height in estimating cardiovascular risks (159). In a study done on patients with end stage renal disease augmentation index wa s found to negatively correlate with body height, and it was", + "individuals. Augmentation index was in reverse correlation with height, in addition it was observed that taller participants had less prevalence of hypertension and use of antihypertensive drugs suggesting th e beneficial role of height in estimating cardiovascular risks (159). In a study done on patients with end stage renal disease augmentation index wa s found to negatively correlate with body height, and it was" + ], + [ + "As seen in this karyotypic spread, the typical human cell has 46 chromosomes with 22 pairs of autosomes (numbered 122) and a pair of sex chromosomes, either XX or XY . Downloaded from http://ahajournals.org by on July 10, 2023", + "FIGURE 3. Telomere arrays of chicken and human chromosomes: the chicken genome contains more telomere sequence than the human", + "In sexually reproducing organisms, body cells contain 2 sets of chromosomes (1 set from each parent). To maintain this state, the egg and sperm that unite during fertilization each contain a single set of chromosomes. During meiosis, diploid cells undergo DNA replication, followed by 2 rounds of cell division, producing 4 gametes, each of which has 1 set of chromosomes (for humans, 23 unpaired chromosomes). Recombination occurs during meiosis. Mendelian diseaseSame as monogenic disease. Named", + "some set. Therefore, chromosome morphology sup-ports the designation of two separate genera [5]. Sex Chromosomes Several studies have revealed high degrees of homology among autosomal chromosomes of bovids with similar banding patterns and gene order among the chromosome arms of ca ttle, river buffalo, sheep, and goats [14, 15]. Bovid sex chromosomes, unlike the highly similar autosomal chromosomes, share a slightly more complex rearrangement of sequences", + "14 Mice share an anatomy, physiology, and genome that is similar, though not identical, to humans (May a nd Lutjen-Drecoll 2002; Smith 2002; Emes, Goodstadt et al. 2003; Huang, Winter et al. 2004). Mice and hum ans also share a su sceptibility to many similar diseases. As an experimental genetic platform for vertebrates, tools for studying and manipulating the mouse genome are near ly, if not completely, unparalleled", + "DELANY ET AL. 920 TABLE 1. Cytogenetic and telomere characteristics of vertebrate animal species (in vivo) Organism Terminal reference 2n/no. of telomere Telomere (maximum longevity) Telomeres array sizes shortening Rainbow trout 5860/116120 20 kb Unknown Oncohynchus mykiss Lejnine et al., 1995(20 yr) African clawed toad 36/72 1050 kb No Xenopus laevisBassham et al., 1998(15 yr) Laboratory mouse 40/80 50150 kb No Mus musculusKipling and Cooke, 1990(2 yr) Wild mouse 40/80 525 kb Yes", + "A human has 23 pairs of chromosomes, i.e. 46 in total. In each pair one chromosome has been inherited from the mother and the other from the father. The chromosomes in a pair are said to be homologous. They have the same genes at the same loci, but they may have different variants, different so called alleles, of the gene. Recall the eye color example from standard high school texts on genetics. We inherit one eye color allele from each parent, either a", + "A human has 23 pairs of chromosomes, i.e. 46 in total. In each pair one chromosome has been inherited from the mother and the other from the father. The chromosomes in a pair are said to be homologous. They have the same genes at the same loci, but they may have different variants, different so called alleles, of the gene. Recall the eye color example from standard high school texts on genetics. We inherit one eye color allele from each parent, either a", + "and zebra sh (http://www.alliancegenome.org, last access: 3 January 2018). 3 The mouse as a model animal for livestock research Mice are mammals, sharing 92 to 95 % of protein cod- ing genes with humans and other mammalian livestock species, such as cattle (Elsik et al., 2009), pigs (Humphray et al., 2007), sheep (Iannuzzi et al., 1999), and goats (Schibler et al., 1998). The mouse genome is structured into 19 autosomes and the sex chromosomes. The mouse", + "Figure 3: Comparison of human and baboon chromosomes. (A) Conservation of microsatellite marker order for orthologs human 12and baboon 11. (B) C hromosome inversion between orthologs hu- man 4 and baboon 5. The y-axis indicates chromosome length incentimorgans. Microsatellite markers identi ed in human have identi cation numbers that begin with D,and microsatellite markers identi ed in baboon have identi cation numbers that begin swith Pha. Figure 2: Papio hamadryas anubis (Olive baboon)" + ], + [ + "ARTICLE nATuRE C ommunICATIons | 3:1079 | DoI: 10.1038/ncomms2086 | www.nature.com/naturecommunications 2012 Macmillan Publishers Limited. All rights reserved.Received 8 may 2012 | Accepted 23 Aug 2012 | Published 25 sep 2012 DOI: 10.1038/ncomms2086 The mammalian brain consists of distinct parts that fulfil different functions. Finlay and Darlington have argued that evolution of the mammalian brain is constrained by", + "ARTICLE nATuRE C ommunICATIons | 3:1079 | DoI: 10.1038/ncomms2086 | www.nature.com/naturecommunications 2012 Macmillan Publishers Limited. All rights reserved.Received 8 may 2012 | Accepted 23 Aug 2012 | Published 25 sep 2012 DOI: 10.1038/ncomms2086 The mammalian brain consists of distinct parts that fulfil different functions. Finlay and Darlington have argued that evolution of the mammalian brain is constrained by", + "Daniel H. Geschwind, Michael J. Hawrylycz, Matthew W. State, Stephan J. Sanders, Patrick F. Sullivan, Mark B. Gerstein , Ed S. Lein , James A. Knowles , Nenad Sestan INTRODUCTION: The brain is responsible for cognition, behavior, and much of what makes us uniquely human. The development of the brain is a highly complex process, and this process is reliant on precise regulation of molecular and cellular events grounded in the spatiotemporal regulation of the transcrip-", + "addition,each study implemented rigorous controls for non-genetic factors suchas age, gender, IQ and performance on the experimental task. They alsocapitalized on existing functional paradigms designed to explorephysiological aspects of distinct neural systems.", + "brain to prevent theapoptosis of irreplaceable neurons, even in the", + "Funding Funding from the BBSRC, EPSRC, ESRC and MRC is gratefully acknowledged. References 1 Brayne C (2007) The elephant in the room: healthy brains in later life, epidemiology and public health. Nat Rev Neurosci ,8, 233239. 2 Gow J, Gilhooly M (2003) Risk Factors for Dementia and Cognitive Decline . Glasgow: NHS Health Scotland. 3 House of Lords (2005) Ageing: scientific aspects. London: The Stationery Office. 4 Stern PC, Carstensen LL (2000) The Aging Mind. Washington, DC: National Academy Press.", + "1124 the brain. Nature Reviews Neuroscience. Nat Rev Neurosci; 2012. pp. 225239. 1125 doi:10.1038/nrn3209 1126 75. van Praag X, Fleshner M, Schwartz MW, Mattson MP. Exercise, energy intake, 1127 glucose homeostasis, and the brain. J Neurosci. 2014;34: 1513915149. 1128 doi:10.1523/JNEUROSCI.2814-14.2014 1129 76. Rafalski VA, Brunet A. Energy metabolism in adult neural stem cell fate. Progress in 1130 Neurobiology. Prog Neurobiol; 2011. pp. 182203. 1131 doi:10.1016/j.pneurobio.2010.10.007", + "1124 the brain. Nature Reviews Neuroscience. Nat Rev Neurosci; 2012. pp. 225239. 1125 doi:10.1038/nrn3209 1126 75. van Praag X, Fleshner M, Schwartz MW, Mattson MP. Exercise, energy intake, 1127 glucose homeostasis, and the brain. J Neurosci. 2014;34: 1513915149. 1128 doi:10.1523/JNEUROSCI.2814-14.2014 1129 76. Rafalski VA, Brunet A. Energy metabolism in adult neural stem cell fate. Progress in 1130 Neurobiology. Prog Neurobiol; 2011. pp. 182203. 1131 doi:10.1016/j.pneurobio.2010.10.007", + "for the creation of redun-dancy in brain circuitry, which is associated with functionalreserve and resiliency. Brain function regulates most of thecompensatory strategy supporting maintenance of homeo-static equilibrium. Both of these processes are essential tohealthy aging and longevity.", + "of complex traits. It has been said that The brain is the chief architect, orchestrator and driver of behavior; behavior, in turn, is the principal function of the brain (Gomez -Marin et al., 2014, p. 1455) , and therefore to understand one we need to understand the other. The brain and the behaviours that it causes are highly complex traits influen ced by many factors including genes (Hager et al., 2012; Hitzemann et al., 2013; McCarroll and Hyman, 2013) , environment (Carola" + ], + [ + "areas that support pos-itive emotions and deactivate brain areas that are linked withaggression, fear and sadness (Diamond, 2004); this nding is consistent with the emotional prole associated with agreeableness.", + "Importantly, regions of the brain responsible for emotional regulation, executive functioning, and their consequential behavioral outcomes are sensitive to in ammation [ 22 ] . The extended limbic system, primitively responsible for fear and pleasure responses, stress, memory, and learning, has been shown to be modulated by immune signaling. Early work established that there is a high density of IL-1 receptors in the dentate gyrus and pyramidal cell layer of the hippocampus, the", + "the midbrain structures are implicated in cardiacresponses to social stress (Wager et al, 2009 ). It is now evident that these same brain regions are involved in emotion regulation. Furthermore, the circuitry involved in physical pain and plea-sure appears to be activated by positive and negative socially induced emotion (Takahashi et al, 2009 ). The possibility therefore arises that positive well-being may be embodied in the acti- vation of neural circuitry in a reciprocal fashion", + "723732. Etkin, A., Egner, T., Peraza, D. M., Kandel, E. R., and Hirsch, J. (2006). Resolving emotional conict: a rolefor the rostral anterior cingulate cortex in modulatingactivity in the amygdala. Neuron, 51 , 871882. Fales, C. L., Barch, D. M., Rundle, M. M., Mintun, M. A., Snyder, A. Z. et al (2008). Altered emotional inter-ference processing in affective and cognitive-controlbrain circuitry in major depression. Biol Psychiatry, 63, 377384. Fanselow, M. S. (2000). Contextual fear, gestalt mem-", + "for cognitive processes such as learning,memory, and emotions.", + "expression of emotional behavior. Sensory inputs with emotional components are transmitted to the amygdala where they are processed and fu rther relayed to other regions to modulate autonomic and behavioral responses, and to form emotional memories (LeDoux, 2000; Rosen, 2004). As a neural substrate of emotionality, many neuropsychiatric disorders have been associated with structural changes i n the amygdala. Individuals with genetically predisposed susceptibility to anxiety and depression have", + "components can act back upon its physical substrate. Thought, emotion, and action trigger neural activity, which can lead to a reorganization of the brain, shaping future psychosocial experience. From this perspective, we are not the passive products of neurophysiology and heredity; rather, through our behavior in the social environment, we become active agents in the con-struction of our own neurobiology and, ultimately, our own lives.", + "et al, 1995 ; Scher et al, 2005 ), (2) are less easily distracted from negative emotion process- ing (Ellenbogen et al, 2002 ; Lyubomirsky et al, 1998 ; Siegle et al, 2002 ; Wenzlaff and Bates, 1998 ), (3) show heightened stress hormone lev- els such as cortisol that may have deleterious effects on the brain (Sapolsky, 2000 ), and (4)", + "et al, 2000 ). Once activated, the amygdala sets in motion a cascade of responses to threat via pro-jections to the hypothalamus and prefrontal cor-tex (LeDoux, 1996 ). A neural region that is criti- cal for regulating responses to emotional stimuli is the ventrolateral prefrontal cortex (VLPFC;Hariri et al, 2002 ). Studies have shown that the labeling of negative affective states activates the right VLPFC and that increased activity inright VLPFC is associated with decreased activ-", + "tially participates in negative emotional states,although it also participates in positive emo- tional states (Zald, 2003 ). The amygdala orches- trates the somatomotor, visceral, and cognitiveresponses to threats by virtue of its connections with cortical brain structures above and hypotha- lamic and brainstem structures below it (LeDouxet al, 1990 ). The nucleus accumbens and ventral striatum participate in reward responses and pos- itive emotional states. Other structures that are" + ], + [ + "pin-releasing hormone (CRH), adrenocorticotropic hormone (ACTH), and glucocorticoids (GC), which are also called stress hormones. These hormones con- tribute to the regulation of immune responses and can also affect neuronal survival, neurogenesis, synaptic plasticity, and behavioral responses [ 1, 2 ] . The HPA axis is a three-tiered biological system that begins at the highest level with the release of CRH from the hypothalamic paraventricular nucleus (PVN). CRH-expressing neu-", + "stressor in uences the interleukin-1beta system, tumor necrosis factor-alpha, transforming growth factor-beta1, and neuropeptide mRNAs in speci c brain regions. Brain Res Bull 51:187193 63. Deak T et al (2005) Stress-induced increases in hypothalamic IL-1: a systematic analysis of multiple stressor paradigms. Brain Res Bull 64:541556 64. Hennessy MB et al (2004) Responses of guinea pig pups during isolation in a novel", + "stressful events. In rats and mice, the secretion of hypothalamicpituitaryadrenal hormones istypically greater, and increased HPA activity often persists into adulthood (Koehl et al, 1999 ). Basal levels of adrenal hormones are more typ-ically reported to be normal in primates, but there may be alterations in the diurnal hormone rhythm or an altered negative feedback, whichresults in protracted cortisol responses once acti-vated. Many effects of prenatal stress on brain", + "Y in depression and stress. Brain Research 1314, 194 205. Mozhui, K., Karlsson, R.M., Kash, T.L., Ihne, J., Norcross, M., Patel, S., Farrell, M.R., Hill, E.E., Graybeal, C., Martin, K.P., Camp, M., Fitzgerald, P.J., Ciobanu, D.C., Sprengel, R., Mishina, M., Wellman, C.L., Winder, D.G., Williams, R.W., Holmes, A., 2010. Strain differences in stress responsivity are associated with divergent amygdala gene expression and glutamate-mediated neuronal excitability. The Journal of", + "Neurobiology of Learning and Memory 185 (2021) 107509 21.Introduction James McGaugh was one of the first neuroscientists to point to the important influence of stress hormones on memory consolidation (McGaugh, Gold, Van Buskirk, & Haycock, 1975 ). He and others considered that hormones released by stressful experiences could enhance memory consolidation, indicating particularly the hormones epinephrine and glucocorticoids as memory modulators (McGaugh &", + "For example, stress is a functional state of psychosocial arousal that focuses and energizes us to confront the stressor, but chronic/toxic levels of stress lead to disruptive changes in brain architecture and dysregulation of stress response mechanisms, such as the hypothalamus-pituitary ( hpA) axis and the autonomic nervous (ANS) system. Under chronic stress, the adrenal glands of mammals (including humans) release the steroid hormone cortisol. Cortisol acts by increas -", + "55:485494. Herman JP, Ostrander MM, Mueller NK, Figueiredo H (2005). Limbic system mechanisms of stress regulation: hypothalamo -pituitary -adrenocortical axis. Prog Neuropsychopharmacol Biol Psychiatry 29:1201 1213. Herry C, Bach DR, Esposito F, Di Salle F, P errig WJ, Scheffler K et al. (2007). Processing of temporal unpredictability in human and animal amygdala. J Neurosci 27:5958 5966. Hitzemann R, Malmanger B, Cooper S, Coulombe S, Reed C, Demarest K et al. (2002).", + "after restraint stress. Acute stress (like acute ethanol) activates the HPA axis and increases brain and circulating levels of GABAergic neuroactive steroids [1] as well as corticosterone, the major corticosteroid synthesized in rodents from DOC. GABAergic neuroactive steroids have anxiolytic properties when administered systemically [54,55]. Thus, we might have predicted that those strains with higher basal DOC levels would have been less", + "present in the brain as well as in the peripheral circulation. It issynthesized from progesterone, mainly in the adrenal zonafasciculata and it is precursor of both the glucocorticoidcorticosterone and the GABAergic neuroactive steroid (3 a,5a)- 3,21-dihydroxypregnan-20-one (tetrahydrodeoxycorticosterone,THDOC). These steroids are all elevated following acute stress[1] or ethanol administration in rats, and their elevation is blunted", + "plasticity and epigenetic regulation as a consequence of stress. Neuropharmacology 62, 3 12. McEwen, B.S., Nasca, C., Gray, J.D., 2016. Stress e ects on neuronal structure: hippo- campus, amygdala, and prefrontal cortex. Neuropsychopharmacology 41, 3 . Mozhui, K., Lu, L., Armstrong, W.E., Williams, R.W., 2012. Sex-speci c modulation of gene expression networks in murine hypothalamus. Front. Neurosci. 6, 63 . Navarro, V.M., 2013. Interactions between kisspeptins and neurokinin B. In: Kisspeptin" + ], + [ + "that corticosterone importantly amplies the SD induced changes", + "be used to predict corticosteroid response [200]. George etal.", + "we do not wish to dispute this viewpoint, it is interesting to note that anti- in ammatory actions of CORT are most pronounced at high and supraphysiological concentrations, whereas lower concentrations of CORT appear to have some immune-potentiating effects (e.g., [ 6 ] ). Whether these low-dose facilitation effects relate more directly to the timing of CORT injection relative to cytokine measure- ments, or represent differential tissue sensitivity to glucocorticoids, remains to be", + "cortisol to the less bioactive cortisone (Seckl,1997 ). While the protection afforded by this bar- rier enzyme can be overwhelmed when cortisol levels get very high, it likely functions effec- tively when cortisol remains within the normalrange (Campbell and Murphy, 1997 ). There is now considerable interest in what types of events or other hormones might lower 11-HSD2 andthereby reduce the buffering benets it affords. On example is elevated catecholamine levels,", + "the balance between cell generation and cell death. Acute increase of corticosterone leads to decreased cell proliferation while chronic increase causes an increase in proliferation rate (Sapolsky et al., 2000). This discrepancy is due to the presence of two receptors with different binding affinities: the glucocorticoid receptor (GR) and mineralocorticoid receptor (MR). The GR present in", + "corticosterone dramatically reduce the delayed-type hypersensitivity response (Dhabhar andMcEwen, 1997 ,1999 ). Sorrells and Sapolsky (2007 ) have provided a thought provoking recent review, contrasting the well-established anti-inammatory aspect of glucocorticoids, with the mounting evidence for their pro-inammatory effects both in the periphery and in the brain fol-lowing chronic exposure. This pattern of results demonstrates that the acute stress response has", + "mature babies in order to stimulate lung maturation. As illustrated here, Dex readily bypasses the protective bar-rier enzyme 11 beta-hydroxysteroid dehydrogenase type2 (11-HSD2), which normally limits fetal exposure tomaternal cortisol by converting it to corticosterone, aless bioactive form. Some concerns linger about long-term effects of fetal exposure to high doses or sustainedcorticosteroid treatmentantenatal glucocorticoid therapies are warranted,", + "first session. Approximately 50 microliters was collected into lithium heparin-coated tubes and then centrifuged for collection of plasma. Samples were stored at 80 degrees until ready for processing. Plasma corticosterone concentration was measured with the use of the DetectX CORT Enzyme Immunoassay kit (ArborAssays K014-H5, Ann Arbor, MI, USA). Room temperature plasma samples were diluted 1:450 in assay buffer and processed according to", + "corticosterone in their drinking water as sole liquid source for seven days prior to i.p. treatment with 4 mg /kg diisopropylurophosphate. Data are mean consumption per day s.e.m. 4.2. Gene Expression in Response to Treatments 4.2.1. IL1b We observed wide variability in the e ect of CORT on expression of Il1b (Figure 2). ANOVA revealed a signicant main e ect for strain (F 32,122 =3.61, p<0.001). The main e ects for sex and", + "cortisol. Support has emerged for the basic tenets of this hypothesis in studies of both humans and animals (Avitsur et al, 2001 ; Miller and Chen, 2006 ; Miller et al, 2002 ,2009 ; Rohleder et al, 2001 ; Stark et al, 2001 ). Most of this work has made use of an ex vivo assay system, where leukocytes are stimulated with a bacterial prod- uct (endotoxin) in the presence of varying levelsof cortisol, which is expected to suppress their ability to synthesize inammatory cytokines. In" + ] + ] +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/human_de_aging.json b/gnqa/paper2_eval/data/dataset/human/human_de_aging.json new file mode 100644 index 0000000..e849c74 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/human_de_aging.json @@ -0,0 +1,100 @@ +{ + "question": [ + "What is the significance of the length of telomeres?", + "Which mouse genes have been associated with longevity?", + "what genetic factor are associated with aging", + "which genes are typically associated with early aging?", + "How do I generate a linkage or association mapping study in mice to understand aging?", + "is there a specific genetic variation that can cause someone to live longer? please make your answer accessible to a non-expert" + ], + "answer": [ + "The length of telomeres is significant for several reasons: 1. **Protection of Chromosomes**: Telomeres are repetitive nucleotide sequences at the ends of eukaryotic chromosomes that protect them from attrition and damage during replication [1], [2], [6]. They prevent the loss of important genetic information and maintain genomic stability [9]. 2. **Aging and Cellular Senescence**: Telomere length is progressively shortened with each round of genomic replication unless restored by the enzyme telomerase [2], [5]. Short telomeres are considered a sign of advanced age and are associated with cellular senescence, where cells lose their ability to divide and function properly [4], [7], [8]. 3. **Disease Association**: Shorter telomeres have been linked to various diseases. For instance, individuals with Type 2 Diabetes (T2D) have been shown to have shorter leukocyte telomeres, which may be associated with disease progression [1]. Similarly, telomere length in smooth muscle cells has been linked to disease severity in atherosclerosis, and shorter leukocyte telomeres are associated with a higher risk of developing cardiovascular disease [3], [8]. 4. **Genomic Integrity**: Telomeres play a crucial role in maintaining genomic integrity and function. Shortening and erosion of telomeres can lead to chromosome end-to-end fusions and genomic instability, which can cause various cellular dysfunctions and diseases [6], [7]. 5. **Biomarker Potential**: Due to their association with aging and disease, telomere length is being studied as a potential biomarker for various health conditions, including cardiovascular diseases and diabetes [3]. In summary, the length of telomeres is crucial for protecting chromosomes, regulating cellular aging, and maintaining genomic stability. It also has significant implications for understanding and potentially diagnosing age-related diseases and conditions.", + "The mouse genes associated with longevity include: 1. **Mouse loci associated with life span**: These loci exhibit sex-specific and epistatic effects [1]. 2. **Mouse growth hormone receptor**: Alterations in this gene result in increased lifespan [3], [4]. 3. **Lmna heterozygosity**: This genetic modification ameliorates progeroid phenotypes and extends lifespan [6]. These references provide evidence of specific genes and loci in mice that have been linked to increased longevity.", + "Genetic factors associated with aging include: 1. **Genome-wide association studies (GWAS)**: These studies offer an unbiased approach to identify new candidate genes for human diseases, including those related to aging. Convergent results from multiple aging-related traits can point out the genes responsible for the general aging of the organism [2]. 2. **Longevity-associated genes**: Dozens of genes have been associated with human longevity, although only a handful have shown consistent effects across populations. These genes are often related to specific biological pathways and processes [4]. 3. **Genes involved in mitochondrial and proteasomal functions**: Aging is associated with alterations in the transcript levels of many genes, including those involved in evolutionarily conserved mitochondrial and proteasomal functions. Some of these genes have been shown to be directly involved in regulating lifespan [7]. 4. **Genes with protective functions**: Certain genes that are overexpressed with age seem to have protective functions, helping organisms manage aging. These genes could be targets for manipulation to potentially influence the aging process [8]. These genetic factors highlight the complex interplay of various genes and pathways in the aging process.", + "Genes typically associated with early aging include those involved in segmental progeroid syndromes, such as the Werner syndrome gene [2]. Additionally, genes critical in pathways previously related to aging, such as the insulin/insulin-like signaling pathway, are also associated with early aging [2].", + "To generate a linkage or association mapping study in mice to understand aging, you can follow these steps: 1. **Select Appropriate Mouse Strains**: Choose inbred mouse strains that are known to have variations in lifespan and aging-related traits. Inbred strains are powerful for identifying genes underlying complex traits such as longevity due to their genetic uniformity [9]. 2. **Identify Aging-Related Traits**: Determine the specific aging-related traits you want to study. This could include longevity, resistance to age-associated diseases, or other biomarkers of aging [4]. 3. **Cross Mouse Strains**: Perform crosses between different inbred strains to generate F1 and F2 generations. This helps in creating genetic diversity necessary for mapping studies [5]. 4. **Phenotyping**: Assay the F2 generation (or other relevant generations) for the aging-related traits of interest. This could involve measuring lifespan, resistance to diseases, or other biomarkers [4]. 5. **Genotyping**: Genotype the mice using molecular markers such as SNPs (Single Nucleotide Polymorphisms) to identify genetic variations across the genome [6]. 6. **Quantitative Trait Locus (QTL) Mapping**: Use QTL mapping techniques to identify chromosomal regions associated with the aging-related traits. This involves statistical analysis to correlate genetic markers with phenotypic traits [6]. 7. **Association Studies**: Conduct association studies to further refine the regions identified by QTL mapping. This involves looking for specific polymorphisms within the identified regions that are significantly associated with the traits [1]. 8. **Replication and Validation**: Validate the findings by replicating the study in different mouse populations or using additional genetic crosses. This helps to confirm the association between the identified genetic regions and aging traits [1]. By following these steps, you can generate a linkage or association mapping study in mice to understand the genetic factors influencing aging.", + "Yes, there are specific genetic variations that can contribute to someone living longer. Research has shown that certain genes and their variations can influence longevity. For example, variations near the genes APOE, FOXO3A, and CHRNA3/5 have been robustly associated with lifespan [10]. These genetic variations can affect how our bodies age and how well we can avoid or delay age-related diseases. In simpler terms, think of these genes as parts of a car. Just like how certain parts can make a car run more efficiently and last longer, these genetic variations can help our bodies function better and live longer. However, it's important to note that living a long life is usually a combination of both genetic factors and lifestyle choices, such as diet, exercise, and avoiding harmful habits." + ], + "contexts": [ + [ + "repetitive nucleotide sequences at the end of each eukaryotic chromosome, which protects them from attrition and damage. Although the relationship between leukocyte telomere length (LTL) and diabetes is still questioned 8, different studies have shown that T2D individuals have shorter leukocyte telomeres than non-T2D individuals9, 10 that may be associated with disease progression11. Indeed, the decreased antioxidant capacity described in patients", + "Telomeres are arrays of linked nucleotide hexamer repeats that are found at the ends of chromosomes in a vast clade of organisms [14]. While the sequence of these telomeric repeats can vary between organisms, their biological function is highly conserved, which is to limit damage inflicted on genes during the replica- tion of chromosomes. Telomere length is progressively shortened with each round of genomic replication, unless it is restored through the action of a ribonucleo-", + "telomere length,a phenomenon attributed to higher levels of oxidativestress at the cellular level (70). More recent studies havelinked telomere length in smooth muscle cells with senes-cence and disease severity in patients with atherosclero-sis (141, 150). Leukocyte telomere length was also short ina cohort of similar patients and associated with a higherrisk of developing occult cardiovascular disease (71).More data are needed to understand and validate the useof leukocyte telomere length as a biomarker", + "age telomere length through accumulation of several short telo- meres (Londono-Vallejo et al., 2001; Martens et al., 2000) is responsible for senescence or whether a speci c chromosome arm limits the replication potential of human cells (Hemann et al., 2001). Individual chromosome arms were shown to have large variations in their length (Lansdorp et al., 1996; Benn, 1997; Londono-Vallejo et al., 2001), and chromosome 17p seemed to be equipped with especially short telomeres in hu-", + "Telomeres are specialized structures that protect the ends of linear chromosomes. They shorten during aging due to the unidirectional activity of DNA polymerase, which leaves a section of DNA unrepli-cated on the lagging strand. Telomeres also are subject to shortening by genotoxic stress, such as oxidative damage (33). Among many eukaryotes, the enzyme telomerase maintains telomere length; but telomerase activity varies over the lifespan and between cell types, tissues, and species (34). In most human", + "TTAGGG sequence that cap the ends of chromosomes, protect-ing them from degradation and fusion. The length of telomererepeats is primarily maintained by active telomerase, which iscomposed of Telomerase RNA (TR) and a catalytic subunitTelomerase Reverse Transcriptase (TERT) (Blackburn, 2001).Extensive evidence has shown that telomere shortening anderosion lead to chromosome end-to-end fusions and genomicinstability (Blasco et al ., 1997; Hande et al ., 1999), causing", + "a pivotal role in maintenance of genomic integrity and func-tion (Moyzis et al., 1988; McElligott and Wellinger, 1997; van Steensel and de Lange, 1997). It is generally accepted that telomeres shorten during DNA replication both in vitro and in vivo. In individuals, short telomeres are considered to be a sign of advanced age. Cawthon and coworkers (2003) showed that telomere shortening in hu-", + "Each cell division shortens telomeric DNA until, at a critical length, the cells lose capping function at thechromosomal ends, activating DNA damage check-points, cell senescence, and eventually apoptosis.Telomere shortening has particular relevance in thesetting of CVD. Leukocyte telomere length (LTL) associates signi cantly with vascular cell senescence,", + "nization may directly affect telomere attrition, resulting in accelerated replicative senescence and progeroid phenotypes [180]. Telomeres are regions constituted by tandem repeats of non-coding DNA sequences 5-(TTAGGG)n-3 and a protein complex called shelterin, bound to them. This structure ensures the stability of the genome and protects the chromosomes from a wrong action of the DNA repair machinery [184] by allowing the formation of a chromatin loop called T-Loop [185].", + "telomeres, the repetitive sequence at the end of linear chromosomes, has garnered much attention for its relation to aging. Telomere repeats serve as an internal clock for cycling cells because each round of replication results in the loss of telomeric DNA in the absence of active telomerase (reviewed in [66]). Eventually, this loss over cellular generations culminates in telomere crisis and a permanent state of" + ], + [ + "11. Gelman R, Watson A, Bronson R et al (1988) Murine chromo- somal regions correlated with longevity. Genetics 118(4):693704 12. Jackson AU, Galecki AT, Burke DT et al (2002) Mouse loci associated with life span exhibit sex-specic and epistatic effects. J Gerontol A Biol Sci Med Sci 57(1):B9B15 13. Foreman JE, Lionikas A, Lang DH et al (2009) Genetic archi- tecture for hole-board behaviors across substantial time intervalsin young, middle-aged and old mice. Genes Brain Behav", + "Long-lived rodents reveal signatures of positive selection in genes associated with lifespan. PLoS Genet. 14:e1007272. doi: 10.1371/journal.pgen.100 7272 Schchter, F., Faure-Delanef, L., Gunot, F., Rouger, H., Froguel, P., Lesueur-Ginot, L., et al. (1994). Genetic associations with human longevity at the APOE and ACE loci. Nat. Genet. 6, 2932. doi: 10.1038/ng0194-29 Schinaman, J. M., Rana, A., Ja, W. W., Clark, R. I., and Walker, D. W. (2019).", + "of the mouse growth hormone receptor results in severely decreased body weights, insulin, and insulin- like growth factor I levels and increased life span. Endocrinology 144:37993810. DOI: https://doi.org/10.1210/en. 2003-0374, PMID: 12933651 de Haan G, Williams RW. 2005. A genetic and genomic approach to identify longevity genes in mice. Mechanisms of Ageing and Development 126:133138. DOI: https://doi.org/10.1016/j.mad.2004.09.012, PMID: 15610771", + "of the mouse growth hormone receptor results in severely decreased body weights, insulin, and insulin- like growth factor I levels and increased life span. Endocrinology 144:37993810. DOI: https://doi.org/10.1210/en. 2003-0374, PMID: 12933651 de Haan G, Williams RW. 2005. A genetic and genomic approach to identify longevity genes in mice. Mechanisms of Ageing and Development 126:133138. DOI: https://doi.org/10.1016/j.mad.2004.09.012, PMID: 15610771", + "Mulvey L, Sinclair A, Selman C (2014) Lifespan modulation in mice and the confounding effects of genetic background. J Genet Genomics 41:497503. doi: 10.1016/j.jgg.2014.06.002 OConnor TP, Lee A, Jarvis JUM, Buffenstein R (2002) Prolonged longevity in naked mole-rats: age-related changes in metabolism, body composition and gastrointestinal function. Comp Biochem Physiol A 133:835842. doi: 10.1016/S1095-6433(02)00198-8 Opazo JC, Palma RE, Melo F, Lessa EP (2005) Adaptive evolution of", + "/ mice by Lmna heterozy- gosity ameliorates progeroid phenotypes and extends lifespan [143, 174, 175].", + "References 1. Hook Met al.Genetic cartography of longevity in humans and mice: Current landscape and horizons. Biochim. Biophys. Acta1864, 27182732 (2018). 2. Kuningas Met al.Genes encoding longevity: from model organisms to humans. Aging Cell7, 270 280 (2008). [PubMed: 18208581] 3. de Magalhes JP, Wuttke D, Wood SH, Plank M & V ora C Genome-environment interactions that modulate aging: Powerful targets for drug discovery. Pharmacol. Rev. 64, 88101 (2012). [PubMed: 22090473]", + "\"Murine chromosomal regions correlated with longevity.\" Genetics 118: 693-704.", + "expression of alpha-1,2-mannosidase I extends lifespan in Drosophila melanogaster and Caenorhabditis elegans . Aging Cell, 2009 , 8(4), 370-9. [73] Wang, H.D.; Kazemi-Esfarjani, P.; Benzer, S. Multiple-stress analysis for isolation of Drosophila longevity genes . Proc Natl Acad Sci U S A , 2004 , 101(34), 12610-5. [74] Lin, Y.J.; Seroude, L.; Benzer, S. Extended life-span and stress resistance in the Drosophila mutant methuselah . Science , 1998 , 282(5390), 943-6.", + "sion analysis of mouse liver genes: effect of age and of thelongevity mutant Prop1df. J Gerontol A Biol Sci Med Sci 56: B72B80, 2001. 12.Fabrizio P, Pozza F, Pletcher SD, Gendron CM, and Longo VD. Regulation of longevity and stress resistance by Sch9 in Yeast. Science 292: 288 290, 2001. 13.Haase D, Lehmann MH, Korner MM, Korfer R, Sigusch HH, and Figulla HR. Identi cation and validation of selective" + ], + [ + "It is undisputed that genetic factors influence aging. In a remarkable", + "perform a study of the genetic sources of biological aging. However, to be successful, the genetic study of acomplex condition requires a heritable phenotype to be developed and validated. Genome-wide association studies offer an unbiased approach to identify newcandidate genes for human diseases. It is hypothesized that convergent results from multiple aging-related traits will point out the genes responsible for the general agingof the organism. This perspective focuses on the", + "population dynamics on the genetic architecture of human longevity. Aging (Albany NY). 2018;10(8):1947 63. 68. Bellenguez C, Kucukali F, Jansen I, Andrade V, Morenau-Grau S, Amin N, et al. Large meta-analysis of genome-wide association studies expands knowledge of the genetic etiology of Alzheimer disease and highlights potential translational opportunities. medRxiv. 2020. 69. Kojima T, Shimazui T, Hinotsu S, Joraku A, Oikawa T, Kawai K, et al. Decreased expression of CXXC4 promotes a", + "In addition to aging- and CR-related genes, another source of candidate genes and pathways for drug designare human longevity-associated genes (Barzilai andShuldiner, 2001; Browner et al., 2004; Kenyon, 2010).Dozens of genes have now been associated with humanlongevity (de Magalha es et al., 2009a), although only ahandful of genes have been shown to have consistenteffects across populations. Many longevity-associated genes are related to spe-", + "Clinical Genetics and Genomics of Aging", + "effect fundamental mechanisms of aging (14, 16). The drawbacksof such studies include the improbability of picking the right geneto study the myriad of known and unknown genes affecting theprocess of interest (17). The linkage study described heremarkedly improves the efficiency of such association studies bydefining a region likely to contain polymorphism(s) with signif-icant influence on life span. Additional association studies with these families and repli-", + "The multifactorial and temporal features of aging can beanalyzed efficiently by genome-wide transcriptional profiling,which has been conducted in various model organisms and hu-mans (Melov and Hubbard 2004). Aging is associated with alter-ations in transcript levels of many genes, including those in-volved in evolutionarily conserved mitochondrial and protea-somal functions (McCarroll et al. 2004), some of which havebeen shown to be directly involved in regulating lifespan in C.", + "overexpressed with age seem to be a response to aging,in that they have been previously found to have protec-tive functions (de Magalha es et al., 2009b). As such,these genes may help organisms manage aging andcould be targets for manipulation. Likewise, gene ex-pression analysis of CR has been conducted to identifyassociated genes (Lee et al., 1999, 2000). A number ofmolecular signatures have emerged from such studiesthat could be useful to identify candidate processes andpathways that affect aging,", + "Mol Genet Genomic Med. 2020;00:e1157. | 1 of 11 https://doi.org/10.1002/mgg3.1157 wileyonlinelibrary.com/journal/mgg3 1 | INTRODUCTION Aging is one of the inevitably dominant risk associated with many diseases. Several biological factors contribute to this etiology which", + "al., 2009; Stanfel et al., 2009). Many of these genesmodulate the response to environmental signals, such asfood availability, and act in signaling pathways that ifunderstood can be targeted (Fig. 1). The genetic regula-tion of aging is therefore an emerging field with multipleapplications in the human nutrition, cosmetic, and phar-maceutical industries. AGING GENES AS TARGETS FOR DRUG DISCOVERY 91" + ], + [ + "lar signatures of mammalian aging. Some of the genes", + "www.ncbi.nlm.nih.gov/homologene) of genes strongly asso-ciated with aging in model organisms. Also included are genesin which mutations result in segmental progeroid syndromes,such as the Werners syndrome gene, as well as genes criticalin pathways previously related to aging, such as the insulin/insulin-like signalling pathway (de Magalhes et al ., 2005a). The", + "overexpressed with age seem to be a response to aging,in that they have been previously found to have protec-tive functions (de Magalha es et al., 2009b). As such,these genes may help organisms manage aging andcould be targets for manipulation. Likewise, gene ex-pression analysis of CR has been conducted to identifyassociated genes (Lee et al., 1999, 2000). A number ofmolecular signatures have emerged from such studiesthat could be useful to identify candidate processes andpathways that affect aging,", + "expression profile of aging in human muscle. Physiol Genomics 2003;14:149-59. 142. Rodwell GE, Sonu R, Zahn JM. A transcriptional profile of aging inthe human kidney. PLoS Biol 2004;e427:2. 143. Hasty P, Campisi J, Hoeijmakers J, van Steeg H, Vijg J. Aging and genome maintenance: lessons from the mouse? Science 2003;299:1355-9. 144. Kyng KJ, May A, Klvraa S, Bohr VA. Gene expression profiling in Werner syndrome closely resembles that of normal aging. Proc Natl Acad Sci U S A 2003;100:12259-64.", + "neurodegenerative diseases. Nature. 2006;443:787 95. 50. de Magalhes JP, Curado J, Church GM. Meta-analysis of age-related gene expression profiles identifies common signatures of aging. Bioinformatics. 2009;25:875 81. 51. Zahn JM, Poosala S, Owen AB, Ingram DK, Lustig A, Carter A, et al. AGEMAP: a gene expression database for aging in mice. PLoS Genet. 2007;3:e201. 52. Liu LF, Shen WJ, Ueno M, Patel S, Kraemer FB. Characterization of age- related gene expression profiling in bone marrow and epididymal", + "Ly DH, Lockhart DJ, Lerner RA, Schultz PG (2000) Mitotic misregulation and human aging. Science 287: 24862492. McCarroll SA, Murphy CT, Zou S, Pletcher SD, Chin CS, et al. (2004) Comparing genomic expression patterns across species identies shared transcriptional prole in aging. Nat Genet 36: 197204. Murphy CT, McCarroll SA, Bargmann CI, Fraser A, Kamath RS, et al. (2003) Genes that act downstream of DAF-16 to inuence the lifespan of Caenorhabditis elegans Nature 424: 277283.", + "genes driving cellular senescence, and perform various integrative analyses. Genes inducing cellular senescence tend to be overexpressed with age in human tissues and are significantly overrepresented in anti-longevity and tumor-suppressor genes, while genes inhibiting cellular senescence overlap with pro-longevity and oncogenes. Furthermore, cellular senescence genes are strongly conserved in mammals but not in invertebrates. We also build", + "exhibits important alterations in global gene expressionproles with age. In mice, aging is accompanied by changesin expression of genes associated with increased inamma-tion, cellular stress, brosis, altered capacity for apoptosis,xenobiotic metabolism, normal cell-cycle control, and DNAreplication [ 5]. Lifelong calorie restriction reversed the", + "stance, genes associated with energy production, which decrease their expression during aging across various tissues and species (Zahn et al. 2006, 2007; de Magalha es et al. 2009), start decreasing at this transition point in our data (group 5; Fig. 2A). Hence, 25 yr of age in humans may mark the beginning of systemic change associated with certain senescence processes. Conservation of expression changes with age We observe that both developmental and aging expression pro-", + "p <10 -6; Table 1 shows the top 25 genes. Many of these genes have been associated with age-related diseases.Several other genes that have been shown to play a role in aging such as lysosomal-associated membrane protein-2 Lamp2 [19] (p = 5.68 -30), Fas [20] (p = 2.70-31) and growth hormone receptor Ghr [21] (p = 1.34-19) also showed a significant co-expression. Anxa2, Anxa3 and Anxa4 also show a low p-value (p < 10-25) as well as several S100 calcium binding proteins which have been" + ], + [ + "effect fundamental mechanisms of aging (14, 16). The drawbacksof such studies include the improbability of picking the right geneto study the myriad of known and unknown genes affecting theprocess of interest (17). The linkage study described heremarkedly improves the efficiency of such association studies bydefining a region likely to contain polymorphism(s) with signif-icant influence on life span. Additional association studies with these families and repli-", + "Map contains 1119 and 1459 curated human and mouse aginggenes, respectively, covering almost all scales of aging, rangingfrom molecular damage to genetic predisposition. Cross-speciescomparison revealed a modest overlap between known humanand mouse aging genes, suggesting both conservation of core sen- escence pathways and fundamental differences in aging between mice and humans (Fig. 2E). Aging-associated genes can alternatively be identified in a", + "11. Gelman R, Watson A, Bronson R et al (1988) Murine chromo- somal regions correlated with longevity. Genetics 118(4):693704 12. Jackson AU, Galecki AT, Burke DT et al (2002) Mouse loci associated with life span exhibit sex-specic and epistatic effects. J Gerontol A Biol Sci Med Sci 57(1):B9B15 13. Foreman JE, Lionikas A, Lang DH et al (2009) Genetic archi- tecture for hole-board behaviors across substantial time intervalsin young, middle-aged and old mice. Genes Brain Behav", + "Along with longevity, a select group of potential aging-related biomarkers will be assayed for each of these mouse models. In addition, it should be possible to assay several of these mouse lines for resistance to specific age-associated diseases, such as diabetes and neurological disorders, by crossing them into the appropriate transgenic disease back- ground. CONCLUSION Our understanding of the basic mechanisms of aging have benefited greatly from the use of simple model systems", + "198 the study of age-related diseases for various reasons: (a) mice are closely related to humans, with nearly 99% of human orthologous in mice; (b) their relatively short lifespan and small size allow surveillance of the aging process within a pertinent time frame and make their housing less expensive; (c) the feasibility of performing genetic manipulations facilitates the engineering of transgenic strains (gain- and loss-of function mice) that model premature aging disorders. In this section, we", + "Hsu HC, Lu L, Yi N, Van Zant G, Williams RW, Mountz JD. Quantitative trait locus (QTL) mapping in aging systems. Methods in Molecular Biology (Clifton, NJ ). 2007; 371:321348. Hunter KW, Crawford NPS. The future of mouse QTL mapping to diagnose disease in mice in the age of whole-genome association studies. Annual Review of Genetics. 2008; 42:131141. Ito R, Robbins TW, Everitt BJ. Differential control over cocaine-seeking behavior by nucleus", + "multiscalar integration of traits. Cell150, 12871299 (2012). [PubMed: 22939713] 33. De Haan G & Van Zant G Genetic analysis of hemopoietic cell cycling in mice suggests its involvement in organismal life span. FASEB J. Off. Publ. Fed. Am. Soc. Exp. Biol. 13, 707713 (1999). 34. Gelman R, Watson A, Bronson R & Yunis E Murine chromosomal regions correlated with longevity. Genetics 118, 693704 (1988). [PubMed: 3163317] 35. Houtkooper RHet al.The metabolic footprint of aging in mice. Sci. Rep1, (2011).", + "mice to identify genetic factors involved in the regulation of cognitive aging that may have gone undetected in either complex human studies or murine studies utilizing only a single genetic background. Aging is a leading risk factor for age-associated de- mentias such as AD, and our work and others suggest that geneticfactors and mechanisms underlying biological processes during midlife play a key role in determining an individual s susceptibility", + "span and have yielded insights into potential biological pathways and processes related to aging. Despite these successes, several problems are inherent in human longevity studies including potentially high degrees ofenvironmental heterogeneity, genetic diversity, and lack of birth matched controls, among others [ 8]. Inbred mouse strains represent a powerful alternative for identifying genes underlying complex trait genes such as longevity [ 9]. Initial mapping approaches include quanti-", + "Recently, the Atlas of Gene Expression in Mouse Aging Project (AGEMAP) reported gene expression proles with age for 8932genes in 16 mouse tissues (Zahn et al ., 2007). We chose not to" + ], + [ + "GENOME-WIDE ASSOCIATION STUDY OF LONGEVITY 479 INCREASES in longevity of the general population world - wide are an unprecedented phenomenon with significant health and social impact. Although environmental factors have led to an increase in life span, there is ample evidence that genetic factors are involved in extreme longevity both in humans (17) and in other organisms (8). The protective genetic factors that lead to longevity are likely to involve", + "that any genetic variant that contributes strongly to extremelongevity would also be rare. One possibility is that a specificmutation could alter the protein-coding region in a gene andconfer a significant increase in longevity. Such a mutation couldact in a dominant or recessive fashion, and might be shared by asignificant fraction of the supercentenarian genomes but not bycontrol genomes. We created a computational pipeline todetermine whether our supercentenarian genomes are enrichedfor such a variant", + "ever, natural human and animal longevity is presumed to be acomplex trait (Finch & Tanzi, 1997). In humans, both candidategene and genome-wide genetic association approaches havebeen applied in an attempt to identify longevity loci. The fre-quency of genetic variants has been typically compared between nonagenarian cases and young controls, revealing", + "genetic makeup of extreme longevity is based on a combination of common and rare variants, with common vari-ants that create the background to survive to relatively common old ages, and specific combinations of uncommon and rare variants that add an additional survival advantage to even older ages. Our analy-sis showed that LAVs discovered through a casecontrol study are not necessarily the variants that make someone live to extreme old age, and additional survival analysis is needed to characterize and", + "genetic determination of human exceptional longevity, they arethe rst step toward the generation of a comprehensive referencepanel of exceptionally long-lived individuals. The data also provideinteresting insights into genetic backgrounds that are conduciveto exceptional longevity and allow us to test different models of exceptional longevity. www.frontiersin.org January 2012 | Volume 2 | Article 90 | 1", + "tremely long lived individuals. Longevity has a genetic component, with an estimated heritability of average life expectancy of approximately 25% (105, 106). Family studies of centenarians, thosewho live to 100 years or more, suggest that the relationship between genetics and longevity is stronger in the oldest-old adults (107, 108), supporting the utility of long-lived individuals as a model system for studying genetic variations that predispose people to longevity.", + "because of genetic variation that becomes particularly important for sur- vival at advanced age (Hjelmborg et al. , 2006). Epidemiological studies have revealed that long-lived individuals (LLI), that is, people surviving to the 95th percentile of the respective birth cohort-specic age distribu- tions (Gudmundsson et al. , 2000), frequently show a favorable (healthy) course of the aging process, with the absence or a delayed onset of age-", + "Studies of centenarians have provided strong evidence to sup-port the hypothesis that a genetic contribution to human excep-tional longevity is decisive, although only a small number ofgenetic variants with modest effects have been irrefutably linkedto this phenotype ( Schachter et al., 1994; Barzilai et al., 2003 ; Christensen et al., 2006 ;Wheeler and Kim, 2011 ). The tech- nology of next generation sequencing provides a tool to gen-erate data that may eventually provide an answer ( Metzker, 2009).", + "genetic contribution to human lifespan variation was estimated at 2530% in twin studies (Gudmundsson et al. , 2000; Skytthe et al. , 2003; Hjelmborg et al. , 2006). The most prominent genetic inuence is observed in families in which the capacity toattain a long lifespan clusters (Perls et al. , 2000; Schoenmaker et al. , 2006). Exceptional longevity can be reached with a low degree of age-related disability (Christensen et al. , 2008; Terry et al. , 2008), raising the question whether protective mecha-", + "age, usually de ned by a threshold, such as 90 years). Up to 25% of the variability in human lifespan has been estimated to be genetic1, but genetic variation at only three loci (near APOE , FOXO3A and CHRNA3/5 )25have so far been demonstrated to be robustly associated with lifespan. Prospective genomic studies of lifespan have been hampered by the fact that subject participation is often only recent, allowing insuf cient follow-up time for a well-powered analysis of" + ] + ], + "task_id": [ + "a", + "b", + "c", + "d", + "e", + "f" + ] +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/human_de_diabetes.json b/gnqa/paper2_eval/data/dataset/human/human_de_diabetes.json new file mode 100644 index 0000000..7200638 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/human_de_diabetes.json @@ -0,0 +1,190 @@ +{ + "question": [ + "How is gene expression in the liver affected by diabetes?", + "Is any of the genes SH2B3, IFIH1 or ERBB3 related to diabetes?", + "nutrition is a factor for diabetes. how can genomics be use to better understand nutritional factors of diabetes", + "nutrition is a factor for diabetes. construct an abstract about how can genomics be use to better understand nutritional factors of diabetes", + "Is the gene TCF7L2 involved in diabetes?", + "Is any of the genes SH2B3, IFIH1 or ERBB3 related to diabetes?", + "How can I use genenetwork to find genes related with diabetes in humans?", + "How can I use the GeneNetwork tool to find genes related with diabetes in humans?", + "what are confounding factors in diabetes?", + "How is the immune system related to diabetes?", + "What are the genomic variants associated with immune system components and diabetes?", + "What is the role of the immune system in the metabolomics of diabetes and associated conditions?", + "Can the landscape of QTL and GWAS hits be used to dissect the role of immune system in diabetes and complications?" + ], + "answer": [ + "Gene expression in the liver is significantly affected by diabetes in several ways: 1. **Hepatic Lipogenic Capacity**: In diabetic obese mice, gene expression profiles suggest a role of hepatic lipogenic capacity in diabetes susceptibility, indicating that genes involved in lipid metabolism are differentially expressed in the liver of diabetic individuals [1]. 2. **High Fat Feeding**: In diabetic Goto-Kakizaki rats, high fat feeding alters liver gene expression, which implies that dietary factors can further influence the gene expression changes associated with diabetes [2]. 3. **Variability of Gene Expression**: Diabetes increases the variability of gene expression levels in the liver, affecting the regulation of genes involved in both glucose and lipid metabolism [3], [4]. 4. **Glucose Homeostasis and Insulin Resistance**: The liver, along with skeletal muscle and adipose tissue, plays a crucial role in maintaining glucose homeostasis. Functional analysis of differentially expressed genes in the liver has identified networks of genes and potential regulators whose activation and inhibition could explain insulin resistance and dysglycemia in diabetic conditions [5], [6], [7], [8]. 5. **Carbohydrate Metabolism**: A glucose-responsive transcription factor that regulates carbohydrate metabolism in the liver is affected by diabetes, indicating changes in the expression of genes involved in glucose metabolism [9]. Overall, diabetes leads to significant changes in the expression of genes in the liver, affecting pathways related to lipid and glucose metabolism, insulin resistance, and overall metabolic homeostasis.", + "Yes, the genes SH2B3, IFIH1, and ERBB3 are related to diabetes: 1. **IFIH1**: This gene is implicated in antiviral responses and has been found to protect against type 1 diabetes [1], [3], [5], [8]. 2. **ERBB3**: This gene is considered to have a role in immune signaling, which is a major feature of type 1 diabetes susceptibility [4]. Additionally, it has been proposed as a candidate gene near a QTL for type 1 diabetes [7]. 3. **SH2B3**: This gene is also considered to have a role in immune signaling related to type 1 diabetes susceptibility [4]. Thus, all three genes, SH2B3, IFIH1, and ERBB3, are related to diabetes.", + "Genomics can be used to better understand the nutritional factors of diabetes through the study of nutrient-gene interactions, which play a significant role in the pathophysiology of diabetes mellitus (DM) [1]. Nutrigenetics and nutrigenomics are fields that focus on how genetic variations affect nutrient metabolism and the body's response to nutrient intake, which can influence the development and progression of DM [3]. By identifying gene variants that contribute to both fetal growth and susceptibility to Type 2 Diabetes Mellitus (T2DM), researchers can better understand how metabolic programming is partly genetically determined [2]. This understanding can help in predicting disease risk more accurately and developing personalized dietary recommendations to prevent and manage DM [4]. Moreover, studying gene-nutrient interactions can reveal early molecular events in DM, contributing to the identification of better biomarkers and diagnostic tools [6]. This approach can also facilitate the development of tailored diets that maximize the use of nutrients and functional ingredients in food, aiding in the prevention and delay of DM and its complications [8]. Overall, genomics provides insights into how nutrients can affect gene expression through various mechanisms, such as directly, through their metabolites, or through signal transduction molecules [9]. This comprehensive understanding can lead to more effective prevention and treatment strategies for diabetes based on individual genetic profiles.", + "Diabetes mellitus (DM) is a global pandemic with increasing incidence worldwide, where nutrients and dietary patterns play a crucial role in its prevention, development, and treatment [1]. The pathogenesis of DM involves complex interactions between genetic predisposition and dietary factors, which are not yet fully understood [1]. Genomics offers a promising avenue to elucidate these interactions by studying nutrient-gene interactions at various levels [1]. Nutritional genomics, including nutrigenetics and nutrigenomics, focuses on how genetic variations affect nutrient metabolism and the body's response to nutrient intake, potentially influencing DM pathogenesis [3]. By identifying gene variants and understanding their interactions with dietary factors, genomics can help predict disease risk more accurately and facilitate the development of personalized dietary interventions [8]. This approach aims to develop tailored diets that maximize the use of nutrients and functional ingredients in food, aiding in the prevention and delay of DM and its complications [6]. Overall, genomics provides valuable insights into the role of gene-nutrient interactions in DM, paving the way for more effective prevention and treatment strategies [7].", + "Yes, the gene TCF7L2 is involved in diabetes, specifically type 2 diabetes. Multiple sources in the provided context confirm this association: 1. Single nucleotide polymorphisms in TCF7L2 are reproducibly associated with type 2 diabetes and reduce the insulin response to glucose in nondiabetic individuals [1]. 2. Common variants in the TCF7L2 gene increase the risk of type 2 diabetes [2]. 3. The TCF7L2 gene confers risk of type 2 diabetes [3], [4]. 4. Variants in the TCF7L2 gene are associated with a significant increase in type 2 diabetes risk, representing the strongest known genetic determinant of the disease [5]. 5. Overexpression of TCF7L2 protein decreases the sensitivity of beta islet cells to secrete insulin, which is involved in the regulation of insulin secretion [6]. 6. TCF7L2 risk alleles result in overexpression in pancreatic cells, causing reduced nutrient-induced insulin secretion and a predisposition to type 2 diabetes [9]. 7. The predominant effect of TCF7L2 dysfunction on type 2 diabetes development is mediated through impairment of insulin secretion [10]. These references collectively support the involvement of TCF7L2 in the pathogenesis and risk of type 2 diabetes.", + "Yes, the genes SH2B3, IFIH1, and ERBB3 are related to diabetes: 1. **IFIH1**: Rare variants of IFIH1, a gene implicated in antiviral responses, protect against type 1 diabetes [1], [3], [5], [6]. 2. **ERBB3**: ERBB3 is considered to be a major feature of type 1 diabetes susceptibility due to its role in immune signaling [4]. Additionally, a genome-wide association study proposed ERBB3 as the best candidate gene near a QTL for type 1 diabetes [8]. 3. **SH2B3**: SH2B3 (also known as LNK) is considered to be a major feature of type 1 diabetes susceptibility due to its role in immune signaling [4]. Thus, all three genes, SH2B3, IFIH1, and ERBB3, are related to diabetes.", + "To use GeneNetwork to find genes related to diabetes in humans, you can follow these steps: 1. **Access the HumanNet Gene Network**: The HumanNet gene network can be accessed through its web interface at [http://www.functionalnet.org/humannet](http://www.functionalnet.org/humannet) [2]. 2. **Search Using Seed Genes**: Once on the HumanNet interface, you can search the network using a set of seed genes. These seed genes can be genes already known to be associated with diabetes or genes you are interested in exploring further [2]. 3. **Review Network Data**: The network data can provide additional support to genome-wide association studies (GWAS) evidence. For example, the network data can help measure the increase in odds for certain genes being associated with type 2 diabetes [2]. 4. **Integrative Analysis**: You can perform integrative analysis to find genes and networks involved in diabetes. This involves combining data from various sources to identify potential genetic interactions and pathways related to diabetes [4]. By following these steps, you can effectively utilize GeneNetwork to identify and analyze genes related to diabetes in humans.", + "To use the GeneNetwork tool to find genes related to diabetes in humans, you can follow these steps: 1. **Access the HumanNet Gene Network**: The HumanNet gene network can be accessed through its web interface at [http://www.functionalnet.org/humannet](http://www.functionalnet.org/humannet) [1]. 2. **Search the Network**: Once you are on the HumanNet web interface, you can search the network using a set of seed genes. This allows you to explore the network for genes that are functionally related to your genes of interest, such as those involved in diabetes [1]. 3. **Integrative Analysis**: You can perform integrative analysis to find genes and networks involved in diabetes. This involves combining data from various sources, such as genome-wide association studies (GWAS) and protein interaction networks, to identify candidate genes and protein network modules associated with diabetes [2], [4], [9]. 4. **Review Network Data**: Consider network data that supports GWAS evidence. For example, network data has been used to add support to GWAS evidence for type 2 diabetes genes, measured as an increase in odds [1]. By following these steps, you can effectively use the GeneNetwork tool to identify and analyze genes related to diabetes in humans.", + "Confounding factors in diabetes are variables that can influence both the exposure and the outcome, potentially leading to misleading associations. Some of the confounding factors in diabetes include: 1. **Physical Activity**: Errors in measuring physical activity can lead to inadequate control of this confounder in epidemiological analyses [1]. 2. **Demographic and Lifestyle Factors**: These include a wide range of variables such as age, sex, socioeconomic status, and lifestyle habits like diet and exercise, which can influence diabetes risk [3]. 3. **Environmental Effects**: These include varying duration of disease, differing access to healthcare, heterogeneity in prescription, and variation in adherence to treatment regimes [5]. 4. **Adiposity**: Adjusting for adiposity and other confounding factors is crucial in studies examining the onset and complications of diabetes [9]. These factors need to be carefully controlled for in studies to ensure accurate and reliable results.", + "The immune system is intricately related to diabetes, particularly Type 1 Diabetes (T1D), through several mechanisms: 1. **Autoimmunity and \u03b2-cell Destruction**: T1D is characterized by the immune system mistakenly attacking and destroying insulin-producing \u03b2-cells in the pancreas. This autoimmune response is a central feature of T1D [6]. 2. **Immune Responsiveness**: Diabetes is associated with diminished immune responsiveness, which can affect the body's ability to manage glucose levels and may influence susceptibility to diabetic complications [2]. 3. **Role of Infections**: Infections or immune stimulation may have a protective effect against the development of diabetes. For instance, viral infections have been shown to trigger immunoregulatory mechanisms that protect against T1D in animal models [1], [8]. 4. **Gut Microbiome and Autoimmunity**: The gut microbiome plays a significant role in the development of autoimmunity related to T1D. Alterations in gut microbiota networks have been observed in children with anti-islet cell autoimmunity, suggesting a link between gut health and immune responses in diabetes [3], [7]. 5. **Immune Pathogenesis in Complications**: Evidence supports an immune pathogenesis for diabetic complications such as autonomic neuropathy, where immune cells infiltrate autonomic ganglia, leading to nerve damage [10]. In summary, the immune system's malfunction, particularly through autoimmunity and altered immune responses, is a key factor in the development and progression of diabetes, especially T1D.", + "The genomic variants associated with immune system components and diabetes include: 1. **SLC30A8 (rs13266634)**: This variant is associated with an increased risk of Type 2 Diabetes Mellitus (T2DM) [1]. 2. **6q23/TNFAIP3**: Identified as a susceptibility locus for Type 1 Diabetes (T1D) [2], [3]. 3. **Pancreatic islet enhancer clusters**: These clusters are enriched in T2DM risk-associated variants [4]. 4. **Regulatory variants affecting monocyte gene expression**: These variants are conditioned by innate immune activity [4]. 5. **>60 loci**: Genome-wide association studies have identified over 60 loci that confer genetic susceptibility to T1D [5], [7]. 6. **CCR5-del32 mutation**: This mutation in the chemokine receptor CCR5 is a modifying pathogenetic factor in T1D [9]. 7. **CCR2 and CCR5 polymorphisms**: These polymorphisms are found in children with insulin-dependent diabetes mellitus [9]. 8. **Novel insights linking immune and metabolic diabetes**: Identified through the first genome-wide association study of latent autoimmune diabetes in adults [10]. These variants highlight the complex interplay between genetic factors influencing both the immune system and diabetes.", + "The role of the immune system in the metabolomics of diabetes and associated conditions is highlighted in the context provided. Specifically, it is noted that serum proteomics has revealed systemic dysregulation of innate immunity in Type 1 diabetes [6]. This indicates that the immune system plays a significant role in the metabolic alterations observed in diabetes. The identification of metabolic biomarkers related to immune system dysregulation can help in detecting individuals at risk for Type 2 diabetes and insulin resistance [6]. This connection underscores the importance of understanding immune system interactions within the broader metabolic landscape of diabetes.", + "Yes, the landscape of QTL (Quantitative Trait Loci) and GWAS (Genome-Wide Association Studies) hits can be used to dissect the role of the immune system in diabetes and its complications. Several pieces of evidence from the provided context support this: 1. **Impact on Immune Phenotypes**: GWAS SNPs for type 1 diabetes (T1D) have been shown to impact immune phenotypes. For example, QTL profiles of 62 T1D GWAS loci grouped by cell populations reveal the distribution of p-values, indicating significant associations between these loci and immune cell traits [1]. 2. **Overlap with Immune-Related Phenotypes**: Many module-QTL loci overlap with GWAS hits for immune-related phenotypes, suggesting that these genetic modules are important in the context of inflammatory diseases, including diabetes [2]. 3. **Genetic Regulation of Immune Phenotypes**: QTL mapping in a study identified nine genome-wide significant QTLs associated with immune-cell proportions, including T cell subpopulations, indicating a genetic regulation of immune phenotypes in T1D [4]. 4. **Impact on Immune-Cell Populations**: Analysis of T1D GWAS loci showed suggestive associations between top SNPs and immune-cell traits, categorized into B cells, T cells, monocytes, and NK cells, further highlighting the impact of these loci on immune cell populations [5]. 5. **Comparative Analysis of Susceptibility Loci**: Comparative analysis of GWAS data sets for diseases like T1D, Crohn's disease (CD), and ulcerative colitis (UC) helps identify additional susceptibility loci and increases statistical power, which is crucial for understanding the genetic basis of immune-related complications in diabetes [6]. 6. **Pathway Identification**: The Immunochip effort has contributed to understanding disease mechanisms by identifying pathways linked to diabetes, which were not previously associated with the disease, indicating the complexity and diversity of diabetes and its immune-related aspects [7]. 7. **Functional Impacts of SNPs**: Although GWAS analyses do not automatically determine the specific genes associated with disease pathogenesis, they provide insights into how disease genes interact and affect immune parameters and functions [8], [9]. In summary, the integration of QTL and GWAS data provides valuable insights into the genetic regulation of immune phenotypes and their role in diabetes and its complications, supporting the use of these landscapes for dissecting the immune system's involvement in the disease." + ], + "contexts": [ + [ + "Lan H, Rabaglia ME, Stoehr JP, Nadler ST, Schueler KL et al (2003) Gene expression proles of nondiabetic and diabetic obese mice suggest a role of hepatic lipogenic capacity in diabetes susceptibility. Diabetes 52:688700Theor Appl Genet (2008) 116:683690 689 123", + "Effects of high fat feeding on liver gene expression in diabetic goto-kakizaki rats, Gene Regul. Syst. Bio 6 (2012) 151 e168. [23] P.J. Kaisaki, G.W. Otto, J.F. McGouran, A. Toubal, K. Argoud, H. Waller-Evans, C. Finlay, S. Cald /C19erari, M.T. Bihoreau, B.M. Kessler, D. Gauguier, R. Mott, Ge- netic control of differential acetylation in diabetic rats, PLoS One 9 (2014) e94555 . [24] S.P. Wilder, P.J. Kaisaki, K. Argoud, A. Salhan, J. Ragoussis, M.T. Bihoreau,", + "Figure 2. Diabetes increases the variability of gene expression levels in other experimental paradigms. ( A) Microarray data from gene", + "also showed differential expression in the liver, where it regulates a number of genes involved in both glucose andlipid metabolism. These results add further support to aTable 3: Numbers of genes for which expressi on levels in pancreas, skel etal muscle, adipose tissue or liver were altered in dia betes as compared to controls P < 0.01 (DGI) P < 0.05 (DGI) P < 0.01 (WTCCC) 11 42 P < 0.05 (WTCCC) 30 115 P < 0.01 in DGI and P < 0.05 in WTCCC or P < 0.01 in WTCCC and P < 0.05 in DGI60", + "toSHR wild type littermates. Liver, together with skeletal muscle and adipose tissue, aredeci- sive organs inmaintaining glucose homeostasis and, hence, thedevelopment ofinsulin resis- tance [75]. Functional analysis ofdifferentially expressed genes intheliver identified networks ofgenes and potential regulators whose activation and inhibition could explain insulin resis- tance and dysglycemia intheheterozygous animals. Wealso recorded significant upregulation", + "toSHR wild type littermates. Liver, together with skeletal muscle and adipose tissue, aredeci- sive organs inmaintaining glucose homeostasis and, hence, thedevelopment ofinsulin resis- tance [75]. Functional analysis ofdifferentially expressed genes intheliver identified networks ofgenes and potential regulators whose activation and inhibition could explain insulin resis- tance and dysglycemia intheheterozygous animals. Wealso recorded significant upregulation", + "toSHR wild type littermates. Liver, together with skeletal muscle and adipose tissue, aredeci- sive organs inmaintaining glucose homeostasis and, hence, thedevelopment ofinsulin resis- tance [75]. Functional analysis ofdifferentially expressed genes intheliver identified networks ofgenes and potential regulators whose activation and inhibition could explain insulin resis- tance and dysglycemia intheheterozygous animals. Wealso recorded significant upregulation", + "toSHR wild type littermates. Liver, together with skeletal muscle and adipose tissue, aredeci- sive organs inmaintaining glucose homeostasis and, hence, thedevelopment ofinsulin resis- tance [75]. Functional analysis ofdifferentially expressed genes intheliver identified networks ofgenes and potential regulators whose activation and inhibition could explain insulin resis- tance and dysglycemia intheheterozygous animals. Wealso recorded significant upregulation", + "mRNA in diabetic liver. Biochem Biophys Res Commun 290: 903-908, 2002. 712 42. Watson PJ, Fairall L, and Schwabe JW . Nuclear hormone receptor co-repressors: 713 structure and function. Mol Cell Endocrinol 348: 440-449, 2012. 714 43. Yamashita H, Takenoshita M, Sakurai M, Bruick RK, Henzel WJ, Sh illinglaw 715 W, Arnot D, and Uyeda K . A glucose-responsive transcr iption factor that regulates 716 carbohydrate metabolism in the liver. Proc Natl Acad Sci U S A 98: 9116-9121, 2001. 717", + "impacts gene expression in a cell type-dependent manner. Science 2009;325:1246 1250diabetes.diabetesjournals.org Locke and Associates 1491Downloaded from http://diabetesjournals.org/diabetes/article-pdf/64/4/1484/580211/db140957.pdf by Kenya Institution user on 11 July 2023" + ], + [ + "associated with increased fasting plasma glucose levels and type2 diabetes risk. Nat Genet. 2009;41(1):89 94. 23. Rees M, Wincovitch S, Schultz J, Waterstradt R, Beer N, Baltrusch S, et al. Cellular characterisation of the GCKR P446L variant associated with type 2 diabe tes risk. Diabetologia. 2012;55 (1):114 22. 24. Nejentsev S, Walker N, Riches D, Egholm M, Todd J, et al. Rare variants of IFIH1 , a gene implicated in antiviral responses, protect against type 1 diabetes. Science. 2009;324(5925):387 9.", + "HLAlinked genes in juvenile diabetes mellitus. Br.Med. J. 3, 133135 (1975). 52. Erlich,H.A. etal. Next generation sequencing reveals the association of DRB3*02:02 with type 1 diabetes. Diabetes 62, 26182622 (2013). 53. CaillatZucman,S. etal. Agedependent HLA genetic heterogeneity of type1 insulindependent diabetes mellitus. J.Clin. Invest. 90, 22422250 (1992). 54. Cucca,F. etal. The distribution of DR4 haplotypes inSardinia suggests a primary association of typeI", + "holdt R, Akolkar B, Erlich HA, Hilner JE, Julier C, Morahan G, Nerup J,Nierras CR, Chen WM, Rich SS, Type 1 Diabetes Genetics Consortium. Ahuman type 1 diabetes susceptibility locus maps to chromosome 21q22.3.Diabetes 2008;57:2858 2861 58. Nejentsev S, Walker N, Riches D, Egholm M, Todd JA. Rare variants of IFIH1, a gene implicated in antiviral responses, protect against type 1diabetes. Science 2009;324:387389 59. Altshuler D, Daly M. Guilt beyond a reasonable doubt. Nat Genet 2007;39: 813 815", + "because of their presumed roles in immune signalling, considered to be a major feature of T1D-susceptibility. These include ERBB3 (receptor tyrosine-protein kinase erbB-3 precursor) at 12q13 and SH2B3/LNK (SH2B adaptor protein 3), TRAFD1 (TRAF-type zinc finger domain containing 1) and PTPN11 (protein tyrosine phos- phatase, non-receptor type 11) at 12q24. For these signal regions in", + "Nejentsev S, Walker N, Riches D, Egholm M, Todd JA (2009) Rare variants of IFIH1, a gene implicated in antiviral responses, protect against type 1 diabetes. Science 324:387389 Nicolson TJ, Bellomo EA, Wijesekara N, Loder MK, Baldwin JM, Gyulkhandanyan AV, Koshkin V, Tarasov AI, Carzaniga R, Kronenberger K, Taneja TK, da Silva Xavier G, Libert S,", + "7 (Wellcome Trust Case Control Consortium 2007) . Separate work that examined liver gene expression in a smaller cohort of human samples with and without Type I diabetes found that ERBB3 did not have a cis -eQTL but that a flanking gene, R PS26, did. Since the disease phenotype and RPS26 both had QTLs in the same location, this suggested the RPS26 was a stronger candidate than ERBB3 . The authors then used mouse liver and adipose expression", + "models. A genome wide association study in a large human population proposed the receptor typrosine kinase ERBB3 as the best candidate gene near a QTL for Type I diabetes", + "61. Nejentsev S, Walker N, Riches D, Egholm M, Todd JA (2009) Rare variants of IFIH1, a gene implicated in antiviral responses, protect against type 1 diabetes. Science 324: 387 389. doi: 10.1126/science. 1167728 PMID: 19264985 62. Nica AC, Ongen H, Irminger JC, Bosco D, Berney T, et al. (2013) Cell-type, allelic, and genetic signa- tures in the human pancreatic beta cell transcriptome. Genome Res 23: 1554 1562. doi: 10.1101/gr. 150706.112 PMID: 23716500", + "gene is associated with insulin-dependent diabetes mellitus. Diabetes 33:176 183, 1984 3. Nistico L, Buzzetti R, Pritchard L, Van der Auwera B, Giovannini C, Bosi E, Larrad M, Rios M, Chow C, Cockram C, Jacobs K, Mijovic C, Bain S,Barnett A, Vandewalle C, Schuit F, Gorus F, Tosi R, Pozzilli P, Todd J: TheCTLA-4 gene region of chromosome 2q33 is linked to, and associated with,type 1 diabetes: Belgian Diabetes Registry. Hum Mol Genet 5:1075 1080, 1996", + "One of these genes associated with type 2 diabetes is the insulin receptor substrate 1 (IRS1, OMIM association num-ber, 147545) (Alharbi, Khan, Abotalib, & AlHakeem, 2014; Alharbi, Khan, Munshi et al., 2014; Brender et al., 2013; Brunetti, Chiefari, & Foti, 2014) and another is the CC motif chemokine receptor5(CCR5, OMIM association num-ber, 601373) (Balistreri et al., 2007; Mokubo et al., 2006; Muntinghe et al., 2009). Insulin initiates a wide range of growth and metabolic ef-" + ], + [ + "understood. It seems that interactions between multiple genes and environmental factors may play a role. One of these factors is dietary factors. There is evidence supporting the role of nutrient- gene interactions in DM pathophysiology [5]. Thus, a greater understanding of potential gene -nutrient interactions may be relevant for DM prevention and treatment. Nutrigenetics and nutrigenomics are defined as the science of the effects of genetic variation on", + "nutrition [12] . The identi cation of gene variants that contribute both to variation in fetal growth and to the susceptibility to T2DM, however, suggests that this metabolic programming could also be partly genetically determined [13] . These complex interactions between genes and environment complicate the task of identifying any single genetic susceptibility factor for T2DM. Three general approaches have been adopted", + "Nutrients 2014, 6 5340 However, while the a pplication of these technologies is becoming more accessible, analysis of the complex large data sets that are generated presents multiple challenges. The aim of the present review was to provide insights regarding the role of nutrient -gene interactions in DM pathogenesis, prevention and treatment. In addition, we explored how an individuals genetic makeup can affect nutrient metabolism and the response to nutrient intake, potentially leading to DM.", + "Nutrients 2014, 6 5343 3. Gene -Nutrient or Dietary Patter n Interactions in T he Development of T2DM Recently, several studies have d emonstrated the significant effects of genotype by environment interactions on T2D M [48,49] . However, further clarification of the role of these interactions at the genome -wide level could help predict disease risk more accurately and facilitate the development of", + "in nutritional epidemiology: applications, needs and new horizons .Hum Genet 125, 507525. Kaput, J., Noble, J., Hatipoglu, B., et al. ( 2007) Application of nutrigenomic concepts to type 2 diabetes melli-tus.Nutr Metab Cardiovasc Dis 17,89103. Ordovas, J.M., Kaput, J., and Corella, D. ( 2007) Nutrition in the genomics era: cardiovascular disease risk and the Mediterranean diet .Mol Nutr Food Res 51, 12931299. van Ommen, B., El-Sohemy , A., Hesketh, J., et al . ( 2010)", + "dietary patterns according to genetic variations, the role of gene -nutrient interactions, gene - diet-phenotype interactions and epigenetic modifications caused by nutrients; these studies will facilitate an understanding of the early molecular events that occur in DM and will contribute to the identification of better biomarke rs and diagnostics tools. In particular, this", + "Abstract: Diabetes mellitus (DM) is considered a global pandemic, and the incidence of DM continues to grow worldwide. Nutrients and dietary patterns are central issues in the prevention, development and treatment of this disease. The pathogenesis of DM is not comp letely understood, but nutrient -gene interactions at different levels, genetic predisposition and dietary factors appear to be involved. Nutritional genomics studies generally focus on", + "approach will help to develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, which will aid in the prevention and delay of DM and its complications. This rev iew discusses the current state of nutrigenetics, nutrigenomics and epigenomics research on DM. Here, we provide an overview of the role of gene variants and nutrient interactions, the importance of nutrients and dietary patterns on gene expression, OPEN ACCESS", + "It was previously report ed that food intake is a key component that affects the incidence of DM. Thus, the identification and analysis of nutrient/gene interactions are necessary steps to understand DM etiopathogenesis. In general, nutrients can affect gene expression via different mechanisms: ( i) directly; (ii) through their metabolites and ( iii) through signal tran sduction molecules (Figure 1).", + "Nutrients 2014, 6 5347 3.4. Importance of Genotype by Macronutrient Interactions for T2DM -Related Traits Recently, using genome -wide complex trait anal ysis, the genome -environment contribution of 14 dietary factors (glycemic load, total energy, protein, total fat, SF A, MUFA, PUFA, n- 3 PUFA, n-6 PUFA, n-3:n-6 PUFA, carbohydrate, alcohol intake, trans fat and fiber) to the total phenotypic variance of 4 T2DM -related traits (fasting glucose, fasting insulin, HOMA -IR and HOMA of cell" + ], + [ + "Abstract: Diabetes mellitus (DM) is considered a global pandemic, and the incidence of DM continues to grow worldwide. Nutrients and dietary patterns are central issues in the prevention, development and treatment of this disease. The pathogenesis of DM is not comp letely understood, but nutrient -gene interactions at different levels, genetic predisposition and dietary factors appear to be involved. Nutritional genomics studies generally focus on", + "ABSTRACT Genomics has contributed to a better understanding of many disorders including diabetes. The following article looks at the ethical, social and legal consequences of genomic medicine and predictive genetic testing for diabetes. This is currently a field in its nascent stage and developing rapidly all over the world. The various ethical facets of genomic medicine in diabetes like its effects", + "Nutrients 2014, 6 5340 However, while the a pplication of these technologies is becoming more accessible, analysis of the complex large data sets that are generated presents multiple challenges. The aim of the present review was to provide insights regarding the role of nutrient -gene interactions in DM pathogenesis, prevention and treatment. In addition, we explored how an individuals genetic makeup can affect nutrient metabolism and the response to nutrient intake, potentially leading to DM.", + "in nutritional epidemiology: applications, needs and new horizons .Hum Genet 125, 507525. Kaput, J., Noble, J., Hatipoglu, B., et al. ( 2007) Application of nutrigenomic concepts to type 2 diabetes melli-tus.Nutr Metab Cardiovasc Dis 17,89103. Ordovas, J.M., Kaput, J., and Corella, D. ( 2007) Nutrition in the genomics era: cardiovascular disease risk and the Mediterranean diet .Mol Nutr Food Res 51, 12931299. van Ommen, B., El-Sohemy , A., Hesketh, J., et al . ( 2010)", + "at the expense of understanding the social context and determinants of the disease.Biogenetic views tend to trump sociological views in the diabetes research imaginary ofconsortium members. However, the genetic epidemiologists who make up part of thediabetes consortium are not ignorant of the effects of proper diet and adequate exercise.Take away the television and the automobile and diabetes would all but disappear, quipped the head of one lab. Neither are researchers unsympathetic to those who sufferfrom", + "approach will help to develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, which will aid in the prevention and delay of DM and its complications. This rev iew discusses the current state of nutrigenetics, nutrigenomics and epigenomics research on DM. Here, we provide an overview of the role of gene variants and nutrient interactions, the importance of nutrients and dietary patterns on gene expression, OPEN ACCESS", + "understood. It seems that interactions between multiple genes and environmental factors may play a role. One of these factors is dietary factors. There is evidence supporting the role of nutrient- gene interactions in DM pathophysiology [5]. Thus, a greater understanding of potential gene -nutrient interactions may be relevant for DM prevention and treatment. Nutrigenetics and nutrigenomics are defined as the science of the effects of genetic variation on", + "Nutrients 2014, 6 5343 3. Gene -Nutrient or Dietary Patter n Interactions in T he Development of T2DM Recently, several studies have d emonstrated the significant effects of genotype by environment interactions on T2D M [48,49] . However, further clarification of the role of these interactions at the genome -wide level could help predict disease risk more accurately and facilitate the development of", + "nutrition [12] . The identi cation of gene variants that contribute both to variation in fetal growth and to the susceptibility to T2DM, however, suggests that this metabolic programming could also be partly genetically determined [13] . These complex interactions between genes and environment complicate the task of identifying any single genetic susceptibility factor for T2DM. Three general approaches have been adopted", + "It was previously report ed that food intake is a key component that affects the incidence of DM. Thus, the identification and analysis of nutrient/gene interactions are necessary steps to understand DM etiopathogenesis. In general, nutrients can affect gene expression via different mechanisms: ( i) directly; (ii) through their metabolites and ( iii) through signal tran sduction molecules (Figure 1)." + ], + [ + "single nucleotide polymorphisms in TCF7L2 are reproduc-ibly associated with type 2 diabetes and reduce the insulinresponse to glucose in nondiabetic individuals. Diabetes55:28902895 135. Cauchi S, Meyre D, Dina C, Choquet H, Samson C, Gallina S, Balkau B, Charpentier G, Pattou F, StetsyukV, Scharfmann R, Staels B, Fru hbeck G, Froguel P 2006 Transcription factor TCF7L2 genetic study in the Frenchpopulation: expression in human /H9252-cells and adipose tissue", + "L. Mechanisms by which common variants in the TCF7L2 gene increase risk of type 2 diabetes. J Clin Invest 2007; 117: 2155-2163 [PMID: 17671651 DOI: 10.1172/JCI30706] 164 Gloyn AL , Braun M, Rorsman P. Type 2 diabetes susceptibility gene TCF7L2 and its role in beta-cell function. Diabetes 2009; 58: 800-802 [PMID: 19336690 DOI: 10.2337/db09-0099] 165 da Silva Xavier G , Loder MK, McDonald A, Tarasov AI, Carzaniga R, Kronenberger K, Barg S, Rutter GA. TCF7L2 regulates late", + "transcription factor 7-like 2 ( TCF7L2 ) gene confers risk of type 2 diabetes. Nat Genet. 2006; 38:320323. [PubMed: 16415884] 172. Gloyn AL, Noordam K, Willemsen MA, Ellard S, Lam WW, et al. Insights into the biochemical and genetic basis of glucokinase activation from naturally occurring hypoglycemia mutations. Diabetes. 2003; 52:24332440. [PubMed: 12941786] 173. Pearson ER, Donnelly LA, Kimber C, Whitley A, Doney AS, et al. Variation in TCF7L2", + "2 (TCF7L2 ) gene confers risk of Type 2 diabetes. Nat. Genet. 38(3), 320323 (2006). 143Florez JC, Jablonski KA, Bayley N et al. TCF7L2 polymorphisms and progression to diabetes in the Diabetes Prevention Program. N. Engl. J. Med. 355(3), 241250 (2006). 144Damcott CM, Pollin TI, Reinhart LJ et al. Polymorphisms in the transcription factor 7-like 2 ( TCF7L2 ) gene are associated with", + "rs7903146 and rs12255372 in intron 3 of the TCF7L2 gene [20], associated with a ~45% increase in Type 2 diabetes risk per allele. As such, the TCF7L2 locus presently repre- sents the strongest known genetic determinant of Type 2diabetes. Risk allele carriers show impaired insulin produc-tion [21] and b-cell dysfunction in vitro [22]. TCF7L2 (previously referred to as TCF-4) is a high-mobility group box-containing transcription factor involved in Wingless-type MMTV integration site (Wnt)", + "genes which also play a significant role in the risk and pathogenesis of the disease[158,159]. The association of TCF7L2 gene variants with type 2 diabetes and its mechanism of action received special attention by several investigators[161,162]. Over expression of the protein was shown to decrease the sensitivity of beta islet cells to secrete insulin[163,164] and was more precisely involved in the regulation of secretary granule fusion that constitute a late event in insulin secretion", + "et al. Variant of transcription factor 7-like 2 (TCF7L2) gene confers risk of type 2 diabetes. Nat Genet . 2006;38:320-23. Sladek R, Rocheleau G, Rung J, Dina C, Shen L, Serre D, et al. A genome- [9] wide association study identifies novel risk loci for type 2 diabetes. Nature . 2007;445:881-85. Kirchhoff K, Machicao F, Haupt A, Schafer SA, Tschritter O, Staiger H, et al. [10] Polymorphisms in the TCF7L2, CDKAL1 and SLC30A8 genes are associated", + "transcription factor 7-like 2 (TCF7L2) gene confers risk of type 2diabetes. Nat Genet 38:320 3231422 Diabetologia (2007) 50:1418 1422", + "approximately double odds ratio compared to most other diabetes susceptibility polymorphisms. TCF7L2 is a transcription factor involved in the Wnt signaling pathway that is ubiquitously expressed, and it has been observed that TCF7L2 risk alleles result in the overexpression of TCF7L2 in pancreatic cells. This overexpression causes reduced nutrient -induced insulin secretion, which results in a direct predisposition to T2DM as well as an indirect predisp osition via an increase in hepatic glucose", + "diabetes. The gene seems to be widely expressed [ 18] and the transcription factor product is known to be involved in the Wnt signalling cascade. Current evidence strongly supports the idea that the predominant effect of TCF7L2 dysfunction on type 2 diabetes development is mediated through impairment of insulin secretion [ 11,1517,20], a finding that would be consistent, for example, with theknown effects of other (non-homologous) TCF genes (TCF1 [also known as HNF1A ] and TCF2 [also known as" + ], + [ + "associated with increased fasting plasma glucose levels and type2 diabetes risk. Nat Genet. 2009;41(1):89 94. 23. Rees M, Wincovitch S, Schultz J, Waterstradt R, Beer N, Baltrusch S, et al. Cellular characterisation of the GCKR P446L variant associated with type 2 diabe tes risk. Diabetologia. 2012;55 (1):114 22. 24. Nejentsev S, Walker N, Riches D, Egholm M, Todd J, et al. Rare variants of IFIH1 , a gene implicated in antiviral responses, protect against type 1 diabetes. Science. 2009;324(5925):387 9.", + "HLAlinked genes in juvenile diabetes mellitus. Br.Med. J. 3, 133135 (1975). 52. Erlich,H.A. etal. Next generation sequencing reveals the association of DRB3*02:02 with type 1 diabetes. Diabetes 62, 26182622 (2013). 53. CaillatZucman,S. etal. Agedependent HLA genetic heterogeneity of type1 insulindependent diabetes mellitus. J.Clin. Invest. 90, 22422250 (1992). 54. Cucca,F. etal. The distribution of DR4 haplotypes inSardinia suggests a primary association of typeI", + "holdt R, Akolkar B, Erlich HA, Hilner JE, Julier C, Morahan G, Nerup J,Nierras CR, Chen WM, Rich SS, Type 1 Diabetes Genetics Consortium. Ahuman type 1 diabetes susceptibility locus maps to chromosome 21q22.3.Diabetes 2008;57:2858 2861 58. Nejentsev S, Walker N, Riches D, Egholm M, Todd JA. Rare variants of IFIH1, a gene implicated in antiviral responses, protect against type 1diabetes. Science 2009;324:387389 59. Altshuler D, Daly M. Guilt beyond a reasonable doubt. Nat Genet 2007;39: 813 815", + "because of their presumed roles in immune signalling, considered to be a major feature of T1D-susceptibility. These include ERBB3 (receptor tyrosine-protein kinase erbB-3 precursor) at 12q13 and SH2B3/LNK (SH2B adaptor protein 3), TRAFD1 (TRAF-type zinc finger domain containing 1) and PTPN11 (protein tyrosine phos- phatase, non-receptor type 11) at 12q24. For these signal regions in", + "Nejentsev S, Walker N, Riches D, Egholm M, Todd JA (2009) Rare variants of IFIH1, a gene implicated in antiviral responses, protect against type 1 diabetes. Science 324:387389 Nicolson TJ, Bellomo EA, Wijesekara N, Loder MK, Baldwin JM, Gyulkhandanyan AV, Koshkin V, Tarasov AI, Carzaniga R, Kronenberger K, Taneja TK, da Silva Xavier G, Libert S,", + "61. Nejentsev S, Walker N, Riches D, Egholm M, Todd JA (2009) Rare variants of IFIH1, a gene implicated in antiviral responses, protect against type 1 diabetes. Science 324: 387 389. doi: 10.1126/science. 1167728 PMID: 19264985 62. Nica AC, Ongen H, Irminger JC, Bosco D, Berney T, et al. (2013) Cell-type, allelic, and genetic signa- tures in the human pancreatic beta cell transcriptome. Genome Res 23: 1554 1562. doi: 10.1101/gr. 150706.112 PMID: 23716500", + "7 (Wellcome Trust Case Control Consortium 2007) . Separate work that examined liver gene expression in a smaller cohort of human samples with and without Type I diabetes found that ERBB3 did not have a cis -eQTL but that a flanking gene, R PS26, did. Since the disease phenotype and RPS26 both had QTLs in the same location, this suggested the RPS26 was a stronger candidate than ERBB3 . The authors then used mouse liver and adipose expression", + "models. A genome wide association study in a large human population proposed the receptor typrosine kinase ERBB3 as the best candidate gene near a QTL for Type I diabetes", + "and 16p13.2 (near TMEM114 ) have not previously been implicated in b-cell function, type 2 diabetes susceptibility, or related phenotypes. However, in publically available gene expression data from the MuTHER consortium, rs4148941 acts as eQTL for CHST3 in lymphoblast cell lines ( P=5310251) and SPOCK2 in both adipose tissue (P=1310221) and lymphoblast cell line ( P=331024) (22). Given the additional trend toward association with GLP-1 RA treatment response in diabetic patients, further", + "IGFBP1, and IGFBP3. The IGF pathway is nowsuspected to play a role in diabetes because of observedassociations with IGF2BP2 (2729)." + ], + [ + "9. Ehm MG, Karnoub MC, Sakul H, Gottschalk K, Holt DC, Weber JL, American Diabetes Association GENNID Study Group. Genetics of NIDDM, et al. Genome wide search for type 2 diabetes susceptibil-ity genes in four American populations. Am J Hum Genet. 2000;66:187181. 10. McCarthy M, Zeggini E. Genome-wide association studies in type 2 diabetes. Curr Diab Rep. 2009;9:16471. 11. Hivert MF, Jablonski KA, Perreault L, Saxena R,", + "that from orthologous genes of yeast, worm, and fly. The resulting HumanNet gene network can be accessed through a web interface (http://www.functionalnet.org/humannet). Using this interface, researchers can easily search the network using a set of seedTable 1. Selected top-ranked Crohns disease and type 2 diabetes genes for which network data added support to GWAS evidence, measured as an increase in odds (prior =1.7 for each) Crohns disease", + "twins. Diabetologia 30, 763768 (1987). 3. Neel, J. V. in The Genetics of Diabetes Mellitus (eds W. Creutzfeldt, J. Kbberling, & J. V. Neel) 1-11 (Springer, 1976). 4. International HapMap Consortium, etal. A second generation human haplotype map of over 3.1 million SNPs. Nature 449, 851861 (2007). 5. Sabeti, P . C. etal. Genome-wide detection and characterization of positive selection in human populations. Nature 449, 913918 (2007). 6. Genomes Project, C. etal. A global reference", + "Genome Biology 2007, 8:R253Open Access2007Bergholdtet al.Volume 8, Issue 11, Article R253Research Integrative analysis for finding genes and networks involved in diabetes and other complex diseases Regine Bergholdt*, Zenia M Strling, Kasper Lage, E Olof Karlberg, Pll lason, Mogens Aalund, Jrn Nerup*, Sren Brunak, Christopher T Workman and Flemming Pociot* Addresses: *Steno Diabetes Center, Niels Steensensvej 2, DK-2820 Gentofte, Denmark. Center for Biological Sequence Analysis, Technical", + "77. Bergholdt R, Brorsson C, Lage K, Nielsen JH, Brunak S, Pociot F. Expression proling of human genetic and protein interaction networks intype 1 diabetes. PLoS One 2009;4:e6250 78. Bergholdt R, Storling ZM, Lage K, Karlberg EO, Olason PI, Aalund M, Nerup J, Brunak S, Workman CT, Pociot F. Integrative analysis for ndinggenes and networks involved in diabetes and other complex diseases.Genome Biol 2007;8:R253 79. Oresic M, Simell S, Sysi-Aho M, Na nto -Salonen K, Seppa nen-Laakso T,", + "31. Saxena, R. et al. Genome-wide association analysis identies loci for type 2 diabetes and triglyceride levels. Science 316, 13311336 (2007). 32. Franke, L. et al. Reconstruction of a functional human gene network, with an application for prioritizing positional candidate genes. Am. J. Hum. Genet. 78, 10111025 (2006). 33. Su, Z., Marchini, J. & Donnelly, P. HAPGEN2: simulation of multiple disease SNPs. Bioinformatics 27,23042305 (2011).", + "Genetic exploration of GDM is in its initial stage. The genetics of GDM, focusing on human association studies with candidate genes common to both T2DM and GDM is elegantly summarized by Robitaille and Grant (2008). The purpose of this chapter is to provide a comprehensive overview to include recent literature on susceptible gene variants that may contribute to both GDM and T2DM. SEARCH STRATEGIES A systematic literature search using PubMed was performed to identify stud-", + "Human Molecular Genetics 16(1): 3649, 2007). The DiabetesGenetics Initiative (DGI) study was used for the analysis, as we had access to genotype data in this study. The unadjusted gene p-value, P BestSNP g is the association p-value of the best regional SNP for gene g(y-axis in A). Phenotype permutation analysis was used as the gold standard to test goodness of gene score correction as it corrects forall confounders without requiring a priori knowledge of the confounders ( P Gene", + "version 2.0: users manual. PGL tech rep 2. Population Ge-netics Laboratory, Department of Genetics, Southwest Foun-dation for Biomedical Research, San Antonio Elbein SC (1997) The genetics of human noninsulin-dependent (type 2) diabetes mellitus. J Nutr 127:1891S1896S Elbein S, Hoffman M, Leppert M, Hasstedt S (1997) Linkage of fasting glucose in relatives of an NIDDM sib pair tomarkers on chromosome 9p. Diabetes 57 Suppl 1:51A Elston RC (1998) Methods of linkage analysisand the as-", + "Diabetes Study (DDS): a platform for chronic disease research.Glob Health Epidemiol Genom 1:e2. https://doi.org/10.1017/ gheg.2015.3 17. Genomes Project C, Auton A, Brooks LD et al (2015) A global reference for human genetic variation. Nature 526:68 74 18. Howie BN, Donnelly P, Marchini J (2009) A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet 5(6):e1000529. https://doi. org/10.1371/journal.pgen.1000529" + ], + [ + "that from orthologous genes of yeast, worm, and fly. The resulting HumanNet gene network can be accessed through a web interface (http://www.functionalnet.org/humannet). Using this interface, researchers can easily search the network using a set of seedTable 1. Selected top-ranked Crohns disease and type 2 diabetes genes for which network data added support to GWAS evidence, measured as an increase in odds (prior =1.7 for each) Crohns disease", + "Genome Biology 2007, 8:R253Open Access2007Bergholdtet al.Volume 8, Issue 11, Article R253Research Integrative analysis for finding genes and networks involved in diabetes and other complex diseases Regine Bergholdt*, Zenia M Strling, Kasper Lage, E Olof Karlberg, Pll lason, Mogens Aalund, Jrn Nerup*, Sren Brunak, Christopher T Workman and Flemming Pociot* Addresses: *Steno Diabetes Center, Niels Steensensvej 2, DK-2820 Gentofte, Denmark. Center for Biological Sequence Analysis, Technical", + "9. Ehm MG, Karnoub MC, Sakul H, Gottschalk K, Holt DC, Weber JL, American Diabetes Association GENNID Study Group. Genetics of NIDDM, et al. Genome wide search for type 2 diabetes susceptibil-ity genes in four American populations. Am J Hum Genet. 2000;66:187181. 10. McCarthy M, Zeggini E. Genome-wide association studies in type 2 diabetes. Curr Diab Rep. 2009;9:16471. 11. Hivert MF, Jablonski KA, Perreault L, Saxena R,", + "77. Bergholdt R, Brorsson C, Lage K, Nielsen JH, Brunak S, Pociot F. Expression proling of human genetic and protein interaction networks intype 1 diabetes. PLoS One 2009;4:e6250 78. Bergholdt R, Storling ZM, Lage K, Karlberg EO, Olason PI, Aalund M, Nerup J, Brunak S, Workman CT, Pociot F. Integrative analysis for ndinggenes and networks involved in diabetes and other complex diseases.Genome Biol 2007;8:R253 79. Oresic M, Simell S, Sysi-Aho M, Na nto -Salonen K, Seppa nen-Laakso T,", + "31. Saxena, R. et al. Genome-wide association analysis identies loci for type 2 diabetes and triglyceride levels. Science 316, 13311336 (2007). 32. Franke, L. et al. Reconstruction of a functional human gene network, with an application for prioritizing positional candidate genes. Am. J. Hum. Genet. 78, 10111025 (2006). 33. Su, Z., Marchini, J. & Donnelly, P. HAPGEN2: simulation of multiple disease SNPs. Bioinformatics 27,23042305 (2011).", + "Page 16 of 21 Tohetal. BMC Biology (2022) 20:245 Identification ofdiabeteslinked genes bytext mining We used four techniques to derive a set of genes associ - ated with type 2 diabetes and with diet-induced diabe - tes. First, we compiled an expert-curated gene-disease association database from standard resources, the Com - parative Toxicogenomics Database [35] and PharmGKB [36]. The result gave 277 genes associated with type 2 diabetes, but none associated with diet-induced dia -", + "2 diabetes alone and in combination with HumanNet and measuring performance as AUC ( <5% FPR) for recovering the top 20 genes from a type 2 diabetes meta-analysis of 4549 cases and 5579 controls (Zeggini et al. 2008). As for Crohns disease, consideration of the network boosts performance across a wide range of parameter values. Notably, consideration of the network strongly implicates the genes CTNNB1 and BACH2 in type 2 diabetes;", + "twins. Diabetologia 30, 763768 (1987). 3. Neel, J. V. in The Genetics of Diabetes Mellitus (eds W. Creutzfeldt, J. Kbberling, & J. V. Neel) 1-11 (Springer, 1976). 4. International HapMap Consortium, etal. A second generation human haplotype map of over 3.1 million SNPs. Nature 449, 851861 (2007). 5. Sabeti, P . C. etal. Genome-wide detection and characterization of positive selection in human populations. Nature 449, 913918 (2007). 6. Genomes Project, C. etal. A global reference", + "type 1 diabetes genome scan data, and a high -confidence human protein interaction network. Resulting networks were ranked by the significance of the enrichment of proteins from interacting regions. We identified a number of new prot ein network modules and novel candidate genes/ proteins for type 1 diabetes. We propose this type of integrative analysis as a general method for the elucidation of genes and networks involv ed in diabetes and other complex diseases. Background", + "gene prioritization are explained in detail in the Appendix, Supplemental Digital Content 1 , http://links.lww.com/A1049. In addition, the complete list of the training genes, including both the Gene HGNC symbol, and gene name are shown in the Appendix , Supplemental Digital Content 1 , http://links.lww.com/A1049. Moreover, from the freely available site http:// www.broad.mit.edu/ diabetes/, we downloaded the results of the GWA study in 3000 Scandinavian individuals about the genetic variants that inu-" + ], + [ + "confounding, which is plausible in observational studies of incident type 2 diabetes. Measurements of confounders (eg, physical activity) are susceptible to errors and are not adequately controlled for in epidemiological analyses. 5 Although results from clinical trials6,7 have shown no e ect of vitamin D supplementation on the incidence of type 2 diabetes, these ndings require cautious interpretation because of issues with doses, combination treatment with calcium, compliance, and generalisability. 3", + "common (confounding factors) that are the real causes of diabetes. In this study, the researchers use Mendelian randomization to examine whether increased blood CRP causes diabetes. Some variants of CRP (the gene that encodes CRP) increase the amount of CRP in the blood. Because these variants are inherited randomly, there is no likelihood ofconfounding factors, and an association between these variants and the development of insulin resistance and diabetes indicates, therefore, that", + "residual confounding. As shown inTable 2, many of the included studiesadjusted for a wide range of potentialconfounders, including demographicand lifestyle factors. The strength of theadjusted RRs for adiponectin levels anddiabetes risk and the consistency of as-sociations across diverse populations re-duce the likelihood that residual con-founding by these variables can explainthe findings. Another issue is whetheradiponectin has a causal effect on dia-betes or is only a surrogate marker forother", + "diabetes are related to impaired glucose counterregulation and hypoglycemia unawareness, one should also keep in mind that hypoglycemia can be multifactorial and be the result of several unrelated diseases. These include liver disease, malnutrition, sepsis, burns, total parenteral nutrition, malignancy and administration of certain medications known to reduce plasma glucose concentrations (Table 1).27 In principle, the same risk factors for hypoglycemia apply to", + "exists in the overall sample. In the case of type 2 diabetes,one would ideally stratify on the basis of insulin resistanceand/or severity of insulin secretion defect. However, con-founding environmental effects, including varying durationof disease, differing access to health care, heterogeneity inprescription, and variation in adherence to treatmentregimes, make inferences about insulin action in diabeticpatients problematic, especially inferences based solely onoral glucose tolerance test (OGTT) data", + "of diabetes remains one of the great challenges in human genetics. Diabetes is a result of complex interactions between genetic and non-genetic (including environmental) factors. Although diabetes and its related traits have been shown to cluster within families, their .transmission does not follow a Mendelian fashion, except for some rare syndromes such as MODY. Diabetes could be the result of few common variants with a relatively large effect, such as HLA alleles at the MHC locus and VNTR", + "predisposing to diabetes through effects on insulin sensitivity, however, may be more dif cult to track down because of strong", + "is still unclear. Genetic studies in both animalsand humans are complex, given the many susceptibility andprotective loci that contribute to the overall risk of diabetes", + "adjustment for adiposity and other confounding factors [4 10]. Preventing or delaying onset of diabetes and its compli- cations is an important therapeutic aim, and there is interest in inammatory effectors including CRP as drug targets [11,12]. It is therefore highly desirable to establish which mediators in the inammatory cascade are causal for diabetes. Mendelian randomization involves comparison of pheno- type and genotype effects in observational studies [13]. If the", + "adjusting for sex, diabetes duration, HbA1c, and smoking, assuming either additive or dominant effects of the polymorphisms.N. VIONNET AND ASSOCIATES DIABETES, VOL. 55, NOVEMBER 2006 3169Downloaded from http://diabetesjournals.org/diabetes/article-pdf/55/11/3166/649912/zdb01106003166.pdf by Kenya Institution user on 14 July 2023" + ], + [ + "disordering particular lymphocyte subsets [57]. Viral anti-body-free BB rats show an increased frequency and accel-erated onset of diabetes, suggesting that infection may havea protective effect against the development of diabetes bythese animals [230]. Thus, we speculate that infection orimmune stimulation in humans may also reduce the pen-etrance of susceptibility genes, which could account for thelow concordance rate between identical twins of less than40% for the development of T1D [13]. Conclusion", + "ished immune responsiveness, a well-characterized feature of diabetes ( Shanmugam et al., 2003 ; Mowat and Baum, 1971 ). Further, we considered that the genetic component of an individuals response to glucose may influence their susceptibility to diabetic complications like retinopathy. Cell lines from individuals with diabetes with and without retinopathy reveal differences in the response to glucose at a molec-", + "diabetes. ISME J. 5,8291 (2011). 30. Brown, C. T. et al. Gut microbiome metagenomics analysis suggests a functional model for the development of autoimmunity for type 1 diabetes.PLoS ONE 6,e25792 (2011). 31. Endesfelder, D. et al. Compromised gut microbiota networks in children with anti-islet cell autoimmunity. Diabetes 63,2006 2014 (2014). 32. Kostic, A. D. et al. The dynamics of the human infant gut microbiome in development and in progression toward type 1 diabetes. Cell Host Microbe 17, 260273 (2015).", + "+T cells related to diabetes-associated", + "the innate immune system (8, 36, 37) are known to play important roles in the development of diabetes itself, no study to date has linked these ideas with the", + "same or related viruses might complete the process of immune-mediated b-cell destruction. Alternatively, chil- dren genetically predisposed to develop autoimmunediabetes might have an altered immune system that is more likely to respond to viral exposures with strongly detectable antibody levels against certain viral antigens.If so, the detectable levels of antibodies to multiple viral antigens in diabetic patients would not indicate a causal", + "with -cell autoimmunity and those without. Diabetes 62, 12381244 (2013). 9. Mario, E. et al. Gut microbial metabolites limit the frequency of autoimmune T cells and protect against type 1 diabetes. Nat. Immunol. 18, 552562 (2017). 10. Needell, J. C. & Zipris, D. The role of the intestinal microbiome in type 1 diabetes pathogenesis. Curr. Diab. Rep. 16, 89 (2016). 11. Davis-Richardson, A. G. et al. Bacteroides dorei dominates gut microbiome prior", + "141. Filippi CM, Estes EA, Oldham JE, von Herrath MG. Immuno- regulatory mechanisms triggered by viral infections protect fromtype 1 diabetes in mice. J Clin Invest 119: 15151523, 2009. 142. Filippi CM, von Herrath MG. Viral trigger for type 1 diabetes: pros and cons. Diabetes 57: 28632871, 2008. 143. Flohe SB, Wasmuth HE, Kerad JB, Beales PE, Pozzilli P. A wheat-based, diabetes-promoting diet induces a Th1-type cytokinebias in the gut of NOD mice. Cytokine 21: 149154, 2003.", + "12451252 (2008). 77. Hofer,J. etal. Elevated proportions of recent thymic emigrants in children and adolescents with type1 diabetes. Rejuvenation Res. 12, 311320 (2009). 78. Wong,F.S. How does Bcell tolerance contribute to the protective effects of diabetes following induced mixed chimerism in autoimmune diabetes? Diabetes 63, 18551857 (2014). 79. Roep,B.O. & Peakman,M. Antigen targets of type1 diabetes autoimmunity. Cold Spring Harb. Perspect. Med. 2, a007781 (2012).", + "Immune Hypothesis: Evidence supporting an immune pathogenesis is strongest for diabetic autonomic neuropathy. Autonomic ganglia heavily infiltrated by lymphocytes, plasma cells, and macrophages were found at autopsy in five type 1 diabetics with symptomatic autonomic neuropathy. Striking cervical sympathetic ganglia atrophy was reported in another with severe sensory and autonomic neuropathy.32 Autoimmune pathogenesis may be involved in proximal diabetic" + ], + [ + "Imran Ali Khan et al., Genetic Variants in Indian Diabetes Patients www.jcdr.net Journal of Clinical and Diagnostic Research. 2015 Nov, Vol-9(11): GC01-GC05 44of the pancreas and islets during embryonic growth [3]. Genetic variants in this gene are associated with increased risk of T2DM in a variety of study populations [28,29]. In the first published GWAS for T2DM, SLC30A8 (rs13266634) was revealed to be associated with diabetes (OR, 1.26; p = 5.0 10-7).", + "diabetes and celiac disease. N Engl J Med 2008; 359: 27672777. 11 Fung E, Smyth DJ, Howson JM, Cooper JD, Walker NM, Stevens H et al. Analysis of 17 autoimmune disease-associated variants in type 1 diabetes identifies 6q23/TNFAIP3 as asusceptibility locus. Genes Immun 2008; 10: 188191. 12 Cooper JD, Smyth DJ, Smiles AM, Plagnol V, Walker NM, Allen JE et al. Meta-analysis of genome-wide association study data identifies additional type 1 diabetes risk loci. Nat Genet 2008; 40: 13991401.", + "10. Smyth, D.J. et al. Shared and distinct genetic variants in type 1 diabetes and celiac disease. N. Engl. J. Med. 359, 27672777 (2008). 11. Fung, E. et al. Analysis of 17 autoimmune disease-associated variants in type 1 diabetes identies 6q23/TNFAIP3 as a susceptibility locus. Genes Immun. 10, 188191 (2009). 12. Cooper, J.D. et al. Meta-analysis of genome-wide association study data identies additional type 1 diabetes risk loci. Nat. Genet. 40, 13991401 (2008).", + "14. Pasquali L, Gaulton KJ, Rodriguez-Segui SA, Mularoni L, Miguel-Escalada I, et al. (2014) Pancreatic islet enhancer clusters enriched in type 2 diabetes risk-associated variants. Nat Genet 46: 136 143. doi:10.1038/ng.2870 PMID: 24413736 15. Fairfax BP, Humburg P, Makino S, Naranbhai V, Wong D, et al. (2014) Innate immune activity condi- tions the effect of regulatory variants upon monocyte gene expression. Science 343: 1246949. doi: 10. 1126/science.1246949 PMID: 24604202", + "The Journal of Immunology Systematic Evaluation of Genes and Genetic Variants Associated with Type 1 Diabetes Susceptibility Ramesh Ram,*,Munish Mehta,*,Quang T. Nguyen,*,Irma Larma,*, Bernhard O. Boehm,,xFlemming Pociot,{Patrick Concannon,,#and Grant Morahan*, Genome-wide association studies have found >60 loci that confer genetic susceptibility to type 1 diabetes (T1D). Many of these are", + "disease and type II diabetes. Genes Immun. 10, 654658 (2009). 41. Hindorff, L.A. et al. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc. Natl. Acad. Sci. USA 106, 93629367 (2009). 42. Nicolson, T.J. et al. Insulin storage and glucose homeostasis in mice null for the granule zinc transporter ZnT8 and studies of the type 2 diabetes-associated variants. Diabetes 58, 20702083 (2009).", + "The composition and activity of the human immune system is under genetic control, and people with certain changes in their genes are more susceptible than others to develop type 1 diabetes. Previous studies have identified around 60 locations in the human DNA (known as loci) associated with the condition, but it remains unclear how these loci influence the immune system and whether diabetes will emerge. Chu, Janssen, Koenen et al. explored how variations in genetic information can influence the", + "mellitus-associated genetic variants contribute to overlapping immune regulatory networks. Front Genet 2018; 9:535. 13 Syreeni A, Sandholm N, Cao J et al. Genetic determinants of glycated hemoglobin in type 1 diabetes. Diabetes 2019; 68: 858 67. 14 Sidore C, Busonero F, Maschio A et al. Genome sequencing elucidates Sardinian genetic architecture and augmentsGenes affecting type 1 diabetes diagnosis age / A. Syreeni et al .", + "Genetic Variants in Type 1 Diabetes and Celiac Disease n engl j med 359;26 www.nejm.org december 25, 2008 2777Kalev I, Oselin K, Prlist P, et al. CC-26. chemokine receptor CCR5-del32 mutation as a modifying pathogenetic factor in type I diabetes. J Diabetes Complications 2003;17:387-91. Szalai C, Csszr A, Czinner A, et al. 27. Chemokine receptor CCR2 and CCR5 polymorphisms in children with insulin-dependent diabetes mellitus. Pediatr Res 1999;46:82-4. Yang B, Houlberg K, Millward A, De - 28.", + "13(1):2337. https://doi.org/10.1038/s41467-022-29932-y 5. Burgess S, Butterworth A, Thompson SG (2013) Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol 37(7):658 665. https://doi. org/10.1002/gepi.21758 6. Cousminer DL, Ahlqvist E, Mishra R et al (2018) First genome- wide association study of latent autoimmune diabetes in adults reveals novel insights linking immune and metabolic diabetes. Diabetes Care 41(11):2396 2403. https://doi.org/10.2337/dc18-" + ], + [ + "allows the detection of systemic metabolic imbalances, thereby providing a disease specific picture of human physiology. doi:10.1371/journal.pone.0013953.g003Metabolomics of Diabetes PLoS ONE | www.plosone.org 9 November 2010 | Volume 5 | Issue 11 | e13953", + "Metabolomics studies allow metabolites involved in disease mechanisms to be discovered by monitoring metabolite level changes in predisposed individuals compared with healthy ones (Shaham et al, 2008; Newgard et al, 2009; Zhao et al, 2010; Pietilainen et al, 2011; Rhee et al, 2011; Wang et al,2 0 1 1 ; Cheng et al, 2012; Goek et al, 2012). Altered metabolite levels may serve as diagnostic biomarkers and enable preventive action. Previous cross-sectional metabolomics studies of T2D", + "doi:10.1371/journal.pone.0013953.t006Metabolomics of Diabetes PLoS ONE | www.plosone.org 8 November 2010 | Volume 5 | Issue 11 | e13953", + "monitoring and preventing progression to costly co-morbidities. The principal concept of metabolomics being able to find some metabolites differing in a control and a type 2 diabetic group is established. It is not our goal here to show this once again. The questions we ask are rather How well are different approaches suited to attain this goal? and What are optimal settings under which such studies can be successful?. Others have already investigated these questions before [16,17,18]. However, we", + "H, Raftery D, Nair KS. Quantitative me-tabolomics by H-NMR and LC-MS/MSconrms altered metabolic pathways in diabetes. PLoS ONE 2010;5:e10538 2. Li LO, Hu YF, Wang L, Mitchell M, Berger A, Coleman RA. Early hepatic insulin re-sistance in mice: a metabolomics analysis.Mol Endocrinol 2010;24:657 666 3. Bain JR, Stevens RD, Wenner BR, Ilkayeva O, Muoio DM, Newgard CB. Metabolomicsapplied to diabetes research: moving frominformation to knowledge. Diabetes 2009; 58:2429 2443", + "70 Zhang Q, Fillmore TL, Schepmoes AA et al. Serum proteomics reveals systemic dysregulation of innate immunity in Type 1 diabetes. J. Exp. Med. 210(1), 191203 (2013). 71 Roberts LD, Koulman A, Griffin JL. Towards metabolic biomarkers of insulin resistance and Type 2 diabetes: progress from the metabolome. Lancet Diabetes Endocrinol. 2(1), 6575 (2014). \t Illustrates\tpotential\tmetabolic\tbio-markers\twhich\tmay\tbe\t used\tto\tdetect\tpeople\tat-risk\tfor\tT2D/insulin\tresistance,", + "Serum or plasma concentrations of sugars and sugar metabo- lites (e.g., glucose, mannose, desoxyhexose, and 1,5-anhy-droglucoitol), ketone bodies ( -hydroxybutyrate), lipids (e.g., phosphatidyl-cholines and nonesterified fatty acids), branched-chain amino acids, and other metabolites were found to be associated with insulin resistance or diabetes status (see Supplementary Data online for full references). A proof-of- concept multi-platform, metabolome-wide study based on the", + "Serum or plasma concentrations of sugars and sugar metabo- lites (e.g., glucose, mannose, desoxyhexose, and 1,5-anhy-droglucoitol), ketone bodies ( -hydroxybutyrate), lipids (e.g., phosphatidyl-cholines and nonesterified fatty acids), branched-chain amino acids, and other metabolites were found to be associated with insulin resistance or diabetes status (see Supplementary Data online for full references). A proof-of- concept multi-platform, metabolome-wide study based on the", + "Conclusions/Significance: Our study depicts the promising potential of metabolomics in diabetes research by identification of a series of known and also novel, deregulated metabolites that associate with diabetes. Key observations include perturbations of metabolic pathways linked to kidney dysfunction (3-indoxyl sulfate), lipid metabolism (glyceropho-", + "with signicant limitations and potential for misuse oftechnologies and overinterpretation of data. Here we seekto provide a critical evaluation of progress to date inapplication of metabolomics technologies for the under-standing of diabetes and obesity mechanisms, for sub-classication of different forms of diabetes to assist intailoring of therapeutic strategies, and for more detailedevaluation of the safety and efcacy of drugs used totreat the disease.Overview of current metabolomics" + ], + [ + "'&'.+* .%(\"'.+ * $$* ! \f\r \t\f\u000b '&'.+* .%(\"'.+ * $$*\t\u000b r Figure 2. Impact of type 1 diabetes (T1D) genome- wide association studies (GWAS) single- nucleotide polymorphisms (SNPs) on immune phenotypes. (A)Quantile- quantile (Q- Q) plots of quantitative trait locus (QTL) profiles of 62 T1D GWAS loci grouped by cell populations. The distribution of p- values", + "diseases, including T2D. Many of the module-QTL locioverlap with GWAS hits for immune-related pheno- types, suggesting that the modules described here might be of importance in the context of inflammatory dis- eases. Similar analyses should be performed for co- expression modules in other more T2D-relevant tissues to provide further insight into the causal networks underlying T2D aetiology. Similarly, network rewiring in T2D might be more strongly detectable in other tissues", + "(58)], revealing some interesting possible candidate functionalgenes other than those associated with the HLA and related sys-tems. In addition, early GWAS on type 1 diabetes by Todd et al.(23) revealed suggestive functional effects of non-HLA variants involved in immune functions. Another interesting application of", + "Research article Genetics and Genomics | Medicine Chu, Janssen, Koenen etal. eLife 2022;11:e73709. DOI: https://doi.org/10.7554/eLife.73709 9 of 17Genetic regulation of immune phenotypes in T1D To further explore potential genetic regulation of immune phenotypes on the whole- genome level, we performed QTL mapping in 300DM. This identified nine genome- wide significant QTLs (p- value < 5 108) associated with immune- cell proportion, including four associated with T cell subpopu-", + "studies (r2> 0.8) and performed a chi- square test on clinical status by using PLINK 1.9. Samples in 300DM were taken as cases and samples in 500FG as controls. Impact of T1D GWAS loci on immune phenotypes To detect the impact of T1D GWAS loci on immune- cell populations, we grouped all traits into four categories (B cells, T cells, monocytes, and NK cells), and counted the number of suggestive associ- ations (p- value < 0.05) between the 63 top SNPs from T1D GWAS loci and immune- cell traits. 1000", + "In the present study, we interrogated GWAS data sets on CD, UC and T1D for known susceptibility loci implicated inthese diseases. Our comparative analysis serves several impor-tant roles: rst, the ability to identify additional susceptibilityloci for one disease by testing known loci for another disease,similar to previous studies ( 12,13). This approach increases statistical power by limiting the number of hypotheses", + "Conclusions A major challenge is to translate GWAS ndings intocausal variants and target genes. The Immunochipeffort has greatly contributed to our understanding of disease mechanisms by identifying pathways, which could not be linked to diabetes by existing hypotheticalmodels. Diabetes is probably a much more diverse disease than the current subdivision into T1DM and T2D implies and a more precise subdivisioninto subgroups may also pave the way for a more", + "edge of the role(s) of genetic variation (SNPs) in population-level sus-ceptibility to T1D ( Ram et al., 2016a ). However, GWAS analyses do not automatically determine the particular gene(s) in a speci c locus that are mechanistically associated with disease pathogenesis, or elucidate the manner in which disease gene(s) interact ( Zhong et al., 2010). The diculty associated with ascribing functional impacts to SNPs is partly explained by the fact that most disease-associated SNPs identi ed by", + "(Supplementary file 1C). We next investigated whether these genetic risk loci for T1D affect immune parameters and func- tion. The quantile- quantile plot of the association of the 63 T1D GWAS loci with different cell types and cytokines illustrates an inflated deviation from an expected uniform distribution (Figure 2A, Figure2figure supplement 1). We further tested whether this deviation can be explained by chance", + "Fadason et al. demonstrated that functionally relevant type 2 diabetes- associated SNPs are spatially linked with speci c changes in the ex- pression levels of genes within disease-associated tissues ( Fadason et al., 2017 ). Similarly, a study demonstrated that integrating chro- matin interactions with GWAS analyses is important in elucidatingcausal genes that modulate regulatory networks in autoimmune dis- eases ( McGovern et al., 2016). As such, the spatial organization of DNA" + ] + ] +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/human_de_gn.json b/gnqa/paper2_eval/data/dataset/human/human_de_gn.json new file mode 100644 index 0000000..6d85f5f --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/human_de_gn.json @@ -0,0 +1,470 @@ +{ + "question": [ + "What are the potential benefits and risk associated with gene editing technologies like CRISPRR-Cas9?", + "How does epigenetics inluence gene expression without changing the underlying DNA sequence?", + "Describe the role of mitochondrial DNA in heredity and how it differs from nuclear DNA.", + "What are the ethical considerations surrounding prenatal genetic testing and the selective termination of pregnancies based on genetic factors?", + "Create a how-to guide for genetic sequencing.", + "Which genes give a predisposition to developing T1D?", + "What is ensembl", + "Which database can I use for genetic, genomics, phenotype, and disease-related data generated from rat research?", + "What is RGD?", + "What resources can I use to do pathway analyses?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "Explain the process of DNA replication and how it ensures accurate copying of genetic information during cell division.", + "What are the potential benefits and risks associated with gene editing technologies like CRISPR-Cas9?", + "How does one tell the difference between X and Y DNA, with repsect to DNA tracing and determining QTLs?", + "For text and biological resources, do you mean add some books (on biology stuff) or/and web resources (as ensembl) on your system?", + "what is ensembl?", + "What is the difference between QTL mapping and GWAS?", + "How do I determine which gene in my QTL is causal for the trait?", + "Why do males have two Y chromosomes and females only one?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "How can I add a new species to the GeneNetwork database?", + "which genes are typically associated with diabetes in QTL analyses?", + "In which diseases is the gene TCF7L2 involved?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Can you explain what a ribosomal binding site at a high level and make it accessable to a non-expert?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Can you explain the difference between sequencing with short reads vs long reads? Please make you answer accessible to a non-expert.", + "Can you explain why using a pangenome-based reference might be more useful than simply using a single linear reference? Please make you answer accessible to a non-expert.", + "Is all genetic regulation done through DNA (e.g., prompters, repressors, activators) or are there other forms of genetic regulation? Please make you answer accessible to a non-expert.", + "What are the different relationship between traits?", + "Can landscape of QTL and GWAS hits be used to find relationships between traits?" + ], + "answer": [ + "### Potential Benefits of CRISPR-Cas9: 1. **Accelerated Genomic Research**: CRISPR-Cas9 tools have significantly accelerated the pace of genomic research by allowing highly efficient knockouts or edits of virtually any gene in cells or model organisms [1]. 2. **Clinical Trials and Stability**: Multiple CRISPR-Cas9-based clinical trials are in progress, and early trial results suggest that CRISPR-engineered cells are stable and do not cause acute adverse reactions in humans [1]. 3. **Functional Genomics**: CRISPR is set to make a significant impact on genomic screening and scientific discovery, with recent demonstrations showing great promise [2]. 4. **Targeted Gene Regulation and Epigenome Modifications**: CRISPR-Cas9 has been linked to various effector proteins to enable targeted gene regulation and epigenome modifications [3]. 5. **Precision Medicine**: CRISPR-Cas9 holds significant promise for the development of new disease models and precision-targeted therapies, particularly in molecular medicine [4]. 6. **Versatility in Applications**: CRISPR-Cas9 can induce genome edits by creating targeted DNA breaks that trigger site-specific DNA repair. It can also control the transcriptional output of genes or alter genome sequences using nucleotide base editing, which does not require DNA break repair [8]. ### Potential Risks of CRISPR-Cas9: 1. **Off-Target Effects**: CRISPR-Cas9 is known to generate off-target alterations, which can result in unwanted mutations and potentially cytotoxic effects [6]. 2. **Technical Challenges**: There are several technical challenges that need to be addressed to maximize the benefits of CRISPR technology [2]. 3. **Long-Term Safety**: While early trial results are promising, the long-term safety of CRISPR-engineered cells is yet to be determined [1]. 4. **Mismatch Tolerance**: The CRISPR-Cas9 system can tolerate certain mismatches to the DNA target, which could be a disadvantage as it may lead to unintended edits [7]. In summary, while CRISPR-Cas9 offers numerous benefits such as accelerating genomic research, enabling precision medicine, and providing versatile applications, it also poses risks like off-target effects, technical challenges, and concerns about long-term safety.", + "Epigenetics influences gene expression without changing the underlying DNA sequence through several mechanisms. These include: 1. **DNA Methylation**: This involves the addition of methyl groups to DNA, primarily at CpG sites, which can affect gene transcription by converting cytosine to 5-methylcytosine, thereby repressing gene activity [1], [4]. 2. **Histone Modifications**: Chemical modifications to histone proteins, such as methylation, acetylation, phosphorylation, and ubiquitination, can alter the chromatin structure, making genes more or less accessible for transcription [1], [6]. 3. **Non-coding RNAs (ncRNAs)**: These RNAs, including microRNAs (miRNAs), can regulate gene expression at the post-transcriptional level by binding to messenger RNAs (mRNAs) and preventing their translation into proteins [1], [6]. These epigenetic modifications are influenced by both genetic variants and environmental factors such as pollution, tobacco smoking, obesity, lack of physical activity, and alcohol consumption [2], [6]. These changes are heritable and can be passed from one generation to the next without altering the DNA sequence itself [4], [5].", + "Mitochondrial DNA (mtDNA) plays a unique role in heredity and differs significantly from nuclear DNA (nDNA) in several ways: 1. **Inheritance Pattern**: Mitochondrial DNA is inherited exclusively from the mother, a phenomenon known as maternal or extranuclear inheritance [4], [6], [7]. This contrasts with nuclear DNA, which is inherited from both parents. 2. **Replication and Segregation**: mtDNA replicates autonomously and independently of the cell cycle, unlike nuclear DNA which replicates during cell division [1], [5]. Mitochondria are semiautonomous organelles with their own replication, transcription, and translation systems [2]. 3. **Genetic Variation and Mutation Rate**: Mitochondrial DNA has a higher mutation rate compared to nuclear DNA, approximately tenfold higher [2]. This can lead to a condition called heteroplasmy, where both mutated and normal mitochondria coexist within the same cell [1], [2]. 4. **Structure and Size**: The mitochondrial genome is a short circular molecule, significantly smaller than the nuclear genome. The human mitochondrial genome is only 16,569 base pairs long and encodes 37 genes, whereas the nuclear genome consists of approximately 1500 genes related to mitochondrial function [8], [9]. 5. **Functional Role**: mtDNA carries essential genetic information for cellular metabolism and energy generation, which is crucial for the functioning of the cell [10]. This is distinct from the broader range of functions encoded by nuclear DNA. In summary, mitochondrial DNA is maternally inherited, replicates independently, has a higher mutation rate, and is structurally distinct from nuclear DNA. These differences underscore the unique role of mtDNA in heredity and cellular function.", + "The ethical considerations surrounding prenatal genetic testing and the selective termination of pregnancies based on genetic factors are multifaceted and complex. Key considerations include: 1. **Parental Beliefs and Attitudes**: Decisions about prenatal testing and subsequent actions, such as termination, are heavily influenced by parental beliefs about abortion, attitudes regarding disability, and their perceptions of the usefulness of the information revealed by genetic tests [2]. These personal beliefs and attitudes play a crucial role in shaping the ethical landscape of prenatal genetic testing. 2. **Informed Consent**: Ensuring that parents are fully informed about the implications of genetic testing is critical. This includes understanding the potential outcomes, the accuracy of the tests, and the possible decisions they may face based on the results [4]. Informed consent is a cornerstone of ethical medical practice, particularly in the context of genetic testing. 3. **Respect for Parental Decision-Making**: Ethical considerations must balance clinical benefits and risks with respect for parental decision-making. Parents should generally have the final authority in making decisions about their children's health care, including whether to undergo genetic testing and how to respond to the results [5]. 4. **Social and Psychological Implications**: The ability to predict future diseases through genetic testing, coupled with limited options for prevention or treatment, has significant social and psychological implications. These must be addressed to ensure that parents are not unduly burdened by the information provided by genetic tests [7]. 5. **Access and Equity**: Ethical issues also arise from the accessibility of genetic testing and the opportunities it creates. There are concerns about equitable access to these technologies and the potential for disparities in who can benefit from them [1]. 6. **Family Communication Challenges**: Genetic testing results can create communication challenges within families, as they navigate the complex information and make decisions that affect their future [1]. In summary, the ethical considerations surrounding prenatal genetic testing and selective termination involve respecting parental beliefs and decision-making, ensuring informed consent, addressing social and psychological impacts, and promoting equitable access to genetic testing technologies. These considerations must be carefully balanced to navigate the ethical complexities of prenatal genetic testing.", + "### How-to Guide for Genetic Sequencing Genetic sequencing is a powerful tool used to determine the order of nucleotides in DNA. This guide will walk you through the basic steps involved in genetic sequencing, referencing key resources for further information. #### Step 1: Sample Preparation 1. **Collect DNA Sample**: Obtain a sample from the organism or tissue of interest. This could be blood, saliva, or tissue biopsy. 2. **Extract DNA**: Use a DNA extraction kit to isolate DNA from the sample. This involves cell lysis, removal of proteins and other contaminants, and purification of DNA. #### Step 2: Library Preparation 1. **Fragment DNA**: Break the DNA into smaller fragments. This can be done using mechanical shearing or enzymatic digestion. 2. **Add Adapters**: Ligate specific adapter sequences to the ends of the DNA fragments. These adapters are necessary for the fragments to bind to the sequencing platform. #### Step 3: Amplification 1. **PCR Amplification**: Amplify the DNA fragments using Polymerase Chain Reaction (PCR). This step increases the quantity of DNA to ensure there is enough material for sequencing. #### Step 4: Sequencing 1. **Choose a Sequencing Platform**: Select an appropriate sequencing technology based on your needs. Common platforms include Illumina, Ion Torrent, and Pacific Biosciences [6], [9]. 2. **Load DNA onto the Sequencer**: Follow the manufacturer's instructions to load your prepared DNA library onto the sequencing machine. 3. **Run the Sequencer**: Initiate the sequencing run. The machine will read the nucleotide sequences of the DNA fragments and generate raw sequence data. #### Step 5: Data Analysis 1. **Base Calling**: Use software like Phred to convert raw data into nucleotide sequences [10]. 2. **Sequence Assembly**: Assemble the short DNA sequences into longer contiguous sequences (contigs) using tools like Phrap or CAP3 [10]. 3. **Variant Calling**: Identify genetic variants by comparing the assembled sequences to a reference genome. This can be done using various bioinformatics tools. #### Step 6: Interpretation 1. **Annotation**: Annotate the sequences to identify genes, regulatory elements, and other functional regions. 2. **Data Management**: Store and manage the high-throughput sequence data efficiently. This is crucial for handling large datasets generated by next-generation sequencing technologies [3], [4]. #### Additional Resources - **Next-Generation Sequencing (NGS)**: For a detailed understanding of NGS technologies and their applications, refer to Shendure and Ji's review on next-generation DNA sequencing [1], [5]. - **Sequencing Platforms**: For comparisons of different sequencing platforms, see the work by Quail et al. [6]. - **Bioinformatics Tools**: Explore various bioinformatics tools and software for sequence analysis and data management [10]. By following these steps and utilizing the referenced resources, you can successfully perform genetic sequencing and analyze the resulting data.", + "The genes that give a predisposition to developing Type 1 Diabetes (T1D) include: 1. **Major Histocompatibility Complex (MHC)** genes located in the HLA region of chromosome 6 [1], [2], [6]. 2. **Insulin gene (INS)** located in the IDDM2 locus on chromosome 11 [3]. 3. **CTLA-4 (Cytotoxic T-Lymphocyte Associated 4)** [2]. 4. **PTPN22 (Protein Tyrosine Phosphatase, Non-Receptor Type 22)** [2]. 5. **IL2RA/CD25 (Interleukin 2 Receptor Alpha)** [2]. 6. **IFIH1/MDA5 (Interferon-Induced Helicase 1)** [2]. These genes are strongly associated with T1D and contribute to the genetic susceptibility to the disease.", + "Ensembl is a comprehensive genomic database that identifies the positions of known human genes from public sequence database entries, often using tools like GENEWISE to predict their exon structures. It aims to provide a more accurate reflection of reality compared to ab initio predictions alone [1]. Additionally, Ensembl includes various genomic features such as different repeat classes, cytological bands, CpG island predictions, tRNA gene predictions, and expressed sequence clusters from the UniGene database [7]. It also reflects the progress of projects like the International Haplotype Map Project and allows users to view speculative data and develop personalized annotations [4].", + "You can use the Rat Genome Database (RGD) for genetic, genomics, phenotype, and disease-related data generated from rat research. The RGD is specifically mentioned as a platform for genetic and genomic research, including disease portals and phenotypic data [2], [5]. Additionally, it serves as a repository for various types of data such as QTLs, strains used as disease models, expression data, and more [5]. The database can be accessed at http://www.rgd.mcw.edu [1], [5].", + "RGD refers to a resource that provides information regarding physiological traits studied, strain combinations used, associated linkage statistics, and the genomic coordinates of the pQTL (protein Quantitative Trait Loci) region. For pQTL regions identified from RGD, the original data were examined, and the 99% confidence interval within the 2 logarithm of the odds (LOD) drop from the peak of linkage was estimated [1].", + "For pathway analyses, you can use the following resources: 1. **KEGG (Kyoto Encyclopedia of Genes and Genomes)**: This database allows you to cross-reference a list of genes with biological functions and genetic networks, including metabolic, signaling, or other regulation pathways [1], [4], [9], [10]. 2. **Reactome**: A cross-referenced, manually curated, and peer-reviewed pathway database that provides enhanced pathway visualization [2], [3], [5], [7], [9], [10]. 3. **WikiPathways**: A multifaceted pathway database that bridges metabolomics to other omics research [2], [3], [9], [10]. 4. **HumanCyc**: Primarily a database of metabolic pathways [4]. 5. **Pathway Commons**: A web resource for biological pathway data [6]. 6. **BioCarta**: Provides pathway maps [8]. 7. **BioGRID**: A genetic and protein interaction database [8]. 8. **DAVID (Database for Annotation, Visualization, and Integrated Discovery)**: Offers gene ontology and pathway analysis [8]. 9. **WebGestalt**: Provides gene ontology and pathway analysis [8]. These resources can help you determine whether a pathway is over-represented in your gene list and whether it is over-activated or under-activated [1].", + "Once a sperm combines with an egg, the traits passed onto the resulting lifeform are determined by several genetic and epigenetic factors: 1. **Chromosomal Segments**: Individuals inherit a patchwork of chromosomal segments from maternal and paternal chromosomes, which are combined during fertilization to form the zygote [2]. 2. **Mendelian Laws**: The Mendel laws of segregation and independent assortment play a crucial role. Each parent passes a randomly selected allele for a given trait to the offspring, ensuring genetic diversity [5]. 3. **Gene Activation and Regulation**: The process of gene activation and regulation is complex and involves several mechanisms: - **Imprinting**: Genes can be marked with small chemical tags that determine whether they are activated or remain inactive after fertilization, depending on whether the modification was made by the father (in the sperm) or the mother (in the egg) [3], [4]. - **Maternal Effect**: The mother can alter gene activity in her offspring via the placenta [4]. - **Cis-regulation**: Instructions encoded within the embryo's DNA can directly control if and when a nearby gene becomes activated [4]. 4. **Epigenetic Marks**: Epigenetic marks, which are less constrained and can be maintained, affect gene expression and phenotype later in development [6]. These factors collectively determine how traits are passed onto the resulting lifeform after the sperm combines with the egg.", + "Genetic tracing is often matrilineal rather than patrilineal primarily because mitochondrial DNA (mtDNA) is inherited solely from the maternal lineage. Unlike nuclear DNA, which is a combination of both paternal and maternal contributions, mtDNA is passed down from mothers to all their children without any admixture from the father [4], [5]. This uniparental inheritance of mtDNA simplifies the tracing of maternal lineage as it remains relatively unchanged through generations, making it a reliable marker for genetic studies [4], [5]. Additionally, humans have developed complex mechanisms to ensure this uniparental inheritance of mtDNA, which further supports its use in genetic tracing [4].", + "DNA replication is a critical process that ensures the accurate copying of genetic information during cell division. Here is an explanation of the process and how it maintains accuracy: 1. **Separation of DNA Strands**: During replication, the two strands of the mother cell DNA are separated [1], [2]. This separation is essential to expose the nucleotide sequences of each strand, which will serve as templates for the synthesis of new complementary strands. 2. **Formation of New Strands**: New nucleotides are assembled to form two double helices that are identical to the original DNA molecule [1], [2]. Each original strand pairs with a newly synthesized complementary strand, resulting in two DNA molecules, each consisting of one old and one new strand. This method is known as semi-conservative replication. 3. **Initiation at Replication Origins**: Replication begins at specific locations on the DNA molecule called replication origins. During the S phase of the cell cycle, replication starts from these origins [3]. 4. **Replication Fork and Enzyme Activity**: The replication process involves the formation of a replication fork, where enzymes like DNA helicase unwind the DNA double helix. DNA polymerase then adds new nucleotides to the growing DNA strand by matching them with the complementary nucleotides on the template strand. 5. **Error Minimization and Repair Mechanisms**: Cells have mechanisms to monitor replication and minimize errors. For instance, cell-cycle checkpoints ensure coordination of DNA synthesis and repair with cell division [6]. Additionally, specific repair proteins can revert certain DNA injuries, ensuring the fidelity of the replication process [4]. 6. **Postreplicative Repair**: If replication encounters obstacles such as DNA adducts, cells can induce DNA damage tolerance mechanisms. These mechanisms allow the completion of replication through error-free template switching using the sister chromatid or through translesion polymerases [9]. By following these steps and employing various error-checking and repair mechanisms, DNA replication ensures that genetic information is accurately copied and transmitted to daughter cells during cell division.", + "### Potential Benefits of CRISPR-Cas9: 1. **Accelerated Genomic Research**: CRISPR-Cas9 tools have significantly accelerated the pace of genomic research by enabling highly efficient knockouts or edits of virtually any gene in cells or model organisms [1]. 2. **Clinical Trials and Stability**: Multiple CRISPR-Cas9-based clinical trials are in progress, and early trial results suggest that CRISPR-engineered cells are stable and do not cause acute adverse reactions in humans [1]. 3. **Functional Genomics**: CRISPR-Cas9 is set to make a significant impact on genomic screening and scientific discovery, with recent demonstrations showing great promise [2]. 4. **Targeted Gene Regulation and Epigenome Modifications**: CRISPR-Cas9 has been linked to various effector proteins to enable targeted gene regulation and epigenome modifications [3]. 5. **Precision Medicine**: CRISPR-Cas9 holds significant promise for the development of new models and precision-targeted therapies for diseases such as Alzheimer's Disease (AD) [5]. 6. **Versatility and Efficiency**: CRISPR-Cas9 provides a highly versatile platform that allows fast and efficient genome editing in an ever-growing list of organisms [10]. ### Potential Risks of CRISPR-Cas9: 1. **Off-Target Effects**: CRISPR-Cas9 is known to generate off-target alterations, which can result in unwanted mutations and potentially cytotoxic effects [4]. 2. **Technical Challenges**: There are several technical challenges that need to be addressed to maximize the benefits of CRISPR-Cas9 technology [2]. 3. **Long-Term Safety**: While early trial results are promising, the long-term safety of CRISPR-Cas9-engineered cells is yet to be determined [1]. 4. **Mismatch Tolerance**: The CRISPR-Cas9 system can tolerate certain mismatches to the DNA target, which could potentially lead to unintended edits [8]. By considering these benefits and risks, researchers and clinicians can better navigate the development and application of CRISPR-Cas9 technologies.", + "To differentiate between X and Y DNA in the context of DNA tracing and determining quantitative trait loci (QTLs), one must consider the unique characteristics of each chromosome: 1. **Recombination Rates**: The Y chromosome largely does not undergo recombination, whereas the X chromosome has a slower recombination rate compared to autosomes. This difference in recombination rates has significant implications for the detection of QTLs [1]. 2. **QTL Mapping**: QTLs are stretches of DNA closely linked to genes that influence specific phenotypes. The process of QTL mapping involves measuring phenotypes within a population and genotyping at numerous marker loci [2], [4]. Given the unique recombination characteristics of the X and Y chromosomes, the strategies for mapping QTLs on these chromosomes may differ. 3. **Genetic Markers and Phenotypic Traits**: When measuring correlations between genetic markers and phenotypic traits, the lack of recombination on the Y chromosome and the slower recombination on the X chromosome must be taken into account. This affects how QTLs linked to these chromosomes are identified and analyzed [10]. In summary, the primary differences between X and Y DNA in the context of DNA tracing and QTL determination lie in their recombination rates and the subsequent impact on QTL detection and mapping strategies.", + "For text and biological resources, it seems you are referring to adding web resources such as Ensembl to your system. This is evident from the context which mentions various web-based biological data management systems and genome browsers like BioMart, GBrowse, and Ensembl [1], [4]. These resources provide portals to current and archived public assemblies, as well as tools for searching and annotating genome assemblies [4], [6]. Therefore, it appears you are more focused on integrating web resources rather than books.", + "Ensembl is a genomic database that identifies the positions of known human genes from public sequence database entries, usually using tools like GENEWISE to predict their exon structures. It aims to provide a more accurate reflection of reality compared to ab initio predictions alone [1]. Ensembl also includes many genomic features other than predicted genes, such as different repeat classes, cytological bands, CpG island predictions, tRNA gene predictions, and expressed sequence clusters from the UniGene database [4].", + "The primary difference between QTL mapping and GWAS lies in their approaches and resolutions for identifying genetic associations with traits. 1. **QTL Mapping**: - **Approach**: QTL mapping involves identifying segments of the genome (quantitative trait loci) that are associated with a particular phenotype. It typically uses linkage analysis to find statistically significant linkages between phenotypes and genotypes [7], [8]. - **Resolution**: Historically, QTL mapping has had low resolution, meaning it identifies larger genomic regions associated with traits rather than pinpointing specific genetic variants [6]. 2. **GWAS (Genome-Wide Association Studies)**: - **Approach**: GWAS is a holistic, whole-genome approach that analyzes the association between DNA polymorphisms and phenotypic traits across the entire genome. It establishes correlations between changes in DNA at specific loci and changes in disease traits within populations [2], [4]. - **Resolution**: GWAS allows for much finer mapping of QTLs compared to traditional linkage analysis, providing higher resolution in identifying specific genetic variants associated with traits [3]. In summary, while QTL mapping identifies broader genomic regions linked to traits with lower resolution, GWAS provides a finer, more detailed mapping of genetic associations across the entire genome [2], [3], [4], [6], [7], [8].", + "To determine which gene in your QTL is causal for the trait, you can follow these steps: 1. **Identify Candidate Genes**: Start by identifying candidate genes within the QTL interval. This can be done by looking at genes that are expressed in tissues likely to influence the traits of interest [6]. 2. **Sequence Candidate Genes**: Sequence the cDNA of strong candidate genes to identify amino acid polymorphisms. This helps in detecting sequence differences in coding or regulatory regions between the two parental inbred strains [1], [6]. 3. **Expression Studies**: Test for mRNA and protein expression differences in target tissues of the original strains used to detect the QTL. This will help refine the list of candidate genes [1]. 4. **Fine Mapping**: Perform fine mapping of the QTL interval to shorten the list of plausible candidate polymorphisms [6]. 5. **Correlation with Trait**: Use gene expression data to screen for genes within the QTL interval whose expression correlates with the trait of interest [7]. 6. **Proof of Causality**: After refining the list of candidate genes, test them rigorously for proof of cause and effect. This involves proving definitively which nucleotide polymorphism underlies the QTL [1], [6]. By following these steps, you can systematically narrow down and identify the causal gene within your QTL.", + "The query appears to be based on a misunderstanding. Males do not have two Y chromosomes; they have one X and one Y chromosome, while females have two X chromosomes. This is clearly explained in the provided context: - Males have an XY genotype, meaning they have one X chromosome and one Y chromosome [3]. - Females have an XX genotype, meaning they have two X chromosomes [3]. Additionally, it is noted that for each chromosome pair, one chromosome is inherited from the mother and one from the father [2]. Therefore, males inherit their Y chromosome from their father and their X chromosome from their mother, while females inherit one X chromosome from each parent.", + "To differentiate between X and Y DNA with respect to DNA tracing and determining quantitative trait loci (QTLs), consider the following points: 1. **Recombination Rates**: - The Y chromosome largely does not undergo recombination, which means it is passed down relatively unchanged from father to son. This makes it useful for tracing paternal lineage [1]. - The X chromosome, on the other hand, does undergo recombination, but at a slower rate compared to autosomes. This slower recombination rate has significant implications for the detection of QTLs on the X chromosome [1]. 2. **QTL Mapping**: - QTLs are stretches of DNA closely linked to genes that underlie specific phenotypes. The process of QTL mapping involves identifying these regions to understand the genetic basis of heritable traits [2]. - The detection of QTLs on the X chromosome may be influenced by its slower recombination rate, which can affect the resolution and accuracy of QTL mapping compared to autosomes [1]. In summary, the primary differences between X and Y DNA in the context of DNA tracing and QTL determination lie in their recombination behaviors and how these behaviors impact the detection and mapping of QTLs. The Y chromosome's lack of recombination makes it a stable marker for paternal lineage tracing, while the X chromosome's slower recombination rate affects the detection of significant QTLs [1], [2].", + "Once a sperm combines with an egg, the traits passed onto the resulting lifeform are determined by several factors: 1. **Chromosomal Segments from Parents**: Individuals inherit a patchwork of chromosomal segments from both maternal and paternal chromosomes [1]. 2. **Mendelian Laws**: The Mendel laws of segregation and independent assortment play a crucial role. The law of segregation states that each parent passes a randomly selected allele for a given trait to their offspring [5]. 3. **Gene Activation and Regulation**: The process involves controlling which genes are active, when they activate, and for how long. This control can be achieved through mechanisms such as imprinting, maternal effect, cis-regulation, and other instructions encoded within the embryo's DNA [3], [4]. 4. **Epigenetic Marks**: Epigenetic marks, which are chemical modifications to DNA or histones, can affect gene expression and phenotype later in development [7]. These combined genetic and epigenetic factors determine how traits are passed onto and expressed in the resulting lifeform.", + "The provided context does not include specific instructions on how to add a new species to the GeneNetwork database. However, it does mention that data from multiple species have been integrated into GeneNetwork [1], [6]. This suggests that the process is possible, but the exact steps are not detailed in the given excerpts. To add a new species to the GeneNetwork database, you may need to refer to the official GeneNetwork website (www.genenetwork.org) [5], [7], or contact their support team for detailed guidance. Additionally, reviewing the materials and methods information page linked to datasets in GeneNetwork [2] might provide further insights into the process.", + "In QTL (Quantitative Trait Loci) analyses, several genes have been identified as being associated with diabetes. Specifically: 1. **RASA1, Nnt, and PSK1** on chromosome 13 have been identified as candidate genes underlying diabetes-related traits [4]. 2. **PAX4** near the locus 7q32 has been identified in a genome-wide association study in a Chinese population as a susceptibility locus for type 2 diabetes [6]. 3. **Genes on chromosomes 8q24 and 7q11** have been associated with insulin sensitivity and insulin secretion, which are critical factors in the development of type 2 diabetes [2]. 4. **Genes on 5q11-q13 and 7q32** have been identified as significant susceptibility factors for type 1 diabetes [9]. These genes and loci are typically associated with diabetes in QTL analyses, highlighting the complex genetic architecture of the disease.", + "The gene TCF7L2 is primarily involved in Type 2 diabetes. This association is well-documented across multiple sources: 1. Single nucleotide polymorphisms in TCF7L2 are reproducibly associated with Type 2 diabetes and reduce the insulin response to glucose in nondiabetic individuals [2]. 2. The TCF7L2 locus represents the strongest known genetic determinant of Type 2 diabetes, with risk allele carriers showing impaired insulin production and beta-cell dysfunction [3]. 3. Variants of the TCF7L2 gene confer risk of Type 2 diabetes [4], [5]. 4. TCF7L2 is considered the strongest genetic determinant for the risk of developing Type 2 diabetes [7]. 5. Overexpression of TCF7L2 protein decreases the sensitivity of beta islet cells to secrete insulin, which is involved in the regulation of insulin secretion [9]. Thus, the gene TCF7L2 is significantly involved in Type 2 diabetes.", + "Once a sperm combines with an egg, the traits passed onto the resulting lifeform are determined by several factors: 1. **Chromosomal Segments from Parents**: Individuals inherit a patchwork of chromosomal segments from both maternal and paternal chromosomes [1]. 2. **Mendelian Laws**: The Mendel laws of segregation and independent assortment play a crucial role. The law of segregation states that each parent passes a randomly selected allele for a given trait to their offspring [4]. 3. **Gene Activation and Regulation**: The process involves controlling which genes are active, when they activate, and for how long. This control can be achieved through mechanisms such as imprinting, maternal effect, cis-regulation, and other instructions encoded within the embryo's DNA [3], [5]. 4. **Epigenetic Marks**: Soon after fertilization, epigenetic marks can affect gene expression and phenotype later in development [8]. 5. **Heritable Material**: The information passed from parents to offspring is coded in DNA molecules [7]. These factors collectively determine how traits are inherited and expressed in the resulting lifeform.", + "A ribosomal binding site (RBS) is a specific sequence of nucleotides in messenger RNA (mRNA) that is crucial for the initiation of protein synthesis. At a high level, you can think of it as a \"landing pad\" for the ribosome, which is the molecular machine that reads the mRNA and translates it into a protein. Here's a simple analogy: Imagine the ribosome as a train and the mRNA as a track. The ribosomal binding site is like a train station where the train (ribosome) stops to pick up passengers (the instructions for making a protein). Without this station, the train wouldn't know where to start its journey. In more technical terms, the RBS helps the ribosome to correctly position itself on the mRNA so that it can start translating the genetic code into a protein at the right spot. This process is essential for the proper expression of genes and the production of proteins that perform various functions in the cell [1], [2], [3].", + "Once a sperm combines with an egg, the traits passed onto the resulting lifeform are determined by several factors: 1. **Chromosomal Segments**: Individuals inherit a patchwork of chromosomal segments from maternal and paternal chromosomes due to phenomena such as mutations and gene conversion events during meiosis [1]. 2. **Mendelian Laws**: The Mendel laws of segregation and independent assortment play a crucial role. The law of segregation states that each parent passes a randomly selected allele for a given trait to their offspring [4]. 3. **Gene Activation and Regulation**: The process of trait determination also involves controlling which genes are active, when they activate, and for how long. This control can be achieved through mechanisms such as imprinting, maternal effect, and cis-regulation [3], [5]. 4. **Epigenetic Marks**: Epigenetic marks, which are chemical modifications to DNA that do not change the DNA sequence but affect gene expression, also play a role. These marks can be inherited and influence gene expression and phenotype later in development [8]. 5. **Heritable Material**: The information passed from parents to offspring is coded in DNA molecules, and any mutations in these genes can be inherited by the offspring [7]. In summary, the combination of chromosomal inheritance, Mendelian genetic laws, gene activation and regulation mechanisms, epigenetic marks, and the heritable DNA material all contribute to how traits are passed onto the resulting lifeform after fertilization.", + "Sure! Let's break down the difference between short-read and long-read sequencing in a way that's easy to understand. ### Short-Read Sequencing: 1. **Read Length**: Short-read sequencing generates small fragments of DNA, typically around 50-300 base pairs long [4]. 2. **Technology**: This method is commonly used by platforms like Illumina [3]. 3. **Advantages**: - **High Throughput**: It can produce a large amount of data quickly and is relatively cost-effective [4]. - **Accuracy**: Generally, short-read sequencing has a lower error rate per base compared to long-read sequencing [4]. 4. **Limitations**: - **Complex Regions**: It struggles to accurately sequence complex regions of the genome, such as those with long repetitive elements or structural variations [6]. - **Assembly**: Because the reads are short, assembling them into a complete genome can be challenging and may leave gaps or errors [6]. ### Long-Read Sequencing: 1. **Read Length**: Long-read sequencing produces much longer fragments, often several kilobases (thousands of base pairs) in length [6]. 2. **Technology**: There are two main types: - **Single-Molecule Real-Time Sequencing**: This method reads long DNA fragments directly without needing to amplify them [8]. - **Synthetic Long-Reads**: This method uses short-read technology to create long reads in a computational process [2]. 3. **Advantages**: - **Complex Regions**: It can span complex or repetitive regions in a single read, making it easier to resolve these areas accurately [7]. - **Transcriptomics**: Long reads can cover entire mRNA transcripts, helping researchers understand gene structures and variations better [7]. 4. **Limitations**: - **Cost and Throughput**: Long-read sequencing is generally more expensive and produces less data per run compared to short-read sequencing [4]. - **Error Rate**: The error rate per base can be higher than that of short-read sequencing, although this is improving with new technologies [4]. In summary, short-read sequencing is like reading a book by looking at many small snippets of text, which is fast and accurate but can be tricky if the text is very repetitive or complex. Long-read sequencing, on the other hand, is like reading longer passages at a time, which helps to understand the context better but might be slower and more expensive.", + "Using a pangenome-based reference can be more useful than using a single linear reference for several reasons: 1. **Capturing Genetic Diversity**: A single linear reference genome represents just one version of a species' DNA, which might not include all the genetic variations found in different individuals. A pangenome, on the other hand, includes multiple versions of genes and sequences from various individuals, capturing a broader spectrum of genetic diversity [3]. 2. **Reducing Reference Bias**: When using a single linear reference, there's a risk of reference bias, which means that genetic variations not present in the reference genome might be missed or misinterpreted. A pangenome reduces this bias by including multiple genetic variants, making it more likely to accurately represent the genetic makeup of different individuals [1]. 3. **Improving Disease Research**: By including a wider range of genetic variations, a pangenome can help researchers better understand the genetic basis of diseases. This comprehensive approach allows for more accurate identification of disease-associated genetic variants, which can lead to better diagnostics and treatments [6]. 4. **Enhanced Analysis Tools**: The use of a pangenome can improve the performance of genetic analysis tools. For example, it can provide a more accurate benchmark for evaluating new sequencing technologies and analysis methods, leading to more reliable results [3]. 5. **Overcoming Limitations of Single References**: A single reference genome assumes that the order and structure of genes are the same across all individuals, which is not always true. This assumption can lead to errors in genetic studies. A pangenome, by including multiple sequences, can account for these structural variations and provide a more accurate representation of the genome [2]. In summary, a pangenome-based reference is more useful because it captures the genetic diversity of a species, reduces reference bias, improves disease research, enhances analysis tools, and overcomes the limitations of using a single linear reference. This makes it a more comprehensive and accurate tool for genetic studies.", + "Not all genetic regulation is done solely through DNA elements like promoters, repressors, and activators. There are other forms of genetic regulation that play crucial roles in controlling gene expression. Here are some key points to consider: 1. **Non-Coding DNA**: Recent research has shown that much of the non-coding part of the genome, which does not code for proteins, is still active in regulating gene expression. This includes various regulatory activities that are not directly related to the traditional DNA elements like promoters and repressors [1]. 2. **Chromatin Structure**: The structure of chromatin, which is the complex of DNA and proteins in the cell nucleus, plays a significant role in gene regulation. For example, histone acetylation, which involves adding acetyl groups to histone proteins, can decondense chromosomal structure and make DNA more accessible for transcription [9]. 3. **Epigenetic Regulation**: Epigenetics involves changes in gene expression that do not alter the DNA sequence itself. This can include modifications like DNA methylation and histone modification, which affect how tightly DNA is wound around histones and thus its accessibility for transcription [6], [7]. 4. **Post-Transcriptional Regulation**: After DNA is transcribed into RNA, there are additional layers of regulation. This includes processes like RNA splicing, editing, and degradation, which can influence how much of the RNA is available to be translated into protein [7]. 5. **Translational and Post-Translational Regulation**: Even after RNA is translated into protein, there are mechanisms that regulate the activity, stability, and localization of proteins. These include modifications like phosphorylation and ubiquitination, which can alter protein function and lifespan [7]. In summary, while DNA elements like promoters, repressors, and activators are important for genetic regulation, there are multiple other layers of regulation involving chromatin structure, epigenetic modifications, and post-transcriptional and post-translational processes that also play critical roles in controlling gene expression.", + "The different relationships between traits can be categorized into several types based on the provided context: 1. **Correlation Among Traits in a Pair**: This refers to how traits within a pair are related to each other in terms of their correlation [1], [2], [3]. 2. **Correlation Between a Trait Pair and Other Factors**: This involves examining how a pair of traits correlates with other external factors or conditions [1], [2], [3]. 3. **High-Order Organization of Traits**: - **Groups of Tightly Related Traits**: These are traits that share the same transcript mechanisms and are highly correlated with each other (modules 1, 2, 6, 7, 8) [6], [7], [8]. - **Groups of Distinct Traits with Shared Mechanisms**: These traits share the same transcript mechanisms but do not necessarily have high correlations among themselves (modules 3, 4, 5) [6], [7], [8]. - **Overlapping Traits in Different Groups**: Different groups of traits may have overlapping traits but typically differ in their underlying mechanisms [6], [7], [8]. These relationships highlight the complexity and interconnectedness of traits, showing that they can be related through direct correlations, shared mechanisms, or overlapping characteristics.", + "Yes, the landscape of QTL (Quantitative Trait Loci) and GWAS (Genome-Wide Association Studies) hits can be used to find relationships between traits. This can be achieved through several methods: 1. **Correlated Traits in Different Environments**: Multiple GWAS for the same trait in different environments can be treated as correlated traits, which helps in exploring the genetic and phenotypic basis of local adaptation [1]. 2. **Mapping Pleiotropy**: Newer approaches map pleiotropy by simultaneously associating genomic loci with multiple traits, which can reveal relationships between traits [2]. 3. **QTL-Trait-Trait Triads**: Causal inference in GWAS and QTL studies involves identifying pairs of traits with a common QTL and determining whether the QTL directly affects each of the two traits independently or if it affects only one trait, which then influences the other [4]. 4. **Colocalization and Integration of Data**: Methods such as Bayesian tests for colocalization between pairs of genetic association studies using summary statistics, and Mendelian randomization integrating GWAS and eQTL data, can reveal genetic determinants of complex and clinical traits, thereby identifying relationships between traits [5]. These methods collectively demonstrate that the landscape of QTL and GWAS hits can indeed be used to find relationships between traits." + ], + "contexts": [ + [ + "neered nucleases, CRISPR-Cas9 tools have accelerated the pace of genomic research by permitting highly efficient knockouts or edits of virtually any gene in cells or model organisms. Multiple CRISPR-Cas9based clinical trials are in progress or are expected to begin soon. Although Cas9- engineered cells havent yet dem - onstrated efficacy at scale, early trial results suggest that such cells are stable and dont cause acute adverse reactions in humans. Long-term safety is yet to be de -", + "stageissetforCRISPRtomakeanenormousimpactongenomic screening and thus scientic discovery in the coming years, and recent demonstrations of this system have shown great promise (Shalem etal., 2015 ).However,a number of technical challenges must be addressed in order to maximize the benet of this technology. In this review, we will discuss current applications of CRISPR in functional genomics and provide a perspective on futuredevelopmentsinthisarea. CRISPR/Cas9 Genome Editing", + "heralding the age of genome editing. Furthermore, Cas9 or guide RNAs have been linked to various effector proteins to enable targeted gene regulation 12,13 and epigenome modifications14,15. It is worth noting, however, that many of these feats had been demonstrated previously using other nucleases or DNA-binding proteins 1,16. In this Perspective, I shed light on early genome editing platforms that laid the groundwork for the widespread use of CRISPRCas9 in research and medicine (Fig. 1 ).", + "CRISPR/CAS9 HOLDS SIGNIFICANT PROMISE FOR THE DEVELOPMENT OFNEW AD MODELS AND PRECISIONTARGETED AD THERAPY Clustered regularly interspaced short palindromic repeat (CRISPR)-Cas nucleases have revolutionizedthe eld of gene editing and have tremendous appli-cation in the eld of molecular medicine [98102].Despite a signicant surge in CRISPR/Cas9-mediated genome editing in various disease models,the progress in the eld of AD has lagged behindsubstantially. We believe that genome editing can sig-", + "81. Applications for CRISPRCas9 beyond genome editing", + "cline- or Tet-regulated Cas9 system. Current CRISPR/Cas systems arefrom Streptococcus pyogenes ,Streptococcus thermophilus ,Neisseria meningitides and Treponema denticola .2.5. Caveats of advanced genome editing tools Off-target effects . The DNA-binding domains of ZFNs and TALENs need to be very speci c for the target site to avoid off-target cleavage, which results in unwanted mutations and potentially cytotoxic effects [27]. CRISPR/Cas9 is also known to generate off-target alterations,", + "on transcriptional interfere nce (CRISPRi) and activation (CRISPRa) have also harnessed Cas9-based technologies for use in genome-wide studies ( 59,174). In addition, recent improvements in lentiviral library generation and propagation,as well as large-scale DNA and RNA synthesis, have allowedCRISPR-Cas9 technology to be exploited across multiple modelplatforms ( 59,175178). nCas9 The CRISPR-Cas9 system can tolerate certain mismatches to the DNA target since the required gRNAs are short. A disadvantage,", + "CRISPR-Cas9 can be used to in - duce genome edits by creating targeted DNA breaks that trigger site-specific DNA repair. In next- generation formats, it can also control the transcriptional output of genes or alter genome se - quences using a process of nu - cleotide base editing that does not require repair of DNA breaks. As these technolo - gies continue to mature, it will become increasingly possible to alter cellular genomes efficiently and accurately. Coming on the heels of engi -", + "S.P . Raikwar et al. / Alzheimers Disease: New Therapeutic Horizons 333 gene editing efciency of the CRISPR/Cas9 systems.", + "13. Kleinstiver BP, etal. High-fidelity CRISPRCas9 nucleases with no detectable genome-wide off-target effects. Nature. 2016;529:4905. 14. Brane A, Tollefsbol T.Targeting telomeres and telomerase: studies in aging and disease uti- lizing CRISPR/Cas9 technology. Cells. 2019;8:186. 15. Wang H, etal. One-step generation of mice carrying mutations in multiple genes by CRISPR/ Cas-mediated genome engineering. Cell. 2013;153:9108." + ], + [ + "to regulate lifetime and aging processes. In fact, epigenetics modulate gene expression without altering the DNA sequence. This is possible by means of different kinds of epigenetic modifications, including DNA methylation and histone modifications (which might affect gene transcription), and noncoding (nc)RNAs (which might change gene expression at the post-transcriptional level)[59]. Given the crucial role of epigenetics in the modulation of gene expression, its alteration can contribute to", + "can regulate gene expression while the underlying DNA sequence remains the same. The epigenome is influenced both by underlying genetic variants as well as by environ- mental factors including the social environment, health behaviors, and environmental pollutants [ 11]. Methylation of CpG dinucleotides, the best understood epigenetic mechanism, is also dynamic over the life course. It is well established that epigenomic patterns of DNA methylation change with age [ 12]. A recent study in lymphocytes", + "Epigenetics Changes arising from alterations in gene expression levels that are caused by reversible chemical modification of DNA, but not changes to the DNA sequence passed on from parents to offspring.", + "Epigenetic changes refer to heritable changes in gene expression which do not involve changes in DNA sequences. Several epigenetic mechanisms have been found to regulate gene expression. Whilst the most studied mechanism relates to DNA methylation, other changes, including histone modi cations and non-coding RNAs, also play an important role, and can be transmitted from one generation to the next. DNA methylation involves the addition of methyl groups to DNA, mainly at CpG sites, which converts cytosine", + "EPIGENETIC STUDIES An epigenetic mechanism is a biochemical alteration to the DNA molecule that does not change the sequence of the DNA but does in uence gene expression. Epigenetics is often de ned as the study of mitotically and/or meiotically heri- table changes in gene function that cannot be explained by changes in DNA sequence (Russo, Martienssen, & Riggs, 1996, p. 1). The epigenetic/epigenomic approach shares many advantages and disad-", + "ity and expression of genes without changing their DNA sequence [ 4]. These modications are: DNA methylation, histone modications, and ncRNAs including miRNA [4]. The en- vironment and lifestyle can induce epigenetic changes, such as pollution, tobacco smoking, obesity, lack of physical activity, and alcohol consumption [ 108]. Furthermore, exposure to such environmental factors can have a buttery effect: epigenetic modications may", + "epigenetics is the study of mitotically heritable alterations in gene expression potential that are not caused by changes in DNA sequence (Jaenisch and Bird, 2003 ). Hence, rather than encompassing all of developmental biology, modern epigenetics is focused on understanding the spe-ci c molecular mechanisms that convey cellular memory. Within the nucleus, the mammalian genome is wrapped", + "gene expression can also occur by trans-epigenetics ( Bonasio et al., 2010 ), in which proteins and RNAs inuence gene expres-sion and repression. Stable transcription factor networks are anexample of trans -epigenetics ( Young, 2011 ). Clearly, enzymes that modify DNA and histones (methyltransferases, demethy-lases, acetyltransferases, deacetylases) are central epigeneticregulatory mechanisms ( Rando and Chang, 2009 ). The essence of epigenetics is not only the establishment, but", + "pay attention to epigenetic effects on gene expressionmeaning changes that are heritable but that do not involve any change in DNA sequence (see Rutter 2006). Three key points are relevant. First, genes only have effects when they are expressed. Many genes are expressed in only some body tissues and only at certain phases in development. Second, there are multiple inherited DNA elements that do not code for proteins but yet which have important effects through their in uence on gene expression. We need to", + "genetics of gene expression (i.e. regular genetical genomics) and the genetics of epigenetics could be studied simultaneously, thus revealing genes that directly or indirectly affect epigenetic gene states. An additional issue that could be addressed by such an approach is to estimate the percentage of variation in gene expression that can be explained by different epigenetic conformations." + ], + [ + "drial DNA sequence variation seems impossible withoutan understanding of some important differences betweennuclear and mitochondrial genetics (Table I). Mitochon-drial DNA replicates autonomously and is inherited viathe cytoplasm of the parent cell with the individualmitochondrion being the segregating unit (Attardi et al.,1995). Thus, in the case of mitochondrial mutations bothmutated as well as normal mitochondria may be presentwithin the same cell. This situation has been termedheteroplasmy and can", + "cMitochondria are semiautonomous organelles; possess their own replication-, transcription- and translation system cExclusively maternal inheritance of mitochondrial DNA cMitotic segregation of mitochondrial DNAcan lead to hetero- plasmy, i.e., the proportion of genetically different populations ofmitochondria differs between generations of mitotically activecells cApproximately tenfold higher mutation rate compared with nuclear", + "DIFFERENCES BETWEEN MITOCHONDRIAL AND NUCLEAR GENETICS Arealisticassessmentoftherelevanceofmitochon-", + "In the fifth mode of inheritance, the disease mutation lies not on a chromosome in the nucleus but rather in mitochondrial DNA outside the nucleus. Mitochondria are inherited exclu- sively from an offsprings mother; because of this phenome- non, the mutation and thus the disease can be passed only from a mother to her offspring. This is maternal inheritance, also known as extranuclear inheritance (Figure 11). Representative disorders include various mitochondrial myopathies.", + "The regulation of the mitochondrial genome also reflects its prokaryotic ancestry. While nuclear DNA undergoes replication during cell division, mtDNA replication occurs independently of cell cycle. The majority of the compo-nents for mtDNA replication are imported nuclear-encoded proteins, including the catalytic subunit of mtDNA poly -", + "Unlike the nuclear genome, which requires both paternal and maternal contributions, mtDNA is inherited solely from the maternal lineage. It is unclear what advantage a uniparental mtDNA transmission confers, but one possibil-ity is to minimize the number of distinct genomes to maxi-mize the efficiency of a multi-genomic system (Hill etal. 2019). In fact, humans have developed complex, redundant mechanisms to ensure uniparental inheritance of mtDNA (DeLuca and OFarrell 2012; Rojansky etal. 2016). Paternal", + "mitochondria and sperm are not, mitochondrial DNAis usually inherited from the mother. Therefore, mito-chondrial genes and diseases due to DNA-sequencevariants in them are transmitted in a matrilineal pat-tern that is distinctly different from the pattern of in-heritance of nuclear genes. MONOGENIC CONDITIONS Over the course of the 20th century, a combination", + "2. Mitochondrial DNA structure and properties Mitochondrial genomes (mt-genomes) are short circular molecules that, with the exception of viruses,represent the most economically packed forms ofDNA in the whole biosphere. The human mt-genomeis only 16,569bp long [9]; within this extension, wend the coding sequences for seven subunits of theNADH-ubiquitone reductase (respiratory complex I),the apocytochrome bof the ubiquitone cytochrome creductase (respiratory complex III), three subunits", + "Abstract The human mitochondrial genome consists of approximately 1500 genes, 37 encoded by the maternally inherited mitochondrial DNA (mtDNA) and the remainder encoded in the nuclear DNA (nDNA). The mtDNA is present in thousands of copies per cell and encodes", + "(mtDNA). MtDNA carries important genetic information concerning cellular metabolismand the generation of energy. It has been suggested that mitochondria and mtDNA could be of significance during early embryo development. Our work confirms this hypothesis. Specif- ically, our findings implicate mitochondria and their genome in female reproductive agingand the generation of embryonic chromosome abnormalities. Importantly, we describe a di-" + ], + [ + "1999) raises practical and ethical issues of access to resulting opportunities and creates family communication challenges. Currently, prenatal testing for chromosomal diseases has become increasingly common (Moyer et al., 1999). Options such as pre-implantation genetic diagnosis (PGD) can identify over 1,250 disease-related mutations creating an opportunity for parents to select unaffected embryos for implantation in the womb (R. M. Green, 2008). Test results provide potential parents with information", + "undergo prenatal testing have determined that partners base their decision upon several factors, including, but not limited to: parental beliefs about abor-tion, attitudes regarding disability and their perceptions of the usefulness of having the information revealed by genetic tests (Moyer et al., 1999, p. 522). Abortion beliefs constitute a key issue in the decision-making process. Even though a majority of parents receiving abnormal prenatal test results terminate their pregnancies (Redlinger-Grosse,", + "Hum Genet 1995;57:12331241. 24. Committee on Bioethics. Ethical and policy issues in genetic testing and screening of children. Pediatrics 2013;131:620622. 25. Ross LF, Saal HM, David KL, Anderson RR. Technical report: ethical and policy issues in genetic testing and screening of children. Genet Med 2013;15: 234245. 26. Wilfond B, Ross LF. From genetics to genomics: ethics, policy, and parental decision-making. J Pediatr Psychol 2009;34:639647.", + "Informed Consent and Genetic Testing Genetic testing is increasingly used across the life continuum for screening, diagnosis, and de termining the best treatment of diseases. Obstetric and pediat ric nurses have traditionally been involved in the genetic testing process with prenatal screening for genetic conditions such as spina bifida and Down syndrome, and newborn screening for genetic conditions such", + "Objective Ethical evaluation of genetic testing in children is traditionally based on balancing clinical benefits and risks. However, this focus can be inconsistent with the general practice of respecting parentaldecision-making about their childrens health care. We argue that respect for parental decision-making should play a larger role in shaping pediatric genetic testing practices, and play a similar role regarding decisions", + "prenatal decisions. Further research needs to investigate how different families engage in such discussions and decision-making pro-cesses, especially as prenatal testing becomes more common and better able to predict or prevent a wider range of genetic conditions.", + "all of the complex ethical and legal issues rel- evant to genetic testing would disappear if there were effective preventions or treatments available for genetic conditions. The ability to predict future disease in conjunction with a limited ability to do much about it has im- portant social and psychological implications that must be addressed in conducting genetic research. One final factor worth consideration in un- derstandingthesensitivitytogeneticmedicine", + "Newborn screening by tandem mass spec-trometry: ethical and social issues. Can J Public Health 2007; 98: 284286. 65 Belle-Isle L: Genetic testing for late onset dis- eases: a population and public health per-spective. Health Policy Res Bull 2001; 1: 11 12. 66 Williams-Jones B: Private genetic testing in Canada: a summary. Health Law Rev 2001; 9: 1013. 67 Begleiter ML: Training for genetic counsel- lors. Nat Rev Genet 2002; 3: 557561. 68 Carroll JC, Reid AJ, Woodward CA, Per-", + "Although risk-based genetic testing for common diseases raise similar ethical issues to more traditional genetic testing for rare diseases, new challenges are raised due to the type of information revealed and access to tests. With thoughtful deliberation with health professionals, patients and families, test developers and laboratories, insurers and other stakeholders, these issues can be addressed to ensure the safe and appropriate use of these promising new clinical applications. REFERENCES", + "against testing, parents should generally be given final decision-making authority. Ethical Considerations in Developing Policy for Comprehensive Genomic Testing In the near future, genomic testing is likely to become more accessible and will provide both information aboutthe risks of common conditions such as heart disease, diabetes, and hypertension as well as predictions aboutindividual responses to specific pharmaceuticals and other medical therapies (Aspinall & Hamermesh, 2007)." + ], + [ + "36. Sequencing, H.G. Finishing the euchromatic sequence of the human genome. Nature 2004 ,431, 931945. 37. Heather, J.M.; Chain, B. The sequence of sequencers: The history of sequencing DNA. Genomics 2016 ,107, 18. [CrossRef] 38. Rothberg, J.M.; Leamon, J.H. The development and impact of 454 sequencing. Nat. Biotechnol. 2008 ,26, 11171124. [CrossRef] [PubMed] 39. Shendure, J.; Ji, H. Next-generation DNA sequencing. Nat. Biotechnol. 2008 ,26, 11351145. [CrossRef] [PubMed]", + "22. Karow, J. Qiagen launches GeneReader NGS System atAMP; presents performance evaluation by broad. GenomeWeb [online], https:// www.genomeweb.com/ molecular-diagnostics/qiagen-launches-genereader- ngs-system-amp-presents-performance-evaluation (4Nov 2015). 23. Smith,D.R. & McKernan,K. Methods of producing and sequencing modified polynucleotides . US Patent 8058030 (2011). 24. Margulies,M. etal. Genome sequencing in microfabricated high-density picolitre reactors. Nature 437, 376380 (2005).", + "11 BIOINFORMATIC CHALLENGES FOR GENOMIC MEDICINE Processing and managing of high-throughput sequence data High throughput sequencing offers severa l advantages relative to array-based genotyping or expression assays. First, unlike genotyping arrays, whole genome sequencing is not limited to interrogating onl y known sequence variants. Similarly, RNA- sequencing (RNA-seq) enables expression quanti fication of novel transcripts that are not", + "11 BIOINFORMATIC CHALLENGES FOR GENOMIC MEDICINE Processing and managing of high-throughput sequence data High throughput sequencing offers severa l advantages relative to array-based genotyping or expression assays. First, unlike genotyping arrays, whole genome sequencing is not limited to interrogating onl y known sequence variants. Similarly, RNA- sequencing (RNA-seq) enables expression quanti fication of novel transcripts that are not", + "High-throughput bacterial genome sequencing: an embarrassment of choice, aworldof opportunity.NatRevMicrobiol2012;10:599-606. 11.CroucherNJ,DidelotX.Theapplicationof genomicstotracingbacterialpathogen transmission.CurrOpinMicrobiol2015;23:62-7. 12.ShendureJ,JiH.Next-generationDNAsequencing.NatBiotechnol2008;26:1135- 45. 13.MillerJR,KorenS,SuttonG.Assemblyalgorithmsfornext-generationsequencing data.Genomics2010;95:315-27. 14.OlsonND,LundSP,ColmanRE,FosterJT,SahlJW,SchuppJM,etal.Bestpractices", + "sequencing. Genome Res. 20, 11651173 (2010). 64. English,A.C. etal. Assessing structural variation in a personal genome-towards a human reference diploid genome. BMC Genomics 16, 286 (2015). 65. Carneiro,M.O. etal. Pacific Biosciences sequencing technology for genotyping and variation discovery in human data. BMC Genomics 13, 375 (2012). 66. Quail,M.A. etal. A tale of three next generation sequencing platforms: comparison of Ion T orrent, Pacific Biosciences and Illumina MiSeq sequencers.", + "Nat. Biotechnol. 30, 10331036 (2012). 111. Chrystoja,C.C. & Diamandis,E.P . Whole genome sequencing as a diagnostic test: challenges and opportunities. Clin. Chem. 60, 724733 (2014). 112. McGuire,A.L. etal. Point-counterpoint. Ethics and genomic incidental findings. Science 340, 10471048 (2013). 113. Bowers,J. etal. Virtual terminator nucleotides for next-generation DNA sequencing. Nat. Methods 6, 593595 (2009). 114. Heger,M. Chinas Direct Genomics unveils new", + "sequencing. Bioinformatics 31, 20402042 (2015). 46. Qiagen. Oncology insights enabled by knowledge base- guided panel design and the seamless workflow of the GeneReader NGS system Press Release. Qiagen [online], http://www.genereaderngs.com/PROM-9192- 001_1100403_WP_GeneReader_NGS_0116_NA.pdf (2016). 47. Forgetta,V. etal. Sequencing of the Dutch elm disease fungus genome using the Roche/454 GS-FLX Titanium System in a comparison of multiple genomics core", + "FURTHER INFORMATION 10X Genomics: http://www.10xgenomics.com 454 Sequencing: http://www.454.com Advances in Genome Biology and Technology (AGBT): http://www.agbt.org BGISEQ500: http://seq500.com/en/portal/Sequencer.shtml Illumina: http://www.illumina.com Ion Torrent: https://www.thermofisher.com/us/en/home/ brands/ion-torrent.html Oxford Nanopore Technologies: https://www.nanoporetech. com Pacific Biosciences: http://www.pacb.com Personal Genome Project: http://www.personalgenomes.org", + "DNA), and provide the means to link sequences containing applications. First, base- callers like Phred (4,5) extract raw sequences from raw data. There are also contig assemblers like Phrap (University of Washington, http://bozeman. mbt.washington.edu/phrap.docs/phrap.html ) or CAP3 (6) that assemble frag- ments to contigs and packages like consed (7) or GAP4 (8), which are used to finish sequencing projects. These programs are not explained in detail here." + ], + [ + "are involved in the development of the disease [127 ]. There is evidence that more than twenty regions of the genome are involved in t he genetic susceptibility to T1D. The genes most strongly associated with T1D are loc ated in the HLA region of chromosome 6 [128]. Similar to T1D, T2D has a stro ng genetic component. To date, more than 50 candidate genes for T2D have been inve stigated in various populations worldwide. Candidate genes are selected due to the ir interference with pancreatic", + "pre-existing statistical support for a role in T1D-susceptibility: these are the major histocompatibility complex (MHC), the genes encod- ing insulin, CTLA-4 (cytotoxic T-lymphocyte associated 4) and PTPN22 (protein tyrosine phosphatase, non-receptor type 22), and the regions around the interleukin 2 receptor alpha ( IL2RA/CD25 ) and interferon-induced helicase 1 genes ( IFIH1 /MDA5)94. However, these signals can explain only part of the familial aggregation of T1D.", + "C. The Insulin Gene A lesser genetic predisposition to T1D is conferred by the IDDM2 locus on chromosome 11 containing the insu-lin gene region. A polymorphic region located 5 =of the insulin gene was rst reported in 1984 to be associatedwith T1D in caucasoids (39). Now established as a pri- TYPE 1 DIABETES: FROM CAUSE TO CURE 81 Physiol Rev VOL 91 JANUARY 2011 www.prv.org Downloaded from journals.physiology.org/journal/physrev (041.090.188.152) on July 14, 2023.", + "ception of the insulin gene (434). The genetic susceptibil-ity component of T1D allows some targeting of primarypreventive care to family members of diagnosed T1Dpatients, but there is no complete inheritance of the dis-ease. Nevertheless, the risk for developing T1D comparedwith people with no family history is /H110111015 times greater. Although /H1101170% of individuals with T1D carry", + "Genes signifying increased risk for both type 1 and type 2 dia-betes have been identified. Genomewide association studies have identified over 50 loci associated with an increased genetic risk of type 1 diabetes. Several T1D candidate genes for increased risk of developing type 1 diabetes have been sug-gested or identified within these regions, but the molecular basis by which they contribute to islet cell inflammation and beta cell destruction is not fully understood. 12 Also, several", + "14 carried out on large cohorts including collections of families with affected sibling pairs (Pociot et al., 2010). These studies have provided evidence for over forty T1D susceptibility regions , but the exact mechanisms by which the variation found in these regions confer susceptibility to T1D is still not clear (Noble and Erlich, 2012). The most important genes contributing to T1D susceptibility are located in the MHC class II region , also referred to as t he Human Leukocyte", + "The ultimate proof of an inherited contribution to disease pathogenesis comes from the identication of susceptibility genes. As described below, an increasing number of T2D susceptibility genes have been discovered in the past decade, especially,but not exclusively, in monogenic subtypes. Collectively, these probably account for294 A. L. Gloyn and M. I. McCarthy", + "loci contribute to Type 1 Diabetes (T1D) susceptibility and age at T1D onset. Hum. Immunol. 66,301313 (2005). 9. Aly, T. A. et al. Extreme genetic risk for type 1A diabetes. Proc. Natl Acad. Sci. USA 103, 14074 14079 (2006). 10. Noble, J. A. et al. The HLA class I A locus affects susceptibility to type 1 diabetes. Hum. Immunol. 63,657664 (2002). 11. Honeyman, M. C., Harrison, L. C., Drummond, B., Colman, P. G. & Tait, B. D. Analysis of families at risk for insulin-dependent diabetes mellitus reveals that", + "failure linked to T2D genetic risk and pathophysiology. Single celltranscriptome analysis of human islet cells indicate that multiplemonogenic diabetes genes are highly expressed in beta cells (e.g., PDX1, PAX4, INS, HNF1A, andGCK)[27]. However, other non-beta cell types express genes mutated in monogenic diabetes (such as PAX6 and RFX6 ), congenital hyperinsulinemia ( HADH, UCP2 ) and those implicated as T2D GWAS target/effector genes [28].", + "chain promoter (Serreze and Leiter 2001). This observation, alongwith human genetic studies, suggests that increased T1D risk in humans may also result from the combination of rare and common variants within the human population (Concannon et al. 2009b). Despite the identification of several Iddgenes to date, this limited collection does not fully explain T1D pathogenesis or the underlying genetic architecture for T1D risk. One of the many Idd" + ], + [ + "supported by a signicant BLAST match to one or more expressed sequences or proteins. Ensembl also identies the positions of known human genes from public sequence database entries, usually using GENEWISE to predict their exon structures. The total set of Ensembl genes should therefore be a much more accurate reection of reality than ab initio predictions alone, but it is clear that some novel genes are missed (Hogenesch et al. , 2001). Of the many novel genes that are detected, some are", + "supported by a signicant BLAST match to one or more expressed sequences or proteins. Ensembl also identies the positions of known human genes from public sequence database entries, usually using GENEWISE to predict their exon structures. The total set of Ensembl genes should therefore be a much more accurate reection of reality than ab initio predictions alone, but it is clear that some novel genes are missed (Hogenesch et al. , 2001). Of the many novel genes that are detected, some are", + "supported by a signicant BLAST match to one or more expressed sequences or proteins. Ensembl also identies the positions of known human genes from public sequence database entries, usually using GENEWISE to predict their exon structures. The total set of Ensembl genes should therefore be a much more accurate reection of reality than ab initio predictions alone, but it is clear that some novel genes are missed (Hogenesch et al. , 2001). Of the many novel genes that are detected, some are", + "populations as Ensembl reects the progress of the International Haplotype Map Project (Thorisson et al. , 2005). More speculative data, such as GENSCAN-predicted exons that have not been incorporated into Ensembl-conrmed genes, may also be viewed. This means that the display can be used as a workbench for the user to develop personalized an- notation. For example, one may discover novel exons by nding GENSCAN exon predictions which coincide with good matches to a fragment of the draft mouse", + "populations as Ensembl reects the progress of the International Haplotype Map Project (Thorisson et al. , 2005). More speculative data, such as GENSCAN-predicted exons that have not been incorporated into Ensembl-conrmed genes, may also be viewed. This means that the display can be used as a workbench for the user to develop personalized an- notation. For example, one may discover novel exons by nding GENSCAN exon predictions which coincide with good matches to a fragment of the draft mouse", + "populations as Ensembl reects the progress of the International Haplotype Map Project (Thorisson et al. , 2005). More speculative data, such as GENSCAN-predicted exons that have not been incorporated into Ensembl-conrmed genes, may also be viewed. This means that the display can be used as a workbench for the user to develop personalized an- notation. For example, one may discover novel exons by nding GENSCAN exon predictions which coincide with good matches to a fragment of the draft mouse", + "Ostell/Spidey/ SSAHA at Sanger Institute http://www.sanger.ac.uk/Software/analysis/SSAHA/ human and mouse genomes, where there are large full-length cDNA collections to guide the hunt for genes, Ensembl should be very reliable. From the beginning, many genomic features other than predicted genes were included in Ensembl: different repeat classes, cytological bands, CpG island predic- tions, tRNA gene predictions, expressed sequence clusters from the UniGene database", + "Ostell/Spidey/ SSAHA at Sanger Institute http://www.sanger.ac.uk/Software/analysis/SSAHA/ human and mouse genomes, where there are large full-length cDNA collections to guide the hunt for genes, Ensembl should be very reliable. From the beginning, many genomic features other than predicted genes were included in Ensembl: different repeat classes, cytological bands, CpG island predic- tions, tRNA gene predictions, expressed sequence clusters from the UniGene database", + "Ostell/Spidey/ SSAHA at Sanger Institute http://www.sanger.ac.uk/Software/analysis/SSAHA/ human and mouse genomes, where there are large full-length cDNA collections to guide the hunt for genes, Ensembl should be very reliable. From the beginning, many genomic features other than predicted genes were included in Ensembl: different repeat classes, cytological bands, CpG island predic- tions, tRNA gene predictions, expressed sequence clusters from the UniGene database", + "comprehensive, powerful, flexible and interactive gene set enrichment analysis toolkit. Nucleic Acids Research ,45(W1), W130W137. [44] Zhang, B., Kirov, S., & Snoddy, J. (2005). WebGestalt: an integrated system for exploring gene sets in various biological contexts. Nucleic Acids Research ,33(Web Server issue), W741-8. [45] McLaren, W., Gil, L., Hunt, S. E., Riat, H. S., Ritchie, G. R. S., Thormann, A., Flicek, P ., et al. (2016). The ensembl variant effect predictor. Genome Biology ,17(1), 122." + ], + [ + "417 Use of Rat Genomics for Investigating the Metabolic Syndrome and phenotypic traits are available to the scientific community in databases, such as Ensembl ( http://www.ensembl.or g), the Rat Genome Database ( http://www.rgd.mcw.ed u), eQTL Explorer ( http://www. web.bioinformatics.ic.ac.uk/eqtlexplore r) or GeneNetwork ( http://www.genenetwork.or g). Additional online rat genetic resources have been recently reviewed by Twigger et al. (11).", + "Howard Jacob (Medical College of Wisconsin) discussed the Rat Genome Database disease portals, a platform for genetic and genomic research. Thereare 845 strains of rats, 573 of which are inbred,including substrains. Historically, biologists usingthe rat as a model have been disease focused,studying diseases, related phenotypes, pathways, and biological processes. The Rat Genome Database", + "10. Consortium STAR, Saar K, Beck A, Bihoreau MT, Birney E, Brocklebank D, Chen Y et al (2008) SNP and haplotype mapping for genetic analysis in the rat. Nat Genet 40:560566 11. Twigger SN, Pruitt KD, Fernndez-Surez XM, Karolchik D, Worley KC, Maglott DR et al (2008) What everybody should know about the rat genome and its online resources. Nat Genet 40:523527 12. Butcher LM, Beck S (2008) Future impact of integrated high-throughput methylome anal- yses on human health and disease. J Genet", + "for linkage analyses using new methods of efficient genotyping based on genechip microarrays (10). In addition, over 800,000 ESTs and 5,000 annotated rat gene sequences are available for functional analyses of candidate genes. Development of new methodologies for high throughput phenotyping, such as expres- sion profiling, are becoming routinely used. Most of these genetic 2. Recent Advances in Rat Genetics and Genomics", + "serves as a repository of all rat QTLs related to thedisease area as well as associated mouse and humanQTLs, strains used as disease models, phenotypedata, related references, expression data, genome-wide views of disease genes, and QLS via GViewer,comparative maps of disease-related regions, cus-tomization of data sets and download options, and analysis and visualization of function and cellular localization makeup of gene sets (http://www.rgd.mcw.edu/). ENU mutagenesis is now being done with rats.", + "3. Can data sharing in rodent phenotyping help with replicability? Laboratory mice and rats are the main mammalian models currently used for high-throughput genomic and behavior genetic research, and are employed primarily to explore and test gene function. This is con- sidered by some to be the great challenge facing biologists today (Collins et al., 2007 ). Rodent models are used extensively as part of preclinical development and testing of treatments for disease in hu-", + "Bioinformatics and Statistical Analysis R was used for basic analysis of phenotypic data. GeneNetwork (www.genenetwork.org) was used for correlation and genetic analyses. The original phenotypes published in this paper and all microarray data generated in these cohorts are available for public analysis or download using the GeneNetwork database (Species: Mouse, Group: BXD, Type: Adipose mRNA, Liver mRNA, or Muscle mRNA, then select the EPFL datasets). The three", + "[23]. Shimoyama M, De Pons J, Hayman GT, Laulederkind SJ, Liu W, Nigam R, Petri V , Smith JR, Tutaj M, Wang S-J, The Rat Genome Database 2015: genomic, phenotypic and environmental variations and disease, Nucleic acids research 43(D1) (2014) D743D750. [PubMed: 25355511] [24]. Dickinson ME, Flenniken AM, Ji X, Teboul L, Wong MD, White JK, Meehan TF, Weninger WJ, Westerberg H, Adissu H, High-throughput discovery of novel developmental phenotypes, Nature 537(7621) (2016) 508. [PubMed: 27626380]", + "database (dbSNP) build 130 to identify genes located inthe vicinity of selected SNPs. Homologues of the genes formouse and rat were identified using the NCBI's Homolo-Gene release 64. We included only those genes that wereevolutionarily conserved in three different species namelyhuman, mouse and rat. Analysis of microarray data", + "(data not shown). Therefore, it seems logical to position the rat field so themechanistic, disease-based research canbe integrated into the awesome power ofthe human and mouse genome projects. Progress of the Rat Genome Project Recognizing the usefulness of the rat as amodel system, NIH, led by the NationalHeart, Lung, and Blood Institute(NHLBI), has funded the Rat GenomeProject (RGP), the Rat Expressed Se-quence Tag (RGP EST) Project, and the Rat" + ], + [ + "were identied using the RGD (68). This resource provides infor-mation regarding the physiological trait studied, strain combina-tion used, associated linkage statistics, and the genomic coordi-nates of the pQTL region. For pQTL regions identied from RGD,the original data (Supplementary Table S3) were examined, and the99% condence interval [within the 2 logarithm of the odds (LOD)drop from the peak of linkage] was estimated. Cis-eQTLs were", + "RGCs. The discovery of this relationship may help inguiding studies that explore the disease mechanismsassociated with altered protein transport and foldingin RGCs. In glaucoma, the identication and conr-mation of these two proteins in RGC health and dis-ease holds great promise for the development ofmolecular targets to slow or reverse RGC damage, which, in turn, will preserve vision. Experimental procedures Human donor eyes Human donor eyes were collected in accordance with the", + "RGCs. The discovery of this relationship may help inguiding studies that explore the disease mechanismsassociated with altered protein transport and foldingin RGCs. In glaucoma, the identication and conr-mation of these two proteins in RGC health and dis-ease holds great promise for the development ofmolecular targets to slow or reverse RGC damage, which, in turn, will preserve vision. Experimental procedures Human donor eyes Human donor eyes were collected in accordance with the", + "(http://www.cbil.upenn.edu/PaGE/). All microarray platforms and image-analysis software are supported. In addition, RAD is being used for CGH, ChIP , and SAGE data. RAD can produce MAGE-ML les for export of data to other databases or software packages. RAD is part of a more general Genomics Unied Schema, which provides a platform to integrate gene and transcript data from a variety of organisms. Advantages RAD is a scalable, Web-accessible database that can accommodate data from sev-", + "(http://www.cbil.upenn.edu/PaGE/). All microarray platforms and image-analysis software are supported. In addition, RAD is being used for CGH, ChIP , and SAGE data. RAD can produce MAGE-ML les for export of data to other databases or software packages. RAD is part of a more general Genomics Unied Schema, which provides a platform to integrate gene and transcript data from a variety of organisms. Advantages RAD is a scalable, Web-accessible database that can accommodate data from sev-", + "(http://www.cbil.upenn.edu/PaGE/). All microarray platforms and image-analysis software are supported. In addition, RAD is being used for CGH, ChIP , and SAGE data. RAD can produce MAGE-ML les for export of data to other databases or software packages. RAD is part of a more general Genomics Unied Schema, which provides a platform to integrate gene and transcript data from a variety of organisms. Advantages RAD is a scalable, Web-accessible database that can accommodate data from sev-", + "differentiallysusceptibletodeath,withalpha-RGCsandintrinsicallyphotosensitiveRGCs (ipRGCs) being less sensitive to cell death than other RGC subtypes in a mouse model of glaucoma. Keywo rds: retinal ganglion cells, gene regulatory networks, transcription factors, recombinant inbred strain, subtypes INTRODUCTION Theretinalganglioncell(RGC)isthenaloutputneuronoftheretina,projectingthroughtheoptic nerve to the brain, where it targets a number of functionally distinct areas: for visual perception,", + "AG18245 (DG), NIAAA U01AA014425 (LL), and P20 DA021131 (RW). We thank Derek Rains, Gurjit Rai, Meifen Lu, Richard Cushing, Erich Brauer, and Alan Weatherford for their invaluable technical assistance. Abbreviations BrdU bromodeoxyuridine CV cresyl violet GF growth fraction LOD likelihood of the odds LRS likelihood ratio statistic NSCs neural stem cells OB olfactory bulb DG dentate gyrus QTL quantitative trait locus RI recombinant inbred RMS rostral migratory stream SGZ subgranular zone", + "Rdh10, Lrat,) whose biology functions are directly associated w ith the metabolism of retinoid. RGR (retinal G protein-coupled receptor, protein of Rgr ) is a protein that structurally resembles visual pigments and other G protein-coupled recepto rs. Light isomerizes 11- cis - into all-trans - retinal, triggering a conformational transition of the opsin molecule that initiates phototransduction . After bleaching all- trans -retinal leaves the opsin, and light sensitivity mu st be restored by", + "GeneNetwork system, we were able to define robust expression covariance signatures for RGCs and confirmed membership of Chrna6 within the RGC cell type of the retina using new array data sets and RT-PCR tracking through a progressive RGC loss mouse line. Chrna6 can be added as reliable biomarker for RGCs and RGC loss secondary to glaucoma. It is important to note that in addition to providing evidence for Chrna6 expression as a" + ], + [ + "[3] and KEGG [4] all allow a list of genes to be crossed with biological functions and genetic networks, including metabolic, signalling or other regulation pathways. Basic statistical analysis (e.g., [5,6]) can then determine whether a pathway is over-represented in the list, and whether it is over-activated or under-activated. However, one can argue that introducing information on the path- way at this point in the analysis process sacrifices some statistical power to the simplicity of the approach. For", + "Sidiropoulos, K., Viteri, G., Sevilla, C., Jupe, S., Webber, M., Orlic -Milacic, M., et al. (2017). Reactome enhanced pathway visualization. Bioinformatics 33, 3461 3467. doi:10.1093/bioinformatics/btx441. Slenter, D. N., Kutmon, M., Hanspers, K., Riutta, A., Windsor, J., Nunes, N., et al. (2018). WikiPathways: a multifaceted pathway database bri dging metabolomics to other omics research. Nucleic Acids Res. 46, D661 D667. doi:10.1093/nar/gkx1064.", + "Sidiropoulos, K., Viteri, G., Sevilla, C., Jupe, S., Webber, M., Orlic -Milacic, M., et al. (2017). Reactome enhanced pathway visualization. Bioinformatics 33, 3461 3467. doi:10.1093/bioinformatics/btx441. Slenter, D. N., Kutmon, M., Hanspers, K., Riutta, A., Windsor, J., Nunes, N., et al. (2018). WikiPathways: a multifaceted pathway database bri dging metabolomics to other omics research. Nucleic Acids Res. 46, D661 D667. doi:10.1093/nar/gkx1064.", + "analysis, we restrict the analysis to curated, peer-reviewedpathways based on experimental evidence, and pathways inferred via gene homology. We draw candidate pathways from the collections listed in Figure 6 (see also Supplementary Materials). KEGG [146] and HumanCyc [147] are primarily databases of metabolic pathways, and are unlikely to be relevant to someJoint Analysis of Variants and Pathways in Disease PLOS Genetics | www.plosgenetics.org 11 October 2013 | Volume 9 | Issue 10 | e1003770", + "textual interface, also linking out to the original articles. Analysing participating pathways is an important aspect of any gene s functional analysis strategy. In this view, REACTOME (http://www.reactome.org) [13] is a cross referenced, manually curated and peer reviewed pathway database. LitInspector (http://www.litinspector.org) [14]and NetPath (http://www.netpath.org/index.html) [15] allow one to access curated signal transduction related lit-", + "I, Babur O, Anwar N, Schultz N, Bader GD, Sander C (2011) Pathway Commons, a web resource for biological pathway data. Nucleic Acids Res 39(Database issue):D685D690. doi: 10.1093/nar/gkq1039 6. Baker EJ, Jay JJ, Bubier JA, Langston MA, Chesler EJ (2012) GeneWeaver: a web-based system for integrative functional genomics. Nucleic Acids Res 40(Database issue):D1067D1076. doi: 10.1093/nar/gkr968 7. Bubier JA, Phillips CA, Langston MA, Baker", + "67. Krmer, A., Green, J., Pollard, J. Jr. & Tugendreich, S. Causal analysis approaches in ingenuity pathway analysis. Bioinformatics 30, 523530 (2014). 68. Jassal, B. et al. The reactome pathway knowledgebase. Nucleic Acids Res. 48, D498D503 (2020). 69. Okonechnikov, K., Conesa, A. & Garca-Alcalde, F. Qualimap 2: advanced multi-sample quality control for high-throughput sequencing data. Bioinformatics 32, 292294 (2016).", + "Biocarta pathway maps www.biocarta.com BioGRID genetic and protein interaction database thebiogrid.org AnalysisPLINK processing and QC of genetic data sets pngu.mgh.harvard.edu/ purcell/plink Bioconductor processing and QC of expression data sets www.bioconductor.org DAVID gene ontology, pathway analysis david.abcc.ncifcrf.gov WebGestalt gene ontology, pathway analysis bioinfo.vanderbilt.edu/webgestalt Sage", + "2004; Gene Ontology Consortium, 2015; The Gene Ontology Consortium, 2019) , KEGG pathways (Kanehisa and Goto, 2000; Kanehisa et al., 2012) , Panther pathways (Mi et al., 2019a, 2019b) , Reactome pathways (Sidiropoulos et al., 2017; Jassal et al., 2020) , and Wikipathway pathways (Pico et al., 2008; Slenter et al., 2018) (Figure 31). As many different annotations as wanted can be chosen by clicking on the + icon ( Figure 31). Also note, that the user can", + "2004; Gene Ontology Consortium, 2015; The Gene Ontology Consortium, 2019) , KEGG pathways (Kanehisa and Goto, 2000; Kanehisa et al., 2012) , Panther pathways (Mi et al., 2019a, 2019b) , Reactome pathways (Sidiropoulos et al., 2017; Jassal et al., 2020) , and Wikipathway pathways (Pico et al., 2008; Slenter et al., 2018) (Figure 31). As many different annotations as wanted can be chosen by clicking on the + icon ( Figure 31). Also note, that the user can" + ], + [ + "the egg and the sperm. Such a process would result in genetic changes that will be copied into every cell of the future adult, including reproductive cells (Stock & Campbell, 2000), opening the door to irreversibly alter the human species. Inevitably, signifi cant self-disclosure and discussion challenges await families", + "phenomena such as mutations and gene conversion events) occur in relevant meioses leading up to the formation of the gametes (i.e., egg and sperm) which are combined during fertilization and the formation of zygotes. Thus, individuals inherit a patch- work of chromosomal segments from maternal and paternal chromosomes.", + "a fertilized egg is a complicated process that relies on controlling: which genes are active; whenthese genes activate; and for how long they are active. In broad terms, there are four ways that thiscontrol can be achieved: First, inside the sperm or egg, genes can be marked with small chemical tags that flag these genes", + "to be activated (or remain inactive) after fertilization, depending on whether the modification wasmade by the father (in the sperm) or the mother (in the egg); this process is known as imprinting. Second, the mother can alter the gene activity in her offspring via the placenta; this process is known as maternal effect. Third, instructions encoded within the embryos DNA can directly control if, andwhen, a nearby gene becomes activated; this is known as cis-regulation. Finally, similar instructions", + "(Figures 8 and 9). Two gametes (egg and sperm) ultimately join into a single cell, the zygote, which has the full comple-ment of 23 chromosome pairs restored. If all goes well, the zygote gives rise to a live offspring. The Mendel Laws: Segregation and Independent Assortment Both of the Mendel laws pertain directly to the process of meiosis. The first Mendel law, the law of segregation, states that each parent passes a randomly selected allele for a given", + "sex chromosome effects. (B)Soon after fertilization, male and female cells have sex-specic transcriptomes, epigenomes, and phenotypes (for example, male embryos grow faster than female embryos). At implantation, lineage determination begins and gene expression differences are reduced. Epigenetic marks, however, are less constrained and some are maintained, affecting gene expression, and phenotype later in development. Once specic lineages are established, differences in", + "the subset of that genetic information that is active. But how does the differentiation process begin? The key insight in resolving this conundrum came from fly genetics and was the realization that the egg is not a homogenous sack of protoplasm. The maternally-derived genes active in the fertilized egg are asymmetrically distributed such that at the first cell division each daughter cell receives a different complement of factors. Development continues as a", + "genes. An altered gene may be passed on to every cell that develops from it. The resulting features my help, harm, or have little or no effect on the offsprings success in its environment. (AAAS, pg. 109, 5B:9-12#4 ) 6. Heritable material: The information passed from parents to offspring is coded in DNA molecules (AAAS, pg 108, 5B:9-12#3) 7. Mutagens: Gene mutations can be caused by such things as radiation and chemicals. When they occur in sex cells, the mutations can be passed onto offspring; if they", + "or father (sperm cell). Each gamete has a set of 23 unpaired chromosomes. Two human gametes (egg and sperm) combine to create a cell (zygote) that contains the full human genome of 23 paired chromosomes.Genetic Information Nondiscrimination Act (GINA) US federal legislation that makes it unlawful to discriminate against individuals on the basis of their genetic profiles in regard to health insurance and employment. These protections are intended to encourage Americans to take advantage of", + "spermatozoa: more than the sum of its parts? DNA, histones, pro - tamines and epigenetics. Reproduction 139:287301 Nilsson EE, Sadler-Riggleman I, Skinner MK (2018) Environmentally induced epigenetic transgenerational inheritance of disease. Envi-ron Epigenet 4:dvy016Pembrey M, Saffery R, Bygren LO, Network in Epigenetic Epide-" + ], + [ + "variation with cultural practices around lineage. In certain societies, individuals place greater importance on (and have greater knowledge about) one side of the family than another (unilineal descent). Thus, individuals in patrilineal groups trace relationships through males only so that your fathers brothers children are members of your family, but not your fathers sisters (Kottak, 2007 ). They are members of their husbands group or family. Efforts to create", + "maternal lineage membership with those who weredirectly genotyped. Based on these pedigree (matrilineal) relation-", + "in three-generation families, and read pair tracing DNMs with phased variants. In the former approach, we determined the parent of origin as in our previous analysis4. For example, if an offspring of the proband was a carrier of the DNM allele and had haplotype sharing to paternal chromosome of the proband, we assigned the mutation to the father. Meanwhile, if the offspring was not a DNM allele carrier, we would assign it to the maternal germline. We restricted the haplo -", + "Unlike the nuclear genome, which requires both paternal and maternal contributions, mtDNA is inherited solely from the maternal lineage. It is unclear what advantage a uniparental mtDNA transmission confers, but one possibil-ity is to minimize the number of distinct genomes to maxi-mize the efficiency of a multi-genomic system (Hill etal. 2019). In fact, humans have developed complex, redundant mechanisms to ensure uniparental inheritance of mtDNA (DeLuca and OFarrell 2012; Rojansky etal. 2016). Paternal", + "c) Mitochondrial DNA (maternal line testing) markers: mitochondrial DNA or mtDNA haploid is the maternally inherited mitochondrial genome (mtDNA) [ 44]. All children inherit mtDNA from their mother, with no admixture from the father. Like Y-line DNA, mtDNA is passed intact from one generation to the next but through maternal line. Mitochondrial DNA does not follow any surname. In fact, the surname changes in every generation when women marry. Polymorphisms of mtDNA", + "a family pedigree may be hampered if the participant is not familiar with her mothers relatives, but her mothers brothers children (her cousins) may be able to supplement her overall family history. Knowledge about the cultural system of unilineal descent avoids assuming the universality of bilateral descent. Cultural beliefs such as these also have implications in the conduct of genetic research in terms of confidentiality and autonomy (Benkendorf et al.,", + "225 three-generation families using haplotype sharing (Fig. 1c and Methods), 80.4% were found to be of paternal origin (Extended Data Fig. 1). Figure 1e shows a strong relationship between the number of paternal DNMs and the fathers age at conception (1.47 per year, 95% CI 1.341.59) and a weaker impact of the mothers age on the number of maternal DNMs (0.37 per year, 95% CI 0.300.45). The parental origin of all DNMs was also assessed by read pair", + "sistent with a maternal imprinting effect in familiesfrom France [18], the USA[10, 18, 21] (Figure 2; Table3) and Canada [27]. However, in a large family dataset from the UK, and in smaller data sets fromDenmark and Sardinia, the transmission of VNTRsusceptibility alleles is more pronounced frommothersthanfromfathers,andnowsignicantlysoinUK families (Figure 2; Table 3). Comparison of theresults from the USAwith those from the UK suggestthat unexplained inter-population differences in thisparent-of-origin", + "started with the largest matrilineage and worked down the list. Theparticipants selected for mtDNA sequencing were selected inde-pendent of their cognitive or dementia status. 274 matrilineageswere represented by this dataset. As a result, the sequencedmitochondrial genomes also represent as many different majormitochondrial haplogroups and clusters as possible (Table 1).Selection was made blind to case-control status. 287 samples weresent to Family Tree DNA (www.familytreedna.com) for Sangersequencing of", + "genetics-based population divergence studies. Am J Phys Anthropol 128(2):415 423.22. Helgason A, Hrafnkelsson B, Gulcher JR, War d R, Stefnsson K (2003) A populationwide coalescent analysis of Icelandic matrilineal and patrilineal genealogies: Evidence for a faster evolutionary rate of mtDNA lineages than Y chromosomes. Am J Hum Genet 72(6): 1370 1388. 23. Amster G, Sella G (2015) Life history effects on the molecular clock of autosomes and sex chromosomes. Proc Natl Acad Sci USA 113(6):1588 1593." + ], + [ + "the DNA, i.e. the whole genome. During replication the two strands of themother cell DNA are separated, and new nucleotides are put together to maketwo double helices identical to the original one, see Figure 2.1. TAAGACCG AT T CTGGCCCGTGGC. . . . . . .. . ATTCTGGCTAAGACCG. . . . . . . . Figure 2.1: A DNA chain consists of two strands of complementary nucleotides. When DNA is replicated, two double chains identical to the original one are created.", + "the DNA, i.e. the whole genome. During replication the two strands of themother cell DNA are separated, and new nucleotides are put together to maketwo double helices identical to the original one, see Figure 2.1. TAAGACCG AT T CTGGCCCGTGGC. . . . . . .. . ATTCTGGCTAAGACCG. . . . . . . . Figure 2.1: A DNA chain consists of two strands of complementary nucleotides. When DNA is replicated, two double chains identical to the original one are created.", + "The mechanism to maintain the rDNA copy number The gene amplication mechanism that counteracts recombination-mediated loss of rDNA copies is well studied in budding yeast [ 6,11]. During the S phase of the cell cycle, replication starts from replication origins, and isinhibited at the replication fork barrier site (RFB) by the function of the fork blocking protein, Fob1 (Fig. 3)[12]. This inhibition works as a recombinational hotspot toinduce amplication for copy number recovery as follow;", + "S and G2 when the DNA is replicated, providing a pristine secondcopy of the sequence (sister chromatid) for aligning the breaks. Incontrast, the less-accurate end joining is most relevant in the G1phase of the cell cycle, when a second copy is not available 14. Finally, some single repair proteins directly revert certain injuries, such as O6-methylguanine methyltransferase, which removes O6-methyl guanine. This highly mutagenic lesion permits base", + "Replication", + "genotoxic agents and to guarantee faithfulchromosome duplication and transmission to the offspring. In addition to DNA damage repair, cells monitor replication to minimize er-rors of DNA synthesis. In eukaryotes, cell-cycle checkpoints guarantee coordination of DNA synthesis and DNA repair with cell division.Genome instability is mainly due to sporadic replication or repair errors but can also take place in response to developmental or environ-mental signals, as occurs in meiosis, and antigen", + "This section will explain how cells normally divide. It will also desc ribe how an unexpected change in the structure of DNA can sometimes cause harm to th e body. New tools to study genetic variations of common diseases and to identify genetic variatio ns common to specific diseases will also be presented. Cell Division Humans grow and develop as a result of a process called cell division. There are two types of cell division mitosis and meiosis.", + "and replicated (by a templating mechanism). Each DNA molecule in a cell forms a single chromosome. (NRC, pg. 185, 9-12:C2#1) 4. Genes as information for building proteins: The genetic information in DNA molecules provide the instructions on assembling protein molecules. The code is virtually the same for all life forms. (AAAS, pg. 114, 5C:9-12#4 ) 5. Molecular nature of genes and mutations: Genes are segments of DNA molecules. Inserting, deleting, or substituting DNA segments can alter genes. An altered", + "When a replication fork encounters a DNA adduct, cells induce DNA damage toler-ance mechanisms that allow completion of replication. Adducts can be bypassed by postreplicative repair via translesion poly-merases (either faithful or error-prone) or via error-free template switching using the sister chromatid (64, 105). Postreplicativerepair guarantees genome stability by allowing completion of replication (albeit at the expense", + "genome instability in part because of the unique structureof replicating DNA molecules (Figure 2). When single-strand lesions occur in non-replicating molecules of DNA,the overall integrity of chromosomes is maintained byhydrogen bond base pairing on either side of these lesionsuntil they are repaired (Figure 2A). In contrast to non-replicating DNA, replicating DNA at replication forkscontains unwound, highly recombinogenic single-strandedtemplate DNA before this DNA is converted to double-strand DNA by" + ], + [ + "neered nucleases, CRISPR-Cas9 tools have accelerated the pace of genomic research by permitting highly efficient knockouts or edits of virtually any gene in cells or model organisms. Multiple CRISPR-Cas9based clinical trials are in progress or are expected to begin soon. Although Cas9- engineered cells havent yet dem - onstrated efficacy at scale, early trial results suggest that such cells are stable and dont cause acute adverse reactions in humans. Long-term safety is yet to be de -", + "stageissetforCRISPRtomakeanenormousimpactongenomic screening and thus scientic discovery in the coming years, and recent demonstrations of this system have shown great promise (Shalem etal., 2015 ).However,a number of technical challenges must be addressed in order to maximize the benet of this technology. In this review, we will discuss current applications of CRISPR in functional genomics and provide a perspective on futuredevelopmentsinthisarea. CRISPR/Cas9 Genome Editing", + "heralding the age of genome editing. Furthermore, Cas9 or guide RNAs have been linked to various effector proteins to enable targeted gene regulation 12,13 and epigenome modifications14,15. It is worth noting, however, that many of these feats had been demonstrated previously using other nucleases or DNA-binding proteins 1,16. In this Perspective, I shed light on early genome editing platforms that laid the groundwork for the widespread use of CRISPRCas9 in research and medicine (Fig. 1 ).", + "cline- or Tet-regulated Cas9 system. Current CRISPR/Cas systems arefrom Streptococcus pyogenes ,Streptococcus thermophilus ,Neisseria meningitides and Treponema denticola .2.5. Caveats of advanced genome editing tools Off-target effects . The DNA-binding domains of ZFNs and TALENs need to be very speci c for the target site to avoid off-target cleavage, which results in unwanted mutations and potentially cytotoxic effects [27]. CRISPR/Cas9 is also known to generate off-target alterations,", + "CRISPR/CAS9 HOLDS SIGNIFICANT PROMISE FOR THE DEVELOPMENT OFNEW AD MODELS AND PRECISIONTARGETED AD THERAPY Clustered regularly interspaced short palindromic repeat (CRISPR)-Cas nucleases have revolutionizedthe eld of gene editing and have tremendous appli-cation in the eld of molecular medicine [98102].Despite a signicant surge in CRISPR/Cas9-mediated genome editing in various disease models,the progress in the eld of AD has lagged behindsubstantially. We believe that genome editing can sig-", + "81. Applications for CRISPRCas9 beyond genome editing", + "CRISPR-Cas9 can be used to in - duce genome edits by creating targeted DNA breaks that trigger site-specific DNA repair. In next- generation formats, it can also control the transcriptional output of genes or alter genome se - quences using a process of nu - cleotide base editing that does not require repair of DNA breaks. As these technolo - gies continue to mature, it will become increasingly possible to alter cellular genomes efficiently and accurately. Coming on the heels of engi -", + "on transcriptional interfere nce (CRISPRi) and activation (CRISPRa) have also harnessed Cas9-based technologies for use in genome-wide studies ( 59,174). In addition, recent improvements in lentiviral library generation and propagation,as well as large-scale DNA and RNA synthesis, have allowedCRISPR-Cas9 technology to be exploited across multiple modelplatforms ( 59,175178). nCas9 The CRISPR-Cas9 system can tolerate certain mismatches to the DNA target since the required gRNAs are short. A disadvantage,", + "13. Kleinstiver BP, etal. High-fidelity CRISPRCas9 nucleases with no detectable genome-wide off-target effects. Nature. 2016;529:4905. 14. Brane A, Tollefsbol T.Targeting telomeres and telomerase: studies in aging and disease uti- lizing CRISPR/Cas9 technology. Cells. 2019;8:186. 15. Wang H, etal. One-step generation of mice carrying mutations in multiple genes by CRISPR/ Cas-mediated genome engineering. Cell. 2013;153:9108.", + "Since its discovery, CRISPR-Cas technology has ignited a biological revolu- tion by providing a highly versatile platform that allows fast and efficient genome editing in an ever-growing list of organisms. In this chapter we will first describe the most recent advances in the development and application of the CRISPR-Cas platform in biomedical research. Then we will discuss the most recent and notable basic research applications of this technology in the study of the molecular causes" + ], + [ + "While most of the Y chromosome does not undergo recombination, the recombination rate of the X chromosomeis slower than that of the autosomes. This has important consequences on the detection of significant QTLs. For a comprehensive view of these issues, see(43). 9.Probe hybridization artifacts When several probes are available for the same gene, it is not uncommon to observe a difference in the mapping results", + "8 QTL Mapping Allelic variation exists among natural populations and inbred strains, and this is reflective of the segregation of quantitative tr ait loci (QTLs) [96]. QTLs are stretches of DNA that are closely linked to genes that underlie a phenotype of interest. QTL analysis has been proven to be an invaluable tool to help unravel heritable traits, by enabling researchers to map different quantitative traits back to the genomic location involved in the regulation of these phenotypes.", + "8 QTL Mapping Allelic variation exists among natural populations and inbred strains, and this is reflective of the segregation of quantitative tr ait loci (QTLs) [96]. QTLs are stretches of DNA that are closely linked to genes that underlie a phenotype of interest. QTL analysis has been proven to be an invaluable tool to help unravel heritable traits, by enabling researchers to map different quantitative traits back to the genomic location involved in the regulation of these phenotypes.", + "The basic pr emise of QTL an alysis is simple (Ph illips and Belknap, 2002 ) . First, one must meas ure a speci c phen otype within a popul ation. Next, the population must be genotyped at a hundred or more marker loci186 Boehm II et al.", + "genes underlying QTLs in animals and plants (see for example Shirley et al 2004,Korstanje & Paigen 2002, Fridman et al 2004). I should also point out, though, that even in a single QTL region isolated in a congenic strain, it is possible that there is more than one allele that aects the phenotype. So, you have a fair pointabout the challenges and complexities of QTL analysis. Koolhaas: There are dierent questions underlying both approaches. The QTL", + "genes underlying QTLs in animals and plants (see for example Shirley et al 2004,Korstanje & Paigen 2002, Fridman et al 2004). I should also point out, though, that even in a single QTL region isolated in a congenic strain, it is possible that there is more than one allele that aects the phenotype. So, you have a fair pointabout the challenges and complexities of QTL analysis. Koolhaas: There are dierent questions underlying both approaches. The QTL", + "through analysis of line crosses, quantitative trait loci (QTL) mapping, and verification of candidate genes with quantitative complementation tests or genetic engineering (e.g.,McGuire and Tully 1987; Chandra et al. 2001; Dierick and Greenspan 2006; Edwardset al. 2006). They can also be used to study the underlying physiological, neural, andmolecular mechanisms of the differences in behavior between selected and controllines, or between divergently selected lines.", + "through analysis of line crosses, quantitative trait loci (QTL) mapping, and verification of candidate genes with quantitative complementation tests or genetic engineering (e.g.,McGuire and Tully 1987; Chandra et al. 2001; Dierick and Greenspan 2006; Edwardset al. 2006). They can also be used to study the underlying physiological, neural, andmolecular mechanisms of the differences in behavior between selected and controllines, or between divergently selected lines.", + "genetic background. Gene identification of QTL should be distinguished from identification of the quanti- tative trait nucleotide (QTN). The latter is a daunting task, since SNPs are so frequent. Final proof for a QTN in mice would require placing a genomic segment containing theputative QTN from a donor mouse strain on the background of another strain using homologous recombination and reproducing the phenotype of the donor strain.", + "measuring correlations between genetic markers and phenotypic traits in a population. Individuals are scored for their phenotype for a particular trait, and their genotype at a marker. If there is a differ- ence in mean phenotype between those individuals with one geno- type at a particular locus compared with the other, than we can infer that there is a QTL linked to that marker [ 40 , 153 ]. 2.3 Analysis and QTL MappingDavid G. Ashbrook and Reinmar Hager" + ], + [ + "for people to exchange data easily over the Web. Two other notable developments are BioMart and GBrowse. The BioMart project (http://www.biomart.org/), originally a spin-off from Ensembl, offers a generic data management system that allows complex searches of biological data such as sequence annotation. The GBrowse project (Stein et al. , 2002; http://www.gmod.org/) has produced a generic genome browser that can be customized to organize, display and query a new genome scale data set. These", + "for people to exchange data easily over the Web. Two other notable developments are BioMart and GBrowse. The BioMart project (http://www.biomart.org/), originally a spin-off from Ensembl, offers a generic data management system that allows complex searches of biological data such as sequence annotation. The GBrowse project (Stein et al. , 2002; http://www.gmod.org/) has produced a generic genome browser that can be customized to organize, display and query a new genome scale data set. These", + "for people to exchange data easily over the Web. Two other notable developments are BioMart and GBrowse. The BioMart project (http://www.biomart.org/), originally a spin-off from Ensembl, offers a generic data management system that allows complex searches of biological data such as sequence annotation. The GBrowse project (Stein et al. , 2002; http://www.gmod.org/) has produced a generic genome browser that can be customized to organize, display and query a new genome scale data set. These", + "(http://ensembl.org/ ) and the National Center for Biotechnology Information (NCBI) (http://www.ncbi.nlm.nih.gov/ ) all provide portals to the most current, and archived public assemblies. These sites also provide means of searching the assem- blies, such as BLAST (Altschul et al. , 1997), BLAT (Kent, 2002) and SSAHA (Ning et al. , 2001) as well as precomputed annotation for the genome assemblies that can be readily incorporated into comparative genomic analyses.", + "(http://ensembl.org/ ) and the National Center for Biotechnology Information (NCBI) (http://www.ncbi.nlm.nih.gov/ ) all provide portals to the most current, and archived public assemblies. These sites also provide means of searching the assem- blies, such as BLAST (Altschul et al. , 1997), BLAT (Kent, 2002) and SSAHA (Ning et al. , 2001) as well as precomputed annotation for the genome assemblies that can be readily incorporated into comparative genomic analyses.", + "(http://ensembl.org/ ) and the National Center for Biotechnology Information (NCBI) (http://www.ncbi.nlm.nih.gov/ ) all provide portals to the most current, and archived public assemblies. These sites also provide means of searching the assem- blies, such as BLAST (Altschul et al. , 1997), BLAT (Kent, 2002) and SSAHA (Ning et al. , 2001) as well as precomputed annotation for the genome assemblies that can be readily incorporated into comparative genomic analyses.", + "resources. We present an easy-to-adopt module that weaves together several important bioin-formatic tools so students can grasp how these tools are used in answering research questions.Students integrate information gathered from websites dealing with anatomy (Mouse BrainLibrary), quantitative trait locus analysis (WebQTL from GeneNetwork), bioinformatics and geneexpression analyses (University of California, Santa Cruz Genome Browser, National Center forBiotechnology Informations Entrez Gene, and the", + "References Altman RB. Building successful biological databases. Briefings in Bioinformatics. 2004; 5:45. [PubMed: 15153301] Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: Tool for the unification of biology. The Gene Ontology Consortium. Nature Genetics. 2000; 25:2529. [PubMed: 10802651] Ashish N, Ambite JL, Muslea M, Turner JA. Neuroscience data integration through mediation: an", + "Sequences, Protein Structures, Complete Genomes, Tax- onomy, Medical Genetics resources (see later), and others (see http://www.ncbi.nlm.nih.gov/Database/index.html for a complete listing of databases). Entrez PubMed provides access to full-text articles at journal websites and other related web resources, some of which are free to the public. This site also provides links to other molecular biology resources. The National Center for Biotechnology Information ( http://", + "Sequences, Protein Structures, Complete Genomes, Tax- onomy, Medical Genetics resources (see later), and others (see http://www.ncbi.nlm.nih.gov/Database/index.html for a complete listing of databases). Entrez PubMed provides access to full-text articles at journal websites and other related web resources, some of which are free to the public. This site also provides links to other molecular biology resources. The National Center for Biotechnology Information ( http://" + ], + [ + "supported by a signicant BLAST match to one or more expressed sequences or proteins. Ensembl also identies the positions of known human genes from public sequence database entries, usually using GENEWISE to predict their exon structures. The total set of Ensembl genes should therefore be a much more accurate reection of reality than ab initio predictions alone, but it is clear that some novel genes are missed (Hogenesch et al. , 2001). Of the many novel genes that are detected, some are", + "supported by a signicant BLAST match to one or more expressed sequences or proteins. Ensembl also identies the positions of known human genes from public sequence database entries, usually using GENEWISE to predict their exon structures. The total set of Ensembl genes should therefore be a much more accurate reection of reality than ab initio predictions alone, but it is clear that some novel genes are missed (Hogenesch et al. , 2001). Of the many novel genes that are detected, some are", + "supported by a signicant BLAST match to one or more expressed sequences or proteins. Ensembl also identies the positions of known human genes from public sequence database entries, usually using GENEWISE to predict their exon structures. The total set of Ensembl genes should therefore be a much more accurate reection of reality than ab initio predictions alone, but it is clear that some novel genes are missed (Hogenesch et al. , 2001). Of the many novel genes that are detected, some are", + "Ostell/Spidey/ SSAHA at Sanger Institute http://www.sanger.ac.uk/Software/analysis/SSAHA/ human and mouse genomes, where there are large full-length cDNA collections to guide the hunt for genes, Ensembl should be very reliable. From the beginning, many genomic features other than predicted genes were included in Ensembl: different repeat classes, cytological bands, CpG island predic- tions, tRNA gene predictions, expressed sequence clusters from the UniGene database", + "Ostell/Spidey/ SSAHA at Sanger Institute http://www.sanger.ac.uk/Software/analysis/SSAHA/ human and mouse genomes, where there are large full-length cDNA collections to guide the hunt for genes, Ensembl should be very reliable. From the beginning, many genomic features other than predicted genes were included in Ensembl: different repeat classes, cytological bands, CpG island predic- tions, tRNA gene predictions, expressed sequence clusters from the UniGene database", + "Ostell/Spidey/ SSAHA at Sanger Institute http://www.sanger.ac.uk/Software/analysis/SSAHA/ human and mouse genomes, where there are large full-length cDNA collections to guide the hunt for genes, Ensembl should be very reliable. From the beginning, many genomic features other than predicted genes were included in Ensembl: different repeat classes, cytological bands, CpG island predic- tions, tRNA gene predictions, expressed sequence clusters from the UniGene database", + "database, which aims to compile a non-redundant, curated data set representing current knowledge of known genes (Wheeler et al. , 2002; http://www.ncbi.nlm.nih. gov/entrez/query.fcgi?db=gene). Like the Ensembl protocol, many Acembly- predicted structures (the NCBI estimate 42 per cent) are incomplete. These struc- tures can be displayed alongside ab initio gene models, Ensembl-predicted genes, and matching UniGene clusters to allow users to make their own conclusions about the likeliest gene structure.", + "database, which aims to compile a non-redundant, curated data set representing current knowledge of known genes (Wheeler et al. , 2002; http://www.ncbi.nlm.nih. gov/entrez/query.fcgi?db=gene). Like the Ensembl protocol, many Acembly- predicted structures (the NCBI estimate 42 per cent) are incomplete. These struc- tures can be displayed alongside ab initio gene models, Ensembl-predicted genes, and matching UniGene clusters to allow users to make their own conclusions about the likeliest gene structure.", + "database, which aims to compile a non-redundant, curated data set representing current knowledge of known genes (Wheeler et al. , 2002; http://www.ncbi.nlm.nih. gov/entrez/query.fcgi?db=gene). Like the Ensembl protocol, many Acembly- predicted structures (the NCBI estimate 42 per cent) are incomplete. These struc- tures can be displayed alongside ab initio gene models, Ensembl-predicted genes, and matching UniGene clusters to allow users to make their own conclusions about the likeliest gene structure.", + "populations as Ensembl reects the progress of the International Haplotype Map Project (Thorisson et al. , 2005). More speculative data, such as GENSCAN-predicted exons that have not been incorporated into Ensembl-conrmed genes, may also be viewed. This means that the display can be used as a workbench for the user to develop personalized an- notation. For example, one may discover novel exons by nding GENSCAN exon predictions which coincide with good matches to a fragment of the draft mouse" + ], + [ + "traditional QTL mapping and GWASsapproaches can benefit from systems-biological approaches by filling in criticalinformation about the molecular phenotypes that stand between DNAvariation and complex disease (figure5). The incorporation of data fromhigh-throughput molecular profilingtechnologies, such as gene expressionmicroarrays, can better define a diseaseby identifying groups of genes thatrespond to or covary with disease-associated traits. Network analysis ofdisease-associated genes allows", + "knowledge of the true QTL location (Doss et al. 2005 ), which can be used to empirically estimate the power of aGWAS performed at a similar scale (Hao et al. 2008 ; Schadt et al. 2008 ). A GWAS on its own does little more than establish correlations between changes in DNA at agiven locus and changes in a disease trait of interest, with respect to populations of interest. Further, these studies on", + "genotypes. Since association studies allow for a mu ch finer mapping of the QTL than that obtained with linkage analysis, there is a trade-off to consider between power and resolution when choosing the mapping stra tegy. Genome-wide associa- tion studies (GWAS) have naturally been used to per form genetical genomics studies in humans [18, 24-27] and are emerging in m odel organisms studies using outbred populations [28]. 8.2.2 Combining studies", + "genetically also mapped to the same genomic location. In order to locate the positions of genes that are responsible for a certain trait, GWAS can be conducted. GWAS is a quan- titative approach to analyze the association of whole genome DNA polymorphisms and a phe- notypic trait, thereby localizing the genes un- derlining the trait. Genome-Wide Association Studies (GWAS) GWAS is a holistic whole-genome approach to robustly determine the association of DNA polymorphisms with correlated phenotypic", + "(PHMs) use principles of MR embedded within a Bayesian hierarchical model to detect interac-tions between regulatory elements [ 98]. Furthermore, GWAS is often integrated with the QTL analysis despite the fact that many GWAS loci are not strong eQTL loci [ 56]. GWAS-eQTL colocalization methods, including RTC [ 145], QTLMacth [ 158], Sherlock [ 159], and coloc [ 160], are based on the concept that disease-", + "association studies (GWAS) or linkage studies (Enoch 2013). QTL mapping studies historically had very low resolution,and many have been performed using populations for whichlimited genetic data exist. Publications of gene expressionstudies typically highlight a few interesting gene centered results, but the bulk of information is rejected due to concern", + "pairs that include many genes within the seg- ment. On the other hand, GWAS may point to several or even many genomic locations for the trait of interest, complicating further functional analysis. Analysis of Quantitative Trait Loci (QTL) QTL analysis reveals statistically signicant linkage between phenotypes and genotypes, thereby providing explanation for the genetic basis of variation in complex traits (Falconer and Mackay, 1996; Lynch and Walsh, 1998). In a sense, QTL analysis can be viewed as incom-", + "QTL mapping QTL mapping using GeneNetwork has been described in detail elsewhere ( Mulligan et al., 2017 ). However, in brief, quantitative trait loci (QTLs) are segments of the genome affecting a particular phenotype ( Falconer and Mackay, 1996 ). QTL mapping, identifying", + "3. Genetic Mapping Methods Several statistical approaches have been developed for genome-wide linkage analysis of traditional phenotypes. The same approaches can be used to map eQTLs. These approaches range from single marker tests ( t-test, ANOVA, and simple regression analysis) to multiple locus mapping methods. The only major difference is that eQTL studies involve tens of thousands of expression traits and require fast algorithms. Since an eQTL study tests for", + "plete GWAS analysis with limited number of markers that does not cover the entire genome. As such, if one or few QTLs are found, there may be more QTLs in the genome to be dis- covered. More importantly, in the absence of closely linked markers in the genomic regions containing signicant QTLs for the trait, the most signicant genes responsible for the trait can be missed. However, because of historical reasons such as the lack of genome-wide mark- ers, or the lack of funding, QTL analysis is still" + ], + [ + "candidate genes. These candidate genes must then betested for a causal link to the phenotype. A good starting point would be sequencing the cDNA of strong candidate genes to identify amino acid polymorphisms and testingfor mRNA and protein expression differences in target tissues of the original strains used to detect the QTL. Sequencing and expression studies will rene the list ofcandidate genes that can then be tested rigorously for proof of cause and effect. The nal proof of a causal gene", + "candidate genes. These candidate genes must then betested for a causal link to the phenotype. A good starting point would be sequencing the cDNA of strong candidate genes to identify amino acid polymorphisms and testingfor mRNA and protein expression differences in target tissues of the original strains used to detect the QTL. Sequencing and expression studies will rene the list ofcandidate genes that can then be tested rigorously for proof of cause and effect. The nal proof of a causal gene", + "do you identify the responsible gene within a QTL that you have identified? Generally, one starts by performing a strain survey to find two parental inbred strains that have a markedly different trait. One can now look up many different traits of inbred mice online at the Mouse Phenome Database ( http://phenome. jax.org/pub-cgi/phenome/mpdcgi?rtn=docs/home ). However, the trait you may want to study may not be present in wild type mice, so you may want to cross", + "used to test the hypothesis at locus-specific sig-nificance (LRS 12). In doing so, an additional 7 cQTLs are observed as consistent in both diets(Fig. 2I, red number). Solving QTLs: Finding the quantitative trait gene For cis-QTLs, the causal factors can be quickly identified: With few exceptions, they will be driv-en by variants within the gene itself or imme-diately adjacent. For trans-QTLs, mQTLs, and cQTLs, the identification of the causal quanti-", + "data is to find a quantitative trait locus, or QTL. A QTL (http://gn1.genenetwork.org/glossary.html#Q ) is an area on a chromosome that can contain one or many genes, that is linked to a change in phenotype. After a QTL that is responsible for the apparent variation in phenotype has been identified , one can start stu dying the genes within that locus to identify the likely causal gene . Once the data is normalized appropriately (in our case, no normalization was required) , the QTL", + "candidate genes that are expressed in tissues likely to inuence the traits of interest(Su et al 2004). These candidate genes are then sequenced in the two parental inbred strains looking for sequence dierences in coding or regulatory regions. After ne mapping the QTL interval and shortening the list of plausible candidate polymorphisms, the major challenge remains /C246 proving denitively which nucleotide polymorphism underlies the QTL. The most direct proof", + "because these strains have been genotyped at more than 14,000 markers, including single nucleotide polymorphisms (SNP). Hundreds of genes may lie within a QTL interval, so identifying the underlying genes requires complementary methods. One method is to use BXD gene expression data (a public resource at www.genenetwork.org) to screen for genes within the QTL interval whose expression correlates with the trait of interest [23].", + "candidate genes that are expressed in tissues likely to inuence the traits of interest(Su et al 2004). These candidate genes are then sequenced in the two parental inbred strains looking for sequence dierences in coding or regulatory regions. After ne mapping the QTL interval and shortening the list of plausible candidate polymorphisms, the major challenge remains /C246 proving denitively which nucleotide polymorphism underlies the QTL. The most direct proof", + "curate approaches to identify various types of QTL according to their molecular features, in par- ticular to control various confounding factors, such as dietary habit and population structure. Fine Mapping of Causal Variants and Causal Genes Despite the identi cation of large numbers of QTLs, it remains challenging to establish causal", + "to date, only a small handful of genes have been definitively identified for complex traits. Our own efforts to identify a causal gene were stymied by the compound nature of QTLs and the high gene density in Qrr1 , and in Vol8a . Furthermore, it is now becoming clear that in addition to the canonical candidate genes, there are multiple spliced variants, microRNAs, and epigenetic factors to be considered. With what appears to be an increasingly complex genom ic landscape, it is now all" + ], + [ + "that accounts for the significant difference. One explanationis a contribution of the Y chromosome from the B strain. Sincethe cross was non-reciprocal all F2 mice carried the B strain Ychromosome. Thus, males carrying Chr X B QTL alleles andthe B Y chromosome differ in two ways from females carry-ing Chr X A alleles (or AB but B alleles are recessive) and noY chromosome, but in only one way from males carrying ChrX A/J QTL alleles because they share the B Y chromosome.However, pursuit of the identity of", + "women comprises 2 X chromosomes and in men 1 X and 1 Y chromosome (Figure 2). For each chromosome pair, 1 chro- mosome was inherited from the mother and 1 from the father. The full set of chromosomes is collectively called the genome. The human genome is largely contained within the nucleus of each cell, where it is separated from the rest of the cell functions. However, a small amount of DNA exists outside the nucleus in the mitochondria and is considered to be part of the human genome.", + "betweenmalesandfemalesisthesexchromosomes.MaleshaveanXYgenotypeand femaleshaveanXXgenotype.TheXisamuchlargerchromosome,165.5x106bpsvs. 16.0x106bps,withapproximately30timesmoregenesthantheYchromosome.To compensateforthelargernumberofgenes,andtoensurefemalesdonothaveover expressionofgenesresidingontheXchromosome,oneoftheXchromosomesis inactivated(7).TheXinactivationoccursearlyindevelopmentandisarandomprocess. Onlyasmallportionoftheinactivatedchromosomeretainstranscriptionalability.This", + "mammals. Instead of a dominant gene for maleness on the Y chromosome, it is the ratioof X chromosomes to autosomes that determines gender. The 2:2 ratio of XX femalesand the 1:2 ratio in XY males produce different ratios of regulatory proteins encoded byX-linked and autosomal genes. Those regulatory genes in turn cause transcripts of theregulatory Sex-lethal (Sxl) gene to be spliced differently in males and females, which be-", + "mammals. Instead of a dominant gene for maleness on the Y chromosome, it is the ratioof X chromosomes to autosomes that determines gender. The 2:2 ratio of XX femalesand the 1:2 ratio in XY males produce different ratios of regulatory proteins encoded byX-linked and autosomal genes. Those regulatory genes in turn cause transcripts of theregulatory Sex-lethal (Sxl) gene to be spliced differently in males and females, which be-", + "gins the process of sexual differentiation. A fly with two X chromosomes can thereforecarry a Y and still be a fertile female, leading to a paradoxical sex chromosome system inwhich males inherit X chromosomes from their fathers (figure 16.13). Rice and Chippindale (2001) used a combination of these genetic techniques to test", + "gins the process of sexual differentiation. A fly with two X chromosomes can thereforecarry a Y and still be a fertile female, leading to a paradoxical sex chromosome system inwhich males inherit X chromosomes from their fathers (figure 16.13). Rice and Chippindale (2001) used a combination of these genetic techniques to test", + "ity on the X chromosome compared to the other five strains(Figure 2B ). Compared to females, males had a deficiency of heterozygous X-linked SNP loci ( Supplementary Figure S2 ), which was expected because males are hemizygous. The resid-ual X-linked heterozygous SNPs in males could be due to mis-assembled autosomal contigs on the X chromosome, multiplecopies on the X, or homology between X and autosomalsequences. Chromosome XAutosomesProportion of SNP lociHomozygous SNPs Heterozygous SNPs", + "sex chromosome Y chromosome: One of the two sex chromosomes, X and Y. See also; X chromosome, sex chromosome", + "one Y chromosome. Human chromosomes are typically displayed pictorially in a karyotype, as shown in Figure 9, arranged according to length and position of the centromere (i.e., the most con-stricted area of a chromosome). The ends of the chromosomesare called telomeres. Most human karyotypes look identicalbecause they are constructed from cells arrested in the phaseof the cell cycle when chromosomes are most condensed. During this phase of the cell cyc le, allelic differences cannot be detected." + ], + [ + "While most of the Y chromosome does not undergo recombination, the recombination rate of the X chromosomeis slower than that of the autosomes. This has important consequences on the detection of significant QTLs. For a comprehensive view of these issues, see(43). 9.Probe hybridization artifacts When several probes are available for the same gene, it is not uncommon to observe a difference in the mapping results", + "8 QTL Mapping Allelic variation exists among natural populations and inbred strains, and this is reflective of the segregation of quantitative tr ait loci (QTLs) [96]. QTLs are stretches of DNA that are closely linked to genes that underlie a phenotype of interest. QTL analysis has been proven to be an invaluable tool to help unravel heritable traits, by enabling researchers to map different quantitative traits back to the genomic location involved in the regulation of these phenotypes.", + "8 QTL Mapping Allelic variation exists among natural populations and inbred strains, and this is reflective of the segregation of quantitative tr ait loci (QTLs) [96]. QTLs are stretches of DNA that are closely linked to genes that underlie a phenotype of interest. QTL analysis has been proven to be an invaluable tool to help unravel heritable traits, by enabling researchers to map different quantitative traits back to the genomic location involved in the regulation of these phenotypes.", + "genetic background. Gene identification of QTL should be distinguished from identification of the quanti- tative trait nucleotide (QTN). The latter is a daunting task, since SNPs are so frequent. Final proof for a QTN in mice would require placing a genomic segment containing theputative QTN from a donor mouse strain on the background of another strain using homologous recombination and reproducing the phenotype of the donor strain.", + "The basic pr emise of QTL an alysis is simple (Ph illips and Belknap, 2002 ) . First, one must meas ure a speci c phen otype within a popul ation. Next, the population must be genotyped at a hundred or more marker loci186 Boehm II et al.", + "verify the difference, and the data were then ana-lyzed by the QTL detection method of Belknap et al.(1997) based on allele frequency differences betweenthe two lines. When a difference was confirmed,individual genotypes and individual behavioral re-sponses to MA were used to estimate the position ofthe bQTL using the interval mapping methods as implemented in R/qtl (Broman et al. 2003). The lat-", + "X axis depicts 19 autosomes and X chromoso me. The Y axis is the likelihood ratio statistic from a single QTL model. Two QTLs, on chromosomes 1 and 11, are significant at a mu ltiple test corrected permut ation threshold as shown. Chromosome 1 and 11 likeli hood ratio statistic plots Figure 2 Chromosome 1 and 11 likelih ood ratio statistic plots . Interval mapping plots of chromosomes 1 and 11, showing more detail of Figure 1. 2 LOD support inte rvals are shown in Mb on the X axis.", + "genes underlying QTLs in animals and plants (see for example Shirley et al 2004,Korstanje & Paigen 2002, Fridman et al 2004). I should also point out, though, that even in a single QTL region isolated in a congenic strain, it is possible that there is more than one allele that aects the phenotype. So, you have a fair pointabout the challenges and complexities of QTL analysis. Koolhaas: There are dierent questions underlying both approaches. The QTL", + "genes underlying QTLs in animals and plants (see for example Shirley et al 2004,Korstanje & Paigen 2002, Fridman et al 2004). I should also point out, though, that even in a single QTL region isolated in a congenic strain, it is possible that there is more than one allele that aects the phenotype. So, you have a fair pointabout the challenges and complexities of QTL analysis. Koolhaas: There are dierent questions underlying both approaches. The QTL", + "model at the QTL assumes that the original lines arexed for different alleles although genes can besegregating elsewhere. Hence, it is possible to combineinformation about the QTL across families. The assumption of xation at the QTL can be tested by" + ], + [ + "phenomena such as mutations and gene conversion events) occur in relevant meioses leading up to the formation of the gametes (i.e., egg and sperm) which are combined during fertilization and the formation of zygotes. Thus, individuals inherit a patch- work of chromosomal segments from maternal and paternal chromosomes.", + "the egg and the sperm. Such a process would result in genetic changes that will be copied into every cell of the future adult, including reproductive cells (Stock & Campbell, 2000), opening the door to irreversibly alter the human species. Inevitably, signifi cant self-disclosure and discussion challenges await families", + "a fertilized egg is a complicated process that relies on controlling: which genes are active; whenthese genes activate; and for how long they are active. In broad terms, there are four ways that thiscontrol can be achieved: First, inside the sperm or egg, genes can be marked with small chemical tags that flag these genes", + "to be activated (or remain inactive) after fertilization, depending on whether the modification wasmade by the father (in the sperm) or the mother (in the egg); this process is known as imprinting. Second, the mother can alter the gene activity in her offspring via the placenta; this process is known as maternal effect. Third, instructions encoded within the embryos DNA can directly control if, andwhen, a nearby gene becomes activated; this is known as cis-regulation. Finally, similar instructions", + "(Figures 8 and 9). Two gametes (egg and sperm) ultimately join into a single cell, the zygote, which has the full comple-ment of 23 chromosome pairs restored. If all goes well, the zygote gives rise to a live offspring. The Mendel Laws: Segregation and Independent Assortment Both of the Mendel laws pertain directly to the process of meiosis. The first Mendel law, the law of segregation, states that each parent passes a randomly selected allele for a given", + "the subset of that genetic information that is active. But how does the differentiation process begin? The key insight in resolving this conundrum came from fly genetics and was the realization that the egg is not a homogenous sack of protoplasm. The maternally-derived genes active in the fertilized egg are asymmetrically distributed such that at the first cell division each daughter cell receives a different complement of factors. Development continues as a", + "sex chromosome effects. (B)Soon after fertilization, male and female cells have sex-specic transcriptomes, epigenomes, and phenotypes (for example, male embryos grow faster than female embryos). At implantation, lineage determination begins and gene expression differences are reduced. Epigenetic marks, however, are less constrained and some are maintained, affecting gene expression, and phenotype later in development. Once specic lineages are established, differences in", + "genes. An altered gene may be passed on to every cell that develops from it. The resulting features my help, harm, or have little or no effect on the offsprings success in its environment. (AAAS, pg. 109, 5B:9-12#4 ) 6. Heritable material: The information passed from parents to offspring is coded in DNA molecules (AAAS, pg 108, 5B:9-12#3) 7. Mutagens: Gene mutations can be caused by such things as radiation and chemicals. When they occur in sex cells, the mutations can be passed onto offspring; if they", + "or father (sperm cell). Each gamete has a set of 23 unpaired chromosomes. Two human gametes (egg and sperm) combine to create a cell (zygote) that contains the full human genome of 23 paired chromosomes.Genetic Information Nondiscrimination Act (GINA) US federal legislation that makes it unlawful to discriminate against individuals on the basis of their genetic profiles in regard to health insurance and employment. These protections are intended to encourage Americans to take advantage of", + "spermatozoa: more than the sum of its parts? DNA, histones, pro - tamines and epigenetics. Reproduction 139:287301 Nilsson EE, Sadler-Riggleman I, Skinner MK (2018) Environmentally induced epigenetic transgenerational inheritance of disease. Envi-ron Epigenet 4:dvy016Pembrey M, Saffery R, Bygren LO, Network in Epigenetic Epide-" + ], + [ + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "addition to this, GeneNetwork can be used to study correlations between traits and to perform data mining in genomic regions containing candidates for quantitative trait genes (Hoffman et al., 2011). All datasets in GeneNetwork are linked to a materials and methods information page that summarizes experimental details relating to the dataset. Databases within GeneNetwork include the transcriptome database, the BXD published", + "publication, and links to the dataset database and to the published paper (4C). There is also an option to add this trait to your collection by pressing the Add button (4D), or to view this trait in an ear lier version of GeneNetwork, GN1 (4E).", + "Bayesian inference of species networks from multilocus sequence data. Mol. Biol. Evol. 35, 504517 (2018). 167. Flouri, T ., Jiao, X., Rannala, B. & Yang, Z. A Bayesian implementation of the multispecies coalescent model with introgression for phylogenomic analysis. Mol. Biol. Evol. 37, 12111223 (2020). 168. Kubatko, L. in Handbook of Statistical Genomics (eds Balding, D., Moltke, I. & Marioni, J.) 219245 (Wiley, 2019). 169. Rannala, B., Edwards, S., Leach, A. D. & Yang, Z.", + "subnetworks GeneNetwork (www.genenetwork.org) is a depository of data- sets and tools for use in complex systems biology approaches in order to generate or predict higher order gene function ( 23, 24 ).", + "on different cross types, such as F 2crosses (B6BTBRF2, B6D2F2, BH/HB F2, CastB6/B6Cast F2, B6JxB6N F2), butalso on more complex outbred crosses such as the HS, the CC, and the Hybrid Mouse Diversity Panel. Recently, data from other species has also been integrated into GeneNet- work (human, rat, monkey, fruit ies, and others) to facilitate the translational research of results into other species. To this end, GeneNetwork provides many tools for the analysis of", + "GeneNetwork (www.genenetwork.org). The web -based software further allows extraction of sets of", + "Phenotypes Database attheGeneNetwork (www.", + "Phenotypes Database attheGeneNetwork (www.", + "Phenotypes Database attheGeneNetwork (www." + ], + [ + "genes that are responsible for obesity-associated diabetes. By the generation of subcongenic lines of a QTL, if pos- sible starting with chromosome substitution strains, thensmall critical regions that harbor the gene(s) in question can be identied with certainty. Sequence analysis and mRNA proling together with gene targeting in-vitro andin-vivo may lead to a solid chain of evidence linking sequence differences with altered molecular, cellular, and", + "tensive nondiabetic families, the QTLs on chromosomes 8q24 and 7q11, which are located in regions previouslyidentied as harboring type 2 diabetesassociated genes,may govern insulin sensitivity and insulin secretion in thepresence of insulin resistance before development of overttype 2 diabetes. Follow-up ne-scale mapping aroundthese loci and well-designed candidate gene studies, inparticular, are strongly encouraged. ACKNOWLEDGMENTS", + "studies used the QTL approach for statistical analysis of genotypes and phenotypes measured in the crosses. The concept of genetic dissection of diabetes into quantitative endophenotypes was introduced and resulted in the detection of genetic loci responsible for the control of fasting glycemia [39,42] , fasting insulinemia [39,43] , glucose tolerance [39,41,42] , insulin secretion induced by glucose or arginine [39], body weight [39,41,44] , adiposity [39], b-", + "indicating that risk factors exist on both genetic back- grounds [ 29]. QTL mapping studies indicate that these murine metabolic traits have a complex genetic architec- ture that is not dominated by any single allele [ 2931], much like humans [ 32,33]. Prior work identied candidate genes on Chr 13 that might underlie diabetes-related traits, including RASA1, Nnt, andPSK1. RASA1 show strong sequence differences between B6 and D2 strains [ 34]. Rasche et al. [ 35] reported that", + "genetic background [4]. Linkage analyses have shown that several quantitative trait loci interact with each other and with the environment to elicit obesity syndromes that are potentially diabetic. Several recent genome-wide associa- tion studies have identified novel candidate genes for T2DM but the effect of these variants on disease suscepti- bility is generally low, with odds ratios mostly around 1.5 [5-11]. Multiple studies on the transcriptome level have been per-", + "(2011). 7. Steinthorsdottir, V. et al. Identification of low-frequency and rare sequence variants associated with elevated or reduced risk of type 2 diabetes. Nat. Genet. 46, 294298 (2014).8. Ma, R. C. et al. Genome-wide association study in a Chinese population identifies a susceptibility locus for type 2 diabetes at 7q32 near PAX4. Diabetologia 56, 12911305 (2013). 9. Huyghe, J. R. et al. Exome array analysis identifies new loci and low-frequency", + "nificant QTL, strongly associated with body weight (Galli et al.1996; Gauguier et al. 1996). Moreover, Gauguier and colleagues(1996) mapped a QTL linked to postprandial insulin secretion intheregionofChr4wherewedetectedasuggestiveQTL.DifferentNIDDM models (obese OLETF rats and lean GK rats) may carryalleles conferring NIDDM susceptibility in the same genes. Thecombined results imply the possibility of common genetic factorsunderlyingNIDDMinhumans,notwithstandingthehighdegreeofgenetic heterogeneity in human", + "data indicates that variants regulating islet gene transcription influence type 2 diabetes(T2D) predisposition and glucose homeostasis. However, the specific genes through whichthese regulatory variants act remain poorly characterized. We generated expression quanti-tative trait locus (eQTL) data in 118 human islet samples using RNA-sequencing and high-density genotyping. We identified fourteen loci at which cis-exon-eQTL signals overlapped", + "linkage analysis assists in the identication of possiblegene-gene interactions and that 5q11-q13 and 7q32together constitute a signicant susceptibility factorfor type 1 diabetes. Diabetes 53:15841591, 2004Type 1 diabetes is a common multifactorial dis- ease characterized by autoimmune destructionof the insulin-producing /H9252-cells in the endocrine pancreas, resulting in deranged metabolic ho-", + "model for common forms of NIDDM in humans associ-ated with obesity. This study identies the location of amajor QTL and additional independent QTLs contrib-uting to development of hyperglycemia in TH malemice. We have also elucidated genegene interactionsbetween QTLs in the development of NIDDM, detect-ing new QTLs that reveal their signicant effects onlywhen they interact with other QTLs. This complexinheritance pattern associated with genegene inter-actions may be of prime importance in" + ], + [ + "T. I., de Bakker, P . I. et al (2006). TCF7L2", + "single nucleotide polymorphisms in TCF7L2 are reproduc-ibly associated with type 2 diabetes and reduce the insulinresponse to glucose in nondiabetic individuals. Diabetes55:28902895 135. Cauchi S, Meyre D, Dina C, Choquet H, Samson C, Gallina S, Balkau B, Charpentier G, Pattou F, StetsyukV, Scharfmann R, Staels B, Fru hbeck G, Froguel P 2006 Transcription factor TCF7L2 genetic study in the Frenchpopulation: expression in human /H9252-cells and adipose tissue", + "rs7903146 and rs12255372 in intron 3 of the TCF7L2 gene [20], associated with a ~45% increase in Type 2 diabetes risk per allele. As such, the TCF7L2 locus presently repre- sents the strongest known genetic determinant of Type 2diabetes. Risk allele carriers show impaired insulin produc-tion [21] and b-cell dysfunction in vitro [22]. TCF7L2 (previously referred to as TCF-4) is a high-mobility group box-containing transcription factor involved in Wingless-type MMTV integration site (Wnt)", + "et al. Variant of transcription factor 7-like 2 (TCF7L2) gene confers risk of type 2 diabetes. Nat Genet . 2006;38:320-23. Sladek R, Rocheleau G, Rung J, Dina C, Shen L, Serre D, et al. A genome- [9] wide association study identifies novel risk loci for type 2 diabetes. Nature . 2007;445:881-85. Kirchhoff K, Machicao F, Haupt A, Schafer SA, Tschritter O, Staiger H, et al. [10] Polymorphisms in the TCF7L2, CDKAL1 and SLC30A8 genes are associated", + "transcription factor 7-like 2 ( TCF7L2 ) gene confers risk of type 2 diabetes. Nat Genet. 2006; 38:320323. [PubMed: 16415884] 172. Gloyn AL, Noordam K, Willemsen MA, Ellard S, Lam WW, et al. Insights into the biochemical and genetic basis of glucokinase activation from naturally occurring hypoglycemia mutations. Diabetes. 2003; 52:24332440. [PubMed: 12941786] 173. Pearson ER, Donnelly LA, Kimber C, Whitley A, Doney AS, et al. Variation in TCF7L2", + "L. Mechanisms by which common variants in the TCF7L2 gene increase risk of type 2 diabetes. J Clin Invest 2007; 117: 2155-2163 [PMID: 17671651 DOI: 10.1172/JCI30706] 164 Gloyn AL , Braun M, Rorsman P. Type 2 diabetes susceptibility gene TCF7L2 and its role in beta-cell function. Diabetes 2009; 58: 800-802 [PMID: 19336690 DOI: 10.2337/db09-0099] 165 da Silva Xavier G , Loder MK, McDonald A, Tarasov AI, Carzaniga R, Kronenberger K, Barg S, Rutter GA. TCF7L2 regulates late", + "tion. Although the disease progression results from aninterplay of environmental factors and genetic predisposi- tion, in recent years TCF7L2 gene has been considered the strongest genetic determinant for the risk of developingT2DM [ 24,19,20]. The gene encodes a transcription factor of the canonical Wnt signaling pathway, expressed in several tissues, known to have developmental roles indetermining cell fate, survival, proliferation and movement [9]. Wnt signaling plays an important role also in B-cell", + "transcription factor 7-like 2 (TCF7L2) gene confers risk of type 2diabetes. Nat Genet 38:320 3231422 Diabetologia (2007) 50:1418 1422", + "genes which also play a significant role in the risk and pathogenesis of the disease[158,159]. The association of TCF7L2 gene variants with type 2 diabetes and its mechanism of action received special attention by several investigators[161,162]. Over expression of the protein was shown to decrease the sensitivity of beta islet cells to secrete insulin[163,164] and was more precisely involved in the regulation of secretary granule fusion that constitute a late event in insulin secretion", + "Muggeo M, Stoico V, Negri C, Pignatti PF, Bonora E, Bonadonna RC (2011) Variants and haplotypes of TCF7L2 are associatedwithb-cell function in patients with newly diagnosed type 2 diabetes: the Verona Newly Diagnosed Type 2 Diabetes Study (VNDS) 1. J Clin Endocrinol Metab 96(2):E389E393 13. Grundy SM, Cleeman JI, Merz CN, Brewer HB Jr, Clark LT, Hunninghake DB, Pasternak RC, Smith SC Jr, Stone NJ, National Heart, Lung, and Blood Institute, American College of Cardiol-" + ], + [ + "phenomena such as mutations and gene conversion events) occur in relevant meioses leading up to the formation of the gametes (i.e., egg and sperm) which are combined during fertilization and the formation of zygotes. Thus, individuals inherit a patch- work of chromosomal segments from maternal and paternal chromosomes.", + "the egg and the sperm. Such a process would result in genetic changes that will be copied into every cell of the future adult, including reproductive cells (Stock & Campbell, 2000), opening the door to irreversibly alter the human species. Inevitably, signifi cant self-disclosure and discussion challenges await families", + "a fertilized egg is a complicated process that relies on controlling: which genes are active; whenthese genes activate; and for how long they are active. In broad terms, there are four ways that thiscontrol can be achieved: First, inside the sperm or egg, genes can be marked with small chemical tags that flag these genes", + "(Figures 8 and 9). Two gametes (egg and sperm) ultimately join into a single cell, the zygote, which has the full comple-ment of 23 chromosome pairs restored. If all goes well, the zygote gives rise to a live offspring. The Mendel Laws: Segregation and Independent Assortment Both of the Mendel laws pertain directly to the process of meiosis. The first Mendel law, the law of segregation, states that each parent passes a randomly selected allele for a given", + "to be activated (or remain inactive) after fertilization, depending on whether the modification wasmade by the father (in the sperm) or the mother (in the egg); this process is known as imprinting. Second, the mother can alter the gene activity in her offspring via the placenta; this process is known as maternal effect. Third, instructions encoded within the embryos DNA can directly control if, andwhen, a nearby gene becomes activated; this is known as cis-regulation. Finally, similar instructions", + "the subset of that genetic information that is active. But how does the differentiation process begin? The key insight in resolving this conundrum came from fly genetics and was the realization that the egg is not a homogenous sack of protoplasm. The maternally-derived genes active in the fertilized egg are asymmetrically distributed such that at the first cell division each daughter cell receives a different complement of factors. Development continues as a", + "genes. An altered gene may be passed on to every cell that develops from it. The resulting features my help, harm, or have little or no effect on the offsprings success in its environment. (AAAS, pg. 109, 5B:9-12#4 ) 6. Heritable material: The information passed from parents to offspring is coded in DNA molecules (AAAS, pg 108, 5B:9-12#3) 7. Mutagens: Gene mutations can be caused by such things as radiation and chemicals. When they occur in sex cells, the mutations can be passed onto offspring; if they", + "sex chromosome effects. (B)Soon after fertilization, male and female cells have sex-specic transcriptomes, epigenomes, and phenotypes (for example, male embryos grow faster than female embryos). At implantation, lineage determination begins and gene expression differences are reduced. Epigenetic marks, however, are less constrained and some are maintained, affecting gene expression, and phenotype later in development. Once specic lineages are established, differences in", + "or father (sperm cell). Each gamete has a set of 23 unpaired chromosomes. Two human gametes (egg and sperm) combine to create a cell (zygote) that contains the full human genome of 23 paired chromosomes.Genetic Information Nondiscrimination Act (GINA) US federal legislation that makes it unlawful to discriminate against individuals on the basis of their genetic profiles in regard to health insurance and employment. These protections are intended to encourage Americans to take advantage of", + "Proponents of the evo-devo view rightly point out that evolution occurs through changes in the development of traits, which may or may not have changes in DNA as their root cause. The processes that produce traits occur during development and involve more than just genes. All animals begin life as a fertilized egg, a single cell containing mitochondria and other organelles, and enough maternally derived RNA and proteins to kick start development and" + ], + [ + "promoters ,regulatory proteins and their binding sites, ribosomal binding sites terminators ,et. RegulonDB contains both documentation and prediction objects. In addition it is linked with Swiss -prot, with microarray databases for analysis and visualization of microarray experiments.[5] WIT The WIT (What Is There) (http://wit.mcs.anl.gov/WIT2/) is a comparable computational system for analysis of sequenced genomes and generation of metabolic", + "promoters ,regulatory proteins and their binding sites, ribosomal binding sites terminators ,et. RegulonDB contains both documentation and prediction objects. In addition it is linked with Swiss -prot, with microarray databases for analysis and visualization of microarray experiments.[5] WIT The WIT (What Is There) (http://wit.mcs.anl.gov/WIT2/) is a comparable computational system for analysis of sequenced genomes and generation of metabolic", + "promoters ,regulatory proteins and their binding sites, ribosomal binding sites terminators ,et. RegulonDB contains both documentation and prediction objects. In addition it is linked with Swiss -prot, with microarray databases for analysis and visualization of microarray experiments.[5] WIT The WIT (What Is There) (http://wit.mcs.anl.gov/WIT2/) is a comparable computational system for analysis of sequenced genomes and generation of metabolic", + "173. Griffey, R. H.; Greig, M. J.; Haoyun, A.; Sasmor, H.; Manalili, S. Targeted Site-Specific Gas-Phase Cleavage of Oligoribonucleotides. Application in Mass Spectrometry-Based Identification of Ligand Binding Sites. J. Am. Chem. Soc. 1999, 121, 474475. 174. Hanson, C. L.; Fucini, P.; Ilag, L. L.; Nierhaus, K. H.; Robinson, C. V. Dissociation of Intact Escherichia coli Ribosomes in a Mass Spectrome- terEvidence for Conformational Change in a Ribosome Elongation", + "or chloramphenicol Immobilized targetDissociation of ribosome and release of mRNA5Poly(AAA)3 mRNA Isolation of mRNART-PCRdsDNA Mutagenesis by error-prone PCR Fig. 35.5. Schematic presentation of a ribosome display round. The gene of interest is transcribed from dsDNA into mRNA and translated into proteins by in vitro techniques. The ribosomes remain tethered to the mRNA by either cold shock or chloramphenicol. This step ensures that the genotype remains coupled to the phenotype. The proteins are", + "270 G.L. Sutphin e t a l. gene (Hinneb usch 2005 ). The m echanism of re gulation i s t hought to in v o lv e r el- ati v e a v a ilability of the l ar ge and small r ibosome s ub units. Specically , w hen 60Sribosomal sub unit l e v els a re lo w , ternary comple x e s containing initiation f actors and 40S ribosomal sub units are p roposed to more frequently scan through the", + "then used to develop synthetic gene networks with defined outputs, without significant post-hoc adjustments 22,4751. Alternatively, syn- thetic ribosome binding site (RBS) sequences can be used to optimize protein expression levels. Recently, Salis et al. 52 have developed a thermodynamic model for predicting the relative translational ini -", + "Philips, R.M., 2017 How Many Ribosomes Are in a Cell? [WWW Document]. URL http:// book.bionumbers.org/how-many-ribosomes-are-in-a-cell/ ((accessed 7.24.16) n.d.). R Core Team, 2014. R: a Language and Environment for Statistical Computing. R Founda- tion for Statistical Computing, Vienna, Austria. Sigurdson, A.J., Ha, M., Hauptmann, M., Bhatti, P., Sram, R.J., Beskid, O., Tawn, E.J.,", + "structure, and to find sites that are likely to be cleaved or modified; interac- tion or catalytic mechanisms can be simulated. Bioinformatic resources on the WWW range from the determination of the molecular weight to complex threading and three-dimensional (3D) prediction algorithms. A huge list of tools can be found on the ExPASy proteomic tools homepage (65). Because of the great variety of programs available, several of these single tools have", + "tiation rates for a protein with different upstream RBS sequences, a model that can also be used to rationally forward-engineer RBS sequences to give desired protein expression. In addition, protein degradation can be controlled by tagging proteins with degradation-targeting peptides that impart different degradation dynamics 53. By automating the construction and characterization of biomo-" + ], + [ + "phenomena such as mutations and gene conversion events) occur in relevant meioses leading up to the formation of the gametes (i.e., egg and sperm) which are combined during fertilization and the formation of zygotes. Thus, individuals inherit a patch- work of chromosomal segments from maternal and paternal chromosomes.", + "the egg and the sperm. Such a process would result in genetic changes that will be copied into every cell of the future adult, including reproductive cells (Stock & Campbell, 2000), opening the door to irreversibly alter the human species. Inevitably, signifi cant self-disclosure and discussion challenges await families", + "a fertilized egg is a complicated process that relies on controlling: which genes are active; whenthese genes activate; and for how long they are active. In broad terms, there are four ways that thiscontrol can be achieved: First, inside the sperm or egg, genes can be marked with small chemical tags that flag these genes", + "(Figures 8 and 9). Two gametes (egg and sperm) ultimately join into a single cell, the zygote, which has the full comple-ment of 23 chromosome pairs restored. If all goes well, the zygote gives rise to a live offspring. The Mendel Laws: Segregation and Independent Assortment Both of the Mendel laws pertain directly to the process of meiosis. The first Mendel law, the law of segregation, states that each parent passes a randomly selected allele for a given", + "to be activated (or remain inactive) after fertilization, depending on whether the modification wasmade by the father (in the sperm) or the mother (in the egg); this process is known as imprinting. Second, the mother can alter the gene activity in her offspring via the placenta; this process is known as maternal effect. Third, instructions encoded within the embryos DNA can directly control if, andwhen, a nearby gene becomes activated; this is known as cis-regulation. Finally, similar instructions", + "the subset of that genetic information that is active. But how does the differentiation process begin? The key insight in resolving this conundrum came from fly genetics and was the realization that the egg is not a homogenous sack of protoplasm. The maternally-derived genes active in the fertilized egg are asymmetrically distributed such that at the first cell division each daughter cell receives a different complement of factors. Development continues as a", + "genes. An altered gene may be passed on to every cell that develops from it. The resulting features my help, harm, or have little or no effect on the offsprings success in its environment. (AAAS, pg. 109, 5B:9-12#4 ) 6. Heritable material: The information passed from parents to offspring is coded in DNA molecules (AAAS, pg 108, 5B:9-12#3) 7. Mutagens: Gene mutations can be caused by such things as radiation and chemicals. When they occur in sex cells, the mutations can be passed onto offspring; if they", + "sex chromosome effects. (B)Soon after fertilization, male and female cells have sex-specic transcriptomes, epigenomes, and phenotypes (for example, male embryos grow faster than female embryos). At implantation, lineage determination begins and gene expression differences are reduced. Epigenetic marks, however, are less constrained and some are maintained, affecting gene expression, and phenotype later in development. Once specic lineages are established, differences in", + "or father (sperm cell). Each gamete has a set of 23 unpaired chromosomes. Two human gametes (egg and sperm) combine to create a cell (zygote) that contains the full human genome of 23 paired chromosomes.Genetic Information Nondiscrimination Act (GINA) US federal legislation that makes it unlawful to discriminate against individuals on the basis of their genetic profiles in regard to health insurance and employment. These protections are intended to encourage Americans to take advantage of", + "Proponents of the evo-devo view rightly point out that evolution occurs through changes in the development of traits, which may or may not have changes in DNA as their root cause. The processes that produce traits occur during development and involve more than just genes. All animals begin life as a fertilized egg, a single cell containing mitochondria and other organelles, and enough maternally derived RNA and proteins to kick start development and" + ], + [ + "for sequencing on existing short-read instrumentation, after which data are split by barcode and reassembled with the knowledge that fragments sharing barcodes Barcodes A series of known bases addedto a template molecule either through ligation or amplification. After sequencing, these barcodes can be used to identify which sample a particular read is derived from. Figure 5 | Real-time and synthetic long-read sequencing approaches.", + "sequence 2D read. Synthetic long-reads. Unlike true sequencing platforms, synthetic long-read technology relies on a system of barcoding to associate fragments that are sequenced on existing short-read sequencers61. These approaches par - tition large DNA fragments into either microtitre wells or an emulsion such that very few molecules exist in each partition. Within each partition the template frag - ments are sheared and barcoded. This approach allows", + "sequencing. This platform is used by the Illumina suite of platforms. 36. Dohm,J.C., Lottaz,C., Borodina,T . & Himmelbauer,H. Substantial biases in ultra-short read data sets from high-throughput DNA sequencing. Nucleic Acids Res. 36, e105 (2008). 37. Nakamura,K. etal. Sequence-specific error profile ofIllumina sequencers. Nucleic Acids Res. 39, e90 (2011). 38. Minoche,A.E., Dohm,J.C. & Himmelbauer,H. Evaluation of genomic high-throughput sequencing data generated on Illumina HiSeq and genome", + "Comparison of short-read platforms. Individual short- read sequencing platforms vary with respect to through - put, cost, error profile and read structure (TABLE1 ). Despite the existence of several NGS technology pro - viders, NGS research is increasingly being conducted within the Illumina suite of instruments21. Although this implies high confidence in their data, it also raises concerns about systemic biases derived from using a single sequencing approach2628. As a consequence, new", + "short-read sequencing. arXiv, arXiv:1203.3907v2, https://arxiv.org/abs/ 12073907 . Garrison, E., Sire n, J., Novak, A.M., Hickey, G., Eizenga, J.M., Dawson, E.T., Jones, W., Garg, S., Markello, C., Lin, M.F., et al. (2018). Variation graph toolkit improves read mapping by representing genetic variation in the reference. Nat. Biotechnol. 36, 875879 . Giambartolomei, C., Vukcevic, D., Schadt, E.E., Franke, L., Hingorani, A.D.,", + "or transcriptomic structure53. Long-read sequencing Overview. It has become apparent that genomes are highly complex with many long repetitive elements, copy number alterations and structural variations that are relevant to evolution, adaptation and disease5456. However, many of these complex elements are so long that short-read paired-end technologies are insufficient to resolve them. Long-read sequencing delivers reads in excess of several kilobases, allowing for the resolution of", + "these large structural features. Such long reads can span complex or repetitive regions with a single continuous read, thus eliminating ambiguity in the positions or size of genomic elements. Long reads can also be useful for transcriptomic research, as they are capable of span - ning entire mRNA transcripts, allowing researchers to identify the precise connectivity of exons and discern geneisoforms. Currently, there are two main types of long-read tech -", + "nologies: single-molecule real-time sequencing approaches and synthetic approaches that rely on existing short- read technologies to construct long reads insilico . The single-molecule approaches differ from short-read approaches in that they do not rely on a clonal popula - tion of amplified DNA fragments to generate detectable Figure 2 | Sequencing by ligation methods. a | SOLiD sequencing. Following cluster generation or bead deposition onto a slide, fragments are sequenced by ligation, in", + "Tools for alignment-free analyses of sequencing data The vast majority of next-generation sequencing experiments in mouse have read alignment to a reference genome as their first step. However, the primary data from any sequencing experiment are the reads themselves. Recognition that the raw reads are information-rich has led to the development of alignment-free algorithms for error correction (among many others, Chaisson and Pevzner 2008 ), abundance estimation ( Patro et al. 2014 ), and de novo", + "(right). Sequencing adaptors (depicted by short red bars and short purple bars) are subsequently ligated to each cDNA fragment (green lines) and short sequence reads (single end or paired ends) from each cDNA are generated using high-throughput sequencing technology. The resulting sequence reads [short lines beneath the genome sequence with three genes shown (fat blue bars)] are aligned with the reference genome to" + ], + [ + "When reliable prior knowledge exists about the variant composition in a pan-genome (typi- cally obtained via read-to-reference mapping), there are computational tools that can transform a linear reference sequence and a set of variant calls into graphs (18).This approach bypasses the computationallyexpensiveall-versus-allalignmentstepalongwiththeuncertaintiesofsubsequent graph construction, but the trade-off is increased reference bias and a potentially incomplete", + "(Karolchik et al. 2014 )] and Ensembl ( Flicek et al. 2013 ). Use of a single haploid reference sequence as an anchor for all studies of genetic variation in mouse offers many practical advantages. But the dependency on a reference genome requires several assumptions about the nature of genetic variation which may be violated in practicethe strongest of which is that of genomic collinearity (i.e., conserved marker order) between strains. We consider the", + "for at least 500 ancestrally diverse humans. This resource willalso provide a set of highly accurate genomes that can be used as a benchmarking dataset to improve short-read analysis tools. Even more importantly, these genomes allow completelynew designs for more effective short-read analysis strategiesthat overcome many of the limitations described above. Transitioning to a pan-genome reference will require develop-", + "2018;562(7726):203-209. http://doi.org/10.1038/s41586-018-0579-z 110. Li R, Li Y, Zheng H, et al. Building the sequence map of the human pan-genome. Nat Biotechnol . 2010;28(1):57-63. http://doi.org/10. 1038/nbt.1596 111. Vernikos G, Medini D, Riley DR, Tettelin H. Ten years of pan- genome analyses. Curr Opin Microbiol . 2015;23:148-154. http:// doi.org/10.1016/j.mib.2014.11.016 112. Miga KH, Wang T. The need for a human pangenome reference sequence. Annu Rev Genomics Hum Genet . 2021;22:81-102. http://", + "Whilemostpan-genomesconstructedtodateareprimarilygene-basedbecauseoftherelative easeofcomparingandcategorizingdiscreteunitsdefinedbytranscriptionandtranslation,theim- portanceofnoncodingandrepetitivesequencesisunquestionable.Itwouldthereforebeextremely powerfultodefineacomprehensivesequence-basedpan-genomethatincludesinformationabout therelativepositionofallsequences.Unfortunately,interpretingnoncodingsequencevariationischallenging.Indeed,evenforclassesofnoncodingsequencesofknownimportance,e.g.,promot-", + "assessment will improve our understanding of the reference to better assemble and interpret future genome sequences. We have previously developed a method to assess the risk of a patient for 55 diseases using a quantitative human disease -SNP association database, and showed that we could suggest useful and clinical relevant information using his personal genome sequence (16). Here, we queried the reference genome sequence against our databa se and identified 3,556 disease -susceptib ility", + "The shortcomings of a single, linear reference genome per species are well appreciated, and richer reference datastructures are an active area of research (Church et al. 2015 ). An alternative is de novo assembly of the genomes of commonly used strains. The Sanger Mouse GenomesProject is using a combination of long-insert jumping libraries and optical mapping to build de novo assemblies", + "undertake comprehensive and powerful explorations rather than being confined to testing hypoth - eses focused on candidate path - ways. With the completion of the first reference sequence of the human genome,3 attention shifted from searching for genes to dis - covering their functions. System - atic genetic mapping in families and populations helped scientists pinpoint the genetic variants that contribute to human disease.", + "points, nding statistical associations, modeling and run ning predic- tors, or constructing and pruning networks of detected rela tions. In the following paragraphs I will explore these opportunities in detail. 1.4.1 Population reference genomes Genomes are relatively similar between individuals, there fore, instead of assembling the complete sequence for each person, we only de termine points of DNA variation compared to a reference genome. Subs equently,", + "having a reference genome for a related specie s certainly makes the process easier. The availability of long-read sequences vastly improves our ability to assemble new genomes, and new technologies, such as PacBio and Nanopore, are now able to give reads between 100-1000 kilobases, an order of m agnitude longer than current Illumina sequencing (Shendure et al. 2017). Combining these new technologies with traditional short read NGS will greatly improve our ability" + ], + [ + "al., 2012 ; Hindhorff, 2009; Barrett et al ., 2007 ). Recent efforts by the Encyclopedia of DNA elements (ENCODE) consortium, to characterise the human genome, have revealed that most of the non -coding part of the genome is not inactive but is associated with different forms of regulatory activity (ENCODE, 2012 ; Thurman, 2012 ). One important regulatory process that takes place within the genome is the (in-) activation of gene expression through the interaction", + "network of transcriptional regulators. Nature 403, 335338 (2000). 18. Gardner,T ., Cantor,C. & Collins,J. Construction of a genetic toggle switch in Escherichia coli. Nature 403, 339342 (2000). 19. Kauffman,S.A. Metabolic stability and epigenesis in randomly constructed genetic nets. J.Theor. Biol. 22, 437467 (1969). 20. Thomas,R. Boolean formalization of genetic control circuits. J.Theor. Biol. 42, 563585 (1973). REVIEWS NATURE REVIEWS | GENETICS ADV ANCE ONLINE PUBLICATION | 11", + "25 2.8 REGULATION OF GENE EXPRESSION Apart from the protein coding sequences, there are other biologically relevant nucleic acid sequences that play other important roles in the genome such as regulation of gene expression and maintenance of the chromatin structure (Pique -Regis et al., 2011). Regu lation of gene expression involves a process that leads to increase or decrease in the production of specific", + "expression is regulated at many levels, but gene transcription represents an essential and, in many cases, dominant point of control. Protein-coding genes are transcribed from promoters, which represent genomic regions that recruit basal transcrip- tion factors and RNA polymerase II. Physiological levels of gene expression and responses to internal and external signals require the actions of additional sequence-specific transcrip- tion factors that recruit nucleosome-remodeling complexes,", + "regulatory elements and variants thereof that may affect gene expression particularly through the binding of transcription factors (TFs) to DNA. The suggestion that the genetic determinants of complex diseases are perh aps better sought in problems associated with gene regulation is due to findings that many of the disease associated variants occur in non -coding DNA sequences within the genome (ENCODE, 2012; Schuab et", + "through multiple cell divisions at the transcriptio nal and epigenetic level need to be more 204 carefully examined and have evolved as an exciting area of research. 205 206 Epigenetics and transcriptional regulation 207 Regulation of gene expression relies on the ac cessibility of DNA to various transcription 208 factors, co-activators/co-repressors, and the transcriptional machinery. DNA is first wrapped 209", + "post-translationally, translationally, transcriptionally, or epigenetically (Lempradl et al, 2015; Zong et al, 2017) . It seems likely that these different layers of regulation can operate cooperatively on different time- scales . More permanent adaptations might be expected following persistent regulation on a more transient levelfor example, lowered transcriptional activity of a gene might follow a period of low functional activity of its protein. Elucidating the means of such", + "important component in the regulation of gene expression with between 10 and 20% of the transcriptome being regulated by DNA variation. 2. Technologies The study of DNA and its downstream effects is very much a technology driven process. Most of the rst screens looking at DNA changes in disease involved looking at segregation in fam- ilies because there were no reasonable technologies at the time", + "the cytosine and adenine nucleotides[31]. In addition, the c hromosomal structure of DNA can be decondensated by histone acetylatio n (trans- fer of acetyl groups to DNA organizational elements), makin g it more accessible for transcription[87]. The transcriptional ex pression of genes is further regulated by genetic variants themselves[7]. Fi nally, proteins form a complex network of interactions[265] that, in turn, a lso regulate gene expression[331].", + "eterogeneity and common, small effect genetic variants will be assessed. h D (c) Regulatory Signals: Co-regulation of genes via shared transcriptional networks provides the basis for context-dependent gene expression, an understanding of which is vital to the understanding of disease etiology and disease progression. In particular, transcription factors (TF) and their transcription factor binding sites (TFBS) provide a key component in the understanding of how co-regulation is achieved." + ], + [ + "3, 4 and 5 suggest previously unknown connections between traits. We next characterized pairs of traits within each group of traits (trait pairs) to show that the quality of these pairs is not lower than in existing methods. We focused on three main properties of trait pairs: the correlation among traits in a pair; the correlation between a trait pair and the", + "3, 4 and 5 suggest previously unknown connections between traits. We next characterized pairs of traits within each group of traits (trait pairs) to show that the quality of these pairs is not lower than in existing methods. We focused on three main properties of trait pairs: the correlation among traits in a pair; the correlation between a trait pair and the", + "3, 4 and 5 suggest previously unknown connections between traits. We next characterized pairs of traits within each group of traits (trait pairs) to show that the quality of these pairs is not lower than in existing methods. We focused on three main properties of trait pairs: the correlation among traits in a pair; the correlation between a trait pair and the", + "taxonomy of traits is that it allows researchers to turn theirattention to the ways temperament and personality traitsexpress themselves in daily life and to the fundamental pro-cesses underlying variations in these traits. In this section, we rst describe the traits and then review some of the mostinteresting current work on the psychological and evolutionaryunderpinnings of each trait. A more detailed description of thecomponents of these traits is found in Caspi and Shiner (2006).Because relatively less", + "ditions and related totraits ofinter est,often bycomparing two groups differing forthetrait. Darvasi (2003) states that thereisanundeclar eddispute among resear chers who study complex traits :::Onone side areclassical geneticists :::ontheother areproponents ofgene expr ession analysis :::.Darvasi goes ontooutline thepossible advantages ofcombining these techniques over and above either technique alone. Inaddition tobetter correlating ge-", + "three types of high-order organization of traits. (i) Groups of tightly related traits that share thesame transcripts mechanisms (modules 1, 2, 6, 7, 8, e.g., Figure 3 ). (ii) Groups of distinct traits that share the same transcripts mechanism, but not necessarily high correlations among them (modules 3, 4, 5, e.g., Figure 4 ). (iii) Different groups commonly have overlapping traits, but typically differ in their underlying mechanisms ( Figure 2B ).", + "three types of high-order organization of traits. (i) Groups of tightly related traits that share thesame transcripts mechanisms (modules 1, 2, 6, 7, 8, e.g., Figure 3 ). (ii) Groups of distinct traits that share the same transcripts mechanism, but not necessarily high correlations among them (modules 3, 4, 5, e.g., Figure 4 ). (iii) Different groups commonly have overlapping traits, but typically differ in their underlying mechanisms ( Figure 2B ).", + "three types of high-order organization of traits. (i) Groups of tightly related traits that share thesame transcripts mechanisms (modules 1, 2, 6, 7, 8, e.g., Figure 3 ). (ii) Groups of distinct traits that share the same transcripts mechanism, but not necessarily high correlations among them (modules 3, 4, 5, e.g., Figure 4 ). (iii) Different groups commonly have overlapping traits, but typically differ in their underlying mechanisms ( Figure 2B ).", + "of varying effect sizes (small to moderate), interact with each other across time to manifest as individual genotypic and phenotypic traits. These traits contribute to normal variation in human behavior. Yet, these trait variants also increase the susceptibility of a disorder or a condition for many others.", + "action will open a Correlation Plot page in which you can examine the relationship between the two traits. Look for linearity and outliers. 3.3.1. Selection and Saving Multiple Traits The list of traits on the Correlation Results page represents traits that may be related in some way. You may want to select a group of them for further analysis. For example, use the checkboxes to the left of each entry to check entries 1, 9, 10, 14, 16, 18, traits related to brain size. Click the Add to collection" + ], + [ + "ST, see [40,120122]). Such tools may also offer a way of incorporating GxE interactions, as multiple GWAS for the same trait in different environments can be treated as correlatedtraits [123]. As association data for a greater variety of populations, species, and traits becomes available, we view the methods described outhere as a productive way forward in developing a quantitativeframework to explore the genetic and phenotypic basis of local adaptation. Materials and Methods", + "has been achieved by quantitative trait loci mapping, admixture mapping and GW AS131, which have limited power to detect small-effect-size genes. Newer approaches map pleiotropy by simultaneously associating genomic loci with multiple traits 54 and can also detect epistatic interactions using machine learning algorithms 132.Detecting the genomic signatures of correlational selectionCorrelational selection could potentially be inferred from signatures of selective sweeps at loci under strong selection", + "pairs that include many genes within the seg- ment. On the other hand, GWAS may point to several or even many genomic locations for the trait of interest, complicating further functional analysis. Analysis of Quantitative Trait Loci (QTL) QTL analysis reveals statistically signicant linkage between phenotypes and genotypes, thereby providing explanation for the genetic basis of variation in complex traits (Falconer and Mackay, 1996; Lynch and Walsh, 1998). In a sense, QTL analysis can be viewed as incom-", + "studies. There are many possible causal networks even in a simple syst em consisting of a genomic locus (QTL) and two traits, T1 and T2 ( Figure 1 ). Causal inference in GWLS and GWAS involves, in its simplest form, the i dentification of pairs of traits with a common QTL (QTL-trait-trait triads) and dete rmining whether the QTL directly affects each of two traits (independent), or if the QTL affects only one trait", + "tions by matching patterns of expression QTL and GWAS. Am. J. Hum. Genet. 92, 92 160. Giambartolomei, C. et al. (2014) Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10, e1004383 161. Porcu, E. et al. (2019) Mendelian randomization integrating GWAS and eQTL data reveals genetic determinants of com-plex and clinical traits. Nat. Commun. 10, 3300 162. Zhu, Z. et al. (2016) Integration of summary data from GWAS", + "knowledge of the true QTL location (Doss et al. 2005 ), which can be used to empirically estimate the power of aGWAS performed at a similar scale (Hao et al. 2008 ; Schadt et al. 2008 ). A GWAS on its own does little more than establish correlations between changes in DNA at agiven locus and changes in a disease trait of interest, with respect to populations of interest. Further, these studies on", + "Another method to identify candidate genes is to leverage data generated in another population or species. Phenome-wide association studies (PheWAS) take a gene or variant of interest and nd all reported associations in GWAS datasets. A number of these GWAS tools exist, using either different methods, or different human cohorts (https://atlas.ctglab. nl/PheWAS, http://pheweb.sph.umich.edu/, accessed on 2 February 2022). Mouse QTL mapping has high power but low precision (i.e., we can detect a QTL, but", + "Another method to identify candidate genes is to leverage data generated in another population or species. Phenome-wide association studies (PheWAS) take a gene or variant of interest and nd all reported associations in GWAS datasets. A number of these GWAS tools exist, using either different methods, or different human cohorts (https://atlas.ctglab. nl/PheWAS, http://pheweb.sph.umich.edu/, accessed on 2 February 2022). Mouse QTL mapping has high power but low precision (i.e., we can detect a QTL, but", + "Another method to identify candidate genes is to leverage data generated in another population or species. Phenome-wide association studies (PheWAS) take a gene or variant of interest and nd all reported associations in GWAS datasets. A number of these GWAS tools exist, using either different methods, or different human cohorts (https://atlas.ctglab. nl/PheWAS, http://pheweb.sph.umich.edu/, accessed on 2 February 2022). Mouse QTL mapping has high power but low precision (i.e., we can detect a QTL, but", + "narrow regions ofthegenome harboring trait associated genetic variants. Itisstill, however, a challenge toidentify causal genes and several approaches have been developed that canassist inbridging thisgap. Specifically, systems genetics approaches involving theintegration of other types of-omics data have proven useful [25]. Two systems genetics approaches for informing GWAS areexpression quantitative trait loci(eQTL) discovery and co-expression" + ] + ] +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_aging_1 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_aging_1 new file mode 100644 index 0000000..677e295 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_aging_1 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2018 - Mechanisms of Vascular Aging.pdf", + "2016 - The dog aging project translational geroscience in companion.pdf", + "2015 - Cellular and Molecular Biology of Aging Endothelial Cells.pdf", + "2012 - Aging, Rejuvenation, and Epigenetic.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2015 - Cellular and Molecular Biology of Aging Endothelial Cells.pdf", + "2017 - Epigenetic aging signatures in mice livers.pdf", + "2017 - Diverse interventions that extend mouse.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf" + ], + "extraction_id": [ + "3e65812c-453e-53aa-83ab-92f2ce15da29", + "2c1fcce1-b723-5f9f-8f66-49ed7895f2ac", + "86f9502b-7a3a-501f-9053-8af1d37043b4", + "d23b6aab-f299-5370-b3b6-0615112681f0", + "a47672ed-9f4d-5aa8-8b7e-f10753246a6e", + "42c88d1d-4bb6-50f8-9010-379e15650d96", + "0e789eef-b085-5fc2-b10a-8572bc28fa1b", + "5d4bf4c1-5bb4-5de6-a1bb-0485163a5373", + "d634b92e-0802-5ba8-a4c5-9e45462cd7d5", + "a47672ed-9f4d-5aa8-8b7e-f10753246a6e" + ], + "document_id": [ + "659b84b6-63dd-5bb1-80ee-7478ed3c47e3", + "e841c6bd-78b8-56e1-b3dd-e2bcc8a0f590", + "815d7f3e-e219-502f-aba0-57a68ae787d3", + "bde26feb-f423-51b0-89ec-6f079bfc8b17", + "62b635c3-040e-512a-b016-6ef295308a1e", + "62b635c3-040e-512a-b016-6ef295308a1e", + "815d7f3e-e219-502f-aba0-57a68ae787d3", + "b20b11a6-1490-51b8-9218-c441a2e65ba7", + "dc7ad71a-a4d7-5901-a016-9a6fb2b91a2f", + "62b635c3-040e-512a-b016-6ef295308a1e" + ], + "id": [ + "chatcmpl-ADZV87184EnuXO9GIujWS8NC7oWU2", + "57be0715-77c8-55e3-8239-56e1fa11a543", + "03e62089-fef5-5ed5-bf7f-36ff595fbaea", + "fe5b60e5-ded6-5950-bc1c-72cb39e16234", + "d7dcefa4-133c-594c-b8a8-38fe945c6b5c", + "907d7d31-04db-5f66-b390-7740142af182", + "40cbc230-7175-522e-b0ae-3901f2cfac0b", + "a9666b11-4567-52dd-90c8-be2238dafdcb", + "729598dc-94e6-5f52-ae19-071c959c7dd2", + "cbc86652-98e1-5464-a0ce-2272111246df", + "f8630239-fd67-5214-a5cd-f965d878f712" + ], + "contexts": [ + "168. Yin L, Ye S, Chen Z, Zeng Y . Rapamycin preconditioning attenuates tran- sient focal cerebral ischemia/reperfusion injury in mice. Int J Neurosci. 2012;122:748756. doi: 10.3109/00207454.2012.721827 169. Spilman P, Podlutskaya N, Hart MJ, Debnath J, Gorostiza O, Bredesen D, Richardson A, Strong R, Galvan V . Inhibition of mTOR by rapamy-cin abolishes cognitive deficits and reduces amyloid-beta levels in a mouse model of Alzheimers disease. PLoS One. 2010;5:e9979. doi: 10.1371/journal.pone.0009979", + "Anisimov VN, Zabezhinski MA, Popovich IG, Piskunova TS, Semenchenko AV, Tyndyk ML, Yurova MN, Rosenfeld SV,Blagosklonny MV (2011b) Rapamycin increases lifespan and inhibits spontaneous tumorigenesis in inbred female mice. Cell Cycle 10:42304236 Augustine JJ, Bodziak KA, Hricik DE (2007) Use of sirolimus in solid organ transplantation. Drugs 67:369391 Bannister CA, Holden SE, Jenkins-Jones S, Morgan CL, Halcox JP,", + "ACCEPTED MANUSCRIPTACCEPTED MANUSCRIPT mTOR complex 2 (mTORC2), the less clearly identified and less sensitive to rapamycin. Most information to date on the r ole of mTOR has studied the insulin/nutrient signaling via the mTORC1 and significantly less in known about the role of mTORC2 ( in this review, future references measure either mTORC1 or general mTOR activity )[251]. Earlier this decade studies showed that decreasing TOR signaling, genetically or with rapamycin,", + "Harrison, D.E., Strong, R., Sharp, Z.D., Nelson, J.F., Astle, C.M., Flurkey, K.,Nadon, N.L., Wilkinson, J.E., Frenkel, K., Carter, C.S., et al. (2009). Rapamycin Cell148, January 20, 2012 2012 Elsevier Inc. 55", + "96. Lamming DW, Ye L, Katajisto P, Goncalves MD, Saitoh M, Stevens DM, etal. Rapamycin- induced insulin resistance is mediated by mTORC2 loss and uncoupled from longevity. Science. 2012;335:163843. 97. Tataranni T, Biondi G, Cariello M, Mangino M, Colucci G, Rutigliano M, etal. Rapamycin- induced hypophosphatemia and insulin resistance are associated with mTORC2 activation and klotho expression. Am J Transplant. 2011;11(8):165664.", + "ing these aspects in future studies on the effects of resveratrol could help to study in greater depth the mechanisms of action of this compound [56]. Rapamycin Rapamycin is a macrolide isolated from Streptomyces hygroscopicus, a bacteria from Pascua Island (Rapa Nui). It has functions as an antibiotic, an immune sup- pressant drug, and it is also proposed as a CRM.After the first studies, it was found that rapamycin could induce the extension of the replicative life of yeast through the", + "[257] Wilkinson JE, Burmeister L, Brooks SV, Chan CC, Friedline S, Harrison DE, et al. Rapamycin slows aging in mi ce. Aging Cell. 2012;11:675 -82. [258] Selman C, Tullet JM, Wieser D, Irvine E, Lingard SJ, Choudhury AI, et al. Ribosomal protein S6 kinase 1 signaling regulates mammalian life span. Science. 2009;326:140 -4. [259] Reihl K, Seals D, Henson G, LaRocca T, Mag erko K, Bosshardt G, et al. Dietary rapamycin selectively improves arterial function in old mice. FASEB Journal. 2013;27:1194.17.", + "29. Wilkinson JE, Burmeister L, Brooks SV, Chan C-C, Friedline S, Harrison DE, et al. Rapamycin slows aging in mice. Aging Cell. 2012;11:675 82. 30. Lamming DW, Ye L, Katajisto P, Goncalves MD, Saitoh M, Stevens DM, et al. Rapamycin-induced insulin resistance is mediated by mTORC2 loss and uncoupled from longevity. Science. 2012;335:1638 43. 31. Zampieri M, Ciccarone F, Calabrese R, Franceschi C, Brkle A, Caiafa P. Reconfiguration of DNA methylation in aging. Mech Ageing Dev. 2015;151:60 70.", + "files [55, 62]. Of note, rapamycin in particular appears to induce additional changes u nrelated to age-associated changes. While both CR and rapamycin induced these non-age-related effects, this effect was much more marked for rapamycin. These non age-related epigenetic changes include gains of methylation at genes, enhancers and CpG islands and losses of methylation at genes and enhancers. Conceivably, such non age-related effects of rapamycin in", + "23 94. Chakrabarti P, English T, Shi J, Smas CM, Kandror KV .Mammalian target of rapamycin complex 1 suppresses lipolysis, stimulates lipogenesis, and promotes fat storage. Diabetes. 2010;59:77581. 95. Miller RA, Harrison DE, Astle CM, Fernandez E, Flurkey K, Han M, et al. Rapamycin- mediated lifespan increase in mice is dose and sex dependent and metabolically distinct from dietary restriction. Aging Cell. 2014;13:46877." + ], + "answer": "Here are the studies that include rapamycin: 1. **Yin L, Ye S, Chen Z, Zeng Y (2012)**: \"Rapamycin preconditioning attenuates transient focal cerebral ischemia/reperfusion injury in mice\" [1]. 2. **Spilman P, Podlutskaya N, Hart MJ, Debnath J, Gorostiza O, Bredesen D, Richardson A, Strong R, Galvan V (2010)**: \"Inhibition of mTOR by rapamycin abolishes cognitive deficits and reduces amyloid-beta levels in a mouse model of Alzheimer's disease\" [1]. 3. **Anisimov VN, Zabezhinski MA, Popovich IG, Piskunova TS, Semenchenko AV, Tyndyk ML, Yurova MN, Rosenfeld SV, Blagosklonny MV (2011)**: \"Rapamycin increases lifespan and inhibits spontaneous tumorigenesis in inbred female mice\" [2]. 4. **Lamming DW, Ye L, Katajisto P, Goncalves MD, Saitoh M, Stevens DM, et al. (2012)**: \"Rapamycin-induced insulin resistance is mediated by mTORC2 loss and uncoupled from longevity\" [5]. 5. **Tataranni T, Biondi G, Cariello M, Mangino M, Colucci G, Rutigliano M, et al. (2011)**: \"Rapamycin-induced hypophosphatemia and insulin resistance are associated with mTORC2 activation and klotho expression\" [5]. 6. **Wilkinson JE, Burmeister L, Brooks SV, Chan CC, Friedline S, Harrison DE, et al. (2012)**: \"Rapamycin slows aging in mice\" [7]. 7. **Reihl K, Seals D, Henson G, LaRocca T, Magerko K, Bosshardt G, et al. (2013)**: \"Dietary rapamycin selectively improves arterial function in old mice\" [7]. 8. **Miller RA, Harrison DE, Astle CM, Fernandez E, Flurkey K, Han M, et al. (2014)**: \"Rapamycin-mediated lifespan increase in mice is dose and sex dependent and metabolically distinct from dietary restriction\" [10]. These references provide a comprehensive list of studies involving rapamycin.", + "question": "List as many studies as you can that include rapamycin." +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_aging_10 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_aging_10 new file mode 100644 index 0000000..ff7c2dc --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_aging_10 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2001 - The genetics of aging.pdf", + "2011 - A genome-wide association study confirms APOE as the major gene influencing.pdf", + "2009 - The Human Ageing Genomic Resources online.pdf", + "2018 - Sex Differences in Aging Genomic Instability.pdf", + "2007 - Genetic correlates of brain aging on MRI and cognitive test measures a genome-wide association and linkage analysis in the Framingham study.pdf", + "2021 - Genome-wide association studies identify.pdf", + "2021 - Footprints in the Sand Deep Taxonomic Comparisons in Vertebrate Genomics to Unveil the Genetic Programs of Human Longevity.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2016 - Progress on the role of DNA methylation in aging.pdf" + ], + "extraction_id": [ + "76bae746-eabf-51ed-a01f-d32ecc89c11b", + "210aa417-372c-5bf6-b961-e281a1817458", + "7ada6b55-99c2-5e20-bf96-d153f927256c", + "0104338d-cc9c-538f-be29-8343a64da37d", + "3f7edb2f-b8c8-511a-9fb3-c746b6f7f213", + "cd7730b6-22dc-5256-9310-79fc348b3226", + "34223e0e-590c-5f26-b120-b7250cd91b99", + "fd143ef8-c605-51d8-9969-4e681493b826", + "4ea8424f-1cd8-569c-a1df-3f0f54206e70", + "bcb3c620-b960-5af6-95ea-13215c31672e" + ], + "document_id": [ + "aa9a9193-b6f3-5ef8-aefd-e01ec44abb46", + "63b27b06-db2c-5542-9b1a-cb9ebe64d339", + "e43cd3b6-ad8e-5422-ba7c-ceb6e66cc529", + "8cfb5529-7f0c-58fc-b6e4-b3ee800fb72f", + "56b25b5a-fc9e-5d61-8502-1c110466ba16", + "60c2e869-1fee-53ea-b332-26d9c2abc747", + "0dc45abe-ab02-5b07-9916-7093b53323c0", + "62b635c3-040e-512a-b016-6ef295308a1e", + "62b635c3-040e-512a-b016-6ef295308a1e", + "e4cdc02f-4415-5638-aab8-f848b4d64a22" + ], + "id": [ + "chatcmpl-ADZWBvqLDNy2CwnWNpuZ7tlbpCfWr", + "e09c33ea-4139-5cc2-9cf5-a40045f26a0c", + "2d0a20b8-4196-5451-9d99-282f82234464", + "3117c019-7311-53ae-8ab1-927ca822c709", + "a9434032-4a9d-54f8-a7a6-16110d1b3118", + "3ff3cf0b-c00f-5548-b4fb-21a57042c91c", + "9fa00091-9661-57bd-91c7-f0bf436805a7", + "f6ed1f0c-e4ea-5459-bb63-136decc11eee", + "12868be9-1c5a-57ea-a5e3-f521b977cef2", + "a0672677-71ad-5603-8427-a0648eec407f", + "c1b5a31a-066d-571b-af1f-db746d9d17f6" + ], + "contexts": [ + "Recent developments on the genetics of aging can be seen as several streams of effort. In general, humans show a relatively modest ( <50%) heritability of", + "effect genetic variants on human longevity. Aging 2, 612620. Yu, C.E., Seltman, H., Peskind, E.R., Galloway, N., Zhou, P.X., Rosenthal, E., Wijsman, E.M., Tsuang, D.W., Devlin, B., Schellenberg, G.D., 2007. Comprehensive analysis of APOE and selected proximate markers for late-onset Alzheimers disease: patterns of linkage disequilibrium and disease/marker association. Genomics", + "It is undisputed that genetic factors influence aging. In a remarkable", + "males: what are the molecular and evolutionary causes? Aging Cell. 2007;6:225233. doi:10.1111/j.1474-9726.2007.00279.x 63. Benayoun BA, Pollina EA, Brunet A. Epigenetic regulation of ageing: link- ing environmental inputs to genomic stability. Nat Rev Mol Cell Biol. 2015;16:593610. doi:10.1038/nrm4048 64. Sen P, Shah PP, Nativio R, Berger SL. Epigenetic mechanisms of longevity and aging. Cell. 2016;166:822839. doi:10.1016/j.cell.2016.07.050", + "Genet 1998, 81:92-97. 3. Pedersen NL, Posner SF, Gatz M: Multiple-threshold models for genetic influences on age of onset for Alzheimer disease: findings in Swedish twins. Am J Med Genet 2001, 105:724-728. 4. Gudmundsson H, Gudbjartsson DF, Frigge M, Gulcher JR, Stefansson K: Inheritance of human longevity in Iceland. Eur J Hum Genet 2000, 8:743-749. 5. Flossmann E, Schulz UG, Rothwell PM: Systematic review of methods and results of studie s of the genetic epidemiology", + "population dynamics on the genetic architecture of human longevity. Aging (Albany NY). 2018;10(8):1947 63. 68. Bellenguez C, Kucukali F, Jansen I, Andrade V, Morenau-Grau S, Amin N, et al. Large meta-analysis of genome-wide association studies expands knowledge of the genetic etiology of Alzheimer disease and highlights potential translational opportunities. medRxiv. 2020. 69. Kojima T, Shimazui T, Hinotsu S, Joraku A, Oikawa T, Kawai K, et al. Decreased expression of CXXC4 promotes a", + "discover core mechanisms of regulation.ANALYSIS OF HUMAN VARIATION IN THE GENETIC CONTROL OF LONGEVITY Heritability studies have convincingly demonstrated that at least some fraction of human lifespan is heritable. In tandem, large-scale genome-wide association studies (GWAS) have identied numerous loci associated with age-related traits (Buniello et al., 2019). While genetic studies have functionally shown an inverse eect of multiple age-related, disease-", + "than in healthy elderly patients [71]. Concluding Remarks The study of the human aging process is complex and multifactorial, where genetic and environmental variables are key players in its development. That is why we sug- gest a series of different biomarkers which include hormonal, inflammatory, and oxidative stress biomarkers. However, it is possible that other biomarkers such as DNA damage, telomere length determination, DNA repair mechanisms and p53", + "Clinical Genetics and Genomics of Aging", + "standing the cause and mechanisms of aging is imperative in assisting to suppress age-related diseases and promote healthylongevity. It is well-known that aging is influenced by a combin- ation of genetic and environmental factors. Previous twin stud- ies have shown that the genetic contribution to general human longevity is about 2030% [ 4,5], whereas environmental factors in human aging and longevity still account for the largest effect. Epigenetic factors influence the regulation of gene expres-" + ], + "answer": "Genetic factors play a significant role in influencing aging in humans. Here are some key points based on the provided context: 1. **Heritability of Longevity**: Human longevity shows a relatively modest heritability of less than 50% [1]. Twin studies have estimated the genetic contribution to general human longevity to be about 20-30% [10]. 2. **Genome-Wide Association Studies (GWAS)**: Large-scale GWAS have identified numerous loci associated with age-related traits, indicating that specific genetic variations can influence aging [7]. 3. **Specific Genetic Variants**: The APOE gene and its variants have been comprehensively analyzed for their association with late-onset Alzheimer's disease, which is an age-related condition [2]. This suggests that certain genetic markers are linked to age-related diseases. 4. **Epigenetic Regulation**: Epigenetic mechanisms, which involve changes in gene expression without altering the DNA sequence, also play a crucial role in aging. Environmental inputs can affect genomic stability through epigenetic regulation [4]. 5. **Inheritance Studies**: Studies on the inheritance of human longevity, such as those conducted in Iceland, have provided insights into the genetic factors that contribute to a longer lifespan [5]. In summary, aging in humans is influenced by a combination of genetic factors, including specific genetic variants, heritability, and epigenetic regulation [1], [2], [4], [5], [7], [10].", + "question": "What genetic factors influence aging in humans?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_aging_11 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_aging_11 new file mode 100644 index 0000000..c32fefd --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_aging_11 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2009 - The Human Ageing Genomic Resources online.pdf", + "2012 - Genome-Environment Interactions That Modulate.pdf", + "2010 - Do different neurons age differently Direct genome-wide analysis of aging in single identified cholinergic neurons.pdf", + "2012 - Genome-Environment Interactions That Modulate.pdf", + "2012 - Human Ageing Genomic Resources Integrated.pdf", + "2018 - Biological Processes Modulating Longevity across Primates.pdf", + "2011 - Genetics and genomics of human ageing.pdf", + "2010 - Genetics and genomics of human ageing.pdf", + "2008 - Estrogen, not intrinsic aging, is the major regulator of delayed.pdf", + "2013 - Gene expression changes with age in skin.pdf" + ], + "extraction_id": [ + "52c67b46-63f2-54ae-a78e-e9d54a55f6e4", + "d59d7882-333d-5576-86ab-3cfa6354b946", + "81c68113-aa96-5af3-b4fc-5898fa20e379", + "d59d7882-333d-5576-86ab-3cfa6354b946", + "25e9d8a3-54ac-5412-8efb-3b56d93f363f", + "c07d6709-8dbe-5437-b7df-0849b92c0ea0", + "07a34581-749c-5556-bdea-806b2c9c7915", + "59227f74-f1c7-58ad-a886-aa9e3799a132", + "eeffae01-ce08-54a8-955f-6f0c9d07eedc", + "dfb687b2-f1ff-5e22-8a67-4a1db9ebeb3c" + ], + "document_id": [ + "e43cd3b6-ad8e-5422-ba7c-ceb6e66cc529", + "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "153b070f-0291-5ed4-ad33-edea5e3fa8f7", + "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "5f554cc7-c94d-5fbd-9567-528499663ed6", + "930103c1-e98e-524c-aa68-233a45dc6726", + "08eee102-d627-5f1b-84c7-603c38981adf", + "633f3149-e966-53ef-aa7d-b759398ed541", + "04a3d8f1-64c1-5e25-ab0a-3eb749c06c92", + "5c121bbb-57b8-51cc-8461-effa1bfd87b9" + ], + "id": [ + "chatcmpl-ADZWHUX5oZWH5Bj3eh2vkudPOLcus", + "8fd5ab85-67ed-55e6-bbfa-09436c4fdbfb", + "413f8f54-b5cc-5089-9f5c-d9e3b8bcf594", + "6f04401a-b938-5a60-8b69-d37f9086748c", + "786d2756-4c4d-5ac0-8d3d-63f914d51664", + "0ae63c75-df5f-59b0-9561-30d5115f0f74", + "f2fbfb29-0a51-5f94-8b67-d47ab4de68bd", + "fd6cfc2c-76b1-5620-a68c-fb37db9b6f78", + "df45a752-e866-54bb-ab49-daff9a702eef", + "66f72bdc-d38b-5c7a-afdd-4c7549ce2131", + "d53018ae-0881-5ef4-9c49-48623e8aa342" + ], + "contexts": [ + "www.ncbi.nlm.nih.gov/homologene) of genes strongly asso-ciated with aging in model organisms. Also included are genesin which mutations result in segmental progeroid syndromes,such as the Werners syndrome gene, as well as genes criticalin pathways previously related to aging, such as the insulin/insulin-like signalling pathway (de Magalhes et al ., 2005a). The", + "overexpressed with age seem to be a response to aging,in that they have been previously found to have protec-tive functions (de Magalha es et al., 2009b). As such,these genes may help organisms manage aging andcould be targets for manipulation. Likewise, gene ex-pression analysis of CR has been conducted to identifyassociated genes (Lee et al., 1999, 2000). A number ofmolecular signatures have emerged from such studiesthat could be useful to identify candidate processes andpathways that affect aging,", + "OTHER AGING RELATED GENES", + "In addition to aging- and CR-related genes, another source of candidate genes and pathways for drug designare human longevity-associated genes (Barzilai andShuldiner, 2001; Browner et al., 2004; Kenyon, 2010).Dozens of genes have now been associated with humanlongevity (de Magalha es et al., 2009a), although only ahandful of genes have been shown to have consistenteffects across populations. Many longevity-associated genes are related to spe-", + "potentially associated with human ageing. For eachgene, a description compiled from the studies that linkthe gene to ageing is provided. It should be noted thatour focus is on genes that might affect the ageingprocess, rather than individual age-related pathologies; genes affecting multiple, even if not all, age-related", + "Pleiotropies and Aging-Related Genesets To study genes that have been previously related to aging, a list of curated human genes associated with aging in different model systems was obtained from the GenAge data set ( de Magalh ~aes et al. 2005 ). We used gene ontology (GO) anno-", + "aging in human muscle reveals a common aging signa-ture. PLoS Genet. 2, e115. ( doi:10.1371/journal.pgen. 0020115 ) 64 Lener, T ., Moll, P . R., Rinnerthaler, M., Bauer, J., Aberger, F. & Richter, K. 2006 Expression proling ofaging in the human skin. Exp. Gerontol. 41, 387397. (doi:10.1016/j.exger.2006.01.012 ) 65 Kim, S. K. 2008 Genome-wide views of aging gene net- works . Molecular Biology of Aging Monograph 9. Cold Spring Harbor, CT: Cold Spring Harbor LaboratoryPress.", + "aging in human muscle reveals a common aging signa-ture. PLoS Genet. 2, e115. ( doi:10.1371/journal.pgen. 0020115 ) 64 Lener, T ., Moll, P . R., Rinnerthaler, M., Bauer, J., Aberger, F. & Richter, K. 2006 Expression proling ofaging in the human skin. Exp. Gerontol. 41, 387397. (doi:10.1016/j.exger.2006.01.012 ) 65 Kim, S. K. 2008 Genome-wide views of aging gene net- works . Molecular Biology of Aging Monograph 9. Cold Spring Harbor, CT: Cold Spring Harbor LaboratoryPress.", + "tive-gerontogenes and genes with established aging-relatedfunctions were identified by interrogation of the GenAgeonline database [12], from aging-associated Gene Ontology( G O ) g r o u p s a n d f r o m h a n d a n n o t a t i o n ( s e e M a t e r i a l s a n dmethods/Results for a detailed description of the analysis). We show that the fundamenta l changes in genes and proc-", + "on model organisms [3] or have been confined to specificaging-associated disorders such as progeria syndromes [4]. A study of postmortem human brain tissue from 30 individuals aged 26 to 106 years [5] showed that approxi- mately 4% of approximately 11,000 genes analyzed show a significant age-related expression change (1.5-fold or more) in individuals aged >40 years. These genes were reported to play central roles in synaptic plasticity, vesi- cular transport, and mitoch ondrial function. Another" + ], + "answer": "Several genes have been associated with aging. Here are some key points from the provided context: 1. **Genes in Model Organisms**: A list of genes strongly associated with aging in model organisms includes those involved in segmental progeroid syndromes, such as the Werner syndrome gene, and genes critical in pathways previously related to aging, such as the insulin/insulin-like signaling pathway [1]. 2. **Gene Expression Analysis**: Studies have identified genes overexpressed with age that have protective functions, suggesting they help manage aging and could be targets for manipulation. Gene expression analysis of caloric restriction (CR) has also been conducted to identify associated genes [2]. 3. **Human Longevity-Associated Genes**: Dozens of genes have been associated with human longevity, although only a handful have shown consistent effects across populations [4]. 4. **GenAge Data Set**: A curated list of human genes associated with aging in different model systems is available from the GenAge data set [6]. 5. **GenAge Online Database**: Genes with established aging-related functions were identified by interrogation of the GenAge online database, aging-associated Gene Ontology groups, and hand annotation [9]. These references collectively highlight the involvement of various genes and pathways in the aging process.", + "question": "what genes are associated with aging?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_aging_12 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_aging_12 new file mode 100644 index 0000000..73a4c9b --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_aging_12 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2012 - Genome-Environment Interactions That Modulate.pdf", + "2009 - The Human Ageing Genomic Resources online.pdf", + "2010 - Do different neurons age differently Direct genome-wide analysis of aging in single identified cholinergic neurons.pdf", + "2011 - A genome-wide association study of aging.pdf", + "2009 - The Human Ageing Genomic Resources online.pdf", + "2018 - Biological Processes Modulating Longevity across Primates.pdf", + "2009 - The Human Ageing Genomic Resources online.pdf", + "2009 - The Human Ageing Genomic Resources online.pdf", + "2010 - Genetics and genomics of human ageing.pdf", + "2011 - Genetics and genomics of human ageing.pdf" + ], + "extraction_id": [ + "d59d7882-333d-5576-86ab-3cfa6354b946", + "52c67b46-63f2-54ae-a78e-e9d54a55f6e4", + "81c68113-aa96-5af3-b4fc-5898fa20e379", + "a5be18f8-c263-5635-87d7-57c5addd65e5", + "52c67b46-63f2-54ae-a78e-e9d54a55f6e4", + "c07d6709-8dbe-5437-b7df-0849b92c0ea0", + "52c67b46-63f2-54ae-a78e-e9d54a55f6e4", + "52c67b46-63f2-54ae-a78e-e9d54a55f6e4", + "59227f74-f1c7-58ad-a886-aa9e3799a132", + "07a34581-749c-5556-bdea-806b2c9c7915" + ], + "document_id": [ + "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "e43cd3b6-ad8e-5422-ba7c-ceb6e66cc529", + "153b070f-0291-5ed4-ad33-edea5e3fa8f7", + "8e9c1150-1047-54a2-bf85-1cc5000a6811", + "e43cd3b6-ad8e-5422-ba7c-ceb6e66cc529", + "930103c1-e98e-524c-aa68-233a45dc6726", + "e43cd3b6-ad8e-5422-ba7c-ceb6e66cc529", + "e43cd3b6-ad8e-5422-ba7c-ceb6e66cc529", + "633f3149-e966-53ef-aa7d-b759398ed541", + "08eee102-d627-5f1b-84c7-603c38981adf" + ], + "id": [ + "chatcmpl-ADZWOuZDmIcGuvC8wjb6oX7vSBFDg", + "786d2756-4c4d-5ac0-8d3d-63f914d51664", + "a21de3e8-ed2c-5c06-a351-ccb8f92f4e21", + "6f04401a-b938-5a60-8b69-d37f9086748c", + "06e319e1-b054-5f33-9b40-ee892f507736", + "9defe0af-80a1-56da-90df-551fd55baa13", + "f2fbfb29-0a51-5f94-8b67-d47ab4de68bd", + "8fd5ab85-67ed-55e6-bbfa-09436c4fdbfb", + "650300e1-898c-56e2-9358-0bb6625b0073", + "df45a752-e866-54bb-ab49-daff9a702eef", + "fd6cfc2c-76b1-5620-a68c-fb37db9b6f78" + ], + "contexts": [ + "In addition to aging- and CR-related genes, another source of candidate genes and pathways for drug designare human longevity-associated genes (Barzilai andShuldiner, 2001; Browner et al., 2004; Kenyon, 2010).Dozens of genes have now been associated with humanlongevity (de Magalha es et al., 2009a), although only ahandful of genes have been shown to have consistenteffects across populations. Many longevity-associated genes are related to spe-", + "GenAge features a data set of genes that may regulate agingin humans or that at least appear to be considerably associated with the human aging phenotype. This data set includes orthologues derived from established databases, mainly In-Paranoid (OBrien et al ., 2005) but also HomoloGene (http://", + "OTHER AGING RELATED GENES", + "processes in human longevity and aging. Ten of the 22 suggestive associations identied in our analyses are in ornear genes that are highly expressed in the brain (HECW2[Rotin and Kumar, 2009], HIP1 [Blanpied et al., 2003], BIN2, GRIA1), were previously related to the regulation of neuronal excitability and plasticity (KCNQ4 [Van Eyken et al., 2006], LMO4 [Joshi et al., 2009; Leuba et al., 2004],", + "genes analyzed for their possible association with human lon-gevity (http://genomics.senescence.info/genes/longevity.html).All longevity association studies in humans we could find by thetime of the latest update were added to this list. These includestudies reporting negative results, which we see as essentialsince many genes display population-specific associations withlongevity. Fig. 1 From the main page of the Human Ageing", + "Pleiotropies and Aging-Related Genesets To study genes that have been previously related to aging, a list of curated human genes associated with aging in different model systems was obtained from the GenAge data set ( de Magalh ~aes et al. 2005 ). We used gene ontology (GO) anno-", + "www.ncbi.nlm.nih.gov/homologene) of genes strongly asso-ciated with aging in model organisms. Also included are genesin which mutations result in segmental progeroid syndromes,such as the Werners syndrome gene, as well as genes criticalin pathways previously related to aging, such as the insulin/insulin-like signalling pathway (de Magalhes et al ., 2005a). The", + "shown that genes associated with aging and/or longevity inmodel organisms are evolutionary conserved in terms of havingmore homologues than predicted by chance (Budovsky et al .,2007, 2008) and exhibiting slower molecular evolution rates (de Magalhes & Church, 2007). Therefore, it is now clear that atleast some genes identified in model organisms may be relevantto human aging. To allow researchers to focus specifically on human aging,", + "aging in human muscle reveals a common aging signa-ture. PLoS Genet. 2, e115. ( doi:10.1371/journal.pgen. 0020115 ) 64 Lener, T ., Moll, P . R., Rinnerthaler, M., Bauer, J., Aberger, F. & Richter, K. 2006 Expression proling ofaging in the human skin. Exp. Gerontol. 41, 387397. (doi:10.1016/j.exger.2006.01.012 ) 65 Kim, S. K. 2008 Genome-wide views of aging gene net- works . Molecular Biology of Aging Monograph 9. Cold Spring Harbor, CT: Cold Spring Harbor LaboratoryPress.", + "aging in human muscle reveals a common aging signa-ture. PLoS Genet. 2, e115. ( doi:10.1371/journal.pgen. 0020115 ) 64 Lener, T ., Moll, P . R., Rinnerthaler, M., Bauer, J., Aberger, F. & Richter, K. 2006 Expression proling ofaging in the human skin. Exp. Gerontol. 41, 387397. (doi:10.1016/j.exger.2006.01.012 ) 65 Kim, S. K. 2008 Genome-wide views of aging gene net- works . Molecular Biology of Aging Monograph 9. Cold Spring Harbor, CT: Cold Spring Harbor LaboratoryPress." + ], + "answer": "Several genes have been associated with aging in humans according to the provided context: 1. **GenAge Data Set**: This data set includes genes that may regulate aging in humans or are considerably associated with the human aging phenotype [2]. 2. **HECW2, HIP1, BIN2, GRIA1, KCNQ4, LMO4**: These genes are highly expressed in the brain and have been previously related to the regulation of neuronal excitability and plasticity [4]. 3. **Werners Syndrome Gene**: Mutations in this gene result in segmental progeroid syndromes, which are critical in pathways previously related to aging, such as the insulin/insulin-like signaling pathway [7]. These references indicate that there are multiple genes associated with aging in humans, with some being highly expressed in specific tissues like the brain and others being involved in critical aging-related pathways.", + "question": "Which genes are associated with aging in human" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_aging_13 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_aging_13 new file mode 100644 index 0000000..27527e2 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_aging_13 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2009 - The Human Ageing Genomic Resources online.pdf", + "2012 - Genome-Scale Studies of Aging Challenges and Opportunities.pdf", + "2020 - A multidimensional systems biology.pdf", + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2009 - The Human Ageing Genomic Resources online.pdf", + "2018 - Human Ageing Genomic Resources new and updated.pdf", + "2009 - The Human Ageing Genomic Resources online.pdf", + "2009 - The Human Ageing Genomic Resources online.pdf", + "2012 - Human Ageing Genomic Resources Integrated.pdf", + "2009 - The Human Ageing Genomic Resources online.pdf" + ], + "extraction_id": [ + "aecbe8a8-aeed-5cfa-b0f3-be29f19d849d", + "53c57cc4-4d43-505a-974c-442d06e144df", + "fe4ec57e-6ae7-59c4-b8fa-da73fe77ce96", + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "6b10898e-0906-5fff-9c70-b3be2d562fda", + "03c88365-c56c-56f2-a15f-e183398d3dfe", + "7ada6b55-99c2-5e20-bf96-d153f927256c", + "aecbe8a8-aeed-5cfa-b0f3-be29f19d849d", + "25e9d8a3-54ac-5412-8efb-3b56d93f363f", + "aecbe8a8-aeed-5cfa-b0f3-be29f19d849d" + ], + "document_id": [ + "e43cd3b6-ad8e-5422-ba7c-ceb6e66cc529", + "b77aace0-fa36-5fd4-8e2a-c8932198acd1", + "d040bfe3-e409-5b5c-b8f8-f3dd4fc060e3", + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "e43cd3b6-ad8e-5422-ba7c-ceb6e66cc529", + "82726cea-f77c-5a92-9f2e-ecccc369953a", + "e43cd3b6-ad8e-5422-ba7c-ceb6e66cc529", + "e43cd3b6-ad8e-5422-ba7c-ceb6e66cc529", + "5f554cc7-c94d-5fbd-9567-528499663ed6", + "e43cd3b6-ad8e-5422-ba7c-ceb6e66cc529" + ], + "id": [ + "chatcmpl-ADZWTp42DWHZeK1fZT0MSpkOitZfP", + "496d27de-6dd0-5f6a-bedb-64d4c252981d", + "df726361-271a-5dbb-b6d1-03dab5a63006", + "9716c2c9-6f43-57f2-bad4-6d96c82d5c16", + "c63cfaee-749e-547b-9c0a-086266f10670", + "42464f0d-d8ce-5f73-9c7c-0cdec45e7f4f", + "3153cd1e-de1c-52fb-aede-4065019d8c6b", + "676b5bff-01e8-58cf-93e5-ac14d8e82760", + "4c4f5670-cb9a-59b5-b9cc-ba5bce662035", + "cf8bf1ec-4919-59b2-a60d-183fc5a04bb0", + "1d7f120f-20c4-5d6c-983f-41534fb30503" + ], + "contexts": [ + "the different pathways linked with aging and even study genenetworks. In such works, GenAge is an adequate resource asit provides a framework for the functional genomics of aging.For example, Xue et al . (2007) used GenAge to construct a modular network of aging and obtain insights into aging, including thefact that genes connecting different modules are more likely toaffect longevity and/or aging, an hypothesis the authors validatedexperimentally in worms (Xue et al", + "[111], and for generation of networks based on known gene interactions such as GeneMania [112] and Cytoscape [113], as well as for identifying cross-species orthology relation-ships [114], network-based thinking has been increasingly applied to the study of aging and lifespan [115-118]. Re-cently, the novel computational method of network identifi- cation by regression (NIR) [119] has been used to identify", + "networks can be built using protein interaction and gene co-expression data. A previous paper used protein- protein interactions to build genetic networks identifying potential longevity genes along with links between genes and aging-related diseases [ 30]. Here, we present the network of proteins and genes co-expressed with the CellAge senescence genes. Assaying the networks, we find links between senescence and immune system func- tions and find genes highly connected to CellAge genes", + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "of GenAge involved finding novel genes that may be linked toaging by way of an analysis of proteinprotein interactions. Theprinciple being that proteins not previously thought to berelated to aging which interact with a large number of proteinsdirectly linked to aging might too be involved in aging and arethus promising candidates for future studies (de Magalhes &Toussaint, 2004; Budovsky et al ., 2007). Similar works are made", + "2009, with over 400 genes added in the current update (Ta-ble1), includingmiRNAs for thefirst time. GenAge has proven a valuable resource for ageing re- search, as evidence by many publications. A systems levelanalysis of the GenAge human genes database identified a robust group of ageing-specific network characteristics, re- vealingageinggenesasnetworkhubs( 11).Moreover,inan analysis of genes in the ageing human brain, 54 genes with sustained, consistent expression and 23 genes with DNA", + "a curated database of genes potentiallyassociated with human aging, and a list of genes testedfor their association with human longevity. A myriad ofbiological data and information is included for hundredsof genes, making GenAge a reference for research thatreflects our current understanding of the genetic basis ofaging. GenAge can also serve as a platform for thesystems biology of aging, and tools for the visualizationof proteinprotein interactions are also included. AnAgeis a database of aging in", + "et al ., 2007). In a sense, GenAge offers an overall view of what is presently known about thegenetics of aging in model organisms and in humans that canbe used for numerous studies, including in contemporary functionalgenomics and systems biology methods.Table 2 Criteria used to select entries for inclusion in the GenAge human data set Main reason for selectionNumber of genes Evidence directly linking the gene product to aging in humans 3", + "senescence.info/genes/) is to host high-quality curatedgene-centric information relevant to human ageing.Although initially GenAge was designed to include onlyhuman genes potentially associated with ageing, thedatabase has signicantly grown since, and several newgene sets have been added to it. For example, GenAge includes, since 2008, a list of genes from model organisms based on genetic manipulation experiments (2). Currently, the database is divided into three main", + "information source on the genetics of aging. In particular forthe human data set, a wealth of biological data is provided foreach entry, including relevant information in the context ofbiogerontology (see example below) to an extent that is notavailable in larger, more generic databases like Entrez Gene.Therefore, to learn about the involvement of a given gene inaging, a quick search in GenAge is the best place to start.GenAge features a sophisticated search engine and its user-friendly interface is easy" + ], + "answer": "GeneNetwork is a resource that has been significantly updated and enhanced to include data from 10 species, multi-omics analysis, updated code, and new tools. It serves as an exciting resource for predictive medicine and systems genetics, constantly being maintained and improved [4]. In relation to aging research, GeneNetwork is used to study genetic networks and pathways linked with aging. For example, researchers use GeneNetwork to construct modular networks of aging, which can provide insights into how different genes interact and affect longevity and aging processes [1]. This network-based approach allows for the identification of potential longevity genes and the links between genes and aging-related diseases [3]. Thus, GeneNetwork plays a crucial role in the functional genomics of aging by enabling the analysis and visualization of complex genetic interactions and their implications for aging and longevity.", + "question": "What is GeneNetwork and how does it relate to aging research?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_aging_2 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_aging_2 new file mode 100644 index 0000000..2e25fb7 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_aging_2 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2002 - Pharmacology, Genomics, and the Evolutionary Biology.pdf", + "2001 - A genome-wide scan for linkage to human.pdf", + "2010 - Genetics and genomics of human ageing.pdf", + "2011 - Genetics and genomics of human ageing.pdf", + "2023 - A transcriptome-based single-cell biological age model.pdf", + "2011 - A genome-wide association study of aging.pdf", + "2021 - Footprints in the Sand Deep Taxonomic Comparisons in Vertebrate Genomics to Unveil the Genetic Programs of Human Longevity.pdf", + "2002 - Pharmacology, Genomics, and the Evolutionary Biology.pdf", + "2011 - Genomics of human longevity.pdf", + "2011 - A genome-wide association study of aging.pdf" + ], + "extraction_id": [ + "aa03a9d5-4e30-5fb0-bee1-6dd8e6a549b3", + "17246c43-2e44-579b-867d-3dc7150ceedd", + "04babc6e-5138-5804-a150-70254859800d", + "27e291f1-e6bf-5e76-9245-522de74ea63b", + "0fd46f00-d3e1-54f4-9395-6c3e8294ed51", + "253a4339-29d4-58c2-8a01-5137d94873b6", + "34223e0e-590c-5f26-b120-b7250cd91b99", + "e501662f-ffca-563b-97a7-b682a5d7f6ba", + "7b101eb3-7990-5345-b510-c0be15f063a7", + "beab62d0-2e6f-5d77-b0a2-7375a9ed1364" + ], + "document_id": [ + "1bc636a3-6ce0-5fea-b549-0dae90a78f1b", + "1431984a-82d9-51d4-a23c-5f76a02ab554", + "633f3149-e966-53ef-aa7d-b759398ed541", + "08eee102-d627-5f1b-84c7-603c38981adf", + "9be234b7-f37d-5cd5-8895-bfe676441b2f", + "8e9c1150-1047-54a2-bf85-1cc5000a6811", + "0dc45abe-ab02-5b07-9916-7093b53323c0", + "1bc636a3-6ce0-5fea-b549-0dae90a78f1b", + "2e038219-fdaa-506f-9cd3-51379054130e", + "8e9c1150-1047-54a2-bf85-1cc5000a6811" + ], + "id": [ + "chatcmpl-ADZVIyiCYn4oPG1At1d3sSBHcOoYZ", + "bf384c33-974b-57c3-867d-3515e1d45c49", + "e0cce1c5-8709-5218-99b6-48a6ba242931", + "62e2bf90-fdb9-5499-a063-cee6c92feb40", + "25a0cb1d-0207-5197-9b6a-389b16c1f17e", + "9f9fef49-0bda-5948-93bd-0f8f43bbefdf", + "f1f870c1-b1ed-5eeb-8831-3484d35414b2", + "f6ed1f0c-e4ea-5459-bb63-136decc11eee", + "86393802-9171-57d8-806d-6d2ccfb3f0b2", + "45e52016-b6ef-5efb-a9e8-5a88341f3300", + "146229ff-4d17-5319-88e4-6040f30cf0c1" + ], + "contexts": [ + "that is differentiated at hundreds of loci. Many ofthe loci that control aging in Drosophila will not have the same effect on human aging. On the other hand,we expect that other loci will work in a parallelmanner in humans. We have no way of knowing a priori which group any particular locus will belong in. Thus, the individual mutants that increase Drosophila lifespan may or may not come from loci", + "effect fundamental mechanisms of aging (14, 16). The drawbacksof such studies include the improbability of picking the right geneto study the myriad of known and unknown genes affecting theprocess of interest (17). The linkage study described heremarkedly improves the efficiency of such association studies bydefining a region likely to contain polymorphism(s) with signif-icant influence on life span. Additional association studies with these families and repli-", + "understanding of molecular mechanisms underlyingthe human ageing process. Like other complexhuman traits, nding common variants that accountfor the entire genetic component of human lifespan variability has proved difcult. If rare variants rather than common variants explain most of the genetic vari-ation in ageing among humans, new genotypingtechniques and new analysis methods must be devel-oped to nd genes and pathways involved in ageing.Next-generation sequencing technologies are faster", + "understanding of molecular mechanisms underlyingthe human ageing process. Like other complexhuman traits, nding common variants that accountfor the entire genetic component of human lifespan variability has proved difcult. If rare variants rather than common variants explain most of the genetic vari-ation in ageing among humans, new genotypingtechniques and new analysis methods must be devel-oped to nd genes and pathways involved in ageing.Next-generation sequencing technologies are faster", + "Map contains 1119 and 1459 curated human and mouse aginggenes, respectively, covering almost all scales of aging, rangingfrom molecular damage to genetic predisposition. Cross-speciescomparison revealed a modest overlap between known humanand mouse aging genes, suggesting both conservation of core sen- escence pathways and fundamental differences in aging between mice and humans (Fig. 2E). Aging-associated genes can alternatively be identified in a", + "Several explanations are possible for the lack of genome- wide signicant ndings. First, mortality is arguably 1 ofthe most complex phenotypes, and several trajectories to-ward extreme old age have been identied (Evert et al.,2003). Multiple genes could mediate the aging process butwould have their effects through numerous different patho-physiological processes and diseases that act as intermediate", + "discover core mechanisms of regulation.ANALYSIS OF HUMAN VARIATION IN THE GENETIC CONTROL OF LONGEVITY Heritability studies have convincingly demonstrated that at least some fraction of human lifespan is heritable. In tandem, large-scale genome-wide association studies (GWAS) have identied numerous loci associated with age-related traits (Buniello et al., 2019). While genetic studies have functionally shown an inverse eect of multiple age-related, disease-", + "[12]More than 1000 loci exhibit age-dependent changes in geneexpression (1264 genes). This is a substantialproblem, because not all of these loci will be causally involved in aging, and there are so many to sort out. An additional application of gene chip technologyis to compare ies with and without a lifespanmodulating physiological treatment. Pletcher et al.", + "such alleles. The frequency of genetic variants wastypically compared between highly aged cases andyoung controls, revealing loci at which genetic variantsmay contribute to a higher or lower probability ofsurvival into old age. So far, this approach hasmainly been applied to study single candidate genessuch as the mammalian orthologues of loci in IIS sig-nalling pathways that emerged from lifespan extensionstudies in animal models. An interesting observationthat needs to be taken into human studies is the", + "Kenyon, 2010; Vellai et al., 2003 ). However, in humans, common variants within genes involved in these pathways have not been consistently associated with lifespan ( Chris-tensen et al., 2006; Kenyon, 2010; Kuningas et al., 2008; Vijg and Suh, 2005 ). The lack of success in the identication of genes related to aging in humans may be due to the complexity of the phenotype. One approach to investigate aging and longevity is to compare frequencies of genetic variants between no-" + ], + "answer": "Mapping gene loci that control aging in humans is difficult due to several reasons: 1. **Complexity of the Phenotype**: Aging is a highly complex phenotype influenced by numerous genes and their interactions with environmental factors. This complexity makes it challenging to identify specific genetic loci that control aging [10]. 2. **Multiple Pathophysiological Processes**: Multiple genes could mediate the aging process through various pathophysiological processes and diseases, adding layers of complexity to the genetic mapping [6]. 3. **Rare vs. Common Variants**: Finding common genetic variants that account for the entire genetic component of human lifespan variability has proven difficult. If rare variants explain most of the genetic variation in aging, new genotyping techniques and analysis methods are required to identify these genes and pathways [3], [4]. 4. **High Number of Loci**: There are more than 1000 loci that exhibit age-dependent changes in gene expression. Not all of these loci are causally involved in aging, making it a substantial challenge to sort out which ones are relevant [8]. 5. **Species Differences**: Many loci that control aging in model organisms like Drosophila may not have the same effect on human aging. This lack of direct correlation between species complicates the identification of relevant human aging genes [1]. 6. **Heritability and Genetic Variation**: While some fraction of human lifespan is heritable, the genetic variation contributing to aging is complex and involves numerous loci associated with age-related traits, further complicating the mapping process [7]. These factors collectively contribute to the difficulty in mapping gene loci that control aging in humans.", + "question": "Why is it so diffuclut to map gene loci that control aging in humans?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_aging_3 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_aging_3 new file mode 100644 index 0000000..1d57222 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_aging_3 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Protecting the Aging Genome.pdf", + "2013 - Pathways, Networks and Systems Medicine Conferences.pdf", + "2012 - Pleiotropic Cellular Functions of PARP1 in Longevity.pdf", + "2008 - Biotools for Determining the Genetics of Susceptibility to Infectious Diseases.pdf", + "2008 - (Infectious Disease) Karl A. Western (auth.), Vassil St. Georgiev PhD, Karl A. Western MD, John J. McGowan PhD (eds.) - National Institute of Allergy and Infectious Diseases, NIH_ Frontiers in Researc (3).pdf", + "1999 - The NOD mouse model of type 1 diabetes.pdf", + "2012 - Genome-Wide Analysis of Yeast Aging.pdf", + "2005 -Liang- GENETIC REGULATION OF HEMATOPOIETIC STEM CELL NUMBERS IN MICE.pdf", + "2005 - GENETIC REGULATION OF HEMATOPOIETIC STEM CELL NUMBERS IN MICE.pdf", + "2006 - Molecular pathogenesis of thyroid cancer the significance.pdf" + ], + "extraction_id": [ + "58c6c8e0-734b-539d-8e50-fd3cb02f650e", + "ee9fd19c-ae3c-5da6-9fcd-264bafc68b55", + "254dda83-4350-5b57-b6e4-638addaf7ce3", + "30fc6495-2cc4-5c3a-9d49-555478243db1", + "630a9f0d-d04f-581b-a9a0-3d2de4fff6fe", + "4c08454a-1c63-52e3-b382-0a33cd46b523", + "5436985c-1a11-544d-b935-fe68ee75a956", + "20ef68d8-5bd7-5ed1-937f-4be14c6ce1b0", + "70332eb3-8348-53b6-abd6-724515f363db", + "48a746ad-a07a-5c53-89dc-3c2768900f0d" + ], + "document_id": [ + "bb774030-2570-5596-b2ab-b8f57ff81086", + "b50a9732-7d01-5d4d-8f33-a9d43dbc7df3", + "e67324c0-474b-5280-8cbc-3778c6c0e5f0", + "fcbbb3ce-6524-50e3-9f8d-c191dc551231", + "4db8c752-c8e2-5f6d-a091-dc4f1d0c48bc", + "cc139813-6771-5434-b948-381291c86509", + "cf24db9a-e013-5780-8b0f-369c56143f29", + "6f3d464d-8df0-560e-b579-942810e1107c", + "815a8b4a-6902-5bb9-87e0-563e1ba7a38a", + "d05e3aba-f8c1-5c5c-afff-679fa14b9a16" + ], + "id": [ + "chatcmpl-ADZVPjiJlVpPgOZJvVtmrRMtG8Eg7", + "dfd32439-5b44-5e43-a85b-6dd58810b9ed", + "c02a78d4-b932-5d71-b183-8b1965fef470", + "fdfc9b00-1bce-5f6b-b20f-c516c7b5448a", + "2258748b-d21f-577b-a1f8-0ba4f61b6e30", + "de267316-5a20-5a54-b22f-30c8e0bf426e", + "31910427-870d-5c8c-846f-d355211c632a", + "ee2ce54b-850a-5d36-8781-f8b23585f97d", + "c48f36fa-a9c7-5b9c-a7d1-26850026f3a3", + "3a15c325-3b6e-54ff-a58a-0e61631ed073", + "fe4906aa-37b1-5514-962c-1e8dc5b2fb13" + ], + "contexts": [ + "Cell Death A form of programmed cell death, apoptosis is necessary for normal cell turnover and is essential to a plethora of other biological processes. Apoptosis can be executed via Bcl-2 activation of caspases, via signals from the death receptor on the plasma membrane, or via induction by granzyme Bsecreted from cytotoxic T cells (Tc cells) [ 35]. Endonucleases and proteases are activated by active caspases, eventually leading to the death of the cell. With age, however, apoptotic activity changes.", + "(during development and for maintenance of homeostasis) in multi -cellular organism is apoptosis, which is character ized by a sequence of well -defined events resulting in cell destruction. Dysregulation of apoptosis is responsible for many physiological health problems and diseases; therefore, it is necessary to understand the responsible signaling pathways and complex interplay of cellularprocesses. Results: A combined mathematical model of apoptosis", + "is, apoptosis and necrosis. Apoptosis is considered as thedefault pathway, where cell death occurs in a controlledmanner resulting in the elimination of cells by macrophageswithout secondary damage of the surrounding cells. In con-trast, necrosis is considered an uncontrolled process whichleads to disruption of cells promoting tissue inammation[187]. Several transition states between the two pathways", + "tion of cells undergoing apoptosis. Immunol Today 14: 131 136. 82. Platt N, Silva RP, da Gordon S (1998) Recognizing death: the phagocytosis of apoptotic cells. Trends Cell Biol 8: 365 372. 83. Giles KM, Hart SP, Haslett C, Rossi AG, Dransfield I (2000) An appetite for apoptotic cells? Controversies and challenges. Br J Haematol 109: 1 12.", + "tion of cells undergoing apoptosis. Immunol Today 14: 131 136. 82. Platt N, Silva RP, da Gordon S (1998) Recognizing death: the phagocytosis of apoptotic cells. Trends Cell Biol 8: 365 372. 83. Giles KM, Hart SP, Haslett C, Rossi AG, Dransfield I (2000) An appetite for apoptotic cells? Controversies and challenges. Br J Haematol 109: 1 12.", + "the induc-tion of apoptosis.", + "to cancer , b ut probably not rele v ant to the i ntrinsic aging process i n yeast. Apoptosis Cell suicide, or apoptosis, i s a well-studied biological phenomenon in multicellular or g anisms t hat allo ws specic cells to be remo v e d during t he de v e lopment of com- ple x tissues, o r potentially dangerous damaged cells to be destro yed for t he benetof the w hole o r g anism. T he lack of an apparent e v olutionary benet for s uch a p ro-", + "15Apoptosis is caused by the activation of the caspase cascade, which isinitiated by two signaling routes (stress-induced death and death-domainreceptor-induced death) (Domen 2001). This process can be prevented by anti-apoptotic molecules, such as Bcl-2 (Domen and Weissman 2000). Directevidence for the involvement of apoptosis in HSC number regulation came fromthe findings that overexpression of the anti-apoptotic gene bcl-2 led to increasednumbers of Thy-1.1low, Sca-1+, c-kit+, Lin- cells, a population", + "15Apoptosis is caused by the activation of the caspase cascade, which isinitiated by two signaling routes (stress-induced death and death-domainreceptor-induced death) (Domen 2001). This process can be prevented by anti-apoptotic molecules, such as Bcl-2 (Domen and Weissman 2000). Directevidence for the involvement of apoptosis in HSC number regulation came fromthe findings that overexpression of the anti-apoptotic gene bcl-2 led to increasednumbers of Thy-1.1low, Sca-1+, c-kit+, Lin- cells, a population", + "Apoptosis modulating genesApopotosis or programmed cell death is associated withalterations in cell morphology, particularly the nucleus, withendonucleatytic cleavage of DNA into nucleosomal lengthfragments.Apoptosis may resultfrom withdrawalofgrowth signals.Fas, a transmembrane protein of the nerve growth factor/tumor necrosis factor receptor family signals apoptotic de-ath signals apoptotic death in some cell types. Fas but notbel-2 gene expression is negatively regulated by TSH (Ka-wakami et al., 1996)," + ], + "answer": "Apoptosis is a form of programmed cell death that is essential for normal cell turnover and various biological processes [1]. It is characterized by a sequence of well-defined events leading to cell destruction [2]. Apoptosis occurs in a controlled manner, resulting in the elimination of cells by macrophages without causing secondary damage to surrounding cells [3]. This process can be initiated through several pathways, including Bcl-2 activation of caspases, signals from death receptors on the plasma membrane, or induction by granzyme B secreted from cytotoxic T cells [1].", + "question": "What is apoptosis?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_aging_4 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_aging_4 new file mode 100644 index 0000000..a42f857 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_aging_4 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2010 - Do different neurons age differently Direct genome-wide analysis of aging in single identified cholinergic neurons.pdf", + "2020 - Whole-genome sequencing of Chinese.pdf", + "2011 - EXPLOITING NATURAL AND INDUCED GENETIC VARIATION TO STUDY HEMATOPOIESIS.pdf", + "2000 - Genome-wide study of aging and oxidative stress.pdf", + "2009 - The Human Ageing Genomic Resources online.pdf", + "2020 - A multidimensional systems biology.pdf", + "2008 - Combining transcriptional profiling and genetic linkage analysis to uncover gene networks operating in hematopoietic stem cells and their progeny.pdf", + "2012 - Genome-Environment Interactions That Modulate.pdf", + "2012 - Genome-Environment Interactions That Modulate.pdf", + "2012 - Genome-Environment Interactions That Modulate.pdf" + ], + "extraction_id": [ + "81c68113-aa96-5af3-b4fc-5898fa20e379", + "0d3deffe-1f4d-5a6b-9acb-56d56141ad60", + "2b1a11ea-1574-5df6-b73a-a34052098751", + "ac5d00c0-f445-5c6a-b248-12c82c985d9a", + "52c67b46-63f2-54ae-a78e-e9d54a55f6e4", + "9d1656aa-32d2-5094-8232-4817655b1cbd", + "bf7b1e3c-bb4f-5a88-9167-a8c3b90cd68a", + "d59d7882-333d-5576-86ab-3cfa6354b946", + "d59d7882-333d-5576-86ab-3cfa6354b946", + "a01ca925-4ccf-5863-a162-7bd4c754fe89" + ], + "document_id": [ + "153b070f-0291-5ed4-ad33-edea5e3fa8f7", + "9ac921c7-3991-579b-bd53-7966b91e3aae", + "6f250b15-61b3-57ed-8900-5aa4a173fa8c", + "3fc2266c-d677-54f9-b3a2-5129eedf214a", + "e43cd3b6-ad8e-5422-ba7c-ceb6e66cc529", + "d040bfe3-e409-5b5c-b8f8-f3dd4fc060e3", + "af6e0103-849d-542f-bca7-0251082bc0b3", + "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec" + ], + "id": [ + "chatcmpl-ADZVSukRfQ2bwSsJtuTxllhMDtRvP", + "6f04401a-b938-5a60-8b69-d37f9086748c", + "02b405a4-71d7-5b85-9138-8a97c537601c", + "8f8848f4-d5fb-5f8c-a6b1-0f965f2abbc6", + "b58deffd-3cd3-5b7b-893d-b9cfc880830b", + "8fd5ab85-67ed-55e6-bbfa-09436c4fdbfb", + "61baeaa5-d65a-54b5-bfee-9bab8bbf1985", + "2eb33321-d0fe-5fc4-aab0-7184f2b397e0", + "b719fbc0-94e4-5df0-abb7-0d13fc36214c", + "413f8f54-b5cc-5089-9f5c-d9e3b8bcf594", + "3c369292-4b9c-5156-a80f-4b3301026f30" + ], + "contexts": [ + "OTHER AGING RELATED GENES", + "ation of the process of aging. Studies revealed from 300 to 750 genes related to longev- ity that are critically involved in a variety of life activities, such as growth and developme nt, energy metabolism, oxi- dative stress, genomic stability maintenance, and neurocog- nition [ 4]. These candidate genes include mainly APOE, a gene involved in lipoprotein metabolism [ 5,6]. Others are those involved in cell cycle regulation, cell growth and signal transduction, the maintenance of genome stability,", + "down-regulated during aging were genes involved in DNA repair and chromatin remodelling. 55 While these studies revealed thousands of age-regulated genes, the ultimate causes of these expression perturbations remain unknown. Analyzing age-dependent gene expression changes using multi-dimensional genetical genomics could bring the identification of genes causing the age-induced alterations and thereby future therapeutic intervention strategies one step closer. Adding the dimension of epigenetics", + "dam-age, as well as genes involved in inducing apoptosis (10, 11). Theaging process is also accompanied by changes in the expressionpatterns of a number of genes (1214). How the regulation ofgene expression in aging correlates with that in response tooxidative stress, however, is understood poorly.", + "www.ncbi.nlm.nih.gov/homologene) of genes strongly asso-ciated with aging in model organisms. Also included are genesin which mutations result in segmental progeroid syndromes,such as the Werners syndrome gene, as well as genes criticalin pathways previously related to aging, such as the insulin/insulin-like signalling pathway (de Magalhes et al ., 2005a). The", + "genes driving cellular senescence, and perform various integrative analyses. Genes inducing cellular senescence tend to be overexpressed with age in human tissues and are significantly overrepresented in anti-longevity and tumor-suppressor genes, while genes inhibiting cellular senescence overlap with pro-longevity and oncogenes. Furthermore, cellular senescence genes are strongly conserved in mammals but not in invertebrates. We also build", + "those down-regulated during aging were genes involved in DNA repair and chromatin remodelling (Chambers et al. 2007b ). While these studies revealed thousands of age- regulated genes, the ultimate causes of these expressionperturbations remain unknown. Analyzing age-dependent gene expression changes using multidimensional genetical genomics could bring the identification of genes causingthe age-induced alterations and thereby future therapeutic intervention strategies one step closer.", + "lar signatures of mammalian aging. Some of the genes", + "overexpressed with age seem to be a response to aging,in that they have been previously found to have protec-tive functions (de Magalha es et al., 2009b). As such,these genes may help organisms manage aging andcould be targets for manipulation. Likewise, gene ex-pression analysis of CR has been conducted to identifyassociated genes (Lee et al., 1999, 2000). A number ofmolecular signatures have emerged from such studiesthat could be useful to identify candidate processes andpathways that affect aging,", + "al., 2009; Stanfel et al., 2009). Many of these genesmodulate the response to environmental signals, such asfood availability, and act in signaling pathways that ifunderstood can be targeted (Fig. 1). The genetic regula-tion of aging is therefore an emerging field with multipleapplications in the human nutrition, cosmetic, and phar-maceutical industries. AGING GENES AS TARGETS FOR DRUG DISCOVERY 91" + ], + "answer": "Several genes are involved in the aging process, as identified in the provided context: 1. **APOE**: This gene is involved in lipoprotein metabolism and is one of the candidate genes related to longevity [2]. 2. **Genes involved in cell cycle regulation, cell growth, and signal transduction**: These genes play a role in maintaining genome stability and are implicated in the aging process [2]. 3. **Genes involved in DNA repair and chromatin remodeling**: These genes are down-regulated during aging, indicating their involvement in the aging process [3], [7]. 4. **Genes inducing apoptosis**: These genes are also associated with the aging process [4]. 5. **Werners syndrome gene**: Mutations in this gene result in segmental progeroid syndromes, which are related to aging [5]. 6. **Genes in the insulin/insulin-like signaling pathway**: These genes are critical in pathways previously related to aging [5]. 7. **Genes driving cellular senescence**: These genes tend to be overexpressed with age in human tissues and are significantly overrepresented in anti-longevity and tumor-suppressor genes [6]. These genes collectively contribute to various aspects of the aging process, including genomic stability, cellular senescence, and response to oxidative stress.", + "question": "which genes are involved in the aging process" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_aging_5 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_aging_5 new file mode 100644 index 0000000..55613f3 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_aging_5 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2012 - Genome-Environment Interactions That Modulate.pdf", + "2005 - Genomes Optimize Reproduction Aging as a Consequence of the Developmental Program.pdf", + "2007 - Genome Dynamics and Transcriptional Deregulation.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2005 - Genomes Optimize Reproduction Aging as a Consequence of the Developmental Program.pdf", + "2005 - Aging and Genome Maintenance.pdf", + "2001 - The genetics of aging.pdf", + "2009 - Genomic instability and DNA damage responses in progeria arising.pdf", + "2017 - An integrative metabolomics.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf" + ], + "extraction_id": [ + "ac2b646d-b25b-55d2-b1f9-1180a7f0b7bf", + "dd3d8efb-1ce0-532e-9c77-caccb0820944", + "e9cec19b-9005-57e0-991e-c8b0125040df", + "65bc0695-ec07-50fa-83c7-f36763dc96db", + "dd3d8efb-1ce0-532e-9c77-caccb0820944", + "05fb83b5-b589-565f-89fa-c7a2fe1ec048", + "3f6cd069-806a-513a-a5bf-e547fd1e9737", + "02f84d0b-eb00-5252-a647-b2b042f1f7a9", + "af201c05-daed-5cba-abc8-e714483e602f", + "af6e2449-9591-51e8-bd8a-4f6eab30843e" + ], + "document_id": [ + "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "94d264da-5e72-5eb9-9fd9-a81ac2b91b77", + "296e3322-a519-5e4f-b955-8cc03c68e78f", + "62b635c3-040e-512a-b016-6ef295308a1e", + "94d264da-5e72-5eb9-9fd9-a81ac2b91b77", + "ecf53e34-4491-5db1-ad37-304671799179", + "aa9a9193-b6f3-5ef8-aefd-e01ec44abb46", + "b7d96f9f-8ad4-5f8f-94f9-60404806d478", + "cb0831f4-540a-5620-b69e-03d6127f84e5", + "62b635c3-040e-512a-b016-6ef295308a1e" + ], + "id": [ + "chatcmpl-ADZVYj6XpaNI2z12sJiqcMEMc2C8W", + "b008df20-7a1c-5cee-9d71-a9758492b256", + "7e3dbd4f-cc54-549c-b7f2-d7a57b176185", + "b4eea796-bb34-591b-8873-b6d8b773d24f", + "1b4ba691-cd5b-52dd-9b50-ac404c6559cd", + "37d5273c-5359-598a-ae9a-eb407314774b", + "1acd92f0-07a2-53ac-86d8-2195a37e8cab", + "228391ea-b709-5d78-96d7-c480802f5cbc", + "1f14235a-ae99-58f9-8f39-a6dd29c2f676", + "c5c49956-fdf4-5aef-8852-2c6d14fd05b4", + "2d467a17-2367-5736-b0dd-20239f85cfa2" + ], + "contexts": [ + "in the aging process.", + "age-related decline results from damaging by-products of metabolism and/or inefficient repairmechanisms (27, 32). According to this view, dam-agewhich can take on many formsaccumu-lates throughout the life span (38). The exponentialincrease in mortality and the functional declinethat characterize aging, however, only begin aftersexual maturity, whether this occurs at age 13, as inhumans, age 5, as in monkeys, or at less than 2months, as in mice. Therefore, one alternative viewis that aging is perhaps", + "of a pro-cess of mutation accumulation in somatic cells. While im-plicated as a general cause of aging, no specic mecha-nism has been proposed as to how mutation accumulationcould ever lead to the multitude of degenerative processesthat comprise aging. We have now demonstrated that alarge variety of mutations accumulate with age at greatlydifferent rates in a tissue-specic manner. More recentlywe have shown that while some organs, such as brain, donot seem to accumulate mutations with age at all,", + "this process between proteins and other macromolecules responsible for ageing, while the theory of free radicals suggests that ageing is the result of inadequate pro- tection against cell and tissue damage by free radicals and oxidative stress through- out life. Finally, the wear-and-tear theory poses that the cumulative damage that eventually leads to ageing and death is, in fact, the result of the continuous function- ing of vital processes, during which stochastic errors gradually arise.", + "Many mechanistic theories of aging argue that", + "cell senescence and cell death pathways, are a major cause of aging pheno-types, such as organ atrophy. This would appear to be a pre-programmed cause of aging, since it is a consistent response of a sizable fraction of the cell population. However, cellular responses to damage are unlikely to be the onlyexplanation for aging, since even very old organisms still appear to have am-ple tissue capacity left to function optimally.", + "function during aging.", + "INTRODUCTION The aging process represents progressive changes in a cell or an organism which culminate in death due to accumulated defects in function leading to system failure [1]. These defe cts result in part from accumulated damage to DNA. Such damage may result www.impactaging.com AGING, January 2009, Vol. 1. No 1 Review", + "that induce complex molecular changes and, in turn, a deterioration of cellular structures and function. These changes are major causes of age-related diseases like cancer or cardiovascular disorders [1, 2]. The main mo- lecular adaptations occurring during aging are loss ofgenomic stability due to reduced DNA repair capacities [3], loss of proliferative potential caused by increased senescence [1, 4], and age-related alterations in the DNA-methylation patterns that affect cellular plasticity", + "cause in turn metabolic and cognitive alterations, resulting in increasing vulnerabil- ity to environmental challenge and a growing risk for disease and death [1]. Since aging comprises the greatest risk factor for a variety of chronic diseases, includ- ing cancer, cardiovascular disorders, and neurodegenerative diseases [2], one of the goals of biomedical research is to decipher the molecular mechanism underlying aging, which in turn might facilitate the development of treatments aimed at delay-" + ], + "answer": "The aging process is caused by several factors, including: 1. **Accumulated Damage**: Age-related decline results from damaging by-products of metabolism and/or inefficient repair mechanisms, leading to damage that accumulates throughout the lifespan [2]. 2. **Mutation Accumulation**: A process of mutation accumulation in somatic cells, although no specific mechanism has been proposed for how this leads to the multitude of degenerative processes that comprise aging [3]. 3. **Free Radicals and Oxidative Stress**: The theory of free radicals suggests that aging results from inadequate protection against cell and tissue damage by free radicals and oxidative stress throughout life [4]. 4. **Wear-and-Tear**: The wear-and-tear theory posits that cumulative damage from the continuous functioning of vital processes leads to aging and death due to stochastic errors gradually arising [4]. 5. **Cell Senescence and Death Pathways**: Cell senescence and cell death pathways are major causes of aging phenotypes, such as organ atrophy, which appear to be pre-programmed responses of a sizable fraction of the cell population [6]. 6. **Accumulated Defects in Function**: Progressive changes in a cell or organism lead to accumulated defects in function, resulting in system failure and death [8]. 7. **Loss of Genomic Stability**: Loss of genomic stability due to reduced DNA repair capacities, loss of proliferative potential caused by increased senescence, and age-related alterations in DNA-methylation patterns that affect cellular plasticity [9]. These factors collectively contribute to the aging process and the associated decline in physiological functions.", + "question": "what causes the aging process" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_aging_6 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_aging_6 new file mode 100644 index 0000000..adf6e59 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_aging_6 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2010 - Do different neurons age differently Direct genome-wide analysis of aging in single identified cholinergic neurons.pdf", + "2020 - A multidimensional systems biology.pdf", + "2012 - Genome-Environment Interactions That Modulate.pdf", + "2020 - Whole-genome sequencing of Chinese.pdf", + "2012 - Genome-Environment Interactions That Modulate.pdf", + "2009 - The Human Ageing Genomic Resources online.pdf", + "2011 - EXPLOITING NATURAL AND INDUCED GENETIC VARIATION TO STUDY HEMATOPOIESIS.pdf", + "2000 - Genome-wide study of aging and oxidative stress.pdf", + "2007 - Temporal and spatial transcriptional profiles.pdf", + "2008 - Evolution of the Aging Brain Transcriptome and Synaptic.pdf" + ], + "extraction_id": [ + "81c68113-aa96-5af3-b4fc-5898fa20e379", + "9d1656aa-32d2-5094-8232-4817655b1cbd", + "d59d7882-333d-5576-86ab-3cfa6354b946", + "0d3deffe-1f4d-5a6b-9acb-56d56141ad60", + "a01ca925-4ccf-5863-a162-7bd4c754fe89", + "52c67b46-63f2-54ae-a78e-e9d54a55f6e4", + "2b1a11ea-1574-5df6-b73a-a34052098751", + "ac5d00c0-f445-5c6a-b248-12c82c985d9a", + "2e42619b-d0b2-5d33-aab8-6f04002ee807", + "bab54a5c-0b3c-5c5b-9b2b-5e7a67492a9c" + ], + "document_id": [ + "153b070f-0291-5ed4-ad33-edea5e3fa8f7", + "d040bfe3-e409-5b5c-b8f8-f3dd4fc060e3", + "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "9ac921c7-3991-579b-bd53-7966b91e3aae", + "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "e43cd3b6-ad8e-5422-ba7c-ceb6e66cc529", + "6f250b15-61b3-57ed-8900-5aa4a173fa8c", + "3fc2266c-d677-54f9-b3a2-5129eedf214a", + "38f27ec7-08bf-5397-b2b8-bde95e0dc3f8", + "cf413489-3986-5a5f-925d-58f94fa57428" + ], + "id": [ + "chatcmpl-ADZVfJ7vrTDhDZNUBDMrr0RnqmSWE", + "6f04401a-b938-5a60-8b69-d37f9086748c", + "61baeaa5-d65a-54b5-bfee-9bab8bbf1985", + "b719fbc0-94e4-5df0-abb7-0d13fc36214c", + "02b405a4-71d7-5b85-9138-8a97c537601c", + "4d6876c5-9226-587c-8d3e-d4957ee42dba", + "8fd5ab85-67ed-55e6-bbfa-09436c4fdbfb", + "8f8848f4-d5fb-5f8c-a6b1-0f965f2abbc6", + "43abb9e9-5ffb-58d8-b5b9-251c50c1283d", + "bf2cd208-273f-5848-b243-df8b95ea7833", + "9430a0cd-5e05-536b-9d47-5b0b0674df5d" + ], + "contexts": [ + "OTHER AGING RELATED GENES", + "genes driving cellular senescence, and perform various integrative analyses. Genes inducing cellular senescence tend to be overexpressed with age in human tissues and are significantly overrepresented in anti-longevity and tumor-suppressor genes, while genes inhibiting cellular senescence overlap with pro-longevity and oncogenes. Furthermore, cellular senescence genes are strongly conserved in mammals but not in invertebrates. We also build", + "lar signatures of mammalian aging. Some of the genes", + "ation of the process of aging. Studies revealed from 300 to 750 genes related to longev- ity that are critically involved in a variety of life activities, such as growth and developme nt, energy metabolism, oxi- dative stress, genomic stability maintenance, and neurocog- nition [ 4]. These candidate genes include mainly APOE, a gene involved in lipoprotein metabolism [ 5,6]. Others are those involved in cell cycle regulation, cell growth and signal transduction, the maintenance of genome stability,", + "genes (http://genomics.senescence.info/genes/), more than700 genes have been identified that regulate lifespan inmodel organisms (de Magalha es et al., 2009a). Many ofthese genes and their associated pathwayssuch as theinsulin/IGF1/GH pathwayhave been shown to affect lon-gevity across different model organisms (Kenyon, 2010).Therefore, at least some mechanisms of aging are evolu-tionarily conserved and may have potential therapeuticapplications (Baur et al., 2006). For example, evidencesuggests the use of", + "www.ncbi.nlm.nih.gov/homologene) of genes strongly asso-ciated with aging in model organisms. Also included are genesin which mutations result in segmental progeroid syndromes,such as the Werners syndrome gene, as well as genes criticalin pathways previously related to aging, such as the insulin/insulin-like signalling pathway (de Magalhes et al ., 2005a). The", + "down-regulated during aging were genes involved in DNA repair and chromatin remodelling. 55 While these studies revealed thousands of age-regulated genes, the ultimate causes of these expression perturbations remain unknown. Analyzing age-dependent gene expression changes using multi-dimensional genetical genomics could bring the identification of genes causing the age-induced alterations and thereby future therapeutic intervention strategies one step closer. Adding the dimension of epigenetics", + "Aging is a biological process universal to eukaryotic organ- isms, and its underlying mechanisms are under intensive study. Genetic analyses of yeast, nematode, fly, and mouse haveuncovered a number of genes, whether mutated or misexpressed,that would increase the lifespans of these organisms (1). These genes include superoxide dismutase , a free-radical scavenger; methuselah , a potential G protein-coupled receptor, in Drosoph- ila melanogaster ; and p66 shc, an oxidative stress-response gene, in", + "The multifactorial and temporal features of aging can beanalyzed efficiently by genome-wide transcriptional profiling,which has been conducted in various model organisms and hu-mans (Melov and Hubbard 2004). Aging is associated with alter-ations in transcript levels of many genes, including those in-volved in evolutionarily conserved mitochondrial and protea-somal functions (McCarroll et al. 2004), some of which havebeen shown to be directly involved in regulating lifespan in C.", + "5. Jiang CH, Tsien JZ, Schultz PG, Hu Y (2001) The effects of aging on gene expression in the hypothalamus and cortex of mice. Proc Natl Acad Sci U S A 98: 19301934. 6. Lu T, Pan Y, Kao SY, Li C, Kohane I, et al. (2004) Gene regulation and DNA damage in the ageing human brain. Nature 429: 883891. 7. Fraser HB, Khaitovich P, Plotkin JB, Paabo S, Eisen MB (2005) Aging and gene expression in the primate brain. PLoS Biol 3: e274. 8. Zahn JM, Poosala S, Owen AB, Ingram DK, Lustig A, et al. (2007) AGEMAP: a" + ], + "answer": "Several genes are involved in the aging process, as identified in various studies: 1. **APOE**: This gene is involved in lipoprotein metabolism and is critically related to longevity [4]. 2. **Superoxide dismutase**: A free-radical scavenger gene that has been shown to increase lifespan in model organisms [8]. 3. **Methuselah**: A potential G protein-coupled receptor gene in *Drosophila melanogaster* associated with increased lifespan [8]. 4. **p66shc**: An oxidative stress-response gene involved in aging [8]. 5. **Insulin/IGF1/GH pathway genes**: These genes are part of a pathway that affects longevity across different model organisms [5]. 6. **Genes involved in DNA repair and chromatin remodeling**: These genes are down-regulated during aging [7]. 7. **Genes associated with cellular senescence**: These include genes that induce or inhibit cellular senescence, which are overexpressed with age and are linked to anti-longevity and tumor-suppressor functions [2]. Additionally, studies have identified between 300 to 750 genes related to longevity that are involved in various life activities such as growth, development, energy metabolism, oxidative stress, genomic stability maintenance, and neurocognition [4].", + "question": "which genes are involved in aging" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_aging_7 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_aging_7 new file mode 100644 index 0000000..ce4d749 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_aging_7 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2010 - Do different neurons age differently Direct genome-wide analysis of aging in single identified cholinergic neurons.pdf", + "2020 - Whole-genome sequencing of Chinese.pdf", + "2011 - EXPLOITING NATURAL AND INDUCED GENETIC VARIATION TO STUDY HEMATOPOIESIS.pdf", + "2009 - The Human Ageing Genomic Resources online.pdf", + "2000 - Genome-wide study of aging and oxidative stress.pdf", + "2012 - Genome-Environment Interactions That Modulate.pdf", + "2012 - Genome-Environment Interactions That Modulate.pdf", + "2000 - Genome-wide study of aging and oxidative stress.pdf", + "2008 - Genome-wide analysis of aging and learning-related genes.pdf", + "2008 - Combining transcriptional profiling and genetic linkage analysis to uncover gene networks operating in hematopoietic stem cells and their progeny.pdf" + ], + "extraction_id": [ + "81c68113-aa96-5af3-b4fc-5898fa20e379", + "0d3deffe-1f4d-5a6b-9acb-56d56141ad60", + "2b1a11ea-1574-5df6-b73a-a34052098751", + "52c67b46-63f2-54ae-a78e-e9d54a55f6e4", + "ac5d00c0-f445-5c6a-b248-12c82c985d9a", + "d59d7882-333d-5576-86ab-3cfa6354b946", + "a01ca925-4ccf-5863-a162-7bd4c754fe89", + "ac5d00c0-f445-5c6a-b248-12c82c985d9a", + "593d5c1f-316a-5eab-814c-4ba150111d3e", + "bf7b1e3c-bb4f-5a88-9167-a8c3b90cd68a" + ], + "document_id": [ + "153b070f-0291-5ed4-ad33-edea5e3fa8f7", + "9ac921c7-3991-579b-bd53-7966b91e3aae", + "6f250b15-61b3-57ed-8900-5aa4a173fa8c", + "e43cd3b6-ad8e-5422-ba7c-ceb6e66cc529", + "3fc2266c-d677-54f9-b3a2-5129eedf214a", + "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "3fc2266c-d677-54f9-b3a2-5129eedf214a", + "cae3fe6b-9fe6-561d-8a2f-fe9fbd580f3d", + "af6e0103-849d-542f-bca7-0251082bc0b3" + ], + "id": [ + "chatcmpl-ADZVlFDc0TCB7W4npOF5tI5Kq0cCy", + "6f04401a-b938-5a60-8b69-d37f9086748c", + "02b405a4-71d7-5b85-9138-8a97c537601c", + "8f8848f4-d5fb-5f8c-a6b1-0f965f2abbc6", + "8fd5ab85-67ed-55e6-bbfa-09436c4fdbfb", + "b58deffd-3cd3-5b7b-893d-b9cfc880830b", + "413f8f54-b5cc-5089-9f5c-d9e3b8bcf594", + "3c369292-4b9c-5156-a80f-4b3301026f30", + "43abb9e9-5ffb-58d8-b5b9-251c50c1283d", + "b284606e-a2db-5151-9d30-b591493b984d", + "2eb33321-d0fe-5fc4-aab0-7184f2b397e0" + ], + "contexts": [ + "OTHER AGING RELATED GENES", + "ation of the process of aging. Studies revealed from 300 to 750 genes related to longev- ity that are critically involved in a variety of life activities, such as growth and developme nt, energy metabolism, oxi- dative stress, genomic stability maintenance, and neurocog- nition [ 4]. These candidate genes include mainly APOE, a gene involved in lipoprotein metabolism [ 5,6]. Others are those involved in cell cycle regulation, cell growth and signal transduction, the maintenance of genome stability,", + "down-regulated during aging were genes involved in DNA repair and chromatin remodelling. 55 While these studies revealed thousands of age-regulated genes, the ultimate causes of these expression perturbations remain unknown. Analyzing age-dependent gene expression changes using multi-dimensional genetical genomics could bring the identification of genes causing the age-induced alterations and thereby future therapeutic intervention strategies one step closer. Adding the dimension of epigenetics", + "www.ncbi.nlm.nih.gov/homologene) of genes strongly asso-ciated with aging in model organisms. Also included are genesin which mutations result in segmental progeroid syndromes,such as the Werners syndrome gene, as well as genes criticalin pathways previously related to aging, such as the insulin/insulin-like signalling pathway (de Magalhes et al ., 2005a). The", + "dam-age, as well as genes involved in inducing apoptosis (10, 11). Theaging process is also accompanied by changes in the expressionpatterns of a number of genes (1214). How the regulation ofgene expression in aging correlates with that in response tooxidative stress, however, is understood poorly.", + "overexpressed with age seem to be a response to aging,in that they have been previously found to have protec-tive functions (de Magalha es et al., 2009b). As such,these genes may help organisms manage aging andcould be targets for manipulation. Likewise, gene ex-pression analysis of CR has been conducted to identifyassociated genes (Lee et al., 1999, 2000). A number ofmolecular signatures have emerged from such studiesthat could be useful to identify candidate processes andpathways that affect aging,", + "al., 2009; Stanfel et al., 2009). Many of these genesmodulate the response to environmental signals, such asfood availability, and act in signaling pathways that ifunderstood can be targeted (Fig. 1). The genetic regula-tion of aging is therefore an emerging field with multipleapplications in the human nutrition, cosmetic, and phar-maceutical industries. AGING GENES AS TARGETS FOR DRUG DISCOVERY 91", + "Aging is a biological process universal to eukaryotic organ- isms, and its underlying mechanisms are under intensive study. Genetic analyses of yeast, nematode, fly, and mouse haveuncovered a number of genes, whether mutated or misexpressed,that would increase the lifespans of these organisms (1). These genes include superoxide dismutase , a free-radical scavenger; methuselah , a potential G protein-coupled receptor, in Drosoph- ila melanogaster ; and p66 shc, an oxidative stress-response gene, in", + "nicance of genes that were found to be aected by aging,the most prominent appeared to be involved in processesthat involve cell division, cell death and apoptosis, migra-tion of cells, and dierentiation, all of which are consistentwith changes in the dierent stages of neurogenesis. Thesechanges at the molecular level agree with studies at the cel- lular level that report changes in rate of migration, dieren- tiation and neurogenesis with aging ( Seki & Arai, 1995;", + "those down-regulated during aging were genes involved in DNA repair and chromatin remodelling (Chambers et al. 2007b ). While these studies revealed thousands of age- regulated genes, the ultimate causes of these expressionperturbations remain unknown. Analyzing age-dependent gene expression changes using multidimensional genetical genomics could bring the identification of genes causingthe age-induced alterations and thereby future therapeutic intervention strategies one step closer." + ], + "answer": "Several genes are involved in the aging process, each playing a role in various biological activities and pathways. Key genes and their associated functions include: 1. **APOE**: Involved in lipoprotein metabolism [2]. 2. **Superoxide dismutase**: A free-radical scavenger [8]. 3. **Methuselah**: A potential G protein-coupled receptor in *Drosophila melanogaster* [8]. 4. **p66shc**: An oxidative stress-response gene [8]. 5. **Genes involved in DNA repair and chromatin remodeling**: These genes are down-regulated during aging [3], [10]. 6. **Genes involved in cell cycle regulation, cell growth, and signal transduction**: These genes help maintain genome stability [2]. 7. **Genes involved in inducing apoptosis**: These genes are associated with damage response [5]. 8. **Genes in the insulin/insulin-like signaling pathway**: Critical in pathways previously related to aging [4]. These genes collectively contribute to processes such as growth and development, energy metabolism, oxidative stress response, genomic stability maintenance, and neurocognition [2].", + "question": "what genes are involved in the aging process" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_aging_8 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_aging_8 new file mode 100644 index 0000000..b1bb064 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_aging_8 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2012 - Genome-wide association analysis of age-at-onset.pdf", + "2007 - Genetic correlates of brain aging on MRI and cognitive test measures a genome-wide association and linkage analysis in the Framingham study.pdf", + "2009 - MicroRNA Implications for Alzheimer Disease and other Human CNS.pdf", + "2018 - Genomics New Light on Alzheimer?s.pdf", + "2012 - Mitochondrial Genomic Analysis of Late Onset.pdf", + "2021 - A genome-wide association study with 1,126,563.pdf", + "2017 - Genomic Variants, Genes, and Pathways.pdf", + "2003 - The application of functional genomics.pdf", + "2018 - Cognitive decline and dementia in diabetes mellitus.pdf", + "2012 - Genome-wide association study of Alzheimer?s disease.pdf" + ], + "extraction_id": [ + "2a2e5ce1-cc56-579c-bf79-f9057f4c9671", + "b545e588-2876-5928-9c01-710c1371b44e", + "4b383c2a-f0de-5420-af8d-07060b8874f3", + "64f3adb4-e745-5738-af28-43c2a870c086", + "b826d64d-9d95-5522-8179-82f79d957c03", + "c4d63e5d-36ac-572e-8269-f9efd9c0437e", + "7cff03ac-de86-5e70-bbcb-dadc2fa447c3", + "171377f4-24a7-5cde-adff-c9c7096edc75", + "d2b4b131-b7c9-595b-813a-b0940c4e87c0", + "1f9039f1-91e9-538b-b709-a1880cf47007" + ], + "document_id": [ + "9874359e-5f5d-5e6a-9844-cd9a1d2cae24", + "56b25b5a-fc9e-5d61-8502-1c110466ba16", + "70d08119-d16d-5e9b-89ed-ec8547be125a", + "940593d2-04c3-59b9-a5bf-976febbc6f71", + "5404a17c-34a9-5881-8b1a-2acacdc996a8", + "77fafe7f-6f8f-5779-9d49-77213f149512", + "a5bf6a11-3ed5-5222-bc4d-d5149188cdbd", + "ec5f7b7d-6bd2-5580-bf3e-3c8b64a74169", + "fc7027a3-f885-55b8-b56d-bb8117e2a2f1", + "7e7a8526-ff6e-5c83-ae72-e45509e3b788" + ], + "id": [ + "chatcmpl-ADZVsDdzFGdoBGkCMl1sqg3Cv85XM", + "6ac3f90f-ec8a-55c7-a3f7-d597d9d7cb2e", + "4014c984-d6d9-5eb2-a25e-9e9fe15d1b41", + "cd8f4d4a-2b1a-542f-a3f3-364a83fb10fb", + "f24834c0-1862-5d9f-bdb6-2af38505aa5c", + "064a3510-9a3d-5b93-b848-69478e02e013", + "794ac337-0c08-52ca-9603-fe840fa343af", + "fc44e06f-a727-5544-ad7c-6ba3632552b7", + "5e6b9b4b-1e03-585a-af52-18a054e1e603", + "784e7626-1d9c-521b-84f3-965965435366", + "dac0ab78-d01f-5f95-a129-559cbe6791ec" + ], + "contexts": [ + "Introduction Alzheimers disease (AD), a devastating neurodegen- erative disease, is the most common form of dementiaamong the elderly. Genetically, AD is a complex and multifactorial disease with the possible involvement of multiple genes. The rare early-onset form of the diseaseusually follows an autosomal-dominant inheritance pattern and to date three genes have been identified: amyloid precursor protein ( APP) and presenilin 1 and 2(PSEN1 andPSEN2 ). The common late-onset form of", + "Background Age-related neurological diseases such as stroke and dementia represent a substantial population burden, and one in three persons will develop either stroke or demen- tia in their lifetime [1]. Twin studies suggest that 3778% of the variance in the age of onset of Alzheimer's disease (AD), the most common cause of dementia in the elderly, can be attributed to additive genetic effects [2,3]. Con- versely, cognitively healthy aging also has a substantial", + "cognitive status in Alzheimer's disease. Neurobiol. Aging 1996 , 17: 921-933. [3] Ertekin-Taner, N. Genetics of Alzheimer's disease: a centennial review. Neurol. Clin. 2007 , 25: 611-667. [4] Bernardi, L., Tomaino, C., Anfossi, M., Gallo, M., Geracitano, S., Puccio, G., Colao, R., Frangipane, F., Mirabelli, M., Smirne, N., Giovanni Maletta, R., Bruni, A.C. Late onset familial Alzheimer's disease: novel presen ilin 2 mutation and PS1 E 318G polymor- phism. J. Neurol. 2008 , 255: 604-606.", + "Keywords: alzheimers disease; genomics; GWAS; genetic risk factors; epigenetic modication; aging 1. Introduction Alzheimers disease (AD) is the most common cause of dementia, accounting for approximately 6080% of dementia cases, followed by vascular dementia (approximately 10%), Lewy Body or Parkinsons disease-related dementia, and alcohol-mediated dementia [ 1]. Mild cognitive impairment, one of the representative early symptoms of AD, makes this disease distinguishable from other types", + "14. Heyman A, Wilkinson WE, Hurwitz BJ, Schmechel D, Sigmon AH, et al. (1983) Alzheimers disease: genetic aspects and associated clinical disorders. AnnNeurol 14: 507515. 15. Farrer LA, Myers RH, Connor L, Cupples LA, Growdon JH (1991) Segregation analysis reveals evidence of a major gene for Alzheimer disease. Am J HumGenet 48: 10261033. 16. Duara R, Lopez-Alberola RF, Barker WW, Loewenstein DA, Zatinsky M, et al. (1993) A comparison of familial and sporadic Alzheimers disease. Neurology 43: 13771384.", + "(2016). 3. DeTure, M. A. & Dickson, D. W . The neuropathological diagnosis of Alzheimers disease. Mol. Neurodegener. 14, 32 (2019). 4. Gatz, M. et al. Heritability for Alzheimers disease: the study of dementia in Swedish twins. J. Gerontol. A Biol. Sci. Med. Sci. 52, M117M125 (1997). 5. Gatz, M. et al. Role of genes and environments for explaining Alzheimer disease. Arch. Gen. Psychiatry 63, 168174 (2006).", + "Lett 379(3):199204. Avramopoulos D. 2009. Genetics of Alzheimers disease: Recent advances. Genome Med 1(3):34. Bachman DL, Wolf PA, Linn R, Knoefel JE, Cobb J, Belanger A, DAgostino RB, White LR. 1992. Prevalence of dementia and probable seniledementia of the Alzheimer type in the Framingham study. Neurology42(1):115119. Barral S, Cheng R, Reitz C, Vardarajan B, Lee J, Kunkle B, Beecham G,", + "[11] and the exclusion of cerebrovascular factors as inherentetiopathogenic determinants of neuronal deathin AD, taking into account that in patients olderthan 70 years of age the vast majority of caseswith dementia show a clear cerebrovascular com-promise [12]. In addition, most studies attempt- ing to correlate clinical features with singlegenotypes are partially biased due to heterogene-ity and inaccuracy in phenotype recruitment.Furthermore, 6080% of the therapeutic fail-ures in AD", + "associated with Alzheimers disease neuropathology. J. Alzheimers Dis. 60, 10351043 (2017). 63. Gottesman, R. F. etal. Association between midlife vascular risk factors and estimated brain amyloid deposition. JAMA 317, 14431450 (2017). 64. Moran, C. etal. T ype 2 diabetes mellitus and biomarkers of neurodegeneration. Neurology 85, 11231130 (2015). 65. Vemuri, P . etal. Age, vascular health, and Alzheimer disease biomarkers in an elderly sample. Ann. Neurol. 82, 706718 (2017).", + "Introduction Alzheimers disease (AD), the most common form of dementia, is highly heritable (heritability of up to 76%) but genetically complex.1Neuropatho- logically, the disease is characterized by extracellular senile plaques containing b-amyloid (A b) and intra- cellular neurofibrillary tangles containing hyperpho-sphorylated tau protein. 1Before 2009, four genes had been definitively implicated in its aetiology. Muta- tions of the amyloid precursor protein (APP) gene" + ], + "answer": "The genotypes related to Alzheimer's disease (AD) and dementia that have commonalities with those for aging include several key genetic factors: 1. **Amyloid Precursor Protein (APP)**: Mutations in the APP gene are implicated in the early-onset form of Alzheimer's disease. This gene is also associated with the aging process, as the accumulation of amyloid plaques is a hallmark of both AD and age-related cognitive decline [1], [10]. 2. **Presenilin 1 and 2 (PSEN1 and PSEN2)**: These genes are also involved in the early-onset form of AD and are linked to the production of amyloid-beta, a protein that accumulates in the brains of individuals with AD and is also associated with aging [1]. 3. **APOE \u03b54 Allele**: The APOE \u03b54 allele is a well-known genetic risk factor for late-onset Alzheimer's disease. It is also associated with aging, as it influences lipid metabolism and is linked to age-related cognitive decline [2], [4]. 4. **Genetic Variance in Age of Onset**: Twin studies suggest that 37-78% of the variance in the age of onset of Alzheimer's disease can be attributed to additive genetic effects, indicating a significant overlap between the genetic factors influencing AD and those affecting the aging process [2]. These genotypes highlight the genetic commonalities between Alzheimer's disease, dementia, and the aging process, emphasizing the multifactorial nature of these conditions.", + "question": "Describe the genotypes related to Alzheimers and dementia which have commonalities with those for aging." +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_aging_9 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_aging_9 new file mode 100644 index 0000000..6ec3e5f --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_aging_9 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2007 - Genetic correlates of brain aging on MRI and cognitive test measures a genome-wide association and linkage analysis in the Framingham study.pdf", + "2009 - MicroRNA Implications for Alzheimer Disease and other Human CNS.pdf", + "2012 - Genome-wide association analysis of age-at-onset.pdf", + "2003 - The application of functional genomics.pdf", + "2018 - Cognitive decline and dementia in diabetes mellitus.pdf", + "2012 - Genome-wide association study of Alzheimer?s disease.pdf", + "2018 - Genomics New Light on Alzheimer?s.pdf", + "2012 - Mitochondrial Genomic Analysis of Late Onset.pdf", + "2003 - Results of a high-resolution genome screen.pdf", + "2017 - Genomic Variants, Genes, and Pathways.pdf" + ], + "extraction_id": [ + "b545e588-2876-5928-9c01-710c1371b44e", + "4b383c2a-f0de-5420-af8d-07060b8874f3", + "2a2e5ce1-cc56-579c-bf79-f9057f4c9671", + "171377f4-24a7-5cde-adff-c9c7096edc75", + "d2b4b131-b7c9-595b-813a-b0940c4e87c0", + "1f9039f1-91e9-538b-b709-a1880cf47007", + "64f3adb4-e745-5738-af28-43c2a870c086", + "b826d64d-9d95-5522-8179-82f79d957c03", + "053cb638-e8ee-593b-9e3c-fe745534adfc", + "7cff03ac-de86-5e70-bbcb-dadc2fa447c3" + ], + "document_id": [ + "56b25b5a-fc9e-5d61-8502-1c110466ba16", + "70d08119-d16d-5e9b-89ed-ec8547be125a", + "9874359e-5f5d-5e6a-9844-cd9a1d2cae24", + "ec5f7b7d-6bd2-5580-bf3e-3c8b64a74169", + "fc7027a3-f885-55b8-b56d-bb8117e2a2f1", + "7e7a8526-ff6e-5c83-ae72-e45509e3b788", + "940593d2-04c3-59b9-a5bf-976febbc6f71", + "5404a17c-34a9-5881-8b1a-2acacdc996a8", + "37eda0c5-73f9-5615-be6f-7016071ec1f4", + "a5bf6a11-3ed5-5222-bc4d-d5149188cdbd" + ], + "id": [ + "chatcmpl-ADZVxBA3IhLP4BoAeswrss7YVgqiu", + "4014c984-d6d9-5eb2-a25e-9e9fe15d1b41", + "cd8f4d4a-2b1a-542f-a3f3-364a83fb10fb", + "6ac3f90f-ec8a-55c7-a3f7-d597d9d7cb2e", + "5e6b9b4b-1e03-585a-af52-18a054e1e603", + "784e7626-1d9c-521b-84f3-965965435366", + "dac0ab78-d01f-5f95-a129-559cbe6791ec", + "f24834c0-1862-5d9f-bdb6-2af38505aa5c", + "064a3510-9a3d-5b93-b848-69478e02e013", + "f95a098d-6950-551a-8854-2c4b956cb10b", + "fc44e06f-a727-5544-ad7c-6ba3632552b7" + ], + "contexts": [ + "Background Age-related neurological diseases such as stroke and dementia represent a substantial population burden, and one in three persons will develop either stroke or demen- tia in their lifetime [1]. Twin studies suggest that 3778% of the variance in the age of onset of Alzheimer's disease (AD), the most common cause of dementia in the elderly, can be attributed to additive genetic effects [2,3]. Con- versely, cognitively healthy aging also has a substantial", + "cognitive status in Alzheimer's disease. Neurobiol. Aging 1996 , 17: 921-933. [3] Ertekin-Taner, N. Genetics of Alzheimer's disease: a centennial review. Neurol. Clin. 2007 , 25: 611-667. [4] Bernardi, L., Tomaino, C., Anfossi, M., Gallo, M., Geracitano, S., Puccio, G., Colao, R., Frangipane, F., Mirabelli, M., Smirne, N., Giovanni Maletta, R., Bruni, A.C. Late onset familial Alzheimer's disease: novel presen ilin 2 mutation and PS1 E 318G polymor- phism. J. Neurol. 2008 , 255: 604-606.", + "Introduction Alzheimers disease (AD), a devastating neurodegen- erative disease, is the most common form of dementiaamong the elderly. Genetically, AD is a complex and multifactorial disease with the possible involvement of multiple genes. The rare early-onset form of the diseaseusually follows an autosomal-dominant inheritance pattern and to date three genes have been identified: amyloid precursor protein ( APP) and presenilin 1 and 2(PSEN1 andPSEN2 ). The common late-onset form of", + "[11] and the exclusion of cerebrovascular factors as inherentetiopathogenic determinants of neuronal deathin AD, taking into account that in patients olderthan 70 years of age the vast majority of caseswith dementia show a clear cerebrovascular com-promise [12]. In addition, most studies attempt- ing to correlate clinical features with singlegenotypes are partially biased due to heterogene-ity and inaccuracy in phenotype recruitment.Furthermore, 6080% of the therapeutic fail-ures in AD", + "associated with Alzheimers disease neuropathology. J. Alzheimers Dis. 60, 10351043 (2017). 63. Gottesman, R. F. etal. Association between midlife vascular risk factors and estimated brain amyloid deposition. JAMA 317, 14431450 (2017). 64. Moran, C. etal. T ype 2 diabetes mellitus and biomarkers of neurodegeneration. Neurology 85, 11231130 (2015). 65. Vemuri, P . etal. Age, vascular health, and Alzheimer disease biomarkers in an elderly sample. Ann. Neurol. 82, 706718 (2017).", + "Introduction Alzheimers disease (AD), the most common form of dementia, is highly heritable (heritability of up to 76%) but genetically complex.1Neuropatho- logically, the disease is characterized by extracellular senile plaques containing b-amyloid (A b) and intra- cellular neurofibrillary tangles containing hyperpho-sphorylated tau protein. 1Before 2009, four genes had been definitively implicated in its aetiology. Muta- tions of the amyloid precursor protein (APP) gene", + "Keywords: alzheimers disease; genomics; GWAS; genetic risk factors; epigenetic modication; aging 1. Introduction Alzheimers disease (AD) is the most common cause of dementia, accounting for approximately 6080% of dementia cases, followed by vascular dementia (approximately 10%), Lewy Body or Parkinsons disease-related dementia, and alcohol-mediated dementia [ 1]. Mild cognitive impairment, one of the representative early symptoms of AD, makes this disease distinguishable from other types", + "14. Heyman A, Wilkinson WE, Hurwitz BJ, Schmechel D, Sigmon AH, et al. (1983) Alzheimers disease: genetic aspects and associated clinical disorders. AnnNeurol 14: 507515. 15. Farrer LA, Myers RH, Connor L, Cupples LA, Growdon JH (1991) Segregation analysis reveals evidence of a major gene for Alzheimer disease. Am J HumGenet 48: 10261033. 16. Duara R, Lopez-Alberola RF, Barker WW, Loewenstein DA, Zatinsky M, et al. (1993) A comparison of familial and sporadic Alzheimers disease. Neurology 43: 13771384.", + "disease. Nat. Genet. ,19, 321 322. 7. Bergem, A.L., Engedal, K. and Kringlen, E. (1997) The role of heredity in late-onset Alzheimer disease and vascular dementia. A twin study. Arch. Gen. Psychiat. ,54, 264 270. 8. Payami, H., Grimslid, H., Oken, B., Camicioli, R., Sexton, G., Dame, A., Howieson, D. and Kaye, J. (1997) A prospective study of cognitive health inthe elderly (Oregon Brain Aging Study): effects of family history andapolipoprotein E genotype. Am. J. Hum. Genet. ,60, 948 956.", + "Lett 379(3):199204. Avramopoulos D. 2009. Genetics of Alzheimers disease: Recent advances. Genome Med 1(3):34. Bachman DL, Wolf PA, Linn R, Knoefel JE, Cobb J, Belanger A, DAgostino RB, White LR. 1992. Prevalence of dementia and probable seniledementia of the Alzheimer type in the Framingham study. Neurology42(1):115119. Barral S, Cheng R, Reitz C, Vardarajan B, Lee J, Kunkle B, Beecham G," + ], + "answer": "The genotypes related to Alzheimer's disease (AD) and dementia that have commonalities with those for aging include several key genetic factors: 1. **Apolipoprotein E (APOE)**: The APOE gene, particularly the APOE \u03b54 allele, is a well-known genetic risk factor for late-onset Alzheimer's disease. It is also associated with cognitive decline in aging populations [9]. Studies have shown that individuals carrying the APOE \u03b54 allele have an increased risk of developing Alzheimer's disease and exhibit more rapid cognitive decline as they age [8]. 2. **Amyloid Precursor Protein (APP)**: Mutations in the APP gene are implicated in the early-onset form of Alzheimer's disease. While these mutations are rare, they contribute to the understanding of the disease's pathology, which overlaps with mechanisms involved in aging, such as amyloid plaque formation [3], [6]. 3. **Presenilin 1 and 2 (PSEN1 and PSEN2)**: Mutations in these genes are also associated with early-onset Alzheimer's disease. These mutations affect the production of amyloid-beta, a key component of the plaques found in the brains of Alzheimer's patients. The accumulation of amyloid-beta is a process that is also observed in the aging brain, albeit to a lesser extent in cognitively healthy individuals [3], [6]. 4. **Genetic Variance in Age of Onset**: Twin studies suggest that a significant portion of the variance in the age of onset of Alzheimer's disease can be attributed to genetic factors, indicating a genetic overlap between aging and the development of Alzheimer's disease [1]. 5. **Vascular Health and Aging**: Vascular health is another important factor that links aging with Alzheimer's disease. Midlife vascular risk factors, such as hypertension and diabetes, are associated with increased amyloid deposition in the brain, which is a hallmark of Alzheimer's disease. This suggests that the genetic factors influencing vascular health and aging may also play a role in the development of Alzheimer's disease [5]. In summary, the genotypes related to Alzheimer's disease and dementia that share commonalities with those for aging include APOE, APP, PSEN1, and PSEN2, as well as genetic factors influencing vascular health and the age of onset of the disease [1], [3], [5], [6], [8], [9].", + "question": "Describe the genotypes related to Alzheimer's and dementia which have commonalities with those for aging." +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_diabetes_1 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_diabetes_1 new file mode 100644 index 0000000..4b5b06e --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_diabetes_1 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2015 - Pharmacogenetics and individual responses to treatment of hyperglycemia.pdf", + "2015 - Pharmacogenetics and individual responses to treatment of hyperglycemia.pdf", + "2021 - Monogenic diabetes a gateway to precision medicine.pdf", + "2020 - Precision Medicine in Diabetes.pdf", + "2014 - Heritability of variation in glycaemic response to metformin.pdf", + "2015 - Pharmacogenetics and individual responses to treatment of hyperglycemia.pdf", + "2012 - Type 2 Diabetes Genetics Beyond GWAS.pdf", + "2018 - Human Genetics of Obesity and Type 2 Diabetes Mellitus.pdf", + "2010 - Pharmacogenetics of Anti-Diabetes Drugs.pdf", + "2011 - Inherited destiny Genetics and gestational diabetes mellitus.pdf" + ], + "extraction_id": [ + "026d2a7d-a7b7-5342-981a-2664a998c79b", + "026d2a7d-a7b7-5342-981a-2664a998c79b", + "baea9ac6-7ff9-5724-87ed-81b17e2469cd", + "c27447b1-5f7e-5b8b-9172-baba74ffc29b", + "90ea6bd5-5140-5c73-ace7-fd5030e83c6d", + "026d2a7d-a7b7-5342-981a-2664a998c79b", + "a3a875fa-e55b-52d0-b9bf-72b96330c393", + "e18fd615-3cde-5dc2-ab7d-a9e17d4c8ed6", + "a1359f6d-8f61-51ca-8b02-45420e345946", + "48c3e4a4-db23-5fca-9c46-775e80894655" + ], + "document_id": [ + "46081466-a50f-59d8-893d-8b8883b38507", + "46081466-a50f-59d8-893d-8b8883b38507", + "0b6ff786-6a7b-5d24-ba5e-7a61fee7757f", + "0ad5b2de-d782-5d43-b294-bff5c7befd2d", + "458da117-3235-5852-aff2-529c0bf16074", + "46081466-a50f-59d8-893d-8b8883b38507", + "d59a38d7-889b-51b5-b896-c305c82a2169", + "2083de31-17c6-5d1e-9aa6-2efc6c1d9ac2", + "ffeebaf9-ff76-5751-9b8b-7a2a4a4f1dc3", + "6d341cd2-ae56-5807-9aff-39298efc4d06" + ], + "id": [ + "chatcmpl-ADZQAXp2EmWZCiBbiRu4ySm4isUy8", + "6aa611a9-aa5b-5dc5-a760-eaf1f95b8109", + "4352b950-a365-523c-b704-9eb4eddaf448", + "db50a759-ac52-5e02-a5c1-5c898f16bd27", + "c372d094-ceb2-56d1-82f3-c63f65e5c5c1", + "f187dbbd-3380-566a-ab25-18fc923e2263", + "9ced327e-3feb-5b7b-a938-30ad544113e2", + "b4516514-f107-5b15-b73d-0d3d89dce5a8", + "6707ac07-6096-5eaa-b6c4-315faa4c2813", + "c2b8b8a1-d19e-5f7e-aa22-a421367e4fdd", + "35d3fc6c-28a8-53fe-9574-e92d87f01c19" + ], + "contexts": [ + "interindividual variation in responses to antidiabetic treatment and may provide the foundation for future genotype-based treatment standards. Pharmacogenetics and Genomics 25:475 484 Copyright 2015 Wolters Kluwer Health, Inc. All rights reserved. Pharmacogenetics and Genomics 2015, 25:475 484 Keywords: antidiabetic treatment, diabetes type 2, disease progression, genotype, pharmacogenetics aSection of Metabolic Genetics, Novo Nordisk Research Foundation Center for", + "treatment guidelines. Yet, the interindividual response to therapy and slope of disease progression varies markedly among patients with type 2 diabetes. Gene gene, gene environment, and gene treatment interactions may explain some of the variation in disease progression. Several genetic variants have been suggested to beassociated with response to antidiabetic drugs. Some are present in drug receptors or drug metabolizers ( OCT genes, KCNJ11 ,ABCC8 , and CYP2C9 ). Numerous type 2 diabetes", + "mic control in the majority of insulin-treated patients. Diabet Med . 2009;26(4):437441. 20. Pearson ER, et al. Sensitivity to sulphonylureas in patients with hepatocyte nuclear factor-1alpha gene mutations: evidence for pharmacogenetics in diabetes. Diabet Med . 2000;17(7):543545. 21. Pearson ER, et al. Genetic cause of hypergly- caemia and response to treatment in diabetes. Lancet . 2003;362(9392):12751281. 22. Fantasia KL, Steenkamp DW. Optimal glycemic", + "When considering etiological varia- tion, recent work partitioning diabe-tes-associated genetic variants by theirpresumed etiological process (parti-tioned polygenic scores) (6,42,101)may de ne genetically driven dominant processes. These processes, such asb-cell dysfunction, lipodystrophy, or obe- sity, could respond differently to drugsthat act on these pathways, such assulfonylureas, glucagon-like peptide 1 re- ceptor agonist (GLP-1RA), DPP4i, and thiazolidinediones.", + "source of such variation might help to identify patients most likely not to respond to metformin and could help to develop more e ective agents by providing insight into the biological mechanism of metformin. As with other complex traits, glycaemic response to metformin is probably determined by the interplay between genetic and environmental factors. Clinical variables such as BMI, drug adherence, and dosing only account for part of the variation. 3 Pharmacogenetic", + "Pharmacogenetics and individual responses to treatment of hyperglycemia in type 2 diabetes Line Engelbrechtsena, Ehm Anderssona, Soeren Roepstorffb, Torben Hansenaand Henrik Vestergaarda The aim of this study was to summarize current knowledge and provide perspectives on the relationships between human genetic variants, type 2 diabetes, antidiabetic treatment, and disease progression. Type 2 diabetes is a complex disease with clear-cut diagnostic criteria and", + "Genomics. 2010; 20:3844. [PubMed: 19898263] 168. Jablonski KA, McAteer JB, de Bakker PI, Franks PW, Pollin TI, et al. Common variants in 40 genes assessed for diabetes incidence and response to metformin and lifestyle intervention in the diabetes prevention program. Diabetes. 2010; 59:26722681. [PubMed: 20682687] 169. Wolford JK, Yeatts KA, Dhanjal SK, Black MH, Xiang AH, et al. Sequence variation in PPARG may underlie differential response to troglitazone. Diabetes. 2005; 54:33193325. [PubMed: 16249460]", + "10.1007/s00125-017-4227-1. 42. Hattersley AT, et al. Precision diabetes: learning from monogenic diabetes. Diabetologia. 2017;60:769777. doi: 10.1007/s00125-017-4226-2. 43. Florez JC. The pharmacogenetics of metformin. Diabetologia. 2017;60:16481655. doi: 10.1007/s00125-017-4335-y. 44. Maruthur NM, et al. The pharmacogenetics of type 2 diabetes: a system-atic review. Diabetes Care. 2014;37:876886. doi: 10.2337/dc13-1276. 45. Zhou K, et al. Variation in the glucose transporter gene SLC2A2 is associ-", + "typically based on efficacy, yet favorable respon ses to such therapeutics are oftentimes variable and difficult to pred ict. Characterization of drug response is expected to substantially enhance our ability to provide patients with the most effective treatment strategy given their indivi dual backgrounds, yet pharmacogenetic study of diabetes medications is still in its infancy. To date, major pharmacogenetic studies have focused on", + "treatment or adverse effects and dosing of medications are not likely to be adversely affected by environmental exposures and tend to have large effect sizes [95]. There fore, some of the variability in response or dosing could be due to genetic variation. Pharmacogenetics in the area of diabetes is still in its infancy, although there have been studies examining KCNJ11 and sulfonylurea therapy for both rare [96,97] and common [98,99] variants and res" + ], + "answer": "The genetic bases for the varying efficacy of diabetes treatments among individuals are multifaceted and involve several genetic variants that influence drug response. Key points include: 1. **Genetic Variants in Drug Receptors and Metabolizers**: Variations in genes such as OCT, KCNJ11, ABCC8, and CYP2C9 have been associated with differing responses to antidiabetic drugs. These genes are involved in drug receptors or drug metabolism, which can affect how well a patient responds to a particular treatment [2]. 2. **Gene-Gene and Gene-Environment Interactions**: The interplay between different genes and between genes and environmental factors can also contribute to the variability in treatment efficacy. This includes how genetic predispositions interact with lifestyle factors and other environmental influences [2]. 3. **Specific Genetic Mutations**: For instance, mutations in the hepatocyte nuclear factor-1alpha gene have been linked to sensitivity to sulphonylureas, highlighting a specific pharmacogenetic interaction in diabetes treatment [3]. 4. **Genetically Driven Dominant Processes**: Certain genetic variants may drive dominant processes such as beta-cell dysfunction, lipodystrophy, or obesity, which in turn can influence the effectiveness of drugs targeting these pathways, such as sulfonylureas, GLP-1 receptor agonists, DPP4 inhibitors, and thiazolidinediones [4]. 5. **Pharmacogenetic Studies**: Although pharmacogenetic research in diabetes is still developing, studies have shown that genetic variations can significantly impact drug response. For example, variations in the glucose transporter gene SLC2A2 have been associated with differential responses to metformin [8]. 6. **Complex Interplay of Factors**: The glycaemic response to treatments like metformin is likely determined by a complex interplay of genetic and environmental factors, with clinical variables such as BMI, drug adherence, and dosing also playing roles [5]. These genetic factors underscore the importance of personalized medicine in diabetes treatment, where understanding an individual's genetic makeup can help tailor more effective therapeutic strategies.", + "question": "What are the genetic bases for the varying efficacy of diabetes treatments among individuals?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_diabetes_10 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_diabetes_10 new file mode 100644 index 0000000..149e49a --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_diabetes_10 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2004 - Diabetes Genes a.pdf", + "2018 - Animal models of obesity.pdf", + "2012 - Systems Biology Approaches to Nutrition.pdf", + "2021 - Monogenic diabetes a gateway to precision medicine.pdf", + "2003 - A functional polymorphism in the promoterenhancer region of the FOXP3Scurfin gene associated with type 1 diabetes.pdf", + "2017 - diabetes-mellitus-in-developing-countries-and-underserved-commun-2017.pdf", + "1997 - Development of non-insulin-dependent diabetes mellitus.pdf", + "2017 - Differentiation of Diabetes by Pathophysiology.pdf", + "2004 - Diabetes Genes a.pdf", + "1984 - A Polymorphic Locus.pdf" + ], + "extraction_id": [ + "0d4b1f7a-5757-5129-835a-6ed0334a1e0e", + "f3ffcc7b-7d0c-5f1a-9a5a-762cf6c85403", + "5364714e-1909-58f7-a15d-fb60df4e7bee", + "9b3c9e69-82ff-5bfc-9b5b-77038a59d8e9", + "aacbb5a1-c294-5568-ba02-3d4342091e86", + "d5004507-475d-5ac1-9aa6-b5fd664b8bf7", + "e27a0679-e4b0-5e9f-8d98-1cd4e8b08b00", + "a9accd40-eb89-5595-bf27-b6b82b49f4d4", + "4beabe81-e24e-535c-9df3-bfaa9cfdde90", + "57f307f8-2493-5438-ad08-b4d85288b94e" + ], + "document_id": [ + "805ad0d5-6a3d-5e5f-b1ba-121e10c6daaa", + "9e9af9c7-814f-562e-a04d-878528a38002", + "6955478b-950d-5d29-b24c-3a5ca656f3ae", + "0b6ff786-6a7b-5d24-ba5e-7a61fee7757f", + "4a3964a4-0aea-58ee-b749-33e0d8c62228", + "8a9451b9-d7e8-5417-b6a5-5fd1b791cc4d", + "8598a406-5a79-5f9e-8a1b-bf69daf071bf", + "9cfaef1e-fb60-5c2b-94f0-632c89b2eb16", + "805ad0d5-6a3d-5e5f-b1ba-121e10c6daaa", + "df2478dd-6970-5d8d-99e5-bb23f46bcdb1" + ], + "id": [ + "chatcmpl-ADZR8nkJvPePGl3BA4Ofgj0JbQyIv", + "c3e7c5c2-d8d3-55ba-ba38-b8ca5eb2487a", + "24aac9a6-38ed-5a54-b60a-0604d31e5972", + "65469d6d-578f-5c78-97cf-d06b2f483dcf", + "92749b8d-6c7a-5f2b-a873-a04904cc247b", + "18ba0b3f-51a8-5af0-98ec-3b45f1e3219a", + "2454130e-8098-5c7f-944b-c5933a8409f8", + "856c7a02-c233-5b00-ae1c-55a5e2b1a2ed", + "3313b0de-44f4-5cb5-9735-2fefd5ebf0bb", + "1e84a9e4-7bd1-51ad-80b5-3a371c090151", + "3b29472a-7875-5761-86d5-cbc57c20db85" + ], + "contexts": [ + "two broad etiopathogenetic groups. In one group (type I diabetes), the cause is an absolute deficiency of insulin secretion. Individuals at increased risk of developing this type of diabetes can often be identified by serological evidence of an autoimmune process of the pancreatic islets and by genetic markers. In the second and more prevalent group (type 2 diabetes), the cause is a combination of resistance to insulin action with inadequate compensatory insulin secretory response.", + "Diabetes mellitus. Type1 diabetes mellitus (T1DM) and T2DM have different causes, but both ultimately lead to pancreatic -cell dysfunction. Damaging the pancreas chemically or mechanically can induce experimental diabetes mellitus. Pancreatic damage can be achieved by surgically removing parts of or all of the pancreatic tissue (pancreatectomy) to reduce or fully ablate endogenous insulin production282. The benefit of this method is the lack of toxic adverse effects (compared with diabetogenic", + "Diabetes is a disorder of carbohydrate metabolism charac-terized primarily by hyperglycemia resulting from ineffec-tive uptake of glucose by tissues. Type 1 diabetes is an autoimmune disease that typically occurs early in life and results in total loss of insulin production, whereas type 2 diabetes develops over time as tissues develop a resistance to insulin, and insulin release from the pancreas slowly diminishes. As carbohydrates have the greatest effect on blood glucose of all macronutrients, their", + "diabetes but a rare cause of diabetes diag - nosed in childhood or adulthood. Diabetes . 2008;57(4):10341042. 152. Molven A, et al. Mutations in the insulin gene can cause MODY and autoantibody-negative type 1 diabetes. Diabetes . 2008;57(4):11311135. 153. Gloyn AL, et al. Mutations in the genes encoding the pancreatic beta-cell KATP channel subunits Kir6.2 (KCNJ11) and SUR1 (ABCC8) in diabe - tes mellitus and hyperinsulinism. Hum Mutat. 2006;27(3):220231.", + "Type 1 diabetes is an autoimmune disease caused by T-cell-mediated destruction of insulin-producing beta cellsin the pancreatic islets of Langerhans (Atkinson andMaclaren 1994). Various aberrations in immune regula-tion have been described in both human patients andanimal models of type 1 diabetes (Rosmalen et al. 2002).A recent study has demonstrated that the disturbance ofcentral and/or peripheral tolerance mechanisms existed indiabetes-prone humans and animals (Sakaguchi 2000).With respect to the", + "disorder caused by different factors characterized by a chronic high level of blood sugar with distur-bances to carbohydrate, fat, and protein metabo-lism resulting from defects in insulin secretion, insulin action, or both [ 83 ]. Scientists have divided diabetes into three different types: Type 1 F. Assah and J.C. Mbanya", + "(Fig. 1), indicating that insulin resistance and insulin secretory defect played a cooperative role in the development and exac- erbation of diabetes, even though neither was strong enough alone to cause overt diabetes. From another point of view, even if genetically determined insulin resistance itself might not be sufficient for the development of diabetes, insulin resis- tance results in diabetes if pancreatic /H9252 cell function is im- paired genetically (this study) or nongenetically. Development", + "tors, and other environmental factors that trigger isletautoimmunity and/or type 1 diabetes. Type 2 Diabetes Type 2 diabetes develops when b-cells fail to secrete suf- cient insulin to keep up with demand, usually in the context of increased insulin resistance. A minority of peo- ple diagnosed with type 2 diabetes also have evidence ofislet autoimmunity (57,58). Obesity is a major risk factor for type 2 diabetes (59,60) with complex genetic and en- vironmental etiology.", + "have environmental (islet-injuring drugs or a particular diet) and/or genetic (monogenic or polygenic) causes. We have grouped the models by cause and type of diabetes. While this grouping is reasonable and instructive, it can over-emphasize distinctions. For example, it is believed that beta cell failure (and/or poor islet regeneration) contributes to type 2 diabetes, but in their pure, severe form these processes cause type I diabetes. MODELS OF INSULIN-DEFICIENT DIABETES", + "Diabetes mellitus comprises a heterogenous group of disorders that have been classified as either insulin-dependent (IDDM) or non-insulin-depend- ent (NIDDM).1 Their causes are poorly understood but appear to involve some form of interaction between ge- netic and environmental factors.2-4 Some of the environmen- tal factors that can contribute to IDDM include viral infections and chemicals, while obesity is a common predisposing fac- tor for NIDDM. Genes that confer susceptibility or can cause" + ], + "answer": "Diabetes can be caused by different factors depending on the type. 1. **Type 1 Diabetes**: This type is primarily caused by an absolute deficiency of insulin secretion due to an autoimmune process that destroys insulin-producing beta cells in the pancreatic islets [1], [5]. Genetic markers and serological evidence can often identify individuals at increased risk [1]. 2. **Type 2 Diabetes**: This type is caused by a combination of resistance to insulin action and an inadequate compensatory insulin secretory response [1]. It develops over time as tissues become resistant to insulin, and the pancreas's ability to release insulin diminishes [3]. Obesity is a major risk factor for type 2 diabetes, with both genetic and environmental factors playing a role [8]. 3. **General Factors**: Both types of diabetes ultimately lead to pancreatic beta-cell dysfunction [2]. Environmental factors, such as viral infections and chemicals, can contribute to the development of diabetes, particularly type 1, while obesity is a common predisposing factor for type 2 diabetes [10]. In summary, diabetes is caused by a combination of genetic, autoimmune, and environmental factors that affect insulin production and action [1], [3], [8], [10].", + "question": "What causes diabetes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_diabetes_11 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_diabetes_11 new file mode 100644 index 0000000..97776c1 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_diabetes_11 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2004 - Diabetes Genes a.pdf", + "2016 - NIH working group report using genomic information to guide weight management From universal.pdf", + "2012 - Diabetes and Hypertension Is There a Common Metabolic Pathway.pdf", + "2004 - Diabetes Genes a.pdf", + "2004 - Diabetes Genes b.pdf", + "2004 - Diabetes Genes a.pdf", + "2005 - Metabolic Syndrome A Comprehensive Perspective Based on Interactions Between Obesity Diabetes and Inflammation.pdf", + "2017 - diabetes-mellitus-in-developing-countries-and-underserved-commun-2017.pdf", + "2004 - Diabetes Genes a.pdf", + "2018 - Type 2 Diabetes in adolescents and young adults.pdf" + ], + "extraction_id": [ + "ad5bdba5-b3c6-50ac-a4b3-3089e7bed0da", + "bbaf5afd-56e0-5ded-bf17-c8c36d67122c", + "e0a47978-ae34-5905-baff-36a3364d21af", + "424d7751-3dbf-5e10-83ca-12101841d17c", + "cef57178-c218-52d3-b049-aa6ca097fd73", + "ad5bdba5-b3c6-50ac-a4b3-3089e7bed0da", + "c6cfb382-639a-5dd4-a9c8-c8f57b6daabc", + "f7fe5916-4f25-5740-8737-f668f216575d", + "4657f231-5e0c-5572-ad75-22c74f55a70f", + "6c730685-6ec0-52a4-8f33-671a39616a86" + ], + "document_id": [ + "805ad0d5-6a3d-5e5f-b1ba-121e10c6daaa", + "0ee28c8a-3618-559e-be0a-30f2579a0d1f", + "37b08243-09de-5a78-b2bb-1eade3c714af", + "805ad0d5-6a3d-5e5f-b1ba-121e10c6daaa", + "da2f2624-e3e6-5e2d-b406-941db2fe7671", + "805ad0d5-6a3d-5e5f-b1ba-121e10c6daaa", + "de2aa54c-eb0f-5dc3-ac92-23ee3215dd2a", + "8a9451b9-d7e8-5417-b6a5-5fd1b791cc4d", + "805ad0d5-6a3d-5e5f-b1ba-121e10c6daaa", + "cc708325-df0a-55ec-9e9b-2bf97835c992" + ], + "id": [ + "chatcmpl-ADZRC8tLslwOrPHuuXyMSXCo1Prfp", + "5479de8e-2994-5b99-a0a7-915840f1de0d", + "cdd1e1cb-6b89-5045-96e2-280f6d615ab4", + "50f8c1de-8641-5cb6-8080-620f15810922", + "9da658e9-223d-527d-a913-b1d8eac31de2", + "e317892f-8310-5414-869e-b759258b2eeb", + "a3060853-46b0-506a-b3ed-9e85c2c450da", + "8c4e8b2c-6730-541c-8a2e-22fbd7ddb487", + "09070d01-4946-559c-9b44-f502c7b066c3", + "eb818d5f-6b01-53ef-8343-1823c449f779", + "e08c0b4a-24f9-576e-b5cf-74641fe81fd0" + ], + "contexts": [ + "2 diabetes suggest that regular exercise might play an important role in decreasing the very high incidence of premature coronary artery disease. Although there are no randomized controlled trials assessing reduction in cardiovascular events induced by physical activity in type 2 diabetes, available evidence is consistent with the concept that physical activity may play an important role in reducing cardiovascular risk in type 2 diabetes. 44 Large", + "tern of weight change impact health. For example, in the DiabetesPrevention Program (DPP; described in more detail later), both short- and intermediate-term weight loss were associated with reduced diabetes risk and intermediate cardiometabolic risk factor levels, whereas weight cycling (defined as number of 5 lb [2.25 kg] weight cycles) raised diabetes risk, fasting glucose levels, insulinresistance, and systolic blood pressure. Initial (baseline to 1 month)", + "sclerosis Risk in Communities (ARIC) study, the highestquartile of leisure activity (primarily cycling and walking)had a 34% lower odds of developing hypertension over 6 years compared to the least active [ 107]. Thus, physical activity reduces the risk of developing diabetes and hyper- tension. The mechanism involves changes in body weight and glucose tolerance, as well as other factors [ 107]. The effect of obesity susceptibility genes on the onset of", + "exercise can reduce the incidence of type 2 diabetes. Tuomilehto and coworkers demonstrated that the individuals on a consistent diet and exercise program had 10% incidence of diabetes during 4 years of follow-up compared to 22% for patients in the control group, who met only once a year with the dietician and the physician.40 A six-year randomized trial conducted by Pan and colleagues demonstrated that exercise resulted in 46% reduction", + "Exercise Exercise has been shown to prevent development of Type 2 diabetes in high-risk groups. A number of studies have looked at the effect of insulin on delaying the onset of diabetes. In a study of 5990 male alumni from an American university followed over 10 years, 202 pts (3.3 percent) developed Type 2 diabetes mellitus. The relative risk was lower in patients who exercised regularly even when adjusted for obesity, hypertension, and a family history of diabetes. The benefit was greatest in", + "nonrandomized studies of both men and women with type 2 diabetes and impaired glucose tolerance have found that physical activity is associated with a decreased risk for cardiovascular disease. It also appears that the amount of physical activity is inversely associated with coronary events.5354 RISK OF EXERCISE IN PATIENTS WITH DIABETES The risks associated with exercise can be divided into metabolic, vascular, neurologic and musculoskeletal (Table 4).", + "74 The mechanism underlying this effect of exercise is not known;however, it is noteworthy that lifestyle change is a very effectiveway to reduce the rate of development of diabetes in a predia-betic population, as shown by the diabetes prevention study. 75,76 Both a reduction in macronutrient intake and exercise cause areduction in inflammation. References 1. Reaven GM. Banting lecture 1988. Role of insulin resistance in human disease. Diabetes . 1988;37:15951607.", + "physical training on carbohydrate metabolism and associated cardiovascular risk factors in patients with diabetes. Diabetes Rev. 1995;3:378407. 23. Rogers MA, Yamamoto C, King DS, Harberg JM, Ensani AA, Holloszy JO. Improvement in glucose tolerance after one week of exercise in patients with mild NIDDM. Diabetes Care. 1988;11:6138. 24. Eriksson KF, Lindgarde F. Prevention of type 2 dia- betes mellitus by diet and physical exercise. Diabetologia. 1991;34:8918.", + "migrant and other observational studie!f86970 and prospective studies in subjects at high risk for developing type 2 diabetes.717273 Recently, large interventional trials have reinforced the benefits of exercise in reducing the risk for type 2 diabetes. These include the Malmo study from Sweden45, the Da Quing study from China74 and the recently concluded Finnish Diabetes Prevention Study.75 These prospective but not randomized studies show a reduction in the risk of 560", + "reduce systolic blood pressure, reduce total cholesterol, raise HDL cholesterol, and improve endothelial function in overweight patients with young-onset type 2 diabetes. 47 However, any potential benefits to the cardiovascular disease risk profile are lost within 36 months after cessation of exercise training, and do not confer protection against later cardiovascular events. 47,121 Additionally, reviews49,121,122 of the limited number of studies done to" + ], + "answer": "Yes, cycling, as a form of physical activity, can reduce the risk of diabetes. According to the context provided: - The Atherosclerosis Risk in Communities (ARIC) study found that higher levels of leisure activity, which primarily included cycling and walking, were associated with a 34% lower odds of developing hypertension over six years compared to the least active group. This study also indicated that physical activity reduces the risk of developing diabetes and hypertension through mechanisms involving changes in body weight and glucose tolerance [3]. - Additionally, other studies have shown that exercise, including activities like cycling, can prevent the development of Type 2 diabetes in high-risk groups [5]. Thus, engaging in regular cycling can contribute to a reduced risk of developing diabetes.", + "question": "Does cycling reduce risk of diabetes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_diabetes_12 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_diabetes_12 new file mode 100644 index 0000000..4425d61 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_diabetes_12 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2008 - Glossary of Genetics Genomics Terms.pdf", + "2015 - The genetics of diabetic complications.pdf", + "2009 - From Disease Association to Risk Assessment.pdf", + "2014 - Identification of novel risk genes associated with type 1 diabetes mellitus.pdf", + "2017 - Machine Learning and Data Mining Methods in Diabetes Research.pdf", + "1994 - Genetic Predisposition to Diabetic Nephropathy.pdf", + "2007 - Network-Based Analysis.pdf", + "2007 - Network-Based Analysis.pdf", + "2007 - Network-Based Analysis.pdf", + "2008 - High-Density Single Nucleotide Polymorphism.pdf" + ], + "extraction_id": [ + "53e868dd-b318-5cf3-8b2e-98a548aab7cf", + "27de21d5-8e86-5233-8196-ff09c1916eb8", + "6f819601-6eea-54a4-ab88-27e1b0602287", + "cce6eb13-6c59-5916-a108-477128ed6912", + "46f1cae6-a01f-5445-b20f-0eadf892f8bf", + "9f693d00-d331-5924-a0bc-8ec8614ccd6e", + "295a5916-ff2d-54b2-a0fe-4b279e71b5ad", + "334686b0-71fb-5820-9649-3cdf355f1dfe", + "63fadd0c-3522-5339-b534-807144901fa9", + "e04a055d-630a-50b5-a468-560e25bf1d40" + ], + "document_id": [ + "c66d2572-071d-5aaf-829c-b3ca6cf6d697", + "5bc1f058-caf2-5cb4-9623-b1d04b074a3c", + "a61066d0-0d1a-5f10-96c3-aa96bacdad5e", + "97fe33b0-a6c7-59b6-bd34-05528e77293f", + "e2dcbb80-5ad7-5441-b170-9b46607445b0", + "e9dce475-d40f-5cda-a53d-3e722191d447", + "1f23601c-2dab-570a-a2ca-039283831b17", + "1f23601c-2dab-570a-a2ca-039283831b17", + "1f23601c-2dab-570a-a2ca-039283831b17", + "ce79f562-c274-5cbe-bae2-e5b688348b04" + ], + "id": [ + "chatcmpl-ADZRGfPVf97ITQ8FTyJHnwW98zLJf", + "54ff4672-bf7f-5158-b228-ca3d45e0cb0d", + "8a7d2ffb-20b3-572a-99af-ec120e268bd3", + "506f423f-23f7-5d72-b614-1ccc9b38e853", + "d84c2221-5d5b-5df9-bafd-2dd17e9fb132", + "069b11b5-0785-599a-b92e-543e133c1c65", + "f0848c4e-5c55-5e13-8ac5-75065aaed286", + "9c16d623-9eb8-57fe-8ae8-48009f766d64", + "9fd6df7d-c275-573d-8c8e-afe69ec5c544", + "5005ed0b-8b17-540f-8106-94593c601084", + "81a23927-18e2-54fe-94c2-6b64cc3c7020" + ], + "contexts": [ + "Genetic factors appear to play a role in determining an individuals risk of developing diabetes. It is hoped", + "Diabetes (GoKinD) study: a genetics collection available for identifying genetic susceptibility factors for diabetic nephropathy in type1 diabetes. J. Am. Soc. Nephrol. 17, 17821790 (2006). 137. Scott, R.A. etal. Large-scale association analyses identify new loci influencing glycaemic traits and provide insight into the underlying biological pathways. Nat. Genet. 44, 9911005 (2012). Author contributions All authors researched the data for the article,", + "identifying genetic susceptibility factors for diabetic nephropathy in type 1 diabetes. J Am Soc Nephrol 17: 17821790. 44. Manolio TA, Rodriguez LL, Brooks L, Abecasis G, Ballinger D, et al. (2007) New models of collaboration in genome-wide association studies: the Genetic Association Information Network. Nat Genet 39: 10451051. 45. Mailman MD, Feolo M, Jin Y, Kimura M, Tryka K, et al. (2007) The NCBI dbGaP database of genotypes and phenotypes. Nat Genet 39: 11811186.", + "in Diabetes (GoKinD) study: a genetics collection availablefor identifying genetic susceptibility factors for diabeticnephropathy in type 1 diabetes. J Am Soc Nephrol 2006; 177: 1782 1790. 10. Pezzolesi MG, Poznik GD, Mychaleckyj JC, et al. Genome- wide association scan for diabetic nephropathysusceptibility genes in type 1 diabetes. Diabetes 2009; 586: 14031410. 11. Paterson AD, Lopes-Virella MF, Waggott D, et al.", + "beta cell function, insulin mode of action, glucose metabolism and/or other risk factors. It is a fact that advances in genotyping technology, over the past few years, have facilitated rapid progress in large-scale gene tic studies. Identification of a large number of novel genetic variants increasing suscept ibility diabetes and related traits opened up opportunities, not existing thus far, to associate this genetic information", + "DISCUSSION The findings of previous epidemiological and family studies suggest that diabetic nephropathy results from an interaction between metabolic abnormalities that are typical of poorlycontrolled IDDM and predisposing genetic factors (4,5). Thenature of the genetic factors, however, has remained un- known (22). Using a candidate gene approach, we have found in this", + "PLoS Genetics | www.plosgenetics.org June 2007 | Volume 3 | Issue 6 | e96 0963 Type 2 Diabetes Network-Based Analysis", + "PLoS Genetics | www.plosgenetics.org June 2007 | Volume 3 | Issue 6 | e96 0971 Type 2 Diabetes Network-Based Analysis", + "PLoS Genetics | www.plosgenetics.org June 2007 | Volume 3 | Issue 6 | e96 0967 Type 2 Diabetes Network-Based Analysis", + "High-Density Single Nucleotide Polymorphism Genome-Wide Linkage Scan for Susceptibility Genes forDiabetic Nephropathy in Type 1 Diabetes Discordant Sibpair Approach John J. Rogus,1,2G. David Poznik,1Marcus G. Pezzolesi,1,2Adam M. Smiles,1Jonathon Dunn,1 William Walker,1Krzysztof Wanic,1,2Dariusz Moczulski,1,2,3Luis Canani,1,2,4Shinichi Araki,1,2,5 Yuichiro Makita,1,2,6James H. Warram,1and Andrzej S. Krolewski1,2 OBJECTIVE Epidemiological and family studies have demon-" + ], + "answer": "GeneNetwork can assist in identifying genetic factors involved in diabetes by leveraging large-scale genetic studies and databases that focus on genetic susceptibility factors. For instance, the Diabetes (GoKinD) study provides a genetics collection specifically aimed at identifying genetic susceptibility factors for diabetic nephropathy in type 1 diabetes [2], [4]. Additionally, advances in genotyping technology have facilitated rapid progress in large-scale genetic studies, enabling the identification of numerous novel genetic variants that increase susceptibility to diabetes and related traits [5]. These resources and technological advancements can be utilized within GeneNetwork to analyze genetic data and identify potential genetic factors involved in diabetes.", + "question": "How can GeneNetwork assist in identifying genetic factors involved in diabetes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_diabetes_13 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_diabetes_13 new file mode 100644 index 0000000..8a4a369 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_diabetes_13 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2010 - Systems genetics analyses predict a transcription role for P2P-R Molecular confirmation that P2P-R is a transcriptional co-repressor.pdf", + "2008 - Genetic Analysis of Posterior Medial Barrel Subfield Size.pdf", + "2011 - Peroxisomal L-bifunctional enzyme (Ehhadh) is essential for the production of medium-chain dicarboxylic acids.pdf", + "2009 - Metabolomics Applied to Diabetes Research.pdf", + "2020 - GeneNetwork a toolbox for systems genetics.pdf", + "2017 - GeneNetwork a toolbox for systems genetics.pdf", + "2011 - Using the PhenoGen Website for \u201cIn Silico\u201d Analysis of Morphine-Induced Analgesia Identifying Candidate Genes.pdf", + "2015 - Cell cycle gene expression networks discovered using systems biology Significance in carcinogenesis.pdf", + "2013 - Pathways, Networks and Systems Medicine Conferences.pdf" + ], + "extraction_id": [ + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "ec624ebb-489a-5437-a721-f01cf981d0a7", + "66aad1b1-a76d-58a8-aa40-76a6b58c4964", + "a8b40857-7ae8-512a-9817-bea1ae3345ba", + "380e9a2e-8f9f-5f9e-ba20-3695b1c60fda", + "4ca2fc9e-7d42-5ea3-b1b7-a296bfbc6a09", + "7dd82b3f-58bd-5915-9eea-250f11412ff2", + "0e3a5e40-06b0-58d4-b495-3093954ed17b", + "5b6d04d2-3aa2-5a43-814a-b13e60e3bb1d", + "9ca6d444-064c-5743-b029-9d634685f11b" + ], + "document_id": [ + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "e4d1e2e9-f267-5814-8c7b-dc11d7eec9bf", + "76a715a4-8222-598b-8e65-6d5b6e807989", + "ac61753e-bcb2-55c3-804b-e821e3d1a4ad", + "a6ae2fb6-88ae-588f-a98d-b6092f886ed9", + "d11a87ca-4989-59af-95e3-ab90af7d9212", + "682c3a51-0aa5-54a3-a6e7-a09b81c0e8b6", + "eb266fa1-8dec-5c56-a3d5-b508bd6bd448", + "6f354254-4f4d-52ad-bed7-9356f43c0b20", + "b50a9732-7d01-5d4d-8f33-a9d43dbc7df3" + ], + "id": [ + "chatcmpl-ADZRLVC30o2qvIhM1bclRsts27OFA", + "c63cfaee-749e-547b-9c0a-086266f10670", + "a9508122-3b14-5365-979c-ba580bdcb78f", + "21936758-94b1-506f-9229-77e26001ae44", + "8b8a24da-a175-5cb8-91bd-8966fca5d344", + "418060c8-fafb-5010-a512-55819ed36a3d", + "7ce6c0fe-8b0a-5ce9-83d1-6e6b99b4f24d", + "30e2423f-2b2b-5c7d-8808-b025242fa0c7", + "fa07b1bf-94e6-515b-8400-cf3afa8b8741", + "dcb29dfe-ba22-54bc-91f7-af3261a18fd2", + "f163b61d-987b-50eb-aef2-ee0dc0eddb9f" + ], + "contexts": [ + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "GeneNetwork http://www.genenetwork.org is anexample of a bioinformatics tool that can be used to explore systems genetics data. The importance of defining biological networks and predicting molecular interactions has been emphasized by several reports [1,2]. Such studies emphasize that when knowledge about DNA variation within popula- tions is interfaced with data on gene expression, protein interactions and DNA-protein binding, biological networks can be constructed that are predictive of the", + "GeneNetwork provides users with an array of analyticaltools to compare a given trait with a number of data setsavailable from other experimenters. Microarray data ofgene expression in the brain and data of other phenotypes are two such examples of possible tools. For this study, we", + "subnetworks GeneNetwork (www.genenetwork.org) is a depository of data- sets and tools for use in complex systems biology approaches in order to generate or predict higher order gene function ( 23, 24 ).", + "of these tools to diabetes andmetabolic disease research at the cellular, animal model,and human disease levels are summarized, with a partic-ular focus on insights gained from the more quantitativetargeted methodologies. We also provide early examplesof integrated analysis of genomic, transcriptomic, andmetabolomic datasets for gaining knowledge about meta-bolic regulatory networks and diabetes mechanisms andconclude by discussing prospects for future insights.", + "including correlation and network analysis to compare associations between tissues and between other rodent or human data sets[32] Many of the Data Sets are amenable to systems genetics mapping and other methods and are accessible at GeneNetwork. The Description and Usage column provides details about the data set and potential", + "including correlation and network analysis to compare associations between tissues and between other rodent or human data sets[32] Many of the Data Sets are amenable to systems genetics mapping and other methods and are accessible at GeneNetwork. The Description and Usage column provides details about the data set and potential", + "data are entered into GeneNetwork after they have been shepherded through a system like PhenoGen that has extensive capabilities for normalization and quality control. A comparison of the brain gene expression datasets and some of the tools for data analysis available on PhenoGen and GeneNetwork is shown in Table 3, and more detailed information on features provided by each site is outlined in the Supplementary DiscussionHoffman et al. Page 5 Addict Biol . Author manuscript; available in PMC 2012 July 1.", + "of importance in the emergence of precision medicine ( Curtis, 2015 ; Desautels et al., 2014 ; Glade Bender et al., 2015 ; Jorgensen, 2015 ; Kummar et al., 2015 ; Marquet et al., 2015 ; Rubin, 2014 ) wherein therapeutic strategies need to be aligned with specific properties of tumors. Methods GeneNetwork and WebGestalt GeneNetwork is an open access, online data analysis resource for systems biology and systems genetics. It contains a large number of microarray datasets from multiple tissues of", + "results in applying the method to type 2 diabetes mellitus suggest it may hold promise as a useful research tool for complex diseases . Further details on the methodol ogy is available from the following paper: Liu M, Liberzon A, Kong SW, Lai WR, Park PJ et al (2007) Network -based analysis of affected biological processes in type 2 diabetes models. PLoS Genet 3(6):e96. doi:10.1371/journal.pgen.0030096." + ], + "answer": "GeneNetwork offers several specific tools that are particularly useful for diabetes research: 1. **Multi-Omics Analysis**: GeneNetwork includes data from multiple species and supports multi-omics analysis, which integrates genomic, transcriptomic, and metabolomic datasets. This integration is crucial for understanding the complex regulatory networks and mechanisms underlying diabetes [1], [5]. 2. **Correlation and Network Analysis**: These tools allow researchers to compare associations between tissues and between rodent or human datasets. This can help identify key molecular interactions and pathways involved in diabetes [6], [7]. 3. **Systems Genetics Mapping**: Many datasets within GeneNetwork are amenable to systems genetics mapping, which can be used to link genetic variations to phenotypic traits related to diabetes. This helps in identifying genetic factors that contribute to the disease [6]. 4. **Microarray Data Analysis**: GeneNetwork provides access to a large number of microarray datasets from multiple tissues. This data can be used to study gene expression patterns and identify genes that are differentially expressed in diabetic conditions [3], [9]. 5. **Predictive Medicine and Systems Genetics**: GeneNetwork is designed as a resource for predictive medicine and systems genetics, which are essential for developing personalized treatment strategies for diabetes [1]. These tools are applied in diabetes research to gain insights into the genetic and molecular basis of the disease, identify potential biomarkers, and develop targeted therapeutic strategies. For example, integrated analysis of genomic, transcriptomic, and metabolomic datasets can reveal metabolic regulatory networks and mechanisms specific to diabetes [5]. Correlation and network analysis can help in understanding how different tissues interact and contribute to the disease pathology [6], [7].", + "question": "What specific tools within GeneNetwork are most useful for diabetes research, and how are they applied?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_diabetes_14 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_diabetes_14 new file mode 100644 index 0000000..f56cff3 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_diabetes_14 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2012 - Integrative Analysis of a Cross-Loci Regulation Network.pdf", + "2019 - IRS1\u2010 rs10498210 GA and CCR5\u201059029 AG polymorphisms in patients with type 2 diabetes in Kurdistan.pdf", + "2014 - Pathophysiology and treatment of type 2 diabetes.pdf", + "2012 - Systems Biology Approaches to Nutrition.pdf", + "2012 - Systems Biology Approaches to Nutrition.pdf", + "2012 - Systems Biology Approaches to Nutrition.pdf", + "2000 - Pathophysiology and Pharmacological Treatment.pdf", + "2012 - Systems Biology Approaches to Nutrition.pdf", + "2004 - Diabetes Genes a.pdf", + "2004 - Diabetes Genes a.pdf" + ], + "extraction_id": [ + "63fe12a0-20b1-5f8b-9fd6-adaecaa66eeb", + "5b74e0f4-8b7a-5ef2-ab41-99819fe185cc", + "b4a31e40-c59e-525c-afcf-6f1efae2ef3a", + "4cf7634b-caa6-589c-939d-3bf8d9410e46", + "4cf7634b-caa6-589c-939d-3bf8d9410e46", + "3faeb0aa-9894-58e7-a2a6-c5f9ceb5cd22", + "8bbb1489-4c01-5d58-99f3-c007c9e4713a", + "ae5e854d-a344-56f3-a597-fb8e1e745af7", + "77878a83-0136-583f-a237-5443ce967a1a", + "a7e3dee0-551a-57ea-8a22-04b5f84252da" + ], + "document_id": [ + "de5b6ecc-8a65-5dc7-9a4a-49e6df22472e", + "18afbfee-ddee-54b3-88cc-342812a65d09", + "ab9288ab-e3ad-58f1-b5ba-183ee17ce4bd", + "6955478b-950d-5d29-b24c-3a5ca656f3ae", + "6955478b-950d-5d29-b24c-3a5ca656f3ae", + "6955478b-950d-5d29-b24c-3a5ca656f3ae", + "27206293-61c6-5a22-a342-6dfe1a759f04", + "6955478b-950d-5d29-b24c-3a5ca656f3ae", + "805ad0d5-6a3d-5e5f-b1ba-121e10c6daaa", + "805ad0d5-6a3d-5e5f-b1ba-121e10c6daaa" + ], + "id": [ + "chatcmpl-ADZRS3ZXELoaA2dkAlgxXgB5ABMRV", + "1da7c09b-640d-5791-b411-4f87dfc2e03c", + "7b13b8a2-7d7f-5675-b3f0-c9aa48566b45", + "089f1f4d-d9d9-5245-b7ca-cf6e2ff87111", + "dd5661a5-6349-5abf-95c6-4abe424ada78", + "4f477c98-cd75-5955-ac3e-72a44d53f45e", + "2ace6cbd-084e-57e5-b6ed-1f42527b59b9", + "eafb2d74-ca99-52c6-a93d-89711b063a53", + "63d20d73-d6a3-56cb-91f6-34c66f439c7f", + "39b2638a-6bc8-5d16-bde4-4abd573cca47", + "8240c5cc-7d4f-51b4-935a-eb47314b3ec0" + ], + "contexts": [ + "Figure 3. Schematics view of insulin regulation. Elevated glucose level by either food intake or liver glycogenolysis is sensed by islet and leads to insulin secretion to the bloodstream. The increased insulin stimulates peripheral tissues to absorb glucose, and as a consequence, the glucose le vel", + "plays an important role in regulating insulin secretion in beta cells of the pancreas. It has been shown that glucosestimu-lated insulin secretion may be triggered by the autocrine ac-tivation of the insulin signaling pathway, including insulin receptor phosphorylation, tyrosine phosphorylation in IRS1, and the activation of PI3Kinase. Putting together these data leads to the hypothesis that a single molecular impairment in the pathway of insulin signaling, including an incomplete interaction between", + "(A) Insulin interacts in the liver to suppress glucose production, and in muscle and adipose tissue to stimulate uptake of glucose, aminoacids, and fatty acids. The amount of insulin released to maintain normal glucose homoeostasis is established by prevailing insulin sensitivity. This feedback is probably mediated through neuronal and humoral mechanisms, but exact mediators are still not known. (B) When insulin resistance develops in insulin-sensitive tissues, feedback to cells ensures that the cells", + "Insulin Action In healthy, normal individuals, blood glucose concentra- tion is maintained within a narrow range. After an over-night fast or between meals, blood glucose normally falls within the range of 3.5 5.5 mM. Immediately after a meal containing carbohydrate, blood glucose concentration rises to a peak of 6 10 mM followed by a sharp decline back to baseline within 60 minutes. This exquisite control is achieved by a ne balance between glucose absorption", + "from the gut, glucose production by the liver, and glucose extraction from the blood into the cells and tissues. Insulin plays a central role in the regulation of blood", + "glucose transport into the cell. Concomitantly, insulin stimulates intracellular utili-zation of glucose by many other tissues as well. In the fasting state, the main physiological function of insulin is to suppress glucose production by the liver and prevent uncontrolled lipolysis and ketogenesis, without which dia-betic ketoacidosis would quickly develop. Hence, if either of these aspects of insulin action is impaired, then periph-eral or liver hepatic insulin resistance or both are said to be present.", + "and suppression ofglucose production are regulated by insulin.", + "the pancreas in response to an increase in blood glucose, such as that which follows a carbohydrate - containing meal. Insulin acts to decrease blood glucose levels by increasing glucose uptake by tissues and by decreasing gluconeogenesis by the liver. To increase tissue uptake, insulin triggers the translocation of GLUT4 receptors to the cell surface in skeletal muscle and adipose tissue. Insulin also stimulates each of the regulatory enzymes in the glycolytic pathway, while also inhibiting the key", + "insulin suppresses both hepatic and renal glucose release, 3031 and stimulates glucose uptake exogenous insulin administration causes systemic glucose utilization to exceed systemic glucose release so that plasma glucose concentrations decrease. As the plasma glucose levels decrease there is a characteristic hierarchy of responses (Figure 1 ). Reduction of insulin secretion, the first in the cascade of hypoglycemia counterregulation, 2 derepresses glucose", + "Counter-regulatory hormones antagonize the glucose lowering action of insulin, and act to raise the blood glucose level. Glucagon, a potent counter-regulatory hormone inhibited by insulin, is secreted from pancreatic alpha cells when cells perceive low glucose. In diabetes, pancreatic insulin levels are reduced and glucagon is chronically elevated. In DKA, in addition to low insulin action, there is the cellular perception of low glucose , which" + ], + "answer": "Insulin plays a central role in the regulation of blood glucose levels by facilitating the uptake of glucose into peripheral tissues and suppressing glucose production by the liver. When blood glucose levels rise, such as after food intake, insulin is secreted by the pancreas into the bloodstream [1]. This secretion stimulates peripheral tissues, including muscle and adipose tissue, to absorb glucose, thereby lowering blood glucose levels [1], [3], [8]. Insulin also acts in the liver to suppress glucose production and gluconeogenesis, further contributing to the reduction of blood glucose levels [3], [6], [8]. Additionally, insulin triggers the translocation of GLUT4 receptors to the cell surface in skeletal muscle and adipose tissue, enhancing glucose uptake by these tissues [8]. This finely tuned balance between glucose absorption, production, and extraction ensures that blood glucose levels are maintained within a narrow range [4], [5].", + "question": "What role does insulin play in the regulation of blood glucose levels?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_diabetes_15 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_diabetes_15 new file mode 100644 index 0000000..45ce004 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_diabetes_15 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2017 - Regular exercise participation improves genomic stability in diabetic patients an exploratory study to analyse telomere length and DNA damage.pdf", + "2017 - Age at natural menopause and risk of type 2 diabetes a prospective cohort study.pdf", + "2018 - Type 2 Diabetes in adolescents and young adults.pdf", + "2012 - Meta-Analysis of the Relationship between Common.pdf", + "2011 - Interaction Between Exercise and Genetics.pdf", + "2017 - Differentiation of Diabetes by Pathophysiology.pdf", + "2018 - Type 2 Diabetes in adolescents and young adults.pdf", + "2018 - Type 2 Diabetes in adolescents and young adults.pdf", + "2018 - Type 2 Diabetes in adolescents and young adults.pdf", + "2015 - A Chromosome 13 locus is associated with male-specific mortality in mice.pdf" + ], + "extraction_id": [ + "0e53122e-a308-55f7-8ee8-a0857ac9c52f", + "7d48e6ab-4fe7-539f-beee-b65ee92cb187", + "ec07ba94-177b-599e-8104-92b2e0b943e6", + "a1598191-38cd-5ece-ad1f-24f100ce43a4", + "3c217c69-d626-541a-a846-3277ddbf3e12", + "47e113ad-dfd3-5a57-be05-7cbb1457f0b6", + "ec07ba94-177b-599e-8104-92b2e0b943e6", + "18efcee1-5d44-55fc-9d04-e8caaf1a13ab", + "ea61883c-6516-509f-b336-e50da1272f69", + "5cc56e3b-53ab-5299-814d-014e2ed31d2f" + ], + "document_id": [ + "dcaf7b09-2d54-5cbf-b061-e3c4e6c6c518", + "7457a250-85c1-520b-a177-f7775a60b14f", + "cc708325-df0a-55ec-9e9b-2bf97835c992", + "0135b1a8-aa80-5d2f-a64c-daf7806868bd", + "c36db75e-4b76-540d-9efb-d0e156e61541", + "9cfaef1e-fb60-5c2b-94f0-632c89b2eb16", + "cc708325-df0a-55ec-9e9b-2bf97835c992", + "cc708325-df0a-55ec-9e9b-2bf97835c992", + "cc708325-df0a-55ec-9e9b-2bf97835c992", + "ad8f2626-87fb-520e-8cef-ee9a9cc3ab0b" + ], + "id": [ + "chatcmpl-ADZRXnhQYKwt6bdryLveg63fKEuX4", + "a2125212-46c1-5020-ab62-196db673c2f5", + "df41de1c-0fa9-5a3b-a610-20c98a3f2b2d", + "c03fd521-abb7-53c7-a2f9-61fea38ca541", + "f2086a09-1ba9-5cc7-8573-3a3c654ee2c6", + "dee77427-41b1-5ebb-a50c-8d29bc152f88", + "765594a9-3100-57fb-a061-3d72b771fce7", + "2747746a-5ede-5c96-aa43-8b71a29c1ce0", + "c0c7ac1b-e32e-5971-a63c-7d2ee7b48d5f", + "91e2be43-bd18-5304-9755-3ee18dcf0040", + "13911a7c-bc0a-5643-bbe6-f4ec9a9a2292" + ], + "contexts": [ + "The biological processes linking aging and disease risk are poorly understood. Still, aging is considered to date as one of the main factors responsible for several complex diseases including cancer, cardiovascular diseases, and diabetes. Particularly, type 2 diabetes (T2D) has become very prevalent all over the world, with a projected increas- ing growth rate for the years ahead 1. The pathophysiological mechanism that underlines diabetic complications", + "unclear whether age at menopause is associated with risk of type2d i a b e t e s[ 3,4]. Data from cross-sectional studies examining the association between age at menopause and type 2 diabetes are contradictory, with a few studies reporting no association and some other reporting higher odds of having type 2 diabetes with early onset of menopause [ 57]. Recently, a nested case cohort study reported that an increased risk of type 2 diabetes is associ-", + "The mechanisms leading to development of type 2 diabetes in young people are similar to those in older patients; however, the speed of onset, severity, and interplay of reduced insulin sensitivity and defective insulin secretion might be different in patients who develop the disease at a younger age. 18 In adolescents with type 2 diabetes, as in later onset type 2 diabetes, the initial deterioration in -cell function is characterised by loss of first-phase nutrient-stimulated insulin secretion.", + "anincreased risk of developing type 2 diabetes (T2D) later in their", + "T2D is associated with age, and Western populations are aging rapidly. The second major explanation is our lifestyles have changed dramatically in recent years. Epidemiological studies have identified strong T2D risk relationships for obesity, sedentary behavior [24], and diets rich in energy [5], processed carbohydrates [6], and animal fats [7]. Collectively, these lifestyle factors impede the actions of insulin and raise hepatic glucose production, which can result in the diminution of endog-enous", + "tion. Many people with type 2 diabetes ultimately requirei n s u l i nt h e r a p y ,w h i c hr e ects long-standing type 2 diabetes and greatly diminished b-cell function but also likely includes individuals who have slowly progressingautoimmune diabetes with adult onset (LADA) or otherambiguous forms of diabetes. Age. Data from randomized controlled trials in people with type 2 diabetes under the age of 18 years or over the age of 65 years are scarce. Bene cial effects of tight", + "strong inverse association between BMI and age at diagnosis of type 2 diabetes. When type 2 diabetes presents in later life, the severity of insulin resistance is often greater among individuals with a history of protracted and severe obesity, particularly with excess visceral adiposity. 28", + "patients with young-onset type 2 diabetes than in patients without diabetes, whereas the risk of myocardial infarction was much less (typically 24 times higher) in patients with type 2 diabetes presenting in middle and later life. 106 In Hong Kong, where 20% of type 2 diabetes diagnosed since 1995 occurs in people aged 40 years or younger, a 7-year prospective study 107 showed that when adjusted for age,", + "type 2 diabetes, the major predisposing risk factors are obesity, family history, and sedentary lifestyle. Onset of diabetes at a younger age (defined here as up to age 40 years) is associated with longer disease exposure and increased risk for chronic complications. Young-onset type 2 diabetes also affects more individuals of working age, accentuating the adverse societal effects of the disease. Furthermore, evidence is accumulating that young-onset type 2 diabetes has a more aggressive disease phenotype,", + "pathophysiology of type 2 diabetes. Diabetes 60(10):26242634. doi:10.2337/db11-0415Aging Clin Exp Res 123" + ], + "answer": "Aging is considered one of the main factors responsible for the development of type 2 diabetes (T2D) [1]. As populations in Western countries are aging rapidly, the prevalence of T2D is increasing [5]. This is partly due to lifestyle changes that impede insulin action and increase hepatic glucose production, which are more pronounced in older adults [5]. Additionally, the severity of insulin resistance tends to be greater in older individuals, especially those with a history of prolonged and severe obesity [7]. Therefore, aging significantly increases the risk of developing type 2 diabetes.", + "question": "How does aging affect the risk of developing type 2 diabetes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_diabetes_16 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_diabetes_16 new file mode 100644 index 0000000..baa98fa --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_diabetes_16 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2017 - diabetes-mellitus-in-developing-countries-and-underserved-commun-2017.pdf", + "2018 - Global aetiology and epidemiology of type 2 diabetes mellitus and its complications.pdf", + "2014 - Pathophysiology and treatment of type 2 diabetes.pdf", + "2011 - Lifestyle and Genetics in Obesity and type 2 Diabetes.pdf", + "2010 - Interactions of Dietary Whole-Grain Intake.pdf", + "2008 - Public Health Genomics Approach to Type 2 Diabetes.pdf", + "2009 - Zinc and Diabetes - clinical links and molecular mechanisms.pdf", + "2011 - Type 2 diabetes across generations from pathophysiology to prevention and management.pdf", + "2007 - Physical activity modifies the effect of SNPs in the SLC2A2 (GLUT2).pdf", + "2011 - Lifestyle and Genetics in Obesity and type 2 Diabetes.pdf" + ], + "extraction_id": [ + "e6158348-e782-5e6d-9d89-3169b8fa630f", + "b534ab93-c837-5d89-809d-92062b1d49a4", + "35936d60-f8db-502e-be2c-4fe39f60fddd", + "93638ea5-6d1f-5b6a-9629-798804de24dd", + "6283c124-b479-5050-86ca-dc42390147a1", + "12668f1a-1631-5cce-bb6a-80b4de3fbb9e", + "ef8e6aa1-b7e0-5988-b9fb-a339317f9a66", + "de689016-3a4c-53b2-b3bf-a25ccbcbbb02", + "65609b08-1113-5a7f-9117-73476bcf50de", + "93638ea5-6d1f-5b6a-9629-798804de24dd" + ], + "document_id": [ + "8a9451b9-d7e8-5417-b6a5-5fd1b791cc4d", + "8bc8f3d4-968f-5252-ab4c-832b92e9ec0d", + "ab9288ab-e3ad-58f1-b5ba-183ee17ce4bd", + "a16d3328-039c-530a-bfe5-f6f80ecf2ad0", + "e4d4a19e-18a0-5a08-9ab7-537f31b7cdc1", + "47186d35-9c05-5b0a-b8cd-21d2e0e688d8", + "72ab8458-928b-56b9-9547-1ba4b59dfab9", + "0f49b102-1d7e-5702-af30-35e5f2ed93a6", + "6f5ced46-b777-563a-b644-432f4e7e2644", + "a16d3328-039c-530a-bfe5-f6f80ecf2ad0" + ], + "id": [ + "chatcmpl-ADZRbty26XP7vi2KOPG4Rh8fHX6iY", + "4e079c08-9095-5ec2-8c19-c6d0b222891e", + "b81dd6ab-e06e-55a2-bc0a-c89c5e883d3a", + "d090cda1-cf6a-5f2f-899a-3c7c763d0c8c", + "2b361786-7027-54e1-825d-34abc3a3fe98", + "89339b65-325f-588f-9f25-761124f0012f", + "74ec2f7f-a933-53b3-a78a-c69b9796c1c5", + "e6e5b010-d608-5a19-ae74-d571499fbb7b", + "2dc0e0fa-b061-5c09-8af3-02a44811042e", + "0e465787-e5b0-5f33-88cf-9bd1d0624f68", + "4d08d1ea-03a2-53d9-bb9d-df46c3fc2dcb" + ], + "contexts": [ + "of Type 2 Diabetes The lifestyle intervention using physical exercise and modi cation of nutrition is ef cient in pre- venting type 2 diabetes in patients with impaired glucose tolerance [ 99 ]. Clinical trials con rm that lifestyle interventions (dietary modi cation and increased physical activity) reduce the risk of progressing from impaired glucose tolerance to type 2 diabetes [ 105 ]. Assessing T2D risk accord- ing to FINDRISK scale [ 106 ] is quite common in", + "Major clinical trials have demonstrated that diet and lifestyle modifications are effective in preventing T2DM in high-risk individuals. T2DM management strategies including lifestyle modifications, social support and ensuring medication adherence are key to reducing the incidence of diabetes mellitus complications. REVIEWS NATURE REVIEWS | ENDOCRINOLOGY VOLUME 14 | FEBRUARY 2018 | 89", + "focused on people with impaired glucose tolerance or impaired fasting glucose because of their high risk of development of type 2 diabetes. Several studies have examined the ability of lifestyle modi cation and drugs to slow progression to diabetes (table 2). Findings from these trials have nearly all shown a bene t, with lifestyle modi cations being more e cacious than any drug, with the exception of the thiazolidinedione anti diabetics. 163175", + "no or just minor weight loss was achieved, diabetes incidence was also reduced ( Pan et al., 1997 ; Ramachandran et al., 2006 ). In addition, on the long term weight was partially or totally regained in all of the studies ( Knowler et al., 2009 ; Li et al., 2008 ; Lindstrom et al., 2006 ; Lindstrom et al., 2003 ). Despite this regain T2DM risk remained low or decreased further, thus the e ect of lifestyle is unlikely to be solely due to", + "proven particularly effective for preven-tion and management of type 2 diabetes.For example, improvement in dietaryquality, in conjunction with other lifestylemodications like increased physical ac-tivity, was shown to be more effectivethan pharmacological treatment in pre-vention of diabetes in individuals at highrisk (1). Further, lifestyle modicationmay mitigate the risk associated with thestrongest known diabetes risk loci (2).While the existence of environmental in-uences on genetic risk (and vice", + "spite of our incomplete knowledge of the genetics of type 2diabetes today, the burden of type 2 diabetes can be amelio-rated at the population level. Recent studies have found thatlifestyle changes through diet and exercise can prevent or", + "Lifestyle modification including exercise, nutrition and behavioral changes is the cornerstone to prevent and treat type 2 diabetes. Oral antidiabetic medication either as single agent or combination therapy is frequently required to maintain metabolic control, as assessed by monitoring ofglycated hemoglobin A 1C(HbA 1C) levels. Eventually, asignificant proportion of patients with type 2 diabetes require the exogenous administration of insulin [40].", + "diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. N Engl J Med 2001; 344: 134350. 114 Knowler WC, Barrett-Connor E, Fowler SE, et al. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med 2002; 346: 393403. 115 Ramachandran A, Snehalatha C, Mary S, Mukesh B, Bhaskar AD,", + "type 2 diabetes. Physical activity, favorable dietary changes,and weight reduction were essential components of a success-ful lifestyle intervention in two large randomized controlled trials on the prevention of type 2 diabetes in high-risk individ-uals with impaired glucose tolerance (IGT), including theFinnish Diabetes Prevention Study (DPS) (44) and the Diabe-tes Prevention Program (DPP) (22). In the DPS, increasedphysical activity was associated with a decreased risk of type", + "demonstrate that lifestyle modi cation comprising higher levels of PA and prudent food consumption may be e ective in obesity and T2DM prevention. The positive e ect of lifestyle on body weight seems somewhat transient, whereas the e ect on T2DM is sustained for longer periods. Furthermore, lifestyle modi ca- tion appears to have an e ect on diabetes risk independently of body weight and even of weight loss. Lifestyle and Genetics in Obesity and Type 2 Diabetes" + ], + "answer": "Lifestyle changes, particularly those involving diet and physical activity, have been shown to be effective in managing and even reversing type 2 diabetes (T2DM). Several sources in the provided context support this: 1. **Prevention and Management**: Lifestyle interventions, including dietary modifications and increased physical activity, have been proven effective in preventing the progression from impaired glucose tolerance to type 2 diabetes [1], [2], [3], [5], [9]. These interventions are more efficacious than pharmacological treatments in some cases [5]. 2. **Sustained Effects**: The positive effects of lifestyle changes on type 2 diabetes risk are sustained over longer periods, even if weight is partially or totally regained [4], [10]. This suggests that the benefits of lifestyle modifications are not solely dependent on weight loss. 3. **Cornerstone of Treatment**: Lifestyle modification, including exercise, nutrition, and behavioral changes, is considered the cornerstone for both preventing and treating type 2 diabetes [7]. In summary, lifestyle changes can indeed play a significant role in reversing type 2 diabetes, as evidenced by multiple clinical trials and studies [1], [2], [3], [4], [5], [7], [9], [10].", + "question": "Can lifestyle changes reverse type 2 diabetes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_diabetes_2 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_diabetes_2 new file mode 100644 index 0000000..20550b1 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_diabetes_2 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2008 - Glossary of Genetics Genomics Terms.pdf", + "2013 - Genetic association of ADIPOQ gene variants with type 2 diabetes, obesity.pdf", + "2015 - The Association of Type 2 Diabetes Loci.pdf", + "2016 - Genome-Wide Association Studies of Type 2 Diabetes.pdf", + "2012 - Systems Biology Approaches to Nutrition.pdf", + "2019 - Genetic Risk Scores for Diabetes Diagnosis.pdf", + "2012 - Gene-Environment Interactions in the Development of Type 2 Diabetes.pdf", + "2018 - Quantitative Relationship Between Cumulative Risk Alleles Based.pdf", + "2015 - Diabetes mellitus The epidemic of the century.pdf", + "2012 - The Pathogenesis and Natural History of Type 1 diabetes.pdf" + ], + "extraction_id": [ + "53e868dd-b318-5cf3-8b2e-98a548aab7cf", + "c2875fb7-31e1-51f2-87b8-f2c21d597d08", + "8703f848-f3bc-58b2-932a-a49b1f0fb002", + "c92c81bb-ede1-5e01-af7d-e244214fc856", + "eb3de845-98db-505c-bb7f-c0f3259875fc", + "a8162fba-c5da-504f-a018-b6242a026bc5", + "b961664b-5008-547c-a302-ee8c719f68fd", + "6db9f25e-36fd-51c0-be36-6dfacd963b1b", + "b1c7a0c1-90a0-54fe-a662-9113e44e2c9f", + "b797dd19-b8f4-5dc9-93ee-ace7594bf3bf" + ], + "document_id": [ + "c66d2572-071d-5aaf-829c-b3ca6cf6d697", + "6a2afe9a-51c0-52a6-be40-c034fb45c69a", + "a2abccec-e5cb-56ae-93b9-3040bc09f148", + "185aad8a-6a5b-5b18-81c4-ef251edef5e7", + "6955478b-950d-5d29-b24c-3a5ca656f3ae", + "8c66aca1-d4ba-534d-a037-4273de340ee1", + "ea9601ed-ad83-506e-b1b7-e7211671ff73", + "d585896e-1c32-51cb-827d-e4fd3b3943f3", + "e114dd28-fd39-56df-bdeb-8806474a6c10", + "acad2a9b-1149-539b-b335-661176d631f1" + ], + "id": [ + "chatcmpl-ADZQIhRURTB7PnDm4Bf2cVOJhSbs0", + "54ff4672-bf7f-5158-b228-ca3d45e0cb0d", + "0ccd2114-85e2-5aa3-85b5-3ae4b202037a", + "bf4247f8-f82c-5c40-b5af-3a68476f54bf", + "4b289db2-bda2-51d1-8f65-1cda62a4e40f", + "9fc663d2-2833-51e7-ae6a-55b007a6e27c", + "a67fe95c-11ac-5d06-8757-209f9abd0fd8", + "14608f3c-f5fa-52d6-b2c7-6ce6fd40985f", + "32b978f9-4bce-5f39-a655-09685b0d0f1f", + "74ab0f97-7758-5b01-b178-afee23d2e6cc", + "10d30a80-821a-5d09-988b-60bc71eae43c" + ], + "contexts": [ + "Genetic factors appear to play a role in determining an individuals risk of developing diabetes. It is hoped", + "ger, will develop diabetes because the prevalence of diabetes increases with age. In order to circumvent this problem, age was adjusted for in2 K. Ramya et al. / Gene xxx (2013) xxx xxx Please cite this article as: Ramya, K., et al., Genetic association of ADIPOQ gene variants with type 2 diabetes, obesity and serum adiponectin levels in south Indian population, Gene (2013), http://dx.doi.org/10.1016/j.gene.2013.09.012", + "elderly population. PLoS One 9: e100548. doi: 10.1371/journal.pone.0100548 PMID: 24959828 23. Strawbridge RJ, Dupuis J, Prokopenko I, Barker A, Ahlqvist E, Rybin D, et al. (2011) Genome-wide association identifies nine common variants associated with fasting proinsulin levels and provides new insights into the pathophysiology of type 2 diabetes. Diabetes 60: 2624 2634. doi: 10.2337/db11-0415 PMID: 21873549", + "information for diabetes risk prediction - differences according to sex, age, family history and obesity. PloS One 8(5):e64307. doi: 10.1371/journal.pone.0064307 Neel JV (1962) Diabetes mellitus: a thrifty genotype rendered detrimental by progress? Am J Hum Genet 14:353362 Neel JV (1999) The thrifty genotype in 1998. Nutr Rev 57(5 Pt 2):S2S9 Palmer ND, McDonough CW, Hicks PJ, Roh BH, Wing MR, An SS, Hester JM, Cooke JN,", + "insulin resistance, hypertension, and dyslipidemia (Obesity Education Initiative Expert Panel, 1998 ). Insulin resist-ance increases with age, and the incidence of diabetes rises sharply in the elderly (American Diabetes Association, 2010a ). In a few patients, genetic mutations appear to be associ- ated with T2D (Roche et al. , 2005 ; American Diabetes Association, 2010a ). For example, recent work using the DPP data has led to the identi cation of 27 single nucle-", + "early-onset diabetes in some pedigrees, but it also maybe observed in individuals who retain normal glucose tolerance into late adulthood and beyond ( ). Studying individuals from HNF A-MODY families, Lango Allen et al. () found that a -SNP T Dr s P S was signi cantly associated with earlier age of diabetes diagnosis, with each additional risk allele accelerating diagnosis by ~ months. Clinical application of predictive scores", + "12. de Miguel-Yanes JM, Shrader P, Pencina MJ, Fox CS, Manning AK, et al. 2011. Genetic risk reclassi- cation for type 2 diabetes by age below or above 50 years using 40 type 2 diabetes risk single nucleotide polymorphisms. Diabetes Care 34:12125 13. Dempe A, Scherag A, Hein R, Beckmann L, Chang-Claude J, Schafer H. 2008. Gene-environment interactions for complex traits: denitions, methodological requirements and challenges. Eur. J. Hum. Genet. 16:116472", + "diabetes risk genes predicts impaired glucose tolerance in female andobese individuals. PLoS One . 2012;7:e38224 . 74. Stevens JW, Khunti K, Harvey R, et al. Preventing the progression to type 2 diabetes mellitus in adults at high risk: a systematic review and network meta-analysis of lifestyle, pharmacological and surgicalinterventions. Diabetes Res Clin Pract . 2015;107:320 331(in eng).Cumulative Risk Alleles and Type 2 Diabetes Mellitus 18jJ Epidemiol 2018;28(1):3-18", + "and protects against oxidative stress-induced insulin-deficient diabetes. PLoS One 2014; 9: e87941 [PMID: 24498408 DOI: 10.1371/journal.pone.0087941] 23 Maahs DM , West NA, Lawrence JM, Mayer-Davis EJ. Epidemiology of type 1 diabetes. Endocrinol Metab Clin North Am 2010; 39: 481-497 [PMID: 20723815 DOI: 10.1016/j.ecl.2010.05.011] 24 Daneman D . Type 1 diabetes. Lancet 2006; 367: 847-858 [PMID: 16530579 DOI: 10.1016/S0140-6736(06)68341-4]", + "Sosenko JM, Skyler JS, Krischer JP , Greenbaum CJ, Mahon J, Rafkin LE, Cuthbertson D, Cowie C, Herold K, Eisen-barth G, et al. 2010. Glucose excursions between states of glycemia with progression to type 1 diabetes in the diabetes prevention trial-type 1 (DPT-1). Diabetes 59: 23862389. Steck AK, Armstrong TK, Babu SR, Eisenbarth GS. 2011. Type 1 Diabetes Genetics Consortium. Stepwise or linear decrease in penetrance of type 1 diabetes with lower-risk HLA genotypes over the past 40 years. Diabetes 60:" + ], + "answer": "Protective genetic factors against diabetes in elderly populations can be understood through several key insights from the provided context: 1. **Genome-Wide Association Studies (GWAS)**: Research has identified common genetic variants associated with fasting proinsulin levels, which provide new insights into the pathophysiology of type 2 diabetes. These findings suggest that certain genetic variants may play a protective role by influencing insulin regulation and glucose metabolism in the elderly [3]. 2. **Single Nucleotide Polymorphisms (SNPs)**: Studies have identified specific SNPs that are associated with diabetes risk. For example, some genetic mutations are linked to early-onset diabetes, but individuals with certain protective alleles may retain normal glucose tolerance into late adulthood. This indicates that specific genetic configurations can delay or prevent the onset of diabetes in elderly populations [6]. 3. **Gene-Environment Interactions**: The interaction between genetic factors and environmental influences is crucial in understanding diabetes risk. Protective genetic factors may interact with lifestyle and environmental factors to reduce the likelihood of developing diabetes in older adults [7]. 4. **Oxidative Stress Protection**: Certain genetic factors may protect against oxidative stress-induced insulin-deficient diabetes. This suggests that genetic mechanisms that enhance the body's ability to manage oxidative stress could be protective against diabetes in the elderly [9]. In summary, protective genetic factors against diabetes in elderly populations include specific genetic variants that influence insulin regulation, SNPs that delay diabetes onset, gene-environment interactions, and genetic mechanisms that protect against oxidative stress. These factors collectively contribute to a reduced risk of developing diabetes in older adults.", + "question": "Explain Protective Genetic Factors Against Diabetes in Elderly Populations" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_diabetes_3 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_diabetes_3 new file mode 100644 index 0000000..5caf9a4 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_diabetes_3 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2011 - Lifestyle and Genetics in Obesity and type 2 Diabetes.pdf", + "2011 - Lifestyle and Genetics in Obesity and type 2 Diabetes.pdf", + "2010 - Candidate Gene and Genome-Wide Association Studies in Behavioral Medicine.pdf", + "2017 - diabetes-mellitus-in-developing-countries-and-underserved-commun-2017.pdf", + "2016 - Epigenetics and aging.pdf", + "2005 - Metabolic Syndrome A Comprehensive Perspective Based on Interactions Between Obesity Diabetes and Inflammation.pdf", + "2011 - Lifestyle and Genetics in Obesity and type 2 Diabetes.pdf", + "2021 - Gene-by-environment modulation of lifespan and weight gain in the murine BXD family.pdf", + "2020 - Precision Medicine in Diabetes.pdf", + "2011 - Type 2 diabetes across generations from pathophysiology to prevention and management.pdf" + ], + "extraction_id": [ + "93638ea5-6d1f-5b6a-9629-798804de24dd", + "93638ea5-6d1f-5b6a-9629-798804de24dd", + "3bf4c712-4a5a-5a67-9e2a-d83fba8c1cb4", + "bc31e1f8-f149-50c4-82c1-86e2d465202c", + "4fb7ef96-fe5a-5d81-bf28-c756656f1cbb", + "c6cfb382-639a-5dd4-a9c8-c8f57b6daabc", + "551087b1-8e80-5a7b-839a-304f566a6417", + "bca61863-81b3-5ef7-850d-10cc9577a9e1", + "68183d3e-4c95-5363-92b8-891dccf7e3d6", + "de689016-3a4c-53b2-b3bf-a25ccbcbbb02" + ], + "document_id": [ + "a16d3328-039c-530a-bfe5-f6f80ecf2ad0", + "a16d3328-039c-530a-bfe5-f6f80ecf2ad0", + "17637a6f-804e-50e4-9cf5-37318e17f15c", + "8a9451b9-d7e8-5417-b6a5-5fd1b791cc4d", + "71b206ec-81bd-5194-8b21-ae522f8cbc2d", + "de2aa54c-eb0f-5dc3-ac92-23ee3215dd2a", + "a16d3328-039c-530a-bfe5-f6f80ecf2ad0", + "4d082da4-fa48-5170-8147-c4fea47a5d4b", + "0ad5b2de-d782-5d43-b294-bff5c7befd2d", + "0f49b102-1d7e-5702-af30-35e5f2ed93a6" + ], + "id": [ + "chatcmpl-ADZQPOsxOK9DJcrr7qBEh29WBnCmr", + "4d08d1ea-03a2-53d9-bb9d-df46c3fc2dcb", + "be87703d-e7b2-5db5-9983-5412e09a57ba", + "5c99d3b9-8b1a-5be4-8689-97662557dac4", + "4c5eb67d-3bdd-58d7-bf5e-d1d08a47118d", + "3fd5d259-8fd4-5b0d-bb64-134424baeef2", + "8c4e8b2c-6730-541c-8a2e-22fbd7ddb487", + "6f12fbd4-284d-5d41-9d60-54aa268a635d", + "06c32067-10ea-599a-9af2-9413ad8c8984", + "57012499-8167-5e51-8cb5-b436460e24a2", + "2dc0e0fa-b061-5c09-8af3-02a44811042e" + ], + "contexts": [ + "demonstrate that lifestyle modi cation comprising higher levels of PA and prudent food consumption may be e ective in obesity and T2DM prevention. The positive e ect of lifestyle on body weight seems somewhat transient, whereas the e ect on T2DM is sustained for longer periods. Furthermore, lifestyle modi ca- tion appears to have an e ect on diabetes risk independently of body weight and even of weight loss. Lifestyle and Genetics in Obesity and Type 2 Diabetes", + "suggested to attenuate its negative e ect on metabolic pro le, body weight, and diabetes risk ( Franks et al., 2007 ; Kilpelainen et al., 2008 ; Lindi et al., 2002 ; Ruchat et al., 2010 ) ( Table 1 ). The notion that lifestyle modi cation can eliminate the increased risk for development of T2DM in subjects with genetic suscepti-bility is also supported by ndings of Barwell et al. (2008) who", + "M., Bray, G. A. et al (2006). Effect of weight loss withlifestyle intervention on risk of diabetes. Diabetes Care, 29 , 21022107. Herder, C., Peltonen, M., Koenig, W., Sutfels, K., Lindstrom, J. et al (2009). Anti-inammatory effect oflifestyle changes in the Finnish Diabetes PreventionStudy. Diabetologia, 52 , 433442. Hung, J., McQuillan, B. M., Thompson, P . L., and Beilby,", + "22 Medications for Diabetes Prevention Even in the most successful of the randomized controlled trials, the risk reduction for incident diabetes following lifestyle intervention was ~60 % [ 48 51 ]. That raises the argument as to", + "SRT2104 extend the life span of obese mice and protect against age- related changes in multiple tissues ( 215). The antidiabetic drug metformin also induces effects similar to CR (216). Diabetes is considered an age-associated disease, and disturbances in insulin signaling and carbohydrate homeostasis may essentially lead toother age-related complications, including cancer, if untreated. Along with its antidiabetic properties, metformin supplementation has been", + "74 The mechanism underlying this effect of exercise is not known;however, it is noteworthy that lifestyle change is a very effectiveway to reduce the rate of development of diabetes in a predia-betic population, as shown by the diabetes prevention study. 75,76 Both a reduction in macronutrient intake and exercise cause areduction in inflammation. References 1. Reaven GM. Banting lecture 1988. Role of insulin resistance in human disease. Diabetes . 1988;37:15951607.", + "uals, but also for low-risk lean individuals ( Kriska et al., 2003 ; Meisinger et al., 2005 ; Schulze et al., 2006 ). Furthermore, health-ier lifestyle has been shown to be associated with decreased incidence of obesity- and T2DM-related complications such as hypertension and cardiovascular disease ( Manson et al., 2002 ; Stampfer et al., 2000 ). Evidence from randomized controlled trails The e cacy of lifestyle changes in obesity and T2DM prevention", + "extends lifespan. Cell Rep. 20, 451463 (2017). [PubMed: 28700945] 64. Barzilai N & Ferrucci L Insulin resistance and aging: A cause or a protective response? J. Gerontol. Ser. A 67, 13291331 (2012). 65. Holmes MV , Ala-Korpela M & Smith GD Mendelian randomization in cardiometabolic disease: challenges in evaluating causality. Nat. Rev. Cardiol. 14, 577590 (2017). [PubMed: 28569269] 66. Holmes MVet al.Mendelian randomization of blood lipids for coronary heart disease. Eur. Heart J.", + "70. Knowler WC, Barrett-Connor E, Fowler SE,et al.; Diabetes Prevention Program ResearchGroup. Reduction in the incidence of type 2diabetes with lifestyle intervention or metfor-min. N Engl J Med 2002;346:393 403 71. Crandall J, Schade D, Ma Y, et al.; DiabetesPrevention Program Research Group. The in-uence of age on the effects of lifestyle mod-", + "diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. N Engl J Med 2001; 344: 134350. 114 Knowler WC, Barrett-Connor E, Fowler SE, et al. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med 2002; 346: 393403. 115 Ramachandran A, Snehalatha C, Mary S, Mukesh B, Bhaskar AD," + ], + "answer": "Lifestyle modifications, including increased physical activity (PA) and prudent food consumption, have been shown to be effective in preventing obesity and type 2 diabetes mellitus (T2DM) [1]. These modifications can have a sustained positive effect on diabetes risk, even independently of body weight and weight loss [1]. This suggests that the benefits of lifestyle changes extend beyond mere weight management. Furthermore, lifestyle modifications can attenuate the negative effects on metabolic profiles, body weight, and diabetes risk, even in individuals with genetic susceptibility to T2DM [2]. This indicates that lifestyle changes can potentially eliminate the increased risk of developing T2DM in genetically predisposed individuals. The effectiveness of lifestyle changes in reducing the rate of diabetes development has been demonstrated in prediabetic populations, where both reduced macronutrient intake and increased exercise have been shown to lower inflammation, a key factor in diabetes progression [6]. Additionally, randomized controlled trials have shown that lifestyle interventions can reduce the incidence of T2DM by approximately 60% [4]. In summary, lifestyle modifications play a crucial role in reducing the risk of aging-associated diabetes by improving metabolic health, reducing inflammation, and mitigating genetic risks, thereby offering a comprehensive approach to diabetes prevention [1], [2], [4], [6].", + "question": "Explain Effect of Lifestyle Modifications on Aging-Associated Diabetes Risk" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_diabetes_4 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_diabetes_4 new file mode 100644 index 0000000..fc0bdbd --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_diabetes_4 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2015 - Pharmacogenetics and individual responses to treatment of hyperglycemia.pdf", + "2010 - Genome-wide association study (GWAS)-identified disease risk alleles do not compromisehuman longevity.pdf", + "2011 - Genomics of human longevity.pdf", + "2021 - Gene-by-environment modulation of lifespan and weight gain in the murine BXD family.pdf", + "2016 - Whole-Genome Sequencing of a Healthy Aging Cohort.pdf", + "2017 - Four Genome-Wide Association Studies Identify New.pdf", + "2008 - Glossary of Genetics Genomics Terms.pdf", + "2019 - Bioinformatic prediction of critical genes and pathways.pdf", + "2011 - Genomics of human longevity.pdf", + "2019 - Genetic Risk Scores for Diabetes Diagnosis.pdf" + ], + "extraction_id": [ + "32275a81-cd67-525e-b6c1-c68dc441ab62", + "680423ed-71cc-5049-a80f-c78fe86e35ff", + "7c183ae5-f10e-5f0c-962e-32135887b3bd", + "bca61863-81b3-5ef7-850d-10cc9577a9e1", + "c55b4a12-6cc8-5594-87d4-53e4f8f023d1", + "a6075268-c86f-536b-a6b4-d2e18be9f117", + "53e868dd-b318-5cf3-8b2e-98a548aab7cf", + "4109e561-4721-5f4e-b4d5-4353f8d1741d", + "7c183ae5-f10e-5f0c-962e-32135887b3bd", + "a8162fba-c5da-504f-a018-b6242a026bc5" + ], + "document_id": [ + "46081466-a50f-59d8-893d-8b8883b38507", + "200c2966-b647-552f-8504-0d6fb7f50bfa", + "2e038219-fdaa-506f-9cd3-51379054130e", + "4d082da4-fa48-5170-8147-c4fea47a5d4b", + "3a287979-e475-545b-99e6-4c1925653a79", + "c10653f6-b3d7-5b92-9271-ab8fcc7905a7", + "c66d2572-071d-5aaf-829c-b3ca6cf6d697", + "01201944-11f2-52d9-ac3e-7af685d4a4c4", + "2e038219-fdaa-506f-9cd3-51379054130e", + "8c66aca1-d4ba-534d-a037-4273de340ee1" + ], + "id": [ + "chatcmpl-ADZQVK9rNW7qGGShVvwBLR6uFNp9v", + "849d5eca-38a4-553e-83da-a967ba81614c", + "260a4030-b151-5afd-ae06-86246ee73a7a", + "558acee9-89ff-599a-8502-bc181bc94995", + "06c32067-10ea-599a-9af2-9413ad8c8984", + "19faf41b-7716-5244-a9c3-196c2e5cd477", + "369b0a64-a439-573a-99dd-67d911026c37", + "54ff4672-bf7f-5158-b228-ca3d45e0cb0d", + "a45fa299-f675-5050-a510-dfa6d0954a25", + "cfe4eab8-fb34-5d0b-ae67-79c3d9993e15", + "a67fe95c-11ac-5d06-8757-209f9abd0fd8" + ], + "contexts": [ + "Longitudinal Study of Aging. The natural history of progression from normalglucose tolerance to type 2 diabetes in the Baltimore Longitudinal Study of Aging. Diabetes 2003; 52:1475 1484. 22 Hornbak M, Allin KH, Jensen ML, Lau CJ, Witte D, Jrgensen ME ,e ta l .A combined analysis of 48 type 2 diabetes genetic risk variants shows nodiscriminative value to predict time to first prescription of a glucose lowering drug in Danish patients with screen detected type 2 diabetes. PLoS One 2014; 9:e104837.", + "A set of currently known alleles increasing the risk for coronary artery disease, cancer, and type 2 diabetes as identi ed by genome- wide association studies was tested for compatibility with human longevity. Here, we show that nonagenarian siblings from long- lived families and singletons older than 85 y of age from the general population carry the same number of disease risk alleles as young controls. Longevity in this study population is not compromised by", + "52561.x ) 17 Atzmon, G., Schechter, C., Greiner, W ., Davidson, D., Rennert, G. & Barzilai, N. 2004 Clinical phenotype of families with longevity. J. Am. Geriatr. Soc. 52, 274 277. ( doi:10.1111/j.1532-5415.2004.52068.x ) 18 Rozing, M. P . et al. 2009 Human insulin/IGF-1 and familial longevity at middle age. Aging (Albany NY )1, 714722. 19 Rozing, M. P . et al. 2010 Favorable glucose tolerance and lower prevalence of metabolic syndrome in", + "extends lifespan. Cell Rep. 20, 451463 (2017). [PubMed: 28700945] 64. Barzilai N & Ferrucci L Insulin resistance and aging: A cause or a protective response? J. Gerontol. Ser. A 67, 13291331 (2012). 65. Holmes MV , Ala-Korpela M & Smith GD Mendelian randomization in cardiometabolic disease: challenges in evaluating causality. Nat. Rev. Cardiol. 14, 577590 (2017). [PubMed: 28569269] 66. Holmes MVet al.Mendelian randomization of blood lipids for coronary heart disease. Eur. Heart J.", + "et al., 2012 ), possibly due to the indirect and/or a mixed relation- ship between individual genetic disease risk loci and exceptional longevity (as discussed by Fortney et al., 2015 ) versus the poten- tially more direct relationship between aging in the absence of disease and overall genetic disease risk. On the other hand, no difference in genetic risk is observed for type 2 diabetes genetic risk and cancer. Some of these ndings (type 2 diabetes, colon, and lung cancer) can be explained by the", + "5. Garagnani P, Giuliani C, Pirazzini C, etal. Centenarians as super-controls to assess the biological relevance of genetic risk factors for common age-related diseases: a proof of principle on type 2 diabetes. Aging (Albany NY). 2013;5:373385. doi:10.18632/aging.100562 6. Sebastiani P, Nussbaum L, Andersen SL, Black MJ, Perls TT. Increasing sibling relative risk of survival to older and older ages and the importance", + "Genetic factors appear to play a role in determining an individuals risk of developing diabetes. It is hoped", + "The pursuit of longevity has been the goal of humanity since ancient times. Genetic alterations have been demonstrated to affect lifespan. As increasing numbers of pro-longevity genes and anti-longevity genes have been discovered in Drosophila, screening for functionally important genes among the large number of genes has become difficult. The aim of the present study was to explore critical genes and pathways affecting longevity in Drosophila melanogaster. In this study, 168 genes associated with", + "offspring without diabetes mellitus of nonagenariansiblings: the Leiden Longevity Study. J. Am. Geriatr. Soc. 58, 564569. ( doi:10.1111/j.1532-5415.2010. 02725.x ) 20 Suh, Y . et al. 2008 Functionally signicant insulin-like growth factor I receptor mutations in centenarians.Proc. Natl Acad. Sci. USA 105, 34383442. ( doi:10. 1073/pnas.0705467105 ) 21 Heijmans, B. T ., Beekman, M., Houwing-Duistermaat, J. J., Cobain, M. R., Powell, J., Blauw, G. J., van der", + "early-onset diabetes in some pedigrees, but it also maybe observed in individuals who retain normal glucose tolerance into late adulthood and beyond ( ). Studying individuals from HNF A-MODY families, Lango Allen et al. () found that a -SNP T Dr s P S was signi cantly associated with earlier age of diabetes diagnosis, with each additional risk allele accelerating diagnosis by ~ months. Clinical application of predictive scores" + ], + "answer": "Longevity genes play a significant role in protecting against diabetes by influencing various biological pathways that contribute to better glucose tolerance and metabolic health. Here are some key points explaining their role: 1. **Favorable Glucose Tolerance and Lower Prevalence of Metabolic Syndrome**: Research has shown that individuals from long-lived families tend to have favorable glucose tolerance and a lower prevalence of metabolic syndrome, which are critical factors in reducing the risk of type 2 diabetes [3]. 2. **Insulin/IGF-1 Pathway**: The insulin/IGF-1 signaling pathway is crucial in regulating lifespan and metabolic processes. Mutations in the insulin-like growth factor I receptor (IGF-1R) have been found in centenarians, suggesting that these mutations may contribute to longevity by enhancing insulin sensitivity and glucose metabolism, thereby protecting against diabetes [9]. 3. **Genetic Risk and Longevity**: Studies have indicated that nonagenarians and centenarians carry the same number of disease risk alleles for type 2 diabetes as younger controls, yet they do not exhibit the disease. This suggests that longevity genes may counteract the effects of diabetes risk alleles, possibly through mechanisms that enhance metabolic health and insulin sensitivity [2], [5]. 4. **Use of Centenarians as Super-Controls**: Centenarians are often used as super-controls in genetic studies to assess the biological relevance of genetic risk factors for common age-related diseases, including type 2 diabetes. This approach helps identify genetic factors that contribute to both longevity and protection against diabetes [6]. In summary, longevity genes contribute to protecting against diabetes by promoting favorable glucose tolerance, enhancing insulin sensitivity, and counteracting the effects of diabetes risk alleles through various genetic and metabolic pathways [2], [3], [5], [6], [9].", + "question": "Explain The Role of Longevity Genes in Protecting Against Diabetes" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_diabetes_5 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_diabetes_5 new file mode 100644 index 0000000..024af91 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_diabetes_5 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2017 - diabetes-mellitus-in-developing-countries-and-underserved-commun-2017.pdf", + "2010 - Epidemiology of diabetes.pdf", + "2015 - Diabetes mellitus The epidemic of the century.pdf", + "2017 - diabetes-mellitus-in-developing-countries-and-underserved-commun-2017.pdf", + "2016 - Dissecting diabetes metabolic disease.pdf", + "2011 - Interaction Between Exercise and Genetics.pdf", + "2015 - Recent Trends in Therapeutic Approaches for Diabetes Management A Comprehensive Updat.pdf", + "2015 - Recent Trends in Therapeutic Approaches for Diabetes Management A Comprehensive Updat.pdf", + "2017 - Machine Learning and Data Mining Methods in Diabetes Research.pdf", + "2023 - Childhood adiposity and novel subtypes of adult-onset diabetes a Mendelian randomisation and genome-wide genetic correlation study.pdf" + ], + "extraction_id": [ + "d5004507-475d-5ac1-9aa6-b5fd664b8bf7", + "4307e79a-c3ae-51d7-8510-820375d2c4ca", + "6a734fb4-5ce1-5f11-b1fb-288e38ef9a6c", + "b2cd4ee5-81b3-5701-8cd1-8dbea4242cc1", + "998a92ba-e7fc-5553-b629-7b5797fbfafe", + "ed6dcfee-8273-5512-8fb4-fc51a9c921da", + "b8e47ab6-95e0-5fbb-bc40-fa9e46c0b1dc", + "e4d87eba-dfd4-51e5-a560-1ad46924edf1", + "81a02908-ff22-5136-be83-d53e04a81541", + "f0e064be-81a0-5ee9-88da-2a7049c65520" + ], + "document_id": [ + "8a9451b9-d7e8-5417-b6a5-5fd1b791cc4d", + "7f1cb121-3a35-571e-81c9-96a3afd66448", + "e114dd28-fd39-56df-bdeb-8806474a6c10", + "8a9451b9-d7e8-5417-b6a5-5fd1b791cc4d", + "eee2f79d-e093-52fb-871a-798fd859235e", + "c36db75e-4b76-540d-9efb-d0e156e61541", + "ec4921c2-af14-56cc-aed3-65f8ea236bde", + "ec4921c2-af14-56cc-aed3-65f8ea236bde", + "e2dcbb80-5ad7-5441-b170-9b46607445b0", + "fff2bd78-2ac2-5672-b8fd-ed82ab7c910b" + ], + "id": [ + "chatcmpl-ADZQdCy9515POnQgOjqu9IhwdWHwq", + "2454130e-8098-5c7f-944b-c5933a8409f8", + "6ba4950a-304f-5257-bd31-3e83a2f52df1", + "008aa60f-789b-519b-b81d-f437042c3df8", + "4660d51a-178a-5a14-a27a-2eeef1b0bf95", + "64fa332d-1415-584b-8b7c-43e8e3e698dc", + "3ef149b8-30fa-533b-b950-fc4122586080", + "ecc77a70-68dc-51a8-92a3-50f417deb98e", + "b169069b-43f2-5c24-8431-adfcaad27942", + "ae1db826-0202-53c9-a251-0fc9216bbf5c", + "ddc1154f-5406-5028-bacb-47a2ee6fbcf4" + ], + "contexts": [ + "disorder caused by different factors characterized by a chronic high level of blood sugar with distur-bances to carbohydrate, fat, and protein metabo-lism resulting from defects in insulin secretion, insulin action, or both [ 83 ]. Scientists have divided diabetes into three different types: Type 1 F. Assah and J.C. Mbanya", + "Type 1 and type 2 diabetes are the two main types, with type 2 diabetesaccounting for the majority ( >85%) of total diabetes prevalence. Both", + "classical classification of diabetes as proposed by the American Diabetes Association (ADA) in 1997 as type 1, type 2, other types, and gestational diabetes mellitus (GDM) is still the most accepted classification and adopted by ADA[1]. Wilkin[8] proposed the accelerator hypothesis that argues type 1 and type 2 diabetes are the same disorder of insulin resistance set against different genetic backgrounds[9]. The difference bet - ween the two types relies on the tempo, the faster", + "41 diabetes mellitus (formerly insulin- dependent diabetes mellitus IDDM) or type 1 diabetes is also known as juvenile onset diabetes. Type 2 diabetes mellitus (non-insulin-dependent diabe-tes mellitus (formerly non-insulin- dependent dia-betes, NIDDM) or type 2 diabetes adult-onset diabetes) is found in individuals who are insulin-resistant and who usually have relative insulin de ciency. Gestational diabetes mellitus (GDM), the third type, is de ned as any degree of glucose", + "Diabetes is a metabolic disease characterized by uncontrolled hyper-glycemia resulting from the variable combination of dysfunctional in-sulin secretion by pancreatic beta cells and insulin resistance. It is generally classi ed into monogenic diabetes (maturity onset diabetes of the young [MODY], neonatal diabetes, mitochondrial diabetes[54,55] , syndromes of insulin resistance) [56], type 1 diabetes (T1D) and type 2 diabetes (T2D). The metabolic syndrome is a combination of", + "Diabetes mellitus is a group of metabolic diseases characterized by hyperglycemia (elevated levels of glucose in the blood) resulting from defects in insulin secretion, insulin action, or both. There are two major types of diabetes mellitus: type 1 (T1D) and T2D, although several other rarer forms also exist [13]. T1D is an autoimmune disease that usually occurs in childhood, but the onset may occur at any age. T1D results from a cellular-mediated autoimmune destruction of the beta-cells in the pancreatic", + "2. Classification of Diabetes On the basis of insulin deficiency, diabetes can be classifiedintothefollowingtypesasfollows.2.1. Insulin Dependent Diabetes Mellitus (IDDM). It is also known as juvenile onset diabetes or type 1 diabetes, which accounts for 510% of the patients, resulting from cellular-mediated autoimmune destruction of the pancreatic cells. Thediseasecanaffectpeopleofallagesbutusuallyoccursin childrenoryoungadults.Regularsupplyofinsulininjections", + "2 JournalofDiabetesResearch Type I diabetes IDDM Type II diabetes NIDDM Gestational diabetesPancreas Islet of Langerhans-glucagon beta cells: insulin Genomic mutationsadministration for survival sugar levels Insulin resistance Defective insulin production Increased mortalityY ounger populationGlobal pandemicHuman body and diabetes pregnancy, it needs complete care and glucose monitorin g glycemic status individual level identification/development of lead moleculesRegular insulin Exercise", + "However, there are two major clinical types, type 1 diabetes (T1D) and type 2 diabetes (T2D), according to the etiopathology of t he disorder. T2D appears to be the", + "SIDD Severe insulin-deficient diabetes SIRD Severe insulin-resistant diabetes Introduction In 2018, a ground-breaking study identified five novel subtypes of adult-onset diabetes: severe autoimmune diabetes (SAID, including type 1 diabetes and latent autoimmune diabetes in adults [LADA]) and four subtypes of type 2 diabetes (severe insulin-deficient diabetes [SIDD], severe insulin-resistant diabetes [SIRD], mild obesity-related diabetes [MOD] and mild age-" + ], + "answer": "Diabetes is classified into several types based on different criteria. The main types of diabetes are: 1. **Type 1 Diabetes (T1D)**: Also known as insulin-dependent diabetes mellitus (IDDM) or juvenile onset diabetes, it is an autoimmune disease that usually occurs in childhood but can onset at any age [4], [6], [7]. 2. **Type 2 Diabetes (T2D)**: Also known as non-insulin-dependent diabetes mellitus (NIDDM) or adult-onset diabetes, it is characterized by insulin resistance and relative insulin deficiency [4], [6]. 3. **Gestational Diabetes Mellitus (GDM)**: This type occurs during pregnancy and is defined as any degree of glucose intolerance with onset or first recognition during pregnancy [4]. 4. **Other Types**: These include monogenic diabetes (such as maturity onset diabetes of the young [MODY], neonatal diabetes, mitochondrial diabetes), and syndromes of insulin resistance [5]. Additionally, a 2018 study identified five novel subtypes of adult-onset diabetes, which include severe autoimmune diabetes (SAID, including type 1 diabetes and latent autoimmune diabetes in adults [LADA]), severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes (MOD), and mild age-related diabetes [10].", + "question": "What are the types of diabetes" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_diabetes_6 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_diabetes_6 new file mode 100644 index 0000000..9527cf4 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_diabetes_6 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2010 - Epidemiology of diabetes.pdf", + "2015 - Diabetes mellitus The epidemic of the century.pdf", + "2017 - diabetes-mellitus-in-developing-countries-and-underserved-commun-2017.pdf", + "2023 - Childhood adiposity and novel subtypes of adult-onset diabetes a Mendelian randomisation and genome-wide genetic correlation study.pdf", + "2015 - Genetics, genomics and personalized medicine in Type 2 Diabetes.pdf", + "2017 - Machine Learning and Data Mining Methods in Diabetes Research.pdf", + "2018 - Novel subgroups of adult-onset diabetes and their association.pdf", + "2021 - Genomic Medicine in Diabetes Improving the Diagnostic Rate of Monogenic Diabetes.pdf", + "2007 - Bioethnic Conscription Genes, Race.pdf", + "2017 - Painting a new picture of personalised medicine for diabetes.pdf" + ], + "extraction_id": [ + "4307e79a-c3ae-51d7-8510-820375d2c4ca", + "6a734fb4-5ce1-5f11-b1fb-288e38ef9a6c", + "b2cd4ee5-81b3-5701-8cd1-8dbea4242cc1", + "f0e064be-81a0-5ee9-88da-2a7049c65520", + "670074e5-275c-5999-9fb2-2370a1ce3dbf", + "81a02908-ff22-5136-be83-d53e04a81541", + "20a6e2db-c742-5f28-a310-62f3bf58d92a", + "499fe6d8-73ba-5835-91a7-af3376d1651b", + "d824748c-69ce-5124-8a76-99c3cf221f8a", + "2ee5d7fa-babf-5feb-b40a-fd453b4b3f31" + ], + "document_id": [ + "7f1cb121-3a35-571e-81c9-96a3afd66448", + "e114dd28-fd39-56df-bdeb-8806474a6c10", + "8a9451b9-d7e8-5417-b6a5-5fd1b791cc4d", + "fff2bd78-2ac2-5672-b8fd-ed82ab7c910b", + "d8b85c3e-62f3-5e67-99b0-d0a2f225aff0", + "e2dcbb80-5ad7-5441-b170-9b46607445b0", + "c9a39a25-de31-5553-941b-bf1298cf1693", + "e315a891-ba59-57e9-856b-602544375324", + "d90126d9-fd87-5b38-87f7-08415f690836", + "e226b2b1-0bc4-5d79-b931-ad47f21be045" + ], + "id": [ + "chatcmpl-ADZQhFOO3LRPtv9Lg1g6L8gDOic6T", + "6ba4950a-304f-5257-bd31-3e83a2f52df1", + "008aa60f-789b-519b-b81d-f437042c3df8", + "4660d51a-178a-5a14-a27a-2eeef1b0bf95", + "ddc1154f-5406-5028-bacb-47a2ee6fbcf4", + "945f57d6-b790-5c1b-a94b-c3076ab28adc", + "ae1db826-0202-53c9-a251-0fc9216bbf5c", + "191582b1-0a31-5791-b123-4e1fa2672962", + "ee7614a8-89a2-503a-9da2-4207c22225bc", + "13ab2950-2bdc-57d2-840a-042157d2b9e8", + "6a7f929c-ba32-51ea-93e1-2b760bcb156d" + ], + "contexts": [ + "Type 1 and type 2 diabetes are the two main types, with type 2 diabetesaccounting for the majority ( >85%) of total diabetes prevalence. Both", + "classical classification of diabetes as proposed by the American Diabetes Association (ADA) in 1997 as type 1, type 2, other types, and gestational diabetes mellitus (GDM) is still the most accepted classification and adopted by ADA[1]. Wilkin[8] proposed the accelerator hypothesis that argues type 1 and type 2 diabetes are the same disorder of insulin resistance set against different genetic backgrounds[9]. The difference bet - ween the two types relies on the tempo, the faster", + "41 diabetes mellitus (formerly insulin- dependent diabetes mellitus IDDM) or type 1 diabetes is also known as juvenile onset diabetes. Type 2 diabetes mellitus (non-insulin-dependent diabe-tes mellitus (formerly non-insulin- dependent dia-betes, NIDDM) or type 2 diabetes adult-onset diabetes) is found in individuals who are insulin-resistant and who usually have relative insulin de ciency. Gestational diabetes mellitus (GDM), the third type, is de ned as any degree of glucose", + "SIDD Severe insulin-deficient diabetes SIRD Severe insulin-resistant diabetes Introduction In 2018, a ground-breaking study identified five novel subtypes of adult-onset diabetes: severe autoimmune diabetes (SAID, including type 1 diabetes and latent autoimmune diabetes in adults [LADA]) and four subtypes of type 2 diabetes (severe insulin-deficient diabetes [SIDD], severe insulin-resistant diabetes [SIRD], mild obesity-related diabetes [MOD] and mild age-", + "7 American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care 37(Suppl. 1), S81S90 (2014). 8 Daneman D. Type 1 diabetes. Lancet 367(9513), 847858 (2006). 9 Kahn SE, Cooper ME, Del Prato S. Pathophysiology and treatment of Type 2 diabetes: perspectives on the past, present, and future. Lancet 383(9922), 10681083 (2014). \t Describes\tthe\tpathophysiology\tof\tType\t2\tdiabetes\t(T2D)\tin \t detail\twith\tprospective\tof\t -cell\tdysfunction\tand\tpotential", + "However, there are two major clinical types, type 1 diabetes (T1D) and type 2 diabetes (T2D), according to the etiopathology of t he disorder. T2D appears to be the", + "type 1 diabetes, 723 (53%) had LADA, 162 (12%) had secondary diabetes (coexisting pancreatic disease), and 519 (38%) were unclassifiable because of missing data. The remaining 12 112 (883%) patients were considered to have type 2 diabetes (appendix). To classify patients into novel diabetes subgroups, first", + "4 monogenic diabetes not only provides opportunities for etiology- based treatment of the minority of individuals with highly penetrant variants, but also informs broader understanding of diabetes etiology. Types of monogenic diabetes Maturity onset diabetes of the young (MODY) MODY comprises most monogenic diabetes cases, with classical characteristics", + "19 RACIALIZED ETIOLOGIES OF DIABETES Diabetes is not one disease but many. More than 90 percent of all diabetics", + "with young-onset diabetes. Diabetologia 55:1265 1272 13. Schwartz SS, Epstein S, Corkey BE, Grant SF, Gavin JR 3rd, Aguilar RB (2016) The time is right for a new classification system for diabetes: rationale and implications of the -cell-centric classi- fication schema. Diabetes Care 39:179 186 14. Gale EAM (2006) Declassifying diabetes. Diabetologia 49:1989 1995 15. V oight BF, Scott LJ, Steinthorsdottir V et al (2010) Twelve type 2" + ], + "answer": "There are several types of diabetes. The classical classification includes type 1, type 2, other types, and gestational diabetes mellitus (GDM) [2]. Additionally, there are subtypes of adult-onset diabetes, including severe autoimmune diabetes (SAID), severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes (MOD), and mild age-related diabetes [4]. Monogenic diabetes, such as Maturity Onset Diabetes of the Young (MODY), also exists [8]. Therefore, the main types of diabetes are: 1. Type 1 diabetes 2. Type 2 diabetes 3. Other types of diabetes 4. Gestational diabetes mellitus (GDM) 5. Monogenic diabetes (e.g., MODY) In summary, there are at least five main types of diabetes, with additional subtypes identified in recent studies.", + "question": "How many types of diabetes exist?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_diabetes_7 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_diabetes_7 new file mode 100644 index 0000000..2ec5b11 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_diabetes_7 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2017 - Regular exercise participation improves genomic stability in diabetic patients an exploratory study to analyse telomere length and DNA damage.pdf", + "2009 - Antidiabetic drug metformin (GlucophageR) increasesbiogenesis of Alzheimer\u2019s amyloid peptides viaup-regulatingBACE1transcription.pdf", + "2016 - The dog aging project translational geroscience in companion.pdf", + "2018 - Type 2 Diabetes in adolescents and young adults.pdf", + "2004 - Diabetes Mellitus and Risk of Alzheimer Disease and Decline in Cognitive Function.pdf", + "2012 - Systems Biology Approaches to Nutrition.pdf", + "2016 - Whole-Genome Sequencing of a Healthy Aging Cohort.pdf", + "2010 - Genetics, pathogenesis and clinical interventions in type\u20091 diabetes.pdf", + "2016 - The genetic architecture of type 2 diabetes.pdf", + "2016 - Genetic predisposition for beta cell fragility underlies type 1 and type 2 diabetes.pdf" + ], + "extraction_id": [ + "0e53122e-a308-55f7-8ee8-a0857ac9c52f", + "660377a1-3bd9-5628-ba52-4603b485267a", + "0d62683a-9b2d-535a-9464-15df3187bff3", + "ec07ba94-177b-599e-8104-92b2e0b943e6", + "d301fa41-68c5-5d02-94cc-ef3f83f2df8a", + "eb3de845-98db-505c-bb7f-c0f3259875fc", + "c55b4a12-6cc8-5594-87d4-53e4f8f023d1", + "6d537deb-a8fa-59cf-a961-aa3da56b18da", + "8b8b572d-68f5-5470-b5ed-ec5c6219dd5e", + "77681744-9c95-530f-afec-248e183fd78c" + ], + "document_id": [ + "dcaf7b09-2d54-5cbf-b061-e3c4e6c6c518", + "78818ecc-83cb-5189-8eba-2ed7cb6d3a7f", + "e841c6bd-78b8-56e1-b3dd-e2bcc8a0f590", + "cc708325-df0a-55ec-9e9b-2bf97835c992", + "a6fca397-1f0f-5a8d-846d-78e92ef5c088", + "6955478b-950d-5d29-b24c-3a5ca656f3ae", + "3a287979-e475-545b-99e6-4c1925653a79", + "46ac7ad7-a672-5d91-abf8-9c2f6307cd38", + "d7e2a9de-46f1-5191-9cb0-dd68eb9f365a", + "5b239c51-7b4c-58e0-acca-2061593fe317" + ], + "id": [ + "chatcmpl-ADZQlbMdmyuTz6o9831V2evAJSxPf", + "a2125212-46c1-5020-ab62-196db673c2f5", + "962238ce-db77-5c91-8f41-33640d0bf501", + "6da64757-8c4d-5ec8-9c70-8da1be37af81", + "2747746a-5ede-5c96-aa43-8b71a29c1ce0", + "916717da-d554-5e4c-95d5-780d96c8bad2", + "9fc663d2-2833-51e7-ae6a-55b007a6e27c", + "19faf41b-7716-5244-a9c3-196c2e5cd477", + "5bc52c12-3339-542b-82a2-b839203370b9", + "13ca56ac-b751-5bc8-b557-e7a7a12a1b04", + "652c144e-94d8-519b-8d1f-1bcb2bf1b7b3" + ], + "contexts": [ + "The biological processes linking aging and disease risk are poorly understood. Still, aging is considered to date as one of the main factors responsible for several complex diseases including cancer, cardiovascular diseases, and diabetes. Particularly, type 2 diabetes (T2D) has become very prevalent all over the world, with a projected increas- ing growth rate for the years ahead 1. The pathophysiological mechanism that underlines diabetic complications", + "fects correlate with the functional alterations associated withaging of the brain and with AD pathogenesis (411). The vastmajority of AD cases are late onset and sporadic in origin withaging being the most profound risk factor. Insulin signaling isknown to be involved in the process of brain aging (1220).Insulin dysfunction/resistance in diabetes mellitus (DM) is notonly a common syndrome in the elderly but also considered a riskfactor for AD, especially for vascular dementia (21, 22). The link", + "striking similarities to people with respect to age-associ- ated increases in risk for several diseases, the relative risk for individual diseases is not always shared. For example,although the prevalence of type II diabetes in older dogs increases with age, it is still much lower than the current prevalence of type II diabetes in people, and the mostcommon form of diabetes in dogs resembles type I diabetes in people (Nelson and Reusch 2014 ). Whether this reects", + "strong inverse association between BMI and age at diagnosis of type 2 diabetes. When type 2 diabetes presents in later life, the severity of insulin resistance is often greater among individuals with a history of protracted and severe obesity, particularly with excess visceral adiposity. 28", + "COMMENT In a cohort of more than 800 older persons, we found thatdiabetes mellitus sometime in the study was associated withan increased risk of developing AD during a mean of 5.5years of observation. The risk of incident AD was 65% higherin those with diabetes mellitus than in those without it.Overall, results were similar in analyses restricted to dia-", + "insulin resistance, hypertension, and dyslipidemia (Obesity Education Initiative Expert Panel, 1998 ). Insulin resist-ance increases with age, and the incidence of diabetes rises sharply in the elderly (American Diabetes Association, 2010a ). In a few patients, genetic mutations appear to be associ- ated with T2D (Roche et al. , 2005 ; American Diabetes Association, 2010a ). For example, recent work using the DPP data has led to the identi cation of 27 single nucle-", + "et al., 2012 ), possibly due to the indirect and/or a mixed relation- ship between individual genetic disease risk loci and exceptional longevity (as discussed by Fortney et al., 2015 ) versus the poten- tially more direct relationship between aging in the absence of disease and overall genetic disease risk. On the other hand, no difference in genetic risk is observed for type 2 diabetes genetic risk and cancer. Some of these ndings (type 2 diabetes, colon, and lung cancer) can be explained by the", + "equal number of adults over 18 are thought to develop the disease,although incidence in older people receives less media/research attention. In this review, we discuss our current understanding of the cellular/molecular mechanisms of disease aetiology and progres-sion, the usefulness and limitations of rodent models of spontaneousdiabetes, the factors that are influencing the current increased inci-dence and the clinical opportunities for those affected.", + "associated with maturity onset diabetes of the young and early onset-age of type 2 diabetes. J. Diabetes Complications 26, 343347 (2012). 19. Langenberg, C. et al. Design and cohort description of the InterAct Project: an examination of the interaction of genetic and lifestyle factors on the incidence of type 2 diabetes in the EPIC Study. Diabetologia 54, 22722282 (2011).", + "in the precipitation of diabetes. Saturated fatty acids drive the apoptosis and senescence of beta cells27,41, with increased oxidative stress42 and endoplasmic reticulum stress41. As increased body mass index is asso - ciated with earlier onset of T1D43, it is possible that dietary fat is acting as a sensitizer similar to insHEL, in effect lowering the threshold for autoimmune stress to precipitate clinical diabetes. The male-specific susceptibility to diabetes in this model is in sharp" + ], + "answer": "Yes, there is a direct association between aging and susceptibility to having diabetes. Aging is considered one of the main factors responsible for several complex diseases, including diabetes, particularly type 2 diabetes (T2D) [1]. Additionally, insulin resistance, which is a key factor in the development of diabetes, increases with age, and the incidence of diabetes rises sharply in the elderly [6].", + "question": "Is there a direct association between aging and susceptibility to having diabetes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_diabetes_8 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_diabetes_8 new file mode 100644 index 0000000..619d0b1 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_diabetes_8 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2008 - Glossary of Genetics Genomics Terms.pdf", + "2012 - Predicting Diabetes Our Relentless Quest for Genomic Nuggets.pdf", + "2007 - Genome\u2013wide association studies provide new insights into type 2 diabetes aetiology..pdf", + "2004 - Diabetes Genes b.pdf", + "2013 - Systems Biology Approach Reveals Genome to Phenome Correlation in Type 2 Diabetes.pdf", + "2010 - Diabetes in Asia.pdf", + "2004 - Diabetic nephropathy Linking histology, cell biology.pdf", + "2004 - Diabetes Genes a.pdf", + "2011 - Type 2 diabetes across generations from pathophysiology to prevention and management.pdf", + "2004 - Diabetes Genes a.pdf" + ], + "extraction_id": [ + "53e868dd-b318-5cf3-8b2e-98a548aab7cf", + "19b662f1-779b-57d2-bb51-e5505479ddc6", + "cd034e2b-72bd-5cda-a456-48cf17ead1bf", + "35324efd-a618-54aa-b9a5-541a9435134c", + "9369222f-e125-58c0-8f2b-cf5daa867f77", + "e99fe157-eda9-5e56-9ec9-8f428de2a161", + "7d723588-2c9a-5f6f-8ed1-f484fada8d13", + "4beabe81-e24e-535c-9df3-bfaa9cfdde90", + "baec13ec-c42b-51b4-9974-8ef1c2d10ddc", + "5a2221e0-dabc-523c-8358-3e43789e8f7a" + ], + "document_id": [ + "c66d2572-071d-5aaf-829c-b3ca6cf6d697", + "7ef99c69-e777-521f-bb99-26bcb81de748", + "2ad9b6c6-56ed-5ba6-ad88-c1a6777f5196", + "da2f2624-e3e6-5e2d-b406-941db2fe7671", + "ea7c2799-c259-5d0e-b40b-ecebe0a9fc9f", + "0be842b8-7f69-503b-baed-c336e5c834d6", + "b0732aa4-4250-563c-b4e7-b98bb8706299", + "805ad0d5-6a3d-5e5f-b1ba-121e10c6daaa", + "0f49b102-1d7e-5702-af30-35e5f2ed93a6", + "805ad0d5-6a3d-5e5f-b1ba-121e10c6daaa" + ], + "id": [ + "chatcmpl-ADZQoBhwZsCCyM8hyEreAlAw5KAiT", + "54ff4672-bf7f-5158-b228-ca3d45e0cb0d", + "ceedd7ed-ae19-5971-89f8-cccc731741b6", + "12ee4112-ee8b-584a-bbab-b47863449868", + "65084469-bf7c-508c-a211-1f28f7626638", + "3bbf736e-7d8b-5e67-a4bf-e1ae28738bf3", + "6c14eef8-bb27-503a-9523-9e7a16d71021", + "8397e685-13d3-5487-a9c6-856cc119cef9", + "44725666-366f-5123-92dd-ab2cf29e88c1", + "2aa9f009-ae05-5c93-ac3a-58b1f516d844", + "54d5bc85-a2f5-58f6-814f-b511f2e0c4cf" + ], + "contexts": [ + "Genetic factors appear to play a role in determining an individuals risk of developing diabetes. It is hoped", + "the diabetes epidemic, and its predilection for certain ethnic groups, are unknown. However, interactions between genetic pre-disposition and environmental triggers (or accelerants) are generally presumed to un- derlie the etiology of diabetes (3 5) (Fig. 1). The best known environmental risk factors are dietary habits, physical inactivity, and obesity; interventions that ameliorate theserisk factors prevent the development oftype 2 diabetes (6,7). By contrast, knowledge of the genetic", + "increases the risk of type 2 diabetes. Such a strong environmental component to a dis - ease should perhaps have deterred geneticists from studying the disorder. However, there are many obese people who do not suffer from diabetes and many non-obese people who do, showing that obesity is not the only factor involved in the aetiology of type 2 diabetes (FIG. 1). In the past 10 years, geneticists have devoted a large amount of effort to finding type 2 diabetes genes. These efforts have", + "future diabetes, however, is not possible on a genetic basis alone. For example, the concordance rate for identical twins is < 50%, indicating that either environmental or developmental events (such as T cell development) affect the progression of diabetes. The ability of serologic studies to identify individuals at risk for diabetes in the general population is under investigation. Among relatives of patients with diabetes, serologic markers can identify patients at high risk.3", + "genes relate directly to insulin secretion and indirectly, through collaborating with other genes, to insulin resistance. Thisseems to support the epidemiological evidence that environmentally triggered insulin resistance interacts with geneticallyprogrammed bcell dysfunction to precipitate diabetes. Citation: Jain P, Vig S, Datta M, Jindel D, Mathur AK, et al. (2013) Systems Biology Approach Reveals Genome to Phenome Correlation in Type 2 Diabetes. PLoS ONE 8(1): e53522. doi:10.1371/journal.pone.0053522", + "Genetic factors Type 2 diabetes has a strong genetic component and most Asian patients have a rst-degree relative with diabetes. 48,49 Much progress has been made in our understanding of the genetics of this disease. Importantly, most of the loci originally associated with diabetes in European populations have been replicated in Asian populations. Whereas monogenic forms of diabetes result from rare genetic mutations with large e ects, such as those seen in maturity-onset diabetes of young people,", + "literature abounds with evidence for genetic mediation ofthe initiation and progression of diabetic nephropathy.First, there is familial clustering that is not completelyexplained by environmental factors [3947]. Our indexcase and her family are perfect examples of genetic pre-disposition to diabetes and its complications, or, at thevery least, familial clustering. Parving and colleagues es-timated that glycemic control, hypertension, and albu-minuria account for only one-third of the variability", + "GENETIC MODELS OF DIABETES Classically, genetic models of diabetes and obesity have been produced in two ways. One is serendipitous observation of a spontaneously arising extreme phenotype, followed by selective breeding to fix the trait. The resulting model will often be monogenic, i.e. due to a single mutation. The other approach is by repeated selective breeding of initially normal appearing members of a genetically diverse ( outbred) population that are at", + "36 Herder C, Roden M. Genetics of type 2 diabetes: pathophysiologic and clinical relevance. Eur J Clin Invest 2011; 41: 67992. 37 Dabelea D, Hanson RL, Lindsay RS, et al. Intrauterine exposure to diabetes conveys risks for type 2 diabetes and obesity: a study of discordant sibships. Diabetes 2000; 49: 220811. 38 Voight BF, Scott LJ, Steinthorsdottir V, et al. Twelve type 2 diabetes susceptibility loci identi ed through large-scale association analysis. Nat Genet 2010; 42: 57989.", + "Environmental influences interact with genetic factors to determine susceptibility to type 2 diabetes by affecting either insulin action, insulin secretion or both. The prevalence of type 2 diabetes has increased markedly in populations that have rapidly adopted a Western lifestyle (for example the Pima Indians) and in many populations that have migrated to regions with a more affluent lifestyle compared to their native country (see Chapter IV.2)." + ], + "answer": "Genetics plays a significant role in influencing the emergence of diabetes. Several key points highlight this influence: 1. **Genetic Predisposition**: Genetic factors are crucial in determining an individual's risk of developing diabetes [1]. There is a strong genetic component to type 2 diabetes, with many patients having a first-degree relative with the disease [6]. 2. **Interaction with Environmental Factors**: The development of diabetes is generally presumed to result from interactions between genetic predisposition and environmental triggers such as dietary habits, physical inactivity, and obesity [2]. This interaction suggests that while genetics set the stage, environmental factors act as accelerants. 3. **Insulin Secretion and Resistance**: Specific genes are directly related to insulin secretion and indirectly to insulin resistance. This supports the idea that genetically programmed beta-cell dysfunction, when combined with environmentally triggered insulin resistance, precipitates diabetes [5]. 4. **Familial Clustering**: There is evidence of familial clustering of diabetes, indicating a genetic predisposition to the disease and its complications [7]. 5. **Genetic Models**: Genetic models of diabetes have been developed through selective breeding, which often results in monogenic forms of the disease due to single mutations [8]. 6. **Concordance in Twins**: The concordance rate for diabetes in identical twins is less than 50%, indicating that while genetics play a significant role, environmental or developmental events also affect the progression of diabetes [4]. In summary, genetics significantly influences the emergence of diabetes by predisposing individuals to the disease, which is then often triggered or exacerbated by environmental factors.", + "question": "How does genetics influence the emergency of diabetes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_diabetes_9 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_diabetes_9 new file mode 100644 index 0000000..3b2d9f4 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_diabetes_9 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2010 - Pharmacogenetics of Anti-Diabetes Drugs.pdf", + "2002 - Genetic Effects on Age-Dependent Onset and Islet Cell.pdf", + "2017 - diabetes-mellitus-in-developing-countries-and-underserved-commun-2017.pdf", + "2004 - Interaction and Association Analysis of a Type 1 Diabetes Susceptibility Locus.pdf", + "2008 - Clinical Risk Factors, DNA Variants.pdf", + "2011 - Obesity and Type 2 Diabetes What Can Be Unified.pdf", + "2017 - Spectrum of mutations in monogenic diabetes genes identified from high-throughput DNA sequencing of 6888 individuals.pdf", + "2010 - Genomics, Type 2 Diabetes, and Obesity.pdf", + "2015 - Diabetes mellitus The epidemic of the century.pdf", + "2015 - Diabetes mellitus The epidemic of the century.pdf" + ], + "extraction_id": [ + "73e1aaff-7ef6-5ca2-9c94-23f5674a4f88", + "3dbc9428-663d-58ba-9268-3d87957bad4d", + "d7bd898b-1d46-557a-b065-f94fc5310b2a", + "d71343b2-f7c5-52b4-96f9-bcc98f97fe81", + "2643b341-8c50-5cea-af36-86a8b070a80e", + "037a2c9e-51be-5bd8-957a-4b3dfcc82e46", + "b24927c4-ee83-51a8-b431-b43be7d3b678", + "8a28c11f-e0d2-526b-ac85-2f2fbf054fc5", + "7cfe9f29-a0ee-56d3-be3b-1b238a43bc07", + "7cfe9f29-a0ee-56d3-be3b-1b238a43bc07" + ], + "document_id": [ + "ffeebaf9-ff76-5751-9b8b-7a2a4a4f1dc3", + "2598ed03-43da-5894-8a62-41c8f53559d1", + "8a9451b9-d7e8-5417-b6a5-5fd1b791cc4d", + "4246f8d0-69e8-56cf-9674-d379467dfb61", + "0018610a-9c86-5e2d-a27d-f66cf4f8519d", + "07e715c3-4f95-54f4-8461-5062e62d0fe6", + "18a8a000-69ed-5d34-b13f-f5ae016d1067", + "3d629777-f1b6-5450-94ef-56736e5a4e10", + "e114dd28-fd39-56df-bdeb-8806474a6c10", + "e114dd28-fd39-56df-bdeb-8806474a6c10" + ], + "id": [ + "chatcmpl-ADZQut2fnFLEnMmGsKclOiiWtUmw9", + "437a7129-63b8-5f34-8273-2eef9535e987", + "e2cc78dc-8af3-5459-bc88-84939f5aced4", + "47e8bd94-fd61-57f2-b1d0-cc139d71936a", + "a495dcc8-5cee-58a9-9f15-95be8fbc9b6a", + "aa72551a-ac0c-5d7d-8057-34f229f68eb1", + "9a42b2f0-cad6-56af-9aa4-0d2f9bedd351", + "748d13eb-eec0-5f79-8138-e3227a188b52", + "b4efc562-0077-5428-be43-f3eeafeb6847", + "d184bcc3-8c38-5969-859a-22db976fec35", + "3e22864f-a062-55b2-a9a3-a64cde8bd388" + ], + "contexts": [ + "gene are associated with NIDDM in Caucasians. Diabetes 1996 , 45, 825-831. 46. Tarasov, A.I.; Nicolson, T.J. ; Riveline, J.P.; Taneja, T.K. ; Baldwin, S.A.; Baldwin, J.M.; Charpentier, G.; Gautier, J.F. ; Froguel, P.; Vaxillaire, M.; et al. A rare mutation in ABCC8/SUR1 leading to altered ATP-sensitive K+ channel activ ity and beta-cell glucose sensing is associated with type 2 diabetes in adults. Diabetes 2008 , 57, 1595-1604.", + "gene is associated with insulin-dependent diabetes mellitus. Diabetes 33:176 183, 1984 6. Bennett ST, Lucassen AM, Gough SCL, Powell EE, Undlien DE, Pritchard LE, Merriman ME, Kawaguchi Y, Drons eld MJ, Pociot F, Nerup J, Bouzekri N, Cambon-Thomasen A, R nningen KS, Barnett AH, Bain SC, Todd JA: Susceptibility to human type 1 diabetes at IDDM2 is determinedby tandem repeat variation at the insulin gene minisatellite locus. Nat Genet 9:284 292, 1995", + "of Diabetes Results of several genome-wide association stud- ies (GWAS) have linked the following common gene variants with a 1520% increased risk of diabetes: reduced insulin secretion via reduce beta-cell mass (CDKAL1, CDKN2A, CDKN2B) and beta-cell dysfunction (MTNR1B, TCF7L2, KCNJ11) and increased insulin resistance related to obesity (FTO) and unrelated to obesity (IRS1, PPARG) [ 11 ]. While most of the early studies", + "gene is associated with insulin-dependent diabetes mellitus. Diabetes 33:176 183, 1984 3. Nistico L, Buzzetti R, Pritchard L, Van der Auwera B, Giovannini C, Bosi E, Larrad M, Rios M, Chow C, Cockram C, Jacobs K, Mijovic C, Bain S,Barnett A, Vandewalle C, Schuit F, Gorus F, Tosi R, Pozzilli P, Todd J: TheCTLA-4 gene region of chromosome 2q33 is linked to, and associated with,type 1 diabetes: Belgian Diabetes Registry. Hum Mol Genet 5:1075 1080, 1996", + "ly associated with type 2 diabetes: TCF7L2, KCNJ11, and PPARG . 5-7 However, in 2007, a number of novel genetic variants ( CDKAL1, IGF2BP2, the locus on chromosome 9 close to CDKN2A/CDKN2B, FTO, HHEX, SLC30A8, and WFS1)8-14 were shown to in - crease susceptibility to type 2 diabetes in repro - ducible studies. Furthermore, a recent meta-analy - sis identified six novel variants ( JAZF1, CDC123/ CAMK1D, TSPAN8/LGR5, THADA, ADAMTS9, and NOTCH2 ) that are associated with type 2 dia - betes. 15", + "date gene approaches now have identified /H1101140 genes as- sociated with type 2 diabetes (17, 18) and a similar num-ber, albeit largely different, with obesity. Most type 2diabetes genes appear to be related to /H9252-cell dysfunction,", + "HNF1A ,HNF4A ,HNF1B ,INS,NEUROD1 ,PDX1 ,PAX4 , ABCC8 ,KCNJ11 ,KLF11 ,CEL, and BLK), 6 genes associ- ated with recessive diseases that include diabetes as a phenotype ( WFS1 ,NEUROG3 ,EIF2AK3 ,GLIS3 ,RFX6 , andSLC19A2 ), and 3 genes in which heterozygous mu- tations have been shown to cause diabetes mellitus (PAX6 ,GATA6 , and PPARG ). Our primary objectives were to (1) identify subjects with potentially undiag- nosed monogenic diabetes, (2) compare and contrast the", + "4. ORahilly S. Human genetics illumi - nates the paths to metabolic disease. Na - ture 2009;462:307-14. 5. McCarthy MI. Growing evidence for diabetes susceptibility genes from genome scan data. Curr Diab Rep 2003;3:159-67. 6. Hattersley AT, McCarthy MI. What makes a good genetic association study? Lancet 2005;366:1315-23. 7. Altshuler D, Hirschhorn JN, Klanne - mark M, et al. The common PPARgamma Pro12Ala polymorphism is associated with decreased risk of type 2 diabetes. Nat Genet 2000;26:76-80.", + "genes including interlukin-6 ( IL-6), tumor necrosis factor- and IL-10 genes were found to be associated with greater risk of developing type 2 diabetes[171], in addition to genetic variants in the genes for IL12B , IL23R and IL23A genes[172]. In a study involving the hormone sensitive lipase re sponsible for lipolysis in adipose tissues, a deletion null mutation, which resulted in the absence of the protein from adipocytes, was reported to be associated with diabetes[173]. Nine", + "2 diabetes[144,149,150], however, not all of these genes showed consistent and reproducible association with the disease[151]. Genome wide association studies (GWAS) in various populations identified 70 loci associated with type 2 diabetes and revealed positive linkage of many mutations and SNPs that influence the expression and physiological impact of the related proteins and risk to develop type 2 diabetes. One study involved several thousand type 2 diabetes patients and" + ], + "answer": "Several genes have been associated with diabetes, both type 1 and type 2, according to the provided context: 1. **Type 1 Diabetes:** - **Insulin gene minisatellite locus (IDDM2)**: Susceptibility to type 1 diabetes is determined by tandem repeat variation at this locus [2]. - **CTLA-4 gene region of chromosome 2q33**: This gene region is linked to and associated with type 1 diabetes [4]. 2. **Type 2 Diabetes:** - **ABCC8/SUR1**: A rare mutation in this gene affects ATP-sensitive K+ channel activity and beta-cell glucose sensing, leading to type 2 diabetes [1]. - **Common gene variants**: Several genome-wide association studies (GWAS) have linked common gene variants with increased risk of type 2 diabetes, including: - **CDKAL1, CDKN2A, CDKN2B**: Associated with reduced insulin secretion via reduced beta-cell mass [3]. - **MTNR1B, TCF7L2, KCNJ11**: Associated with beta-cell dysfunction [3]. - **FTO**: Associated with increased insulin resistance related to obesity [3]. - **IRS1, PPARG**: Associated with increased insulin resistance unrelated to obesity [3]. - **Other genes**: Additional genes associated with type 2 diabetes include: - **TCF7L2, KCNJ11, PPARG, CDKAL1, IGF2BP2, FTO, HHEX, SLC30A8, WFS1** [5]. - **JAZF1, CDC123/CAMK1D, TSPAN8/LGR5, THADA, ADAMTS9, NOTCH2** [5]. - **Genes related to beta-cell dysfunction**: Many type 2 diabetes genes appear to be related to beta-cell dysfunction [6]. - **Monogenic diabetes genes**: Genes such as **HNF1A, HNF4A, HNF1B, INS, NEUROD1, PDX1, PAX4, ABCC8, KCNJ11, KLF11, CEL, BLK** are associated with monogenic forms of diabetes [7]. - **Genes associated with recessive diseases that include diabetes**: **WFS1, NEUROG3, EIF2AK3, GLIS3, RFX6, SLC19A2** [7]. - **Genes with heterozygous mutations causing diabetes mellitus**: **PAX6, GATA6, PPARG** [7]. - **Inflammatory genes**: Genes such as **IL-6, TNF-\u03b1, IL-10, IL12B, IL23R, IL23A** are associated with a greater risk of developing type 2 diabetes [9]. These references provide a comprehensive list of genes associated with both type 1 and type 2 diabetes.", + "question": "what genes are associated with diabetes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_1 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_1 new file mode 100644 index 0000000..93c6c86 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_1 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2019 - Shared and distinct genetic risk factors for childhood-onset.pdf", + "2008 - Genetic Effects on Environmental Vulnerability to Disease Novartis Foundation Symposium 293.pdf", + "2018 - The Genetics and Genomics of Asthma.pdf", + "2010 - Recent advances in the genetics and genomics of asthma.pdf", + "2019 - Shared and distinct genetic risk factors for childhood-onset.pdf", + "2008 - Genetic Effects on Environmental Vulnerability to Disease Novartis Foundation Symposium 293.pdf", + "2019 - Leveraging genomics to uncover.pdf", + "2018 - The Genetics and Genomics of Asthma.pdf", + "2018 - The Genetics and Genomics of Asthma.pdf", + "2019 - Shared and distinct genetic risk factors for childhood-onset.pdf" + ], + "extraction_id": [ + "306aeeff-1e54-5e8d-9d06-10fc3c995f69", + "110ccbf7-ee1f-5326-ac9a-a5ea4a842751", + "47d93beb-84d7-55ab-af76-a671ea6dc488", + "d398c492-cb9a-5aba-bfd4-4a51bd6eb831", + "cfc7a3ec-7c07-5966-a022-decf1c4f8276", + "22dfc0a4-f5ac-5fa0-911a-32a5e71c8608", + "531fbafe-c4a1-55b2-b832-dfb9a7f67e96", + "af4e210e-5d90-5f49-996a-fa177eaf155e", + "b95d9c35-eefe-579a-bf1d-12811d1b5a07", + "cfc7a3ec-7c07-5966-a022-decf1c4f8276" + ], + "document_id": [ + "6f5caedc-c2b4-54e4-8bc6-8b78cab6bb0e", + "5d65e407-34e5-5c1c-b394-989b7a09b57d", + "47cf2a47-d8d2-583b-8c12-2a7cfbe92e5e", + "656c0e8b-d154-551a-bae1-986e418a6aa4", + "6f5caedc-c2b4-54e4-8bc6-8b78cab6bb0e", + "5d65e407-34e5-5c1c-b394-989b7a09b57d", + "5da46d3b-fa82-57f6-b3e5-c82784347881", + "47cf2a47-d8d2-583b-8c12-2a7cfbe92e5e", + "47cf2a47-d8d2-583b-8c12-2a7cfbe92e5e", + "6f5caedc-c2b4-54e4-8bc6-8b78cab6bb0e" + ], + "id": [ + "chatcmpl-ADZIhpxkquCUzShWFEamc7p1ntYgw", + "21c02be1-f8a1-5d70-abb7-00a866f4a734", + "183e3af7-7138-554b-bcda-b76e7eeef30a", + "21d3694f-032b-5d8c-93e2-58e85ec92903", + "b64a52ee-d2e8-50a7-a101-b255cd905180", + "46ee340b-b11c-52ef-a48d-d2c8135b63d8", + "1fa74fa4-0f13-55be-8164-ee57dfbc80af", + "545f48b6-3b45-5a1a-8def-7a5900ecc40a", + "a0a06ce6-5b8f-52ff-8904-901ae666f247", + "4d8496c2-5415-58d4-a27d-8f0f7f8d147a", + "47361fa0-f820-5114-8074-ad63e0815d81" + ], + "contexts": [ + "children is driven more by dysregulated allergy and epithelial barrier function genes, whereas the cause of adult-onset asthma is more lung-centred and environmentally determined, but with immune-mediated mechanisms driving disease progression in both children and adults. Funding US National Institutes of Health. Copyright 2019 Elsevier Ltd. All rights reserved. Introduction Asthma is the most prevalent chronic respiratory disease worldwide.1 The diagnosis of asthma is based on the", + "asthma has increased with alarming frequency in industrialized cities worldwide (e.g. Elias et al 2003). These diseases generally are complex, with clear contribu-tions of genetic background and exposure to environmental stimuli (see Kleeberger & Peden 2005). It is unlikely that the increased incidence in disease can be attributed only to genetics as increases in disease-causing genetic mutations to account for the increase would require multiple generations. Therefore the role of environmental exposures", + "living all represent risk factors for asthma, while early farm exposures and breastfeeding confer protective effects. Such observations have been assimilated into the hygiene hypothesis, rst set out in 1989 (136), positing that reduced early microbial exposure and its impacts on immunity underliethe postIndustrial Revolution atopy and asthma epidemic. Responsible for a transformation in our understanding of microbial factors in asthma has been a revolution of a different kind. Only", + "tobacco smoke exposure and with early-onset asthma (before age 4) [49/C15/C15]. Further studies of preschool asth- matics have shown the 17q21 variants are associated with an almost two-fold increased risk of developing recurrent wheeze, asthma, asthma exacerbations and bronchial hyper-responsiveness, but are not associated with eczema, rhinitis or allergic sensitization, indicating that they are specic determinants of nonatopic asthma in children [47].", + "for childhood-onset asthma supports the widely held idea that asthma in childhood is due to impaired barrier function in the skin and other epithelial surfaces. This model proposes that compromised epithelial barriers promote sensitisation to food and airway allergens and to wheezing illnesses in early life. 46,47 In fact, childhood onset-specific loci identified in this study have been associated with atopic dermatitis or food allergies, such as FLG on 1q21.3 with the atopic march, 41 atopic", + "relation to asthma and other atopic diseases). The prompt in the asthma example came from the observation of the apparent effect of being reared in a farm envi-ronment. Of course, it was crucial to replicate that observation in different social contexts and it was also important to have some leverage on a likely biological mediating pathway (in that case exposure to endotoxins). Similarly, the G E", + "[11] Shaaban R, Zureik M, Soussan D, Neukirch C, Heinrich J, Sunyer J, et al. Rhinitis and onset of asthma: a longitudinal population-based study. Lancet (London, England) 2008;372(9643):104957. [12] de NijsSB, VenekampLN, BelEH. Adult-onset asthma: is it really different? Eur Respir Rev 2013;22(127):44. [13] RackemannFM. Intrinsic asthma. J Allergy 1940;11(2):14762. [14] JarvisD, NewsonR, LotvallJ, HastanD, TomassenP, KeilT, etal. Asthma in adults and its as -", + "GG19CH10_Cookson ARI 26 July 2018 9:47 Epigenetic Features of Asthma: Within the Lung A study of the epigenome in primary airway epithelial cells from 74 asthmatic and 41 non-asthmatic adults (111) revealed a regulatory locus on chromosome 17q1221 (the same locus identied by asthma GWASs) associated with asthma risk and epigenetic signatures of specic asthma endo-types. ORMDL3 expression was related to the differentially methylated region at this locus, while", + "studies have identied a range of pre-, peri-, and postnatal environmental factors, including modeof delivery, diet, and early lower respiratory tract infection, that confer relative risk or protection. Attempts to map the genetic architecture of asthma have identied a broad spectrum of potential contributory genes. Many of these genes demonstrate inconsistent patterns of replication betweencohorts, most likely reecting a combination of true positive and true negative results and the", + "49 Variants at those loci were all associated with earlier age of asthma onset. We further showed that these loci are associated with childhood-onset asthma, even after exclusion of patients with a history of allergic diseases in prespecified analyses, suggesting both a crucial role for the allergic diathesis in the development of asthma in childhood and a shared architecture between allergic disease and childhood-onset asthma. 33,46 By contrast, the enrichment for genes highly expressed" + ], + "answer": "The most cited environmental factor for the onset of asthma is exposure to tobacco smoke, particularly in early childhood [4]. Additionally, reduced early microbial exposure, as posited by the hygiene hypothesis, is also a significant environmental factor [3].", + "question": "What is the most cited environmental factor for the onset of asthma?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_10 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_10 new file mode 100644 index 0000000..d736997 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_10 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2017 - Regular exercise participation improves genomic stability in diabetic patients an exploratory study to analyse telomere length and DNA damage.pdf", + "2008 - Telomeres and Aging.pdf", + "2002 - Mitochondrial dysfunction leads to telomere attrition.pdf", + "2006 - Sex-specific telomere length profiles.pdf", + "2018 - Sex Differences in Aging Genomic Instability.pdf", + "2008 - Telomeres and Aging.pdf", + "2018 - Repetitive Fragile Sites Centromere Satellite DNA.pdf", + "2006 - Sex-specific telomere length profiles.pdf", + "2010 - Roles of Werner syndrome protein in protection of genome integrity.pdf" + ], + "extraction_id": [ + "efd18101-9cf2-56b5-8f86-c2aba6caa0bc", + "0e53122e-a308-55f7-8ee8-a0857ac9c52f", + "13990eb4-bef2-58ce-bf3e-0e3bc294caab", + "b92ede07-74a7-524a-8d2c-54b2559e8425", + "6d3bfe47-f26e-50dc-8d77-19f3797e53a0", + "396708f1-aa0a-571e-a8d3-7cb8404e9502", + "e57aa746-20f1-50b3-b8ab-3139a9a910fc", + "3b0cb0ab-421d-54d7-9816-c6a2e6f1ac68", + "eb8d8e40-a484-57cb-8125-3fd5eb3f6389", + "32528f9c-b6bb-593e-94c5-1ed12d0ac4ad" + ], + "document_id": [ + "62b635c3-040e-512a-b016-6ef295308a1e", + "dcaf7b09-2d54-5cbf-b061-e3c4e6c6c518", + "61d9c326-d36e-55c1-a891-335dc943e70f", + "d8bc729b-7513-58b7-b12e-0db1fb6d3b7d", + "09c78a17-4a1f-52c1-be4d-994fd9fd71d0", + "8cfb5529-7f0c-58fc-b6e4-b3ee800fb72f", + "61d9c326-d36e-55c1-a891-335dc943e70f", + "262df0d6-ad68-544a-88ed-b4568f305858", + "09c78a17-4a1f-52c1-be4d-994fd9fd71d0", + "ec3e4f66-1619-5f71-9860-c1ad048d1841" + ], + "id": [ + "chatcmpl-ADZJpRmTN4COm0TDjwpOtSCKK6Mex", + "28e98b7e-f273-5bdd-9979-185133f311af", + "bb069c10-45f1-5a83-95e3-4b7655874ba7", + "5f940245-af1d-5eee-84dc-942017c523d0", + "7fad29bd-12bf-53d0-af89-aadd38b974ff", + "607cbd31-d430-5517-8212-208b25af32bf", + "53508a9e-d064-58a3-a4f9-0785470a1462", + "36de43a5-e151-5300-8c34-ed15ec66ea52", + "f181e6da-58b6-5f26-87a2-355e25388673", + "64ef9964-1831-5a7a-8a69-5e8d0c332d37", + "dd9a3905-0225-5345-891b-4469af6336ee" + ], + "contexts": [ + "Telomeres are arrays of linked nucleotide hexamer repeats that are found at the ends of chromosomes in a vast clade of organisms [14]. While the sequence of these telomeric repeats can vary between organisms, their biological function is highly conserved, which is to limit damage inflicted on genes during the replica- tion of chromosomes. Telomere length is progressively shortened with each round of genomic replication, unless it is restored through the action of a ribonucleo-", + "repetitive nucleotide sequences at the end of each eukaryotic chromosome, which protects them from attrition and damage. Although the relationship between leukocyte telomere length (LTL) and diabetes is still questioned 8, different studies have shown that T2D individuals have shorter leukocyte telomeres than non-T2D individuals9, 10 that may be associated with disease progression11. Indeed, the decreased antioxidant capacity described in patients", + "telomere length,a phenomenon attributed to higher levels of oxidativestress at the cellular level (70). More recent studies havelinked telomere length in smooth muscle cells with senes-cence and disease severity in patients with atherosclero-sis (141, 150). Leukocyte telomere length was also short ina cohort of similar patients and associated with a higherrisk of developing occult cardiovascular disease (71).More data are needed to understand and validate the useof leukocyte telomere length as a biomarker", + "TTAGGG sequence that cap the ends of chromosomes, protect-ing them from degradation and fusion. The length of telomererepeats is primarily maintained by active telomerase, which iscomposed of Telomerase RNA (TR) and a catalytic subunitTelomerase Reverse Transcriptase (TERT) (Blackburn, 2001).Extensive evidence has shown that telomere shortening anderosion lead to chromosome end-to-end fusions and genomicinstability (Blasco et al ., 1997; Hande et al ., 1999), causing", + "age telomere length through accumulation of several short telo- meres (Londono-Vallejo et al., 2001; Martens et al., 2000) is responsible for senescence or whether a speci c chromosome arm limits the replication potential of human cells (Hemann et al., 2001). Individual chromosome arms were shown to have large variations in their length (Lansdorp et al., 1996; Benn, 1997; Londono-Vallejo et al., 2001), and chromosome 17p seemed to be equipped with especially short telomeres in hu-", + "Telomeres are specialized structures that protect the ends of linear chromosomes. They shorten during aging due to the unidirectional activity of DNA polymerase, which leaves a section of DNA unrepli-cated on the lagging strand. Telomeres also are subject to shortening by genotoxic stress, such as oxidative damage (33). Among many eukaryotes, the enzyme telomerase maintains telomere length; but telomerase activity varies over the lifespan and between cell types, tissues, and species (34). In most human", + "ends. For example, chromosome 17p typi-cally has shorter telomeres than most other chromosomeends (26, 137). In human nucleated blood cells, the aver-age telomere length shows a highly signicant declinewith age that is most pronounced for the cells of theimmune system (Fig. 2). Telomeres prevent the ends oflinear chromosomes from appearing as DNA double-strand (ds) breaks and protect chromosome ends fromdegradation and fusion. It has been proposed that telo-meres can switch between an open state (in", + "telomeres, the repetitive sequence at the end of linear chromosomes, has garnered much attention for its relation to aging. Telomere repeats serve as an internal clock for cycling cells because each round of replication results in the loss of telomeric DNA in the absence of active telomerase (reviewed in [66]). Eventually, this loss over cellular generations culminates in telomere crisis and a permanent state of", + "a pivotal role in maintenance of genomic integrity and func-tion (Moyzis et al., 1988; McElligott and Wellinger, 1997; van Steensel and de Lange, 1997). It is generally accepted that telomeres shorten during DNA replication both in vitro and in vivo. In individuals, short telomeres are considered to be a sign of advanced age. Cawthon and coworkers (2003) showed that telomere shortening in hu-", + "Telomeres are nucleoprotein complexes situated at the ends of thelinear chromosomes that prevent chromosome termini from beingrecognized as broken DNA ends ( i.e., DSBs). In most of the organisms studied, telomeres consist of long repetitive G-rich and C-rich DNAstrands, the ribonucleoprotein telomerase, and telomere bindingand associated proteins [179] . Loss of telomeric repeats or loss of" + ], + "answer": "The length of telomeres is significant for several reasons: 1. **Protection of Chromosomes**: Telomeres are repetitive nucleotide sequences at the ends of chromosomes that protect them from attrition and damage during replication [2], [4], [7]. They prevent chromosome ends from being recognized as broken DNA ends, which could otherwise lead to degradation and fusion [4], [10]. 2. **Aging and Cellular Senescence**: Telomere length decreases with each round of genomic replication due to the unidirectional activity of DNA polymerase, which leaves a section of DNA unreplated on the lagging strand [1], [6]. This progressive shortening acts as an internal clock for cellular aging, eventually leading to cellular senescence when telomeres become critically short [8]. 3. **Disease Association**: Shorter telomeres have been linked to various diseases. For example, individuals with Type 2 Diabetes (T2D) have been shown to have shorter leukocyte telomeres, which may be associated with disease progression [2]. Similarly, shorter telomeres in smooth muscle cells have been linked to senescence and disease severity in patients with atherosclerosis [3]. Leukocyte telomere length has also been associated with a higher risk of developing cardiovascular disease [3]. 4. **Genomic Stability**: Telomere shortening and erosion can lead to chromosome end-to-end fusions and genomic instability, which are detrimental to cellular function and can lead to various genetic disorders [4]. 5. **Biomarker Potential**: Due to their association with aging and disease, telomere length is being studied as a potential biomarker for various conditions, including cardiovascular diseases and diabetes [3]. In summary, telomere length is crucial for maintaining chromosome integrity, regulating cellular aging, and potentially serving as a biomarker for disease progression and risk assessment.", + "question": "What is the significance of the length of telomeres?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_11 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_11 new file mode 100644 index 0000000..2ea8436 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_11 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2009 - Opening Up the Conversation on Genetics.pdf", + "2015 - Constraint and divergence of global gene expression in the mammalian embryo.pdf", + "2015 - Constraint and divergence of global gene expression in the mammalian embryo.pdf", + "2007 - Promoting_Student_Scientific_Literacy_of_Molecular Genetics and Genomics.pdf", + "2019 - Sexual Dimorphism in the Age of Genomics How, When, Where.pdf", + "2008 - Study Design and Statistical Issues.pdf", + "2015 - Basic Concepts and Potential Applications of Genetics and Genomics for Cardiovascular and Stroke Clinicians.pdf", + "2008 - Genotype-phenotype relationships and the patterning of complex traits as exemplified in the mammalian dentition.pdf", + "2019 - The influence of paternal diet on sncRNA-mediated epigenetic.pdf", + "2019 - Mother or Father who is in the front line.pdf" + ], + "extraction_id": [ + "51dbd5e2-fde6-5097-aa05-fcf57d3ca6b1", + "261c4af7-f63d-51ac-b164-0d9e7a64bff9", + "261c4af7-f63d-51ac-b164-0d9e7a64bff9", + "67369433-749b-5d6a-b5ef-3f0afe78b767", + "e22bb6fb-bec4-5c4c-8690-c96d0b8d13d4", + "06bf0605-388a-592c-96ad-3a53bb36362c", + "8a1ce8fa-b5f4-5942-b7b1-14a8a7887710", + "5aab3e60-b8b0-52ad-b4d3-817cf012cfa5", + "84335575-34d7-56b6-aa06-5a8ac13d637a", + "297793b1-93f9-5626-ac63-6d8675c02d27" + ], + "document_id": [ + "b62a8f54-c2f5-5bbb-9324-af80f7537167", + "3d9005f1-8f71-5d39-8749-4ebeab962cab", + "3d9005f1-8f71-5d39-8749-4ebeab962cab", + "755f34c4-cc06-5275-a744-16d48162b012", + "3f8c03b0-4235-5774-9d26-e43d55c1001b", + "c3bd9cf0-f768-55c4-be94-96590d7acc21", + "8610e699-218a-50e6-8d1d-ef689623266f", + "f6e866b8-b233-5862-bfb8-9949d0dabb97", + "dfcbd6e6-f60d-5eb7-867b-34ec78415e82", + "8011b04b-2199-5913-b8da-42c83334d4b7" + ], + "id": [ + "chatcmpl-ADZK3Fpc5jWofKuSsq6lJRZP4Zmhy", + "60ad1512-b0c0-59cd-ace4-c146e2c04b52", + "a66b8b00-d51c-575b-b6ac-fa445c4ca715", + "df4c6108-740d-5bcf-99e6-dbda74f7e41a", + "8e3fdc2c-0962-5854-83e7-a60ab05cf6de", + "0158f264-120f-5942-ad55-ef5fde1f188a", + "1e151ad5-59d9-598d-97ba-90ba0e64c4cb", + "4472740a-d22d-5bb1-98e3-e91332cbb303", + "47b9142f-98a3-5a45-8eaa-d327c9cc055d", + "be93ee68-72ae-5015-a3f0-19e7bf24827a", + "53364cbf-8069-50ec-b008-5d7f7a8ea1b8" + ], + "contexts": [ + "the egg and the sperm. Such a process would result in genetic changes that will be copied into every cell of the future adult, including reproductive cells (Stock & Campbell, 2000), opening the door to irreversibly alter the human species. Inevitably, signifi cant self-disclosure and discussion challenges await families", + "a fertilized egg is a complicated process that relies on controlling: which genes are active; whenthese genes activate; and for how long they are active. In broad terms, there are four ways that thiscontrol can be achieved: First, inside the sperm or egg, genes can be marked with small chemical tags that flag these genes", + "to be activated (or remain inactive) after fertilization, depending on whether the modification wasmade by the father (in the sperm) or the mother (in the egg); this process is known as imprinting. Second, the mother can alter the gene activity in her offspring via the placenta; this process is known as maternal effect. Third, instructions encoded within the embryos DNA can directly control if, andwhen, a nearby gene becomes activated; this is known as cis-regulation. Finally, similar instructions", + "genes. An altered gene may be passed on to every cell that develops from it. The resulting features my help, harm, or have little or no effect on the offsprings success in its environment. (AAAS, pg. 109, 5B:9-12#4 ) 6. Heritable material: The information passed from parents to offspring is coded in DNA molecules (AAAS, pg 108, 5B:9-12#3) 7. Mutagens: Gene mutations can be caused by such things as radiation and chemicals. When they occur in sex cells, the mutations can be passed onto offspring; if they", + "sex chromosome effects. (B)Soon after fertilization, male and female cells have sex-specic transcriptomes, epigenomes, and phenotypes (for example, male embryos grow faster than female embryos). At implantation, lineage determination begins and gene expression differences are reduced. Epigenetic marks, however, are less constrained and some are maintained, affecting gene expression, and phenotype later in development. Once specic lineages are established, differences in", + "phenomena such as mutations and gene conversion events) occur in relevant meioses leading up to the formation of the gametes (i.e., egg and sperm) which are combined during fertilization and the formation of zygotes. Thus, individuals inherit a patch- work of chromosomal segments from maternal and paternal chromosomes.", + "(Figures 8 and 9). Two gametes (egg and sperm) ultimately join into a single cell, the zygote, which has the full comple-ment of 23 chromosome pairs restored. If all goes well, the zygote gives rise to a live offspring. The Mendel Laws: Segregation and Independent Assortment Both of the Mendel laws pertain directly to the process of meiosis. The first Mendel law, the law of segregation, states that each parent passes a randomly selected allele for a given", + "the subset of that genetic information that is active. But how does the differentiation process begin? The key insight in resolving this conundrum came from fly genetics and was the realization that the egg is not a homogenous sack of protoplasm. The maternally-derived genes active in the fertilized egg are asymmetrically distributed such that at the first cell division each daughter cell receives a different complement of factors. Development continues as a", + "spermatozoa: more than the sum of its parts? DNA, histones, pro - tamines and epigenetics. Reproduction 139:287301 Nilsson EE, Sadler-Riggleman I, Skinner MK (2018) Environmentally induced epigenetic transgenerational inheritance of disease. Envi-ron Epigenet 4:dvy016Pembrey M, Saffery R, Bygren LO, Network in Epigenetic Epide-", + "mediated through the transmission of epigenetic information through the paternal sperm cells [6,80,81]. 4.1. Persistence of Maternal Exposure to A dverse Environmental Conditions along Generations In some cases, developmentally programmed traits may simply be the result of persistent or replicated exposure during critical periods of deve lopment, generation after generation. It has been suggested that the history of seve re socio-political disruptions and economic disadvantage suffered" + ], + "answer": "Once a sperm combines with an egg, the traits passed on to the resulting lifeform are determined by several factors: 1. **Genetic Information**: The fertilized egg, or zygote, contains a full complement of 23 chromosome pairs, which includes a mix of genetic material from both the maternal and paternal chromosomes [7]. This genetic information is coded in DNA molecules and is heritable [4]. 2. **Gene Activation and Regulation**: The process of trait determination involves controlling which genes are active, when they activate, and for how long they remain active. This control can be achieved through several mechanisms: - **Imprinting**: Genes can be marked with small chemical tags that flag them to be activated or remain inactive, depending on whether the modification was made by the father (in the sperm) or the mother (in the egg) [2], [3]. - **Maternal Effect**: The mother can alter gene activity in her offspring via the placenta [3]. - **Cis-Regulation**: Instructions encoded within the embryo's DNA can directly control if and when a nearby gene becomes activated [3]. 3. **Epigenetic Marks**: Soon after fertilization, male and female cells have sex-specific transcriptomes, epigenomes, and phenotypes. Epigenetic marks, which are less constrained, can be maintained and affect gene expression and phenotype later in development [5]. 4. **Mutations and Environmental Factors**: Gene mutations caused by factors such as radiation and chemicals can also be passed on to offspring if they occur in sex cells [4]. Additionally, environmental conditions experienced by the parents can influence the traits of the offspring through epigenetic mechanisms [10]. These combined factors ensure that the resulting lifeform inherits a unique set of traits from both parents, influenced by genetic, epigenetic, and environmental factors.", + "question": "Once a sperm combines with an egg, what determines how traits are passed on to the resulting lifeform?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_12 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_12 new file mode 100644 index 0000000..6d71c04 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_12 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2009 - When Family Means More (or Less) Than Genetics.pdf", + "2012 - Mitochondrial Genomic Analysis of Late Onset.pdf", + "2017 - Parental influence on human germline de novo.pdf", + "2020 - Mitonuclear genomics and aging.pdf", + "2015 - Self-reported race or ethnicity in the age of genomic.pdf", + "2009 - When Family Means More (or Less) Than Genetics.pdf", + "2017 - Parental influence on human germline de novo.pdf", + "2016 - A genetic method for dating ancient genomes provides.pdf", + "1996 - IDDM2-VNTR-encoded Susceptibility to Type 1 Diabetes.pdf", + "2012 - Mitochondrial Genomic Analysis of Late Onset.pdf" + ], + "extraction_id": [ + "baf15552-4198-5701-9175-c3fd31b2068e", + "ed29f84f-f2c9-5cbe-bab1-f5d5d2a334b6", + "a3b7edd7-f50f-53f1-b875-6d6733ddfde9", + "472c8adc-54e7-5c27-a7b8-882b7e49cd2b", + "6d68e979-ad62-5f85-ab03-5e898ce1c73b", + "baf15552-4198-5701-9175-c3fd31b2068e", + "163ce027-26ce-5625-8b63-5b7a910b4462", + "fcf5296e-6be4-5789-b1e1-ac57fef15119", + "a324397e-1525-55ff-a9e8-92dc2aafa237", + "ed29f84f-f2c9-5cbe-bab1-f5d5d2a334b6" + ], + "document_id": [ + "7ba44399-3765-5ef5-9fdd-119b62117f66", + "5404a17c-34a9-5881-8b1a-2acacdc996a8", + "7c8bee23-b142-5fce-be77-6910277a136f", + "e05fdc09-c8d8-5134-a1fd-bf07a1564981", + "51ff0b84-193b-525a-b686-f29a423fcef9", + "7ba44399-3765-5ef5-9fdd-119b62117f66", + "7c8bee23-b142-5fce-be77-6910277a136f", + "5a5e67ea-4830-5fe8-95c3-ccfcc8324036", + "bbaa99aa-3ae9-558d-bc97-7f85b6d0cf81", + "5404a17c-34a9-5881-8b1a-2acacdc996a8" + ], + "id": [ + "chatcmpl-ADZKEWDIh0sRr2bP9lVmh6YIK1qiT", + "f19ba98e-963f-5ecf-ad88-47215a3096e1", + "0e3b3480-c288-53cb-ac18-1d57478f9d34", + "06d4d82e-6eb9-59aa-a762-64de13149041", + "99a2cfc1-5a54-53af-b2a4-4c274e1d5ef1", + "612366c9-fcdc-5081-bc6d-47cd39922eeb", + "2ca2ab07-78b5-5268-93f1-297d83447163", + "db1fe67a-3d0c-549f-a54a-74ea0fa44d11", + "74484e0c-c862-5091-9fb5-957453a069af", + "74ef6cdc-ea40-5d10-9ee8-b4288b3a70b4", + "27f40683-de33-5ec1-852d-6905f2dc389c" + ], + "contexts": [ + "variation with cultural practices around lineage. In certain societies, individuals place greater importance on (and have greater knowledge about) one side of the family than another (unilineal descent). Thus, individuals in patrilineal groups trace relationships through males only so that your fathers brothers children are members of your family, but not your fathers sisters (Kottak, 2007 ). They are members of their husbands group or family. Efforts to create", + "maternal lineage membership with those who weredirectly genotyped. Based on these pedigree (matrilineal) relation-", + "in three-generation families, and read pair tracing DNMs with phased variants. In the former approach, we determined the parent of origin as in our previous analysis4. For example, if an offspring of the proband was a carrier of the DNM allele and had haplotype sharing to paternal chromosome of the proband, we assigned the mutation to the father. Meanwhile, if the offspring was not a DNM allele carrier, we would assign it to the maternal germline. We restricted the haplo -", + "Unlike the nuclear genome, which requires both paternal and maternal contributions, mtDNA is inherited solely from the maternal lineage. It is unclear what advantage a uniparental mtDNA transmission confers, but one possibil-ity is to minimize the number of distinct genomes to maxi-mize the efficiency of a multi-genomic system (Hill etal. 2019). In fact, humans have developed complex, redundant mechanisms to ensure uniparental inheritance of mtDNA (DeLuca and OFarrell 2012; Rojansky etal. 2016). Paternal", + "c) Mitochondrial DNA (maternal line testing) markers: mitochondrial DNA or mtDNA haploid is the maternally inherited mitochondrial genome (mtDNA) [ 44]. All children inherit mtDNA from their mother, with no admixture from the father. Like Y-line DNA, mtDNA is passed intact from one generation to the next but through maternal line. Mitochondrial DNA does not follow any surname. In fact, the surname changes in every generation when women marry. Polymorphisms of mtDNA", + "a family pedigree may be hampered if the participant is not familiar with her mothers relatives, but her mothers brothers children (her cousins) may be able to supplement her overall family history. Knowledge about the cultural system of unilineal descent avoids assuming the universality of bilateral descent. Cultural beliefs such as these also have implications in the conduct of genetic research in terms of confidentiality and autonomy (Benkendorf et al.,", + "225 three-generation families using haplotype sharing (Fig. 1c and Methods), 80.4% were found to be of paternal origin (Extended Data Fig. 1). Figure 1e shows a strong relationship between the number of paternal DNMs and the fathers age at conception (1.47 per year, 95% CI 1.341.59) and a weaker impact of the mothers age on the number of maternal DNMs (0.37 per year, 95% CI 0.300.45). The parental origin of all DNMs was also assessed by read pair", + "genetics-based population divergence studies. Am J Phys Anthropol 128(2):415 423.22. Helgason A, Hrafnkelsson B, Gulcher JR, War d R, Stefnsson K (2003) A populationwide coalescent analysis of Icelandic matrilineal and patrilineal genealogies: Evidence for a faster evolutionary rate of mtDNA lineages than Y chromosomes. Am J Hum Genet 72(6): 1370 1388. 23. Amster G, Sella G (2015) Life history effects on the molecular clock of autosomes and sex chromosomes. Proc Natl Acad Sci USA 113(6):1588 1593.", + "sistent with a maternal imprinting effect in familiesfrom France [18], the USA[10, 18, 21] (Figure 2; Table3) and Canada [27]. However, in a large family dataset from the UK, and in smaller data sets fromDenmark and Sardinia, the transmission of VNTRsusceptibility alleles is more pronounced frommothersthanfromfathers,andnowsignicantlysoinUK families (Figure 2; Table 3). Comparison of theresults from the USAwith those from the UK suggestthat unexplained inter-population differences in thisparent-of-origin", + "started with the largest matrilineage and worked down the list. Theparticipants selected for mtDNA sequencing were selected inde-pendent of their cognitive or dementia status. 274 matrilineageswere represented by this dataset. As a result, the sequencedmitochondrial genomes also represent as many different majormitochondrial haplogroups and clusters as possible (Table 1).Selection was made blind to case-control status. 287 samples weresent to Family Tree DNA (www.familytreedna.com) for Sangersequencing of" + ], + "answer": "Genetic tracing is often matrilineal rather than patrilineal primarily because mitochondrial DNA (mtDNA) is inherited solely from the maternal lineage. Unlike the nuclear genome, which requires contributions from both parents, mtDNA is passed down from mother to offspring without any admixture from the father [4], [5]. This uniparental inheritance of mtDNA simplifies the tracing of maternal lineage as it remains relatively unchanged through generations, making it a reliable marker for genetic studies [4], [5]. Additionally, humans have developed complex mechanisms to ensure this uniparental inheritance of mtDNA, which further supports its use in genetic tracing [4].", + "question": "Why is genetic tracing matrilineal rather than patrilineal?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_13 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_13 new file mode 100644 index 0000000..4263d86 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_13 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2009 - eQTL analysis in mice and rats.pdf", + "2015 - Genetic Control of Survival and Weight Loss during Pneumonic Burk.pdf", + "2015 -Emery- Genetic Control of Survival and Weight Loss during Pneumonic Burk.pdf", + "2005 - quantitative-trait-locus-analysis-of-aggressive-behaviours-in-mi.pdf", + "2005 -Broadkin- quantitative-trait-locus-analysis-of-aggressive-behaviours-in-mi.pdf", + "2006 - From_gene_to_behavior_and_back_again_new.pdf", + "2009 - Experimental_Evolution.pdf", + "2009 - Garland_and_Rose_Experimental_Evolution.pdf", + "2005 - quantitative-trait-analysis-in-the-investigation-of-function-and.pdf", + "2012 - Systems genetic analysis of the effects of iron deficiency in mouse brain.pdf" + ], + "extraction_id": [ + "71981bfb-284e-50ad-854e-2055c07f77a7", + "615ee0cd-5960-57e5-b4e6-56e4b8020a1b", + "268a23e8-f528-5b59-89f2-188331e0a03c", + "9de93371-6239-53c2-b42c-71f615a0614b", + "0a5c759e-8dab-55f1-ac59-e8211ec683b8", + "64c0287d-aeea-52eb-a074-e9591c5593ae", + "8ee78018-b998-590c-99ab-788a447ede81", + "cbce50ea-be78-5d54-beb1-849222c5bfdd", + "0a895880-91c0-5079-b258-73926b38430f", + "6ab990b0-4f9c-5be3-ab79-9ca6835271fa" + ], + "document_id": [ + "8d67ea90-f7b1-5bb8-937c-4a9eceddff43", + "ae1025b0-1410-51ae-9be2-26fa2e9d5808", + "a9aceace-bf48-5472-b54c-59a458a84c62", + "0dc730ba-4ff4-52aa-a988-71075113c416", + "e6027e7f-aec0-5e76-8aff-96b36389e701", + "7a088b36-11b7-5379-bfe5-ce571e11de07", + "34821353-1b74-5ee2-ac39-66dd46f145bf", + "496faa7f-9623-5ab7-9816-7c3755abb3aa", + "dac1c73c-0b5f-5a54-bb12-7e8b654009c0", + "99fc80f0-f3c3-5766-a604-921552bb3298" + ], + "id": [ + "chatcmpl-ADZKK0KDNVDBm3vRB6dGuJYB5JlVa", + "73540700-b5cf-5838-852b-b281ca086140", + "374c456a-d1db-5b4a-8713-97abe4162d77", + "b9d52798-0235-5018-bccd-560565d16cc3", + "c8f17022-aeae-5242-9082-d6d1eee4c4bf", + "1b2de424-be9f-572d-bd62-dc2ecd92192b", + "fef212bc-631b-591d-b8e3-d1523da0507d", + "f72795a1-66c3-5a98-84bc-b085e8008073", + "31a32dc5-81ac-52ba-a463-c61e293f21e5", + "b660d882-1cb0-5150-ae76-8eb3ccb88a58", + "7ef9df1d-b21a-597a-9e74-6eace5d0c33c" + ], + "contexts": [ + "While most of the Y chromosome does not undergo recombination, the recombination rate of the X chromosomeis slower than that of the autosomes. This has important consequences on the detection of significant QTLs. For a comprehensive view of these issues, see(43). 9.Probe hybridization artifacts When several probes are available for the same gene, it is not uncommon to observe a difference in the mapping results", + "8 QTL Mapping Allelic variation exists among natural populations and inbred strains, and this is reflective of the segregation of quantitative tr ait loci (QTLs) [96]. QTLs are stretches of DNA that are closely linked to genes that underlie a phenotype of interest. QTL analysis has been proven to be an invaluable tool to help unravel heritable traits, by enabling researchers to map different quantitative traits back to the genomic location involved in the regulation of these phenotypes.", + "8 QTL Mapping Allelic variation exists among natural populations and inbred strains, and this is reflective of the segregation of quantitative tr ait loci (QTLs) [96]. QTLs are stretches of DNA that are closely linked to genes that underlie a phenotype of interest. QTL analysis has been proven to be an invaluable tool to help unravel heritable traits, by enabling researchers to map different quantitative traits back to the genomic location involved in the regulation of these phenotypes.", + "genes underlying QTLs in animals and plants (see for example Shirley et al 2004,Korstanje & Paigen 2002, Fridman et al 2004). I should also point out, though, that even in a single QTL region isolated in a congenic strain, it is possible that there is more than one allele that aects the phenotype. So, you have a fair pointabout the challenges and complexities of QTL analysis. Koolhaas: There are dierent questions underlying both approaches. The QTL", + "genes underlying QTLs in animals and plants (see for example Shirley et al 2004,Korstanje & Paigen 2002, Fridman et al 2004). I should also point out, though, that even in a single QTL region isolated in a congenic strain, it is possible that there is more than one allele that aects the phenotype. So, you have a fair pointabout the challenges and complexities of QTL analysis. Koolhaas: There are dierent questions underlying both approaches. The QTL", + "The basic pr emise of QTL an alysis is simple (Ph illips and Belknap, 2002 ) . First, one must meas ure a speci c phen otype within a popul ation. Next, the population must be genotyped at a hundred or more marker loci186 Boehm II et al.", + "through analysis of line crosses, quantitative trait loci (QTL) mapping, and verification of candidate genes with quantitative complementation tests or genetic engineering (e.g.,McGuire and Tully 1987; Chandra et al. 2001; Dierick and Greenspan 2006; Edwardset al. 2006). They can also be used to study the underlying physiological, neural, andmolecular mechanisms of the differences in behavior between selected and controllines, or between divergently selected lines.", + "through analysis of line crosses, quantitative trait loci (QTL) mapping, and verification of candidate genes with quantitative complementation tests or genetic engineering (e.g.,McGuire and Tully 1987; Chandra et al. 2001; Dierick and Greenspan 2006; Edwardset al. 2006). They can also be used to study the underlying physiological, neural, andmolecular mechanisms of the differences in behavior between selected and controllines, or between divergently selected lines.", + "genetic background. Gene identification of QTL should be distinguished from identification of the quanti- tative trait nucleotide (QTN). The latter is a daunting task, since SNPs are so frequent. Final proof for a QTN in mice would require placing a genomic segment containing theputative QTN from a donor mouse strain on the background of another strain using homologous recombination and reproducing the phenotype of the donor strain.", + "because these strains have been genotyped at more than 14,000 markers, including single nucleotide polymorphisms (SNP). Hundreds of genes may lie within a QTL interval, so identifying the underlying genes requires complementary methods. One method is to use BXD gene expression data (a public resource at www.genenetwork.org) to screen for genes within the QTL interval whose expression correlates with the trait of interest [23]." + ], + "answer": "To differentiate between X and Y DNA with respect to DNA tracing and determining quantitative trait loci (QTLs), one must consider the unique characteristics of each chromosome: 1. **Recombination Rates**: - The Y chromosome largely does not undergo recombination, which means it is passed down relatively unchanged from father to son. This makes it easier to trace paternal lineage but limits its utility in QTL mapping due to the lack of recombination [1]. - The X chromosome, on the other hand, does undergo recombination, although at a slower rate compared to autosomes. This recombination can complicate tracing but is crucial for detecting significant QTLs [1]. 2. **QTL Mapping**: - QTLs are stretches of DNA closely linked to genes that influence a phenotype of interest. The detection and mapping of QTLs on the X chromosome can be more complex due to its recombination, but this recombination is necessary for identifying the genomic locations involved in regulating phenotypes [2]. - The Y chromosome's lack of recombination means it is less useful for QTL mapping, as the identification of QTLs relies on the recombination events that help pinpoint the genomic regions associated with specific traits [1]. In summary, the primary difference lies in the recombination behavior of the X and Y chromosomes, which affects their utility in DNA tracing and QTL determination. The X chromosome's recombination, although slower, is essential for QTL mapping, whereas the Y chromosome's lack of recombination makes it more suitable for tracing paternal lineage but less useful for QTL analysis.", + "question": "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_14 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_14 new file mode 100644 index 0000000..eb9eb37 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_14 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2016 - A genetic screen identifies hypothalamic Fgf15 as a regulator of glucagon secretion.pdf", + "2015 - Systems genetic analysis of hippocampal neuroanatomy and spatial learning in mice.pdf", + "2007 - Integration of mouse phenome data resources.pdf", + "2016 - Genetic Regulation of Gelsolin in Lung in Mouse Model and its Potential.pdf", + "2005 -Integrated gene expression profiling and linkage analysis in the rat.pdf", + "2019 - The expanded BXD family of mice A cohort for experimental systems genetics and precision medicine.pdf", + "2018 - Molecular Brain Adaptations to Ethanol_ Role of Glycogen Synthase (2).pdf", + "2008 -Han- Comparing Quantitative Trait Loci.pdf", + "2008 - Comparing Quantitative Trait Loci.pdf", + "2008 - Towards systems genetic analyses in barley Integration of phenotypic, expression and genotype data into GeneNetwork.pdf" + ], + "extraction_id": [ + "7eae53fa-ac5e-5cf4-807c-5d13dffdcf83", + "69504f91-c34d-5555-a05a-ac485356cec6", + "6ba5dba3-6135-5545-bec9-eee2e1465e7b", + "311be2a2-4428-5887-8ed2-35875eac9fcb", + "80a6f32f-a473-58ba-98ce-30100f5cc913", + "22772f7f-a42d-5438-a910-9e26c2916be2", + "1047bf10-3878-5b70-8bb2-c0249f2a9c53", + "e0bc4e49-6d6f-5b60-b7bc-18fd622629a8", + "476c90a3-1613-5e45-81b4-358519368bda", + "a6c480d1-b384-5c6f-b21b-94fe0b3b0f4d" + ], + "document_id": [ + "288adb9b-a547-5e61-8593-1b2ab36271d3", + "8708ead5-20bc-5d41-82db-61a807eb3f90", + "08a3ce6e-947b-5ee9-b723-946807cf7d23", + "ec8452c0-1c16-54e6-9b9f-3e741a8c7340", + "7b3a7517-2967-5693-b4e8-8423a9fa432b", + "8df14e3b-644f-5a18-94a6-5ff5a1eae053", + "cc2690a9-5a87-5f09-87d5-115a6a6b8349", + "e6904cbd-8265-5e40-8978-d461ee6e151a", + "bfbddb84-c0e5-5d74-8e2d-9e54e75e8c49", + "8513abbe-65ed-5f35-9f86-ba93cfc5a194" + ], + "id": [ + "chatcmpl-ADZKSZUCeTbC5g92NfqE6Fmp3TXXx", + "a2ffc857-6d79-5889-8344-cae8f1ca5e32", + "1e23f2e3-f4b1-5195-9061-5e525a13fb32", + "6c1e5cb1-ab19-5246-859d-a2f58d48232a", + "51757b6b-0492-5077-ba69-90a2ddf3da9d", + "dae9312b-c464-5fb7-bbc1-06ba2998e462", + "0b3d48d1-f253-508c-9a9e-5060e02d54a6", + "d261c68c-c253-52c9-8e27-f76fb8d0b4f8", + "9fbea8b6-25ad-5da9-bc9a-988784e33f0b", + "bd69b879-f1fe-57ee-8b36-b621708bdcc3", + "969d6ade-dc87-5f19-bd57-3f58882f11e8" + ], + "contexts": [ + "QTL Mapping GeneNetwork ( www.genenetwork.org ) variants data set comprising about", + "Bioinformatics All of the genetic analyses were carried out in GeneNetwork, whichis an open source bioinformatics resource for systems genetics thatexists as both a repository for genetic, genomic and phenotypicdata together with a suite of statistical programs for data analy-sis that includes mapping and evaluating QTLs, examining pheno-type/genotype correlations and building interaction networks. QTL mapping The QTL mapping module of GeneNetwork was used to identify", + "the database is that each data collection is associated with a protocol which describes how the data were generated. The project also provides online analysis tools to allow identification of correlations within its data set. GeneNetwork ( http://www.genenetwork.org ), encompassing WebQTL, is a database of genotypes and complex phenotypes ranging from gene expression to behaviour in standard inbred strains, and six panels of mouse recombinant inbred strains including the two largest", + "QTL/interval analysis QTL mapping was conducted using publically available software on GeneNetwork (http://www .genenetwork .org/webqtl /main .py). One important feature of the GeneNetwork is WebQTL , which is the leading GeneNetwork module , and has been optimized for on-line analysis of traits that are controlled by combinations of allelic variants and environmental factors [15]. A simple graphical user interface", + "WebQTL is the primary module in the Gene- Network online resource (www.genenetwork.org),and provides a powerful environment to analyzetraits controlled by genetic variants (Chesler et al.2004; Wang et al. 2003). It includes data from manypermanent genetic reference populations, including the HXB rat strains, and allows for phenotypic traits,", + "67. As described above, loci are identified in GeneNet work by the computation of a likelihood ratio statistic score and significance was determined using at least 5,000 permutations of the phenotype data. Updated QTL mapping methods , such as R/qtl 2 66,146, Multiple QTL mapping 64, GEMMA 156 and pyLMM 63, have been implimented on t he GeneNetwork2 site 46.", + "genetic mapping, and correlation of quantitative traits such as gene expression data and behavioral parameters (Wang et al, 2003) . GeneNetwork employs genotype data from 3809 markers, selected based on their being informative (i.e., different between progenitor strains). GeneNetwork outputs peak likelihood ratio statistic (LRS) locations for each trait, whic h can be directly converted to", + "tool for combined visualization and exploration of geneexpression data and QTL. The methodology developedin this work is complementary to the analyses that canbe performed on the GeneNetwork website (WebQTL,http://www.genenetwork.org/ ), which allows assessment of the relationship between gene expressions and QTL inrecombinant in bred mice [ 3]. Comparing QTL and microarray data is not completely", + "tool for combined visualization and exploration of geneexpression data and QTL. The methodology developedin this work is complementary to the analyses that canbe performed on the GeneNetwork website (WebQTL,http://www.genenetwork.org/ ), which allows assessment of the relationship between gene expressions and QTL inrecombinant in bred mice [ 3]. Comparing QTL and microarray data is not completely", + "the database entries. Once the resulting record set of the query is returned, it can be further restricted by selecting relevant records based on attached annotations before for- warding it for further analysis. To map genetic loci associated with mRNA abundance or trait phenotypes, any one of the three QTL mapping func- tions currently employed by GeneNetwork's WebQTL module can be used. These are 1. interval mapping, 2. sin- gle-marker regression, or 3. composite mapping [29,30]." + ], + "answer": "For QTL mapping analysis in GeneNetwork2, datasets that are useful include genotypes and complex phenotypes ranging from gene expression to behavior in standard inbred strains and panels of mouse recombinant inbred strains [3]. Additionally, data from permanent genetic reference populations, such as the HXB rat strains, are also valuable [5]. These datasets allow for the identification of correlations within the data and the mapping of genetic loci associated with mRNA abundance or trait phenotypes [10].", + "question": "what type of dataset is useful for qtl mapping analysis in genenetwork2?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_15 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_15 new file mode 100644 index 0000000..79f613c --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_15 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2005 -Dipetrillo- Bioinformatics toolbox QTL.pdf", + "2005 - Bioinformatics toolbox for narrowing rodent quantitative trait loci .pdf", + "2020 - A Multi-Omics Perspective of Quantitative Trait Loci in Precision Medicine.pdf", + "2016 - Genotyping by sequencing for identification and mapping of QTLs for bioenergy-related traits in sweet sorghum.pdf", + "2005 -Dipetrillo- Bioinformatics toolbox QTL.pdf", + "2005 - Bioinformatics toolbox for narrowing rodent quantitative trait loci .pdf", + "2016 - Genetic Regulation of Gelsolin in Lung in Mouse Model and its Potential.pdf", + "2009 - Detection and interpretation of expression quantitative trait loci (eQTL).pdf", + "2020 - A Multi-Omics Perspective of Quantitative Trait Loci in Precision Medicine.pdf", + "2007 - Bioinformatics_for_Geneticists.pdf" + ], + "extraction_id": [ + "63fcced2-fd9b-5b8c-917e-8a5502f89624", + "ede4bc5e-f495-5c65-b2e6-a5dc0625b0d0", + "03e2ebd6-ce89-551c-ba81-59a4ded02515", + "ea640aeb-71cc-578d-8ad3-6940f2b892da", + "294efef3-6516-5c74-8cc5-bc8401f6602b", + "4cf47fab-c25f-52a4-953b-3c3508a26274", + "311be2a2-4428-5887-8ed2-35875eac9fcb", + "2b670f5c-5b0c-5d8f-b236-2cbff81eff5a", + "6f44c216-c9a1-582e-8081-d6ad912369db", + "52bb366e-161f-51fd-a5a2-bef21f1b4c01" + ], + "document_id": [ + "9b089457-5804-594a-99ea-e716b65c216c", + "5d87aefe-dee5-5f25-8b46-d87b24907dcc", + "8503b166-b917-5efb-a356-5ba371504cc1", + "d6da662e-cb6e-5628-8a42-5aca1b978447", + "9b089457-5804-594a-99ea-e716b65c216c", + "5d87aefe-dee5-5f25-8b46-d87b24907dcc", + "ec8452c0-1c16-54e6-9b9f-3e741a8c7340", + "ef974b09-4ea2-5382-85e5-c2169f440fda", + "8503b166-b917-5efb-a356-5ba371504cc1", + "4ea8e1a8-e113-5f02-ad78-880b9c51a101" + ], + "id": [ + "chatcmpl-ADZKWNKiSvqnmJLiG5DiGZqUhCrfq", + "7a9f6af0-22c9-5bd7-a443-f0b0111551fa", + "b89fda54-1dd8-5033-9caa-c8e6079d4e28", + "db4d7722-ff83-54a4-9fb6-23d331ead769", + "5604e763-06b5-5528-be49-9003bf547ae2", + "7019c554-cbae-528e-8207-b8575d99daf4", + "3fe2119e-e576-5608-91e1-2a010b91515c", + "51757b6b-0492-5077-ba69-90a2ddf3da9d", + "7cd326b3-1669-55f1-b4ce-376b5159a6fb", + "ae35202f-70ed-5fb8-a075-ce1e63616fb2", + "0049fb65-142a-54a1-8ab5-2d747bc521a0" + ], + "contexts": [ + "rodent QTLs. Here we discuss each tool, illustrate itsapplication and generate a bioinformatics strategy fornarrowing QTLs. Combining these bioinformatics toolswith classical experimental methods should accelerateQTL gene identication. Introduction Quantitative trait locus (QTL) analysis is a method to localize chromosomal regions harboring genetic variants that affect a continuously distributed, polygenic phenotype(including many common diseases) [1]. It is particularly", + "rodent QTLs. Here we discuss each tool, illustrate itsapplication and generate a bioinformatics strategy fornarrowing QTLs. Combining these bioinformatics toolswith classical experimental methods should accelerateQTL gene identication. Introduction Quantitative trait locus (QTL) analysis is a method to localize chromosomal regions harboring genetic variants that affect a continuously distributed, polygenic phenotype(including many common diseases) [1]. It is particularly", + "Table 2. Computational Approaches for Identi cation of QTLs Tools Link Programming languageRefs Linear models CPMAtranseqtl https://github.com/cotsapaslab/CPMAtranseqtl R/Python [ 176] eMap www.gnu.org/software/gsl/ R FastMap https://sourceforge.net/projects/fastmapunix/ JAVA [ 134] lme4qtl https://github.com/variani/lme4qtl R[ 175] Matrix eQTL www.bios.unc.edu/research/genomic_software/ Matrix_eQTLR/Matlab [ 133] Meta-eQTL https://haok01.u.hpc.mssm.edu/meta_eQTL/ R/C [ 177]", + "2012). Tools for QTL analysis have been de veloped and released for researchers such as R/qtl, QTL cartographer, M apQTL, and WebQTL. Recently, Wang et al. (2012) developed a free software for QTL mapping called QTL IciMapping which constructs genetic linkage maps and QTL analysis by simple interval mapping and inclusive composite interval mapping. QTL IciMapping is available for segregating and inbred PREVIEW", + "incorrect, the analysis can separate the QTL peak into twoTable 1. Summary of bioinformatics tools for dissecting rodent QTLs Bioinformatics tool Summary Resolution Comparative genomics Identies regions of chromosomal synteny in QTLs that are concordant across species1020 Mb Combined cross analysis Recodes genotype information from multiple crosses detecting a shared QTL into one susceptibility and one resistance genotype to combine the crosses in a singleQTL analysis1020 Mb Interval-specic haplotype", + "incorrect, the analysis can separate the QTL peak into twoTable 1. Summary of bioinformatics tools for dissecting rodent QTLs Bioinformatics tool Summary Resolution Comparative genomics Identies regions of chromosomal synteny in QTLs that are concordant across species1020 Mb Combined cross analysis Recodes genotype information from multiple crosses detecting a shared QTL into one susceptibility and one resistance genotype to combine the crosses in a singleQTL analysis1020 Mb Interval-specic haplotype", + "QTL/interval analysis QTL mapping was conducted using publically available software on GeneNetwork (http://www .genenetwork .org/webqtl /main .py). One important feature of the GeneNetwork is WebQTL , which is the leading GeneNetwork module , and has been optimized for on-line analysis of traits that are controlled by combinations of allelic variants and environmental factors [15]. A simple graphical user interface", + "model selection approach for mapping multiple interacting QTL [376] and Plink, a library for association QTL mapping on single nu cleotide polymorphisms (SNP) in natural populations [277]. 3.2.3 Add new analysis tools xQTL workbench supports exible adding of more QTL analysis s oft- ware: any R-based, or command-line tool, can be plugged in. A ll anal- ysis results are uploaded, stored and tracked in the xQTL workbench database through an R-API. When new tools are added, they can b uild", + "717 730 14. Delaneau, O. et al. (2017) A complete tool set for molecular QTL discovery and analysis. Nat. Commun. 8, 1545215. Liu, B.H. (2017) Statistical Genomics: Linkage, Mapping, and QTL Analysis , CRC Press 16. Gibson, G. et al. (2015) Expression quantitative trait locus anal- ysis for translational medicine. Genome Med. 7, 1 14 17. Ritchie, M.D. et al. (2015) Methods of integrating data to uncover genotype-phenotype interactions. Nat. Rev. Genet. 16, 185 197", + "236 CH 10 TOOLS FOR STATISTICAL GENETICS Lastly, Bayesian methods allow the consideration of multiple QTLs, QTL positions and QTL strengths (Jansen, 1996; Satagopan et al. , 1996; Uimari et al. , 1996; Sillanpaa and Arjas, 1998, Borevitz et al. , 2002). Multimapper (Sillanpaa, 1998), for example, allows the automatic building of models of multiple QTLs within the same linkage group. It is designed to work as a companion program to QTL Cartographer (Basten" + ], + "answer": "The bioinformatics tools for QTLs analysis include: 1. **R/qtl** - A comprehensive tool for QTL mapping [4]. 2. **QTL Cartographer** - Used for QTL mapping and analysis [4]. 3. **MapQTL** - Another tool for QTL mapping [4]. 4. **WebQTL** - An online tool optimized for the analysis of traits controlled by combinations of allelic variants and environmental factors [4], [7]. 5. **QTL IciMapping** - A free software for QTL mapping that constructs genetic linkage maps and performs QTL analysis by simple interval mapping and inclusive composite interval mapping [4]. 6. **CPMAtranseqtl** - Available on GitHub, supports R/Python [3]. 7. **eMap** - Available on GNU, supports R [3]. 8. **FastMap** - Available on SourceForge, supports JAVA [3]. 9. **lme4qtl** - Available on GitHub, supports R [3]. 10. **Matrix eQTL** - Available on the UNC website, supports R/Matlab [3]. 11. **Meta-eQTL** - Available on HPC MSSM, supports R/C [3]. 12. **Plink** - A library for association QTL mapping on single nucleotide polymorphisms (SNP) in natural populations [8]. 13. **Multimapper** - Allows the automatic building of models of multiple QTLs within the same linkage group and works as a companion program to QTL Cartographer [10]. These tools can be used in combination with classical experimental methods to accelerate QTL gene identification [1], [2].", + "question": "what are the bioinformatics tools for QTLs analysis?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_16 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_16 new file mode 100644 index 0000000..b345237 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_16 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2009 - Identification of Quantitative Trait Loci in Alcoholism.pdf", + "2018 - Reduced complexity cross design for behavioral genetics.pdf", + "2005 - Genetics of body weight in the LXS recombinant inbred mouse strains.pdf", + "2006 - From_gene_to_behavior_and_back_again_new.pdf", + "2012 - Bioinformatics tools and database resources for systems genetics analysis in mice\u2014a short review and an evaluation of future needs.pdf", + "2012 - Bioinformatics tools and database resources for systems genetics analysis in mice\u2014a short review and an evaluation of future needs.pdf", + "2012 - Genetic regulation of adult hippocampal neurogenesis A systems genetics approach using BXD recombinant inbred mouse strains.pdf", + "2007 - Metabolic and genomic dissection of diabetes in the Cohen rat.pdf", + "2007 - Metabolic and genomic dissection of diabetes.pdf", + "2011 - Genetical genomics approaches for systems genetics.pdf" + ], + "extraction_id": [ + "59e1cde3-dd67-55c0-aceb-0d4dbf22ed4d", + "d18c973d-30ee-5069-a101-b4d3000333eb", + "def0e506-3ca4-5a7f-8a4d-5968e2a36f1e", + "64c0287d-aeea-52eb-a074-e9591c5593ae", + "88873c88-94cd-5caf-b675-a99f0ae6235f", + "88873c88-94cd-5caf-b675-a99f0ae6235f", + "17184903-e412-5545-8dfc-c17e31f5201b", + "a20d5dd5-6dd1-54ab-8c52-647fdf644ae7", + "1aa37aaa-5635-57a5-b8d4-2dd9fa17d028", + "fb1b1f9d-81a6-59b2-b31c-80a5940d8b3f" + ], + "document_id": [ + "11c67421-d1e1-5bde-bf97-3e313232fec7", + "b6797de4-6bdf-52ae-a848-d8fc4f048587", + "1a5be6d7-d1b8-5405-a0cb-696a5eb6a0f1", + "7a088b36-11b7-5379-bfe5-ce571e11de07", + "4bb4798b-3969-5448-ac4b-13c1b8506268", + "4bb4798b-3969-5448-ac4b-13c1b8506268", + "c54da858-9620-588e-8e41-76a960af2ff6", + "ce608956-7efb-5ce8-ab42-400075d012bb", + "5503f978-238f-59bc-ad3f-f500eb712aef", + "de78a01d-8d03-5afb-af5b-ce2ed2167766" + ], + "id": [ + "chatcmpl-ADZKiurNCvLvQlfZEPvqlUva8Sekv", + "5db68dae-9dc1-5065-b61f-067ba20b6e19", + "e5fcabd8-0d42-5aa4-bebb-a355493e8ced", + "8efc851d-4fd4-5355-946a-4e183083eadd", + "fef212bc-631b-591d-b8e3-d1523da0507d", + "9dc3af1c-27a0-5527-b788-719c3ff01cd4", + "4940ec57-f3dc-55f7-9cfa-71f1e5b66287", + "280734af-e950-5339-b984-8718e98448ad", + "9ee9d05e-d3fb-5dd7-b1b5-9862c1894099", + "7e038f11-0794-5424-9465-eb0034442369", + "9a2b996d-7480-57e8-9c6a-da084c4be200" + ], + "contexts": [ + "Methods 31 statistical language/software R (R DEVELOPMENT CORE TEAM 2008) . The core of R/qtl is a set of functions that make use of the hidden Markov model (HMM) technology to calculate QTL genotype probabilities, to simulate from the joint genotype distribution and to calculate the most likely sequence of underlying genotypes (all conditional on the observed marker data) (BROMAN et al. 2003) . R/qtl also calculates several functio ns that are useful for a quality", + "A variety of analytical methodologies are available in the R/qtl package, including, e.g., composite interval mapping or Haley-Knott regression (see Ref. 42for discussion). The scanone function in R/qtl is used to calculate log of the odds (LOD) scores. Per- mutation analysis (perm 1000) is used to establish the signi cance threshold for each phenotype ( P<.05). Additive and/or interactive covariates can be added to the model", + "WebQTL (Chesler et al. 2003; http://www.web- qtl.org/home.html), because each has some uniquecapabilities. R/qtl is an interactive environment for mapping QTLs in experimental crosses, implemented as anadd-on package for the freely available statisticallanguage/software R. Empirical significance valuesare calculated by permutation tests by comparing the peak likelihood ratio statistic (LRS) obtained from 1000 permutations (Churchill and Doerge1994). The permutation test results of highly sig-", + "The basic pr emise of QTL an alysis is simple (Ph illips and Belknap, 2002 ) . First, one must meas ure a speci c phen otype within a popul ation. Next, the population must be genotyped at a hundred or more marker loci186 Boehm II et al.", + "analyses on whole assays of (molecular) phenotypesas a batch. This enables genetical genomics studieswithout waiting times. TIQS is particularly strong inusing a cloud for large scale computing while xQTL uses pbs based traditional clusters and is more developed for data management and definitionof new analyses, so the desire is to work together.Both systems use R as the back-end language for dataanalysis in all platforms, which will enable transfer of analysis protocols between experiments and insti-", + "tional protocols to analyse all expression, proteomicsand metabolomics QTLs on marker maps of everincreasing density. These should include web accesstools for both experts and non-experts in sophisti-cated statistics analysis and high performance computing. The interactive QTL System (TIQS) (http://eqtl .berlios.de) is a web application that guides its usersthrough the analysis steps needed. It maximizes the distribution of computational effort (supporting trad-", + "four commonly used methods for doing a linkage analysis, namely; regression method, likelihood method, variance component method and Bayesian method. For statistical purpose, to check significant thresholds, either permutation test or Bayesian factors are used and for confidence interval check, bootstrapping is the preferred method. For our study, we use WebQTL for QTL mapping. WebQTL (http://webqtl.org) uses interval mapping, to estimate the position of QTLs across a chromosome (Wang et al., 2003,", + "MultiQTL software package, version 2.5 (www.multiqtl.com), aspreviously described in detail (37). In brief, for initial analysis, weused by default an unrestricted model. When the results suggested thepresence of a QTL, we attempted to t the simplest and statisticallyjustied model (dominant, recessive, or additive effect) by comparingit with the nonrestricted model and replacing it if the difference wasnonsignicant. When applicable, we utilized the single-trait, multi-trait, and multienvironment analyses", + "MultiQTL software package, version 2.5 (www.multiqtl.com), aspreviously described in detail (37). In brief, for initial analysis, weused by default an unrestricted model. When the results suggested thepresence of a QTL, we attempted to t the simplest and statisticallyjustied model (dominant, recessive, or additive effect) by comparingit with the nonrestricted model and replacing it if the difference wasnonsignicant. When applicable, we utilized the single-trait, multi-trait, and multienvironment analyses", + "R/QTL [35] is an R package which includes many func tions for mapping, including an algorithm to infer missing genotype data using H idden Markov Models. Gene- Network (www.genenetwork.org [11]) also offers eQTL analysis for user uploaded data, one trait at a time, and genome-wide analysis tools for a number of published datasets. 4. Alternative Illumina data pre-processing Compared with Affymetrix for example, Illumina is a relatively new technology and" + ], + "answer": "The statistical approaches for QTLs (Quantitative Trait Loci) analysis include several methodologies and techniques. Here are some of the key approaches mentioned in the provided context: 1. **Hidden Markov Model (HMM)**: This approach is used to calculate QTL genotype probabilities, simulate from the joint genotype distribution, and calculate the most likely sequence of underlying genotypes, all conditional on the observed marker data [1]. 2. **Composite Interval Mapping (CIM)**: This method is available in the R/qtl package and is used for QTL mapping [2]. 3. **Haley-Knott Regression**: Another method available in the R/qtl package, used for QTL analysis [2]. 4. **Log of the Odds (LOD) Scores Calculation**: The scanone function in R/qtl is used to calculate LOD scores, which are essential for identifying QTLs [2]. 5. **Permutation Analysis**: This technique is used to establish the significance threshold for each phenotype, ensuring that the identified QTLs are statistically significant [2]. 6. **Additive and Interactive Covariates**: These can be added to the model to account for additional sources of variation in the data [2]. 7. **Regression Method**: One of the commonly used methods for linkage analysis in QTL studies [7]. 8. **Likelihood Method**: Another method used for linkage analysis in QTL studies [7]. 9. **Variance Component Method**: This method is also used for linkage analysis in QTL studies [7]. 10. **Bayesian Method**: A method used for linkage analysis, which can also be used to check significant thresholds using Bayesian factors [7]. 11. **Bootstrapping**: This method is preferred for checking confidence intervals in QTL analysis [7]. These approaches provide a comprehensive toolkit for conducting QTL analysis, allowing researchers to identify and validate QTLs effectively.", + "question": "what are the statistical approaches for qtls analysis?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_17 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_17 new file mode 100644 index 0000000..b31471c --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_17 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2017 - INTEGRATIVE ANALYSIS OF GENETIC, GENOMIC AND PHENOTYPIC DATA FOR ETHANOL BEHAVIORS A NETWORK-BASED PIPELINE FOR IDENTIFYING MECHANISMS AND POTENTIAL DRUG TARGETS.pdf", + "2008 - The Environmental Genome Project Reference Polymorphisms for Drug Metabolism Genes and Genome Wide Association Studies.pdf", + "2022 - Using Recurrent Neural Networks for Predicting Type-2 Diabetes from Genomic and Tabular Data.pdf", + "2022 - Using Recurrent Neural Networks for Predicting Type-2 Diabetes from Genomic and Tabular Data.pdf", + "2013 - Genome-Wide Contribution of Genotype by Environment Interaction.pdf", + "2009 - Processing Large-Scale, High-Dimension Genetic and Gene Expression Data.pdf", + "2019 - Beyond Genome-wide Significance Integrative Approaches to the Interpretation and Extension of GWAS Findings for Alcohol Use Disorder.pdf", + "2016- Gene-Based Genome-Wide Association.pdf", + "2008 - The Environmental Genome Project Reference Polymorphisms for Drug Metabolism Genes and Genome Wide Association Studies.pdf", + "2015 - Genetic associations at 53 loci highlight cell types and biological pathways relevant for kidney function.pdf" + ], + "extraction_id": [ + "cc02b251-60c5-571f-9ff8-ef64c61eee5a", + "0f19f50f-ee04-5e99-8547-8a7e71a1dd9c", + "200d489e-301f-50bc-9870-260894c8fc41", + "6b4157fa-dcf0-5b70-b508-38ffb5fcda8d", + "5ade83ec-421a-58be-ac06-c9076076483c", + "1d401588-b6dc-532f-8194-4667a7d31153", + "bca29f20-2764-5d16-888e-3af671c9d8b0", + "db605926-64e1-5fc5-ac90-22f0f33b2a50", + "1b1aabee-8555-5ba8-b147-7f250fdcbc6b", + "0127b2c2-37b8-580d-b974-a2e3c69015ab" + ], + "document_id": [ + "0e2a1075-1e04-5097-b87f-3ca41d55e025", + "15e4c746-42a2-598b-992f-dfbf468865ed", + "be0e50e0-3de8-53c5-8126-a0b618647f80", + "be0e50e0-3de8-53c5-8126-a0b618647f80", + "8c310d76-0a3b-574c-9859-859258870ee5", + "17264155-b665-59db-94cb-f4d67eac20fc", + "f59b3e10-a887-5708-b520-c5e8adb48dcd", + "8cb14287-762d-5366-8ad9-3d638f02d0d6", + "15e4c746-42a2-598b-992f-dfbf468865ed", + "ea82333b-b64c-5416-9843-2e3ffeb1902a" + ], + "id": [ + "chatcmpl-ADZKtYz4STZ5YGDkrchFPqAthSpVB", + "1b947a05-d204-5524-b7a6-4ddce62449f8", + "47097a55-da1c-5802-8ee7-549e16db2927", + "1dbbef8d-ece1-534d-a3f0-0cc46024cae6", + "0b7e9c6d-60e3-5d66-b23f-8222b327d91e", + "43aa64fe-556a-5938-a489-fff5aac6829d", + "6e7cd04d-d23a-5a7d-a0cd-7958608010f2", + "3a9e43ef-294d-5b1b-b4f9-62fa70064045", + "b4a50b95-3a61-5495-b8b2-c18f8edcaa8f", + "5e4b2bf5-f842-5c20-8031-48a29fd3d25a", + "619bcf7e-2724-571a-ba3c-4214ff014f21" + ], + "contexts": [ + "1. Formatting genome wide association study (GWAS) data . For this step, a human GWAS results file is needed that contains SNP names and raw p- values for the association of each SNP with a trait of interest. Because the nodes of the dmGWAS network will represent genes, as opposed to SNPs, gene-wise p-values need to be calculated from the raw SNP p-values. This can be accomplished by using programs like VEGAS2 (Versatile Gene- Based Association Study) [ 10] or KGG (Knowledge-based mining system", + "A general outline for GWAS is provided in Figure 2. These studies usually begin with thousands of individuals who are charact erized for the phenotype of interest using continuous measurements, or dichotomous classi fication as a case (affected) or control (unaffected). Statistical analysis, typically us ing linear or logistic regression, tests the association of each SNP against the phenotype (including relevant covariate variables) to", + "GWAS has also provided polygenic characteristics of diseases. Figure 1 presents a block of GWAS in disease prediction. There are many steps during a gene-set analysis. They are shown below as Steps 1 through Step 6: Step 1: Preliminary genome-wide analysis and data preproces sing; Step 2: Identifying gene-set definitions whose patterns have to be recognized; Step 3: Processing genomic data such as filtering and ident ifying gene patterns;", + "GWAS in disease prediction. There are many steps during a gene-set analysis. They are shown below as Steps 1 through Step 6: Step 1: Preliminary genome-wide analysis and data preprocessing; Step 2: Identifying gene-set denitions whose patterns have to be recognized; Step 3: Processing genomic data such as ltering and identifying gene patterns; Step 4: Identify gene set analysis models, such as identifying the statistical hypothesis; Step 5: Assessing the statistical magnitude;", + "include: 1) generate bed, bimand fam files for GWAS genotype data using PLINK; 2) generategrm.gz and grm.id files using make-grm; 3) prepare a", + "7 Constructing Gene Networks to Enhance GWAS and GOGE Results As discussed, generating a GOGE data set and performing a rst-pass analysis on this scale of data is a major undertaking. The identication of or other DNA markersthat associate with the expression of one or more genes is a primary goal of a GOGE study. However, if analysis of GOGE data stopped at the identication of SNPs that associate with expression, the true v alue of these data would not be realized.", + "Aggregating GWAS data into biological units GWAS data can be further combined into biological units using gene and network-based approaches. Gene-based approaches There is a high multiple testing burden in the context of a GWAS. Gene-based approaches, which aggregate across summary statistics derived from association analyses of multiple loci to derive p-values for association at the level of the gene, developed as one way to reduce", + "Steps involved inthegene-based association testwere described asbelow: 1)Generating intermediate datasets which integrate original GWAS Pvalues, rsID, position and chromo- some column foreach SNP. Atotal of6,559,815 European-specific and 5,351,262 Asian-spe- cific autosomal SNPs were used forsubsequent analysis after excluding theSNPs that could notberecognized byKGG and that located insexchromosomes (XorY);2)Defining asetof", + "248 M. J. RIEDER ET AL. Figure 2 An overview of GWAS. Samples with a phenotype(s) or trait(s) of interest are identified; typically, thousands of samples are required to achieve appropri ate statistical power. Large-scale genotyping is carried out using commercially available chips (Affymetrix or Illumina). P-values are generated from the associa- tion between the phenotype and genotype for each SNP tested. Highly associated SNPs will typically cluster", + "2006). 40. Welter, D. et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. 42,D1001D1006 (2014).41. Wang, X. et al. Comparing methods for performing trans-ethnic meta-analysis of genome-wide association studies. Hum. Mol. Genet. 22,23032311 (2013). 42. Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81,559575 (2007)." + ], + "answer": "### How-to Guide for GWAS Analysis Genome-Wide Association Studies (GWAS) are a powerful tool for identifying genetic variants associated with traits or diseases. Below is a step-by-step guide to performing a GWAS analysis: #### Step 1: Preliminary Genome-Wide Analysis and Data Preprocessing 1. **Sample Collection**: Collect thousands of samples characterized for the phenotype of interest. This can be done using continuous measurements or dichotomous classification as a case (affected) or control (unaffected) [2]. 2. **Genotyping**: Perform large-scale genotyping using commercially available chips such as Affymetrix or Illumina [9]. 3. **Data Formatting**: Prepare the GWAS genotype data by generating bed, bim, and fam files using tools like PLINK [5]. #### Step 2: Identifying Gene-Set Definitions 1. **Gene-Set Identification**: Identify gene-set definitions whose patterns need to be recognized [4]. #### Step 3: Processing Genomic Data 1. **Filtering and Identifying Gene Patterns**: Process the genomic data by filtering and identifying gene patterns [4]. 2. **Intermediate Datasets**: Generate intermediate datasets that integrate original GWAS p-values, rsID, position, and chromosome column for each SNP [8]. #### Step 4: Statistical Analysis 1. **Association Testing**: Perform statistical analysis, typically using linear or logistic regression, to test the association of each SNP against the phenotype, including relevant covariate variables [2]. 2. **Gene-Wise P-Values**: Calculate gene-wise p-values from the raw SNP p-values using programs like VEGAS2 or KGG [1]. #### Step 5: Aggregating GWAS Data 1. **Gene-Based Approaches**: Aggregate GWAS data into biological units using gene-based approaches to reduce the multiple testing burden. This involves deriving p-values for association at the level of the gene [7]. #### Step 6: Assessing Statistical Magnitude 1. **Statistical Hypothesis**: Identify gene set analysis models and assess the statistical magnitude of the associations [4]. #### Step 7: Constructing Gene Networks 1. **Enhancing GWAS Results**: Construct gene networks to enhance GWAS results. This involves identifying DNA markers that associate with the expression of one or more genes [6]. By following these steps, you can systematically perform a GWAS analysis to identify genetic variants associated with your trait or disease of interest.", + "question": "Create a how-to guide for GWAS analysis?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_18 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_18 new file mode 100644 index 0000000..cfe846d --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_18 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2016 - Coming of age ten years of next.pdf", + "2016 - Coming of age ten years of next.pdf", + "2020 - Precision and Personalized Medicine How Genomic.pdf", + "2016 - Coming of age ten years of next.pdf", + "2016 - Coming of age ten years of next.pdf", + "2016 - Coming of age ten years of next.pdf", + "2016 - Coming of age ten years of next.pdf", + "2008 - Gene Expression Profiling.pdf", + "2016 - Coming of age ten years of next.pdf", + "2015 -Pandey- Functional Analysis of Genomic Variation and Impact on Molecular.pdf" + ], + "extraction_id": [ + "cad1dcca-621d-5003-ba3a-81950819bc52", + "5da5fc5d-1fe6-58f0-9141-72b9b2996fff", + "de09f30d-e9ba-5379-8c7a-85b2cd2ed6c8", + "4cdf13c0-c505-5ff9-9a6e-b10e5d1c8819", + "5da5fc5d-1fe6-58f0-9141-72b9b2996fff", + "c91e328e-4a01-5952-85b8-d7b5b47237c5", + "cad1dcca-621d-5003-ba3a-81950819bc52", + "c14d1c74-a14a-5037-8d3f-f32a60faa9a5", + "4cdf13c0-c505-5ff9-9a6e-b10e5d1c8819", + "f4e989e5-c3d0-5d5c-b8c3-95894a14630b" + ], + "document_id": [ + "9dd6e4e9-d136-507b-b628-68c8e1461bd0", + "9dd6e4e9-d136-507b-b628-68c8e1461bd0", + "cd11028a-933b-52a0-9534-c173323056ef", + "9dd6e4e9-d136-507b-b628-68c8e1461bd0", + "9dd6e4e9-d136-507b-b628-68c8e1461bd0", + "9dd6e4e9-d136-507b-b628-68c8e1461bd0", + "9dd6e4e9-d136-507b-b628-68c8e1461bd0", + "59f3b969-089b-5258-93ad-892dbc9ffa9c", + "9dd6e4e9-d136-507b-b628-68c8e1461bd0", + "93381e23-494b-5bc2-9d09-ef315506601f" + ], + "id": [ + "chatcmpl-ADZL3IjE1j3CPp4KvVU6JGEtG2NTv", + "98010acc-fd11-5d33-bced-626ef29f2896", + "13a6b6f9-4a9a-5eb9-ac79-d986d9e613f0", + "8477a774-dddb-5541-b8d7-d51a7e56b0af", + "d2540614-9397-5e3e-8b5f-ad328ca973b2", + "1e324977-2ca5-5062-8a09-7659d516e899", + "a2d9c614-903d-513a-ad88-5a40f3534988", + "97f2aa12-623b-53ec-9793-5834311a37dd", + "3e782f01-a06e-51b6-ac8a-0e0a56939d08", + "199e1929-dc7c-58d4-8c8d-1c931e658e9c", + "d1158643-3625-5855-a03d-eec4ac96eb4d" + ], + "contexts": [ + "FURTHER INFORMATION 10X Genomics: http://www.10xgenomics.com 454 Sequencing: http://www.454.com Advances in Genome Biology and Technology (AGBT): http://www.agbt.org BGISEQ500: http://seq500.com/en/portal/Sequencer.shtml Illumina: http://www.illumina.com Ion Torrent: https://www.thermofisher.com/us/en/home/ brands/ion-torrent.html Oxford Nanopore Technologies: https://www.nanoporetech. com Pacific Biosciences: http://www.pacb.com Personal Genome Project: http://www.personalgenomes.org", + "22. Karow, J. Qiagen launches GeneReader NGS System atAMP; presents performance evaluation by broad. GenomeWeb [online], https:// www.genomeweb.com/ molecular-diagnostics/qiagen-launches-genereader- ngs-system-amp-presents-performance-evaluation (4Nov 2015). 23. Smith,D.R. & McKernan,K. Methods of producing and sequencing modified polynucleotides . US Patent 8058030 (2011). 24. Margulies,M. etal. Genome sequencing in microfabricated high-density picolitre reactors. Nature 437, 376380 (2005).", + "36. Sequencing, H.G. Finishing the euchromatic sequence of the human genome. Nature 2004 ,431, 931945. 37. Heather, J.M.; Chain, B. The sequence of sequencers: The history of sequencing DNA. Genomics 2016 ,107, 18. [CrossRef] 38. Rothberg, J.M.; Leamon, J.H. The development and impact of 454 sequencing. Nat. Biotechnol. 2008 ,26, 11171124. [CrossRef] [PubMed] 39. Shendure, J.; Ji, H. Next-generation DNA sequencing. Nat. Biotechnol. 2008 ,26, 11351145. [CrossRef] [PubMed]", + "sequencing. Genome Res. 20, 11651173 (2010). 64. English,A.C. etal. Assessing structural variation in a personal genome-towards a human reference diploid genome. BMC Genomics 16, 286 (2015). 65. Carneiro,M.O. etal. Pacific Biosciences sequencing technology for genotyping and variation discovery in human data. BMC Genomics 13, 375 (2012). 66. Quail,M.A. etal. A tale of three next generation sequencing platforms: comparison of Ion T orrent, Pacific Biosciences and Illumina MiSeq sequencers.", + "sequencing. Bioinformatics 31, 20402042 (2015). 46. Qiagen. Oncology insights enabled by knowledge base- guided panel design and the seamless workflow of the GeneReader NGS system Press Release. Qiagen [online], http://www.genereaderngs.com/PROM-9192- 001_1100403_WP_GeneReader_NGS_0116_NA.pdf (2016). 47. Forgetta,V. etal. Sequencing of the Dutch elm disease fungus genome using the Roche/454 GS-FLX Titanium System in a comparison of multiple genomics core", + "for sequencing on existing short-read instrumentation, after which data are split by barcode and reassembled with the knowledge that fragments sharing barcodes Barcodes A series of known bases addedto a template molecule either through ligation or amplification. After sequencing, these barcodes can be used to identify which sample a particular read is derived from. Figure 5 | Real-time and synthetic long-read sequencing approaches.", + "160. Glenn,T .C. Field guide to next-generation DNA sequencers. Mol. Ecol. Resour. 11, 759769 (2011). 161. Karow,J. At AGBT , 10X Genomics launches GemCode platform; shipments slated for Q2 as firm battles IP lawsuits. GenomeWeb [online], https://www. genomeweb.com/sample-prep/agbt-10x-genomics- launches-gemcode-platform-shipments-slated-q2-firm- battles-ip-lawsuits (2Mar 2015). Competing interests statement The authors declare competing interests: see Web version for details. FURTHER INFORMATION", + "DNA), and provide the means to link sequences containing applications. First, base- callers like Phred (4,5) extract raw sequences from raw data. There are also contig assemblers like Phrap (University of Washington, http://bozeman. mbt.washington.edu/phrap.docs/phrap.html ) or CAP3 (6) that assemble frag- ments to contigs and packages like consed (7) or GAP4 (8), which are used to finish sequencing projects. These programs are not explained in detail here.", + "Nat. Biotechnol. 30, 10331036 (2012). 111. Chrystoja,C.C. & Diamandis,E.P . Whole genome sequencing as a diagnostic test: challenges and opportunities. Clin. Chem. 60, 724733 (2014). 112. McGuire,A.L. etal. Point-counterpoint. Ethics and genomic incidental findings. Science 340, 10471048 (2013). 113. Bowers,J. etal. Virtual terminator nucleotides for next-generation DNA sequencing. Nat. Methods 6, 593595 (2009). 114. Heger,M. Chinas Direct Genomics unveils new", + "11 BIOINFORMATIC CHALLENGES FOR GENOMIC MEDICINE Processing and managing of high-throughput sequence data High throughput sequencing offers severa l advantages relative to array-based genotyping or expression assays. First, unlike genotyping arrays, whole genome sequencing is not limited to interrogating onl y known sequence variants. Similarly, RNA- sequencing (RNA-seq) enables expression quanti fication of novel transcripts that are not" + ], + "answer": "### How-to Guide for Genetic Sequencing Genetic sequencing is a powerful tool used to determine the order of nucleotides in DNA. This guide will walk you through the basic steps involved in genetic sequencing, referencing specific resources for further information. #### Step 1: Sample Preparation 1. **Collect DNA Sample**: Obtain a DNA sample from the organism or tissue of interest. This can be done using various methods such as blood draws, buccal swabs, or tissue biopsies. 2. **Extract DNA**: Use a DNA extraction kit to isolate DNA from the collected sample. Ensure the DNA is of high quality and free from contaminants. #### Step 2: Library Preparation 1. **Fragment DNA**: Break the DNA into smaller fragments. This can be done using mechanical shearing or enzymatic digestion. 2. **Add Adapters**: Ligate specific adapters to the ends of the DNA fragments. These adapters are necessary for the sequencing process and often contain barcodes for sample identification [6]. #### Step 3: Amplification 1. **PCR Amplification**: Amplify the DNA fragments using Polymerase Chain Reaction (PCR). This step increases the quantity of DNA to ensure there is enough material for sequencing. #### Step 4: Sequencing 1. **Choose a Sequencing Platform**: Select an appropriate sequencing platform based on your needs. Popular platforms include Illumina, Ion Torrent, Pacific Biosciences, and Oxford Nanopore Technologies [1], [4], [7]. 2. **Load the Sample**: Load the prepared DNA library onto the sequencing machine. Follow the manufacturer's instructions for your specific platform. #### Step 5: Data Acquisition 1. **Run the Sequencer**: Start the sequencing run. The machine will read the nucleotide sequences of the DNA fragments and generate raw data. 2. **Monitor the Run**: Keep an eye on the sequencing run to ensure it is proceeding correctly. Some platforms provide real-time monitoring capabilities [6]. #### Step 6: Data Analysis 1. **Base Calling**: Use base-calling software to convert raw sequencing data into nucleotide sequences. Tools like Phred are commonly used for this purpose [8]. 2. **Sequence Assembly**: Assemble the short reads into longer contiguous sequences (contigs) using assemblers like Phrap or CAP3 [8]. 3. **Data Interpretation**: Analyze the assembled sequences to identify genetic variants, structural variations, and other genomic features. This step may involve bioinformatics tools and databases [10]. #### Step 7: Validation and Reporting 1. **Validate Results**: Confirm the accuracy of your sequencing results using additional methods such as Sanger sequencing or quantitative PCR. 2. **Report Findings**: Compile your findings into a comprehensive report. Include details about the methods used, the quality of the data, and any significant genetic variations identified. #### Further Information For more detailed information on specific sequencing technologies and platforms, refer to the following resources: - [10X Genomics](http://www.10xgenomics.com) - [Illumina](http://www.illumina.com) - [Ion Torrent](https://www.thermofisher.com/us/en/home/brands/ion-torrent.html) - [Pacific Biosciences](http://www.pacb.com) - [Oxford Nanopore Technologies](https://www.nanoporetech.com) [1] By following these steps and utilizing the referenced resources, you can successfully perform genetic sequencing and analyze the resulting data.", + "question": "Create a how-to guide for genetic sequencing" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_19 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_19 new file mode 100644 index 0000000..e480449 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_19 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Precision and Personalized Medicine How Genomic.pdf", + "2016 - Coming of age ten years of next.pdf", + "2015 -Pandey- Functional Analysis of Genomic Variation and Impact on Molecular.pdf", + "2015 - Functional Analysis of Genomic Variation and Impact on Molecular and Higher Order Phenotypes.pdf", + "2017 - Infection control in the new age of genomic epidemiology.pdf", + "2016 - Coming of age ten years of next.pdf", + "2016 - Coming of age ten years of next.pdf", + "2016 - Coming of age ten years of next.pdf", + "2016 - Coming of age ten years of next.pdf", + "2008 - Gene Expression Profiling.pdf" + ], + "extraction_id": [ + "de09f30d-e9ba-5379-8c7a-85b2cd2ed6c8", + "5da5fc5d-1fe6-58f0-9141-72b9b2996fff", + "f4e989e5-c3d0-5d5c-b8c3-95894a14630b", + "fa426831-7c04-56c1-a191-1ebbc35342ed", + "04f06fb6-b2ff-57d4-bac0-de5cf4782ff3", + "4cdf13c0-c505-5ff9-9a6e-b10e5d1c8819", + "4cdf13c0-c505-5ff9-9a6e-b10e5d1c8819", + "5da5fc5d-1fe6-58f0-9141-72b9b2996fff", + "cad1dcca-621d-5003-ba3a-81950819bc52", + "c14d1c74-a14a-5037-8d3f-f32a60faa9a5" + ], + "document_id": [ + "cd11028a-933b-52a0-9534-c173323056ef", + "9dd6e4e9-d136-507b-b628-68c8e1461bd0", + "93381e23-494b-5bc2-9d09-ef315506601f", + "263d327b-f5db-54e4-a215-b3f8a51cd7d6", + "8f028916-b990-5e95-b2a6-e69f451cc291", + "9dd6e4e9-d136-507b-b628-68c8e1461bd0", + "9dd6e4e9-d136-507b-b628-68c8e1461bd0", + "9dd6e4e9-d136-507b-b628-68c8e1461bd0", + "9dd6e4e9-d136-507b-b628-68c8e1461bd0", + "59f3b969-089b-5258-93ad-892dbc9ffa9c" + ], + "id": [ + "chatcmpl-ADZLI3pesAhKfrcor9tw6wwv6rY5l", + "8477a774-dddb-5541-b8d7-d51a7e56b0af", + "13a6b6f9-4a9a-5eb9-ac79-d986d9e613f0", + "d1158643-3625-5855-a03d-eec4ac96eb4d", + "cdf2b80f-1509-50a2-9cb2-a36dd6f3f2cc", + "f8ae01ae-cea8-5b8b-95c0-7147055de596", + "d2540614-9397-5e3e-8b5f-ad328ca973b2", + "199e1929-dc7c-58d4-8c8d-1c931e658e9c", + "1e324977-2ca5-5062-8a09-7659d516e899", + "98010acc-fd11-5d33-bced-626ef29f2896", + "3e782f01-a06e-51b6-ac8a-0e0a56939d08" + ], + "contexts": [ + "36. Sequencing, H.G. Finishing the euchromatic sequence of the human genome. Nature 2004 ,431, 931945. 37. Heather, J.M.; Chain, B. The sequence of sequencers: The history of sequencing DNA. Genomics 2016 ,107, 18. [CrossRef] 38. Rothberg, J.M.; Leamon, J.H. The development and impact of 454 sequencing. Nat. Biotechnol. 2008 ,26, 11171124. [CrossRef] [PubMed] 39. Shendure, J.; Ji, H. Next-generation DNA sequencing. Nat. Biotechnol. 2008 ,26, 11351145. [CrossRef] [PubMed]", + "22. Karow, J. Qiagen launches GeneReader NGS System atAMP; presents performance evaluation by broad. GenomeWeb [online], https:// www.genomeweb.com/ molecular-diagnostics/qiagen-launches-genereader- ngs-system-amp-presents-performance-evaluation (4Nov 2015). 23. Smith,D.R. & McKernan,K. Methods of producing and sequencing modified polynucleotides . US Patent 8058030 (2011). 24. Margulies,M. etal. Genome sequencing in microfabricated high-density picolitre reactors. Nature 437, 376380 (2005).", + "11 BIOINFORMATIC CHALLENGES FOR GENOMIC MEDICINE Processing and managing of high-throughput sequence data High throughput sequencing offers severa l advantages relative to array-based genotyping or expression assays. First, unlike genotyping arrays, whole genome sequencing is not limited to interrogating onl y known sequence variants. Similarly, RNA- sequencing (RNA-seq) enables expression quanti fication of novel transcripts that are not", + "11 BIOINFORMATIC CHALLENGES FOR GENOMIC MEDICINE Processing and managing of high-throughput sequence data High throughput sequencing offers severa l advantages relative to array-based genotyping or expression assays. First, unlike genotyping arrays, whole genome sequencing is not limited to interrogating onl y known sequence variants. Similarly, RNA- sequencing (RNA-seq) enables expression quanti fication of novel transcripts that are not", + "High-throughput bacterial genome sequencing: an embarrassment of choice, aworldof opportunity.NatRevMicrobiol2012;10:599-606. 11.CroucherNJ,DidelotX.Theapplicationof genomicstotracingbacterialpathogen transmission.CurrOpinMicrobiol2015;23:62-7. 12.ShendureJ,JiH.Next-generationDNAsequencing.NatBiotechnol2008;26:1135- 45. 13.MillerJR,KorenS,SuttonG.Assemblyalgorithmsfornext-generationsequencing data.Genomics2010;95:315-27. 14.OlsonND,LundSP,ColmanRE,FosterJT,SahlJW,SchuppJM,etal.Bestpractices", + "sequencing. Genome Res. 20, 11651173 (2010). 64. English,A.C. etal. Assessing structural variation in a personal genome-towards a human reference diploid genome. BMC Genomics 16, 286 (2015). 65. Carneiro,M.O. etal. Pacific Biosciences sequencing technology for genotyping and variation discovery in human data. BMC Genomics 13, 375 (2012). 66. Quail,M.A. etal. A tale of three next generation sequencing platforms: comparison of Ion T orrent, Pacific Biosciences and Illumina MiSeq sequencers.", + "Nat. Biotechnol. 30, 10331036 (2012). 111. Chrystoja,C.C. & Diamandis,E.P . Whole genome sequencing as a diagnostic test: challenges and opportunities. Clin. Chem. 60, 724733 (2014). 112. McGuire,A.L. etal. Point-counterpoint. Ethics and genomic incidental findings. Science 340, 10471048 (2013). 113. Bowers,J. etal. Virtual terminator nucleotides for next-generation DNA sequencing. Nat. Methods 6, 593595 (2009). 114. Heger,M. Chinas Direct Genomics unveils new", + "sequencing. Bioinformatics 31, 20402042 (2015). 46. Qiagen. Oncology insights enabled by knowledge base- guided panel design and the seamless workflow of the GeneReader NGS system Press Release. Qiagen [online], http://www.genereaderngs.com/PROM-9192- 001_1100403_WP_GeneReader_NGS_0116_NA.pdf (2016). 47. Forgetta,V. etal. Sequencing of the Dutch elm disease fungus genome using the Roche/454 GS-FLX Titanium System in a comparison of multiple genomics core", + "FURTHER INFORMATION 10X Genomics: http://www.10xgenomics.com 454 Sequencing: http://www.454.com Advances in Genome Biology and Technology (AGBT): http://www.agbt.org BGISEQ500: http://seq500.com/en/portal/Sequencer.shtml Illumina: http://www.illumina.com Ion Torrent: https://www.thermofisher.com/us/en/home/ brands/ion-torrent.html Oxford Nanopore Technologies: https://www.nanoporetech. com Pacific Biosciences: http://www.pacb.com Personal Genome Project: http://www.personalgenomes.org", + "DNA), and provide the means to link sequences containing applications. First, base- callers like Phred (4,5) extract raw sequences from raw data. There are also contig assemblers like Phrap (University of Washington, http://bozeman. mbt.washington.edu/phrap.docs/phrap.html ) or CAP3 (6) that assemble frag- ments to contigs and packages like consed (7) or GAP4 (8), which are used to finish sequencing projects. These programs are not explained in detail here." + ], + "answer": "### How-to Guide for Genetic Sequencing Genetic sequencing is a powerful tool used to determine the order of nucleotides in DNA. This guide will walk you through the basic steps involved in genetic sequencing, referencing specific sources for more detailed information. #### Step 1: Sample Preparation 1. **DNA Extraction**: Begin by extracting DNA from your sample. This can be done using various commercial kits or standard laboratory protocols. 2. **DNA Quantification and Quality Check**: Measure the concentration and purity of the extracted DNA using spectrophotometry or fluorometry. Ensure the DNA is of high quality and free from contaminants. #### Step 2: Library Preparation 1. **Fragmentation**: Fragment the DNA into smaller pieces. This can be achieved through mechanical shearing, enzymatic digestion, or sonication. 2. **End Repair and A-tailing**: Repair the fragmented DNA ends and add an adenine (A) base to the 3' ends to prepare them for adapter ligation. 3. **Adapter Ligation**: Ligate sequencing adapters to the ends of the DNA fragments. These adapters are necessary for the fragments to bind to the sequencing platform. #### Step 3: Amplification and Enrichment 1. **PCR Amplification**: Amplify the adapter-ligated DNA fragments using polymerase chain reaction (PCR). This step increases the quantity of DNA available for sequencing. 2. **Size Selection**: Select DNA fragments of the desired size range using gel electrophoresis or magnetic beads. #### Step 4: Sequencing 1. **Loading the Sequencer**: Load the prepared DNA library onto the sequencing platform. Popular platforms include Illumina, Ion Torrent, and Pacific Biosciences [6], [9]. 2. **Sequencing Run**: Initiate the sequencing run. The platform will read the nucleotide sequences of the DNA fragments and generate raw sequence data [1], [5]. #### Step 5: Data Processing 1. **Base Calling**: Convert raw data into nucleotide sequences using base-calling software such as Phred [10]. 2. **Sequence Assembly**: Assemble the short DNA sequences into longer contiguous sequences (contigs) using software like Phrap or CAP3 [10]. 3. **Quality Control**: Assess the quality of the assembled sequences and remove any errors or low-quality reads. #### Step 6: Data Analysis 1. **Variant Calling**: Identify genetic variants such as single nucleotide polymorphisms (SNPs) and insertions/deletions (indels) using variant calling software. 2. **Annotation**: Annotate the identified variants to understand their potential impact on gene function and association with diseases. #### Step 7: Interpretation and Reporting 1. **Bioinformatics Analysis**: Use bioinformatics tools to interpret the sequencing data in the context of the research question or clinical application [3], [4]. 2. **Report Generation**: Generate a comprehensive report summarizing the findings, including identified variants and their potential implications. #### Additional Resources - For a detailed history and development of sequencing technologies, refer to Heather and Chain's review [1]. - For information on specific sequencing platforms and their comparisons, see Quail et al. [6]. - For best practices in sequencing and data management, consult Olson et al. [5]. By following these steps, you can successfully perform genetic sequencing and analyze the resulting data to gain valuable insights into the genetic makeup of your samples.", + "question": "Create a how-to guide for genetic sequencing." +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_2 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_2 new file mode 100644 index 0000000..9143c59 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_2 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2008 - Gene Expression Profiling.pdf", + "2017 - Metformin alters the gut microbiome of individuals with treatment-naive type 2 diabetes, contributing to the therapeutic effects of the drug.pdf", + "2008 - Gene Expression Profiling.pdf", + "2016 - Sequence Capture and Phylogenetic Utility.pdf", + "2008 - Gene Expression Profiling.pdf", + "2008 - Gene Expression Profiling.pdf", + "2016 - Sequence Capture and Phylogenetic Utility.pdf", + "2016 - Integrated multi-omics of the human gut microbiome in a case study of familial type 1 diabetes.pdf", + "2016 - Sequence Capture and Phylogenetic Utility.pdf", + "2004 - Linking nutrition to genomics.pdf" + ], + "extraction_id": [ + "3f898a5b-0b72-59b9-b923-a5bca2db11c6", + "7595d721-9b06-5442-a876-e389ca4a66be", + "5a11860d-c422-5e6d-8a31-be81de4e1c8d", + "c5beca95-6108-5a67-8f74-fb39b9a36d3c", + "3aa1db4d-6c18-53ab-8859-676d34d2b2ae", + "99821df5-c257-5c1f-9fe8-18d5865d5c1e", + "f9e001fe-b0b0-5cd5-be1b-9377ac52b079", + "1c7453d1-119d-5575-b950-7b400de2b3a4", + "c9f26c8e-b56c-5a1a-95f4-5824f05ba3d0", + "b7d8dfc5-094a-5d4e-969a-97e287939187" + ], + "document_id": [ + "59f3b969-089b-5258-93ad-892dbc9ffa9c", + "448d68d1-19a8-5f4c-a48b-8d33597bd03b", + "59f3b969-089b-5258-93ad-892dbc9ffa9c", + "6232f392-169a-50c5-b8c9-a250f3d840cc", + "59f3b969-089b-5258-93ad-892dbc9ffa9c", + "59f3b969-089b-5258-93ad-892dbc9ffa9c", + "6232f392-169a-50c5-b8c9-a250f3d840cc", + "f0405966-38bf-5a04-aa2c-1474b11362bb", + "6232f392-169a-50c5-b8c9-a250f3d840cc", + "99891ef7-0589-5c41-a61f-1ab1fe1c8939" + ], + "id": [ + "chatcmpl-ADZIljdVVoktIlIQ3BBIkNiAq5m4n", + "4067a893-52a9-5e8e-9221-c32be3241c2a", + "045c27b0-dad8-56f1-8772-ae9d0da11c8a", + "61393b99-58f3-5f1d-899d-809166e88442", + "3a090421-e3e5-5f38-8acf-b8053b43287b", + "29a51de9-1da1-5a4b-9de6-19a88c8593a3", + "559fdf4f-5d14-5277-ba7b-a367d4795ed2", + "3252d040-7281-54ca-a478-46a30b6d84f6", + "f2d72429-c697-5c58-aee0-6cf90b0387e5", + "4498331b-aea3-5c0c-9f0b-77a45cc400a2", + "dbae2fad-ec06-52a8-9dc0-7bc154faecc8" + ], + "contexts": [ + "by shearing. A flow diagram summarizing the extraction of DNA is given in Fig. 1.2. The above-described procedure is suitable for total cellular DNA. If the DNA from a specific organelle or viral particle is needed, it is best to isolate the organelle or virus before extracting its DNA, because the recovery of a particular type of DNA from a mixture is usually rather difficult. Where a high degree of purity is required, DNA may be subjected to density gradient", + "2017 Nature America, Inc., part of Springer Nature. All rights reserved. nature medicine doi:10.1038/nm.434564. Salonen, A. et al. Comparative analysis of fecal DNA extraction methods with phylogenetic microarray: effective recovery of bacterial and archaeal DNA using mechanical cell lysis. J. Microbiol. Methods 81, 127134 (2010). 65. Murphy, N.R. & Hellwig, R.J. Improved nucleic acid organic extraction through use of a unique gel barrier material. Biotechniques 21, 934936, 938939 (1996).", + "is the suitable preparation of the DNA template with a high level of purity and free from contaminating DNA (14). Different procedures are used for DNA extraction with specific protocol for mammals, plants, fungi, bacteria, protozoan, helminthes, insects, and others. In specific cases, such as insects, contamination can be reduced by hypochlorite treatment before extraction to avoid contact with foreign DNA (15). DNA preparation includes the", + "this method is well suited for larger scale investigations of museum insect phylogenomics. We did extract DNA from relatively large insects, where one leg yields more tissue than is availablefrom crushing the entire body of most ants, for example. Thus, it remains now to be tested whether sufficient input DNA can also be obtained from smaller dried insect specimens. None-", + "usually requires that it be isolated and purified to a certain degree. DNA is usually recovered from cells by methods that include cell rupture but that prevent the DNA from fragmenting by mechanical shearing. This is gener- ally undertaken in the presence of EDTA, which chelates the magnesium ions needed as cofactors for enzymes that degrade DNA, termed DNase. Ideally, cell walls, if present, should be digested enzymatically (e.g., lysozyme in the", + "DNA and then using a gene probe representing a protein or enzyme from one of the organisms. In this way, it is possible to search for related genes in different species. This technique is generally termed Zoo blotting. A similar process of nucleic acid blotting can be used to transfer RNA separated by gel electrophoresis onto membranes similar to that used in Southern blotting. This process, termed Northern blotting , allows the identification of specific mRNA", + "6. Staats M, Erkens RH, van de Vossenberg B, Wieringa JJ, Kraaijeveld K, Stielow B, et al. Genomic trea- sure troves: complete genome sequencing of herbarium and insect museum specimens. PLOS ONE. 2013; 8:e69189. doi: 10.1371/journal.pone.0069189 PMID: 23922691 7. Burrell AS, Disotell TR, Bergey CM. The use of museum specimens with high-throughput DNA sequencers. J Hum Evol. 2015; 79:35 44. doi: 10.1016/j.jhevol.2014.10.015 PMID: 25532801", + "were extracted from unthawed, frozen faecal subsamples (150 mg) after pretreatment of the weighed subsamples with 1.5 ml RNAlater ICE (LifeTechnologies) overnight.The faeces-RNAlater ICE mixture was homogenized by bead-beating, as previously described 53. Differential centrifugation and extraction using the All-In-One kit (Norgen Biotek) to recover DNA and proteins were carried out as previously described53. DNA fractions were supplemented with DNA extracted from 200 mg", + "DNA was then extracted destructively by grinding the frozen tissue with a sterile pestle, using aDNeasy Blood and TissueKit (Qiagen, Valencia, CA, USA) and following the manufacturer s protocol, except the DNA was eluted in 130 L ddH 2O instead of the supplied buffer. We ran 10L of each extract for 60 min at 100 volt on 1.5% agarose SB (sodium borate) gels, to estimate size of the genomic DNA. From a pool of 60 successful extractions (12 extractions produced no quantifiable DNA), we", + "Extracting biological information" + ], + "answer": "To extract DNA from flora or fauna, one can follow these general steps: 1. **Cell Rupture**: The cells need to be ruptured to release the DNA. This can be done using mechanical methods such as grinding the tissue with a sterile pestle [9] or using bead-beating for homogenization [8]. Enzymatic digestion of cell walls, if present, can also be used (e.g., lysozyme) [5]. 2. **Preventing DNA Degradation**: During the extraction process, it is crucial to prevent DNA from fragmenting. This can be achieved by using EDTA, which chelates magnesium ions necessary for DNase enzymes that degrade DNA [5]. 3. **Purification**: The DNA needs to be purified to remove contaminants. This can involve differential centrifugation and using specific kits designed for DNA extraction, such as the All-In-One kit [8] or the DNeasy Blood and Tissue Kit [9]. 4. **Specific Protocols for Different Organisms**: Different organisms may require specific protocols. For example, insects may need hypochlorite treatment before extraction to reduce contamination [3]. 5. **High Purity Requirements**: If a high degree of purity is required, DNA can be subjected to density gradient centrifugation [1]. By following these steps, one can effectively extract DNA from various types of flora and fauna.", + "question": "How would one extract the DNA, from say, flora or fauna?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_20 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_20 new file mode 100644 index 0000000..0a09b86 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_20 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2017 - Regular exercise participation improves genomic stability in diabetic patients an exploratory study to analyse telomere length and DNA damage.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2008 - Telomeres and Aging.pdf", + "2006 - Sex-specific telomere length profiles.pdf", + "2018 - Sex Differences in Aging Genomic Instability.pdf", + "2002 - Mitochondrial dysfunction leads to telomere attrition.pdf", + "2006 - Sex-specific telomere length profiles.pdf", + "2017 - The Aging Cardiovascular System.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2018 - Repetitive Fragile Sites Centromere Satellite DNA.pdf" + ], + "extraction_id": [ + "0e53122e-a308-55f7-8ee8-a0857ac9c52f", + "efd18101-9cf2-56b5-8f86-c2aba6caa0bc", + "13990eb4-bef2-58ce-bf3e-0e3bc294caab", + "6d3bfe47-f26e-50dc-8d77-19f3797e53a0", + "396708f1-aa0a-571e-a8d3-7cb8404e9502", + "b92ede07-74a7-524a-8d2c-54b2559e8425", + "eb8d8e40-a484-57cb-8125-3fd5eb3f6389", + "6949970f-7bc7-5585-a57a-96de1b5ba6ec", + "d4afa45a-5efa-577b-822e-7a82c2f6508d", + "3b0cb0ab-421d-54d7-9816-c6a2e6f1ac68" + ], + "document_id": [ + "dcaf7b09-2d54-5cbf-b061-e3c4e6c6c518", + "62b635c3-040e-512a-b016-6ef295308a1e", + "61d9c326-d36e-55c1-a891-335dc943e70f", + "09c78a17-4a1f-52c1-be4d-994fd9fd71d0", + "8cfb5529-7f0c-58fc-b6e4-b3ee800fb72f", + "d8bc729b-7513-58b7-b12e-0db1fb6d3b7d", + "09c78a17-4a1f-52c1-be4d-994fd9fd71d0", + "d3ff8471-986b-5fa0-b9c4-96eaaa8fce7c", + "62b635c3-040e-512a-b016-6ef295308a1e", + "262df0d6-ad68-544a-88ed-b4568f305858" + ], + "id": [ + "chatcmpl-ADZLSMnXSYde3yxfC3WAn4RccN6wO", + "bb069c10-45f1-5a83-95e3-4b7655874ba7", + "28e98b7e-f273-5bdd-9979-185133f311af", + "5f940245-af1d-5eee-84dc-942017c523d0", + "607cbd31-d430-5517-8212-208b25af32bf", + "53508a9e-d064-58a3-a4f9-0785470a1462", + "7fad29bd-12bf-53d0-af89-aadd38b974ff", + "64ef9964-1831-5a7a-8a69-5e8d0c332d37", + "1b453e12-a0c4-59db-a978-bbebd689e7dc", + "65fb74aa-f3c3-5c80-919f-329169db982f", + "f181e6da-58b6-5f26-87a2-355e25388673" + ], + "contexts": [ + "repetitive nucleotide sequences at the end of each eukaryotic chromosome, which protects them from attrition and damage. Although the relationship between leukocyte telomere length (LTL) and diabetes is still questioned 8, different studies have shown that T2D individuals have shorter leukocyte telomeres than non-T2D individuals9, 10 that may be associated with disease progression11. Indeed, the decreased antioxidant capacity described in patients", + "Telomeres are arrays of linked nucleotide hexamer repeats that are found at the ends of chromosomes in a vast clade of organisms [14]. While the sequence of these telomeric repeats can vary between organisms, their biological function is highly conserved, which is to limit damage inflicted on genes during the replica- tion of chromosomes. Telomere length is progressively shortened with each round of genomic replication, unless it is restored through the action of a ribonucleo-", + "telomere length,a phenomenon attributed to higher levels of oxidativestress at the cellular level (70). More recent studies havelinked telomere length in smooth muscle cells with senes-cence and disease severity in patients with atherosclero-sis (141, 150). Leukocyte telomere length was also short ina cohort of similar patients and associated with a higherrisk of developing occult cardiovascular disease (71).More data are needed to understand and validate the useof leukocyte telomere length as a biomarker", + "age telomere length through accumulation of several short telo- meres (Londono-Vallejo et al., 2001; Martens et al., 2000) is responsible for senescence or whether a speci c chromosome arm limits the replication potential of human cells (Hemann et al., 2001). Individual chromosome arms were shown to have large variations in their length (Lansdorp et al., 1996; Benn, 1997; Londono-Vallejo et al., 2001), and chromosome 17p seemed to be equipped with especially short telomeres in hu-", + "Telomeres are specialized structures that protect the ends of linear chromosomes. They shorten during aging due to the unidirectional activity of DNA polymerase, which leaves a section of DNA unrepli-cated on the lagging strand. Telomeres also are subject to shortening by genotoxic stress, such as oxidative damage (33). Among many eukaryotes, the enzyme telomerase maintains telomere length; but telomerase activity varies over the lifespan and between cell types, tissues, and species (34). In most human", + "TTAGGG sequence that cap the ends of chromosomes, protect-ing them from degradation and fusion. The length of telomererepeats is primarily maintained by active telomerase, which iscomposed of Telomerase RNA (TR) and a catalytic subunitTelomerase Reverse Transcriptase (TERT) (Blackburn, 2001).Extensive evidence has shown that telomere shortening anderosion lead to chromosome end-to-end fusions and genomicinstability (Blasco et al ., 1997; Hande et al ., 1999), causing", + "a pivotal role in maintenance of genomic integrity and func-tion (Moyzis et al., 1988; McElligott and Wellinger, 1997; van Steensel and de Lange, 1997). It is generally accepted that telomeres shorten during DNA replication both in vitro and in vivo. In individuals, short telomeres are considered to be a sign of advanced age. Cawthon and coworkers (2003) showed that telomere shortening in hu-", + "Each cell division shortens telomeric DNA until, at a critical length, the cells lose capping function at thechromosomal ends, activating DNA damage check-points, cell senescence, and eventually apoptosis.Telomere shortening has particular relevance in thesetting of CVD. Leukocyte telomere length (LTL) associates signi cantly with vascular cell senescence,", + "nization may directly affect telomere attrition, resulting in accelerated replicative senescence and progeroid phenotypes [180]. Telomeres are regions constituted by tandem repeats of non-coding DNA sequences 5-(TTAGGG)n-3 and a protein complex called shelterin, bound to them. This structure ensures the stability of the genome and protects the chromosomes from a wrong action of the DNA repair machinery [184] by allowing the formation of a chromatin loop called T-Loop [185].", + "telomeres, the repetitive sequence at the end of linear chromosomes, has garnered much attention for its relation to aging. Telomere repeats serve as an internal clock for cycling cells because each round of replication results in the loss of telomeric DNA in the absence of active telomerase (reviewed in [66]). Eventually, this loss over cellular generations culminates in telomere crisis and a permanent state of" + ], + "answer": "The length of telomeres is significant for several reasons: 1. **Protection of Chromosomes**: Telomeres are repetitive nucleotide sequences at the ends of eukaryotic chromosomes that protect them from attrition and damage during replication [1], [2], [6]. They prevent chromosome ends from being recognized as DNA breaks, thereby avoiding unnecessary repair mechanisms that could lead to genomic instability [9]. 2. **Aging and Cellular Senescence**: Telomere length is progressively shortened with each round of genomic replication unless restored by the enzyme telomerase [2], [5]. Short telomeres are considered a sign of advanced age and are associated with cellular senescence, where cells lose their ability to divide and function properly [4], [7], [8]. 3. **Disease Association**: Shorter telomeres have been linked to various diseases. For instance, individuals with Type 2 Diabetes (T2D) have been shown to have shorter leukocyte telomeres, which may be associated with disease progression [1]. Similarly, telomere length in smooth muscle cells has been linked to disease severity in atherosclerosis, and shorter leukocyte telomeres are associated with a higher risk of developing cardiovascular disease [3], [8]. 4. **Genomic Stability**: Telomere shortening and erosion can lead to chromosome end-to-end fusions and genomic instability, which are detrimental to cellular function and can lead to cancer and other genetic disorders [6]. 5. **Biomarker Potential**: Due to their association with aging and disease, telomere length is being studied as a potential biomarker for various conditions, including cardiovascular diseases and diabetes [3]. In summary, the length of telomeres is crucial for maintaining chromosomal integrity, regulating cellular aging, and potentially serving as a biomarker for certain diseases.", + "question": "What is the significance of the length of telomeres?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_21 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_21 new file mode 100644 index 0000000..1cd69f5 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_21 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2016 - Coming of age ten years of next.pdf", + "2016 - Coming of age ten years of next.pdf", + "2020 - Precision and Personalized Medicine How Genomic.pdf", + "2016 - Coming of age ten years of next.pdf", + "2016 - Coming of age ten years of next.pdf", + "2016 - Coming of age ten years of next.pdf", + "2016 - Coming of age ten years of next.pdf", + "2008 - Gene Expression Profiling.pdf", + "2016 - Coming of age ten years of next.pdf", + "2015 -Pandey- Functional Analysis of Genomic Variation and Impact on Molecular.pdf" + ], + "extraction_id": [ + "cad1dcca-621d-5003-ba3a-81950819bc52", + "5da5fc5d-1fe6-58f0-9141-72b9b2996fff", + "de09f30d-e9ba-5379-8c7a-85b2cd2ed6c8", + "4cdf13c0-c505-5ff9-9a6e-b10e5d1c8819", + "5da5fc5d-1fe6-58f0-9141-72b9b2996fff", + "c91e328e-4a01-5952-85b8-d7b5b47237c5", + "cad1dcca-621d-5003-ba3a-81950819bc52", + "c14d1c74-a14a-5037-8d3f-f32a60faa9a5", + "4cdf13c0-c505-5ff9-9a6e-b10e5d1c8819", + "f4e989e5-c3d0-5d5c-b8c3-95894a14630b" + ], + "document_id": [ + "9dd6e4e9-d136-507b-b628-68c8e1461bd0", + "9dd6e4e9-d136-507b-b628-68c8e1461bd0", + "cd11028a-933b-52a0-9534-c173323056ef", + "9dd6e4e9-d136-507b-b628-68c8e1461bd0", + "9dd6e4e9-d136-507b-b628-68c8e1461bd0", + "9dd6e4e9-d136-507b-b628-68c8e1461bd0", + "9dd6e4e9-d136-507b-b628-68c8e1461bd0", + "59f3b969-089b-5258-93ad-892dbc9ffa9c", + "9dd6e4e9-d136-507b-b628-68c8e1461bd0", + "93381e23-494b-5bc2-9d09-ef315506601f" + ], + "id": [ + "chatcmpl-ADZLdc2V8rjlDwihUfGh20lox5Tad", + "98010acc-fd11-5d33-bced-626ef29f2896", + "13a6b6f9-4a9a-5eb9-ac79-d986d9e613f0", + "8477a774-dddb-5541-b8d7-d51a7e56b0af", + "d2540614-9397-5e3e-8b5f-ad328ca973b2", + "1e324977-2ca5-5062-8a09-7659d516e899", + "a2d9c614-903d-513a-ad88-5a40f3534988", + "97f2aa12-623b-53ec-9793-5834311a37dd", + "3e782f01-a06e-51b6-ac8a-0e0a56939d08", + "199e1929-dc7c-58d4-8c8d-1c931e658e9c", + "d1158643-3625-5855-a03d-eec4ac96eb4d" + ], + "contexts": [ + "FURTHER INFORMATION 10X Genomics: http://www.10xgenomics.com 454 Sequencing: http://www.454.com Advances in Genome Biology and Technology (AGBT): http://www.agbt.org BGISEQ500: http://seq500.com/en/portal/Sequencer.shtml Illumina: http://www.illumina.com Ion Torrent: https://www.thermofisher.com/us/en/home/ brands/ion-torrent.html Oxford Nanopore Technologies: https://www.nanoporetech. com Pacific Biosciences: http://www.pacb.com Personal Genome Project: http://www.personalgenomes.org", + "22. Karow, J. Qiagen launches GeneReader NGS System atAMP; presents performance evaluation by broad. GenomeWeb [online], https:// www.genomeweb.com/ molecular-diagnostics/qiagen-launches-genereader- ngs-system-amp-presents-performance-evaluation (4Nov 2015). 23. Smith,D.R. & McKernan,K. Methods of producing and sequencing modified polynucleotides . US Patent 8058030 (2011). 24. Margulies,M. etal. Genome sequencing in microfabricated high-density picolitre reactors. Nature 437, 376380 (2005).", + "36. Sequencing, H.G. Finishing the euchromatic sequence of the human genome. Nature 2004 ,431, 931945. 37. Heather, J.M.; Chain, B. The sequence of sequencers: The history of sequencing DNA. Genomics 2016 ,107, 18. [CrossRef] 38. Rothberg, J.M.; Leamon, J.H. The development and impact of 454 sequencing. Nat. Biotechnol. 2008 ,26, 11171124. [CrossRef] [PubMed] 39. Shendure, J.; Ji, H. Next-generation DNA sequencing. Nat. Biotechnol. 2008 ,26, 11351145. [CrossRef] [PubMed]", + "sequencing. Genome Res. 20, 11651173 (2010). 64. English,A.C. etal. Assessing structural variation in a personal genome-towards a human reference diploid genome. BMC Genomics 16, 286 (2015). 65. Carneiro,M.O. etal. Pacific Biosciences sequencing technology for genotyping and variation discovery in human data. BMC Genomics 13, 375 (2012). 66. Quail,M.A. etal. A tale of three next generation sequencing platforms: comparison of Ion T orrent, Pacific Biosciences and Illumina MiSeq sequencers.", + "sequencing. Bioinformatics 31, 20402042 (2015). 46. Qiagen. Oncology insights enabled by knowledge base- guided panel design and the seamless workflow of the GeneReader NGS system Press Release. Qiagen [online], http://www.genereaderngs.com/PROM-9192- 001_1100403_WP_GeneReader_NGS_0116_NA.pdf (2016). 47. Forgetta,V. etal. Sequencing of the Dutch elm disease fungus genome using the Roche/454 GS-FLX Titanium System in a comparison of multiple genomics core", + "for sequencing on existing short-read instrumentation, after which data are split by barcode and reassembled with the knowledge that fragments sharing barcodes Barcodes A series of known bases addedto a template molecule either through ligation or amplification. After sequencing, these barcodes can be used to identify which sample a particular read is derived from. Figure 5 | Real-time and synthetic long-read sequencing approaches.", + "160. Glenn,T .C. Field guide to next-generation DNA sequencers. Mol. Ecol. Resour. 11, 759769 (2011). 161. Karow,J. At AGBT , 10X Genomics launches GemCode platform; shipments slated for Q2 as firm battles IP lawsuits. GenomeWeb [online], https://www. genomeweb.com/sample-prep/agbt-10x-genomics- launches-gemcode-platform-shipments-slated-q2-firm- battles-ip-lawsuits (2Mar 2015). Competing interests statement The authors declare competing interests: see Web version for details. FURTHER INFORMATION", + "DNA), and provide the means to link sequences containing applications. First, base- callers like Phred (4,5) extract raw sequences from raw data. There are also contig assemblers like Phrap (University of Washington, http://bozeman. mbt.washington.edu/phrap.docs/phrap.html ) or CAP3 (6) that assemble frag- ments to contigs and packages like consed (7) or GAP4 (8), which are used to finish sequencing projects. These programs are not explained in detail here.", + "Nat. Biotechnol. 30, 10331036 (2012). 111. Chrystoja,C.C. & Diamandis,E.P . Whole genome sequencing as a diagnostic test: challenges and opportunities. Clin. Chem. 60, 724733 (2014). 112. McGuire,A.L. etal. Point-counterpoint. Ethics and genomic incidental findings. Science 340, 10471048 (2013). 113. Bowers,J. etal. Virtual terminator nucleotides for next-generation DNA sequencing. Nat. Methods 6, 593595 (2009). 114. Heger,M. Chinas Direct Genomics unveils new", + "11 BIOINFORMATIC CHALLENGES FOR GENOMIC MEDICINE Processing and managing of high-throughput sequence data High throughput sequencing offers severa l advantages relative to array-based genotyping or expression assays. First, unlike genotyping arrays, whole genome sequencing is not limited to interrogating onl y known sequence variants. Similarly, RNA- sequencing (RNA-seq) enables expression quanti fication of novel transcripts that are not" + ], + "answer": "### How-to Guide for Genetic Sequencing Genetic sequencing is a powerful tool used to determine the order of nucleotides in DNA. This guide will walk you through the basic steps involved in genetic sequencing, referencing specific resources for further information. #### Step 1: Sample Preparation 1. **Collect DNA Sample**: Obtain a sample containing DNA. This could be from blood, saliva, or tissue. 2. **Extract DNA**: Use a DNA extraction kit to isolate DNA from the sample. Ensure the DNA is pure and of high quality. #### Step 2: Library Preparation 1. **Fragment DNA**: Break the DNA into smaller fragments. This can be done using mechanical shearing or enzymatic methods. 2. **Add Adapters**: Ligate specific sequences (adapters) to the ends of the DNA fragments. These adapters are necessary for the sequencing process and allow the fragments to bind to the sequencing platform [6]. #### Step 3: Amplification 1. **PCR Amplification**: Amplify the DNA fragments using Polymerase Chain Reaction (PCR). This step increases the quantity of DNA to ensure there is enough material for sequencing. #### Step 4: Sequencing 1. **Choose a Sequencing Platform**: Select a sequencing technology based on your needs. Popular platforms include Illumina, Ion Torrent, Pacific Biosciences, and Oxford Nanopore Technologies [1], [4], [7]. 2. **Load DNA onto Sequencer**: Load the prepared DNA library onto the sequencing machine. Each platform has specific protocols for loading and running the sequencing process. #### Step 5: Data Analysis 1. **Base Calling**: Use software to convert raw sequencing data into nucleotide sequences. Tools like Phred are commonly used for this purpose [8]. 2. **Sequence Assembly**: Assemble the short DNA reads into longer sequences (contigs). Software like Phrap or CAP3 can be used for this step [8]. 3. **Variant Calling**: Identify genetic variants by comparing the assembled sequences to a reference genome. This step is crucial for understanding genetic differences and mutations. #### Step 6: Interpretation 1. **Bioinformatics Analysis**: Use bioinformatics tools to analyze the sequencing data. This includes identifying genes, predicting functions, and understanding genetic variations [10]. 2. **Report Findings**: Summarize the results in a report. This should include information on identified variants, potential implications, and any recommendations for further analysis or validation. #### Additional Resources - **10X Genomics**: Offers advanced solutions for single-cell sequencing and spatial genomics [1]. - **Illumina**: Provides a wide range of sequencing platforms and reagents [1]. - **Pacific Biosciences**: Known for long-read sequencing technology [1], [4]. - **Oxford Nanopore Technologies**: Offers portable and real-time sequencing solutions [1]. By following these steps and utilizing the referenced resources, you can successfully perform genetic sequencing and analyze the resulting data.", + "question": "Create a how-to guide for genetic sequencing" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_22 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_22 new file mode 100644 index 0000000..36c1153 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_22 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2016 - Coming of age ten years of next.pdf", + "2016 - Coming of age ten years of next.pdf", + "2016 - Coming of age ten years of next.pdf", + "2016 - Coming of age ten years of next.pdf", + "2019 - Machine-learning to stratify diabetic patients using novel cardiac biomarkers and integrative genomics.pdf", + "2014 - Computational tools to aid the design and development of a genetic reference population.pdf", + "2020 - Precision and Personalized Medicine How Genomic.pdf", + "2016 - Coming of age ten years of next.pdf", + "2012 - Next Steps in Cardiovascular Disease Genomic Research.pdf", + "2016 - Coming of age ten years of next.pdf" + ], + "extraction_id": [ + "cad1dcca-621d-5003-ba3a-81950819bc52", + "cad1dcca-621d-5003-ba3a-81950819bc52", + "4cdf13c0-c505-5ff9-9a6e-b10e5d1c8819", + "5da5fc5d-1fe6-58f0-9141-72b9b2996fff", + "8c9e74de-fe33-53c9-a26a-c4e4be6ab217", + "a744f8ce-7920-5fb8-acce-912f70112924", + "de09f30d-e9ba-5379-8c7a-85b2cd2ed6c8", + "5da5fc5d-1fe6-58f0-9141-72b9b2996fff", + "c2635fbd-ed4f-574a-be56-076a770af2b4", + "abff2242-b300-56f4-9974-2eefc93ae1aa" + ], + "document_id": [ + "9dd6e4e9-d136-507b-b628-68c8e1461bd0", + "9dd6e4e9-d136-507b-b628-68c8e1461bd0", + "9dd6e4e9-d136-507b-b628-68c8e1461bd0", + "9dd6e4e9-d136-507b-b628-68c8e1461bd0", + "332ac2ec-accc-5370-a4d2-6fec9ce7e072", + "70cbde25-6406-5a31-91ae-57f430e8f267", + "cd11028a-933b-52a0-9534-c173323056ef", + "9dd6e4e9-d136-507b-b628-68c8e1461bd0", + "5cde24e3-2463-5751-8ef3-97cda391449b", + "9dd6e4e9-d136-507b-b628-68c8e1461bd0" + ], + "id": [ + "chatcmpl-ADZLof1DVn6jbHUiHhxz6hRe0WZVb", + "97f2aa12-623b-53ec-9793-5834311a37dd", + "98010acc-fd11-5d33-bced-626ef29f2896", + "d2540614-9397-5e3e-8b5f-ad328ca973b2", + "13a6b6f9-4a9a-5eb9-ac79-d986d9e613f0", + "822f10c2-37f6-5543-a1d4-6f640c464fb7", + "da667832-cd2f-5af6-a0a8-a17542b0a2e2", + "8477a774-dddb-5541-b8d7-d51a7e56b0af", + "1e324977-2ca5-5062-8a09-7659d516e899", + "943d9de4-1181-5811-aa37-e8d560c39562", + "571b0089-beff-5726-a831-5b5c1f95c53a" + ], + "contexts": [ + "160. Glenn,T .C. Field guide to next-generation DNA sequencers. Mol. Ecol. Resour. 11, 759769 (2011). 161. Karow,J. At AGBT , 10X Genomics launches GemCode platform; shipments slated for Q2 as firm battles IP lawsuits. GenomeWeb [online], https://www. genomeweb.com/sample-prep/agbt-10x-genomics- launches-gemcode-platform-shipments-slated-q2-firm- battles-ip-lawsuits (2Mar 2015). Competing interests statement The authors declare competing interests: see Web version for details. FURTHER INFORMATION", + "FURTHER INFORMATION 10X Genomics: http://www.10xgenomics.com 454 Sequencing: http://www.454.com Advances in Genome Biology and Technology (AGBT): http://www.agbt.org BGISEQ500: http://seq500.com/en/portal/Sequencer.shtml Illumina: http://www.illumina.com Ion Torrent: https://www.thermofisher.com/us/en/home/ brands/ion-torrent.html Oxford Nanopore Technologies: https://www.nanoporetech. com Pacific Biosciences: http://www.pacb.com Personal Genome Project: http://www.personalgenomes.org", + "sequencing. Genome Res. 20, 11651173 (2010). 64. English,A.C. etal. Assessing structural variation in a personal genome-towards a human reference diploid genome. BMC Genomics 16, 286 (2015). 65. Carneiro,M.O. etal. Pacific Biosciences sequencing technology for genotyping and variation discovery in human data. BMC Genomics 13, 375 (2012). 66. Quail,M.A. etal. A tale of three next generation sequencing platforms: comparison of Ion T orrent, Pacific Biosciences and Illumina MiSeq sequencers.", + "22. Karow, J. Qiagen launches GeneReader NGS System atAMP; presents performance evaluation by broad. GenomeWeb [online], https:// www.genomeweb.com/ molecular-diagnostics/qiagen-launches-genereader- ngs-system-amp-presents-performance-evaluation (4Nov 2015). 23. Smith,D.R. & McKernan,K. Methods of producing and sequencing modified polynucleotides . US Patent 8058030 (2011). 24. Margulies,M. etal. Genome sequencing in microfabricated high-density picolitre reactors. Nature 437, 376380 (2005).", + "mina barcoded adapters and prepared using a 300-cycle MiSeq Reagent Micro Kit v2 (Illumina, San Diego, CA). PCR amplicons were sequenced on the MiSeq with paired-end (PE) 250 base pair reads. Files were aligned to the bisulfite converted reference genome GRCh38 release 94 implementing Bismark [35, 36]. Alignment was obtained through Bismark using the Bowtie2 [37] engine using non-directional and paired-end. Complete sequencing code is provided (https ://githu b.com/qahat", + "sequencing data to solutions from the genotyping array data. iv PREVIEW", + "36. Sequencing, H.G. Finishing the euchromatic sequence of the human genome. Nature 2004 ,431, 931945. 37. Heather, J.M.; Chain, B. The sequence of sequencers: The history of sequencing DNA. Genomics 2016 ,107, 18. [CrossRef] 38. Rothberg, J.M.; Leamon, J.H. The development and impact of 454 sequencing. Nat. Biotechnol. 2008 ,26, 11171124. [CrossRef] [PubMed] 39. Shendure, J.; Ji, H. Next-generation DNA sequencing. Nat. Biotechnol. 2008 ,26, 11351145. [CrossRef] [PubMed]", + "sequencing. Bioinformatics 31, 20402042 (2015). 46. Qiagen. Oncology insights enabled by knowledge base- guided panel design and the seamless workflow of the GeneReader NGS system Press Release. Qiagen [online], http://www.genereaderngs.com/PROM-9192- 001_1100403_WP_GeneReader_NGS_0116_NA.pdf (2016). 47. Forgetta,V. etal. Sequencing of the Dutch elm disease fungus genome using the Roche/454 GS-FLX Titanium System in a comparison of multiple genomics core", + "Conventional sequencing Next-generation sequencing Sequencing Subcloning in vectors, amplification in hosts for every single DNA fragment Direct DNA fragment sequencing Sequencing of 100 fragments in parallel Optional PCR amplification Parallel sequencing of millions of small fragments Yield 1 /H11003105bp/sequencing run /H110221/H110031011bp/sequencing run Computational requirements Moderate High Cost per megabase High LowAccuracy High HighFuture directions Direct sequencing of DNA molecules", + "Nature Reviews | GeneticsCleavage agent Single-base-encoded probes A probe with a single known base and degenerate bases hybridizes to a template and is imagedResetAfter each imaging step, both the probe and anchor are removed Probe with known base at n+1a SOLiD (Thermo Fisher) b Complete Genomics (BGI) Paired-end sequencingSequencing is performed for both the left and right sides of the adapterTTG AG TC CC GA CT TATA A" + ], + "answer": "### Guide to Genetic Sequencing Genetic sequencing is a powerful tool used to determine the order of nucleotides in DNA. This guide will walk you through the basics of genetic sequencing, the different technologies available, and the steps involved in the sequencing process. #### 1. Introduction to Genetic Sequencing Genetic sequencing involves determining the precise order of nucleotides within a DNA molecule. This information is crucial for understanding genetic information, diagnosing genetic disorders, and conducting research in genomics. #### 2. Types of Sequencing Technologies There are several sequencing technologies available, each with its own advantages and applications: - **Sanger Sequencing**: The first-generation sequencing method, known for its high accuracy but lower throughput. - **Next-Generation Sequencing (NGS)**: Includes various platforms that allow for high-throughput sequencing, enabling the sequencing of millions of fragments simultaneously [9]. #### 3. Next-Generation Sequencing Platforms Several NGS platforms are widely used in the field: - **Illumina**: Known for its high accuracy and throughput, suitable for a wide range of applications [2]. - **Ion Torrent**: Uses semiconductor technology for sequencing, offering a cost-effective solution [2]. - **Pacific Biosciences (PacBio)**: Provides long-read sequencing, which is useful for identifying structural variations and complex regions in the genome [3], [2]. - **Oxford Nanopore Technologies**: Offers portable sequencing devices with real-time data analysis capabilities [2]. - **10X Genomics**: Known for its GemCode platform, which provides linked-read sequencing for better genome assembly [1], [2]. #### 4. Steps in Genetic Sequencing The sequencing process generally involves the following steps: 1. **Sample Preparation**: DNA is extracted from the sample and prepared for sequencing. This may involve fragmentation, adapter ligation, and amplification [5]. 2. **Library Construction**: The prepared DNA fragments are converted into a library suitable for the sequencing platform being used [5]. 3. **Sequencing**: The library is loaded onto the sequencing platform, where the DNA fragments are sequenced in parallel [9]. 4. **Data Analysis**: The raw sequencing data is processed and aligned to a reference genome. Tools like Bismark and Bowtie2 are commonly used for alignment and analysis [5]. 5. **Interpretation**: The processed data is interpreted to identify genetic variants, structural variations, and other genomic features [3]. #### 5. Applications of Genetic Sequencing Genetic sequencing has a wide range of applications, including: - **Medical Diagnostics**: Identifying genetic mutations associated with diseases. - **Genomic Research**: Studying genetic variations and their implications. - **Personal Genomics**: Providing individuals with information about their genetic makeup [2]. #### 6. Future Directions The field of genetic sequencing is rapidly evolving, with ongoing advancements aimed at increasing accuracy, reducing costs, and enabling direct sequencing of DNA molecules without amplification [9]. #### 7. Further Information For more detailed information on specific sequencing platforms and technologies, you can visit the following resources: - **Illumina**: [http://www.illumina.com](http://www.illumina.com) - **Ion Torrent**: [https://www.thermofisher.com/us/en/home/brands/ion-torrent.html](https://www.thermofisher.com/us/en/home/brands/ion-torrent.html) - **Pacific Biosciences**: [http://www.pacb.com](http://www.pacb.com) - **Oxford Nanopore Technologies**: [https://www.nanoporetech.com](https://www.nanoporetech.com) - **10X Genomics**: [http://www.10xgenomics.com](http://www.10xgenomics.com) By understanding the basics of genetic sequencing and the available technologies, you can better appreciate the power and potential of this transformative field in genomics.", + "question": "Create a guide for genetic sequencing" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_23 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_23 new file mode 100644 index 0000000..91915f8 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_23 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2011 - Genetics and Genomics of Cholesterol and Polyunsaturated Fatty Acid Metabolism in Relation to Coronary Heart Disease Risk.pdf", + "2011 - Analysis of cognitive functions in recombinant inbred strains of rats produced by crossbreeding of SHR and BN Lx. lines.pdf", + "2018 - Multivariate analysis of genomics data to identify potential pleiotropic genes.pdf", + "2008 - The Common P446L Polymorphism in GCKR Inversely.pdf", + "2004 - Diabetes Genes a.pdf", + "2018 - Genomic 5-mC contents in peripheral.pdf", + "2021- Development of genome-wide polygenic risk scores for lipid traits and clinical applications for dyslipidemia, subclinical atherosclerosis, and diabetes cardiovascular complications among East Asians.pdf", + "2012 - Systems Biology Approaches to Nutrition.pdf", + "2004 - Diabetes Genes a.pdf", + "2012 - Systems Biology Approaches to Nutrition.pdf" + ], + "extraction_id": [ + "1745eb7d-e39e-5304-96a5-c351809d4795", + "b3d1c55f-bcdc-59b2-8191-623e8e79b87b", + "4bee64c1-92ce-5b8c-925d-f30c4acab84b", + "e54089b3-5559-55f8-b482-ceae887ce6ca", + "9738a79c-f506-5134-87c7-0ef5020c0077", + "3fc1141e-011e-5606-952c-5d7d9201459e", + "a95613b6-a2e8-5d84-841f-ae8879611a9e", + "e860a438-567e-50e4-99a9-759ff52ffdda", + "c194ef31-2e93-5de6-9c35-6365056b1e54", + "e464416a-2dc9-53c0-988c-b0131883aa79" + ], + "document_id": [ + "111e0e1e-d336-55ee-87a8-2f03b02473c2", + "6f628ea8-1286-5d74-80e5-55439f21805d", + "2f7bad8a-28aa-5add-b9c3-8c2d445719f5", + "1d74871a-be20-5ca3-ab8f-0a68e885dcf4", + "805ad0d5-6a3d-5e5f-b1ba-121e10c6daaa", + "f720cb59-3a8f-58e0-9cb8-e34b7d0bb74f", + "ce8040c7-157f-54c5-b28b-3224e8871415", + "6955478b-950d-5d29-b24c-3a5ca656f3ae", + "805ad0d5-6a3d-5e5f-b1ba-121e10c6daaa", + "6955478b-950d-5d29-b24c-3a5ca656f3ae" + ], + "id": [ + "chatcmpl-ADZM6xG6YQyyKS0yjhUsqz3mB8jmi", + "53aa581f-06d1-52b3-b847-08ea3d95a980", + "799c27b2-d017-5ded-bb75-76b3d65b0bf6", + "142eead0-6648-5c97-a2da-770aff4986f6", + "0cbbec43-43bb-502d-a26d-fbc669ff29ee", + "60c771fb-a2fa-5f19-a13c-e4086864bcd5", + "bd69128b-7357-5e87-ab9a-af6f4f3fc733", + "3fd58cb6-d19a-5337-9a84-a8e4e4e0b97c", + "134d285e-3f83-5ed6-ab9d-774b81068a3d", + "7a2c163e-e4ef-58ee-86dc-399d15d20eb7", + "cba6153e-0a7f-540c-897b-40cbf9284ea9" + ], + "contexts": [ + "Deregulated lipid metabolism (dyslipidemia) that manifests as hypercholesterolemia, hypertriglyceridemia, low high -density -lipoprotein (HDL) cholesterol levels or a combination of those is an established risk factor for CHD among other established risk factors. The liver is of major importance in maintaining whole- body lipid metabolic", + "23 Atherogenic dyslipidemia, manifested by raised triglycerides and low concentrations of HDL cholesterol. There could be p resent other lipoprotein abnormalities as well, e.g., increased lipoproteins, elevated apo lipoprotein B, small LDL and HDL particles. All of these abnormalities have been imp licated as being atherogenic (Kolovou et al., 2005; Ginsberget al., 2000). Elevated blood pressure strongly associates with obesity and commonly occu rs in insulin-resistant persons.", + "plasma TGisdetermined bythelevel ofVLDL-TG (the balance between synthesis and clear- ance ofVLDL-TG), and thesynthesis ofVLDL-TG isassociated with total fatmass and liver fat[59]. Thus, thelarge amount offatmass inobese patients leads toincreasing synthesis of VLDL-TG, buttheclearance ofVLDL-TG remains unchanged. Hypertriglyceridemia isaprin- cipal characteristic ofdyslipidemia and islinked tomany other types ofdyslipidemia such as", + "Dyslipidemia status Normolipidemia 2,731 898 (0.33) 1,319 (0.48) 514 (0.19) 42.97End-of-study cases 2,102 611 (0.29) 1,057 (0.50) 434 (0.21) 45.79 0.01, 1.12 (1.021.22)Incident cases 959 293 (0.31) 472 (0.49) 194 (0.20) 44.84 0.9, 0.99 (0.911.09) Overall risk data are P, OR (95% CI) and incident risk data are P, HR (95% CI). Hyperglycemia and type 2 diabetes were dened according to 1997 American Diabetes Association criteria", + "The most characteristic lipoprotein abnormality in patients with diabetes, especially type 2, is elevated triglyceride, i.e. VLDL, reduced HDL, and smaller dense LDL. This lipoprotein profile is sometimes referred to as diabetic dyslipidemia. Moreover, in conjunction with obesity, and insulin resistance this lipoprotein profile constitutes part of the \"polymetabolic syndrome\". The primary lipoprotein abnormality is hypertriglyceridemia .", + "Hyperlipidemia 63 (23%) 100 (38%) < 0.001c Diabetes 66 (24%) 106 (40%) < 0.001c TC (mmol/L) 4.36 0.55 4.37 1.07 0.832b,d TG (mmol/L) 1.01 (0.77~1.28) 1.35 (1.00~1.92) < 0.001d,e HDL-C (mmol/L) 1.26 (1.13~1.42) 1.10 (0.94~1.34) < 0.001d,e LDL-C (mmol/L) 2.57 0.36 2.43 0.88 0.017b,d FBG (mmol/L) 4.71 (4.35~5.15) 5.84 (5.31~6.87) < 0.001e PBLs counts (109/L) 5.30 (4.60~6.29) 6.58 (5.33~7.92) < 0.001e PBLs classifications (PBMCs %)40.31 8.11 34.48 10.16 < 0.001b", + "lipid traits as (lipid follow-up lipid baseline ) / lipid baseline . Dyslipidemia/abnormal lipid levels were defined according to the thresholds used in clinical practice guidelines [ 19]: (1) TC 5.1 mmol/l; TG 1.1 mmol/l; and LDL-C 3.4 mmol/l in children; (2) TC 5.1 mmol/l; TG1.4 mmol/l; and LDL-C 3.4 mmol/l in adolescents; (3) TC 5.2 mmol/l; TG 1.7 or 1.97 mmol/l; and LDL- C1.8 or 2.6 mmol/l in adults or patients with T2D. In the two cohorts of adult women, cIMT was mea-", + "dyslipidemia. It also lowered in ammatory biomarkers (CRP and PAI - 1) associated", + "usually associated with reduced HDL cholesterol and small dense LDL. Biliary cholesterol + Bile acids Blood vessel Figure 3. HDL metabolism: HDL production requires addition of lipid to small, nascent particles. This lipid arrives via hydrolysis of VLDL and chylomicrons with transfer of surface lipids (phospholipid PL, and free cholesterol, FC) via the actions of phospholipid transfer protein (PL TP). A second pathway is via effiux of cellular free cholesterol (FC), a process", + "shift in the composition of the lipoprotein particle from one de ned as VLDL to" + ], + "answer": "Dyslipidemia is defined as deregulated lipid metabolism that manifests as hypercholesterolemia (high cholesterol levels), hypertriglyceridemia (high triglyceride levels), low high-density lipoprotein (HDL) cholesterol levels, or a combination of these conditions [1]. It is an established risk factor for coronary heart disease (CHD) and can involve various lipoprotein abnormalities, such as increased lipoproteins, elevated apolipoprotein B, and small LDL and HDL particles [2].", + "question": "Define dyslipidemia." +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_24 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_24 new file mode 100644 index 0000000..50f3dd9 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_24 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2007 - Functional genomic approach to identify novel genes.pdf", + "2017 - Gene-based genome-wide association study identified 19p13.3 for lean body mass.pdf", + "2008 - Gene Expression Profiling.pdf", + "2007 - Functional genomic approach to identify novel genes.pdf", + "2012 - Quantitative proteomic analysis reveals novel mitochondrial targets.pdf", + "2017 - Systems Genetics Analysis to Identify the Genetic Modulation of a Glaucoma-Associated Gene.pdf", + "2007 - Functional genomic approach to identify novel genes.pdf", + "2007 - Functional genomic approach to identify novel genes.pdf", + "2014 - Evidence for the presence of somatic mitochondrial DNA.pdf", + "2007 - Functional genomic approach to identify novel genes.pdf" + ], + "extraction_id": [ + "3aebacd5-b198-5144-8fe3-34ac09f6e1e8", + "088aee9f-e751-5b68-9142-235a84d4297d", + "d69da2f6-dced-546c-b3d7-bd57253a15a6", + "3aebacd5-b198-5144-8fe3-34ac09f6e1e8", + "ad33e7d7-8861-52b8-92ac-b307a6c42a0d", + "d83136ee-cf42-5167-902b-470a6e0b2d3c", + "47e612a2-c181-5c19-8b1c-c6aaa107e88a", + "90107b5e-bd2c-56ae-a7b9-ac4ca506e3e5", + "655a0cc4-b432-5b84-9eac-43b932700af5", + "3aebacd5-b198-5144-8fe3-34ac09f6e1e8" + ], + "document_id": [ + "6d78d3b0-1754-5f4c-8dbc-c972c68d21cd", + "970711f0-abe2-5129-8374-b7bb24c58b9e", + "59f3b969-089b-5258-93ad-892dbc9ffa9c", + "6d78d3b0-1754-5f4c-8dbc-c972c68d21cd", + "a4ca3799-0273-5765-98fd-4902eeacf894", + "67e804db-8127-5938-8d7f-a5918cdf4f86", + "6d78d3b0-1754-5f4c-8dbc-c972c68d21cd", + "6d78d3b0-1754-5f4c-8dbc-c972c68d21cd", + "a835ef5d-9a88-58ac-a0bc-5411c9f9031a", + "6d78d3b0-1754-5f4c-8dbc-c972c68d21cd" + ], + "id": [ + "chatcmpl-ADZMBjMCSl2sOAftMGQRmV7FIDy31", + "7bab87cd-7bd1-525e-98fe-14e9c51f617a", + "a6bd961a-ed80-5266-8519-4399a1c7a6ba", + "84d74cdd-eccb-55a7-a407-a14f90e30d78", + "cf86caa8-56c6-58f5-8233-e1de07f1ef37", + "53fee260-155f-58c5-810e-ff890292a46b", + "7ff5d5ed-f6a9-52ab-8994-4bdb61161f4f", + "cf251057-96a1-581c-b60a-63a04d845511", + "3b3c603d-bb36-5995-b4c5-fd06e733dfda", + "2e0e68ba-1804-5040-81aa-8746d263083a", + "a2b4ddd9-1f98-5271-85a0-5d79c529253e" + ], + "contexts": [ + "oxidoreductase MitochondriaF29C4.2 IV Cytochrome", + "complex III. It functions to form a part of the mitochondrial respiratory chain. It may also act as a binding fac-tor for the iron-sulfur protein. Mitochondrial Complex III is composed of one mitochondrial-encoded subunit (MT-CYB) and ten nuclear-encoded subunits. The complex is located within the mitochondrial inner mem- brane and plays an important role in biochemical synthesis of ATP . It functions to catalyze electrons to trans-", + "Chapter 36 Directed Protein Evolution 653 3.1.9. SHIPREC Cytochromes are proteins that contain heme groups and are responsible for the transport of electrons. P450 is a family of membrane-bound cytochromes with an absorption maximum of 450 nm when complexed with CO. One of the major roles of the cytochrome P450 system is the detoxification of harmful substances. Sieber et al. (23) produced hybrids of two cytochromes, which share only", + "F42A9.5 cyp-33E2 IV Cytochrome P450 MitochondriaF21D5.8 IV Mitochondrial 28S ribosomal protein S33 MitochondriaC33A12.1 IV NADH: ubiquinone oxidoreductase, ETS complex I subunit MitochondriaZK809.3 IV NADH: ubiquinone oxidoreductase MitochondriaC47E12.2 IV Mitochondrial ADP/ATP carrier protein MitochondriaY57G11C.12 IV NADH: ubiquinone oxidoreductase MitochondriaY41E3.4 ers-1 IV Glutaminyl tRNA synthetase, predicted to be mitochondrial MitochondriaY55F3B_743.b IV Mitochondrial ribosomal protein", + "Process 2.9 2.9 25.4 gi 149058974 rCG44669 (cytochrome c oxidase, subunit VIIc;Cox7c)1.19 0.2121 1.35 1.42 0.05 1.30 1.26 0.0480 1.26 unclassied 29.6 29.7 56.0 gi 149016520 rCG50966 (3-oxoacid-CoA transferase 1(OXCT1/SCOT)1.12 0.3615 1.27 1.08 0.46 1.23 1.33 <0.0001 1.12 metabolism: ketone metabolism 60.9 60.9 67.6 gi 116242506 stress-70 protein, mitochondrial precursor(75 kDa glucose-regulatedprotein) (Heat shock 70kDa protein 9)1.07 0.1432 1.12 1.02 0.39 1.10 1.13 0.0300 1.09 protein folding; protein", + "413 Table 2 Gene ontology Database: molecular function name: Cytochrome c oxidase activity ID:GO:0004129 C = 16 O = 2 E = 0.12 R = 17.06 rawP = 0.0060 adjP = 0.0590 Index User IDGene symbol Gene namesEntrez gene Ensemble 1 ILMN_2657141 Surf1 Surfeit gene 1 20930 ENSMUSG00000015790 2 ILMN_1254971 Cox6b1 Cytochrome c oxidase, subunit VIb polypeptide110323 ENSMUSG00000036751 Database: molecular function Name: NADH dehydrogenase activity ID:GO:0003954", + "F42A9.5 cyp-33E2, cytochrome P450 family 13.81 ( 0.49) 118 0.0010 C47E12.2 Mitochondrial ADP/ATP carrier protein 16.00 ( 0.78) 136 < 0.0001 F21D5.8 Mitochondrial 28S ribosomal protein S33 15.95 ( 0.99) 136 < 0.0001 C33A12.1 NADH: ubiquinone oxidoreductase 16.28 ( 1.05) 139 0.0003 ZK809.3 NADH: ubiquinone oxidoreductase 23.46 ( 1.14) 200 < 0.0001 Y57G11C.12 nuo-3, NADH: ubiquinone oxidoreductase 20.71 ( 1.18) 177 < 0.0001", + "Y66A7A1 100 52 33 4 0 9.00 ( 0.29) 0.0572 210 Y71H2_388.c PP2A regulatory subunit (cytochrome C oxidase subunit) 100 82 48 2 0 5.57 ( 0.20) < 0.0001 130 F54D8.2 Cytochrome c oxidase subunit Vla 100 70 41 22 3 5.62 ( 0.27) < 0.0001 131 F56D2.1 Mitochondrial processing peptidase 100 55 17 3 0 4.46 ( 0.20) 0.4303 104 K04G7.4 Nuo-4, NADH: ubiquinone oxidoreductase 100 78 55 4 0 5.06 ( 0.23) < 0.0001 118 T20H4.5 Ubiquinone Fe-S protein 100 99 89 45 2 7.58 ( 0.18) < 0.0001 177", + "and (Iso211Ser) 1.1383 . (ii) Overview of MT-CYB mutation on electron transport chain. From the complex II the reduced form of ubiquinone move through the hydrophobic region of the membrane by diffusion. When the ubiquinone comes in contact with the next carrier in the electron-transport chain, the electron is transferred to cytochrome reductase, or the cytochrome b-c1 complex (Complex III). The mutated cytochrome b loses the ability to accept incoming", + "c oxidase polypeptide Mitochondria K08F11.4 year-1 IV Tyrosyl-tRNA synthetase, predicted to be mitochondrial MitochondriaE04A4.7 IV Cytochrome c Mitochondria" + ], + "answer": "Cytochromes are proteins that contain heme groups and are responsible for the transport of electrons [3]. They play a crucial role in the mitochondrial respiratory chain, which is essential for the biochemical synthesis of ATP [2]. One specific family of cytochromes, known as cytochrome P450, is involved in the detoxification of harmful substances [3].", + "question": "What is cytochrome?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_25 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_25 new file mode 100644 index 0000000..c14d3d9 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_25 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2009 - eQTL analysis in mice and rats.pdf", + "2015 - Genetic Control of Survival and Weight Loss during Pneumonic Burk.pdf", + "2015 -Emery- Genetic Control of Survival and Weight Loss during Pneumonic Burk.pdf", + "2006 - From_gene_to_behavior_and_back_again_new.pdf", + "2005 - quantitative-trait-locus-analysis-of-aggressive-behaviours-in-mi.pdf", + "2005 -Broadkin- quantitative-trait-locus-analysis-of-aggressive-behaviours-in-mi.pdf", + "2009 - Experimental_Evolution.pdf", + "2009 - Garland_and_Rose_Experimental_Evolution.pdf", + "2005 - quantitative-trait-analysis-in-the-investigation-of-function-and.pdf", + "2016 - Social interactions and indirect genetic effects on complex juvenile and adult traits.pdf" + ], + "extraction_id": [ + "71981bfb-284e-50ad-854e-2055c07f77a7", + "615ee0cd-5960-57e5-b4e6-56e4b8020a1b", + "268a23e8-f528-5b59-89f2-188331e0a03c", + "64c0287d-aeea-52eb-a074-e9591c5593ae", + "9de93371-6239-53c2-b42c-71f615a0614b", + "0a5c759e-8dab-55f1-ac59-e8211ec683b8", + "8ee78018-b998-590c-99ab-788a447ede81", + "cbce50ea-be78-5d54-beb1-849222c5bfdd", + "0a895880-91c0-5079-b258-73926b38430f", + "0b91ce42-1ba4-530c-8d77-6ddbdc0e759d" + ], + "document_id": [ + "8d67ea90-f7b1-5bb8-937c-4a9eceddff43", + "ae1025b0-1410-51ae-9be2-26fa2e9d5808", + "a9aceace-bf48-5472-b54c-59a458a84c62", + "7a088b36-11b7-5379-bfe5-ce571e11de07", + "0dc730ba-4ff4-52aa-a988-71075113c416", + "e6027e7f-aec0-5e76-8aff-96b36389e701", + "34821353-1b74-5ee2-ac39-66dd46f145bf", + "496faa7f-9623-5ab7-9816-7c3755abb3aa", + "dac1c73c-0b5f-5a54-bb12-7e8b654009c0", + "06e126d3-b75d-57db-8edb-09de6ae13b24" + ], + "id": [ + "chatcmpl-ADZMFYjDRlNaYIo2GAk3sVCPv4DGN", + "73540700-b5cf-5838-852b-b281ca086140", + "374c456a-d1db-5b4a-8713-97abe4162d77", + "b9d52798-0235-5018-bccd-560565d16cc3", + "fef212bc-631b-591d-b8e3-d1523da0507d", + "c8f17022-aeae-5242-9082-d6d1eee4c4bf", + "1b2de424-be9f-572d-bd62-dc2ecd92192b", + "f72795a1-66c3-5a98-84bc-b085e8008073", + "31a32dc5-81ac-52ba-a463-c61e293f21e5", + "b660d882-1cb0-5150-ae76-8eb3ccb88a58", + "985378d7-e164-581b-ac1c-97bbcda9c06f" + ], + "contexts": [ + "While most of the Y chromosome does not undergo recombination, the recombination rate of the X chromosomeis slower than that of the autosomes. This has important consequences on the detection of significant QTLs. For a comprehensive view of these issues, see(43). 9.Probe hybridization artifacts When several probes are available for the same gene, it is not uncommon to observe a difference in the mapping results", + "8 QTL Mapping Allelic variation exists among natural populations and inbred strains, and this is reflective of the segregation of quantitative tr ait loci (QTLs) [96]. QTLs are stretches of DNA that are closely linked to genes that underlie a phenotype of interest. QTL analysis has been proven to be an invaluable tool to help unravel heritable traits, by enabling researchers to map different quantitative traits back to the genomic location involved in the regulation of these phenotypes.", + "8 QTL Mapping Allelic variation exists among natural populations and inbred strains, and this is reflective of the segregation of quantitative tr ait loci (QTLs) [96]. QTLs are stretches of DNA that are closely linked to genes that underlie a phenotype of interest. QTL analysis has been proven to be an invaluable tool to help unravel heritable traits, by enabling researchers to map different quantitative traits back to the genomic location involved in the regulation of these phenotypes.", + "The basic pr emise of QTL an alysis is simple (Ph illips and Belknap, 2002 ) . First, one must meas ure a speci c phen otype within a popul ation. Next, the population must be genotyped at a hundred or more marker loci186 Boehm II et al.", + "genes underlying QTLs in animals and plants (see for example Shirley et al 2004,Korstanje & Paigen 2002, Fridman et al 2004). I should also point out, though, that even in a single QTL region isolated in a congenic strain, it is possible that there is more than one allele that aects the phenotype. So, you have a fair pointabout the challenges and complexities of QTL analysis. Koolhaas: There are dierent questions underlying both approaches. The QTL", + "genes underlying QTLs in animals and plants (see for example Shirley et al 2004,Korstanje & Paigen 2002, Fridman et al 2004). I should also point out, though, that even in a single QTL region isolated in a congenic strain, it is possible that there is more than one allele that aects the phenotype. So, you have a fair pointabout the challenges and complexities of QTL analysis. Koolhaas: There are dierent questions underlying both approaches. The QTL", + "through analysis of line crosses, quantitative trait loci (QTL) mapping, and verification of candidate genes with quantitative complementation tests or genetic engineering (e.g.,McGuire and Tully 1987; Chandra et al. 2001; Dierick and Greenspan 2006; Edwardset al. 2006). They can also be used to study the underlying physiological, neural, andmolecular mechanisms of the differences in behavior between selected and controllines, or between divergently selected lines.", + "through analysis of line crosses, quantitative trait loci (QTL) mapping, and verification of candidate genes with quantitative complementation tests or genetic engineering (e.g.,McGuire and Tully 1987; Chandra et al. 2001; Dierick and Greenspan 2006; Edwardset al. 2006). They can also be used to study the underlying physiological, neural, andmolecular mechanisms of the differences in behavior between selected and controllines, or between divergently selected lines.", + "genetic background. Gene identification of QTL should be distinguished from identification of the quanti- tative trait nucleotide (QTN). The latter is a daunting task, since SNPs are so frequent. Final proof for a QTN in mice would require placing a genomic segment containing theputative QTN from a donor mouse strain on the background of another strain using homologous recombination and reproducing the phenotype of the donor strain.", + "measuring correlations between genetic markers and phenotypic traits in a population. Individuals are scored for their phenotype for a particular trait, and their genotype at a marker. If there is a differ- ence in mean phenotype between those individuals with one geno- type at a particular locus compared with the other, than we can infer that there is a QTL linked to that marker [ 40 , 153 ]. 2.3 Analysis and QTL MappingDavid G. Ashbrook and Reinmar Hager" + ], + "answer": "To differentiate between X and Y DNA with respect to DNA tracing and determining quantitative trait loci (QTLs), consider the following points: 1. **Recombination Rates**: - The Y chromosome largely does not undergo recombination, which means it is passed down relatively unchanged from father to son. This makes it easier to trace paternal lineage but limits its utility in QTL mapping due to the lack of recombination [1]. - The X chromosome, on the other hand, does undergo recombination, albeit at a slower rate compared to autosomes. This slower recombination rate can affect the detection of significant QTLs, making it a unique consideration in QTL analysis [1]. 2. **QTL Mapping**: - QTLs are stretches of DNA closely linked to genes that underlie specific phenotypes. The process of QTL mapping involves measuring phenotypes within a population and genotyping at numerous marker loci to identify genomic locations associated with these traits [2], [4]. - The differences in recombination rates between the X and Y chromosomes mean that QTL mapping on the X chromosome can be more complex due to its unique inheritance pattern and recombination characteristics, whereas the Y chromosome's lack of recombination simplifies paternal lineage tracing but complicates QTL mapping [1], [2]. In summary, the primary differences between X and Y DNA in the context of DNA tracing and QTL determination lie in their recombination rates and inheritance patterns. The Y chromosome's lack of recombination makes it straightforward for paternal lineage tracing but less useful for QTL mapping. In contrast, the X chromosome's slower recombination rate compared to autosomes presents unique challenges and considerations in QTL analysis [1], [2].", + "question": "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_26 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_26 new file mode 100644 index 0000000..0e7afb7 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_26 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2012 - Functional genomics research in aquaculture principles and general approaches.pdf", + "2017 - Identification of quantitative trait loci associated with the susceptibility of mouse spermatozoa to cryopreservation.pdf", + "2009 - Garland_and_Rose_Experimental_Evolution.pdf", + "2009 - Experimental_Evolution.pdf", + "2019 - Discovery of early life stress interacting and sex-specific quantitative trait loci impacting cocaine responsiveness.pdf", + "2011 - Evidence for widespread changes in promoter.pdf", + "2017 - Identification of quantitative trait loci associated with the susceptibility of mouse spermatozoa to cryopreservation.pdf", + "2017 - Identification of quantitative trait loci associated with the susceptibility of mouse spermatozoa to cryopreservation.pdf", + "2011 - Using animal models to disentangle the role of genetic, epigenetic, and environmental influences on behavioral outcomes associated with maternal anxiety and depression.pdf", + "2012 - Functional genomics research in aquaculture principles and general approaches.pdf" + ], + "extraction_id": [ + "c3a2c07f-e216-5dc0-92ea-f7c210e90974", + "002f921f-e651-538b-aec0-b357d2c08ee9", + "4a07567a-57db-5110-aa52-cc76b8df0d32", + "5e459c02-b084-5d1a-80fd-90643c6045f5", + "1b0fe3c9-4c86-5e80-9dde-faadbcdd44a1", + "1f77a329-36c6-5cef-884f-221358ef64ec", + "cf45ca6a-fbcd-52bc-b936-4f992c8e5537", + "3a518d3b-30c3-5cee-8344-c21cf4b32941", + "c570b41d-fa75-588a-bce6-30c7a64b6997", + "a59b4a8a-4f9c-59d3-b184-267db613e8cc" + ], + "document_id": [ + "a39b4cc1-8661-578b-a61b-b9962e45fc33", + "e549cb7c-db24-540f-aded-e67442470525", + "496faa7f-9623-5ab7-9816-7c3755abb3aa", + "34821353-1b74-5ee2-ac39-66dd46f145bf", + "92c8a239-31ad-5ef3-bc37-a571afcdd187", + "1c35d1ec-a7c2-56f0-8ccf-e554814e94ef", + "e549cb7c-db24-540f-aded-e67442470525", + "e549cb7c-db24-540f-aded-e67442470525", + "9c2f76c6-b991-58ac-a332-50e1a3c016d8", + "a39b4cc1-8661-578b-a61b-b9962e45fc33" + ], + "id": [ + "chatcmpl-ADZMMx7Aorq6SFjdy1f98MjZd0bQz", + "5e6a7f2c-1232-5c7e-bba5-9779ec280ed0", + "3c7fb887-5dbb-54d9-9664-75ceabd4ebf3", + "1fa77703-e337-518f-bdd8-6c3ebad0754e", + "833bb510-694a-5646-960e-66b5e38ef609", + "30b42710-3270-5e84-9227-266db106f470", + "5db85ce4-25a9-5aba-b5ca-ab394fb0394f", + "80bc495d-85ab-5b77-8984-1f4e80ace849", + "0e360074-4b6b-5b81-a96d-509266b7b637", + "084d7eaf-290a-525b-b01b-f0537e46f56e", + "2a5ee720-370b-5c4b-b7ea-fe4c3b2a9ea4" + ], + "contexts": [ + "ferentiation in animals reared at male- and female-producing temperatures (Fernandino et al., 2011). From a pure experimental point of view, there are several potential sources of environ- mental inuences that need to be under con- trol in order to avoid confounding results when studying gene expression levels (Hodgins-Davis and Townsend, 2009; Table 8.3). One of them is effect of the developmental environment, typi- cally in the range of weeks to years. Size is pos-", + "the fertilization rate (Table 1). There was an interaction between the two factors (strain and", + "subtle, and often uncontrollable, environmentalfactors. Behaviors are often influenced by multiple genes with complex gene-by-gene,gene-by-environment, and environment-by-environment interactions. This is one reason,for example, that single-gene mutants are relatively uninformative (see also Rauser et al.this volume), though we described a case in which such mutants were useful for explor-ing mechanisms underlying the evolution of mating systems in voles.", + "subtle, and often uncontrollable, environmentalfactors. Behaviors are often influenced by multiple genes with complex gene-by-gene,gene-by-environment, and environment-by-environment interactions. This is one reason,for example, that single-gene mutants are relatively uninformative (see also Rauser et al.this volume), though we described a case in which such mutants were useful for explor-ing mechanisms underlying the evolution of mating systems in voles.", + "environment interactions, particularly the contribution of environmen- tal factors in utero (Burmeister, McInnis, & Zllner, 2008; Henriksen, Nordgaard, & Jansson, 2017), and these limitations in turn hinder the development of a mechanistic understanding of aetiology. Here, we dissect the impact of gene prenatal environmental interactions on cocaine responsiveness of adult male and female mice from the BXD recombinant inbred panel. Early life stressors, including prenatal stress (PNS), are important", + "onmental factors, some of which have been shown toalter placental gene expression, as well as epigeneticmarks [10]. These include diet [11,12], smoking [13],and assisted reproductive techniques [14,15]. Mountingevidence implicates epigenetic marks, such as DNA methylation, in mediating environmentally-induced reg- ulation of genome function. More studies into theeffects of the environment on the placental epigenomeare warranted due the importance of this organ in regu-lating pregnancy development.", + "as well as the intrinsic fertilizing ability of the strain. Therefore, the results of the QTL analysis based on the fertilization rates of frozen thawed spermatozoa might have reflected the 220 cumulative effect of these two factors. T o exclude the possible background strain effects, we calculated the ratio of the fertilization rate of frozen thawed spermatozoa per that of fresh spermatozoa in individual male mice (designated here as relative fertilization rate ). As shown", + "male ; Relative fertilization rate (%) = (Fertilization rate with f rozen spermatozoa (%)/Fertilization rate with f resh spermatozoa (%)) 100 (n = 6 for each strain) . Fig. 2. Genome -wide interval mapping for suggestive QTLs affecting the fertilization rate 515 using frozen thawed spermatozoa. (A) Mapping based on the actual fertilization rates. (B) Mapping based on the relative fertilization rates. Critical intervals were selected based on peak", + "duce the behavioral differences observed in these inbred strains.The interaction of genes and the environment to produce phe-notypic outcomes has been acknowledged and accepted for quitesome time in the scientic community. However, the exact mech-anism by which the environment can act on genetic materialhas only recently begun to be investigated in a more systematicmanner. A ROLE FOR EPIGENETICS IN THE LINK BETWEEN MATERNAL CARE AND BEHAVIORAL OUTCOMES IN ANIMAL MODELS", + "I na d d i t i o n ,i ts h o u l db en o t e dt h a tt h ee f - fect of temperature on sex determination has a genetic basis itself and an interaction be- tween families and temperature effect has been reported in several species (Schultz, 1993; Van- deputte et al., 2007). Finally, other environ- mental effects such as pH, hypoxia, and so- cial factors have claimed to be involved on sex determination (reviewed by Guerrero-Est evez and Moreno-Mendoza, 2010). All the informa-" + ], + "answer": "The environment influences fertilization through various factors that can affect both the biological and genetic mechanisms involved. Here are some specific ways in which the environment can impact fertilization: 1. **Developmental Environment**: The developmental environment, which can span from weeks to years, is a significant source of environmental influence. This includes factors such as temperature, which can affect gene expression levels and potentially influence fertilization outcomes [1]. 2. **Strain and Environmental Interactions**: There is an interaction between genetic strain and environmental factors that can influence fertilization rates. For example, the fertilization rate of frozen-thawed spermatozoa compared to fresh spermatozoa can be affected by these interactions, indicating that both intrinsic genetic factors and environmental conditions play a role [2], [7]. 3. **Gene-by-Environment Interactions**: Behaviors and biological processes, including fertilization, are influenced by complex interactions between multiple genes and environmental factors. These interactions can be subtle and often uncontrollable, making it challenging to isolate the specific environmental factors at play [3], [4]. 4. **Epigenetic Modifications**: Environmental factors such as diet, smoking, and assisted reproductive techniques can alter gene expression through epigenetic modifications like DNA methylation. These changes can impact the placental epigenome and, consequently, pregnancy development and fertilization success [6]. 5. **Temperature and Other Environmental Factors**: Temperature has a genetic basis in its effect on sex determination, and interactions between temperature and genetic factors have been observed in several species. Other environmental factors such as pH, hypoxia, and social factors can also influence sex determination and potentially affect fertilization processes [10]. In summary, the environment influences fertilization through a combination of developmental conditions, genetic and environmental interactions, epigenetic modifications, and specific environmental factors like temperature and pH [1], [2], [3], [4], [6], [7], [10].", + "question": "how does environment influence fertilisation" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_27 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_27 new file mode 100644 index 0000000..81e08d6 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_27 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2015_GN_Diabets_notheses.pdf", + "2015 -Bikai- Osteoporosis and Hypertension.pdf", + "2008 - Rutter_s child and adolescent psychiatry-Blackwell Pub (2008).pdf", + "2019 - Novel Genetic Loci Control L5 Vertebral Trabecular Bone and the Response to Low Calcium Intake in Growing BXD Recombinant Inbred Mice.pdf", + "1998 - Type II Diabetes, Essential Hypertension, and Obesity as Syndromes of Impaired Genetic Homeostasis The Thrifty Genotype Hypothesis Enters the 21st Century.pdf", + "2018 - Animal models of obesity.pdf", + "2012 - Systems Biology Approaches to Nutrition.pdf", + "2021 - Estimating genetic and environmental contributions to complex traits and diseases..pdf", + "2015_GN_Diabets_notheses.pdf", + "2015 -Bikai- Osteoporosis and Hypertension.pdf" + ], + "extraction_id": [ + "5c6504ad-cec3-5054-b72e-7c8663b93020", + "2580c29f-1bd9-5a0b-bc39-36d2ac780bcd", + "55640275-345b-5ed5-bc71-5d2ffe9b2fd1", + "f540990d-044f-57aa-855b-ef89677321cb", + "cb4c8b17-644c-58a0-b63f-b7e839419dbf", + "12eaf8f0-a516-575b-af2f-54c390ad052a", + "1d378974-7dbd-54d3-ab94-c2306c450bd0", + "c6f13122-c145-50a6-8cb8-2cd0d8d40f4a", + "c3ea8f4a-36b2-5c54-b573-f1bb8a9f6126", + "b38762d9-8eb9-5290-ad8a-3ff14ffef706" + ], + "document_id": [ + "1744d9bf-29f9-52e3-a7c9-62a916999cda", + "4d27e689-ce69-566f-8d4c-4101abd064e7", + "59daba11-206e-5bbc-8833-9d1b661532b5", + "de8dda5e-0e2f-5aa9-bb13-851c526b36a5", + "5a32199a-ae34-5829-a6f0-025d55231305", + "9e9af9c7-814f-562e-a04d-878528a38002", + "6955478b-950d-5d29-b24c-3a5ca656f3ae", + "2ac6e31c-a3fd-546a-b18a-7f947faf1f4c", + "1744d9bf-29f9-52e3-a7c9-62a916999cda", + "4d27e689-ce69-566f-8d4c-4101abd064e7" + ], + "id": [ + "chatcmpl-ADZMSefJYQo9NCnH90mHvha0SygR5", + "3202ea94-5578-58ff-b5cf-6e856e914c2e", + "70ba6e2c-c4a0-5c07-ba63-d91926f401ff", + "c775c97a-4e53-50b3-aff0-9f947f1cee70", + "6ad9600e-e47a-5a12-8c1d-a1cf84a1342a", + "1ecbfb13-5c51-57ac-b23b-09837def6f70", + "d49fe981-9f4f-59b2-8d91-c325a30ab87e", + "4955053e-da3e-530a-8b72-e8416c962d36", + "6c0bb788-256b-56ce-97db-124b60eeed86", + "3a585d96-582f-5497-b0b1-ca3a7c79c651", + "65d7a65d-a1d7-50f5-923e-f843fc203b21" + ], + "contexts": [ + "economic status of a population, for example childhood nutrition status and the disease environment etc.21 Rare are the stud ies that unveil the relation between height decline and bone loss. A study performed by Galloway et al. on 1,024 subjects (735 women and 289 men) evaluated the correlation between height decline and bone loss with ageing. Their findings show that bone mine ral density (BMD) plays the largest role in determining annual height reduction.22", + "economic status of a population, for example childhood nutrition status and the disease environment etc.21 Rare are the stud ies that unveil the relation between height decline and bone loss. A study performed by Galloway et al. on 1,024 subjects (735 women and 289 men) evaluated the correlation between height decline and bone loss with ageing. Their findings show that bone mine ral density (BMD) plays the largest role in determining annual height reduction.22", + "how many eat a high phenylalanine diet.The relationship between gene and disease remains constantacross sites, but diet will act as an effect modier, controllingthe phenotypic consequences of the gene. Another example is the relationship among peak height velocity (PHV: thegrowth spurt of early adolescence), change of school anddepressive symptoms. The period of PHV may be a time whenyoungsters are particularly vulnerable to symptoms of depres-sion (Simmons & Blyth, 1987), particularly when they haveto", + "Dietary factor s deserve special attention as an environmental factor that interacts with genetics because we are exposed to our diet every day and we can modify it to our own benefit. The findings from several Ca intervention trials in children and adolescents demonstrated that there is a large variability in the acquisition of bone mass , despite the control of age range and pubertal maturation of part icipants.(28) Weaver et al.(102) conducted a 3 -week long, controlled", + "rapidly than Paleolithic people andreaching both maximal adult height andsexual maturity earlier. Wehave earlier speculated thatcompression ofthegrowth history predisposes tohigher blood pressure during adoles- cence andincreases theriskofhypertension inadulthood [57] . Arecent interesting series ofstudies byBarker andcolleagues hasfor- warded theargument thatsome fraction ofthepredisposition tohyperten- sionandNIDDM maybeprogrammed inutero bylowbirth weight. Several", + "diets are likely to vary in composition by batch, season and vendor. Variability in non-nutritive dietary components, such as soluble fibre content and plant- derived phyto- estrogens, affects the progression of DIO and metabolic disease, even affecting behavioural traits151,152. Another consideration is that humans consume ~30% of their daily calories from fat. This fat intake is remarkably consistent across age and BMI153 and lower than the 40% to 60% calories from fat used in many", + "several factors such as age, nutritional status, overall health and geographic location, all of which in uence the diet of", + "4 Hypertension November 2020 estimated the relative influence of genetic and environmental factors on height, weight, BMI, SBP, and DBP, as well as the genetic and environmental correlations of BMI with SBP and DBP. Furthermore, the moderating effects of BMI on SBP and DBP heritabilities were tested to explore potential gene-obe-sity interactions on BP. Contributions to the total phenotypic variances of SBP and", + "individuals. Augmentation index was in reverse correlation with height, in addition it was observed that taller participants had less prevalence of hypertension and use of antihypertensive drugs suggesting th e beneficial role of height in estimating cardiovascular risks (159). In a study done on patients with end stage renal disease augmentation index wa s found to negatively correlate with body height, and it was", + "individuals. Augmentation index was in reverse correlation with height, in addition it was observed that taller participants had less prevalence of hypertension and use of antihypertensive drugs suggesting th e beneficial role of height in estimating cardiovascular risks (159). In a study done on patients with end stage renal disease augmentation index wa s found to negatively correlate with body height, and it was" + ], + "answer": "Diet plays a significant role in determining someone's height, particularly during the growth phases of childhood and adolescence. Several factors related to diet can impact height: 1. **Childhood Nutrition Status**: The nutritional status during childhood is crucial for growth. Proper nutrition ensures that children receive the necessary vitamins and minerals required for bone growth and overall development [1]. 2. **Dietary Factors and Genetics**: Diet interacts with genetics, influencing growth outcomes. For example, calcium intake during childhood and adolescence can affect bone mass acquisition, which is essential for achieving optimal height [4]. 3. **Peak Height Velocity (PHV)**: The period of rapid growth during early adolescence, known as peak height velocity, can be influenced by diet. Adequate nutrition during this period is essential to support the growth spurt [3]. In summary, a well-balanced diet rich in essential nutrients is vital for achieving optimal height, especially during the critical growth periods of childhood and adolescence.", + "question": "how does diet impact someone's height" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_28 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_28 new file mode 100644 index 0000000..9a1b527 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_28 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2015 - Basic Concepts and Potential Applications of Genetics and Genomics for Cardiovascular and Stroke Clinicians.pdf", + "2003 - Telomeres in the chicken genome stability and chromosome ends.pdf", + "2015 - Basic Concepts and Potential Applications of Genetics and Genomics for Cardiovascular and Stroke Clinicians.pdf", + "2010 - Water Buffalo Genome Science Comes of Age.pdf", + "2009 - Genetic pathways of Lyst and exfoliation syndrome.pdf", + "2003 - Telomeres in the chicken genome stability and chromosome ends.pdf", + "2005 - Numerical Algorithms for Mapping of Multiple Quantitative Trait Loci in Experimental Populations.pdf", + "2005 -Ljungberg- Numerical algos for Multi QTL.pdf", + "2018 - Invited review Genetic and genomic_ xmltexbreak_ mouse models for livestock research.pdf", + "2013 - Baboons as a Model to Study Genetics and Epigenetics of Human Disease.pdf" + ], + "extraction_id": [ + "34fa36d0-0b64-5c70-8645-ba3576d9262c", + "02efe8ed-062d-51d2-9dd6-5a29a178b708", + "070b22be-cafb-5fd4-a338-ae3c62939c24", + "86b3157e-5b20-5e1f-aeee-f4a6f652694d", + "4165230b-bfd7-506c-8cfc-02868fa6bf21", + "b5cb2e6d-631c-5dad-bae9-26acf1dd9fb6", + "7c86a795-7202-5bfb-8da3-148cd8e66358", + "1b359995-cabb-5e75-ba37-7df272c6c232", + "fa8c1f01-7655-597d-8718-67ad0bc3b5ee", + "4dd22813-9004-571c-a351-80a2ec0f9b92" + ], + "document_id": [ + "8610e699-218a-50e6-8d1d-ef689623266f", + "c9124b17-6f3f-50fd-b6fc-d329db6b7cdd", + "8610e699-218a-50e6-8d1d-ef689623266f", + "fda7e83a-8e8c-5592-8302-687dab622323", + "5f35f50f-2f13-5b4c-9cfd-a96926e82f8c", + "c9124b17-6f3f-50fd-b6fc-d329db6b7cdd", + "dd7d3ea5-b23a-514e-898f-a4259ce6f6f9", + "bea0655c-7ef4-5754-ba14-817b72a21be2", + "5b167564-85a2-5886-b800-37932c3143a9", + "9f0acb79-6236-5add-b27e-1fb81ee4915d" + ], + "id": [ + "chatcmpl-ADZMXN8MM8gEy7UyxGzfomf1l430J", + "597a0fb1-4a16-5fd3-9bdc-8be977741b82", + "06d5d1e7-9474-5389-9f00-5669172e73a7", + "65b220a4-b96c-5bcb-a65f-ed6954e44757", + "d9101bd9-f565-57c1-98f2-0a43b8a073b1", + "9712b652-cddb-522b-a7b6-053cecb6c9d9", + "53079eb2-6661-5082-8a3a-e9b577cbcbe9", + "b597e6e2-4b16-5955-8b97-972ba3cc7053", + "9e3ef47b-6e78-50d9-bc28-01c227f0a2ce", + "fbf0608e-28ec-540e-9d18-5acbfaacec5d", + "73394dbd-8c20-5c5c-8ac5-ac76d4bab36f" + ], + "contexts": [ + "As seen in this karyotypic spread, the typical human cell has 46 chromosomes with 22 pairs of autosomes (numbered 122) and a pair of sex chromosomes, either XX or XY . Downloaded from http://ahajournals.org by on July 10, 2023", + "FIGURE 3. Telomere arrays of chicken and human chromosomes: the chicken genome contains more telomere sequence than the human", + "In sexually reproducing organisms, body cells contain 2 sets of chromosomes (1 set from each parent). To maintain this state, the egg and sperm that unite during fertilization each contain a single set of chromosomes. During meiosis, diploid cells undergo DNA replication, followed by 2 rounds of cell division, producing 4 gametes, each of which has 1 set of chromosomes (for humans, 23 unpaired chromosomes). Recombination occurs during meiosis. Mendelian diseaseSame as monogenic disease. Named", + "some set. Therefore, chromosome morphology sup-ports the designation of two separate genera [5]. Sex Chromosomes Several studies have revealed high degrees of homology among autosomal chromosomes of bovids with similar banding patterns and gene order among the chromosome arms of ca ttle, river buffalo, sheep, and goats [14, 15]. Bovid sex chromosomes, unlike the highly similar autosomal chromosomes, share a slightly more complex rearrangement of sequences", + "14 Mice share an anatomy, physiology, and genome that is similar, though not identical, to humans (May a nd Lutjen-Drecoll 2002; Smith 2002; Emes, Goodstadt et al. 2003; Huang, Winter et al. 2004). Mice and hum ans also share a su sceptibility to many similar diseases. As an experimental genetic platform for vertebrates, tools for studying and manipulating the mouse genome are near ly, if not completely, unparalleled", + "DELANY ET AL. 920 TABLE 1. Cytogenetic and telomere characteristics of vertebrate animal species (in vivo) Organism Terminal reference 2n/no. of telomere Telomere (maximum longevity) Telomeres array sizes shortening Rainbow trout 5860/116120 20 kb Unknown Oncohynchus mykiss Lejnine et al., 1995(20 yr) African clawed toad 36/72 1050 kb No Xenopus laevisBassham et al., 1998(15 yr) Laboratory mouse 40/80 50150 kb No Mus musculusKipling and Cooke, 1990(2 yr) Wild mouse 40/80 525 kb Yes", + "A human has 23 pairs of chromosomes, i.e. 46 in total. In each pair one chromosome has been inherited from the mother and the other from the father. The chromosomes in a pair are said to be homologous. They have the same genes at the same loci, but they may have different variants, different so called alleles, of the gene. Recall the eye color example from standard high school texts on genetics. We inherit one eye color allele from each parent, either a", + "A human has 23 pairs of chromosomes, i.e. 46 in total. In each pair one chromosome has been inherited from the mother and the other from the father. The chromosomes in a pair are said to be homologous. They have the same genes at the same loci, but they may have different variants, different so called alleles, of the gene. Recall the eye color example from standard high school texts on genetics. We inherit one eye color allele from each parent, either a", + "and zebra sh (http://www.alliancegenome.org, last access: 3 January 2018). 3 The mouse as a model animal for livestock research Mice are mammals, sharing 92 to 95 % of protein cod- ing genes with humans and other mammalian livestock species, such as cattle (Elsik et al., 2009), pigs (Humphray et al., 2007), sheep (Iannuzzi et al., 1999), and goats (Schibler et al., 1998). The mouse genome is structured into 19 autosomes and the sex chromosomes. The mouse", + "Figure 3: Comparison of human and baboon chromosomes. (A) Conservation of microsatellite marker order for orthologs human 12and baboon 11. (B) C hromosome inversion between orthologs hu- man 4 and baboon 5. The y-axis indicates chromosome length incentimorgans. Microsatellite markers identi ed in human have identi cation numbers that begin with D,and microsatellite markers identi ed in baboon have identi cation numbers that begin swith Pha. Figure 2: Papio hamadryas anubis (Olive baboon)" + ], + "answer": "Humans have 46 chromosomes, arranged in 23 pairs [1], [7]. The animal that has the same number of chromosomes as humans is the baboon, specifically the Olive baboon (Papio hamadryas anubis), which also has 46 chromosomes [10].", + "question": "which animal has the same number of chromosomes as human" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_29 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_29 new file mode 100644 index 0000000..81555eb --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_29 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2012 - Genetic architecture supports mosaic brain evolution and independent brain\u2013body size regulation_.pdf", + "2012 - Genetic architecture supports mosaic brain evolution and independent brain\u2013body size regulation_(1).pdf", + "2018 - Integrative functional genomic.pdf", + "2003 - Imaging genomics.pdf", + "2008 - The Aging Brain.pdf", + "2009 - Age-associated cognitive decline.pdf", + "2021 - System genetics in the rat HXBBXH family identifies Tti2 as a pleiotropic quantitative trait gene for adult hippocampal neurogenesis and serum glucose.pdf", + "2022 - System genetics in the rat HXBBXH family identifies Tti2 as a pleiotropic quantitative trait gene for adult hippocampal neurogenesis and serum glucose.pdf", + "2011 - A genome-wide association study of aging.pdf", + "2015 - A Systems-Genetics Analyses of Complex Phenotypes.pdf" + ], + "extraction_id": [ + "e4c6a021-c822-5c6e-96ee-bdfcd9e087b6", + "cb9a0594-ed63-533f-b872-eea0ab9dd781", + "33bb0b60-582f-56b5-87da-66601ba8a482", + "76e11f30-b4f4-5fee-ae1f-eaf8daefc962", + "64f9170a-04bd-57be-ba0b-cc61edec0f37", + "87274deb-c57b-51c7-96f2-17111737c026", + "3c4e5025-5c02-522d-81f0-2354118cbf61", + "347bc44e-9705-5922-bfcd-22d65eb7cd80", + "253a4339-29d4-58c2-8a01-5137d94873b6", + "3f7d819b-ed86-50c7-a0c9-1955df2cead9" + ], + "document_id": [ + "c2d37851-b1a9-5572-8de1-1cc627e5c89e", + "655ce593-3f0f-5065-9ce0-e9c130b6e7e4", + "6e37d26b-e45b-5eb8-8d79-339d9c0e05bd", + "b4aee92d-491c-5f9d-9c40-adb5c5cceeb6", + "874f5d02-35c9-5233-8ded-6e06c7570ca9", + "746ed855-8647-558a-9abc-c0e2d4254868", + "9ab8b190-fb4f-5bb0-8d04-1cd07a42192a", + "4198ec53-60f1-55d1-8759-b9ede1d098c0", + "8e9c1150-1047-54a2-bf85-1cc5000a6811", + "030d0226-b782-5964-8452-339777dc9658" + ], + "id": [ + "chatcmpl-ADZMaWRstSGrYv65Txc4tWg1NphEi", + "340e7007-f00f-56b9-b99c-9bbf6591889a", + "3e7b4f88-a18f-5cdb-aa31-0eb92d4d226c", + "f494980a-326f-5454-8faa-890eed0a343f", + "54eeed5e-a1c7-566a-981d-3c40211b3992", + "772ad124-6371-5435-ad48-4e8546f766a0", + "2e99dbdc-ea40-5e40-864b-4d0ad745bc09", + "e5058bc7-2fc5-5a2b-852e-39efb9adc7c0", + "e6ce00e3-8a5d-5f20-9d18-fb8b8932dc54", + "919fafa2-a013-5549-9f1b-c7ccb2181215", + "2cc5e05a-e8fc-57cb-a7dc-c1d3ea8204a9" + ], + "contexts": [ + "ARTICLE nATuRE C ommunICATIons | 3:1079 | DoI: 10.1038/ncomms2086 | www.nature.com/naturecommunications 2012 Macmillan Publishers Limited. All rights reserved.Received 8 may 2012 | Accepted 23 Aug 2012 | Published 25 sep 2012 DOI: 10.1038/ncomms2086 The mammalian brain consists of distinct parts that fulfil different functions. Finlay and Darlington have argued that evolution of the mammalian brain is constrained by", + "ARTICLE nATuRE C ommunICATIons | 3:1079 | DoI: 10.1038/ncomms2086 | www.nature.com/naturecommunications 2012 Macmillan Publishers Limited. All rights reserved.Received 8 may 2012 | Accepted 23 Aug 2012 | Published 25 sep 2012 DOI: 10.1038/ncomms2086 The mammalian brain consists of distinct parts that fulfil different functions. Finlay and Darlington have argued that evolution of the mammalian brain is constrained by", + "Daniel H. Geschwind, Michael J. Hawrylycz, Matthew W. State, Stephan J. Sanders, Patrick F. Sullivan, Mark B. Gerstein , Ed S. Lein , James A. Knowles , Nenad Sestan INTRODUCTION: The brain is responsible for cognition, behavior, and much of what makes us uniquely human. The development of the brain is a highly complex process, and this process is reliant on precise regulation of molecular and cellular events grounded in the spatiotemporal regulation of the transcrip-", + "addition,each study implemented rigorous controls for non-genetic factors suchas age, gender, IQ and performance on the experimental task. They alsocapitalized on existing functional paradigms designed to explorephysiological aspects of distinct neural systems.", + "brain to prevent theapoptosis of irreplaceable neurons, even in the", + "Funding Funding from the BBSRC, EPSRC, ESRC and MRC is gratefully acknowledged. References 1 Brayne C (2007) The elephant in the room: healthy brains in later life, epidemiology and public health. Nat Rev Neurosci ,8, 233239. 2 Gow J, Gilhooly M (2003) Risk Factors for Dementia and Cognitive Decline . Glasgow: NHS Health Scotland. 3 House of Lords (2005) Ageing: scientific aspects. London: The Stationery Office. 4 Stern PC, Carstensen LL (2000) The Aging Mind. Washington, DC: National Academy Press.", + "1124 the brain. Nature Reviews Neuroscience. Nat Rev Neurosci; 2012. pp. 225239. 1125 doi:10.1038/nrn3209 1126 75. van Praag X, Fleshner M, Schwartz MW, Mattson MP. Exercise, energy intake, 1127 glucose homeostasis, and the brain. J Neurosci. 2014;34: 1513915149. 1128 doi:10.1523/JNEUROSCI.2814-14.2014 1129 76. Rafalski VA, Brunet A. Energy metabolism in adult neural stem cell fate. Progress in 1130 Neurobiology. Prog Neurobiol; 2011. pp. 182203. 1131 doi:10.1016/j.pneurobio.2010.10.007", + "1124 the brain. Nature Reviews Neuroscience. Nat Rev Neurosci; 2012. pp. 225239. 1125 doi:10.1038/nrn3209 1126 75. van Praag X, Fleshner M, Schwartz MW, Mattson MP. Exercise, energy intake, 1127 glucose homeostasis, and the brain. J Neurosci. 2014;34: 1513915149. 1128 doi:10.1523/JNEUROSCI.2814-14.2014 1129 76. Rafalski VA, Brunet A. Energy metabolism in adult neural stem cell fate. Progress in 1130 Neurobiology. Prog Neurobiol; 2011. pp. 182203. 1131 doi:10.1016/j.pneurobio.2010.10.007", + "for the creation of redun-dancy in brain circuitry, which is associated with functionalreserve and resiliency. Brain function regulates most of thecompensatory strategy supporting maintenance of homeo-static equilibrium. Both of these processes are essential tohealthy aging and longevity.", + "of complex traits. It has been said that The brain is the chief architect, orchestrator and driver of behavior; behavior, in turn, is the principal function of the brain (Gomez -Marin et al., 2014, p. 1455) , and therefore to understand one we need to understand the other. The brain and the behaviours that it causes are highly complex traits influen ced by many factors including genes (Hager et al., 2012; Hitzemann et al., 2013; McCarroll and Hyman, 2013) , environment (Carola" + ], + "answer": "The proper functioning of the brain is ensured by several factors: 1. **Precise Regulation of Molecular and Cellular Events**: The development of the brain is a highly complex process that relies on the precise regulation of molecular and cellular events, which are grounded in the spatiotemporal regulation of transcription [3]. 2. **Energy Metabolism**: Energy metabolism plays a crucial role in the fate of adult neural stem cells, which is essential for maintaining brain function [8]. 3. **Functional Reserve and Resiliency**: The creation of redundancy in brain circuitry is associated with functional reserve and resiliency, which are critical for maintaining homeostatic equilibrium and supporting healthy aging and longevity [9]. These factors collectively ensure that the brain works effectively.", + "question": "what ensures brains work" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_3 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_3 new file mode 100644 index 0000000..d731737 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_3 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2016 - A novel heat shock protein alpha 8 (Hspa8) molecular network mediating responses to stress- and ethanol-related behaviors.pdf", + "2012 - Needs Analysis of Genetics and Genomics in Communication Sciences and Disorders.pdf", + "2017 - Primer in Genetics and Genomics.pdf", + "2018 - Identification of non-HLA genes associated with development of islet autoimmunity and type.pdf", + "2020 - Mainstreaming genetics and genomics a systematic review.pdf", + "2009 - Basic Genetics and Genomics A Primer for Nurses.pdf", + "2010 - Genetic variants near TIMP3 and high-density.pdf", + "2004 - Errand Gabpab specify PGC1dependentoxidative phosphorylation gene expressionthat is altered in diabetic muscle.pdf", + "2010 - Genome-wide association identifies OBFC1as a locus involved in human leukocyte telomere biology.pdf", + "2010 - Genome-wide association identifies OBFC1as a locus involved in human leukocyte telomere biology.pdf" + ], + "extraction_id": [ + "600a1af4-0f16-520c-a63f-7e0af523fa3c", + "b7b09b33-3c90-51c9-968c-d47809e9d964", + "53fa3a10-5290-5209-80ce-0655d2c602a5", + "631667de-f20a-59b6-af3c-924b612d21ea", + "0120a9f0-57fd-510d-b975-b1e1f870f9fb", + "2cafe5f4-79a3-5234-948d-d78c20b97650", + "12929889-6359-5c34-8997-95a41f6202a3", + "715eacf0-9e21-593f-b023-84a864eb801f", + "0ed3fd5b-86ce-5587-90b7-1e013a7bb8ad", + "ccda7fa4-0bd0-5af7-919c-47b435ad81ea" + ], + "document_id": [ + "22bb099c-aeca-51e8-a82d-5d091d9f0936", + "c8a76cb1-506d-57e4-a18e-548e777898e2", + "6fae6815-e1b5-564b-81c7-39ed62bbd999", + "fb67c701-af96-57ad-b1e3-1309e1b53a52", + "ea0695f5-c52c-568b-ba97-8fa31405ef30", + "c37e2ace-171b-5776-8969-86eda9736481", + "da9c44fa-16a0-586b-8256-f4b91f4cfef9", + "259bea02-bb3a-57b6-8896-0b41d6cace05", + "76ba50b9-6eb2-51c7-8dd8-82d840d81219", + "76ba50b9-6eb2-51c7-8dd8-82d840d81219" + ], + "id": [ + "chatcmpl-ADZIrY516c5O6uEqljwZdzIXywTvS", + "9e595bc1-a142-525c-97d4-5edde55c5bcf", + "1eed369d-2525-5621-b9a7-c344c2e48f32", + "1ef52b83-a34c-517e-b65f-b8d9c1acb79d", + "ab3b9b3a-2353-5730-8dd2-3b790ca7c5f7", + "d2fe7d2e-8d04-52ce-a8e6-de8437a83014", + "6cce33b0-9975-5727-8b53-2e3478cb3230", + "fb261aba-abc3-5e90-a322-61841f43f60c", + "ab7bbeb8-bc04-527e-8840-3bb5f905dffd", + "c8b4753f-417d-590b-b8b8-866322504fec", + "0b0f3258-b6bb-5de0-91d1-767ef5fce360" + ], + "contexts": [ + "Neurogenetics", + "Genetics Genetics is the study of individual genes and their protein products (Guttmacher &", + "genetics and genomics, article 1DNA, genes, and chromosomes. Biological Research for Nursing ,19, 717. Dueker, N. D., & Pericak-Vance, M. A. (2014). Analysis of genetic linkage data for Mendelian traits. Current Protocols in Human Genetics ,83, 1.4.11.4.31. Fu, M. R., Conley, Y. P., Axelrod, D., Guth, A. A., Yu, G., Fletcher, J., & Zagzag, D. (2016). Precision assessment of heterogeneity of lymphedema phenotype, genotypes and risk prediction. Breast , 29, 231240.", + "genetic factors. 371 372 373 374 375", + "GENETICS in MEDICINE |Volume 22 |Number 7 |July 2020 1153", + "to offspring. Genes are pieces of DNA, and most genes contain the information for making a specific protein. zGenetics - Genetics is a term that refers to the study of genes and their role in inheritance - the way certain traits or conditions are passed down from one generation to another. zGenomics - Genomics is a relatively new term that describes the study of all of a person's genes including interactions of those genes with each other and the person's environment.", + "www.pnas.org/cgi/doi/10.1073/pnas.0912702107 PNAS |April 20, 2010 |vol. 107 |no. 16 |74017406 GENETICS", + "GENETICS Downloaded from https://www.pnas.org by 41.90.188.152 on July 14, 2023 from IP address 41.90.188.152.", + "GENETICS Downloaded from https://www.pnas.org by 41.80.118.137 on October 17, 2023 from IP address 41.80.118.137.", + "GENETICS Downloaded from https://www.pnas.org by 41.80.118.137 on October 17, 2023 from IP address 41.80.118.137." + ], + "answer": "Genetics is the study of individual genes and their protein products [2]. It involves understanding how genes are inherited, how they function, and how they influence traits and conditions passed down from one generation to another [6]. This field also includes the analysis of genetic linkage data for Mendelian traits [3].", + "question": "genetics" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_30 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_30 new file mode 100644 index 0000000..c9827c7 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_30 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2008 - Rutter_s child and adolescent psychiatry-Blackwell Pub (2008).pdf", + "2013 - Neural-Immune Interactions in Brain Function and Alcohol Related Disorders.pdf", + "2010 - Candidate Gene and Genome-Wide Association Studies in Behavioral Medicine.pdf", + "2010 - Candidate Gene and Genome-Wide Association Studies in Behavioral Medicine.pdf", + "2007 - Gene expression profiles in anatomically and functionally distinct regions.pdf", + "2009 - Multiscale Genomic Analysis of the Corticolimbic System_ Uncoveri (1).pdf", + "2009 - Neuroplasticity, Psychosocial Genomics.pdf", + "2010 - Candidate Gene and Genome-Wide Association Studies in Behavioral Medicine.pdf", + "2010 - Candidate Gene and Genome-Wide Association Studies in Behavioral Medicine.pdf", + "2010 - Candidate Gene and Genome-Wide Association Studies in Behavioral Medicine.pdf" + ], + "extraction_id": [ + "5e06bd24-8977-582c-b01b-61be91612e1a", + "fb4ba6b4-c3ea-5671-9da8-15fcadccff59", + "d0222d2f-7e27-59de-9ad0-23febb3564f8", + "4d38ecad-88e4-5f52-8a99-55029773de79", + "b848d23b-0c65-5e44-b190-1ec8e5a76545", + "c755176c-961c-57f0-996c-662de89048d3", + "8cd38348-d367-5c85-829e-e465af8184cb", + "995b3eb6-e505-52a0-a142-ca507eb9a9ac", + "264526ff-3f41-5a6d-88af-6e237cea42cb", + "069a2a63-f01c-5235-a118-3744c21f2baa" + ], + "document_id": [ + "59daba11-206e-5bbc-8833-9d1b661532b5", + "78271275-3409-5fc7-bbdd-53c484178e0b", + "17637a6f-804e-50e4-9cf5-37318e17f15c", + "17637a6f-804e-50e4-9cf5-37318e17f15c", + "d4a001e2-8cac-58cb-be8b-b9afa9382e01", + "3d0df5a3-7d7c-5edc-b94d-cae582f59c12", + "77549d17-8f07-5b62-8134-011a68f2ebd4", + "17637a6f-804e-50e4-9cf5-37318e17f15c", + "17637a6f-804e-50e4-9cf5-37318e17f15c", + "17637a6f-804e-50e4-9cf5-37318e17f15c" + ], + "id": [ + "chatcmpl-ADZMeEIq2Wv2GICWJZcSZAU1k5Qdz", + "34f059bf-1e74-580d-9b52-8c940ff0f302", + "fd7b6e37-2aba-525e-aa22-4a9cef18827d", + "f2dda7e1-1af6-54b0-8ffa-856313872579", + "96a2a72c-b239-58f0-b116-2b1eeb3e8434", + "b2d814c0-e515-54b9-b994-b457ca0e2739", + "45e53d76-dced-5f6b-abf2-c830b41c1c90", + "2fc8ee5e-7a5e-57cc-98e3-e9156aec2571", + "fbdf5982-c2f0-5577-bce3-bc8762aef713", + "2199f4c4-8126-54c8-a323-6704c96bc0f7", + "8b65f73a-2d73-53b2-b418-f8e485d58df3" + ], + "contexts": [ + "areas that support pos-itive emotions and deactivate brain areas that are linked withaggression, fear and sadness (Diamond, 2004); this nding is consistent with the emotional prole associated with agreeableness.", + "Importantly, regions of the brain responsible for emotional regulation, executive functioning, and their consequential behavioral outcomes are sensitive to in ammation [ 22 ] . The extended limbic system, primitively responsible for fear and pleasure responses, stress, memory, and learning, has been shown to be modulated by immune signaling. Early work established that there is a high density of IL-1 receptors in the dentate gyrus and pyramidal cell layer of the hippocampus, the", + "the midbrain structures are implicated in cardiacresponses to social stress (Wager et al, 2009 ). It is now evident that these same brain regions are involved in emotion regulation. Furthermore, the circuitry involved in physical pain and plea-sure appears to be activated by positive and negative socially induced emotion (Takahashi et al, 2009 ). The possibility therefore arises that positive well-being may be embodied in the acti- vation of neural circuitry in a reciprocal fashion", + "723732. Etkin, A., Egner, T., Peraza, D. M., Kandel, E. R., and Hirsch, J. (2006). Resolving emotional conict: a rolefor the rostral anterior cingulate cortex in modulatingactivity in the amygdala. Neuron, 51 , 871882. Fales, C. L., Barch, D. M., Rundle, M. M., Mintun, M. A., Snyder, A. Z. et al (2008). Altered emotional inter-ference processing in affective and cognitive-controlbrain circuitry in major depression. Biol Psychiatry, 63, 377384. Fanselow, M. S. (2000). Contextual fear, gestalt mem-", + "for cognitive processes such as learning,memory, and emotions.", + "expression of emotional behavior. Sensory inputs with emotional components are transmitted to the amygdala where they are processed and fu rther relayed to other regions to modulate autonomic and behavioral responses, and to form emotional memories (LeDoux, 2000; Rosen, 2004). As a neural substrate of emotionality, many neuropsychiatric disorders have been associated with structural changes i n the amygdala. Individuals with genetically predisposed susceptibility to anxiety and depression have", + "components can act back upon its physical substrate. Thought, emotion, and action trigger neural activity, which can lead to a reorganization of the brain, shaping future psychosocial experience. From this perspective, we are not the passive products of neurophysiology and heredity; rather, through our behavior in the social environment, we become active agents in the con-struction of our own neurobiology and, ultimately, our own lives.", + "et al, 1995 ; Scher et al, 2005 ), (2) are less easily distracted from negative emotion process- ing (Ellenbogen et al, 2002 ; Lyubomirsky et al, 1998 ; Siegle et al, 2002 ; Wenzlaff and Bates, 1998 ), (3) show heightened stress hormone lev- els such as cortisol that may have deleterious effects on the brain (Sapolsky, 2000 ), and (4)", + "et al, 2000 ). Once activated, the amygdala sets in motion a cascade of responses to threat via pro-jections to the hypothalamus and prefrontal cor-tex (LeDoux, 1996 ). A neural region that is criti- cal for regulating responses to emotional stimuli is the ventrolateral prefrontal cortex (VLPFC;Hariri et al, 2002 ). Studies have shown that the labeling of negative affective states activates the right VLPFC and that increased activity inright VLPFC is associated with decreased activ-", + "tially participates in negative emotional states,although it also participates in positive emo- tional states (Zald, 2003 ). The amygdala orches- trates the somatomotor, visceral, and cognitiveresponses to threats by virtue of its connections with cortical brain structures above and hypotha- lamic and brainstem structures below it (LeDouxet al, 1990 ). The nucleus accumbens and ventral striatum participate in reward responses and pos- itive emotional states. Other structures that are" + ], + "answer": "Our brains maintain emotions through a complex interplay of various brain regions and neural circuits. Key areas involved include: 1. **Amygdala**: Sensory inputs with emotional components are transmitted to the amygdala, where they are processed and further relayed to other regions to modulate autonomic and behavioral responses, and to form emotional memories [6]. The amygdala orchestrates responses to threats and is involved in both negative and positive emotional states [10]. 2. **Prefrontal Cortex**: The ventrolateral prefrontal cortex (VLPFC) is critical for regulating responses to emotional stimuli. Increased activity in the right VLPFC is associated with decreased activity in the amygdala, which helps in managing negative affective states [9]. 3. **Extended Limbic System**: This system, which includes structures like the hippocampus, is primitively responsible for fear and pleasure responses, stress, memory, and learning. It is modulated by immune signaling, indicating that inflammation can affect emotional regulation [2]. 4. **Midbrain Structures**: These structures are involved in emotion regulation and are activated by both physical pain and pleasure, as well as positive and negative socially induced emotions [3]. 5. **Nucleus Accumbens and Ventral Striatum**: These areas participate in reward responses and positive emotional states [10]. Overall, the brain maintains emotions through the coordinated activity of these regions, which process, regulate, and respond to emotional stimuli, thereby shaping our emotional experiences and behaviors.", + "question": "how do our brains maintain emotions" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_31 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_31 new file mode 100644 index 0000000..0a28905 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_31 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2013 - Neural-Immune Interactions in Brain Function and Alcohol Related Disorders.pdf", + "2013 - Neural-Immune Interactions in Brain Function and Alcohol Related Disorders.pdf", + "2010 - Candidate Gene and Genome-Wide Association Studies in Behavioral Medicine.pdf", + "2014 - Genetic regulatory network analysis reveals that low density lipoprotein receptor-related protein 11 is involved in stress responses in mice.pdf", + "2021 - Prefrontal cortex VAMP1 gene network moderates the effect of the early environment on cognitive flexibility in children.pdf", + "2015 - Great Is Their Sin.pdf", + "2009 - Multiscale Genomic Analysis of the Corticolimbic System_ Uncoveri (1).pdf", + "2011 - Genetic Analysis of the Neurosteroid Deoxycorticosterone and Its Relation to Alcohol Phenotypes Identification of QTLs and Downstream Gene Regulation.pdf", + "2011 - Genetic Analysis of the Neurosteroid Deoxycorticosterone and Its Relation to Alcohol Phenotypes Identification of QTLs and Downstream Gene Regulation.pdf", + "2019 - Exploring the involvement of Tac2 in the mouse hippocampal stress response through gene networking.pdf" + ], + "extraction_id": [ + "e4e689d6-5e01-50cb-bb0f-1d958542a343", + "87cb54ed-b246-52a8-8922-5baa4f2f5e7c", + "c83a0fd3-2bc2-510b-ba66-fad5dab1c430", + "a576772e-e17b-56fc-96b0-bdf8c913b2e8", + "8c989969-10c2-533e-ad71-5e9a54499798", + "2992ae99-13f8-5b72-9a5b-408a1ec77e32", + "bccdd21d-53b6-53c5-89ae-6508fa5ea4a9", + "f854fcfc-5758-5d5f-944d-d1db9e72ccdd", + "0b62b9d2-6622-5882-b3d6-e7f8482a927a", + "cd49980b-e59c-5d64-816d-3a8817f099fc" + ], + "document_id": [ + "78271275-3409-5fc7-bbdd-53c484178e0b", + "78271275-3409-5fc7-bbdd-53c484178e0b", + "17637a6f-804e-50e4-9cf5-37318e17f15c", + "9e59e66c-6b3f-5c99-a12c-7bb6fd0d899f", + "976026ce-9e0c-5b0b-8469-abc8f92dbdf0", + "e5ae9710-3049-5327-82e4-e6626eb670c2", + "3d0df5a3-7d7c-5edc-b94d-cae582f59c12", + "4eef9c8d-17bf-5ed2-a90c-6bc64f7374b1", + "4eef9c8d-17bf-5ed2-a90c-6bc64f7374b1", + "8277ae11-6516-54d2-9723-73749d46db9b" + ], + "id": [ + "chatcmpl-ADZMkkQgFtXLUsuTUrfbvqGBOuc3R", + "3667e7f7-c984-567e-9757-19d7827c2a52", + "db05c1b0-1a66-5a2c-9680-564167f95ffe", + "dfdcca45-79ae-5e00-bae0-175860786128", + "1900d276-5346-5041-b497-41b8f1dde22e", + "cc9faf66-a0d0-5427-9f84-004d1b450b5a", + "7c9bf714-0d21-5104-9aed-4bd1b191fbf4", + "b06f880b-97c9-5541-a76e-a5f37f31fa6a", + "010d5687-d237-51ca-87a1-e7e0af944e39", + "869496a0-2bff-569f-ba3a-03294ebf2e98", + "19df7543-5231-56d5-a59a-e342565b737d" + ], + "contexts": [ + "pin-releasing hormone (CRH), adrenocorticotropic hormone (ACTH), and glucocorticoids (GC), which are also called stress hormones. These hormones con- tribute to the regulation of immune responses and can also affect neuronal survival, neurogenesis, synaptic plasticity, and behavioral responses [ 1, 2 ] . The HPA axis is a three-tiered biological system that begins at the highest level with the release of CRH from the hypothalamic paraventricular nucleus (PVN). CRH-expressing neu-", + "stressor in uences the interleukin-1beta system, tumor necrosis factor-alpha, transforming growth factor-beta1, and neuropeptide mRNAs in speci c brain regions. Brain Res Bull 51:187193 63. Deak T et al (2005) Stress-induced increases in hypothalamic IL-1: a systematic analysis of multiple stressor paradigms. Brain Res Bull 64:541556 64. Hennessy MB et al (2004) Responses of guinea pig pups during isolation in a novel", + "stressful events. In rats and mice, the secretion of hypothalamicpituitaryadrenal hormones istypically greater, and increased HPA activity often persists into adulthood (Koehl et al, 1999 ). Basal levels of adrenal hormones are more typ-ically reported to be normal in primates, but there may be alterations in the diurnal hormone rhythm or an altered negative feedback, whichresults in protracted cortisol responses once acti-vated. Many effects of prenatal stress on brain", + "Y in depression and stress. Brain Research 1314, 194 205. Mozhui, K., Karlsson, R.M., Kash, T.L., Ihne, J., Norcross, M., Patel, S., Farrell, M.R., Hill, E.E., Graybeal, C., Martin, K.P., Camp, M., Fitzgerald, P.J., Ciobanu, D.C., Sprengel, R., Mishina, M., Wellman, C.L., Winder, D.G., Williams, R.W., Holmes, A., 2010. Strain differences in stress responsivity are associated with divergent amygdala gene expression and glutamate-mediated neuronal excitability. The Journal of", + "Neurobiology of Learning and Memory 185 (2021) 107509 21.Introduction James McGaugh was one of the first neuroscientists to point to the important influence of stress hormones on memory consolidation (McGaugh, Gold, Van Buskirk, & Haycock, 1975 ). He and others considered that hormones released by stressful experiences could enhance memory consolidation, indicating particularly the hormones epinephrine and glucocorticoids as memory modulators (McGaugh &", + "For example, stress is a functional state of psychosocial arousal that focuses and energizes us to confront the stressor, but chronic/toxic levels of stress lead to disruptive changes in brain architecture and dysregulation of stress response mechanisms, such as the hypothalamus-pituitary ( hpA) axis and the autonomic nervous (ANS) system. Under chronic stress, the adrenal glands of mammals (including humans) release the steroid hormone cortisol. Cortisol acts by increas -", + "55:485494. Herman JP, Ostrander MM, Mueller NK, Figueiredo H (2005). Limbic system mechanisms of stress regulation: hypothalamo -pituitary -adrenocortical axis. Prog Neuropsychopharmacol Biol Psychiatry 29:1201 1213. Herry C, Bach DR, Esposito F, Di Salle F, P errig WJ, Scheffler K et al. (2007). Processing of temporal unpredictability in human and animal amygdala. J Neurosci 27:5958 5966. Hitzemann R, Malmanger B, Cooper S, Coulombe S, Reed C, Demarest K et al. (2002).", + "after restraint stress. Acute stress (like acute ethanol) activates the HPA axis and increases brain and circulating levels of GABAergic neuroactive steroids [1] as well as corticosterone, the major corticosteroid synthesized in rodents from DOC. GABAergic neuroactive steroids have anxiolytic properties when administered systemically [54,55]. Thus, we might have predicted that those strains with higher basal DOC levels would have been less", + "present in the brain as well as in the peripheral circulation. It issynthesized from progesterone, mainly in the adrenal zonafasciculata and it is precursor of both the glucocorticoidcorticosterone and the GABAergic neuroactive steroid (3 a,5a)- 3,21-dihydroxypregnan-20-one (tetrahydrodeoxycorticosterone,THDOC). These steroids are all elevated following acute stress[1] or ethanol administration in rats, and their elevation is blunted", + "plasticity and epigenetic regulation as a consequence of stress. Neuropharmacology 62, 3 12. McEwen, B.S., Nasca, C., Gray, J.D., 2016. Stress e ects on neuronal structure: hippo- campus, amygdala, and prefrontal cortex. Neuropsychopharmacology 41, 3 . Mozhui, K., Lu, L., Armstrong, W.E., Williams, R.W., 2012. Sex-speci c modulation of gene expression networks in murine hypothalamus. Front. Neurosci. 6, 63 . Navarro, V.M., 2013. Interactions between kisspeptins and neurokinin B. In: Kisspeptin" + ], + "answer": "During stressful experiences, our brains release several hormones, including corticotropin-releasing hormone (CRH), adrenocorticotropic hormone (ACTH), and glucocorticoids (GC) [1]. Additionally, cortisol, a steroid hormone, is released by the adrenal glands under chronic stress [6]. These hormones play significant roles in regulating immune responses, neuronal survival, neurogenesis, synaptic plasticity, and behavioral responses [1].", + "question": "what hormones do our brains release during stressful experiences?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_32 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_32 new file mode 100644 index 0000000..650d433 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_32 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2015 - Genetic dissection of sleep homeostasis.pdf", + "2019 - Leveraging genomics to uncover.pdf", + "2013 - Neural-Immune Interactions in Brain Function and Alcohol Related Disorders.pdf", + "2010 - Candidate Gene and Genome-Wide Association Studies in Behavioral Medicine.pdf", + "2012 - Genetic regulation of adult hippocampal neurogenesis A systems genetics approach using BXD recombinant inbred mouse strains.pdf", + "2010 - Candidate Gene and Genome-Wide Association Studies in Behavioral Medicine.pdf", + "2010 - Candidate Gene and Genome-Wide Association Studies in Behavioral Medicine.pdf", + "2019 - Strain differences in maternal neuroendocrine and behavioral responses to stress and the relation to offspring cocaine responsiveness..pdf", + "2020 - Modeling the Genetic Basis of Individual Differences in Susceptibility to Gulf War Illness.pdf", + "2010 - Candidate Gene and Genome-Wide Association Studies in Behavioral Medicine.pdf" + ], + "extraction_id": [ + "cef725f8-c326-59f4-a65e-62d8c7bd5db5", + "c624519f-327a-5733-9e1e-94d5bec93fd7", + "f6556a02-048a-5e9b-ac7e-ed681db96345", + "f9be673c-af23-5d15-9087-37e818cf1a68", + "3c78be84-90fe-58ce-85e5-e85e2208057f", + "59789bd0-1ee6-51da-b2a1-94f847ff6c63", + "32902b1c-3a3a-5f5b-b651-a6fd0fa653a9", + "29253383-31a5-5fe1-8160-9d6091273a4d", + "1de7e365-88d0-5893-826e-7ac6a69b896e", + "5da98563-71dd-5d71-8303-b52f2fb8c6a7" + ], + "document_id": [ + "ed971d1f-e77e-566b-b549-81cd0038834a", + "5da46d3b-fa82-57f6-b3e5-c82784347881", + "78271275-3409-5fc7-bbdd-53c484178e0b", + "17637a6f-804e-50e4-9cf5-37318e17f15c", + "c54da858-9620-588e-8e41-76a960af2ff6", + "17637a6f-804e-50e4-9cf5-37318e17f15c", + "17637a6f-804e-50e4-9cf5-37318e17f15c", + "d29d8018-09a1-53d4-8f07-9dd110c79b39", + "d235d186-3d1c-5cde-90d5-9c140cd920f4", + "17637a6f-804e-50e4-9cf5-37318e17f15c" + ], + "id": [ + "chatcmpl-ADZMoelW4EZWflXHaXujPl4dX6GM9", + "bf56c010-06d1-598e-81cf-2a2603f0a883", + "76804170-ccb4-5e86-b9ba-533264556893", + "63c085a5-ad08-5f28-b3be-3e62b7739183", + "74ffa8aa-80dc-5e94-a373-c1af483d63f4", + "05e15635-52ee-5d80-9696-15cea22fb7e4", + "5ccf3333-4675-577f-bfce-5d5e72fd7c3f", + "6d2d21e3-a1c5-5a11-a7ca-7fc643cf8b36", + "8f5142d0-8efa-5fe8-b7bf-46dea42ec444", + "7b2a0384-586f-582f-93da-8fd64dc76095", + "2234517f-d2da-535b-8bb4-5ee5d33671e2" + ], + "contexts": [ + "that corticosterone importantly amplies the SD induced changes", + "be used to predict corticosteroid response [200]. George etal.", + "we do not wish to dispute this viewpoint, it is interesting to note that anti- in ammatory actions of CORT are most pronounced at high and supraphysiological concentrations, whereas lower concentrations of CORT appear to have some immune-potentiating effects (e.g., [ 6 ] ). Whether these low-dose facilitation effects relate more directly to the timing of CORT injection relative to cytokine measure- ments, or represent differential tissue sensitivity to glucocorticoids, remains to be", + "cortisol to the less bioactive cortisone (Seckl,1997 ). While the protection afforded by this bar- rier enzyme can be overwhelmed when cortisol levels get very high, it likely functions effec- tively when cortisol remains within the normalrange (Campbell and Murphy, 1997 ). There is now considerable interest in what types of events or other hormones might lower 11-HSD2 andthereby reduce the buffering benets it affords. On example is elevated catecholamine levels,", + "the balance between cell generation and cell death. Acute increase of corticosterone leads to decreased cell proliferation while chronic increase causes an increase in proliferation rate (Sapolsky et al., 2000). This discrepancy is due to the presence of two receptors with different binding affinities: the glucocorticoid receptor (GR) and mineralocorticoid receptor (MR). The GR present in", + "corticosterone dramatically reduce the delayed-type hypersensitivity response (Dhabhar andMcEwen, 1997 ,1999 ). Sorrells and Sapolsky (2007 ) have provided a thought provoking recent review, contrasting the well-established anti-inammatory aspect of glucocorticoids, with the mounting evidence for their pro-inammatory effects both in the periphery and in the brain fol-lowing chronic exposure. This pattern of results demonstrates that the acute stress response has", + "mature babies in order to stimulate lung maturation. As illustrated here, Dex readily bypasses the protective bar-rier enzyme 11 beta-hydroxysteroid dehydrogenase type2 (11-HSD2), which normally limits fetal exposure tomaternal cortisol by converting it to corticosterone, aless bioactive form. Some concerns linger about long-term effects of fetal exposure to high doses or sustainedcorticosteroid treatmentantenatal glucocorticoid therapies are warranted,", + "first session. Approximately 50 microliters was collected into lithium heparin-coated tubes and then centrifuged for collection of plasma. Samples were stored at 80 degrees until ready for processing. Plasma corticosterone concentration was measured with the use of the DetectX CORT Enzyme Immunoassay kit (ArborAssays K014-H5, Ann Arbor, MI, USA). Room temperature plasma samples were diluted 1:450 in assay buffer and processed according to", + "corticosterone in their drinking water as sole liquid source for seven days prior to i.p. treatment with 4 mg /kg diisopropylurophosphate. Data are mean consumption per day s.e.m. 4.2. Gene Expression in Response to Treatments 4.2.1. IL1b We observed wide variability in the e ect of CORT on expression of Il1b (Figure 2). ANOVA revealed a signicant main e ect for strain (F 32,122 =3.61, p<0.001). The main e ects for sex and", + "cortisol. Support has emerged for the basic tenets of this hypothesis in studies of both humans and animals (Avitsur et al, 2001 ; Miller and Chen, 2006 ; Miller et al, 2002 ,2009 ; Rohleder et al, 2001 ; Stark et al, 2001 ). Most of this work has made use of an ex vivo assay system, where leukocytes are stimulated with a bacterial prod- uct (endotoxin) in the presence of varying levelsof cortisol, which is expected to suppress their ability to synthesize inammatory cytokines. In" + ], + "answer": "Corticosterone has several uses and effects as indicated in the provided context: 1. **Amplification of Stress-Induced Changes**: Corticosterone importantly amplifies stress-induced changes [1]. 2. **Anti-inflammatory and Immune Effects**: At high and supraphysiological concentrations, corticosterone has pronounced anti-inflammatory actions. Conversely, at lower concentrations, it appears to have some immune-potentiating effects [3]. 3. **Impact on Cell Proliferation**: Acute increases in corticosterone lead to decreased cell proliferation, while chronic increases cause an increase in proliferation rate. This is due to the presence of two receptors with different binding affinities: the glucocorticoid receptor (GR) and the mineralocorticoid receptor (MR) [5]. 4. **Reduction of Delayed-Type Hypersensitivity Response**: Corticosterone dramatically reduces the delayed-type hypersensitivity response [6]. 5. **Measurement in Research**: Corticosterone levels can be measured in plasma samples using specific enzyme immunoassay kits, which is useful in various research settings [8]. These points highlight the diverse roles and applications of corticosterone in both physiological and research contexts.", + "question": "what is the use of corticosterone?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_4 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_4 new file mode 100644 index 0000000..35b4e62 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_4 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2012 - Systems Biology Approaches to Nutrition.pdf", + "2004 - Nutritional genomics.pdf", + "2008 - (Infectious Disease) Karl A. Western (auth.), Vassil St. Georgiev PhD, Karl A. Western MD, John J. McGowan PhD (eds.) - National Institute of Allergy and Infectious Diseases, NIH_ Frontiers in Researc (3).pdf", + "2008 - Biotools for Determining the Genetics of Susceptibility to Infectious Diseases.pdf", + "2006 - Invited Review Microbial ecology in the age of genomics and metagenomics concepts, tools, and recent advances.pdf", + "2008 - Molecular profiling in the age of cancer genomics.pdf", + "2003 - Molecular profiling in the age.pdf", + "2007 - Bioinformatics_for_Geneticists.pdf", + "003 -Barnes- Bioinformatics_for_Geneticists.pdf", + "2007 - Bioinformatics_for_Genetices_MAZEN_SAEED.pdf" + ], + "extraction_id": [ + "713c3d5f-dea2-5d83-a4f9-8749e8c0a9e8", + "39d8f5af-dff0-58ab-b16f-f0b25f7ccdf0", + "16c8fbb0-ab2a-563f-a6b2-e0d8733b69fb", + "fe6eb7f0-9f09-50f8-a7a1-c71e507226d5", + "c08e6c0a-19fe-52ae-a715-8241e7b9baf8", + "167ddb29-f516-5670-9b89-a5d6c9eb930f", + "4c017db4-38d5-5116-b707-57e836fd043b", + "512cfd3a-f28e-5e11-8caa-6add0151a824", + "564e2e42-51ba-5b46-9375-b9ebee9ceabc", + "dd3030ab-0d42-521f-a769-8ee1b2fd0902" + ], + "document_id": [ + "6955478b-950d-5d29-b24c-3a5ca656f3ae", + "f97bdb31-0a83-5046-a60c-81d633684557", + "4db8c752-c8e2-5f6d-a091-dc4f1d0c48bc", + "fcbbb3ce-6524-50e3-9f8d-c191dc551231", + "a666073a-eb22-53b0-bc94-550e775e456a", + "a3e71525-cef6-50ba-abd1-e3853ddf77fa", + "547b71a7-ac5a-52ca-b9db-04391e9e50c6", + "4ea8e1a8-e113-5f02-ad78-880b9c51a101", + "045edae8-468b-5725-be06-8cb4b8f6a92b", + "139463d1-c63c-5c51-bf9c-9ccc356768e0" + ], + "id": [ + "chatcmpl-ADZIuGqxbDENLCoABzAMboXc8WUvK", + "9b81ec06-a109-5441-93bc-fd3e2d0b7862", + "2369f273-6321-5702-9c80-c29ae74935d0", + "3aafc5c1-96b5-5629-bc72-b00cf5070536", + "6081b16d-3380-5602-9daf-0500940fafbb", + "011b9d94-7b34-597c-9b89-74062b999132", + "32219bd7-c673-5deb-bb35-3bea4ae9bd3a", + "5cebb071-960c-5072-beb7-842815ae89bb", + "59772d2c-7eac-5a4b-b9ef-70735afda23e", + "aa406f0f-8f39-5189-9131-91345a876489", + "f03ab31a-1da3-50f1-9cfe-dc05cee18c05" + ], + "contexts": [ + "is the eld of bioinformatics.", + "the umbrella of bioinformatics or com-putational biology.", + "methods of computer-based information processing for ana-lyzing the structure and function of biologically important molecules. NCBI bioinformatics-related resources may be accessed through its home page at: www.ncbi.nlm.nih.gov. The NCBI has three principal branches: 1. Computational Biology Branch ( http://www.ncbi.nlm. nih.gov/CBBresearch/) 2. Information Engineering Branch ( http://www.ncbi.nlm. nih.gov/IEB/)", + "methods of computer-based information processing for ana-lyzing the structure and function of biologically important molecules. NCBI bioinformatics-related resources may be accessed through its home page at: www.ncbi.nlm.nih.gov. The NCBI has three principal branches: 1. Computational Biology Branch ( http://www.ncbi.nlm. nih.gov/CBBresearch/) 2. Information Engineering Branch ( http://www.ncbi.nlm. nih.gov/IEB/)", + "been successful in microbial ecological research withoutbioinformatics tools. Broadly defined, bioinformatics refersto the use of computers to seek patterns in the observedbiological data and to propose mechanisms for such patterns.As can be seen from below, bioinformatics not only canhelp us directly address experimental research objectives butalso can integrate information from various sources and seekspatterns not achievable through experimentation alone.", + "Since the first protein database was created by Margaret Dayhoffin 1965 in response to the increase in protein sequencing, therehas been an explosion of data from the different modalities. Foreach of the aforementioned levels, bioinformatics plays a crucialand intimate role in each of the steps. In general, there are threelarge categories of bioinformatics applications, including data-bases, algorithms and predictions. The category of databasesallows for the combining and organization of large amounts", + "Since the first protein database was created by Margaret Dayhoffin 1965 in response to the increase in protein sequencing, therehas been an explosion of data from the different modalities. Foreach of the aforementioned levels, bioinformatics plays a crucialand intimate role in each of the steps. In general, there are threelarge categories of bioinformatics applications, including data-bases, algorithms and predictions. The category of databasesallows for the combining and organization of large amounts", + "remit of the early bioinformaticist.1,2T o address these problems, the eld drew from the foundations of statistics, mathematics, physics, computer science and, of course, molecular biology. T oday, predictably, bioinformatics still reects the broad base on which it started, comprising an eclectic collection of scientic specialists. As a result of its inherent diversity, it is difcult to dene the scope of bioinformatics as a discipline. It may be even fruitless to try to draw hard boundaries around the eld.", + "remit of the early bioinformaticist.1,2T o address these problems, the eld drew from the foundations of statistics, mathematics, physics, computer science and, of course, molecular biology. T oday, predictably, bioinformatics still reects the broad base on which it started, comprising an eclectic collection of scientic specialists. As a result of its inherent diversity, it is difcult to dene the scope of bioinformatics as a discipline. It may be even fruitless to try to draw hard boundaries around the eld.", + "remit of the early bioinformaticist.1,2T o address these problems, the eld drew from the foundations of statistics, mathematics, physics, computer science and, of course, molecular biology. T oday, predictably, bioinformatics still reects the broad base on which it started, comprising an eclectic collection of scientic specialists. As a result of its inherent diversity, it is difcult to dene the scope of bioinformatics as a discipline. It may be even fruitless to try to draw hard boundaries around the eld." + ], + "answer": "Bioinformatics broadly refers to the use of computers to seek patterns in observed biological data and to propose mechanisms for such patterns [5]. It involves methods of computer-based information processing for analyzing the structure and function of biologically important molecules [3], [4]. The field draws from the foundations of statistics, mathematics, physics, computer science, and molecular biology, reflecting a broad base of scientific specialties [8], [9], [10].", + "question": "what is bioinformatics" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_5 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_5 new file mode 100644 index 0000000..962ae82 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_5 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2006 - Positional cloning of genes contributing to variability in nociceptive and analgesic phenotypes.pdf", + "2007 - QTL Mapping in Aging Systems.pdf", + "2005 -Knott- Regression based QTL mapping.pdf", + "2005 - Regression-based quantitative trait loci mapping robust, efficient and effective.pdf", + "2005 - Regression-based quantitative trait loci mapping robust, efficient and effective.pdf", + "2005 -Knott- Regression based QTL mapping.pdf", + "2007 - Using quantitative trait loci analysis to select plants for altered radionuclide accumulation.pdf", + "2008 - Genotype-phenotype relationships and the patterning of complex traits as exemplified in the mammalian dentition.pdf", + "2019 - Novel Genetic Loci Control L5 Vertebral Trabecular Bone and the Response to Low Calcium Intake in Growing BXD Recombinant Inbred Mice.pdf", + "2012 - Teaching Neuroinformatics with an Emphasis on Quantitative Locus Anlaysis.pdf" + ], + "extraction_id": [ + "c2c33142-b1dc-5162-a2a1-b452d2385958", + "ace8317f-2e7a-5590-a8e6-5e961480c0fb", + "e12f12c8-b1e0-54fa-86f8-0bcdb580bca1", + "e8203703-d34a-5848-bf54-4e20eb6fc3c5", + "75b53145-3938-5fbe-9cca-0389a68e1955", + "26dd8d34-b134-5426-b717-61b8a3a0f752", + "9ca9216b-e4cb-52c2-a286-f7d5d37936b6", + "b672f393-c45d-5393-96ee-77934e21e9c3", + "92e2d87b-02c9-588b-bc3c-e1034c05826d", + "0184b980-f596-51d9-a1a5-dd9c8d4ba388" + ], + "document_id": [ + "8ba88825-7473-52f8-8a1d-27f25644c4a2", + "35fbcd3c-97e8-57e5-b4c9-08dfbd4bce2e", + "cd41c63b-e5c2-5040-bbc5-ab20925b7d17", + "ba67a5b2-3dc7-57dc-8f8b-2d01433e58c2", + "ba67a5b2-3dc7-57dc-8f8b-2d01433e58c2", + "cd41c63b-e5c2-5040-bbc5-ab20925b7d17", + "682e6f43-10d4-5772-a69a-26e774606ba7", + "f6e866b8-b233-5862-bfb8-9949d0dabb97", + "de8dda5e-0e2f-5aa9-bb13-851c526b36a5", + "f36cbb2c-90f3-5544-8ce8-52b2004f6b49" + ], + "id": [ + "chatcmpl-ADZIye9JJrA436MgjlTpeY9z4NFZS", + "1ec396e1-0218-5f22-8db7-8653770944fb", + "e3149a33-9780-5f50-b582-142cdae5a5d3", + "ef0bab2a-db4a-57ac-9f75-32ec8c4a8f87", + "62ec26e1-3c71-558d-9378-e920e47edb08", + "5b07b911-a624-52ed-8506-ab14cb16a2eb", + "297470d7-ce20-5685-af94-a8ed5c68386b", + "543c9c0c-e8f5-59d8-b1e0-22172ace332e", + "1aa1e57d-cced-59d2-ac5b-9f3be7be2355", + "adbe8575-3c00-53e6-bb98-e86b8d01c7c5", + "22a5b128-d4d2-5fad-a60a-162c1d9a3369" + ], + "contexts": [ + "(although quite demanding) process offollowing the trait across multiple generations by tracing its coinheritance with genetic markers (a technique referred to as linkage mapping). Finding loci responsible for variability in a quantitative trait (quantitative trait locus mapping, or QTL mapping) is much more difficult, as there are many more sources of variation to capture. lnbred mouse strains are the optimum starting point for QTL", + "Genetic linkage analysis can be used to identify regions of the genome that contain genes that predispose to the observed quantitative trait, leading to iden-tification of QTLs. A significant QTL means that different genotypes at a poly-morphic marker locus are associated with different trait values. Linkage isdetermined by the log of odds (LOD) scores or likelihood ratio statistics (LRS)(seeNote 1 ). To calculate a LOD score or an LRS score for a selected quanti-", + "quantitative trait loci in crosses between outbred linesusing least squares. Genetics 136, 11951207. Haseman, J. K. & Elston, R. C. 1972 The investigation of linkage between a quantitative trait and a marker locus.Behav. Genet. 2, 319. Henshall, J. M. & Goddard, M. E. 1999 Multiple trait mapping of quantitative trait loci after selective genotypingusing logistic regression. Genetics 151, 885894. Jansen, R. C. 1993 Interval mapping of multiple quantitative trait loci. Genetics 135, 205211.", + "quantitative trait loci in crosses between outbred linesusing least squares. Genetics 136, 11951207. Haseman, J. K. & Elston, R. C. 1972 The investigation of linkage between a quantitative trait and a marker locus.Behav. Genet. 2, 319. Henshall, J. M. & Goddard, M. E. 1999 Multiple trait mapping of quantitative trait loci after selective genotypingusing logistic regression. Genetics 151, 885894. Jansen, R. C. 1993 Interval mapping of multiple quantitative trait loci. Genetics 135, 205211.", + "Keywords: quantitative trait loci mapping; regression; structured outbred populations 1. HISTORY The idea of using markers associated with a trait of interest, for example, to predict the performance of individuals in the trait, is not new. Initially, however, the markers used were not identied at the molecular level but rather through the phenotype, for example, coat colour or by the use of simple biochemicalprocedures such as blood groups. An early implemen-", + "Keywords: quantitative trait loci mapping; regression; structured outbred populations 1. HISTORY The idea of using markers associated with a trait of interest, for example, to predict the performance of individuals in the trait, is not new. Initially, however, the markers used were not identied at the molecular level but rather through the phenotype, for example, coat colour or by the use of simple biochemicalprocedures such as blood groups. An early implemen-", + "tions between markers and phenotype. Once allelic effects at each locus are identified, different techniques can be used to position precise loci (i.e., QTL) influencing the trait. These techniques include marker regression (30), interval mapping (31), and multiple mapping strategies (32). Marker regression locates QTL with respect to all markers simultaneously by regression onto the marker means. It also estimates the additive (and dominance) effects, tests their signif-", + "successful in identifying genes for simple traits. Quantitative trait mapping and genome wide association studies identify chromosomal regions referred to as quantitative trait loci (QTLs) that are statistically associated with the trait. Usually there are several such associations, each on the order of megabases (Mb) in length containing the usual diversity of single nucleotide polymorphisms (SNPs), one to two thousand per Mb, and there has been little success identifying", + "markers reveal potential gene locations regulating the trait of interest as known as quant itative trait loci (QTL s). Historically, this approach has been successful in identifying genes that are responsible for rare, monogenic bone diseases. More recently, much denser maps of SNP s allow researchers to perform genome -wide linkage analysis for complex trait s like bone phenotypes . However, several difficulties preventing the discovery of causal genes include genetic", + "Quantitative Trait Locus (QTL) analysis, which links phenotype to loci on chromosomes that likely had an impact on the phenotype. Students then are able to sift through a list of genes in the region(s) of the chromosome identified by the QT L analysis and find a candidate gene that has relatively high expression in the brain region of interest. Once such a candidate gene is identified, students can find out more information about the gene," + ], + "answer": "The process of finding a genetic marker followed by a quantitative trait loci (QTL) involves several steps: 1. **Linkage Mapping**: The initial step is to trace the trait across multiple generations by examining its coinheritance with genetic markers. This technique is known as linkage mapping [1]. Genetic linkage analysis is used to identify regions of the genome that contain genes predisposing to the observed quantitative trait [2]. 2. **Identification of QTLs**: Once regions of interest are identified through linkage mapping, the next step is to pinpoint the loci responsible for variability in the quantitative trait. This process is known as quantitative trait locus mapping or QTL mapping [1]. QTL mapping is more challenging due to the numerous sources of variation that need to be captured [1]. 3. **Statistical Analysis**: To determine linkage, statistical methods such as the log of odds (LOD) scores or likelihood ratio statistics (LRS) are used [2]. A significant QTL indicates that different genotypes at a polymorphic marker locus are associated with different trait values [2]. 4. **Techniques for Positioning QTLs**: Various techniques can be employed to position precise loci influencing the trait. These include marker regression, interval mapping, and multiple mapping strategies [7]. Marker regression, for instance, locates QTLs with respect to all markers simultaneously by regression onto the marker means and estimates the additive and dominance effects [7]. 5. **Genome-Wide Association Studies (GWAS)**: In some cases, genome-wide association studies are conducted to identify chromosomal regions referred to as QTLs that are statistically associated with the trait [8]. These regions often contain a high density of single nucleotide polymorphisms (SNPs) [8]. 6. **Candidate Gene Identification**: After identifying the QTL regions, researchers can sift through the list of genes in these regions to find candidate genes that may have a significant impact on the phenotype [10]. By following these steps, researchers can effectively identify genetic markers and subsequently map quantitative trait loci, providing insights into the genetic basis of complex traits.", + "question": "Explain the process of finding a genetic marker followed by a quantitative trait loci." +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_6 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_6 new file mode 100644 index 0000000..45c7db9 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_6 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2018 - Repetitive Fragile Sites Centromere Satellite DNA.pdf", + "2018 - Repetitive Fragile Sites Centromere Satellite DNA.pdf", + "2018 - Repetitive Fragile Sites Centromere Satellite DNA.pdf", + "2018 - Germline de novo mutation clusters arise.pdf", + "2018 - Repetitive Fragile Sites Centromere Satellite DNA.pdf", + "2018 - Repetitive Fragile Sites Centromere Satellite DNA.pdf", + "2018 - Germline de novo mutation clusters arise.pdf", + "2018 - Repetitive Fragile Sites Centromere Satellite DNA.pdf", + "2018 - Repetitive Fragile Sites Centromere Satellite DNA.pdf", + "2018 - Repetitive Fragile Sites Centromere Satellite DNA.pdf" + ], + "extraction_id": [ + "af805fbb-a39f-5a29-a0b0-9add1126b553", + "5f52d45a-991b-54c3-92ae-37dd96e31a42", + "5f52d45a-991b-54c3-92ae-37dd96e31a42", + "403bbc25-ce94-5a4f-a409-436cc02fb204", + "5f52d45a-991b-54c3-92ae-37dd96e31a42", + "907c33dd-34b8-51f5-a91f-fb83cf11f7f9", + "403bbc25-ce94-5a4f-a409-436cc02fb204", + "5f52d45a-991b-54c3-92ae-37dd96e31a42", + "c07e5efe-7d80-547e-847b-eef61bb661cc", + "8cba1054-1540-57ee-a5c4-350f5555081f" + ], + "document_id": [ + "262df0d6-ad68-544a-88ed-b4568f305858", + "262df0d6-ad68-544a-88ed-b4568f305858", + "262df0d6-ad68-544a-88ed-b4568f305858", + "f2b2ca83-a34f-5f99-b9f2-357b2ddbe136", + "262df0d6-ad68-544a-88ed-b4568f305858", + "262df0d6-ad68-544a-88ed-b4568f305858", + "f2b2ca83-a34f-5f99-b9f2-357b2ddbe136", + "262df0d6-ad68-544a-88ed-b4568f305858", + "262df0d6-ad68-544a-88ed-b4568f305858", + "262df0d6-ad68-544a-88ed-b4568f305858" + ], + "id": [ + "chatcmpl-ADZJ5u5h9f6SgdrxrixAsqUmOQgLr", + "72da6034-227d-5dac-9ef6-90c246ec2b40", + "66e5e009-5496-5e18-bfbe-9a9567cad60c", + "2f2342b3-4c07-5bfd-80c6-8bc47fead6b6", + "ab92961e-c267-5e56-aeb9-0d03fd0a4102", + "fb421292-e4ea-510b-8a69-48e12e6e6a43", + "3b5635bb-8308-5c6b-8ee0-d65293257362", + "788b6b85-7ef2-5805-bc0c-d8af71332e0d", + "4802fb82-204d-57b6-b24f-5683f3731aea", + "c8e7e683-487f-5075-bbef-126ca0203c6c", + "5da6f433-231d-586b-a057-558a4c68f741" + ], + "contexts": [ + "Genes 2018 ,9, 615 18 of 20 97. McFarlane, R.J.; Humphrey, T.C. A role for recombination in centromere function. Trends Genet. 2010 ,26, 209213. [CrossRef] 98. Talbert, P .B.; Henikoff, S. Centromeres convert but dont cross. PLoS Biol. 2010 ,8, e1000326. [CrossRef] 99. Durfy, S.J.; Willard, H.F. Concerted Evolution of Primate Alpha Satellite DNA Evidence for an Ancestral Sequence Shared by Gorilla and Human X Chromosome Satellite. J. Mol. Biol. 1990 ,216, 555566. [CrossRef]", + "4.1. Recombination and Repair at Centromeres: Errors in Copying and Mending Highly Repetitive DNA Why are centromeres so cold?, asked Andy Choo in his review of centromeres [ 96]. He was referring to centromere DNA as being cold to recombination. While maternal and paternal chromosomes suffer multiple DNA double-stranded breaks (DSBs) to induce recombination and exchange of genetic information by crossing over during meiosis, centromere loci are refractory", + "exacerbates centromere rearrangements [ 54], indicating that there may be active mechanisms to suppress centromeric recombination and these may, at least in part, involve core centromeric proteins. Centromere alpha-satellite DNA is estimated to represent between 3% and 10% of the human genome [ 101], reviewed in [ 19]. During each round of replication, unperturbed cells suffer over 40 DNA DSBs [ 102], of which at least half are repaired by homologous recombination (HR) in S-phase and G2,", + "347357 (1998). 31. Baudat, F. et al. PRDM9 is a major determinant of meiotic recombination hotspots in humans and mice. Science 327, 836840 (2010). 32. Kong, A. et al. Recombination rate and reproductive success in humans. Nat.Genet. 36, 12031206 (2004). 33. Ottolini, C. S. et al. Genome-wide maps of recombination and chromosome segregation in human oocytes and embryos show selection for maternal recombination rates. Nat. Genet. 47, 727735 (2015).", + "to this process. This led to the assumption that centromeres do not undergo recombination and that the repetitive arrays are maintained as stable. However, this clashed with the notion that centromeres very origin stems from recombination to create the repetitive array, where multiple short- and long-range recombination events may be responsible for the generation and reiteration of blocks of highly homogenized alpha-satellite DNA throughout the centromere [ 97,98]. Furthermore, in addition", + "of these DSBs through recombination-dependent pathways, such as homologous recombination (HR), may disrupt centromere integrity in several ways: (1) Crossover between sister chromatids will lead to sister chromatid exchange (SCE), which has been reported at human cent romeres. (2) Search for the homologous sequence may erroneously identify an identical or nearly identical sequence within the same chromatid downstream or upstream of the break site. Recombination between these two", + "higher in regions of high recombination. Trends Genet. 18, 337340 (2002). 26. Webster, M. T. & Hurst, L. D. Direct and indirect consequences of meiotic recombination: implications for genome evolution. Trends Genet. 28, 101109 (2012). 27. Alexandrov, L. B. et al. Signatures of mutational processes in human cancer. Nature 500, 415421 (2013).", + "to chromosome-specic alpha-satellites, certain centromeric sequences are shared by all chromosomes, evidence that formation of these arrays is dominated by interchromosomal exchanges [ 8,98100]. This invites new questions about the stability of centromere DNA outside of meiosis. Indeed, our recent analysis has shown that centromeres can undergo recombination during a single round of cell division in primary human cells [ 54]. Depletion of CENP-A and other CCAN proteins", + "shown to play a role in DNA repair (reviewed in [ 125]), and in vitro experiments show that this hybridization may facilitate DSB repair by bridging the broken DNA fragments in a Rad52-dependent manner during recombination [126]. Centromeres have been suggested [ 127,128], but not proven, to behave like fragile sites of the human genome. Common fragile sites are described as genomic loci where ongoing replication collides", + "Cell Biol. 2016 ,17, 1629. [CrossRef] [PubMed] 54. Giunta, S.; Funabiki, H. Integrity of the human centromere DNA repeats is protected by CENP-A, CENP-C, and CENP-T. Proc. Natl. Acad. Sci. USA 2017 ,114, 19281933. [CrossRef] [PubMed] 55. Giunta, S. Centromere Chromosome Orientation Fluorescent in situ Hybridization (Cen-CO-FISH) Detects Sister Chromatid Exchange at the Centromere in Human Cells. Bio-Protocol 2018 ,8. [CrossRef]" + ], + "answer": "Recombination in human centromeres is generally suppressed, making these regions \"cold\" to recombination events [2]. This suppression is thought to be actively maintained by core centromeric proteins [3]. Despite this, there is evidence suggesting that centromeres can undergo recombination, particularly through mechanisms that involve homologous recombination (HR) during DNA repair processes [6]. For instance, crossover between sister chromatids, known as sister chromatid exchange (SCE), has been reported at human centromeres [6]. Additionally, centromeres may experience recombination during a single round of cell division in primary human cells [8]. This indicates that while recombination is generally suppressed in centromeres, it can still occur under certain conditions, potentially impacting centromere integrity and stability [5], [6], [8].", + "question": "What about recombination in human centromeres?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_7 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_7 new file mode 100644 index 0000000..5d05576 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_7 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2018 - Repetitive Fragile Sites Centromere Satellite DNA.pdf", + "2018 - Repetitive Fragile Sites Centromere Satellite DNA.pdf", + "2018 - Repetitive Fragile Sites Centromere Satellite DNA.pdf", + "2018 - Repetitive Fragile Sites Centromere Satellite DNA.pdf", + "2018 - Repetitive Fragile Sites Centromere Satellite DNA.pdf", + "2018 - Germline de novo mutation clusters arise.pdf", + "2018 - Repetitive Fragile Sites Centromere Satellite DNA.pdf", + "2018 - Repetitive Fragile Sites Centromere Satellite DNA.pdf", + "2017 - Human female meiosis revised new.pdf", + "2018 - Repetitive Fragile Sites Centromere Satellite DNA.pdf" + ], + "extraction_id": [ + "5f52d45a-991b-54c3-92ae-37dd96e31a42", + "af805fbb-a39f-5a29-a0b0-9add1126b553", + "907c33dd-34b8-51f5-a91f-fb83cf11f7f9", + "5f52d45a-991b-54c3-92ae-37dd96e31a42", + "5f52d45a-991b-54c3-92ae-37dd96e31a42", + "403bbc25-ce94-5a4f-a409-436cc02fb204", + "c07e5efe-7d80-547e-847b-eef61bb661cc", + "5f52d45a-991b-54c3-92ae-37dd96e31a42", + "8e3ed969-da49-5ba4-8382-dc2714b01497", + "8cba1054-1540-57ee-a5c4-350f5555081f" + ], + "document_id": [ + "262df0d6-ad68-544a-88ed-b4568f305858", + "262df0d6-ad68-544a-88ed-b4568f305858", + "262df0d6-ad68-544a-88ed-b4568f305858", + "262df0d6-ad68-544a-88ed-b4568f305858", + "262df0d6-ad68-544a-88ed-b4568f305858", + "f2b2ca83-a34f-5f99-b9f2-357b2ddbe136", + "262df0d6-ad68-544a-88ed-b4568f305858", + "262df0d6-ad68-544a-88ed-b4568f305858", + "95817342-4fb7-543f-beab-f3d7d0a1dd80", + "262df0d6-ad68-544a-88ed-b4568f305858" + ], + "id": [ + "chatcmpl-ADZJEypA9e5sRA4lUD0c4IqRsBYeu", + "66e5e009-5496-5e18-bfbe-9a9567cad60c", + "72da6034-227d-5dac-9ef6-90c246ec2b40", + "3b5635bb-8308-5c6b-8ee0-d65293257362", + "2f2342b3-4c07-5bfd-80c6-8bc47fead6b6", + "fb421292-e4ea-510b-8a69-48e12e6e6a43", + "ab92961e-c267-5e56-aeb9-0d03fd0a4102", + "c8e7e683-487f-5075-bbef-126ca0203c6c", + "4802fb82-204d-57b6-b24f-5683f3731aea", + "dfa6d21d-2407-5738-84df-95b68469c263", + "5da6f433-231d-586b-a057-558a4c68f741" + ], + "contexts": [ + "4.1. Recombination and Repair at Centromeres: Errors in Copying and Mending Highly Repetitive DNA Why are centromeres so cold?, asked Andy Choo in his review of centromeres [ 96]. He was referring to centromere DNA as being cold to recombination. While maternal and paternal chromosomes suffer multiple DNA double-stranded breaks (DSBs) to induce recombination and exchange of genetic information by crossing over during meiosis, centromere loci are refractory", + "Genes 2018 ,9, 615 18 of 20 97. McFarlane, R.J.; Humphrey, T.C. A role for recombination in centromere function. Trends Genet. 2010 ,26, 209213. [CrossRef] 98. Talbert, P .B.; Henikoff, S. Centromeres convert but dont cross. PLoS Biol. 2010 ,8, e1000326. [CrossRef] 99. Durfy, S.J.; Willard, H.F. Concerted Evolution of Primate Alpha Satellite DNA Evidence for an Ancestral Sequence Shared by Gorilla and Human X Chromosome Satellite. J. Mol. Biol. 1990 ,216, 555566. [CrossRef]", + "of these DSBs through recombination-dependent pathways, such as homologous recombination (HR), may disrupt centromere integrity in several ways: (1) Crossover between sister chromatids will lead to sister chromatid exchange (SCE), which has been reported at human cent romeres. (2) Search for the homologous sequence may erroneously identify an identical or nearly identical sequence within the same chromatid downstream or upstream of the break site. Recombination between these two", + "exacerbates centromere rearrangements [ 54], indicating that there may be active mechanisms to suppress centromeric recombination and these may, at least in part, involve core centromeric proteins. Centromere alpha-satellite DNA is estimated to represent between 3% and 10% of the human genome [ 101], reviewed in [ 19]. During each round of replication, unperturbed cells suffer over 40 DNA DSBs [ 102], of which at least half are repaired by homologous recombination (HR) in S-phase and G2,", + "to this process. This led to the assumption that centromeres do not undergo recombination and that the repetitive arrays are maintained as stable. However, this clashed with the notion that centromeres very origin stems from recombination to create the repetitive array, where multiple short- and long-range recombination events may be responsible for the generation and reiteration of blocks of highly homogenized alpha-satellite DNA throughout the centromere [ 97,98]. Furthermore, in addition", + "347357 (1998). 31. Baudat, F. et al. PRDM9 is a major determinant of meiotic recombination hotspots in humans and mice. Science 327, 836840 (2010). 32. Kong, A. et al. Recombination rate and reproductive success in humans. Nat.Genet. 36, 12031206 (2004). 33. Ottolini, C. S. et al. Genome-wide maps of recombination and chromosome segregation in human oocytes and embryos show selection for maternal recombination rates. Nat. Genet. 47, 727735 (2015).", + "shown to play a role in DNA repair (reviewed in [ 125]), and in vitro experiments show that this hybridization may facilitate DSB repair by bridging the broken DNA fragments in a Rad52-dependent manner during recombination [126]. Centromeres have been suggested [ 127,128], but not proven, to behave like fragile sites of the human genome. Common fragile sites are described as genomic loci where ongoing replication collides", + "to chromosome-specic alpha-satellites, certain centromeric sequences are shared by all chromosomes, evidence that formation of these arrays is dominated by interchromosomal exchanges [ 8,98100]. This invites new questions about the stability of centromere DNA outside of meiosis. Indeed, our recent analysis has shown that centromeres can undergo recombination during a single round of cell division in primary human cells [ 54]. Depletion of CENP-A and other CCAN proteins", + "Studying the direct link between re combination and sister chromatid dynamics with combined live cell imaging and genomics will likely yieldimportant insight into the impact that centromeric and telomeric cross- overs have on chromosome segregation.Reconstructing the bivalent con guration from MeioMaps: recombination and its linkwith chromosome segregation The combined assessment of haplotypes that are determined by recombination also allowed the rst direct correlations between", + "Cell Biol. 2016 ,17, 1629. [CrossRef] [PubMed] 54. Giunta, S.; Funabiki, H. Integrity of the human centromere DNA repeats is protected by CENP-A, CENP-C, and CENP-T. Proc. Natl. Acad. Sci. USA 2017 ,114, 19281933. [CrossRef] [PubMed] 55. Giunta, S. Centromere Chromosome Orientation Fluorescent in situ Hybridization (Cen-CO-FISH) Detects Sister Chromatid Exchange at the Centromere in Human Cells. Bio-Protocol 2018 ,8. [CrossRef]" + ], + "answer": "Recombination in human centromeres is a complex process that is generally suppressed to maintain centromere integrity. Here are the key points about how recombination works in human centromeres: 1. **Recombination Suppression**: Centromere DNA is often referred to as being \"cold\" to recombination, meaning that it is generally refractory to the recombination processes that occur during meiosis in other parts of the genome [1]. 2. **Potential Disruptions**: When recombination does occur, it can disrupt centromere integrity in several ways. For instance, crossover between sister chromatids can lead to sister chromatid exchange (SCE), which has been reported at human centromeres. Additionally, the search for homologous sequences may erroneously identify nearly identical sequences within the same chromatid, leading to inappropriate recombination events [3]. 3. **Mechanisms to Suppress Recombination**: There are active mechanisms to suppress recombination at centromeres, which may involve core centromeric proteins. This suppression is crucial because recombination-dependent pathways, such as homologous recombination (HR), could otherwise exacerbate centromere rearrangements [4]. 4. **Recombination in Centromere Formation**: Despite the general suppression, recombination is believed to play a role in the origin and formation of centromeres. Multiple short- and long-range recombination events may be responsible for generating and reiterating blocks of highly homogenized alpha-satellite DNA throughout the centromere [5]. 5. **Recombination Outside Meiosis**: Recent analyses have shown that centromeres can undergo recombination during a single round of cell division in primary human cells, indicating that recombination at centromeres is not entirely restricted to meiosis [8]. In summary, while recombination at human centromeres is generally suppressed to maintain their structural integrity, it can occur under certain conditions and may play a role in the formation and evolution of centromeric DNA.", + "question": "How does recombination work in human centromeres?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_8 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_8 new file mode 100644 index 0000000..6e73b0c --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_8 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2018 - Germline de novo mutation clusters arise.pdf", + "2008 - Mechanisms of Disease genetic insights into the etiology of type 2 diabetes and obesity.pdf", + "2018 - Germline de novo mutation clusters arise.pdf", + "2008 - Loci Related to Metabolic-Syndrome Pathways Including LEPR.pdf", + "2003 - Haplotypes and the systematic analysis of genetic variation in genes and genomes.pdf", + "2018 - Repetitive Fragile Sites Centromere Satellite DNA.pdf", + "2003 - Haplotypes and the systematic analysis of genetic variation in genes and genomes.pdf", + "2003 - Haplotypes and the systematic analysis of genetic variation in genes and genomes.pdf", + "2020 - Prospective avenues for human population genomics and disease mapping in southern Africa.pdf", + "2016 - A genetic method for dating ancient genomes provides.pdf" + ], + "extraction_id": [ + "403bbc25-ce94-5a4f-a409-436cc02fb204", + "0fa3ac68-ea06-5d95-b3fb-f224d40e38a9", + "403bbc25-ce94-5a4f-a409-436cc02fb204", + "74f21fa4-31ff-5aa6-b806-1ffc73b79801", + "de271b3e-86e8-5405-8e15-a54376db728b", + "af805fbb-a39f-5a29-a0b0-9add1126b553", + "e764c7b0-e155-5358-a5c9-a168508a32ea", + "de271b3e-86e8-5405-8e15-a54376db728b", + "4927bfe6-f007-5ad0-88c8-f9ae4bde540f", + "fcf5296e-6be4-5789-b1e1-ac57fef15119" + ], + "document_id": [ + "f2b2ca83-a34f-5f99-b9f2-357b2ddbe136", + "87ceda8f-0ce6-5678-9ade-96a40a991647", + "f2b2ca83-a34f-5f99-b9f2-357b2ddbe136", + "c7583131-8c05-576b-a458-577021374b5d", + "97525d6c-b50d-5826-84eb-71ddd52aea27", + "262df0d6-ad68-544a-88ed-b4568f305858", + "97525d6c-b50d-5826-84eb-71ddd52aea27", + "97525d6c-b50d-5826-84eb-71ddd52aea27", + "e74e3b9c-14d2-54c3-9319-7dc1b45db41e", + "5a5e67ea-4830-5fe8-95c3-ccfcc8324036" + ], + "id": [ + "chatcmpl-ADZJSm9AzU2UQT9a71JIfAJoKh0Fh", + "ab92961e-c267-5e56-aeb9-0d03fd0a4102", + "46297c04-59ef-5f94-af20-5e83036b5ea9", + "788b6b85-7ef2-5805-bc0c-d8af71332e0d", + "9df97195-cdb6-5271-8dd2-89a421f6281a", + "94686ace-46ce-51f1-9b26-07c27baca6b9", + "72da6034-227d-5dac-9ef6-90c246ec2b40", + "9ee9c9e1-70ed-512a-bd20-9f967829f75a", + "acfd48ac-6d04-5691-b2b7-6ebe179c0f0b", + "4bcb02fe-e0e1-5e2b-b0c7-7d27bb03b73c", + "aa3c4d11-71cb-5941-a6b0-56f9358ba565" + ], + "contexts": [ + "347357 (1998). 31. Baudat, F. et al. PRDM9 is a major determinant of meiotic recombination hotspots in humans and mice. Science 327, 836840 (2010). 32. Kong, A. et al. Recombination rate and reproductive success in humans. Nat.Genet. 36, 12031206 (2004). 33. Ottolini, C. S. et al. Genome-wide maps of recombination and chromosome segregation in human oocytes and embryos show selection for maternal recombination rates. Nat. Genet. 47, 727735 (2015).", + "Genet 39: 977983 33 Myers S et al. (2005) A fine-scale map of recombination rates and hotspots across the human genome. Science 310: 321324REVIEW Nature.indt 1 Nature.indt 1 28/11/07 9:46:50 am 28/11/07 9:46:50 am", + "higher in regions of high recombination. Trends Genet. 18, 337340 (2002). 26. Webster, M. T. & Hurst, L. D. Direct and indirect consequences of meiotic recombination: implications for genome evolution. Trends Genet. 28, 101109 (2012). 27. Alexandrov, L. B. et al. Signatures of mutational processes in human cancer. Nature 500, 415421 (2013).", + "D.R., and Donnelly, P. (2004). The ne-scale structure ofrecombination rate variation in the human genome. Science 304, 581584. 33. Winckler, W., Myers, S.R., Richter, D.J., Onofrio, R.C., McDo- nald, G.J., Bontrop, R.E., McVean, G.A., Gabriel, S.B., Reich, D., Donnelly, P., et al. (2005). Comparison of ne-scale recom- bination rates in humans and chimpanzees. Science 308, 107111. 1192 The American Journal of Human Genetics 82, 11851192, May 2008", + "www.pharmaco-genomics.com 569REVIEW 48. Reich DE, Schaffner SF , Daly MJ et al. : Human chromosome sequence variation and the influence of gene history, mutation and recombination. Nat. Genet. 32, 135-142 (2002). The authors provide evidence that recombination hot spots may represent a general feature of the human genome and play a major role in shaping genetic variation in humans. 49. Wall JD, Pritchard JK: Haplotype blocks and linkage disequilibrium in the human", + "Genes 2018 ,9, 615 18 of 20 97. McFarlane, R.J.; Humphrey, T.C. A role for recombination in centromere function. Trends Genet. 2010 ,26, 209213. [CrossRef] 98. Talbert, P .B.; Henikoff, S. Centromeres convert but dont cross. PLoS Biol. 2010 ,8, e1000326. [CrossRef] 99. Durfy, S.J.; Willard, H.F. Concerted Evolution of Primate Alpha Satellite DNA Evidence for an Ancestral Sequence Shared by Gorilla and Human X Chromosome Satellite. J. Mol. Biol. 1990 ,216, 555566. [CrossRef]", + "Variations on a theme: cataloguing human DNA sequence variation. Science 278, 1580- 1581 (1997). 37. Jeffreys AJ, Kauppi L, Neumann R: Intensely punctate meiotic recombination in the class II region of the major histocompatibility complex. Nat. Genet. 29, 217-222 (2001). 38. Chakravarti A, Buetow KH, Antonarakis SE et al.: Nonuniform recombination within the human beta-globin gene cluster. Am. J. Hum. Genet. 36, 1239-1258 (1984). 39. Smith RA, Ho PJ, Clegg JB, Kidd, JR,", + "genome. Nat. Rev. Genet. 4, 587-597 (2003). Important review, including discussion of the recently proposed haplotype-block model of LD. 50. Nachman MW: Variation in recombination rate across the genome: evidence and implications. Curr. Opin. Genet. Dev. 12, 657-663 (2002). 51. Kong A, Gudbjartsson DF , Sainz J et al. : A high-resolution recombination map of the human genome. Nat. Genet. 31, 241-247 (2002). 52. Sabeti PC, Reich DE, Higgins JM et al. :", + "Recombination maps are often used for admixture mapping (Browning and Browning 2007). A recombination map is a genetic map that illustrates the variation of the recombina-tion rate across a region of the genome or the entire genome (Myers etal. 2005). It is dependent on the underlying dis-tribution of recombination events that occur between suc-cessive generations within a given population (Kong etal. 2010). The presence and activity of the PRDM9 zinc finger protein in the population under study, the ratio", + "31. Fu Q, et al. (2015) An early modern human from Romania with a recent Neanderthal ancestor. Nature 524(7564):216 219. 32. Baudat F, et al. (2010) PRDM9 is a major determinant of meiotic recombination hotspots in humans and mice. Science 327(5967):836 840. 33. Lesecque Y, Glmin S, Lartillot N, Mouchiroud D, Duret L (2014) The red queen model of recombination hotspots evolution in the light of archaic and modern human ge- nomes. PLoS Genet 10(11):e1004790." + ], + "answer": "Recombination in the human genome is a complex process with several key aspects highlighted in the provided context: 1. **PRDM9 and Recombination Hotspots**: PRDM9 is identified as a major determinant of meiotic recombination hotspots in humans and mice [1], [10]. This protein plays a crucial role in determining where recombination events are likely to occur during meiosis. 2. **Recombination Rate and Reproductive Success**: There is a relationship between recombination rate and reproductive success in humans, suggesting that recombination may have evolutionary implications [1]. 3. **Genome-wide Maps of Recombination**: Detailed maps of recombination rates and hotspots across the human genome have been created, providing insights into the fine-scale structure of recombination rate variation [2], [4]. These maps are essential for understanding genetic variation and are used in admixture mapping [9]. 4. **Variation in Recombination Rates**: There is significant variation in recombination rates across different regions of the human genome, which has implications for genetic diversity and evolution [8]. 5. **Influence on Genetic Variation**: Recombination hotspots are a general feature of the human genome and play a major role in shaping genetic variation [5]. 6. **Selection for Maternal Recombination Rates**: Genome-wide maps of recombination in human oocytes and embryos show selection for maternal recombination rates, indicating that recombination patterns can be subject to evolutionary pressures [1]. These points collectively highlight the importance of recombination in shaping the human genome, influencing genetic diversity, and having evolutionary consequences.", + "question": "What about recombination in the human genome?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_9 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_9 new file mode 100644 index 0000000..764ad85 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_cs_gn_9 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2016 - Coming of age ten years of next.pdf", + "2020 - Precision and Personalized Medicine How Genomic.pdf", + "2016 - Coming of age ten years of next.pdf", + "2016 - Coming of age ten years of next.pdf", + "2016 - Coming of age ten years of next.pdf", + "2016 - Coming of age ten years of next.pdf", + "2008 - Gene Expression Profiling.pdf", + "2014 - Computational tools to aid the design and development of a genetic reference population.pdf", + "2015 -Pandey- Functional Analysis of Genomic Variation and Impact on Molecular.pdf", + "2015 - Functional Analysis of Genomic Variation and Impact on Molecular and Higher Order Phenotypes.pdf" + ], + "extraction_id": [ + "cad1dcca-621d-5003-ba3a-81950819bc52", + "de09f30d-e9ba-5379-8c7a-85b2cd2ed6c8", + "4cdf13c0-c505-5ff9-9a6e-b10e5d1c8819", + "5da5fc5d-1fe6-58f0-9141-72b9b2996fff", + "cad1dcca-621d-5003-ba3a-81950819bc52", + "5da5fc5d-1fe6-58f0-9141-72b9b2996fff", + "c14d1c74-a14a-5037-8d3f-f32a60faa9a5", + "a744f8ce-7920-5fb8-acce-912f70112924", + "f4e989e5-c3d0-5d5c-b8c3-95894a14630b", + "fa426831-7c04-56c1-a191-1ebbc35342ed" + ], + "document_id": [ + "9dd6e4e9-d136-507b-b628-68c8e1461bd0", + "cd11028a-933b-52a0-9534-c173323056ef", + "9dd6e4e9-d136-507b-b628-68c8e1461bd0", + "9dd6e4e9-d136-507b-b628-68c8e1461bd0", + "9dd6e4e9-d136-507b-b628-68c8e1461bd0", + "9dd6e4e9-d136-507b-b628-68c8e1461bd0", + "59f3b969-089b-5258-93ad-892dbc9ffa9c", + "70cbde25-6406-5a31-91ae-57f430e8f267", + "93381e23-494b-5bc2-9d09-ef315506601f", + "263d327b-f5db-54e4-a215-b3f8a51cd7d6" + ], + "id": [ + "chatcmpl-ADZJdRAJz1ZAYluBcJm58tFEyX9Ng", + "98010acc-fd11-5d33-bced-626ef29f2896", + "8477a774-dddb-5541-b8d7-d51a7e56b0af", + "d2540614-9397-5e3e-8b5f-ad328ca973b2", + "13a6b6f9-4a9a-5eb9-ac79-d986d9e613f0", + "97f2aa12-623b-53ec-9793-5834311a37dd", + "1e324977-2ca5-5062-8a09-7659d516e899", + "3e782f01-a06e-51b6-ac8a-0e0a56939d08", + "da667832-cd2f-5af6-a0a8-a17542b0a2e2", + "d1158643-3625-5855-a03d-eec4ac96eb4d", + "cdf2b80f-1509-50a2-9cb2-a36dd6f3f2cc" + ], + "contexts": [ + "FURTHER INFORMATION 10X Genomics: http://www.10xgenomics.com 454 Sequencing: http://www.454.com Advances in Genome Biology and Technology (AGBT): http://www.agbt.org BGISEQ500: http://seq500.com/en/portal/Sequencer.shtml Illumina: http://www.illumina.com Ion Torrent: https://www.thermofisher.com/us/en/home/ brands/ion-torrent.html Oxford Nanopore Technologies: https://www.nanoporetech. com Pacific Biosciences: http://www.pacb.com Personal Genome Project: http://www.personalgenomes.org", + "36. Sequencing, H.G. Finishing the euchromatic sequence of the human genome. Nature 2004 ,431, 931945. 37. Heather, J.M.; Chain, B. The sequence of sequencers: The history of sequencing DNA. Genomics 2016 ,107, 18. [CrossRef] 38. Rothberg, J.M.; Leamon, J.H. The development and impact of 454 sequencing. Nat. Biotechnol. 2008 ,26, 11171124. [CrossRef] [PubMed] 39. Shendure, J.; Ji, H. Next-generation DNA sequencing. Nat. Biotechnol. 2008 ,26, 11351145. [CrossRef] [PubMed]", + "sequencing. Genome Res. 20, 11651173 (2010). 64. English,A.C. etal. Assessing structural variation in a personal genome-towards a human reference diploid genome. BMC Genomics 16, 286 (2015). 65. Carneiro,M.O. etal. Pacific Biosciences sequencing technology for genotyping and variation discovery in human data. BMC Genomics 13, 375 (2012). 66. Quail,M.A. etal. A tale of three next generation sequencing platforms: comparison of Ion T orrent, Pacific Biosciences and Illumina MiSeq sequencers.", + "22. Karow, J. Qiagen launches GeneReader NGS System atAMP; presents performance evaluation by broad. GenomeWeb [online], https:// www.genomeweb.com/ molecular-diagnostics/qiagen-launches-genereader- ngs-system-amp-presents-performance-evaluation (4Nov 2015). 23. Smith,D.R. & McKernan,K. Methods of producing and sequencing modified polynucleotides . US Patent 8058030 (2011). 24. Margulies,M. etal. Genome sequencing in microfabricated high-density picolitre reactors. Nature 437, 376380 (2005).", + "160. Glenn,T .C. Field guide to next-generation DNA sequencers. Mol. Ecol. Resour. 11, 759769 (2011). 161. Karow,J. At AGBT , 10X Genomics launches GemCode platform; shipments slated for Q2 as firm battles IP lawsuits. GenomeWeb [online], https://www. genomeweb.com/sample-prep/agbt-10x-genomics- launches-gemcode-platform-shipments-slated-q2-firm- battles-ip-lawsuits (2Mar 2015). Competing interests statement The authors declare competing interests: see Web version for details. FURTHER INFORMATION", + "sequencing. Bioinformatics 31, 20402042 (2015). 46. Qiagen. Oncology insights enabled by knowledge base- guided panel design and the seamless workflow of the GeneReader NGS system Press Release. Qiagen [online], http://www.genereaderngs.com/PROM-9192- 001_1100403_WP_GeneReader_NGS_0116_NA.pdf (2016). 47. Forgetta,V. etal. Sequencing of the Dutch elm disease fungus genome using the Roche/454 GS-FLX Titanium System in a comparison of multiple genomics core", + "DNA), and provide the means to link sequences containing applications. First, base- callers like Phred (4,5) extract raw sequences from raw data. There are also contig assemblers like Phrap (University of Washington, http://bozeman. mbt.washington.edu/phrap.docs/phrap.html ) or CAP3 (6) that assemble frag- ments to contigs and packages like consed (7) or GAP4 (8), which are used to finish sequencing projects. These programs are not explained in detail here.", + "sequencing data to solutions from the genotyping array data. iv PREVIEW", + "11 BIOINFORMATIC CHALLENGES FOR GENOMIC MEDICINE Processing and managing of high-throughput sequence data High throughput sequencing offers severa l advantages relative to array-based genotyping or expression assays. First, unlike genotyping arrays, whole genome sequencing is not limited to interrogating onl y known sequence variants. Similarly, RNA- sequencing (RNA-seq) enables expression quanti fication of novel transcripts that are not", + "11 BIOINFORMATIC CHALLENGES FOR GENOMIC MEDICINE Processing and managing of high-throughput sequence data High throughput sequencing offers severa l advantages relative to array-based genotyping or expression assays. First, unlike genotyping arrays, whole genome sequencing is not limited to interrogating onl y known sequence variants. Similarly, RNA- sequencing (RNA-seq) enables expression quanti fication of novel transcripts that are not" + ], + "answer": "### How to Guide for Genetic Sequencing Genetic sequencing is a powerful tool used to determine the order of nucleotides in DNA. This guide will walk you through the basic steps involved in genetic sequencing, from sample preparation to data analysis. #### Step 1: Sample Preparation 1. **Collect DNA Sample**: Obtain a DNA sample from the organism or tissue of interest. This can be done using various methods such as blood draws, buccal swabs, or tissue biopsies. 2. **Extract DNA**: Use a DNA extraction kit to isolate DNA from the collected sample. Ensure the DNA is of high quality and free from contaminants. #### Step 2: Library Preparation 1. **Fragment DNA**: Break the DNA into smaller fragments. This can be done using mechanical shearing or enzymatic digestion. 2. **Add Adapters**: Ligate specific adapters to the ends of the DNA fragments. These adapters are necessary for the sequencing process and allow the fragments to bind to the sequencing platform. #### Step 3: Amplification 1. **PCR Amplification**: Amplify the DNA fragments using Polymerase Chain Reaction (PCR). This step increases the quantity of DNA to ensure there is enough material for sequencing. #### Step 4: Sequencing 1. **Choose a Sequencing Platform**: Select an appropriate sequencing platform based on your needs. Popular platforms include Illumina, Ion Torrent, Pacific Biosciences, and Oxford Nanopore Technologies [1], [5], [6]. 2. **Load the Sample**: Load the prepared DNA library onto the sequencing platform. Each platform has specific protocols for loading samples. 3. **Run the Sequencer**: Initiate the sequencing run. The platform will read the DNA fragments and generate raw sequence data. #### Step 5: Data Analysis 1. **Base Calling**: Use base-calling software to convert raw data into nucleotide sequences. Tools like Phred are commonly used for this purpose [7]. 2. **Sequence Assembly**: Assemble the short DNA sequences into longer contiguous sequences (contigs) using assemblers like Phrap or CAP3 [7]. 3. **Quality Control**: Assess the quality of the assembled sequences. Tools like consed or GAP4 can help in finishing sequencing projects and ensuring accuracy [7]. #### Step 6: Interpretation 1. **Variant Calling**: Identify genetic variants such as single nucleotide polymorphisms (SNPs) and insertions/deletions (indels) using variant calling software. 2. **Annotation**: Annotate the identified variants to understand their potential impact on genes and biological functions. 3. **Data Management**: Store and manage the sequencing data using bioinformatics tools and databases. High-throughput sequencing generates large amounts of data that require efficient processing and management [9], [10]. #### Additional Resources - For more detailed protocols and information on specific sequencing technologies, refer to resources from companies like 10X Genomics, Illumina, and Pacific Biosciences [1]. - For historical context and advancements in sequencing technologies, see references such as Heather and Chain (2016) and Shendure and Ji (2008) [2]. By following these steps, you can successfully perform genetic sequencing and analyze the resulting data to gain insights into the genetic makeup of your sample.", + "question": "Create a how to guide for genetic sequencing" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_diabetes_1 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_diabetes_1 new file mode 100644 index 0000000..a9796ef --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_diabetes_1 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2007 - Combining classical trait and microarray data to dissect transcriptional regulation a case study.pdf", + "2017 - Genomic regulation of type 2 diabetes endophenotypes Contribution.pdf", + "2010 - Neural tube defect genes and maternal diabetes during pregnancy.pdf", + "2009 - Prioritizing genes for follow-up from genome wide association studies using information on gene expression in tissues relevant for type 2 diabetes mellitus.pdf", + "2022 - System Genetics in the Rat Family.pdf", + "2022 - Systems genetics in the rat HXBBXH family identifies Tti2 as a pleiotropic quantitative trait gene for adult hippocampal neurogenesis and serum glucose.pdf", + "2022 -Senko- Hippocampal neurogenesis serum glucose.pdf", + "2022 -Senko- System Genetics in the Rat HXB\uf022BXH Family.pdf", + "2020 - ChREBP downregulates SNAT2 amino acid transporter expression through interactions with SMRT in response to a high-carbohydrate diet.pdf", + "2015 - Targeted Allelic Expression.pdf" + ], + "extraction_id": [ + "1e5ec803-ae2d-5bbd-8d40-438fb1ec1eab", + "a0845748-d229-56b1-8666-5fd7708267b4", + "eaa27c67-ef56-5b12-8dc0-a656cc36c529", + "543f1861-21f2-52de-88e5-fa81a7b6ef64", + "ec24c99e-4654-5fb7-a1ed-ec3f8a941711", + "184f8279-2ea5-5f18-8e15-2804ee9e62d5", + "c597d023-1a22-5849-8c4f-9f3448c22962", + "a56d014f-d78d-582c-845d-2b10823f5424", + "a575ca7c-aa73-5b6a-a152-0ff08ddec434", + "37df3b54-130c-5424-90f6-af59ecb5cdf8" + ], + "document_id": [ + "bb54e43d-7f70-5ee2-a5b9-0e20000dfd97", + "fef1ae33-b3af-50ea-909c-f1b57f7fe981", + "aa74b552-7e06-5596-8dec-298c40ad558c", + "4b1a56e7-6821-5504-b6da-27dcdf57c6a5", + "426b5aeb-1550-5039-8f2a-bd83d17c8648", + "e6323aba-6fec-500b-99e3-a41c2e7f17ff", + "bac2ab98-4317-59ed-99ef-deda8c22786d", + "c67a6829-954a-5202-85fb-7524b03fab28", + "fbfc6093-648c-55f7-9fc8-2ec4964278f1", + "a0f46d1e-81be-5b29-9082-86c1114c3edd" + ], + "id": [ + "chatcmpl-ADYmUfKwQ32pLN2HQWzuhXDWOhemk", + "08c0f648-0618-56cb-935a-c627000943f4", + "1b2895af-da13-52dd-9fd2-133a43b98b5f", + "39d6e4a1-5bbd-5f35-80b2-d3c205a5457c", + "2a71b5a3-67d8-55d8-97f8-cb34cbfcaa41", + "1e08685d-0f9d-5ead-84c1-e97fe346e025", + "4c381a87-dc30-5d3a-95a9-a32255cfe571", + "e8e69e50-076e-5459-ac5a-8e267fa33e13", + "0be84448-80cf-52bd-a84c-668a9ac49b20", + "6b49a027-22fc-59c5-aa87-3155663fd003", + "0feb3ea0-bd53-5e94-8a65-8cd2bdecdf0e" + ], + "contexts": [ + "Lan H, Rabaglia ME, Stoehr JP, Nadler ST, Schueler KL et al (2003) Gene expression proles of nondiabetic and diabetic obese mice suggest a role of hepatic lipogenic capacity in diabetes susceptibility. Diabetes 52:688700Theor Appl Genet (2008) 116:683690 689 123", + "Effects of high fat feeding on liver gene expression in diabetic goto-kakizaki rats, Gene Regul. Syst. Bio 6 (2012) 151 e168. [23] P.J. Kaisaki, G.W. Otto, J.F. McGouran, A. Toubal, K. Argoud, H. Waller-Evans, C. Finlay, S. Cald /C19erari, M.T. Bihoreau, B.M. Kessler, D. Gauguier, R. Mott, Ge- netic control of differential acetylation in diabetic rats, PLoS One 9 (2014) e94555 . [24] S.P. Wilder, P.J. Kaisaki, K. Argoud, A. Salhan, J. Ragoussis, M.T. Bihoreau,", + "Figure 2. Diabetes increases the variability of gene expression levels in other experimental paradigms. ( A) Microarray data from gene", + "also showed differential expression in the liver, where it regulates a number of genes involved in both glucose andlipid metabolism. These results add further support to aTable 3: Numbers of genes for which expressi on levels in pancreas, skel etal muscle, adipose tissue or liver were altered in dia betes as compared to controls P < 0.01 (DGI) P < 0.05 (DGI) P < 0.01 (WTCCC) 11 42 P < 0.05 (WTCCC) 30 115 P < 0.01 in DGI and P < 0.05 in WTCCC or P < 0.01 in WTCCC and P < 0.05 in DGI60", + "toSHR wild type littermates. Liver, together with skeletal muscle and adipose tissue, aredeci- sive organs inmaintaining glucose homeostasis and, hence, thedevelopment ofinsulin resis- tance [75]. Functional analysis ofdifferentially expressed genes intheliver identified networks ofgenes and potential regulators whose activation and inhibition could explain insulin resis- tance and dysglycemia intheheterozygous animals. Wealso recorded significant upregulation", + "toSHR wild type littermates. Liver, together with skeletal muscle and adipose tissue, aredeci- sive organs inmaintaining glucose homeostasis and, hence, thedevelopment ofinsulin resis- tance [75]. Functional analysis ofdifferentially expressed genes intheliver identified networks ofgenes and potential regulators whose activation and inhibition could explain insulin resis- tance and dysglycemia intheheterozygous animals. Wealso recorded significant upregulation", + "toSHR wild type littermates. Liver, together with skeletal muscle and adipose tissue, aredeci- sive organs inmaintaining glucose homeostasis and, hence, thedevelopment ofinsulin resis- tance [75]. Functional analysis ofdifferentially expressed genes intheliver identified networks ofgenes and potential regulators whose activation and inhibition could explain insulin resis- tance and dysglycemia intheheterozygous animals. Wealso recorded significant upregulation", + "toSHR wild type littermates. Liver, together with skeletal muscle and adipose tissue, aredeci- sive organs inmaintaining glucose homeostasis and, hence, thedevelopment ofinsulin resis- tance [75]. Functional analysis ofdifferentially expressed genes intheliver identified networks ofgenes and potential regulators whose activation and inhibition could explain insulin resis- tance and dysglycemia intheheterozygous animals. Wealso recorded significant upregulation", + "mRNA in diabetic liver. Biochem Biophys Res Commun 290: 903-908, 2002. 712 42. Watson PJ, Fairall L, and Schwabe JW . Nuclear hormone receptor co-repressors: 713 structure and function. Mol Cell Endocrinol 348: 440-449, 2012. 714 43. Yamashita H, Takenoshita M, Sakurai M, Bruick RK, Henzel WJ, Sh illinglaw 715 W, Arnot D, and Uyeda K . A glucose-responsive transcr iption factor that regulates 716 carbohydrate metabolism in the liver. Proc Natl Acad Sci U S A 98: 9116-9121, 2001. 717", + "impacts gene expression in a cell type-dependent manner. Science 2009;325:1246 1250diabetes.diabetesjournals.org Locke and Associates 1491Downloaded from http://diabetesjournals.org/diabetes/article-pdf/64/4/1484/580211/db140957.pdf by Kenya Institution user on 11 July 2023" + ], + "answer": "Gene expression in the liver is significantly affected by diabetes in several ways: 1. **Hepatic Lipogenic Capacity**: In diabetic obese mice, gene expression profiles suggest a role of hepatic lipogenic capacity in diabetes susceptibility, indicating that genes involved in lipid metabolism are differentially expressed in the liver of diabetic individuals [1]. 2. **High Fat Feeding**: In diabetic Goto-Kakizaki rats, high fat feeding alters liver gene expression, which implies that dietary factors can further influence the gene expression changes associated with diabetes [2]. 3. **Variability of Gene Expression**: Diabetes increases the variability of gene expression levels in the liver, affecting the regulation of genes involved in both glucose and lipid metabolism [3], [4]. 4. **Glucose Homeostasis and Insulin Resistance**: The liver, along with skeletal muscle and adipose tissue, plays a crucial role in maintaining glucose homeostasis. Functional analysis of differentially expressed genes in the liver has identified networks of genes and potential regulators whose activation and inhibition could explain insulin resistance and dysglycemia in diabetic conditions [5], [6], [7], [8]. 5. **Carbohydrate Metabolism**: A glucose-responsive transcription factor that regulates carbohydrate metabolism in the liver is affected by diabetes, indicating changes in the expression of genes involved in glucose metabolism [9]. Overall, diabetes leads to significant changes in the expression of genes in the liver, affecting pathways related to lipid and glucose metabolism, insulin resistance, and overall metabolic homeostasis.", + "question": "How is gene expression in the liver affected by diabetes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_diabetes_10 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_diabetes_10 new file mode 100644 index 0000000..639579e --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_diabetes_10 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2004 - A New Look at Viruses in Type 1 Diabetes.pdf", + "2020 - Integration of genomics and transcriptomics predicts diabetic retinopathy susceptibility genes.pdf", + "2016 - Integrated multi-omics of the human gut microbiome in a case study of familial type 1 diabetes.pdf", + "2003 - A functional polymorphism in the promoterenhancer region of the FOXP3Scurfin gene associated with type 1 diabetes.pdf", + "2005 - Pathway analysis of coronary atherosclerosis.pdf", + "2003 -Genetic epidemiology of type 1 diabetes.pdf", + "2018 - The human gut microbiome in early-onset type 1 diabetes from the TEDDY study.pdf", + "2011 - Type 1 Diabetes Etiology, Immunology.pdf", + "2017 - Type 1 diabetes mellitus.pdf", + "2004 - Diabetes Genes a.pdf" + ], + "extraction_id": [ + "8bbb4581-dc07-5410-9737-6d249f3740f6", + "018ac588-c327-5122-9c18-18f4d0df0f14", + "092a9b75-9985-5876-a650-59bc3f0d10fb", + "aacbb5a1-c294-5568-ba02-3d4342091e86", + "858559b5-74d3-585a-9f45-ffa065ecb0f7", + "84a487be-a531-5f09-b2d5-d0525c59d581", + "9cca2fe6-7584-5d28-91f3-e06edca7ed54", + "388e7eec-4204-59b5-a42d-e56a9032da0b", + "d342e632-c951-519a-b0de-505f3515403d", + "48f690af-58fa-59e1-a0ca-ce421aaa356c" + ], + "document_id": [ + "38edad91-ff31-504e-91d8-eac3833615b0", + "699a10ff-44d7-5cb3-bc25-ec5ba85cb751", + "f0405966-38bf-5a04-aa2c-1474b11362bb", + "4a3964a4-0aea-58ee-b749-33e0d8c62228", + "fa9c400b-fbfa-54ce-a801-7594b489e42d", + "cbc7f2d3-3f65-50ba-b281-96dd1c77f2c0", + "36096262-86f1-5c7e-bea1-4abbc610a974", + "3c9823cd-3615-53b6-96c8-b7d2123d3eb0", + "8e8b9b6e-8dfb-5aae-8c61-5f53bd4e0242", + "805ad0d5-6a3d-5e5f-b1ba-121e10c6daaa" + ], + "id": [ + "chatcmpl-ADYnJbAeICzXtvQR31T420R0p7Xn0", + "d156068e-31f6-5464-8ef1-eb5e7c58aa8d", + "b205daa9-4723-5641-9ed4-428d83cf7758", + "e7e8ef7b-bad0-54bc-814d-d947ea04756b", + "c851d17c-1ad0-5b9a-8820-ad45d0e4b075", + "0f6e6870-960c-560d-ad61-36c1d4d9970f", + "f6fd1d0f-d88e-55f7-8ed6-bba917a65b8f", + "00b43e01-2296-528e-82e1-5671bffe784d", + "0790a91d-f1c5-519a-9b0e-73a9f73b8da4", + "5daae5a1-9163-5850-874b-ea63ecdd4f87", + "65247182-02f3-501c-94d4-36f4893ff703" + ], + "contexts": [ + "disordering particular lymphocyte subsets [57]. Viral anti-body-free BB rats show an increased frequency and accel-erated onset of diabetes, suggesting that infection may havea protective effect against the development of diabetes bythese animals [230]. Thus, we speculate that infection orimmune stimulation in humans may also reduce the pen-etrance of susceptibility genes, which could account for thelow concordance rate between identical twins of less than40% for the development of T1D [13]. Conclusion", + "ished immune responsiveness, a well-characterized feature of diabetes ( Shanmugam et al., 2003 ; Mowat and Baum, 1971 ). Further, we considered that the genetic component of an individuals response to glucose may influence their susceptibility to diabetic complications like retinopathy. Cell lines from individuals with diabetes with and without retinopathy reveal differences in the response to glucose at a molec-", + "diabetes. ISME J. 5,8291 (2011). 30. Brown, C. T. et al. Gut microbiome metagenomics analysis suggests a functional model for the development of autoimmunity for type 1 diabetes.PLoS ONE 6,e25792 (2011). 31. Endesfelder, D. et al. Compromised gut microbiota networks in children with anti-islet cell autoimmunity. Diabetes 63,2006 2014 (2014). 32. Kostic, A. D. et al. The dynamics of the human infant gut microbiome in development and in progression toward type 1 diabetes. Cell Host Microbe 17, 260273 (2015).", + "+T cells related to diabetes-associated", + "the innate immune system (8, 36, 37) are known to play important roles in the development of diabetes itself, no study to date has linked these ideas with the", + "same or related viruses might complete the process of immune-mediated b-cell destruction. Alternatively, chil- dren genetically predisposed to develop autoimmunediabetes might have an altered immune system that is more likely to respond to viral exposures with strongly detectable antibody levels against certain viral antigens.If so, the detectable levels of antibodies to multiple viral antigens in diabetic patients would not indicate a causal", + "with -cell autoimmunity and those without. Diabetes 62, 12381244 (2013). 9. Mario, E. et al. Gut microbial metabolites limit the frequency of autoimmune T cells and protect against type 1 diabetes. Nat. Immunol. 18, 552562 (2017). 10. Needell, J. C. & Zipris, D. The role of the intestinal microbiome in type 1 diabetes pathogenesis. Curr. Diab. Rep. 16, 89 (2016). 11. Davis-Richardson, A. G. et al. Bacteroides dorei dominates gut microbiome prior", + "141. Filippi CM, Estes EA, Oldham JE, von Herrath MG. Immuno- regulatory mechanisms triggered by viral infections protect fromtype 1 diabetes in mice. J Clin Invest 119: 15151523, 2009. 142. Filippi CM, von Herrath MG. Viral trigger for type 1 diabetes: pros and cons. Diabetes 57: 28632871, 2008. 143. Flohe SB, Wasmuth HE, Kerad JB, Beales PE, Pozzilli P. A wheat-based, diabetes-promoting diet induces a Th1-type cytokinebias in the gut of NOD mice. Cytokine 21: 149154, 2003.", + "12451252 (2008). 77. Hofer,J. etal. Elevated proportions of recent thymic emigrants in children and adolescents with type1 diabetes. Rejuvenation Res. 12, 311320 (2009). 78. Wong,F.S. How does Bcell tolerance contribute to the protective effects of diabetes following induced mixed chimerism in autoimmune diabetes? Diabetes 63, 18551857 (2014). 79. Roep,B.O. & Peakman,M. Antigen targets of type1 diabetes autoimmunity. Cold Spring Harb. Perspect. Med. 2, a007781 (2012).", + "Immune Hypothesis: Evidence supporting an immune pathogenesis is strongest for diabetic autonomic neuropathy. Autonomic ganglia heavily infiltrated by lymphocytes, plasma cells, and macrophages were found at autopsy in five type 1 diabetics with symptomatic autonomic neuropathy. Striking cervical sympathetic ganglia atrophy was reported in another with severe sensory and autonomic neuropathy.32 Autoimmune pathogenesis may be involved in proximal diabetic" + ], + "answer": "The immune system is intricately related to diabetes, particularly Type 1 Diabetes (T1D), through several mechanisms: 1. **Autoimmunity and \u03b2-cell Destruction**: T1D is characterized by the immune system mistakenly attacking and destroying insulin-producing \u03b2-cells in the pancreas. This autoimmune response is a central feature of T1D [6]. 2. **Immune Responsiveness**: Diabetes is associated with diminished immune responsiveness, which can affect the body's ability to manage glucose levels and may influence susceptibility to diabetic complications [2]. 3. **Role of Infections**: Infections or immune stimulation may have a protective effect against the development of diabetes. For instance, viral infections have been shown to trigger immunoregulatory mechanisms that protect against T1D in animal models [1], [8]. 4. **Gut Microbiome and Autoimmunity**: The gut microbiome plays a significant role in the development of autoimmunity related to T1D. Alterations in gut microbiota networks have been observed in children with anti-islet cell autoimmunity, suggesting a link between gut health and immune responses in diabetes [3], [7]. 5. **Immune Pathogenesis in Complications**: Evidence supports an immune pathogenesis for diabetic complications such as autonomic neuropathy, where immune cells infiltrate autonomic ganglia, leading to nerve damage [10]. In summary, the immune system's malfunction, particularly through autoimmunity and altered immune responses, is a key factor in the development and progression of diabetes, especially T1D.", + "question": "How is the immune system related to diabetes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_diabetes_11 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_diabetes_11 new file mode 100644 index 0000000..af8ae20 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_diabetes_11 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2015 - Type 2 Diabetes Mellitus and the Association of Candidate Genes.pdf", + "2009 - Analysis of 19 genes for association with type I diabetes in the Type I Diabetes Genetics Consortium families..pdf", + "2009 - Genome-wide association study and meta-analysis find that over 40 loci affect risk of type 1 diabetes.pdf", + "2015 - Transcript Expression Data from Human.pdf", + "2016 - Systematic Evaluation of Genes and Genetic Variants Associated with Type 1 Diabetes Susceptibility.pdf", + "2010 - Twelve type 2 diabetes susceptibility loci identified.pdf", + "2022 - A genome-wide functional genomics approach uncovers genetic determinants of immune phenotypes in type 1 diabetes.pdf", + "2021- Genome\u2010wide search for genes affecting the age at diagnosis of type 1.pdf", + "2008 - Shared and Distinct Genetic Variants in Type 1 Diabetes.pdf", + "2023 - Childhood adiposity and novel subtypes of adult-onset diabetes a Mendelian randomisation and genome-wide genetic correlation study.pdf" + ], + "extraction_id": [ + "e1e1abb2-882f-5ba4-a51b-3b9bfc4df5aa", + "8ae199fd-0820-54c6-8d5c-aea5bf5fb895", + "a58e318d-3358-518c-ac23-6dd4d7b000f2", + "8fb04ac0-460b-58d3-ad43-2c7720bfd87e", + "082f1c10-0745-5d70-a176-336fc972319c", + "6912cf22-46e3-540b-bafe-f4951ec2bd70", + "fc30d552-be59-5ddf-9bac-e247d536ed96", + "bc2a4183-8ca7-5b72-8e03-25f4933ecc8b", + "20a53007-acf3-5317-89d5-1d69f1845d62", + "defbf2fb-7aa7-538d-b6ac-81ecd607179c" + ], + "document_id": [ + "3236fdee-e304-5b88-921f-52e52dc5afa3", + "a0e27a2d-a07b-5b4d-a93a-907303dd8876", + "e6566ede-0c5a-51d1-aac9-e6e1695e937a", + "2b30d4f3-9ec3-574f-9a36-709b0e09c3f2", + "e4288a56-0280-5681-8eb4-4f52b3160451", + "8be48d47-68bd-5bec-844d-7ddd3e624442", + "368e0215-393e-5bec-a87c-e976adaa3ca5", + "a98a972b-8b50-58c8-9126-1883a96b1a09", + "4a655174-c16b-54d5-901a-6508d638cc23", + "fff2bd78-2ac2-5672-b8fd-ed82ab7c910b" + ], + "id": [ + "chatcmpl-ADYnQqyvhC46GEUXw2f9p5UR309ef", + "d8d64729-8353-5fd3-938f-c7e0467698f9", + "07f9090f-101c-5b89-ab7b-a072dbf1ed4b", + "96b66f03-33dd-5a88-91c8-e0aa13cbcf3d", + "9a17c246-a9c7-5c13-92ef-5d551c7439e9", + "f3e96e99-cd95-5c0c-92c5-72d6edf2f6ff", + "9a6042ed-f076-51c3-b0f3-3d8b94e9852f", + "123d1a9a-12c9-59a2-8f3e-083220452036", + "6fc3a7f1-bd7e-55d9-be9b-1c6f5fb5452e", + "ca60f298-62fe-5fcc-a833-8439733cfae2", + "81df736a-3450-53da-9421-57f7d29e3218" + ], + "contexts": [ + "Imran Ali Khan et al., Genetic Variants in Indian Diabetes Patients www.jcdr.net Journal of Clinical and Diagnostic Research. 2015 Nov, Vol-9(11): GC01-GC05 44of the pancreas and islets during embryonic growth [3]. Genetic variants in this gene are associated with increased risk of T2DM in a variety of study populations [28,29]. In the first published GWAS for T2DM, SLC30A8 (rs13266634) was revealed to be associated with diabetes (OR, 1.26; p = 5.0 10-7).", + "diabetes and celiac disease. N Engl J Med 2008; 359: 27672777. 11 Fung E, Smyth DJ, Howson JM, Cooper JD, Walker NM, Stevens H et al. Analysis of 17 autoimmune disease-associated variants in type 1 diabetes identifies 6q23/TNFAIP3 as asusceptibility locus. Genes Immun 2008; 10: 188191. 12 Cooper JD, Smyth DJ, Smiles AM, Plagnol V, Walker NM, Allen JE et al. Meta-analysis of genome-wide association study data identifies additional type 1 diabetes risk loci. Nat Genet 2008; 40: 13991401.", + "10. Smyth, D.J. et al. Shared and distinct genetic variants in type 1 diabetes and celiac disease. N. Engl. J. Med. 359, 27672777 (2008). 11. Fung, E. et al. Analysis of 17 autoimmune disease-associated variants in type 1 diabetes identies 6q23/TNFAIP3 as a susceptibility locus. Genes Immun. 10, 188191 (2009). 12. Cooper, J.D. et al. Meta-analysis of genome-wide association study data identies additional type 1 diabetes risk loci. Nat. Genet. 40, 13991401 (2008).", + "14. Pasquali L, Gaulton KJ, Rodriguez-Segui SA, Mularoni L, Miguel-Escalada I, et al. (2014) Pancreatic islet enhancer clusters enriched in type 2 diabetes risk-associated variants. Nat Genet 46: 136 143. doi:10.1038/ng.2870 PMID: 24413736 15. Fairfax BP, Humburg P, Makino S, Naranbhai V, Wong D, et al. (2014) Innate immune activity condi- tions the effect of regulatory variants upon monocyte gene expression. Science 343: 1246949. doi: 10. 1126/science.1246949 PMID: 24604202", + "The Journal of Immunology Systematic Evaluation of Genes and Genetic Variants Associated with Type 1 Diabetes Susceptibility Ramesh Ram,*,Munish Mehta,*,Quang T. Nguyen,*,Irma Larma,*, Bernhard O. Boehm,,xFlemming Pociot,{Patrick Concannon,,#and Grant Morahan*, Genome-wide association studies have found >60 loci that confer genetic susceptibility to type 1 diabetes (T1D). Many of these are", + "disease and type II diabetes. Genes Immun. 10, 654658 (2009). 41. Hindorff, L.A. et al. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc. Natl. Acad. Sci. USA 106, 93629367 (2009). 42. Nicolson, T.J. et al. Insulin storage and glucose homeostasis in mice null for the granule zinc transporter ZnT8 and studies of the type 2 diabetes-associated variants. Diabetes 58, 20702083 (2009).", + "The composition and activity of the human immune system is under genetic control, and people with certain changes in their genes are more susceptible than others to develop type 1 diabetes. Previous studies have identified around 60 locations in the human DNA (known as loci) associated with the condition, but it remains unclear how these loci influence the immune system and whether diabetes will emerge. Chu, Janssen, Koenen et al. explored how variations in genetic information can influence the", + "mellitus-associated genetic variants contribute to overlapping immune regulatory networks. Front Genet 2018; 9:535. 13 Syreeni A, Sandholm N, Cao J et al. Genetic determinants of glycated hemoglobin in type 1 diabetes. Diabetes 2019; 68: 858 67. 14 Sidore C, Busonero F, Maschio A et al. Genome sequencing elucidates Sardinian genetic architecture and augmentsGenes affecting type 1 diabetes diagnosis age / A. Syreeni et al .", + "Genetic Variants in Type 1 Diabetes and Celiac Disease n engl j med 359;26 www.nejm.org december 25, 2008 2777Kalev I, Oselin K, Prlist P, et al. CC-26. chemokine receptor CCR5-del32 mutation as a modifying pathogenetic factor in type I diabetes. J Diabetes Complications 2003;17:387-91. Szalai C, Csszr A, Czinner A, et al. 27. Chemokine receptor CCR2 and CCR5 polymorphisms in children with insulin-dependent diabetes mellitus. Pediatr Res 1999;46:82-4. Yang B, Houlberg K, Millward A, De - 28.", + "13(1):2337. https://doi.org/10.1038/s41467-022-29932-y 5. Burgess S, Butterworth A, Thompson SG (2013) Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol 37(7):658 665. https://doi. org/10.1002/gepi.21758 6. Cousminer DL, Ahlqvist E, Mishra R et al (2018) First genome- wide association study of latent autoimmune diabetes in adults reveals novel insights linking immune and metabolic diabetes. Diabetes Care 41(11):2396 2403. https://doi.org/10.2337/dc18-" + ], + "answer": "The genomic variants associated with immune system components and diabetes include: 1. **SLC30A8 (rs13266634)**: This variant is associated with an increased risk of Type 2 Diabetes Mellitus (T2DM) [1]. 2. **6q23/TNFAIP3**: Identified as a susceptibility locus for Type 1 Diabetes (T1D) [2], [3]. 3. **Pancreatic islet enhancer clusters**: These clusters are enriched in T2DM risk-associated variants [4]. 4. **Regulatory variants affecting monocyte gene expression**: These variants are conditioned by innate immune activity [4]. 5. **>60 loci**: Genome-wide association studies have identified over 60 loci that confer genetic susceptibility to T1D [5], [7]. 6. **CCR5-del32 mutation**: This mutation in the chemokine receptor CCR5 is a modifying pathogenetic factor in T1D [9]. 7. **CCR2 and CCR5 polymorphisms**: These polymorphisms are found in children with insulin-dependent diabetes mellitus [9]. 8. **Novel insights linking immune and metabolic diabetes**: Identified through the first genome-wide association study of latent autoimmune diabetes in adults [10]. These variants highlight the complex interplay between genetic factors influencing both the immune system and diabetes.", + "question": "What are the genomic variants associated with immune system components and diabetes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_diabetes_12 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_diabetes_12 new file mode 100644 index 0000000..e684657 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_diabetes_12 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2010 - Metabolic Footprint of Diabetes A Multiplatform.pdf", + "2012 - Novel biomarkers for pre\u2010diabetes identified by metabolomics.pdf", + "2010 - Metabolic Footprint of Diabetes A Multiplatform.pdf", + "2010 - Metabolic Footprint of Diabetes A Multiplatform.pdf", + "2011 - Emerging Applications of Metabolomic.pdf", + "2015 - Genetics, genomics and personalized medicine in Type 2 Diabetes.pdf", + "2011 - The eMERGE Network A consortium of biorepositories linked to electronic medical records data for conducting genomic studies.pdf", + "2011 - Biomarkers for the Prediction of Type 2 Diabetes.pdf", + "2010 - Metabolic Footprint of Diabetes A Multiplatform.pdf", + "2009 - Metabolomics Applied to Diabetes Research.pdf" + ], + "extraction_id": [ + "8ad05bed-b0fd-52d4-badf-a98233a0808d", + "c775612c-c80b-5a50-9417-d6fd89ec07ee", + "2359c12d-8263-5183-a350-fff365318805", + "97b6d492-9139-50ec-9685-53a803f5c995", + "df823d9a-e2de-5dab-b336-af4682b9ce70", + "92a2a3c7-ed41-5394-b716-fdbf5c198a86", + "a35d4e2a-ce04-536d-b88a-8f273aa03f40", + "75f979f5-425b-563c-b4ba-ec3a971f356a", + "6d77a75e-68a4-5c27-b387-449f7f9f9487", + "380e9a2e-8f9f-5f9e-ba20-3695b1c60fda" + ], + "document_id": [ + "b199607e-293e-56e8-88c8-e0716d1ee9eb", + "d93e3562-3419-51a6-86db-8247a9e69361", + "b199607e-293e-56e8-88c8-e0716d1ee9eb", + "b199607e-293e-56e8-88c8-e0716d1ee9eb", + "10c69e6a-3771-5cc6-a915-a31556dec650", + "d8b85c3e-62f3-5e67-99b0-d0a2f225aff0", + "3a174301-2941-578f-8ed6-f16d88fd2230", + "c68d29dd-eaa1-53f8-bc0d-aa85b2f39352", + "b199607e-293e-56e8-88c8-e0716d1ee9eb", + "a6ae2fb6-88ae-588f-a98d-b6092f886ed9" + ], + "id": [ + "chatcmpl-ADYnYRyH5dd9Q9xzg35pmgGcm27tO", + "13c68218-4920-5df7-a0b4-017298c9001a", + "393e2363-48e6-56ad-94d6-39b1915b2f5a", + "3df2fa36-b9aa-51c6-9e36-acfcef1310b6", + "ead10261-182f-5ab1-9af0-ce8a17677d4a", + "024eea85-c974-51fc-8def-89db09ba56b0", + "cef34be2-673e-553f-9c92-1ecef8edec4f", + "5c7dc6d7-800e-5c77-ac61-bd8e3086754c", + "3b9547ce-8316-5256-a68b-256058b3ee79", + "06da63dc-6a8d-5682-80e0-7d37b66cdf6f", + "0cb19f85-21d9-54f1-81a4-43969ac050e8" + ], + "contexts": [ + "allows the detection of systemic metabolic imbalances, thereby providing a disease specific picture of human physiology. doi:10.1371/journal.pone.0013953.g003Metabolomics of Diabetes PLoS ONE | www.plosone.org 9 November 2010 | Volume 5 | Issue 11 | e13953", + "Metabolomics studies allow metabolites involved in disease mechanisms to be discovered by monitoring metabolite level changes in predisposed individuals compared with healthy ones (Shaham et al, 2008; Newgard et al, 2009; Zhao et al, 2010; Pietilainen et al, 2011; Rhee et al, 2011; Wang et al,2 0 1 1 ; Cheng et al, 2012; Goek et al, 2012). Altered metabolite levels may serve as diagnostic biomarkers and enable preventive action. Previous cross-sectional metabolomics studies of T2D", + "doi:10.1371/journal.pone.0013953.t006Metabolomics of Diabetes PLoS ONE | www.plosone.org 8 November 2010 | Volume 5 | Issue 11 | e13953", + "monitoring and preventing progression to costly co-morbidities. The principal concept of metabolomics being able to find some metabolites differing in a control and a type 2 diabetic group is established. It is not our goal here to show this once again. The questions we ask are rather How well are different approaches suited to attain this goal? and What are optimal settings under which such studies can be successful?. Others have already investigated these questions before [16,17,18]. However, we", + "H, Raftery D, Nair KS. Quantitative me-tabolomics by H-NMR and LC-MS/MSconrms altered metabolic pathways in diabetes. PLoS ONE 2010;5:e10538 2. Li LO, Hu YF, Wang L, Mitchell M, Berger A, Coleman RA. Early hepatic insulin re-sistance in mice: a metabolomics analysis.Mol Endocrinol 2010;24:657 666 3. Bain JR, Stevens RD, Wenner BR, Ilkayeva O, Muoio DM, Newgard CB. Metabolomicsapplied to diabetes research: moving frominformation to knowledge. Diabetes 2009; 58:2429 2443", + "70 Zhang Q, Fillmore TL, Schepmoes AA et al. Serum proteomics reveals systemic dysregulation of innate immunity in Type 1 diabetes. J. Exp. Med. 210(1), 191203 (2013). 71 Roberts LD, Koulman A, Griffin JL. Towards metabolic biomarkers of insulin resistance and Type 2 diabetes: progress from the metabolome. Lancet Diabetes Endocrinol. 2(1), 6575 (2014). \t Illustrates\tpotential\tmetabolic\tbio-markers\twhich\tmay\tbe\t used\tto\tdetect\tpeople\tat-risk\tfor\tT2D/insulin\tresistance,", + "Serum or plasma concentrations of sugars and sugar metabo- lites (e.g., glucose, mannose, desoxyhexose, and 1,5-anhy-droglucoitol), ketone bodies ( -hydroxybutyrate), lipids (e.g., phosphatidyl-cholines and nonesterified fatty acids), branched-chain amino acids, and other metabolites were found to be associated with insulin resistance or diabetes status (see Supplementary Data online for full references). A proof-of- concept multi-platform, metabolome-wide study based on the", + "Serum or plasma concentrations of sugars and sugar metabo- lites (e.g., glucose, mannose, desoxyhexose, and 1,5-anhy-droglucoitol), ketone bodies ( -hydroxybutyrate), lipids (e.g., phosphatidyl-cholines and nonesterified fatty acids), branched-chain amino acids, and other metabolites were found to be associated with insulin resistance or diabetes status (see Supplementary Data online for full references). A proof-of- concept multi-platform, metabolome-wide study based on the", + "Conclusions/Significance: Our study depicts the promising potential of metabolomics in diabetes research by identification of a series of known and also novel, deregulated metabolites that associate with diabetes. Key observations include perturbations of metabolic pathways linked to kidney dysfunction (3-indoxyl sulfate), lipid metabolism (glyceropho-", + "with signicant limitations and potential for misuse oftechnologies and overinterpretation of data. Here we seekto provide a critical evaluation of progress to date inapplication of metabolomics technologies for the under-standing of diabetes and obesity mechanisms, for sub-classication of different forms of diabetes to assist intailoring of therapeutic strategies, and for more detailedevaluation of the safety and efcacy of drugs used totreat the disease.Overview of current metabolomics" + ], + "answer": "The role of the immune system in the metabolomics of diabetes and associated conditions is highlighted in the context provided. Specifically, it is noted that serum proteomics has revealed systemic dysregulation of innate immunity in Type 1 diabetes [6]. This indicates that the immune system plays a significant role in the metabolic alterations observed in diabetes. The identification of metabolic biomarkers related to immune system dysregulation can help in detecting individuals at risk for Type 2 diabetes and insulin resistance [6]. This connection underscores the importance of understanding immune system interactions within the broader metabolic landscape of diabetes.", + "question": "What is the role of the immune system in the metabolomics of diabetes and associated conditions?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_diabetes_13 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_diabetes_13 new file mode 100644 index 0000000..4b3a146 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_diabetes_13 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2022 - A genome-wide functional genomics approach uncovers genetic determinants of immune phenotypes in type 1 diabetes.pdf", + "2020 - Whole blood co-expression modules associate with metabolic traits and type 2 diabetes an IMI-DIRECT study.pdf", + "2020 - Polygenic inheritance, GWAS, polygenic risk scores,and the search for functional variants.pdf", + "2022 - A genome-wide functional genomics approach uncovers genetic determinants of immune phenotypes in type 1 diabetes.pdf", + "2022 - A genome-wide functional genomics approach uncovers genetic determinants of immune phenotypes in type 1 diabetes.pdf", + "2010 - Comparative genetic analysis of inflammatory.pdf", + "2016 - Effects of the genome on immune regulation in type 1 diabetes.pdf", + "2018 - The genetic architecture of type 1 diabetes mellitus.pdf", + "2022 - A genome-wide functional genomics approach uncovers genetic determinants of immune phenotypes in type 1 diabetes.pdf", + "2018 - The genetic architecture of type 1 diabetes mellitus.pdf" + ], + "extraction_id": [ + "f716d630-c82a-5987-ab4b-1b8a20d81dfa", + "fbf3d28b-b05c-51a2-b902-94f17ff51d7b", + "3615b8f4-612d-52e5-8581-8c0d97b2a845", + "f4c97581-4139-5397-9f3f-ccbb39846d93", + "bf2387f1-5389-54e8-897e-84575efee7f1", + "1c95778f-bf36-5398-b891-85533d60c80c", + "a744412e-5003-5732-9a73-f1f5267aa715", + "5b8b3673-7fd4-5989-9982-a6d5ea374c8d", + "32bf7dd7-d271-577f-9146-71da2681ec98", + "5b8b3673-7fd4-5989-9982-a6d5ea374c8d" + ], + "document_id": [ + "368e0215-393e-5bec-a87c-e976adaa3ca5", + "a3f00a6f-be97-51ce-9198-87f6469ce2db", + "39ab8f23-a31d-561c-ba90-65b99f64b83e", + "368e0215-393e-5bec-a87c-e976adaa3ca5", + "368e0215-393e-5bec-a87c-e976adaa3ca5", + "ab74ea2b-684a-5f6f-b77b-f3dbd4de86e8", + "9fcfc0c3-80b5-515c-9263-a1a17cfa9a4c", + "341261db-b38a-5bd2-8d8d-fc04a0b3da30", + "368e0215-393e-5bec-a87c-e976adaa3ca5", + "341261db-b38a-5bd2-8d8d-fc04a0b3da30" + ], + "id": [ + "chatcmpl-ADYnd7yDDFmHs6an7OU6ShUeBDi9c", + "f799cd21-0ae3-5c3a-b3d4-9dfa4f5cfcef", + "8090d389-97c8-521f-8139-1947014f3d5e", + "87c0635a-b18d-58dd-8e92-ef98d713b870", + "0cd28c43-f17e-5e9e-8fa9-b81ef89264c3", + "50474cf9-286a-50eb-b344-2837cc7c43a6", + "5f2de1ce-56f7-501d-a0e0-54991c1324f7", + "82f7da2f-7abf-59e1-b259-46a01b375f1c", + "acc9b87c-583b-5ba6-bc6f-b833d2e8d2cb", + "9b1cf5ca-d793-5c2a-a2db-c88f44ac6ec4", + "ce911802-af16-57a4-90e8-e3257a9ee7af" + ], + "contexts": [ + "'&'.+* .%(\"'.+ * $$* ! \f\r \t\f\u000b '&'.+* .%(\"'.+ * $$*\t\u000b r Figure 2. Impact of type 1 diabetes (T1D) genome- wide association studies (GWAS) single- nucleotide polymorphisms (SNPs) on immune phenotypes. (A)Quantile- quantile (Q- Q) plots of quantitative trait locus (QTL) profiles of 62 T1D GWAS loci grouped by cell populations. The distribution of p- values", + "diseases, including T2D. Many of the module-QTL locioverlap with GWAS hits for immune-related pheno- types, suggesting that the modules described here might be of importance in the context of inflammatory dis- eases. Similar analyses should be performed for co- expression modules in other more T2D-relevant tissues to provide further insight into the causal networks underlying T2D aetiology. Similarly, network rewiring in T2D might be more strongly detectable in other tissues", + "(58)], revealing some interesting possible candidate functionalgenes other than those associated with the HLA and related sys-tems. In addition, early GWAS on type 1 diabetes by Todd et al.(23) revealed suggestive functional effects of non-HLA variants involved in immune functions. Another interesting application of", + "Research article Genetics and Genomics | Medicine Chu, Janssen, Koenen etal. eLife 2022;11:e73709. DOI: https://doi.org/10.7554/eLife.73709 9 of 17Genetic regulation of immune phenotypes in T1D To further explore potential genetic regulation of immune phenotypes on the whole- genome level, we performed QTL mapping in 300DM. This identified nine genome- wide significant QTLs (p- value < 5 108) associated with immune- cell proportion, including four associated with T cell subpopu-", + "studies (r2> 0.8) and performed a chi- square test on clinical status by using PLINK 1.9. Samples in 300DM were taken as cases and samples in 500FG as controls. Impact of T1D GWAS loci on immune phenotypes To detect the impact of T1D GWAS loci on immune- cell populations, we grouped all traits into four categories (B cells, T cells, monocytes, and NK cells), and counted the number of suggestive associ- ations (p- value < 0.05) between the 63 top SNPs from T1D GWAS loci and immune- cell traits. 1000", + "In the present study, we interrogated GWAS data sets on CD, UC and T1D for known susceptibility loci implicated inthese diseases. Our comparative analysis serves several impor-tant roles: rst, the ability to identify additional susceptibilityloci for one disease by testing known loci for another disease,similar to previous studies ( 12,13). This approach increases statistical power by limiting the number of hypotheses", + "Conclusions A major challenge is to translate GWAS ndings intocausal variants and target genes. The Immunochipeffort has greatly contributed to our understanding of disease mechanisms by identifying pathways, which could not be linked to diabetes by existing hypotheticalmodels. Diabetes is probably a much more diverse disease than the current subdivision into T1DM and T2D implies and a more precise subdivisioninto subgroups may also pave the way for a more", + "edge of the role(s) of genetic variation (SNPs) in population-level sus-ceptibility to T1D ( Ram et al., 2016a ). However, GWAS analyses do not automatically determine the particular gene(s) in a speci c locus that are mechanistically associated with disease pathogenesis, or elucidate the manner in which disease gene(s) interact ( Zhong et al., 2010). The diculty associated with ascribing functional impacts to SNPs is partly explained by the fact that most disease-associated SNPs identi ed by", + "(Supplementary file 1C). We next investigated whether these genetic risk loci for T1D affect immune parameters and func- tion. The quantile- quantile plot of the association of the 63 T1D GWAS loci with different cell types and cytokines illustrates an inflated deviation from an expected uniform distribution (Figure 2A, Figure2figure supplement 1). We further tested whether this deviation can be explained by chance", + "Fadason et al. demonstrated that functionally relevant type 2 diabetes- associated SNPs are spatially linked with speci c changes in the ex- pression levels of genes within disease-associated tissues ( Fadason et al., 2017 ). Similarly, a study demonstrated that integrating chro- matin interactions with GWAS analyses is important in elucidatingcausal genes that modulate regulatory networks in autoimmune dis- eases ( McGovern et al., 2016). As such, the spatial organization of DNA" + ], + "answer": "Yes, the landscape of QTL (Quantitative Trait Loci) and GWAS (Genome-Wide Association Studies) hits can be used to dissect the role of the immune system in diabetes and its complications. Several pieces of evidence from the provided context support this: 1. **Impact on Immune Phenotypes**: GWAS SNPs for type 1 diabetes (T1D) have been shown to impact immune phenotypes. For example, QTL profiles of 62 T1D GWAS loci grouped by cell populations reveal the distribution of p-values, indicating significant associations between these loci and immune cell traits [1]. 2. **Overlap with Immune-Related Phenotypes**: Many module-QTL loci overlap with GWAS hits for immune-related phenotypes, suggesting that these genetic modules are important in the context of inflammatory diseases, including diabetes [2]. 3. **Genetic Regulation of Immune Phenotypes**: QTL mapping in a study identified nine genome-wide significant QTLs associated with immune-cell proportions, including T cell subpopulations, indicating a genetic regulation of immune phenotypes in T1D [4]. 4. **Impact on Immune-Cell Populations**: Analysis of T1D GWAS loci showed suggestive associations between top SNPs and immune-cell traits, categorized into B cells, T cells, monocytes, and NK cells, further highlighting the impact of these loci on immune cell populations [5]. 5. **Comparative Analysis of Susceptibility Loci**: Comparative analysis of GWAS data sets for diseases like T1D, Crohn's disease (CD), and ulcerative colitis (UC) helps identify additional susceptibility loci and increases statistical power, which is crucial for understanding the genetic basis of immune-related complications in diabetes [6]. 6. **Pathway Identification**: The Immunochip effort has contributed to understanding disease mechanisms by identifying pathways linked to diabetes, which were not previously associated with the disease, indicating the complexity and diversity of diabetes and its immune-related aspects [7]. 7. **Functional Impacts of SNPs**: Although GWAS analyses do not automatically determine the specific genes associated with disease pathogenesis, they provide insights into how disease genes interact and affect immune parameters and functions [8], [9]. In summary, the integration of QTL and GWAS data provides valuable insights into the genetic regulation of immune phenotypes and their role in diabetes and its complications, supporting the use of these landscapes for dissecting the immune system's involvement in the disease.", + "question": "Can the landscape of QTL and GWAS hits be used to dissect the role of immune system in diabetes and complications?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_diabetes_2 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_diabetes_2 new file mode 100644 index 0000000..6b99815 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_diabetes_2 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2012 - Recent Developments in the Genetic and Genomic Basis of Type 2 Diabetes.pdf", + "2017 - Type 1 diabetes mellitus.pdf", + "2010 - Genetics of Type 1 Diabetes What\u2019s Next.pdf", + "2018 - Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls.pdf", + "2011 - Type 2 diabetes and obesity genomics and the clinic.pdf", + "2010 - Genome-wide analysis of transcriptional regulation in the murine liver.pdf", + "2010 - Genome-wide analysis of transcriptional regulation in the murine liver.pdf", + "2015 - Transcript Expression Data from Human.pdf", + "2004 - Interaction and Association Analysis of a Type 1 Diabetes Susceptibility Locus.pdf", + "2019 - IRS1\u2010 rs10498210 GA and CCR5\u201059029 AG polymorphisms in patients with type 2 diabetes in Kurdistan.pdf" + ], + "extraction_id": [ + "1213249d-8ed3-5d13-9137-f11b87a7a78b", + "39b6a474-b721-509f-bbc3-094dc1f49634", + "5557d2db-b55a-59c9-8fe7-89b196a28617", + "43eecb5d-aca2-5c3e-9351-afbef000a795", + "10685e4c-eb4c-562a-a64a-d98e83c12c0b", + "151aa443-b9af-55db-9a30-adc4440ac7ef", + "7cbef74a-2d81-5a3a-a4d4-dfacdb86e632", + "867d0b1b-16a1-53ea-b014-3c204b9001a5", + "d71343b2-f7c5-52b4-96f9-bcc98f97fe81", + "843f5b60-2702-59e4-b237-02d002200e6d" + ], + "document_id": [ + "7d051350-d939-5183-be22-742727573a75", + "8e8b9b6e-8dfb-5aae-8c61-5f53bd4e0242", + "261cbb40-ed6b-554c-a70d-db6b9f14cf74", + "af63c74d-a204-5f9f-9a32-3451b112e5ba", + "5086a525-124e-5a45-b75a-657d67a3250a", + "8a115c1b-662c-5062-b77f-bbde0532bbe9", + "8a115c1b-662c-5062-b77f-bbde0532bbe9", + "2b30d4f3-9ec3-574f-9a36-709b0e09c3f2", + "4246f8d0-69e8-56cf-9674-d379467dfb61", + "18afbfee-ddee-54b3-88cc-342812a65d09" + ], + "id": [ + "chatcmpl-ADYmdeow5Femrvb7YWgDS0ML3p64y", + "6f00dd31-490e-53cd-81b3-c56e13bd7edd", + "fa4e127f-2c54-592b-a478-152bc74e7351", + "f0c9d05b-7999-5cb7-bb48-0666cf74aec0", + "55dca975-78ec-594d-8a30-a0849b683089", + "126bf287-0f5e-52a9-abac-ad59ad3ea153", + "90565c2b-fdb6-5b0f-a710-9086a4cfcd2b", + "ceb7bd13-b917-566f-8e17-40dd523afd42", + "226e2873-a0bf-554d-9576-7fca5f2ffc0f", + "a495dcc8-5cee-58a9-9f15-95be8fbc9b6a", + "997a967e-6428-51c9-9847-24d16f11f9f1" + ], + "contexts": [ + "associated with increased fasting plasma glucose levels and type2 diabetes risk. Nat Genet. 2009;41(1):89 94. 23. Rees M, Wincovitch S, Schultz J, Waterstradt R, Beer N, Baltrusch S, et al. Cellular characterisation of the GCKR P446L variant associated with type 2 diabe tes risk. Diabetologia. 2012;55 (1):114 22. 24. Nejentsev S, Walker N, Riches D, Egholm M, Todd J, et al. Rare variants of IFIH1 , a gene implicated in antiviral responses, protect against type 1 diabetes. Science. 2009;324(5925):387 9.", + "HLAlinked genes in juvenile diabetes mellitus. Br.Med. J. 3, 133135 (1975). 52. Erlich,H.A. etal. Next generation sequencing reveals the association of DRB3*02:02 with type 1 diabetes. Diabetes 62, 26182622 (2013). 53. CaillatZucman,S. etal. Agedependent HLA genetic heterogeneity of type1 insulindependent diabetes mellitus. J.Clin. Invest. 90, 22422250 (1992). 54. Cucca,F. etal. The distribution of DR4 haplotypes inSardinia suggests a primary association of typeI", + "holdt R, Akolkar B, Erlich HA, Hilner JE, Julier C, Morahan G, Nerup J,Nierras CR, Chen WM, Rich SS, Type 1 Diabetes Genetics Consortium. Ahuman type 1 diabetes susceptibility locus maps to chromosome 21q22.3.Diabetes 2008;57:2858 2861 58. Nejentsev S, Walker N, Riches D, Egholm M, Todd JA. Rare variants of IFIH1, a gene implicated in antiviral responses, protect against type 1diabetes. Science 2009;324:387389 59. Altshuler D, Daly M. Guilt beyond a reasonable doubt. Nat Genet 2007;39: 813 815", + "because of their presumed roles in immune signalling, considered to be a major feature of T1D-susceptibility. These include ERBB3 (receptor tyrosine-protein kinase erbB-3 precursor) at 12q13 and SH2B3/LNK (SH2B adaptor protein 3), TRAFD1 (TRAF-type zinc finger domain containing 1) and PTPN11 (protein tyrosine phos- phatase, non-receptor type 11) at 12q24. For these signal regions in", + "Nejentsev S, Walker N, Riches D, Egholm M, Todd JA (2009) Rare variants of IFIH1, a gene implicated in antiviral responses, protect against type 1 diabetes. Science 324:387389 Nicolson TJ, Bellomo EA, Wijesekara N, Loder MK, Baldwin JM, Gyulkhandanyan AV, Koshkin V, Tarasov AI, Carzaniga R, Kronenberger K, Taneja TK, da Silva Xavier G, Libert S,", + "7 (Wellcome Trust Case Control Consortium 2007) . Separate work that examined liver gene expression in a smaller cohort of human samples with and without Type I diabetes found that ERBB3 did not have a cis -eQTL but that a flanking gene, R PS26, did. Since the disease phenotype and RPS26 both had QTLs in the same location, this suggested the RPS26 was a stronger candidate than ERBB3 . The authors then used mouse liver and adipose expression", + "models. A genome wide association study in a large human population proposed the receptor typrosine kinase ERBB3 as the best candidate gene near a QTL for Type I diabetes", + "61. Nejentsev S, Walker N, Riches D, Egholm M, Todd JA (2009) Rare variants of IFIH1, a gene implicated in antiviral responses, protect against type 1 diabetes. Science 324: 387 389. doi: 10.1126/science. 1167728 PMID: 19264985 62. Nica AC, Ongen H, Irminger JC, Bosco D, Berney T, et al. (2013) Cell-type, allelic, and genetic signa- tures in the human pancreatic beta cell transcriptome. Genome Res 23: 1554 1562. doi: 10.1101/gr. 150706.112 PMID: 23716500", + "gene is associated with insulin-dependent diabetes mellitus. Diabetes 33:176 183, 1984 3. Nistico L, Buzzetti R, Pritchard L, Van der Auwera B, Giovannini C, Bosi E, Larrad M, Rios M, Chow C, Cockram C, Jacobs K, Mijovic C, Bain S,Barnett A, Vandewalle C, Schuit F, Gorus F, Tosi R, Pozzilli P, Todd J: TheCTLA-4 gene region of chromosome 2q33 is linked to, and associated with,type 1 diabetes: Belgian Diabetes Registry. Hum Mol Genet 5:1075 1080, 1996", + "One of these genes associated with type 2 diabetes is the insulin receptor substrate 1 (IRS1, OMIM association num-ber, 147545) (Alharbi, Khan, Abotalib, & AlHakeem, 2014; Alharbi, Khan, Munshi et al., 2014; Brender et al., 2013; Brunetti, Chiefari, & Foti, 2014) and another is the CC motif chemokine receptor5(CCR5, OMIM association num-ber, 601373) (Balistreri et al., 2007; Mokubo et al., 2006; Muntinghe et al., 2009). Insulin initiates a wide range of growth and metabolic ef-" + ], + "answer": "Yes, the genes SH2B3, IFIH1, and ERBB3 are related to diabetes: 1. **IFIH1**: This gene is implicated in antiviral responses and has been found to protect against type 1 diabetes [1], [3], [5], [8]. 2. **ERBB3**: This gene is considered to have a role in immune signaling, which is a major feature of type 1 diabetes susceptibility [4]. Additionally, it has been proposed as a candidate gene near a QTL for type 1 diabetes [7]. 3. **SH2B3**: This gene is also considered to have a role in immune signaling related to type 1 diabetes susceptibility [4]. Thus, all three genes, SH2B3, IFIH1, and ERBB3, are related to diabetes.", + "question": "Is any of the genes SH2B3, IFIH1 or ERBB3 related to diabetes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_diabetes_3 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_diabetes_3 new file mode 100644 index 0000000..2951e1a --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_diabetes_3 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2014 - Nutrigenetics and Nutrigenomics Insights into Diabetes Etiopathogenesis.pdf", + "2010 - The Genetics of Type 2 Diabetes.pdf", + "2014 - Nutrigenetics and Nutrigenomics Insights into Diabetes Etiopathogenesis.pdf", + "2014 - Nutrigenetics and Nutrigenomics Insights into Diabetes Etiopathogenesis.pdf", + "2012 - Systems Biology Approaches to Nutrition.pdf", + "2014 - Nutrigenetics and Nutrigenomics Insights into Diabetes Etiopathogenesis.pdf", + "2014 - Nutrigenetics and Nutrigenomics Insights into Diabetes Etiopathogenesis.pdf", + "2014 - Nutrigenetics and Nutrigenomics Insights into Diabetes Etiopathogenesis.pdf", + "2014 - Nutrigenetics and Nutrigenomics Insights into Diabetes Etiopathogenesis.pdf", + "2014 - Nutrigenetics and Nutrigenomics Insights into Diabetes Etiopathogenesis.pdf" + ], + "extraction_id": [ + "c9f99f8c-8432-59fa-8392-38eba8a63d8b", + "55a150e2-71ee-5b88-bbbb-22eecc2ff5e4", + "1e4b6f6f-e67a-53ef-8af6-78aa4c9ce112", + "23654fd6-b738-5c3b-9e6b-9b29ea47df38", + "76e4f0c7-ffb4-5643-b8d7-46cc584cfbf8", + "2618e650-f07c-5d21-a8f9-8f0dcdd51fd6", + "2618e650-f07c-5d21-a8f9-8f0dcdd51fd6", + "2618e650-f07c-5d21-a8f9-8f0dcdd51fd6", + "4f65affc-d78a-5048-86f3-22c0e12d298c", + "4f65affc-d78a-5048-86f3-22c0e12d298c" + ], + "document_id": [ + "ce4f171c-494c-53f2-a770-c3edd3561c40", + "a1d211d4-279e-51d7-b2b2-33bc2763d089", + "ce4f171c-494c-53f2-a770-c3edd3561c40", + "ce4f171c-494c-53f2-a770-c3edd3561c40", + "6955478b-950d-5d29-b24c-3a5ca656f3ae", + "ce4f171c-494c-53f2-a770-c3edd3561c40", + "ce4f171c-494c-53f2-a770-c3edd3561c40", + "ce4f171c-494c-53f2-a770-c3edd3561c40", + "ce4f171c-494c-53f2-a770-c3edd3561c40", + "ce4f171c-494c-53f2-a770-c3edd3561c40" + ], + "id": [ + "chatcmpl-ADYmiyBbswpmJDsIM9Hrs7jxPruIX", + "430a5d63-0af9-569e-b9db-2cc4262f1d77", + "0337dd9b-7228-5664-aff3-fa20c0f04d6d", + "c6c8b56a-1f17-5d96-bea4-787673f11be9", + "e5ac3a01-3a53-5239-b1d0-26ee4e72f37b", + "b9f46fff-157d-5007-ae86-987d9b5022b5", + "0940a12a-fee5-57ca-9a9f-ce720b43119e", + "0752929d-fb98-5c2f-b47f-e493f25ac70d", + "d8db4432-bb66-59de-bb9e-c0667ec9010c", + "f5909a51-0d41-5aee-ac5a-8d47550ef094", + "adf9b377-f569-5f08-be4b-4d9d1913990c" + ], + "contexts": [ + "understood. It seems that interactions between multiple genes and environmental factors may play a role. One of these factors is dietary factors. There is evidence supporting the role of nutrient- gene interactions in DM pathophysiology [5]. Thus, a greater understanding of potential gene -nutrient interactions may be relevant for DM prevention and treatment. Nutrigenetics and nutrigenomics are defined as the science of the effects of genetic variation on", + "nutrition [12] . The identi cation of gene variants that contribute both to variation in fetal growth and to the susceptibility to T2DM, however, suggests that this metabolic programming could also be partly genetically determined [13] . These complex interactions between genes and environment complicate the task of identifying any single genetic susceptibility factor for T2DM. Three general approaches have been adopted", + "Nutrients 2014, 6 5340 However, while the a pplication of these technologies is becoming more accessible, analysis of the complex large data sets that are generated presents multiple challenges. The aim of the present review was to provide insights regarding the role of nutrient -gene interactions in DM pathogenesis, prevention and treatment. In addition, we explored how an individuals genetic makeup can affect nutrient metabolism and the response to nutrient intake, potentially leading to DM.", + "Nutrients 2014, 6 5343 3. Gene -Nutrient or Dietary Patter n Interactions in T he Development of T2DM Recently, several studies have d emonstrated the significant effects of genotype by environment interactions on T2D M [48,49] . However, further clarification of the role of these interactions at the genome -wide level could help predict disease risk more accurately and facilitate the development of", + "in nutritional epidemiology: applications, needs and new horizons .Hum Genet 125, 507525. Kaput, J., Noble, J., Hatipoglu, B., et al. ( 2007) Application of nutrigenomic concepts to type 2 diabetes melli-tus.Nutr Metab Cardiovasc Dis 17,89103. Ordovas, J.M., Kaput, J., and Corella, D. ( 2007) Nutrition in the genomics era: cardiovascular disease risk and the Mediterranean diet .Mol Nutr Food Res 51, 12931299. van Ommen, B., El-Sohemy , A., Hesketh, J., et al . ( 2010)", + "dietary patterns according to genetic variations, the role of gene -nutrient interactions, gene - diet-phenotype interactions and epigenetic modifications caused by nutrients; these studies will facilitate an understanding of the early molecular events that occur in DM and will contribute to the identification of better biomarke rs and diagnostics tools. In particular, this", + "Abstract: Diabetes mellitus (DM) is considered a global pandemic, and the incidence of DM continues to grow worldwide. Nutrients and dietary patterns are central issues in the prevention, development and treatment of this disease. The pathogenesis of DM is not comp letely understood, but nutrient -gene interactions at different levels, genetic predisposition and dietary factors appear to be involved. Nutritional genomics studies generally focus on", + "approach will help to develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, which will aid in the prevention and delay of DM and its complications. This rev iew discusses the current state of nutrigenetics, nutrigenomics and epigenomics research on DM. Here, we provide an overview of the role of gene variants and nutrient interactions, the importance of nutrients and dietary patterns on gene expression, OPEN ACCESS", + "It was previously report ed that food intake is a key component that affects the incidence of DM. Thus, the identification and analysis of nutrient/gene interactions are necessary steps to understand DM etiopathogenesis. In general, nutrients can affect gene expression via different mechanisms: ( i) directly; (ii) through their metabolites and ( iii) through signal tran sduction molecules (Figure 1).", + "Nutrients 2014, 6 5347 3.4. Importance of Genotype by Macronutrient Interactions for T2DM -Related Traits Recently, using genome -wide complex trait anal ysis, the genome -environment contribution of 14 dietary factors (glycemic load, total energy, protein, total fat, SF A, MUFA, PUFA, n- 3 PUFA, n-6 PUFA, n-3:n-6 PUFA, carbohydrate, alcohol intake, trans fat and fiber) to the total phenotypic variance of 4 T2DM -related traits (fasting glucose, fasting insulin, HOMA -IR and HOMA of cell" + ], + "answer": "Genomics can be used to better understand the nutritional factors of diabetes through the study of nutrient-gene interactions, which play a significant role in the pathophysiology of diabetes mellitus (DM) [1]. Nutrigenetics and nutrigenomics are fields that focus on how genetic variations affect nutrient metabolism and the body's response to nutrient intake, which can influence the development and progression of DM [3]. By identifying gene variants that contribute to both fetal growth and susceptibility to Type 2 Diabetes Mellitus (T2DM), researchers can better understand how metabolic programming is partly genetically determined [2]. This understanding can help in predicting disease risk more accurately and developing personalized dietary recommendations to prevent and manage DM [4]. Moreover, studying gene-nutrient interactions can reveal early molecular events in DM, contributing to the identification of better biomarkers and diagnostic tools [6]. This approach can also facilitate the development of tailored diets that maximize the use of nutrients and functional ingredients in food, aiding in the prevention and delay of DM and its complications [8]. Overall, genomics provides insights into how nutrients can affect gene expression through various mechanisms, such as directly, through their metabolites, or through signal transduction molecules [9]. This comprehensive understanding can lead to more effective prevention and treatment strategies for diabetes based on individual genetic profiles.", + "question": "nutrition is a factor for diabetes. how can genomics be use to better understand nutritional factors of diabetes" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_diabetes_4 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_diabetes_4 new file mode 100644 index 0000000..f140ead --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_diabetes_4 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2014 - Nutrigenetics and Nutrigenomics Insights into Diabetes Etiopathogenesis.pdf", + "2018 - Ethical_Social_and_Legal_Consequences.pdf", + "2014 - Nutrigenetics and Nutrigenomics Insights into Diabetes Etiopathogenesis.pdf", + "2012 - Systems Biology Approaches to Nutrition.pdf", + "2007 - Bioethnic Conscription Genes, Race.pdf", + "2014 - Nutrigenetics and Nutrigenomics Insights into Diabetes Etiopathogenesis.pdf", + "2014 - Nutrigenetics and Nutrigenomics Insights into Diabetes Etiopathogenesis.pdf", + "2014 - Nutrigenetics and Nutrigenomics Insights into Diabetes Etiopathogenesis.pdf", + "2010 - The Genetics of Type 2 Diabetes.pdf", + "2014 - Nutrigenetics and Nutrigenomics Insights into Diabetes Etiopathogenesis.pdf" + ], + "extraction_id": [ + "2618e650-f07c-5d21-a8f9-8f0dcdd51fd6", + "6cc02cc8-628d-58b8-b9d3-8fc049773c22", + "1e4b6f6f-e67a-53ef-8af6-78aa4c9ce112", + "76e4f0c7-ffb4-5643-b8d7-46cc584cfbf8", + "79223305-26a8-51d5-a962-bb7d925810d7", + "2618e650-f07c-5d21-a8f9-8f0dcdd51fd6", + "c9f99f8c-8432-59fa-8392-38eba8a63d8b", + "23654fd6-b738-5c3b-9e6b-9b29ea47df38", + "55a150e2-71ee-5b88-bbbb-22eecc2ff5e4", + "4f65affc-d78a-5048-86f3-22c0e12d298c" + ], + "document_id": [ + "ce4f171c-494c-53f2-a770-c3edd3561c40", + "3fb80410-0b56-5c01-b3d6-9388b6029a77", + "ce4f171c-494c-53f2-a770-c3edd3561c40", + "6955478b-950d-5d29-b24c-3a5ca656f3ae", + "d90126d9-fd87-5b38-87f7-08415f690836", + "ce4f171c-494c-53f2-a770-c3edd3561c40", + "ce4f171c-494c-53f2-a770-c3edd3561c40", + "ce4f171c-494c-53f2-a770-c3edd3561c40", + "a1d211d4-279e-51d7-b2b2-33bc2763d089", + "ce4f171c-494c-53f2-a770-c3edd3561c40" + ], + "id": [ + "chatcmpl-ADYmomdJKgdvZlepHclGpceBX99vV", + "0752929d-fb98-5c2f-b47f-e493f25ac70d", + "20d914cb-135d-5033-adc4-61aa7468f8df", + "c6c8b56a-1f17-5d96-bea4-787673f11be9", + "b9f46fff-157d-5007-ae86-987d9b5022b5", + "9bbce823-83c5-5258-af26-f79575042496", + "d8db4432-bb66-59de-bb9e-c0667ec9010c", + "430a5d63-0af9-569e-b9db-2cc4262f1d77", + "e5ac3a01-3a53-5239-b1d0-26ee4e72f37b", + "0337dd9b-7228-5664-aff3-fa20c0f04d6d", + "f5909a51-0d41-5aee-ac5a-8d47550ef094" + ], + "contexts": [ + "Abstract: Diabetes mellitus (DM) is considered a global pandemic, and the incidence of DM continues to grow worldwide. Nutrients and dietary patterns are central issues in the prevention, development and treatment of this disease. The pathogenesis of DM is not comp letely understood, but nutrient -gene interactions at different levels, genetic predisposition and dietary factors appear to be involved. Nutritional genomics studies generally focus on", + "ABSTRACT Genomics has contributed to a better understanding of many disorders including diabetes. The following article looks at the ethical, social and legal consequences of genomic medicine and predictive genetic testing for diabetes. This is currently a field in its nascent stage and developing rapidly all over the world. The various ethical facets of genomic medicine in diabetes like its effects", + "Nutrients 2014, 6 5340 However, while the a pplication of these technologies is becoming more accessible, analysis of the complex large data sets that are generated presents multiple challenges. The aim of the present review was to provide insights regarding the role of nutrient -gene interactions in DM pathogenesis, prevention and treatment. In addition, we explored how an individuals genetic makeup can affect nutrient metabolism and the response to nutrient intake, potentially leading to DM.", + "in nutritional epidemiology: applications, needs and new horizons .Hum Genet 125, 507525. Kaput, J., Noble, J., Hatipoglu, B., et al. ( 2007) Application of nutrigenomic concepts to type 2 diabetes melli-tus.Nutr Metab Cardiovasc Dis 17,89103. Ordovas, J.M., Kaput, J., and Corella, D. ( 2007) Nutrition in the genomics era: cardiovascular disease risk and the Mediterranean diet .Mol Nutr Food Res 51, 12931299. van Ommen, B., El-Sohemy , A., Hesketh, J., et al . ( 2010)", + "at the expense of understanding the social context and determinants of the disease.Biogenetic views tend to trump sociological views in the diabetes research imaginary ofconsortium members. However, the genetic epidemiologists who make up part of thediabetes consortium are not ignorant of the effects of proper diet and adequate exercise.Take away the television and the automobile and diabetes would all but disappear, quipped the head of one lab. Neither are researchers unsympathetic to those who sufferfrom", + "approach will help to develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, which will aid in the prevention and delay of DM and its complications. This rev iew discusses the current state of nutrigenetics, nutrigenomics and epigenomics research on DM. Here, we provide an overview of the role of gene variants and nutrient interactions, the importance of nutrients and dietary patterns on gene expression, OPEN ACCESS", + "understood. It seems that interactions between multiple genes and environmental factors may play a role. One of these factors is dietary factors. There is evidence supporting the role of nutrient- gene interactions in DM pathophysiology [5]. Thus, a greater understanding of potential gene -nutrient interactions may be relevant for DM prevention and treatment. Nutrigenetics and nutrigenomics are defined as the science of the effects of genetic variation on", + "Nutrients 2014, 6 5343 3. Gene -Nutrient or Dietary Patter n Interactions in T he Development of T2DM Recently, several studies have d emonstrated the significant effects of genotype by environment interactions on T2D M [48,49] . However, further clarification of the role of these interactions at the genome -wide level could help predict disease risk more accurately and facilitate the development of", + "nutrition [12] . The identi cation of gene variants that contribute both to variation in fetal growth and to the susceptibility to T2DM, however, suggests that this metabolic programming could also be partly genetically determined [13] . These complex interactions between genes and environment complicate the task of identifying any single genetic susceptibility factor for T2DM. Three general approaches have been adopted", + "It was previously report ed that food intake is a key component that affects the incidence of DM. Thus, the identification and analysis of nutrient/gene interactions are necessary steps to understand DM etiopathogenesis. In general, nutrients can affect gene expression via different mechanisms: ( i) directly; (ii) through their metabolites and ( iii) through signal tran sduction molecules (Figure 1)." + ], + "answer": "Diabetes mellitus (DM) is a global pandemic with increasing incidence worldwide, where nutrients and dietary patterns play a crucial role in its prevention, development, and treatment [1]. The pathogenesis of DM involves complex interactions between genetic predisposition and dietary factors, which are not yet fully understood [1]. Genomics offers a promising avenue to elucidate these interactions by studying nutrient-gene interactions at various levels [1]. Nutritional genomics, including nutrigenetics and nutrigenomics, focuses on how genetic variations affect nutrient metabolism and the body's response to nutrient intake, potentially influencing DM pathogenesis [3]. By identifying gene variants and understanding their interactions with dietary factors, genomics can help predict disease risk more accurately and facilitate the development of personalized dietary interventions [8]. This approach aims to develop tailored diets that maximize the use of nutrients and functional ingredients in food, aiding in the prevention and delay of DM and its complications [6]. Overall, genomics provides valuable insights into the role of gene-nutrient interactions in DM, paving the way for more effective prevention and treatment strategies [7].", + "question": "nutrition is a factor for diabetes. construct an abstract about how can genomics be use to better understand nutritional factors of diabetes" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_diabetes_5 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_diabetes_5 new file mode 100644 index 0000000..2939139 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_diabetes_5 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2009 - Pathomechanisms of Type 2 Diabetes Genes.pdf", + "2015 - Diabetes mellitus The epidemic of the century.pdf", + "2012 - Type 2 Diabetes Genetics Beyond GWAS.pdf", + "2007 - Recent development in pharmacogenomics from candidate genes to genome-wide association studies.pdf", + "2014 - Dorothy Hodgkin Lecture 2014 Understanding genes identified by genome\u2010wide association.pdf", + "2015 - Diabetes mellitus The epidemic of the century.pdf", + "2015 - Type 2 Diabetes Mellitus and the Association of Candidate Genes.pdf", + "2007 - A German genome-wide linkage scan for type 2 diabetes supports the existence of a metabolic syndrome locus on chromosome 1p36.13 and a type 2 diabetes locus on chromosome 16p12.pdf", + "2014 - Nutrigenetics and Nutrigenomics Insights into Diabetes Etiopathogenesis.pdf", + "2007 - TCF7L2 the biggest story in diabetes genetics since HLA.pdf" + ], + "extraction_id": [ + "eff1d167-9689-5c26-9a12-c66714696d86", + "36f9d4f2-293e-53e3-8b4b-12571af6669a", + "a3a875fa-e55b-52d0-b9bf-72b96330c393", + "f2fa55c2-fbca-5f7b-a744-deb279bf9369", + "86253f12-bb43-5236-bfb1-df5dff759f6d", + "7cfe9f29-a0ee-56d3-be3b-1b238a43bc07", + "5ffb710d-ca19-5415-bbb6-34b3f85bf47f", + "198c5f2d-fc43-5744-9cd8-4222c8fa8ab8", + "25187f10-04b3-51c6-8f4c-d4e480353fa2", + "0f7bd536-46b9-52e2-927e-a8309d541066" + ], + "document_id": [ + "cf8ec75c-8ffe-5baa-830d-ac7a4a5964bd", + "e114dd28-fd39-56df-bdeb-8806474a6c10", + "d59a38d7-889b-51b5-b896-c305c82a2169", + "fe012b74-6516-5503-a88a-dc8071869632", + "11d0cb98-a00f-53f1-92e3-e1be17002c02", + "e114dd28-fd39-56df-bdeb-8806474a6c10", + "3236fdee-e304-5b88-921f-52e52dc5afa3", + "f8a85060-6303-5228-ba89-7ee8701bda9f", + "ce4f171c-494c-53f2-a770-c3edd3561c40", + "018b8646-b894-5b7d-a8c8-932a2bc13ca8" + ], + "id": [ + "chatcmpl-ADYmusmSLbuh68YcOiU3r0KXSi3Ub", + "3e678e3c-ad2c-5884-9c88-7f4d54c914bd", + "6617e15c-ab52-596c-b628-60ec5a7001e7", + "1eb3a215-002b-528b-a954-bb9e2419ea6f", + "e456e587-e172-5ae9-b68e-98e38c5052c2", + "5d936c2c-faf7-5b0f-92e1-c3f8f43b3011", + "ed5d8e9e-859e-5256-a7b5-468c1f7837a2", + "263f6b22-d314-5653-bbef-3f0e3e09839b", + "05e76af5-c67b-50ca-a06a-a603d6d4b35e", + "fc63f56e-f1fb-56e0-9e62-b4bdcefb5a53", + "c21b7f01-ff01-5561-8016-c4432d844baf" + ], + "contexts": [ + "single nucleotide polymorphisms in TCF7L2 are reproduc-ibly associated with type 2 diabetes and reduce the insulinresponse to glucose in nondiabetic individuals. Diabetes55:28902895 135. Cauchi S, Meyre D, Dina C, Choquet H, Samson C, Gallina S, Balkau B, Charpentier G, Pattou F, StetsyukV, Scharfmann R, Staels B, Fru hbeck G, Froguel P 2006 Transcription factor TCF7L2 genetic study in the Frenchpopulation: expression in human /H9252-cells and adipose tissue", + "L. Mechanisms by which common variants in the TCF7L2 gene increase risk of type 2 diabetes. J Clin Invest 2007; 117: 2155-2163 [PMID: 17671651 DOI: 10.1172/JCI30706] 164 Gloyn AL , Braun M, Rorsman P. Type 2 diabetes susceptibility gene TCF7L2 and its role in beta-cell function. Diabetes 2009; 58: 800-802 [PMID: 19336690 DOI: 10.2337/db09-0099] 165 da Silva Xavier G , Loder MK, McDonald A, Tarasov AI, Carzaniga R, Kronenberger K, Barg S, Rutter GA. TCF7L2 regulates late", + "transcription factor 7-like 2 ( TCF7L2 ) gene confers risk of type 2 diabetes. Nat Genet. 2006; 38:320323. [PubMed: 16415884] 172. Gloyn AL, Noordam K, Willemsen MA, Ellard S, Lam WW, et al. Insights into the biochemical and genetic basis of glucokinase activation from naturally occurring hypoglycemia mutations. Diabetes. 2003; 52:24332440. [PubMed: 12941786] 173. Pearson ER, Donnelly LA, Kimber C, Whitley A, Doney AS, et al. Variation in TCF7L2", + "2 (TCF7L2 ) gene confers risk of Type 2 diabetes. Nat. Genet. 38(3), 320323 (2006). 143Florez JC, Jablonski KA, Bayley N et al. TCF7L2 polymorphisms and progression to diabetes in the Diabetes Prevention Program. N. Engl. J. Med. 355(3), 241250 (2006). 144Damcott CM, Pollin TI, Reinhart LJ et al. Polymorphisms in the transcription factor 7-like 2 ( TCF7L2 ) gene are associated with", + "rs7903146 and rs12255372 in intron 3 of the TCF7L2 gene [20], associated with a ~45% increase in Type 2 diabetes risk per allele. As such, the TCF7L2 locus presently repre- sents the strongest known genetic determinant of Type 2diabetes. Risk allele carriers show impaired insulin produc-tion [21] and b-cell dysfunction in vitro [22]. TCF7L2 (previously referred to as TCF-4) is a high-mobility group box-containing transcription factor involved in Wingless-type MMTV integration site (Wnt)", + "genes which also play a significant role in the risk and pathogenesis of the disease[158,159]. The association of TCF7L2 gene variants with type 2 diabetes and its mechanism of action received special attention by several investigators[161,162]. Over expression of the protein was shown to decrease the sensitivity of beta islet cells to secrete insulin[163,164] and was more precisely involved in the regulation of secretary granule fusion that constitute a late event in insulin secretion", + "et al. Variant of transcription factor 7-like 2 (TCF7L2) gene confers risk of type 2 diabetes. Nat Genet . 2006;38:320-23. Sladek R, Rocheleau G, Rung J, Dina C, Shen L, Serre D, et al. A genome- [9] wide association study identifies novel risk loci for type 2 diabetes. Nature . 2007;445:881-85. Kirchhoff K, Machicao F, Haupt A, Schafer SA, Tschritter O, Staiger H, et al. [10] Polymorphisms in the TCF7L2, CDKAL1 and SLC30A8 genes are associated", + "transcription factor 7-like 2 (TCF7L2) gene confers risk of type 2diabetes. Nat Genet 38:320 3231422 Diabetologia (2007) 50:1418 1422", + "approximately double odds ratio compared to most other diabetes susceptibility polymorphisms. TCF7L2 is a transcription factor involved in the Wnt signaling pathway that is ubiquitously expressed, and it has been observed that TCF7L2 risk alleles result in the overexpression of TCF7L2 in pancreatic cells. This overexpression causes reduced nutrient -induced insulin secretion, which results in a direct predisposition to T2DM as well as an indirect predisp osition via an increase in hepatic glucose", + "diabetes. The gene seems to be widely expressed [ 18] and the transcription factor product is known to be involved in the Wnt signalling cascade. Current evidence strongly supports the idea that the predominant effect of TCF7L2 dysfunction on type 2 diabetes development is mediated through impairment of insulin secretion [ 11,1517,20], a finding that would be consistent, for example, with theknown effects of other (non-homologous) TCF genes (TCF1 [also known as HNF1A ] and TCF2 [also known as" + ], + "answer": "Yes, the gene TCF7L2 is involved in diabetes, specifically type 2 diabetes. Multiple sources in the provided context confirm this association: 1. Single nucleotide polymorphisms in TCF7L2 are reproducibly associated with type 2 diabetes and reduce the insulin response to glucose in nondiabetic individuals [1]. 2. Common variants in the TCF7L2 gene increase the risk of type 2 diabetes [2]. 3. The TCF7L2 gene confers risk of type 2 diabetes [3], [4]. 4. Variants in the TCF7L2 gene are associated with a significant increase in type 2 diabetes risk, representing the strongest known genetic determinant of the disease [5]. 5. Overexpression of TCF7L2 protein decreases the sensitivity of beta islet cells to secrete insulin, which is involved in the regulation of insulin secretion [6]. 6. TCF7L2 risk alleles result in overexpression in pancreatic cells, causing reduced nutrient-induced insulin secretion and a predisposition to type 2 diabetes [9]. 7. The predominant effect of TCF7L2 dysfunction on type 2 diabetes development is mediated through impairment of insulin secretion [10]. These references collectively support the involvement of TCF7L2 in the pathogenesis and risk of type 2 diabetes.", + "question": "Is the gene TCF7L2 involved in diabetes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_diabetes_6 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_diabetes_6 new file mode 100644 index 0000000..dacccec --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_diabetes_6 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2012 - Recent Developments in the Genetic and Genomic Basis of Type 2 Diabetes.pdf", + "2017 - Type 1 diabetes mellitus.pdf", + "2010 - Genetics of Type 1 Diabetes What\u2019s Next.pdf", + "2018 - Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls.pdf", + "2011 - Type 2 diabetes and obesity genomics and the clinic.pdf", + "2015 - Transcript Expression Data from Human.pdf", + "2010 - Genome-wide analysis of transcriptional regulation in the murine liver.pdf", + "2010 - Genome-wide analysis of transcriptional regulation in the murine liver.pdf", + "2013 - The CTRB12 Locus Affects Diabetes Susceptibility.pdf", + "2009 - Genome-Wide Linkage Scan in Gullah-Speaking African American Families.pdf" + ], + "extraction_id": [ + "1213249d-8ed3-5d13-9137-f11b87a7a78b", + "39b6a474-b721-509f-bbc3-094dc1f49634", + "5557d2db-b55a-59c9-8fe7-89b196a28617", + "43eecb5d-aca2-5c3e-9351-afbef000a795", + "10685e4c-eb4c-562a-a64a-d98e83c12c0b", + "867d0b1b-16a1-53ea-b014-3c204b9001a5", + "151aa443-b9af-55db-9a30-adc4440ac7ef", + "7cbef74a-2d81-5a3a-a4d4-dfacdb86e632", + "97905c56-72e3-546d-ba0e-dfe1023f0c27", + "766aa9ab-312f-5d62-be31-860ba0697180" + ], + "document_id": [ + "7d051350-d939-5183-be22-742727573a75", + "8e8b9b6e-8dfb-5aae-8c61-5f53bd4e0242", + "261cbb40-ed6b-554c-a70d-db6b9f14cf74", + "af63c74d-a204-5f9f-9a32-3451b112e5ba", + "5086a525-124e-5a45-b75a-657d67a3250a", + "2b30d4f3-9ec3-574f-9a36-709b0e09c3f2", + "8a115c1b-662c-5062-b77f-bbde0532bbe9", + "8a115c1b-662c-5062-b77f-bbde0532bbe9", + "5cd422c9-d4bc-5a96-8af8-00561458e67b", + "bd1d6b45-3929-5bd8-a677-d143381a7da5" + ], + "id": [ + "chatcmpl-ADYmzCY2UpsFw8AXX7kNO9LvCTBMY", + "6f00dd31-490e-53cd-81b3-c56e13bd7edd", + "fa4e127f-2c54-592b-a478-152bc74e7351", + "f0c9d05b-7999-5cb7-bb48-0666cf74aec0", + "55dca975-78ec-594d-8a30-a0849b683089", + "126bf287-0f5e-52a9-abac-ad59ad3ea153", + "226e2873-a0bf-554d-9576-7fca5f2ffc0f", + "90565c2b-fdb6-5b0f-a710-9086a4cfcd2b", + "ceb7bd13-b917-566f-8e17-40dd523afd42", + "487d6a88-44ef-520e-a910-5b4b89416880", + "d4d61f22-5ba2-5ef1-a497-167894bf1c7f" + ], + "contexts": [ + "associated with increased fasting plasma glucose levels and type2 diabetes risk. Nat Genet. 2009;41(1):89 94. 23. Rees M, Wincovitch S, Schultz J, Waterstradt R, Beer N, Baltrusch S, et al. Cellular characterisation of the GCKR P446L variant associated with type 2 diabe tes risk. Diabetologia. 2012;55 (1):114 22. 24. Nejentsev S, Walker N, Riches D, Egholm M, Todd J, et al. Rare variants of IFIH1 , a gene implicated in antiviral responses, protect against type 1 diabetes. Science. 2009;324(5925):387 9.", + "HLAlinked genes in juvenile diabetes mellitus. Br.Med. J. 3, 133135 (1975). 52. Erlich,H.A. etal. Next generation sequencing reveals the association of DRB3*02:02 with type 1 diabetes. Diabetes 62, 26182622 (2013). 53. CaillatZucman,S. etal. Agedependent HLA genetic heterogeneity of type1 insulindependent diabetes mellitus. J.Clin. Invest. 90, 22422250 (1992). 54. Cucca,F. etal. The distribution of DR4 haplotypes inSardinia suggests a primary association of typeI", + "holdt R, Akolkar B, Erlich HA, Hilner JE, Julier C, Morahan G, Nerup J,Nierras CR, Chen WM, Rich SS, Type 1 Diabetes Genetics Consortium. Ahuman type 1 diabetes susceptibility locus maps to chromosome 21q22.3.Diabetes 2008;57:2858 2861 58. Nejentsev S, Walker N, Riches D, Egholm M, Todd JA. Rare variants of IFIH1, a gene implicated in antiviral responses, protect against type 1diabetes. Science 2009;324:387389 59. Altshuler D, Daly M. Guilt beyond a reasonable doubt. Nat Genet 2007;39: 813 815", + "because of their presumed roles in immune signalling, considered to be a major feature of T1D-susceptibility. These include ERBB3 (receptor tyrosine-protein kinase erbB-3 precursor) at 12q13 and SH2B3/LNK (SH2B adaptor protein 3), TRAFD1 (TRAF-type zinc finger domain containing 1) and PTPN11 (protein tyrosine phos- phatase, non-receptor type 11) at 12q24. For these signal regions in", + "Nejentsev S, Walker N, Riches D, Egholm M, Todd JA (2009) Rare variants of IFIH1, a gene implicated in antiviral responses, protect against type 1 diabetes. Science 324:387389 Nicolson TJ, Bellomo EA, Wijesekara N, Loder MK, Baldwin JM, Gyulkhandanyan AV, Koshkin V, Tarasov AI, Carzaniga R, Kronenberger K, Taneja TK, da Silva Xavier G, Libert S,", + "61. Nejentsev S, Walker N, Riches D, Egholm M, Todd JA (2009) Rare variants of IFIH1, a gene implicated in antiviral responses, protect against type 1 diabetes. Science 324: 387 389. doi: 10.1126/science. 1167728 PMID: 19264985 62. Nica AC, Ongen H, Irminger JC, Bosco D, Berney T, et al. (2013) Cell-type, allelic, and genetic signa- tures in the human pancreatic beta cell transcriptome. Genome Res 23: 1554 1562. doi: 10.1101/gr. 150706.112 PMID: 23716500", + "7 (Wellcome Trust Case Control Consortium 2007) . Separate work that examined liver gene expression in a smaller cohort of human samples with and without Type I diabetes found that ERBB3 did not have a cis -eQTL but that a flanking gene, R PS26, did. Since the disease phenotype and RPS26 both had QTLs in the same location, this suggested the RPS26 was a stronger candidate than ERBB3 . The authors then used mouse liver and adipose expression", + "models. A genome wide association study in a large human population proposed the receptor typrosine kinase ERBB3 as the best candidate gene near a QTL for Type I diabetes", + "and 16p13.2 (near TMEM114 ) have not previously been implicated in b-cell function, type 2 diabetes susceptibility, or related phenotypes. However, in publically available gene expression data from the MuTHER consortium, rs4148941 acts as eQTL for CHST3 in lymphoblast cell lines ( P=5310251) and SPOCK2 in both adipose tissue (P=1310221) and lymphoblast cell line ( P=331024) (22). Given the additional trend toward association with GLP-1 RA treatment response in diabetic patients, further", + "IGFBP1, and IGFBP3. The IGF pathway is nowsuspected to play a role in diabetes because of observedassociations with IGF2BP2 (2729)." + ], + "answer": "Yes, the genes SH2B3, IFIH1, and ERBB3 are related to diabetes: 1. **IFIH1**: Rare variants of IFIH1, a gene implicated in antiviral responses, protect against type 1 diabetes [1], [3], [5], [6]. 2. **ERBB3**: ERBB3 is considered to be a major feature of type 1 diabetes susceptibility due to its role in immune signaling [4]. Additionally, a genome-wide association study proposed ERBB3 as the best candidate gene near a QTL for type 1 diabetes [8]. 3. **SH2B3**: SH2B3 (also known as LNK) is considered to be a major feature of type 1 diabetes susceptibility due to its role in immune signaling [4]. Thus, all three genes, SH2B3, IFIH1, and ERBB3, are related to diabetes.", + "question": "Is any of the genes SH2B3, IFIH1 or ERBB3 related to diabetes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_diabetes_7 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_diabetes_7 new file mode 100644 index 0000000..c7d4dcb --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_diabetes_7 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2017 - diabetes-mellitus-in-developing-countries-and-underserved-commun-2017.pdf", + "2011 - Prioritizing candidate disease genes by network-based boosting of genome-wide association data.pdf", + "2020 - Insights into pancreatic islet cell dysfunction from type 2 diabetes mellitus genetics..pdf", + "2007 - Integrative analysis for finding genes and networks involved in diabetes and other complex diseases.pdf", + "2010 - Genetics of Type 1 Diabetes What\u2019s Next.pdf", + "2015 - Biological interpretation of genome-wide association studies using predicted gene functions.pdf", + "2011 - Shared Genomics of Type 2 and Gestational Diabetes Mellitus.pdf", + "2010 - Common Inherited Variation in Mitochondrial Genes.pdf", + "1999 - Linkage of Type 2 Diabetes Mellitus and of Age at Onset to a Genetic Location.pdf", + "2019 - Genome-wide association study of type 2 diabetes in Africa.pdf" + ], + "extraction_id": [ + "f7fe5916-4f25-5740-8737-f668f216575d", + "dffdea93-109e-5114-8795-e0fc66d6d3ed", + "f7013243-3e5f-509d-a414-edc4d7f27bc2", + "f13b4fee-14f4-5827-9482-3692165c8ce6", + "e5a38afd-cb9c-5552-9edd-3e9043d4f30d", + "0b09c4c7-a276-517f-a6e1-9388032fe622", + "29039cd9-9414-59e9-b97c-14f6f71ec4a2", + "8e91b32f-a873-5dc7-927d-52786cc44aa8", + "69b05acc-0a98-51de-a69c-1e46ca1c0ba3", + "ef39a6c5-9067-59e8-84ab-8b89071510d5" + ], + "document_id": [ + "8a9451b9-d7e8-5417-b6a5-5fd1b791cc4d", + "db0aa4b3-66ec-5d51-be72-2a1289db944a", + "2a386c81-8f24-5993-8e48-0e89d7fb4fec", + "b91aeacf-6e83-52ac-beb6-034ad77cab18", + "261cbb40-ed6b-554c-a70d-db6b9f14cf74", + "8f9f62fd-9423-55b3-abf9-24cde0d2e775", + "bef0cabe-0bca-5715-9ffc-0b825744fbcf", + "9a5c8cba-06cb-5280-871f-1bbe128c3dc4", + "631b1f41-1064-5fc1-87f9-8a3c9f24ee9d", + "a7e4b6f4-fbb6-5dde-b638-d0d694c8ce87" + ], + "id": [ + "chatcmpl-ADYn4g7NCIHEHW87vnQFVH1QRLe6y", + "e81d17bd-858c-52b7-8c02-2076e59afe20", + "18817608-0557-5acb-a091-9bc4d3640f7e", + "65941ce7-c762-5ae5-b1cd-4c62d8caddac", + "2e004b17-d266-50d9-be7f-33b523e59e54", + "375e0eba-87cf-5081-9f39-da1938e8be9e", + "b3455bcd-494e-5288-93ae-2fd761dd4157", + "51114ced-f323-57b9-87fb-30094a97642c", + "65daaa1d-b4e7-5d6c-aa4f-56b8a88bc1d7", + "ec145460-62ed-5375-b1a9-6231f94db4b9", + "e633c6eb-1fc6-5430-a324-f652c7f3e082" + ], + "contexts": [ + "9. Ehm MG, Karnoub MC, Sakul H, Gottschalk K, Holt DC, Weber JL, American Diabetes Association GENNID Study Group. Genetics of NIDDM, et al. Genome wide search for type 2 diabetes susceptibil-ity genes in four American populations. Am J Hum Genet. 2000;66:187181. 10. McCarthy M, Zeggini E. Genome-wide association studies in type 2 diabetes. Curr Diab Rep. 2009;9:16471. 11. Hivert MF, Jablonski KA, Perreault L, Saxena R,", + "that from orthologous genes of yeast, worm, and fly. The resulting HumanNet gene network can be accessed through a web interface (http://www.functionalnet.org/humannet). Using this interface, researchers can easily search the network using a set of seedTable 1. Selected top-ranked Crohns disease and type 2 diabetes genes for which network data added support to GWAS evidence, measured as an increase in odds (prior =1.7 for each) Crohns disease", + "twins. Diabetologia 30, 763768 (1987). 3. Neel, J. V. in The Genetics of Diabetes Mellitus (eds W. Creutzfeldt, J. Kbberling, & J. V. Neel) 1-11 (Springer, 1976). 4. International HapMap Consortium, etal. A second generation human haplotype map of over 3.1 million SNPs. Nature 449, 851861 (2007). 5. Sabeti, P . C. etal. Genome-wide detection and characterization of positive selection in human populations. Nature 449, 913918 (2007). 6. Genomes Project, C. etal. A global reference", + "Genome Biology 2007, 8:R253Open Access2007Bergholdtet al.Volume 8, Issue 11, Article R253Research Integrative analysis for finding genes and networks involved in diabetes and other complex diseases Regine Bergholdt*, Zenia M Strling, Kasper Lage, E Olof Karlberg, Pll lason, Mogens Aalund, Jrn Nerup*, Sren Brunak, Christopher T Workman and Flemming Pociot* Addresses: *Steno Diabetes Center, Niels Steensensvej 2, DK-2820 Gentofte, Denmark. Center for Biological Sequence Analysis, Technical", + "77. Bergholdt R, Brorsson C, Lage K, Nielsen JH, Brunak S, Pociot F. Expression proling of human genetic and protein interaction networks intype 1 diabetes. PLoS One 2009;4:e6250 78. Bergholdt R, Storling ZM, Lage K, Karlberg EO, Olason PI, Aalund M, Nerup J, Brunak S, Workman CT, Pociot F. Integrative analysis for ndinggenes and networks involved in diabetes and other complex diseases.Genome Biol 2007;8:R253 79. Oresic M, Simell S, Sysi-Aho M, Na nto -Salonen K, Seppa nen-Laakso T,", + "31. Saxena, R. et al. Genome-wide association analysis identies loci for type 2 diabetes and triglyceride levels. Science 316, 13311336 (2007). 32. Franke, L. et al. Reconstruction of a functional human gene network, with an application for prioritizing positional candidate genes. Am. J. Hum. Genet. 78, 10111025 (2006). 33. Su, Z., Marchini, J. & Donnelly, P. HAPGEN2: simulation of multiple disease SNPs. Bioinformatics 27,23042305 (2011).", + "Genetic exploration of GDM is in its initial stage. The genetics of GDM, focusing on human association studies with candidate genes common to both T2DM and GDM is elegantly summarized by Robitaille and Grant (2008). The purpose of this chapter is to provide a comprehensive overview to include recent literature on susceptible gene variants that may contribute to both GDM and T2DM. SEARCH STRATEGIES A systematic literature search using PubMed was performed to identify stud-", + "Human Molecular Genetics 16(1): 3649, 2007). The DiabetesGenetics Initiative (DGI) study was used for the analysis, as we had access to genotype data in this study. The unadjusted gene p-value, P BestSNP g is the association p-value of the best regional SNP for gene g(y-axis in A). Phenotype permutation analysis was used as the gold standard to test goodness of gene score correction as it corrects forall confounders without requiring a priori knowledge of the confounders ( P Gene", + "version 2.0: users manual. PGL tech rep 2. Population Ge-netics Laboratory, Department of Genetics, Southwest Foun-dation for Biomedical Research, San Antonio Elbein SC (1997) The genetics of human noninsulin-dependent (type 2) diabetes mellitus. J Nutr 127:1891S1896S Elbein S, Hoffman M, Leppert M, Hasstedt S (1997) Linkage of fasting glucose in relatives of an NIDDM sib pair tomarkers on chromosome 9p. Diabetes 57 Suppl 1:51A Elston RC (1998) Methods of linkage analysisand the as-", + "Diabetes Study (DDS): a platform for chronic disease research.Glob Health Epidemiol Genom 1:e2. https://doi.org/10.1017/ gheg.2015.3 17. Genomes Project C, Auton A, Brooks LD et al (2015) A global reference for human genetic variation. Nature 526:68 74 18. Howie BN, Donnelly P, Marchini J (2009) A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet 5(6):e1000529. https://doi. org/10.1371/journal.pgen.1000529" + ], + "answer": "To use GeneNetwork to find genes related to diabetes in humans, you can follow these steps: 1. **Access the HumanNet Gene Network**: The HumanNet gene network can be accessed through its web interface at [http://www.functionalnet.org/humannet](http://www.functionalnet.org/humannet) [2]. 2. **Search Using Seed Genes**: Once on the HumanNet interface, you can search the network using a set of seed genes. These seed genes can be genes already known to be associated with diabetes or genes you are interested in exploring further [2]. 3. **Review Network Data**: The network data can provide additional support to genome-wide association studies (GWAS) evidence. For example, the network data can help measure the increase in odds for certain genes being associated with type 2 diabetes [2]. 4. **Integrative Analysis**: You can perform integrative analysis to find genes and networks involved in diabetes. This involves combining data from various sources to identify potential genetic interactions and pathways related to diabetes [4]. By following these steps, you can effectively utilize GeneNetwork to identify and analyze genes related to diabetes in humans.", + "question": "How can I use genenetwork to find genes related with diabetes in humans?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_diabetes_8 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_diabetes_8 new file mode 100644 index 0000000..9b532e2 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_diabetes_8 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2011 - Prioritizing candidate disease genes by network-based boosting of genome-wide association data.pdf", + "2007 - Integrative analysis for finding genes and networks involved in diabetes and other complex diseases.pdf", + "2017 - diabetes-mellitus-in-developing-countries-and-underserved-commun-2017.pdf", + "2010 - Genetics of Type 1 Diabetes What\u2019s Next.pdf", + "2015 - Biological interpretation of genome-wide association studies using predicted gene functions.pdf", + "2022 - A haplotype-resolved genome assembly of the Nile rat facilitates exploration of the genetic basis of diabetes.pdf", + "2011 - Prioritizing candidate disease genes by network-based boosting of genome-wide association data.pdf", + "2020 - Insights into pancreatic islet cell dysfunction from type 2 diabetes mellitus genetics..pdf", + "2007 - Integrative analysis for finding genes and networks involved in diabetes and other complex diseases.pdf", + "2009 - Gene prioritization based on biological plausibility over genome wide association studies renders new loci associated with type 2 diabetes.pdf" + ], + "extraction_id": [ + "dffdea93-109e-5114-8795-e0fc66d6d3ed", + "f13b4fee-14f4-5827-9482-3692165c8ce6", + "f7fe5916-4f25-5740-8737-f668f216575d", + "e5a38afd-cb9c-5552-9edd-3e9043d4f30d", + "0b09c4c7-a276-517f-a6e1-9388032fe622", + "afa54304-6ffc-5f81-9431-d4c19f58527b", + "dcb6101e-cf09-5220-a3c9-ed5106c065b2", + "f7013243-3e5f-509d-a414-edc4d7f27bc2", + "f13b4fee-14f4-5827-9482-3692165c8ce6", + "a6b6c2df-f79b-58b8-a67a-fcf55b18d221" + ], + "document_id": [ + "db0aa4b3-66ec-5d51-be72-2a1289db944a", + "b91aeacf-6e83-52ac-beb6-034ad77cab18", + "8a9451b9-d7e8-5417-b6a5-5fd1b791cc4d", + "261cbb40-ed6b-554c-a70d-db6b9f14cf74", + "8f9f62fd-9423-55b3-abf9-24cde0d2e775", + "6a8eb0a5-807d-5ef9-a732-b1dd722c0499", + "db0aa4b3-66ec-5d51-be72-2a1289db944a", + "2a386c81-8f24-5993-8e48-0e89d7fb4fec", + "b91aeacf-6e83-52ac-beb6-034ad77cab18", + "0fd2b5c8-9bda-5cc8-adb4-231d3842d50f" + ], + "id": [ + "chatcmpl-ADYnAWVHUhI0y6oaOnDZY8VopPust", + "18817608-0557-5acb-a091-9bc4d3640f7e", + "2e004b17-d266-50d9-be7f-33b523e59e54", + "e81d17bd-858c-52b7-8c02-2076e59afe20", + "375e0eba-87cf-5081-9f39-da1938e8be9e", + "b3455bcd-494e-5288-93ae-2fd761dd4157", + "2360c49b-412e-5e9d-b95d-87a67b82e729", + "5e5e6bac-7695-5405-ad90-f24f5336fa34", + "65941ce7-c762-5ae5-b1cd-4c62d8caddac", + "4f009356-41ed-5cdc-9cfa-80cbb913874f", + "c21d117e-a223-5293-b794-6aa60729f7f7" + ], + "contexts": [ + "that from orthologous genes of yeast, worm, and fly. The resulting HumanNet gene network can be accessed through a web interface (http://www.functionalnet.org/humannet). Using this interface, researchers can easily search the network using a set of seedTable 1. Selected top-ranked Crohns disease and type 2 diabetes genes for which network data added support to GWAS evidence, measured as an increase in odds (prior =1.7 for each) Crohns disease", + "Genome Biology 2007, 8:R253Open Access2007Bergholdtet al.Volume 8, Issue 11, Article R253Research Integrative analysis for finding genes and networks involved in diabetes and other complex diseases Regine Bergholdt*, Zenia M Strling, Kasper Lage, E Olof Karlberg, Pll lason, Mogens Aalund, Jrn Nerup*, Sren Brunak, Christopher T Workman and Flemming Pociot* Addresses: *Steno Diabetes Center, Niels Steensensvej 2, DK-2820 Gentofte, Denmark. Center for Biological Sequence Analysis, Technical", + "9. Ehm MG, Karnoub MC, Sakul H, Gottschalk K, Holt DC, Weber JL, American Diabetes Association GENNID Study Group. Genetics of NIDDM, et al. Genome wide search for type 2 diabetes susceptibil-ity genes in four American populations. Am J Hum Genet. 2000;66:187181. 10. McCarthy M, Zeggini E. Genome-wide association studies in type 2 diabetes. Curr Diab Rep. 2009;9:16471. 11. Hivert MF, Jablonski KA, Perreault L, Saxena R,", + "77. Bergholdt R, Brorsson C, Lage K, Nielsen JH, Brunak S, Pociot F. Expression proling of human genetic and protein interaction networks intype 1 diabetes. PLoS One 2009;4:e6250 78. Bergholdt R, Storling ZM, Lage K, Karlberg EO, Olason PI, Aalund M, Nerup J, Brunak S, Workman CT, Pociot F. Integrative analysis for ndinggenes and networks involved in diabetes and other complex diseases.Genome Biol 2007;8:R253 79. Oresic M, Simell S, Sysi-Aho M, Na nto -Salonen K, Seppa nen-Laakso T,", + "31. Saxena, R. et al. Genome-wide association analysis identies loci for type 2 diabetes and triglyceride levels. Science 316, 13311336 (2007). 32. Franke, L. et al. Reconstruction of a functional human gene network, with an application for prioritizing positional candidate genes. Am. J. Hum. Genet. 78, 10111025 (2006). 33. Su, Z., Marchini, J. & Donnelly, P. HAPGEN2: simulation of multiple disease SNPs. Bioinformatics 27,23042305 (2011).", + "Page 16 of 21 Tohetal. BMC Biology (2022) 20:245 Identification ofdiabeteslinked genes bytext mining We used four techniques to derive a set of genes associ - ated with type 2 diabetes and with diet-induced diabe - tes. First, we compiled an expert-curated gene-disease association database from standard resources, the Com - parative Toxicogenomics Database [35] and PharmGKB [36]. The result gave 277 genes associated with type 2 diabetes, but none associated with diet-induced dia -", + "2 diabetes alone and in combination with HumanNet and measuring performance as AUC ( <5% FPR) for recovering the top 20 genes from a type 2 diabetes meta-analysis of 4549 cases and 5579 controls (Zeggini et al. 2008). As for Crohns disease, consideration of the network boosts performance across a wide range of parameter values. Notably, consideration of the network strongly implicates the genes CTNNB1 and BACH2 in type 2 diabetes;", + "twins. Diabetologia 30, 763768 (1987). 3. Neel, J. V. in The Genetics of Diabetes Mellitus (eds W. Creutzfeldt, J. Kbberling, & J. V. Neel) 1-11 (Springer, 1976). 4. International HapMap Consortium, etal. A second generation human haplotype map of over 3.1 million SNPs. Nature 449, 851861 (2007). 5. Sabeti, P . C. etal. Genome-wide detection and characterization of positive selection in human populations. Nature 449, 913918 (2007). 6. Genomes Project, C. etal. A global reference", + "type 1 diabetes genome scan data, and a high -confidence human protein interaction network. Resulting networks were ranked by the significance of the enrichment of proteins from interacting regions. We identified a number of new prot ein network modules and novel candidate genes/ proteins for type 1 diabetes. We propose this type of integrative analysis as a general method for the elucidation of genes and networks involv ed in diabetes and other complex diseases. Background", + "gene prioritization are explained in detail in the Appendix, Supplemental Digital Content 1 , http://links.lww.com/A1049. In addition, the complete list of the training genes, including both the Gene HGNC symbol, and gene name are shown in the Appendix , Supplemental Digital Content 1 , http://links.lww.com/A1049. Moreover, from the freely available site http:// www.broad.mit.edu/ diabetes/, we downloaded the results of the GWA study in 3000 Scandinavian individuals about the genetic variants that inu-" + ], + "answer": "To use the GeneNetwork tool to find genes related to diabetes in humans, you can follow these steps: 1. **Access the HumanNet Gene Network**: The HumanNet gene network can be accessed through its web interface at [http://www.functionalnet.org/humannet](http://www.functionalnet.org/humannet) [1]. 2. **Search the Network**: Once you are on the HumanNet web interface, you can search the network using a set of seed genes. This allows you to explore the network for genes that are functionally related to your genes of interest, such as those involved in diabetes [1]. 3. **Integrative Analysis**: You can perform integrative analysis to find genes and networks involved in diabetes. This involves combining data from various sources, such as genome-wide association studies (GWAS) and protein interaction networks, to identify candidate genes and protein network modules associated with diabetes [2], [4], [9]. 4. **Review Network Data**: Consider network data that supports GWAS evidence. For example, network data has been used to add support to GWAS evidence for type 2 diabetes genes, measured as an increase in odds [1]. By following these steps, you can effectively use the GeneNetwork tool to identify and analyze genes related to diabetes in humans.", + "question": "How can I use the GeneNetwork tool to find genes related with diabetes in humans?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_diabetes_9 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_diabetes_9 new file mode 100644 index 0000000..2474ccd --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_diabetes_9 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2015 - Association between circulating 25-hydroxyvitamin D.pdf", + "2008 - Inflammation, Insulin Resistance and Diabetes.pdf", + "2019 - Adiponectin Levels and Risk of Type 2 Diabetes.pdf", + "2004 - Diabetes Genes a.pdf", + "2001 - A Gene Conferring Susceptibility to Type 2 Diabetes.pdf", + "2004 - Diabetes Genes a.pdf", + "2010 - The Genetics of Type 2 Diabetes.pdf", + "2005 - Animal models of diabetes mellitus.pdf", + "2008 - Inflammation, Insulin Resistance and Diabetes.pdf", + "2006 - Analysis of 14 Candidate Genes for Diabetic Nephropathy.pdf" + ], + "extraction_id": [ + "bea401d8-c665-5910-b3f6-0974617dc32f", + "5f281214-ff2f-5f01-ad99-9faa2edf5751", + "2b483dbc-bc68-5065-b650-811216009db5", + "0b7b0898-2ca5-59aa-aa53-1ab187fe3dd0", + "57ab3edd-7ed7-568a-9e47-b351e2cbf987", + "1a24f9ed-c710-589a-a7f6-7ec46b8cf6a8", + "609eb8ef-0827-567c-98ed-51b1945e5c9d", + "3e91ce60-4e0b-527d-b8c7-147d5717e17a", + "ea677f19-62e2-5f59-b995-babba0066f5f", + "dd18cc20-78a9-5589-b605-d5bf37628258" + ], + "document_id": [ + "3affa62b-11b1-5068-b699-1799d5566ea3", + "71615f74-43a6-5f1c-9300-db97f10b212c", + "584ea813-0169-5c1e-90f2-4abfbcc0b4fa", + "805ad0d5-6a3d-5e5f-b1ba-121e10c6daaa", + "8c07c8b6-4192-56cf-9332-715ff7085577", + "805ad0d5-6a3d-5e5f-b1ba-121e10c6daaa", + "a1d211d4-279e-51d7-b2b2-33bc2763d089", + "2fd381ac-2898-5a8c-af93-bcc86e7dec14", + "71615f74-43a6-5f1c-9300-db97f10b212c", + "6512e711-d879-50b3-96e7-2e97339ebad5" + ], + "id": [ + "chatcmpl-ADYnFB30HdQXCs4mqZmx2zUlQo7uq", + "ad3e5060-4495-5a6f-9bb3-0f372359d852", + "c1c877eb-2dfe-586b-bde8-99b2c0ae8515", + "50aa0f0c-1095-5660-bde3-f6320c88506d", + "0dd93045-9b04-53eb-9d51-1dcc1c9c975a", + "a9ade0cc-6bd0-57d7-ab72-4a0b6735e455", + "8e3bbcea-6f25-57c4-86b8-845b1cd0fc02", + "ee0041e3-b9a3-5a97-9761-55208d06bb2d", + "e35ff76b-92a1-51fa-b28f-d9c90a81f2fd", + "82404153-20f8-53e5-92a9-5cf0818d3c4d", + "b9e891e3-eba8-5a00-86ce-55d5d144361d" + ], + "contexts": [ + "confounding, which is plausible in observational studies of incident type 2 diabetes. Measurements of confounders (eg, physical activity) are susceptible to errors and are not adequately controlled for in epidemiological analyses. 5 Although results from clinical trials6,7 have shown no e ect of vitamin D supplementation on the incidence of type 2 diabetes, these ndings require cautious interpretation because of issues with doses, combination treatment with calcium, compliance, and generalisability. 3", + "common (confounding factors) that are the real causes of diabetes. In this study, the researchers use Mendelian randomization to examine whether increased blood CRP causes diabetes. Some variants of CRP (the gene that encodes CRP) increase the amount of CRP in the blood. Because these variants are inherited randomly, there is no likelihood ofconfounding factors, and an association between these variants and the development of insulin resistance and diabetes indicates, therefore, that", + "residual confounding. As shown inTable 2, many of the included studiesadjusted for a wide range of potentialconfounders, including demographicand lifestyle factors. The strength of theadjusted RRs for adiponectin levels anddiabetes risk and the consistency of as-sociations across diverse populations re-duce the likelihood that residual con-founding by these variables can explainthe findings. Another issue is whetheradiponectin has a causal effect on dia-betes or is only a surrogate marker forother", + "diabetes are related to impaired glucose counterregulation and hypoglycemia unawareness, one should also keep in mind that hypoglycemia can be multifactorial and be the result of several unrelated diseases. These include liver disease, malnutrition, sepsis, burns, total parenteral nutrition, malignancy and administration of certain medications known to reduce plasma glucose concentrations (Table 1).27 In principle, the same risk factors for hypoglycemia apply to", + "exists in the overall sample. In the case of type 2 diabetes,one would ideally stratify on the basis of insulin resistanceand/or severity of insulin secretion defect. However, con-founding environmental effects, including varying durationof disease, differing access to health care, heterogeneity inprescription, and variation in adherence to treatmentregimes, make inferences about insulin action in diabeticpatients problematic, especially inferences based solely onoral glucose tolerance test (OGTT) data", + "of diabetes remains one of the great challenges in human genetics. Diabetes is a result of complex interactions between genetic and non-genetic (including environmental) factors. Although diabetes and its related traits have been shown to cluster within families, their .transmission does not follow a Mendelian fashion, except for some rare syndromes such as MODY. Diabetes could be the result of few common variants with a relatively large effect, such as HLA alleles at the MHC locus and VNTR", + "predisposing to diabetes through effects on insulin sensitivity, however, may be more dif cult to track down because of strong", + "is still unclear. Genetic studies in both animalsand humans are complex, given the many susceptibility andprotective loci that contribute to the overall risk of diabetes", + "adjustment for adiposity and other confounding factors [4 10]. Preventing or delaying onset of diabetes and its compli- cations is an important therapeutic aim, and there is interest in inammatory effectors including CRP as drug targets [11,12]. It is therefore highly desirable to establish which mediators in the inammatory cascade are causal for diabetes. Mendelian randomization involves comparison of pheno- type and genotype effects in observational studies [13]. If the", + "adjusting for sex, diabetes duration, HbA1c, and smoking, assuming either additive or dominant effects of the polymorphisms.N. VIONNET AND ASSOCIATES DIABETES, VOL. 55, NOVEMBER 2006 3169Downloaded from http://diabetesjournals.org/diabetes/article-pdf/55/11/3166/649912/zdb01106003166.pdf by Kenya Institution user on 14 July 2023" + ], + "answer": "Confounding factors in diabetes are variables that can influence both the exposure and the outcome, potentially leading to misleading associations. Some of the confounding factors in diabetes include: 1. **Physical Activity**: Errors in measuring physical activity can lead to inadequate control of this confounder in epidemiological analyses [1]. 2. **Demographic and Lifestyle Factors**: These include a wide range of variables such as age, sex, socioeconomic status, and lifestyle habits like diet and exercise, which can influence diabetes risk [3]. 3. **Environmental Effects**: These include varying duration of disease, differing access to healthcare, heterogeneity in prescription, and variation in adherence to treatment regimes [5]. 4. **Adiposity**: Adjusting for adiposity and other confounding factors is crucial in studies examining the onset and complications of diabetes [9]. These factors need to be carefully controlled for in studies to ensure accurate and reliable results.", + "question": "what are confounding factors in diabetes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_1 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_1 new file mode 100644 index 0000000..886b472 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_1 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2021 - Human Molecular Genetics and Genomics.pdf", + "2015 - A new age in functional genomics using CRISPR Cas9 in arrayed library screening.pdf", + "2016 - Genome editing comes of age.pdf", + "2018 - Neuro-Immuno-Gene- and GenomeEditing-Therapy for Alzheimer\u2019s.pdf", + "2016 - Genome editing comes of age.pdf", + "2016 - Dissecting diabetes metabolic disease.pdf", + "2020 - Functional Genomics in Pancreatic \u03b2 Cells Recent Advances in Gene Deletion and Genome Editing Technologies for Diabetes Research.pdf", + "2021 - Human Molecular Genetics and Genomics.pdf", + "2018 - Neuro-Immuno-Gene- and GenomeEditing-Therapy for Alzheimer\u2019s.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf" + ], + "extraction_id": [ + "08a2c0e6-8ca8-5a72-974c-3f1e27ba1b15", + "49b81415-ef6f-5cc4-bb30-71e971070ebe", + "190e8838-4f61-5431-8848-98564ded7140", + "66dbf4f0-2b37-5219-9eeb-0a560df8d888", + "c7b143d7-347c-5160-bfd4-82283b342d7d", + "ebabc771-1777-56c1-9101-c1677c5ae908", + "fe5bf2df-2eda-5ef0-8aad-79bbc5b898d6", + "08a2c0e6-8ca8-5a72-974c-3f1e27ba1b15", + "3dd04f3c-0dc4-5bf7-aff6-3d9282761a2e", + "54972d7f-0ddc-5076-9d58-890a85f71332" + ], + "document_id": [ + "68e362a5-e580-5a4d-8d41-6a138c873ede", + "20df9469-e1cc-500e-ac30-fbba981d7aee", + "4078087a-c2a4-5c58-95b5-4ae243794800", + "cc0a025b-71e7-5712-bbf7-4ee1e0f769ef", + "4078087a-c2a4-5c58-95b5-4ae243794800", + "eee2f79d-e093-52fb-871a-798fd859235e", + "51350055-d53c-5692-ab53-337b8a8bafd6", + "68e362a5-e580-5a4d-8d41-6a138c873ede", + "cc0a025b-71e7-5712-bbf7-4ee1e0f769ef", + "62b635c3-040e-512a-b016-6ef295308a1e" + ], + "id": [ + "chatcmpl-ADZ9J7DmFDWNhxuisZ7zPEZmBG259", + "ffbca864-26db-5f36-8ad4-3b8d24d46de6", + "2fbabecd-22c1-5570-8f38-bc934d463710", + "6dfc48be-a762-55d6-9aba-799d80e8140d", + "a4aa9de7-cc9f-5c3f-a9fe-c37a47faa5b7", + "ff2d183b-c5be-5e05-94c8-e2db379dcd96", + "a3d6f231-29aa-5cf6-b856-004d3d9dd9c1", + "6ba3cf43-be4d-561f-ad84-f79921cab37e", + "4474c4e9-bc07-5610-8bb2-dafe5c95774b", + "77b11b36-35c8-55dc-a2d1-25d5e4ca218d", + "176b7aa5-17ef-590d-8807-1aa7def904bb" + ], + "contexts": [ + "neered nucleases, CRISPR-Cas9 tools have accelerated the pace of genomic research by permitting highly efficient knockouts or edits of virtually any gene in cells or model organisms. Multiple CRISPR-Cas9based clinical trials are in progress or are expected to begin soon. Although Cas9- engineered cells havent yet dem - onstrated efficacy at scale, early trial results suggest that such cells are stable and dont cause acute adverse reactions in humans. Long-term safety is yet to be de -", + "stageissetforCRISPRtomakeanenormousimpactongenomic screening and thus scientic discovery in the coming years, and recent demonstrations of this system have shown great promise (Shalem etal., 2015 ).However,a number of technical challenges must be addressed in order to maximize the benet of this technology. In this review, we will discuss current applications of CRISPR in functional genomics and provide a perspective on futuredevelopmentsinthisarea. CRISPR/Cas9 Genome Editing", + "heralding the age of genome editing. Furthermore, Cas9 or guide RNAs have been linked to various effector proteins to enable targeted gene regulation 12,13 and epigenome modifications14,15. It is worth noting, however, that many of these feats had been demonstrated previously using other nucleases or DNA-binding proteins 1,16. In this Perspective, I shed light on early genome editing platforms that laid the groundwork for the widespread use of CRISPRCas9 in research and medicine (Fig. 1 ).", + "CRISPR/CAS9 HOLDS SIGNIFICANT PROMISE FOR THE DEVELOPMENT OFNEW AD MODELS AND PRECISIONTARGETED AD THERAPY Clustered regularly interspaced short palindromic repeat (CRISPR)-Cas nucleases have revolutionizedthe eld of gene editing and have tremendous appli-cation in the eld of molecular medicine [98102].Despite a signicant surge in CRISPR/Cas9-mediated genome editing in various disease models,the progress in the eld of AD has lagged behindsubstantially. We believe that genome editing can sig-", + "81. Applications for CRISPRCas9 beyond genome editing", + "cline- or Tet-regulated Cas9 system. Current CRISPR/Cas systems arefrom Streptococcus pyogenes ,Streptococcus thermophilus ,Neisseria meningitides and Treponema denticola .2.5. Caveats of advanced genome editing tools Off-target effects . The DNA-binding domains of ZFNs and TALENs need to be very speci c for the target site to avoid off-target cleavage, which results in unwanted mutations and potentially cytotoxic effects [27]. CRISPR/Cas9 is also known to generate off-target alterations,", + "on transcriptional interfere nce (CRISPRi) and activation (CRISPRa) have also harnessed Cas9-based technologies for use in genome-wide studies ( 59,174). In addition, recent improvements in lentiviral library generation and propagation,as well as large-scale DNA and RNA synthesis, have allowedCRISPR-Cas9 technology to be exploited across multiple modelplatforms ( 59,175178). nCas9 The CRISPR-Cas9 system can tolerate certain mismatches to the DNA target since the required gRNAs are short. A disadvantage,", + "CRISPR-Cas9 can be used to in - duce genome edits by creating targeted DNA breaks that trigger site-specific DNA repair. In next- generation formats, it can also control the transcriptional output of genes or alter genome se - quences using a process of nu - cleotide base editing that does not require repair of DNA breaks. As these technolo - gies continue to mature, it will become increasingly possible to alter cellular genomes efficiently and accurately. Coming on the heels of engi -", + "S.P . Raikwar et al. / Alzheimers Disease: New Therapeutic Horizons 333 gene editing efciency of the CRISPR/Cas9 systems.", + "13. Kleinstiver BP, etal. High-fidelity CRISPRCas9 nucleases with no detectable genome-wide off-target effects. Nature. 2016;529:4905. 14. Brane A, Tollefsbol T.Targeting telomeres and telomerase: studies in aging and disease uti- lizing CRISPR/Cas9 technology. Cells. 2019;8:186. 15. Wang H, etal. One-step generation of mice carrying mutations in multiple genes by CRISPR/ Cas-mediated genome engineering. Cell. 2013;153:9108." + ], + "answer": "### Potential Benefits of CRISPR-Cas9: 1. **Accelerated Genomic Research**: CRISPR-Cas9 tools have significantly accelerated the pace of genomic research by allowing highly efficient knockouts or edits of virtually any gene in cells or model organisms [1]. 2. **Clinical Trials and Stability**: Multiple CRISPR-Cas9-based clinical trials are in progress, and early trial results suggest that CRISPR-engineered cells are stable and do not cause acute adverse reactions in humans [1]. 3. **Functional Genomics**: CRISPR is set to make a significant impact on genomic screening and scientific discovery, with recent demonstrations showing great promise [2]. 4. **Targeted Gene Regulation and Epigenome Modifications**: CRISPR-Cas9 has been linked to various effector proteins to enable targeted gene regulation and epigenome modifications [3]. 5. **Precision Medicine**: CRISPR-Cas9 holds significant promise for the development of new disease models and precision-targeted therapies, particularly in molecular medicine [4]. 6. **Versatility in Applications**: CRISPR-Cas9 can induce genome edits by creating targeted DNA breaks that trigger site-specific DNA repair. It can also control the transcriptional output of genes or alter genome sequences using nucleotide base editing, which does not require DNA break repair [8]. ### Potential Risks of CRISPR-Cas9: 1. **Off-Target Effects**: CRISPR-Cas9 is known to generate off-target alterations, which can result in unwanted mutations and potentially cytotoxic effects [6]. 2. **Technical Challenges**: There are several technical challenges that need to be addressed to maximize the benefits of CRISPR technology [2]. 3. **Long-Term Safety**: While early trial results are promising, the long-term safety of CRISPR-engineered cells is yet to be determined [1]. 4. **Mismatch Tolerance**: The CRISPR-Cas9 system can tolerate certain mismatches to the DNA target, which could be a disadvantage as it may lead to unintended edits [7]. In summary, while CRISPR-Cas9 offers numerous benefits such as accelerating genomic research, enabling precision medicine, and providing versatile applications, it also poses risks like off-target effects, technical challenges, and concerns about long-term safety.", + "question": "What are the potential benefits and risk associated with gene editing technologies like CRISPRR-Cas9?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_10 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_10 new file mode 100644 index 0000000..2f7afe0 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_10 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2007 - Classification of microarray data using gene networks.pdf", + "2020 - Gene network a continuously updated tool for systems genetics analyses.pdf", + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2013 - Integrated Enrichment Analysis of Variants.pdf", + "2013 - Candidate gene association studies a comprehensive guide to useful in silicotools.pdf", + "2017 - Integrative functional genomics for systems genetics in GeneWeaver. org.pdf", + "2023 - Genome-wide RNA polymerase stalling.pdf", + "2011 - The age of the \u201come\u201d Genome, transcriptome and proteome data set collection and analysis.pdf", + "2020 - Gene network a continuously updated tool for systems genetics analyses.pdf", + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf" + ], + "extraction_id": [ + "b509b652-ff49-53b8-9fe1-6b2340c166a6", + "0ddd5599-537b-581d-9775-b4ec0662cfae", + "b1a51c38-5376-51ec-9d6b-a02b63164eb5", + "39015cf6-2e14-5ef7-a5af-b1a87ef22594", + "e4aaaddf-ed9f-5663-b8b7-403b02631793", + "800a4df7-3d75-50cf-bb6c-aef53b97af0f", + "801887dc-6c57-5d4d-8ba3-8a7a84707a8e", + "87e61158-ff52-5bbc-926d-47cd018529aa", + "f9dee762-add3-56b8-baa6-f260e05af531", + "58f46b5e-7cfe-5926-ae36-d0a6d7741171" + ], + "document_id": [ + "639e0456-a445-5e2e-adf5-8eaf987ce2d1", + "374fd6d3-e6c1-560c-a421-a4b393ba23b2", + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "e4b37f87-e940-563c-851c-b272fc30e394", + "72134204-0751-5b57-a051-a0ea2d320fa1", + "cbe10d1f-5271-5c0e-94e3-1479b7e39146", + "78812a12-8d31-5159-8367-b0d38e5bc84b", + "ca99ed69-ee09-5717-95ed-c26eefb5e42d", + "374fd6d3-e6c1-560c-a421-a4b393ba23b2", + "128224f1-3545-52c3-93cb-77c3cf4ec70a" + ], + "id": [ + "chatcmpl-ADZAEvRN04c44oiql0rqsqslEqSpD", + "8f5c5693-f995-5ded-8498-701ff0889deb", + "6ab69fa3-2dcf-50dc-bd36-283407a39451", + "02b60e7c-25ee-5583-822d-a0a4799f4eeb", + "7dbba72c-bf76-5431-aa01-9c828355bed8", + "3539d21a-cc75-54dc-aca3-2d936893481b", + "0c72f387-9074-592d-a87e-7643c2f37d0c", + "5014c31a-1e5c-5101-9c4c-9b6b40c65435", + "e17e2cc4-ca55-55e8-9461-b692c3c5bf00", + "e9748c2d-a9f6-596e-bba2-97bf34ed86d6", + "96f49474-9477-5ac6-8606-81296848493a" + ], + "contexts": [ + "[3] and KEGG [4] all allow a list of genes to be crossed with biological functions and genetic networks, including metabolic, signalling or other regulation pathways. Basic statistical analysis (e.g., [5,6]) can then determine whether a pathway is over-represented in the list, and whether it is over-activated or under-activated. However, one can argue that introducing information on the path- way at this point in the analysis process sacrifices some statistical power to the simplicity of the approach. For", + "Sidiropoulos, K., Viteri, G., Sevilla, C., Jupe, S., Webber, M., Orlic -Milacic, M., et al. (2017). Reactome enhanced pathway visualization. Bioinformatics 33, 3461 3467. doi:10.1093/bioinformatics/btx441. Slenter, D. N., Kutmon, M., Hanspers, K., Riutta, A., Windsor, J., Nunes, N., et al. (2018). WikiPathways: a multifaceted pathway database bri dging metabolomics to other omics research. Nucleic Acids Res. 46, D661 D667. doi:10.1093/nar/gkx1064.", + "Sidiropoulos, K., Viteri, G., Sevilla, C., Jupe, S., Webber, M., Orlic -Milacic, M., et al. (2017). Reactome enhanced pathway visualization. Bioinformatics 33, 3461 3467. doi:10.1093/bioinformatics/btx441. Slenter, D. N., Kutmon, M., Hanspers, K., Riutta, A., Windsor, J., Nunes, N., et al. (2018). WikiPathways: a multifaceted pathway database bri dging metabolomics to other omics research. Nucleic Acids Res. 46, D661 D667. doi:10.1093/nar/gkx1064.", + "analysis, we restrict the analysis to curated, peer-reviewedpathways based on experimental evidence, and pathways inferred via gene homology. We draw candidate pathways from the collections listed in Figure 6 (see also Supplementary Materials). KEGG [146] and HumanCyc [147] are primarily databases of metabolic pathways, and are unlikely to be relevant to someJoint Analysis of Variants and Pathways in Disease PLOS Genetics | www.plosgenetics.org 11 October 2013 | Volume 9 | Issue 10 | e1003770", + "textual interface, also linking out to the original articles. Analysing participating pathways is an important aspect of any gene s functional analysis strategy. In this view, REACTOME (http://www.reactome.org) [13] is a cross referenced, manually curated and peer reviewed pathway database. LitInspector (http://www.litinspector.org) [14]and NetPath (http://www.netpath.org/index.html) [15] allow one to access curated signal transduction related lit-", + "I, Babur O, Anwar N, Schultz N, Bader GD, Sander C (2011) Pathway Commons, a web resource for biological pathway data. Nucleic Acids Res 39(Database issue):D685D690. doi: 10.1093/nar/gkq1039 6. Baker EJ, Jay JJ, Bubier JA, Langston MA, Chesler EJ (2012) GeneWeaver: a web-based system for integrative functional genomics. Nucleic Acids Res 40(Database issue):D1067D1076. doi: 10.1093/nar/gkr968 7. Bubier JA, Phillips CA, Langston MA, Baker", + "67. Krmer, A., Green, J., Pollard, J. Jr. & Tugendreich, S. Causal analysis approaches in ingenuity pathway analysis. Bioinformatics 30, 523530 (2014). 68. Jassal, B. et al. The reactome pathway knowledgebase. Nucleic Acids Res. 48, D498D503 (2020). 69. Okonechnikov, K., Conesa, A. & Garca-Alcalde, F. Qualimap 2: advanced multi-sample quality control for high-throughput sequencing data. Bioinformatics 32, 292294 (2016).", + "Biocarta pathway maps www.biocarta.com BioGRID genetic and protein interaction database thebiogrid.org AnalysisPLINK processing and QC of genetic data sets pngu.mgh.harvard.edu/ purcell/plink Bioconductor processing and QC of expression data sets www.bioconductor.org DAVID gene ontology, pathway analysis david.abcc.ncifcrf.gov WebGestalt gene ontology, pathway analysis bioinfo.vanderbilt.edu/webgestalt Sage", + "2004; Gene Ontology Consortium, 2015; The Gene Ontology Consortium, 2019) , KEGG pathways (Kanehisa and Goto, 2000; Kanehisa et al., 2012) , Panther pathways (Mi et al., 2019a, 2019b) , Reactome pathways (Sidiropoulos et al., 2017; Jassal et al., 2020) , and Wikipathway pathways (Pico et al., 2008; Slenter et al., 2018) (Figure 31). As many different annotations as wanted can be chosen by clicking on the + icon ( Figure 31). Also note, that the user can", + "2004; Gene Ontology Consortium, 2015; The Gene Ontology Consortium, 2019) , KEGG pathways (Kanehisa and Goto, 2000; Kanehisa et al., 2012) , Panther pathways (Mi et al., 2019a, 2019b) , Reactome pathways (Sidiropoulos et al., 2017; Jassal et al., 2020) , and Wikipathway pathways (Pico et al., 2008; Slenter et al., 2018) (Figure 31). As many different annotations as wanted can be chosen by clicking on the + icon ( Figure 31). Also note, that the user can" + ], + "answer": "For pathway analyses, you can use the following resources: 1. **KEGG (Kyoto Encyclopedia of Genes and Genomes)**: This database allows you to cross-reference a list of genes with biological functions and genetic networks, including metabolic, signaling, or other regulation pathways [1], [4], [9], [10]. 2. **Reactome**: A cross-referenced, manually curated, and peer-reviewed pathway database that provides enhanced pathway visualization [2], [3], [5], [7], [9], [10]. 3. **WikiPathways**: A multifaceted pathway database that bridges metabolomics to other omics research [2], [3], [9], [10]. 4. **HumanCyc**: Primarily a database of metabolic pathways [4]. 5. **Pathway Commons**: A web resource for biological pathway data [6]. 6. **BioCarta**: Provides pathway maps [8]. 7. **BioGRID**: A genetic and protein interaction database [8]. 8. **DAVID (Database for Annotation, Visualization, and Integrated Discovery)**: Offers gene ontology and pathway analysis [8]. 9. **WebGestalt**: Provides gene ontology and pathway analysis [8]. These resources can help you determine whether a pathway is over-represented in your gene list and whether it is over-activated or under-activated [1].", + "question": "What resources can I use to do pathway analyses?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_11 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_11 new file mode 100644 index 0000000..8dcfd54 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_11 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2009 - Opening Up the Conversation on Genetics.pdf", + "2008 - Study Design and Statistical Issues.pdf", + "2015 - Constraint and divergence of global gene expression in the mammalian embryo.pdf", + "2015 - Constraint and divergence of global gene expression in the mammalian embryo.pdf", + "2015 - Basic Concepts and Potential Applications of Genetics and Genomics for Cardiovascular and Stroke Clinicians.pdf", + "2019 - Sexual Dimorphism in the Age of Genomics How, When, Where.pdf", + "2008 - Genotype-phenotype relationships and the patterning of complex traits as exemplified in the mammalian dentition.pdf", + "2007 - Promoting_Student_Scientific_Literacy_of_Molecular Genetics and Genomics.pdf", + "2015 - Basic Concepts and Potential Applications of Genetics and Genomics for Cardiovascular and Stroke Clinicians.pdf", + "2019 - The influence of paternal diet on sncRNA-mediated epigenetic.pdf" + ], + "extraction_id": [ + "51dbd5e2-fde6-5097-aa05-fcf57d3ca6b1", + "06bf0605-388a-592c-96ad-3a53bb36362c", + "261c4af7-f63d-51ac-b164-0d9e7a64bff9", + "261c4af7-f63d-51ac-b164-0d9e7a64bff9", + "8a1ce8fa-b5f4-5942-b7b1-14a8a7887710", + "e22bb6fb-bec4-5c4c-8690-c96d0b8d13d4", + "5aab3e60-b8b0-52ad-b4d3-817cf012cfa5", + "67369433-749b-5d6a-b5ef-3f0afe78b767", + "206b8810-b7c1-5195-a10f-4e291864b77c", + "84335575-34d7-56b6-aa06-5a8ac13d637a" + ], + "document_id": [ + "b62a8f54-c2f5-5bbb-9324-af80f7537167", + "c3bd9cf0-f768-55c4-be94-96590d7acc21", + "3d9005f1-8f71-5d39-8749-4ebeab962cab", + "3d9005f1-8f71-5d39-8749-4ebeab962cab", + "8610e699-218a-50e6-8d1d-ef689623266f", + "3f8c03b0-4235-5774-9d26-e43d55c1001b", + "f6e866b8-b233-5862-bfb8-9949d0dabb97", + "755f34c4-cc06-5275-a744-16d48162b012", + "8610e699-218a-50e6-8d1d-ef689623266f", + "dfcbd6e6-f60d-5eb7-867b-34ec78415e82" + ], + "id": [ + "chatcmpl-ADZAJ4XRzNSAEiekxHtxfyNvHLw8G", + "60ad1512-b0c0-59cd-ace4-c146e2c04b52", + "1e151ad5-59d9-598d-97ba-90ba0e64c4cb", + "a66b8b00-d51c-575b-b6ac-fa445c4ca715", + "df4c6108-740d-5bcf-99e6-dbda74f7e41a", + "4472740a-d22d-5bb1-98e3-e91332cbb303", + "0158f264-120f-5942-ad55-ef5fde1f188a", + "47b9142f-98a3-5a45-8eaa-d327c9cc055d", + "8e3fdc2c-0962-5854-83e7-a60ab05cf6de", + "6c8dfaa1-a96f-5f1c-8b5a-870acfd46f5f", + "be93ee68-72ae-5015-a3f0-19e7bf24827a" + ], + "contexts": [ + "the egg and the sperm. Such a process would result in genetic changes that will be copied into every cell of the future adult, including reproductive cells (Stock & Campbell, 2000), opening the door to irreversibly alter the human species. Inevitably, signifi cant self-disclosure and discussion challenges await families", + "phenomena such as mutations and gene conversion events) occur in relevant meioses leading up to the formation of the gametes (i.e., egg and sperm) which are combined during fertilization and the formation of zygotes. Thus, individuals inherit a patch- work of chromosomal segments from maternal and paternal chromosomes.", + "a fertilized egg is a complicated process that relies on controlling: which genes are active; whenthese genes activate; and for how long they are active. In broad terms, there are four ways that thiscontrol can be achieved: First, inside the sperm or egg, genes can be marked with small chemical tags that flag these genes", + "to be activated (or remain inactive) after fertilization, depending on whether the modification wasmade by the father (in the sperm) or the mother (in the egg); this process is known as imprinting. Second, the mother can alter the gene activity in her offspring via the placenta; this process is known as maternal effect. Third, instructions encoded within the embryos DNA can directly control if, andwhen, a nearby gene becomes activated; this is known as cis-regulation. Finally, similar instructions", + "(Figures 8 and 9). Two gametes (egg and sperm) ultimately join into a single cell, the zygote, which has the full comple-ment of 23 chromosome pairs restored. If all goes well, the zygote gives rise to a live offspring. The Mendel Laws: Segregation and Independent Assortment Both of the Mendel laws pertain directly to the process of meiosis. The first Mendel law, the law of segregation, states that each parent passes a randomly selected allele for a given", + "sex chromosome effects. (B)Soon after fertilization, male and female cells have sex-specic transcriptomes, epigenomes, and phenotypes (for example, male embryos grow faster than female embryos). At implantation, lineage determination begins and gene expression differences are reduced. Epigenetic marks, however, are less constrained and some are maintained, affecting gene expression, and phenotype later in development. Once specic lineages are established, differences in", + "the subset of that genetic information that is active. But how does the differentiation process begin? The key insight in resolving this conundrum came from fly genetics and was the realization that the egg is not a homogenous sack of protoplasm. The maternally-derived genes active in the fertilized egg are asymmetrically distributed such that at the first cell division each daughter cell receives a different complement of factors. Development continues as a", + "genes. An altered gene may be passed on to every cell that develops from it. The resulting features my help, harm, or have little or no effect on the offsprings success in its environment. (AAAS, pg. 109, 5B:9-12#4 ) 6. Heritable material: The information passed from parents to offspring is coded in DNA molecules (AAAS, pg 108, 5B:9-12#3) 7. Mutagens: Gene mutations can be caused by such things as radiation and chemicals. When they occur in sex cells, the mutations can be passed onto offspring; if they", + "or father (sperm cell). Each gamete has a set of 23 unpaired chromosomes. Two human gametes (egg and sperm) combine to create a cell (zygote) that contains the full human genome of 23 paired chromosomes.Genetic Information Nondiscrimination Act (GINA) US federal legislation that makes it unlawful to discriminate against individuals on the basis of their genetic profiles in regard to health insurance and employment. These protections are intended to encourage Americans to take advantage of", + "spermatozoa: more than the sum of its parts? DNA, histones, pro - tamines and epigenetics. Reproduction 139:287301 Nilsson EE, Sadler-Riggleman I, Skinner MK (2018) Environmentally induced epigenetic transgenerational inheritance of disease. Envi-ron Epigenet 4:dvy016Pembrey M, Saffery R, Bygren LO, Network in Epigenetic Epide-" + ], + "answer": "Once a sperm combines with an egg, the traits passed onto the resulting lifeform are determined by several genetic and epigenetic factors: 1. **Chromosomal Segments**: Individuals inherit a patchwork of chromosomal segments from maternal and paternal chromosomes, which are combined during fertilization to form the zygote [2]. 2. **Mendelian Laws**: The Mendel laws of segregation and independent assortment play a crucial role. Each parent passes a randomly selected allele for a given trait to the offspring, ensuring genetic diversity [5]. 3. **Gene Activation and Regulation**: The process of gene activation and regulation is complex and involves several mechanisms: - **Imprinting**: Genes can be marked with small chemical tags that determine whether they are activated or remain inactive after fertilization, depending on whether the modification was made by the father (in the sperm) or the mother (in the egg) [3], [4]. - **Maternal Effect**: The mother can alter gene activity in her offspring via the placenta [4]. - **Cis-regulation**: Instructions encoded within the embryo's DNA can directly control if and when a nearby gene becomes activated [4]. 4. **Epigenetic Marks**: Epigenetic marks, which are less constrained and can be maintained, affect gene expression and phenotype later in development [6]. These factors collectively determine how traits are passed onto the resulting lifeform after the sperm combines with the egg.", + "question": "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_12 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_12 new file mode 100644 index 0000000..34fd588 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_12 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2009 - When Family Means More (or Less) Than Genetics.pdf", + "2012 - Mitochondrial Genomic Analysis of Late Onset.pdf", + "2017 - Parental influence on human germline de novo.pdf", + "2020 - Mitonuclear genomics and aging.pdf", + "2015 - Self-reported race or ethnicity in the age of genomic.pdf", + "2009 - When Family Means More (or Less) Than Genetics.pdf", + "2017 - Parental influence on human germline de novo.pdf", + "1996 - IDDM2-VNTR-encoded Susceptibility to Type 1 Diabetes.pdf", + "2012 - Mitochondrial Genomic Analysis of Late Onset.pdf", + "2016 - A genetic method for dating ancient genomes provides.pdf" + ], + "extraction_id": [ + "baf15552-4198-5701-9175-c3fd31b2068e", + "ed29f84f-f2c9-5cbe-bab1-f5d5d2a334b6", + "a3b7edd7-f50f-53f1-b875-6d6733ddfde9", + "472c8adc-54e7-5c27-a7b8-882b7e49cd2b", + "6d68e979-ad62-5f85-ab03-5e898ce1c73b", + "baf15552-4198-5701-9175-c3fd31b2068e", + "163ce027-26ce-5625-8b63-5b7a910b4462", + "a324397e-1525-55ff-a9e8-92dc2aafa237", + "ed29f84f-f2c9-5cbe-bab1-f5d5d2a334b6", + "fcf5296e-6be4-5789-b1e1-ac57fef15119" + ], + "document_id": [ + "7ba44399-3765-5ef5-9fdd-119b62117f66", + "5404a17c-34a9-5881-8b1a-2acacdc996a8", + "7c8bee23-b142-5fce-be77-6910277a136f", + "e05fdc09-c8d8-5134-a1fd-bf07a1564981", + "51ff0b84-193b-525a-b686-f29a423fcef9", + "7ba44399-3765-5ef5-9fdd-119b62117f66", + "7c8bee23-b142-5fce-be77-6910277a136f", + "bbaa99aa-3ae9-558d-bc97-7f85b6d0cf81", + "5404a17c-34a9-5881-8b1a-2acacdc996a8", + "5a5e67ea-4830-5fe8-95c3-ccfcc8324036" + ], + "id": [ + "chatcmpl-ADZAODsOOCY3TdcinzGlvT4IHQgnR", + "f19ba98e-963f-5ecf-ad88-47215a3096e1", + "0e3b3480-c288-53cb-ac18-1d57478f9d34", + "06d4d82e-6eb9-59aa-a762-64de13149041", + "99a2cfc1-5a54-53af-b2a4-4c274e1d5ef1", + "612366c9-fcdc-5081-bc6d-47cd39922eeb", + "2ca2ab07-78b5-5268-93f1-297d83447163", + "db1fe67a-3d0c-549f-a54a-74ea0fa44d11", + "74ef6cdc-ea40-5d10-9ee8-b4288b3a70b4", + "27f40683-de33-5ec1-852d-6905f2dc389c", + "74484e0c-c862-5091-9fb5-957453a069af" + ], + "contexts": [ + "variation with cultural practices around lineage. In certain societies, individuals place greater importance on (and have greater knowledge about) one side of the family than another (unilineal descent). Thus, individuals in patrilineal groups trace relationships through males only so that your fathers brothers children are members of your family, but not your fathers sisters (Kottak, 2007 ). They are members of their husbands group or family. Efforts to create", + "maternal lineage membership with those who weredirectly genotyped. Based on these pedigree (matrilineal) relation-", + "in three-generation families, and read pair tracing DNMs with phased variants. In the former approach, we determined the parent of origin as in our previous analysis4. For example, if an offspring of the proband was a carrier of the DNM allele and had haplotype sharing to paternal chromosome of the proband, we assigned the mutation to the father. Meanwhile, if the offspring was not a DNM allele carrier, we would assign it to the maternal germline. We restricted the haplo -", + "Unlike the nuclear genome, which requires both paternal and maternal contributions, mtDNA is inherited solely from the maternal lineage. It is unclear what advantage a uniparental mtDNA transmission confers, but one possibil-ity is to minimize the number of distinct genomes to maxi-mize the efficiency of a multi-genomic system (Hill etal. 2019). In fact, humans have developed complex, redundant mechanisms to ensure uniparental inheritance of mtDNA (DeLuca and OFarrell 2012; Rojansky etal. 2016). Paternal", + "c) Mitochondrial DNA (maternal line testing) markers: mitochondrial DNA or mtDNA haploid is the maternally inherited mitochondrial genome (mtDNA) [ 44]. All children inherit mtDNA from their mother, with no admixture from the father. Like Y-line DNA, mtDNA is passed intact from one generation to the next but through maternal line. Mitochondrial DNA does not follow any surname. In fact, the surname changes in every generation when women marry. Polymorphisms of mtDNA", + "a family pedigree may be hampered if the participant is not familiar with her mothers relatives, but her mothers brothers children (her cousins) may be able to supplement her overall family history. Knowledge about the cultural system of unilineal descent avoids assuming the universality of bilateral descent. Cultural beliefs such as these also have implications in the conduct of genetic research in terms of confidentiality and autonomy (Benkendorf et al.,", + "225 three-generation families using haplotype sharing (Fig. 1c and Methods), 80.4% were found to be of paternal origin (Extended Data Fig. 1). Figure 1e shows a strong relationship between the number of paternal DNMs and the fathers age at conception (1.47 per year, 95% CI 1.341.59) and a weaker impact of the mothers age on the number of maternal DNMs (0.37 per year, 95% CI 0.300.45). The parental origin of all DNMs was also assessed by read pair", + "sistent with a maternal imprinting effect in familiesfrom France [18], the USA[10, 18, 21] (Figure 2; Table3) and Canada [27]. However, in a large family dataset from the UK, and in smaller data sets fromDenmark and Sardinia, the transmission of VNTRsusceptibility alleles is more pronounced frommothersthanfromfathers,andnowsignicantlysoinUK families (Figure 2; Table 3). Comparison of theresults from the USAwith those from the UK suggestthat unexplained inter-population differences in thisparent-of-origin", + "started with the largest matrilineage and worked down the list. Theparticipants selected for mtDNA sequencing were selected inde-pendent of their cognitive or dementia status. 274 matrilineageswere represented by this dataset. As a result, the sequencedmitochondrial genomes also represent as many different majormitochondrial haplogroups and clusters as possible (Table 1).Selection was made blind to case-control status. 287 samples weresent to Family Tree DNA (www.familytreedna.com) for Sangersequencing of", + "genetics-based population divergence studies. Am J Phys Anthropol 128(2):415 423.22. Helgason A, Hrafnkelsson B, Gulcher JR, War d R, Stefnsson K (2003) A populationwide coalescent analysis of Icelandic matrilineal and patrilineal genealogies: Evidence for a faster evolutionary rate of mtDNA lineages than Y chromosomes. Am J Hum Genet 72(6): 1370 1388. 23. Amster G, Sella G (2015) Life history effects on the molecular clock of autosomes and sex chromosomes. Proc Natl Acad Sci USA 113(6):1588 1593." + ], + "answer": "Genetic tracing is often matrilineal rather than patrilineal primarily because mitochondrial DNA (mtDNA) is inherited solely from the maternal lineage. Unlike nuclear DNA, which is a combination of both paternal and maternal contributions, mtDNA is passed down from mothers to all their children without any admixture from the father [4], [5]. This uniparental inheritance of mtDNA simplifies the tracing of maternal lineage as it remains relatively unchanged through generations, making it a reliable marker for genetic studies [4], [5]. Additionally, humans have developed complex mechanisms to ensure this uniparental inheritance of mtDNA, which further supports its use in genetic tracing [4].", + "question": "Why is genetic tracing matrilineal rather than patrilineal?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_13 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_13 new file mode 100644 index 0000000..49e3211 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_13 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2005 - Numerical Algorithms for Mapping of Multiple Quantitative Trait Loci in Experimental Populations.pdf", + "2005 -Ljungberg- Numerical algos for Multi QTL.pdf", + "2011 - Regulation of ribosomal RNA gene copy number and its role.pdf", + "2001 - Genome maintenance mechanisms.pdf", + "2008 - Rutter_s child and adolescent psychiatry-Blackwell Pub (2008).pdf", + "2013 - Causes of Genome Instability.pdf", + "2009 - Basic Genetics and Genomics A Primer for Nurses.pdf", + "2007 - Promoting_Student_Scientific_Literacy_of_Molecular Genetics and Genomics.pdf", + "2013 - Causes of Genome Instability.pdf", + "2007 - DNA replication stress, genome instability and aging.pdf" + ], + "extraction_id": [ + "3f482661-0759-54cf-9926-8a39abb538bf", + "33c27a82-4633-5f0c-9d9e-716aee665879", + "28addd51-38b1-5405-bed4-140f7224da0b", + "17bbb094-4a6f-5931-be1d-ee46abc25820", + "86760f12-2e7c-56c6-80d8-0d62c611843d", + "67e0ca38-bd7c-551f-9941-bcd6025a630d", + "6e7863c0-dc75-550a-b3ca-9fb0d95af788", + "67369433-749b-5d6a-b5ef-3f0afe78b767", + "e1efc4b8-b33d-513f-b6cb-9f35de5eda30", + "908ae89d-39b7-51ff-9bc4-c4a1de926b87" + ], + "document_id": [ + "dd7d3ea5-b23a-514e-898f-a4259ce6f6f9", + "bea0655c-7ef4-5754-ba14-817b72a21be2", + "20147943-f329-5ac6-8343-3cea72fdc040", + "db0de7b5-6c1c-521c-be6d-0ea91c700277", + "59daba11-206e-5bbc-8833-9d1b661532b5", + "63f87ec0-9437-5d67-b36d-0b24059e9c9a", + "c37e2ace-171b-5776-8969-86eda9736481", + "755f34c4-cc06-5275-a744-16d48162b012", + "63f87ec0-9437-5d67-b36d-0b24059e9c9a", + "dd83ce7f-dfe1-5fa7-8509-bfdb1e27af9f" + ], + "id": [ + "chatcmpl-ADZAStB25S6kWtJMcUZWpQvWYeOgv", + "4c576a55-b4cf-569f-8771-3d2a81480104", + "3a88ba5e-d93b-5c50-a03a-02def897390f", + "8049fc56-1fd2-58ba-9d5c-9529d4cc7e84", + "c793587e-e46f-5b48-9e49-c150637c5f5a", + "440ffc7a-2810-5245-bc20-9284d6861472", + "55f07e34-bcb4-5a68-a222-477378f6c9d0", + "53707c68-7cf1-51aa-9d4c-1eb4a9816182", + "dc2dc054-f0f9-5e78-92b0-1caa0a6239e0", + "f18e23f1-67a3-5d7f-831e-358fa44e7873", + "134f4d33-1645-591b-ac20-d8d8b298bcfc" + ], + "contexts": [ + "the DNA, i.e. the whole genome. During replication the two strands of themother cell DNA are separated, and new nucleotides are put together to maketwo double helices identical to the original one, see Figure 2.1. TAAGACCG AT T CTGGCCCGTGGC. . . . . . .. . ATTCTGGCTAAGACCG. . . . . . . . Figure 2.1: A DNA chain consists of two strands of complementary nucleotides. When DNA is replicated, two double chains identical to the original one are created.", + "the DNA, i.e. the whole genome. During replication the two strands of themother cell DNA are separated, and new nucleotides are put together to maketwo double helices identical to the original one, see Figure 2.1. TAAGACCG AT T CTGGCCCGTGGC. . . . . . .. . ATTCTGGCTAAGACCG. . . . . . . . Figure 2.1: A DNA chain consists of two strands of complementary nucleotides. When DNA is replicated, two double chains identical to the original one are created.", + "The mechanism to maintain the rDNA copy number The gene amplication mechanism that counteracts recombination-mediated loss of rDNA copies is well studied in budding yeast [ 6,11]. During the S phase of the cell cycle, replication starts from replication origins, and isinhibited at the replication fork barrier site (RFB) by the function of the fork blocking protein, Fob1 (Fig. 3)[12]. This inhibition works as a recombinational hotspot toinduce amplication for copy number recovery as follow;", + "S and G2 when the DNA is replicated, providing a pristine secondcopy of the sequence (sister chromatid) for aligning the breaks. Incontrast, the less-accurate end joining is most relevant in the G1phase of the cell cycle, when a second copy is not available 14. Finally, some single repair proteins directly revert certain injuries, such as O6-methylguanine methyltransferase, which removes O6-methyl guanine. This highly mutagenic lesion permits base", + "Replication", + "genotoxic agents and to guarantee faithfulchromosome duplication and transmission to the offspring. In addition to DNA damage repair, cells monitor replication to minimize er-rors of DNA synthesis. In eukaryotes, cell-cycle checkpoints guarantee coordination of DNA synthesis and DNA repair with cell division.Genome instability is mainly due to sporadic replication or repair errors but can also take place in response to developmental or environ-mental signals, as occurs in meiosis, and antigen", + "This section will explain how cells normally divide. It will also desc ribe how an unexpected change in the structure of DNA can sometimes cause harm to th e body. New tools to study genetic variations of common diseases and to identify genetic variatio ns common to specific diseases will also be presented. Cell Division Humans grow and develop as a result of a process called cell division. There are two types of cell division mitosis and meiosis.", + "and replicated (by a templating mechanism). Each DNA molecule in a cell forms a single chromosome. (NRC, pg. 185, 9-12:C2#1) 4. Genes as information for building proteins: The genetic information in DNA molecules provide the instructions on assembling protein molecules. The code is virtually the same for all life forms. (AAAS, pg. 114, 5C:9-12#4 ) 5. Molecular nature of genes and mutations: Genes are segments of DNA molecules. Inserting, deleting, or substituting DNA segments can alter genes. An altered", + "When a replication fork encounters a DNA adduct, cells induce DNA damage toler-ance mechanisms that allow completion of replication. Adducts can be bypassed by postreplicative repair via translesion poly-merases (either faithful or error-prone) or via error-free template switching using the sister chromatid (64, 105). Postreplicativerepair guarantees genome stability by allowing completion of replication (albeit at the expense", + "genome instability in part because of the unique structureof replicating DNA molecules (Figure 2). When single-strand lesions occur in non-replicating molecules of DNA,the overall integrity of chromosomes is maintained byhydrogen bond base pairing on either side of these lesionsuntil they are repaired (Figure 2A). In contrast to non-replicating DNA, replicating DNA at replication forkscontains unwound, highly recombinogenic single-strandedtemplate DNA before this DNA is converted to double-strand DNA by" + ], + "answer": "DNA replication is a critical process that ensures the accurate copying of genetic information during cell division. Here is an explanation of the process and how it maintains accuracy: 1. **Separation of DNA Strands**: During replication, the two strands of the mother cell DNA are separated [1], [2]. This separation is essential to expose the nucleotide sequences of each strand, which will serve as templates for the synthesis of new complementary strands. 2. **Formation of New Strands**: New nucleotides are assembled to form two double helices that are identical to the original DNA molecule [1], [2]. Each original strand pairs with a newly synthesized complementary strand, resulting in two DNA molecules, each consisting of one old and one new strand. This method is known as semi-conservative replication. 3. **Initiation at Replication Origins**: Replication begins at specific locations on the DNA molecule called replication origins. During the S phase of the cell cycle, replication starts from these origins [3]. 4. **Replication Fork and Enzyme Activity**: The replication process involves the formation of a replication fork, where enzymes like DNA helicase unwind the DNA double helix. DNA polymerase then adds new nucleotides to the growing DNA strand by matching them with the complementary nucleotides on the template strand. 5. **Error Minimization and Repair Mechanisms**: Cells have mechanisms to monitor replication and minimize errors. For instance, cell-cycle checkpoints ensure coordination of DNA synthesis and repair with cell division [6]. Additionally, specific repair proteins can revert certain DNA injuries, ensuring the fidelity of the replication process [4]. 6. **Postreplicative Repair**: If replication encounters obstacles such as DNA adducts, cells can induce DNA damage tolerance mechanisms. These mechanisms allow the completion of replication through error-free template switching using the sister chromatid or through translesion polymerases [9]. By following these steps and employing various error-checking and repair mechanisms, DNA replication ensures that genetic information is accurately copied and transmitted to daughter cells during cell division.", + "question": "Explain the process of DNA replication and how it ensures accurate copying of genetic information during cell division." +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_14 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_14 new file mode 100644 index 0000000..d5f4e5d --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_14 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2021 - Human Molecular Genetics and Genomics.pdf", + "2015 - A new age in functional genomics using CRISPR Cas9 in arrayed library screening.pdf", + "2016 - Genome editing comes of age.pdf", + "2016 - Dissecting diabetes metabolic disease.pdf", + "2018 - Neuro-Immuno-Gene- and GenomeEditing-Therapy for Alzheimer\u2019s.pdf", + "2016 - Genome editing comes of age.pdf", + "2021 - Human Molecular Genetics and Genomics.pdf", + "2020 - Functional Genomics in Pancreatic \u03b2 Cells Recent Advances in Gene Deletion and Genome Editing Technologies for Diabetes Research.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf" + ], + "extraction_id": [ + "08a2c0e6-8ca8-5a72-974c-3f1e27ba1b15", + "49b81415-ef6f-5cc4-bb30-71e971070ebe", + "190e8838-4f61-5431-8848-98564ded7140", + "ebabc771-1777-56c1-9101-c1677c5ae908", + "66dbf4f0-2b37-5219-9eeb-0a560df8d888", + "c7b143d7-347c-5160-bfd4-82283b342d7d", + "08a2c0e6-8ca8-5a72-974c-3f1e27ba1b15", + "fe5bf2df-2eda-5ef0-8aad-79bbc5b898d6", + "54972d7f-0ddc-5076-9d58-890a85f71332", + "c072d600-8450-5842-ade1-aefd03854312" + ], + "document_id": [ + "68e362a5-e580-5a4d-8d41-6a138c873ede", + "20df9469-e1cc-500e-ac30-fbba981d7aee", + "4078087a-c2a4-5c58-95b5-4ae243794800", + "eee2f79d-e093-52fb-871a-798fd859235e", + "cc0a025b-71e7-5712-bbf7-4ee1e0f769ef", + "4078087a-c2a4-5c58-95b5-4ae243794800", + "68e362a5-e580-5a4d-8d41-6a138c873ede", + "51350055-d53c-5692-ab53-337b8a8bafd6", + "62b635c3-040e-512a-b016-6ef295308a1e", + "62b635c3-040e-512a-b016-6ef295308a1e" + ], + "id": [ + "chatcmpl-ADZAaZ3EtxdQEfdMEkkYN8ccIeUPg", + "ffbca864-26db-5f36-8ad4-3b8d24d46de6", + "2fbabecd-22c1-5570-8f38-bc934d463710", + "6dfc48be-a762-55d6-9aba-799d80e8140d", + "a3d6f231-29aa-5cf6-b856-004d3d9dd9c1", + "a4aa9de7-cc9f-5c3f-a9fe-c37a47faa5b7", + "ff2d183b-c5be-5e05-94c8-e2db379dcd96", + "4474c4e9-bc07-5610-8bb2-dafe5c95774b", + "6ba3cf43-be4d-561f-ad84-f79921cab37e", + "176b7aa5-17ef-590d-8807-1aa7def904bb", + "e5eef445-772e-5721-bb5f-24566a61e4e3" + ], + "contexts": [ + "neered nucleases, CRISPR-Cas9 tools have accelerated the pace of genomic research by permitting highly efficient knockouts or edits of virtually any gene in cells or model organisms. Multiple CRISPR-Cas9based clinical trials are in progress or are expected to begin soon. Although Cas9- engineered cells havent yet dem - onstrated efficacy at scale, early trial results suggest that such cells are stable and dont cause acute adverse reactions in humans. Long-term safety is yet to be de -", + "stageissetforCRISPRtomakeanenormousimpactongenomic screening and thus scientic discovery in the coming years, and recent demonstrations of this system have shown great promise (Shalem etal., 2015 ).However,a number of technical challenges must be addressed in order to maximize the benet of this technology. In this review, we will discuss current applications of CRISPR in functional genomics and provide a perspective on futuredevelopmentsinthisarea. CRISPR/Cas9 Genome Editing", + "heralding the age of genome editing. Furthermore, Cas9 or guide RNAs have been linked to various effector proteins to enable targeted gene regulation 12,13 and epigenome modifications14,15. It is worth noting, however, that many of these feats had been demonstrated previously using other nucleases or DNA-binding proteins 1,16. In this Perspective, I shed light on early genome editing platforms that laid the groundwork for the widespread use of CRISPRCas9 in research and medicine (Fig. 1 ).", + "cline- or Tet-regulated Cas9 system. Current CRISPR/Cas systems arefrom Streptococcus pyogenes ,Streptococcus thermophilus ,Neisseria meningitides and Treponema denticola .2.5. Caveats of advanced genome editing tools Off-target effects . The DNA-binding domains of ZFNs and TALENs need to be very speci c for the target site to avoid off-target cleavage, which results in unwanted mutations and potentially cytotoxic effects [27]. CRISPR/Cas9 is also known to generate off-target alterations,", + "CRISPR/CAS9 HOLDS SIGNIFICANT PROMISE FOR THE DEVELOPMENT OFNEW AD MODELS AND PRECISIONTARGETED AD THERAPY Clustered regularly interspaced short palindromic repeat (CRISPR)-Cas nucleases have revolutionizedthe eld of gene editing and have tremendous appli-cation in the eld of molecular medicine [98102].Despite a signicant surge in CRISPR/Cas9-mediated genome editing in various disease models,the progress in the eld of AD has lagged behindsubstantially. We believe that genome editing can sig-", + "81. Applications for CRISPRCas9 beyond genome editing", + "CRISPR-Cas9 can be used to in - duce genome edits by creating targeted DNA breaks that trigger site-specific DNA repair. In next- generation formats, it can also control the transcriptional output of genes or alter genome se - quences using a process of nu - cleotide base editing that does not require repair of DNA breaks. As these technolo - gies continue to mature, it will become increasingly possible to alter cellular genomes efficiently and accurately. Coming on the heels of engi -", + "on transcriptional interfere nce (CRISPRi) and activation (CRISPRa) have also harnessed Cas9-based technologies for use in genome-wide studies ( 59,174). In addition, recent improvements in lentiviral library generation and propagation,as well as large-scale DNA and RNA synthesis, have allowedCRISPR-Cas9 technology to be exploited across multiple modelplatforms ( 59,175178). nCas9 The CRISPR-Cas9 system can tolerate certain mismatches to the DNA target since the required gRNAs are short. A disadvantage,", + "13. Kleinstiver BP, etal. High-fidelity CRISPRCas9 nucleases with no detectable genome-wide off-target effects. Nature. 2016;529:4905. 14. Brane A, Tollefsbol T.Targeting telomeres and telomerase: studies in aging and disease uti- lizing CRISPR/Cas9 technology. Cells. 2019;8:186. 15. Wang H, etal. One-step generation of mice carrying mutations in multiple genes by CRISPR/ Cas-mediated genome engineering. Cell. 2013;153:9108.", + "Since its discovery, CRISPR-Cas technology has ignited a biological revolu- tion by providing a highly versatile platform that allows fast and efficient genome editing in an ever-growing list of organisms. In this chapter we will first describe the most recent advances in the development and application of the CRISPR-Cas platform in biomedical research. Then we will discuss the most recent and notable basic research applications of this technology in the study of the molecular causes" + ], + "answer": "### Potential Benefits of CRISPR-Cas9: 1. **Accelerated Genomic Research**: CRISPR-Cas9 tools have significantly accelerated the pace of genomic research by enabling highly efficient knockouts or edits of virtually any gene in cells or model organisms [1]. 2. **Clinical Trials and Stability**: Multiple CRISPR-Cas9-based clinical trials are in progress, and early trial results suggest that CRISPR-engineered cells are stable and do not cause acute adverse reactions in humans [1]. 3. **Functional Genomics**: CRISPR-Cas9 is set to make a significant impact on genomic screening and scientific discovery, with recent demonstrations showing great promise [2]. 4. **Targeted Gene Regulation and Epigenome Modifications**: CRISPR-Cas9 has been linked to various effector proteins to enable targeted gene regulation and epigenome modifications [3]. 5. **Precision Medicine**: CRISPR-Cas9 holds significant promise for the development of new models and precision-targeted therapies for diseases such as Alzheimer's Disease (AD) [5]. 6. **Versatility and Efficiency**: CRISPR-Cas9 provides a highly versatile platform that allows fast and efficient genome editing in an ever-growing list of organisms [10]. ### Potential Risks of CRISPR-Cas9: 1. **Off-Target Effects**: CRISPR-Cas9 is known to generate off-target alterations, which can result in unwanted mutations and potentially cytotoxic effects [4]. 2. **Technical Challenges**: There are several technical challenges that need to be addressed to maximize the benefits of CRISPR-Cas9 technology [2]. 3. **Long-Term Safety**: While early trial results are promising, the long-term safety of CRISPR-Cas9-engineered cells is yet to be determined [1]. 4. **Mismatch Tolerance**: The CRISPR-Cas9 system can tolerate certain mismatches to the DNA target, which could potentially lead to unintended edits [8]. By considering these benefits and risks, researchers and clinicians can better navigate the development and application of CRISPR-Cas9 technologies.", + "question": "What are the potential benefits and risks associated with gene editing technologies like CRISPR-Cas9?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_15 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_15 new file mode 100644 index 0000000..17dff0e --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_15 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2009 - eQTL analysis in mice and rats.pdf", + "2015 - Genetic Control of Survival and Weight Loss during Pneumonic Burk.pdf", + "2015 -Emery- Genetic Control of Survival and Weight Loss during Pneumonic Burk.pdf", + "2006 - From_gene_to_behavior_and_back_again_new.pdf", + "2005 - quantitative-trait-locus-analysis-of-aggressive-behaviours-in-mi.pdf", + "2005 -Broadkin- quantitative-trait-locus-analysis-of-aggressive-behaviours-in-mi.pdf", + "2009 - Experimental_Evolution.pdf", + "2009 - Garland_and_Rose_Experimental_Evolution.pdf", + "2005 - quantitative-trait-analysis-in-the-investigation-of-function-and.pdf", + "2016 - Social interactions and indirect genetic effects on complex juvenile and adult traits.pdf" + ], + "extraction_id": [ + "71981bfb-284e-50ad-854e-2055c07f77a7", + "615ee0cd-5960-57e5-b4e6-56e4b8020a1b", + "268a23e8-f528-5b59-89f2-188331e0a03c", + "64c0287d-aeea-52eb-a074-e9591c5593ae", + "9de93371-6239-53c2-b42c-71f615a0614b", + "0a5c759e-8dab-55f1-ac59-e8211ec683b8", + "8ee78018-b998-590c-99ab-788a447ede81", + "cbce50ea-be78-5d54-beb1-849222c5bfdd", + "0a895880-91c0-5079-b258-73926b38430f", + "0b91ce42-1ba4-530c-8d77-6ddbdc0e759d" + ], + "document_id": [ + "8d67ea90-f7b1-5bb8-937c-4a9eceddff43", + "ae1025b0-1410-51ae-9be2-26fa2e9d5808", + "a9aceace-bf48-5472-b54c-59a458a84c62", + "7a088b36-11b7-5379-bfe5-ce571e11de07", + "0dc730ba-4ff4-52aa-a988-71075113c416", + "e6027e7f-aec0-5e76-8aff-96b36389e701", + "34821353-1b74-5ee2-ac39-66dd46f145bf", + "496faa7f-9623-5ab7-9816-7c3755abb3aa", + "dac1c73c-0b5f-5a54-bb12-7e8b654009c0", + "06e126d3-b75d-57db-8edb-09de6ae13b24" + ], + "id": [ + "chatcmpl-ADZAiadgDkXNlnQWQS4n6DqZI6elc", + "73540700-b5cf-5838-852b-b281ca086140", + "374c456a-d1db-5b4a-8713-97abe4162d77", + "b9d52798-0235-5018-bccd-560565d16cc3", + "fef212bc-631b-591d-b8e3-d1523da0507d", + "c8f17022-aeae-5242-9082-d6d1eee4c4bf", + "1b2de424-be9f-572d-bd62-dc2ecd92192b", + "f72795a1-66c3-5a98-84bc-b085e8008073", + "31a32dc5-81ac-52ba-a463-c61e293f21e5", + "b660d882-1cb0-5150-ae76-8eb3ccb88a58", + "985378d7-e164-581b-ac1c-97bbcda9c06f" + ], + "contexts": [ + "While most of the Y chromosome does not undergo recombination, the recombination rate of the X chromosomeis slower than that of the autosomes. This has important consequences on the detection of significant QTLs. For a comprehensive view of these issues, see(43). 9.Probe hybridization artifacts When several probes are available for the same gene, it is not uncommon to observe a difference in the mapping results", + "8 QTL Mapping Allelic variation exists among natural populations and inbred strains, and this is reflective of the segregation of quantitative tr ait loci (QTLs) [96]. QTLs are stretches of DNA that are closely linked to genes that underlie a phenotype of interest. QTL analysis has been proven to be an invaluable tool to help unravel heritable traits, by enabling researchers to map different quantitative traits back to the genomic location involved in the regulation of these phenotypes.", + "8 QTL Mapping Allelic variation exists among natural populations and inbred strains, and this is reflective of the segregation of quantitative tr ait loci (QTLs) [96]. QTLs are stretches of DNA that are closely linked to genes that underlie a phenotype of interest. QTL analysis has been proven to be an invaluable tool to help unravel heritable traits, by enabling researchers to map different quantitative traits back to the genomic location involved in the regulation of these phenotypes.", + "The basic pr emise of QTL an alysis is simple (Ph illips and Belknap, 2002 ) . First, one must meas ure a speci c phen otype within a popul ation. Next, the population must be genotyped at a hundred or more marker loci186 Boehm II et al.", + "genes underlying QTLs in animals and plants (see for example Shirley et al 2004,Korstanje & Paigen 2002, Fridman et al 2004). I should also point out, though, that even in a single QTL region isolated in a congenic strain, it is possible that there is more than one allele that aects the phenotype. So, you have a fair pointabout the challenges and complexities of QTL analysis. Koolhaas: There are dierent questions underlying both approaches. The QTL", + "genes underlying QTLs in animals and plants (see for example Shirley et al 2004,Korstanje & Paigen 2002, Fridman et al 2004). I should also point out, though, that even in a single QTL region isolated in a congenic strain, it is possible that there is more than one allele that aects the phenotype. So, you have a fair pointabout the challenges and complexities of QTL analysis. Koolhaas: There are dierent questions underlying both approaches. The QTL", + "through analysis of line crosses, quantitative trait loci (QTL) mapping, and verification of candidate genes with quantitative complementation tests or genetic engineering (e.g.,McGuire and Tully 1987; Chandra et al. 2001; Dierick and Greenspan 2006; Edwardset al. 2006). They can also be used to study the underlying physiological, neural, andmolecular mechanisms of the differences in behavior between selected and controllines, or between divergently selected lines.", + "through analysis of line crosses, quantitative trait loci (QTL) mapping, and verification of candidate genes with quantitative complementation tests or genetic engineering (e.g.,McGuire and Tully 1987; Chandra et al. 2001; Dierick and Greenspan 2006; Edwardset al. 2006). They can also be used to study the underlying physiological, neural, andmolecular mechanisms of the differences in behavior between selected and controllines, or between divergently selected lines.", + "genetic background. Gene identification of QTL should be distinguished from identification of the quanti- tative trait nucleotide (QTN). The latter is a daunting task, since SNPs are so frequent. Final proof for a QTN in mice would require placing a genomic segment containing theputative QTN from a donor mouse strain on the background of another strain using homologous recombination and reproducing the phenotype of the donor strain.", + "measuring correlations between genetic markers and phenotypic traits in a population. Individuals are scored for their phenotype for a particular trait, and their genotype at a marker. If there is a differ- ence in mean phenotype between those individuals with one geno- type at a particular locus compared with the other, than we can infer that there is a QTL linked to that marker [ 40 , 153 ]. 2.3 Analysis and QTL MappingDavid G. Ashbrook and Reinmar Hager" + ], + "answer": "To differentiate between X and Y DNA in the context of DNA tracing and determining quantitative trait loci (QTLs), one must consider the unique characteristics of each chromosome: 1. **Recombination Rates**: The Y chromosome largely does not undergo recombination, whereas the X chromosome has a slower recombination rate compared to autosomes. This difference in recombination rates has significant implications for the detection of QTLs [1]. 2. **QTL Mapping**: QTLs are stretches of DNA closely linked to genes that influence specific phenotypes. The process of QTL mapping involves measuring phenotypes within a population and genotyping at numerous marker loci [2], [4]. Given the unique recombination characteristics of the X and Y chromosomes, the strategies for mapping QTLs on these chromosomes may differ. 3. **Genetic Markers and Phenotypic Traits**: When measuring correlations between genetic markers and phenotypic traits, the lack of recombination on the Y chromosome and the slower recombination on the X chromosome must be taken into account. This affects how QTLs linked to these chromosomes are identified and analyzed [10]. In summary, the primary differences between X and Y DNA in the context of DNA tracing and QTL determination lie in their recombination rates and the subsequent impact on QTL detection and mapping strategies.", + "question": "How does one tell the difference between X and Y DNA, with repsect to DNA tracing and determining QTLs?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_16 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_16 new file mode 100644 index 0000000..83f0761 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_16 @@ -0,0 +1,65 @@ +{ + "titles": [ + "003 -Barnes- Bioinformatics_for_Geneticists.pdf", + "2007 - Bioinformatics_for_Genetices_MAZEN_SAEED.pdf", + "2007 - Bioinformatics_for_Geneticists.pdf", + "2007 - Bioinformatics_for_Genetices_MAZEN_SAEED.pdf", + "003 -Barnes- Bioinformatics_for_Geneticists.pdf", + "2007 - Bioinformatics_for_Geneticists.pdf", + "2010 - Teaching Bioinformatics and Neuroinformatics by using Free Web-Based Tools.pdf", + "2012 - Biological Databases for Behavioral Neurobiology.pdf", + "2008 - (Infectious Disease) Karl A. Western (auth.), Vassil St. Georgiev PhD, Karl A. Western MD, John J. McGowan PhD (eds.) - National Institute of Allergy and Infectious Diseases, NIH_ Frontiers in Researc (3).pdf", + "2008 - Biotools for Determining the Genetics of Susceptibility to Infectious Diseases.pdf" + ], + "extraction_id": [ + "49a42e3c-e1f9-5433-9643-192a592454d4", + "a1588a50-8f88-5d50-9232-706bdc46ec88", + "11762190-61cd-585d-96c1-7aa6717d9d47", + "af1c63bf-772d-554e-be88-bd62daee49ee", + "025c4afb-d749-54a7-a183-9a7b8b1332c7", + "c0098aa5-5eba-5b6a-97f2-661388daeb82", + "0017cf22-e712-5a41-9bb7-ea3632bb825a", + "9a38ba60-52da-5f67-9c75-db1ac7a7b7ec", + "16c8fbb0-ab2a-563f-a6b2-e0d8733b69fb", + "fe6eb7f0-9f09-50f8-a7a1-c71e507226d5" + ], + "document_id": [ + "045edae8-468b-5725-be06-8cb4b8f6a92b", + "139463d1-c63c-5c51-bf9c-9ccc356768e0", + "4ea8e1a8-e113-5f02-ad78-880b9c51a101", + "139463d1-c63c-5c51-bf9c-9ccc356768e0", + "045edae8-468b-5725-be06-8cb4b8f6a92b", + "4ea8e1a8-e113-5f02-ad78-880b9c51a101", + "c802c4ea-e99d-501a-ba20-1cd1b369dfc3", + "99d3a5c1-8511-5fba-92ce-1ceef2b6c402", + "4db8c752-c8e2-5f6d-a091-dc4f1d0c48bc", + "fcbbb3ce-6524-50e3-9f8d-c191dc551231" + ], + "id": [ + "chatcmpl-ADZArO5xKdt382Vj4oDYlsZwv0rng", + "fcbb83a7-84f2-55cd-b26d-80883a022c52", + "7f1ea794-1c26-5a90-abe3-f60f338f5985", + "8473f1fc-d615-54de-92de-a3faf5e2045e", + "4741caf7-1306-52e8-874c-fa200f067978", + "f74f9aa9-3464-58e8-a0f6-e3e38efa3c40", + "4121b591-0dda-5347-9833-23e3d9c6d8fe", + "f337b34c-de96-5b8c-ac3e-80417634b5c1", + "df5e9619-d45e-5958-a88d-d33ecc59387d", + "71eac758-37cb-5fec-8380-7d9f4d4c2845", + "e4180707-bb0f-5b00-8de7-f6937bc38e07" + ], + "contexts": [ + "for people to exchange data easily over the Web. Two other notable developments are BioMart and GBrowse. The BioMart project (http://www.biomart.org/), originally a spin-off from Ensembl, offers a generic data management system that allows complex searches of biological data such as sequence annotation. The GBrowse project (Stein et al. , 2002; http://www.gmod.org/) has produced a generic genome browser that can be customized to organize, display and query a new genome scale data set. These", + "for people to exchange data easily over the Web. Two other notable developments are BioMart and GBrowse. The BioMart project (http://www.biomart.org/), originally a spin-off from Ensembl, offers a generic data management system that allows complex searches of biological data such as sequence annotation. The GBrowse project (Stein et al. , 2002; http://www.gmod.org/) has produced a generic genome browser that can be customized to organize, display and query a new genome scale data set. These", + "for people to exchange data easily over the Web. Two other notable developments are BioMart and GBrowse. The BioMart project (http://www.biomart.org/), originally a spin-off from Ensembl, offers a generic data management system that allows complex searches of biological data such as sequence annotation. The GBrowse project (Stein et al. , 2002; http://www.gmod.org/) has produced a generic genome browser that can be customized to organize, display and query a new genome scale data set. These", + "(http://ensembl.org/ ) and the National Center for Biotechnology Information (NCBI) (http://www.ncbi.nlm.nih.gov/ ) all provide portals to the most current, and archived public assemblies. These sites also provide means of searching the assem- blies, such as BLAST (Altschul et al. , 1997), BLAT (Kent, 2002) and SSAHA (Ning et al. , 2001) as well as precomputed annotation for the genome assemblies that can be readily incorporated into comparative genomic analyses.", + "(http://ensembl.org/ ) and the National Center for Biotechnology Information (NCBI) (http://www.ncbi.nlm.nih.gov/ ) all provide portals to the most current, and archived public assemblies. These sites also provide means of searching the assem- blies, such as BLAST (Altschul et al. , 1997), BLAT (Kent, 2002) and SSAHA (Ning et al. , 2001) as well as precomputed annotation for the genome assemblies that can be readily incorporated into comparative genomic analyses.", + "(http://ensembl.org/ ) and the National Center for Biotechnology Information (NCBI) (http://www.ncbi.nlm.nih.gov/ ) all provide portals to the most current, and archived public assemblies. These sites also provide means of searching the assem- blies, such as BLAST (Altschul et al. , 1997), BLAT (Kent, 2002) and SSAHA (Ning et al. , 2001) as well as precomputed annotation for the genome assemblies that can be readily incorporated into comparative genomic analyses.", + "resources. We present an easy-to-adopt module that weaves together several important bioin-formatic tools so students can grasp how these tools are used in answering research questions.Students integrate information gathered from websites dealing with anatomy (Mouse BrainLibrary), quantitative trait locus analysis (WebQTL from GeneNetwork), bioinformatics and geneexpression analyses (University of California, Santa Cruz Genome Browser, National Center forBiotechnology Informations Entrez Gene, and the", + "References Altman RB. Building successful biological databases. Briefings in Bioinformatics. 2004; 5:45. [PubMed: 15153301] Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: Tool for the unification of biology. The Gene Ontology Consortium. Nature Genetics. 2000; 25:2529. [PubMed: 10802651] Ashish N, Ambite JL, Muslea M, Turner JA. Neuroscience data integration through mediation: an", + "Sequences, Protein Structures, Complete Genomes, Tax- onomy, Medical Genetics resources (see later), and others (see http://www.ncbi.nlm.nih.gov/Database/index.html for a complete listing of databases). Entrez PubMed provides access to full-text articles at journal websites and other related web resources, some of which are free to the public. This site also provides links to other molecular biology resources. The National Center for Biotechnology Information ( http://", + "Sequences, Protein Structures, Complete Genomes, Tax- onomy, Medical Genetics resources (see later), and others (see http://www.ncbi.nlm.nih.gov/Database/index.html for a complete listing of databases). Entrez PubMed provides access to full-text articles at journal websites and other related web resources, some of which are free to the public. This site also provides links to other molecular biology resources. The National Center for Biotechnology Information ( http://" + ], + "answer": "For text and biological resources, it seems you are referring to adding web resources such as Ensembl to your system. This is evident from the context which mentions various web-based biological data management systems and genome browsers like BioMart, GBrowse, and Ensembl [1], [4]. These resources provide portals to current and archived public assemblies, as well as tools for searching and annotating genome assemblies [4], [6]. Therefore, it appears you are more focused on integrating web resources rather than books.", + "question": "For text and biological resources, do you mean add some books (on biology stuff) or/and web resources (as ensembl) on your system?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_17 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_17 new file mode 100644 index 0000000..42b1aeb --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_17 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2007 - Bioinformatics_for_Genetices_MAZEN_SAEED.pdf", + "2007 - Bioinformatics_for_Geneticists.pdf", + "003 -Barnes- Bioinformatics_for_Geneticists.pdf", + "003 -Barnes- Bioinformatics_for_Geneticists.pdf", + "2007 - Bioinformatics_for_Genetices_MAZEN_SAEED.pdf", + "2007 - Bioinformatics_for_Geneticists.pdf", + "003 -Barnes- Bioinformatics_for_Geneticists.pdf", + "2007 - Bioinformatics_for_Genetices_MAZEN_SAEED.pdf", + "2007 - Bioinformatics_for_Geneticists.pdf", + "2007 - Bioinformatics_for_Genetices_MAZEN_SAEED.pdf" + ], + "extraction_id": [ + "4ef84d22-b428-5386-bbc0-39dbd364d3d7", + "7524bdfa-63f8-57c1-b5fe-1edcf11c275e", + "c8e9c4b7-19c6-5426-83a2-6f8628b68ceb", + "a3ae6875-b0fc-5a4e-866f-4fee99c7d2a2", + "bb247bfe-333b-553a-94e6-2dc1b13b4723", + "9c89683f-aca5-57f9-b28d-62e9eb64377b", + "23bb58ad-7835-58f4-862f-dd17e1ec5140", + "52fc5fdb-48b5-5c1e-a8d2-1e67d7702c9f", + "20d0c226-76aa-5c1e-85cc-9d5c1bcce2f2", + "bb247bfe-333b-553a-94e6-2dc1b13b4723" + ], + "document_id": [ + "139463d1-c63c-5c51-bf9c-9ccc356768e0", + "4ea8e1a8-e113-5f02-ad78-880b9c51a101", + "045edae8-468b-5725-be06-8cb4b8f6a92b", + "045edae8-468b-5725-be06-8cb4b8f6a92b", + "139463d1-c63c-5c51-bf9c-9ccc356768e0", + "4ea8e1a8-e113-5f02-ad78-880b9c51a101", + "045edae8-468b-5725-be06-8cb4b8f6a92b", + "139463d1-c63c-5c51-bf9c-9ccc356768e0", + "4ea8e1a8-e113-5f02-ad78-880b9c51a101", + "139463d1-c63c-5c51-bf9c-9ccc356768e0" + ], + "id": [ + "chatcmpl-ADZAv1loJK3Vz78QdzvVvEogO2ngd", + "c36215f6-2230-58ef-b3eb-44d1799ba5c2", + "89a578c7-5961-5b88-9a6d-f338216702c3", + "81e589eb-aa51-5f2a-966f-31928fb31943", + "1bf9bb72-ebaa-51d1-82ce-aae2f16dd92b", + "f0c00edb-f07d-5975-a16b-16a072d0f2d4", + "e2e526cb-0ac3-51ff-a1c5-43ff032b5558", + "66294988-1566-5bec-8f63-658ea9011e26", + "b19972d1-7ec6-5f66-ac2f-518e69c5f22b", + "69fdc34c-c187-5c7a-973c-a629045841a7", + "7e15e9b2-c731-5ab0-85c0-b6b432623220" + ], + "contexts": [ + "supported by a signicant BLAST match to one or more expressed sequences or proteins. Ensembl also identies the positions of known human genes from public sequence database entries, usually using GENEWISE to predict their exon structures. The total set of Ensembl genes should therefore be a much more accurate reection of reality than ab initio predictions alone, but it is clear that some novel genes are missed (Hogenesch et al. , 2001). Of the many novel genes that are detected, some are", + "supported by a signicant BLAST match to one or more expressed sequences or proteins. Ensembl also identies the positions of known human genes from public sequence database entries, usually using GENEWISE to predict their exon structures. The total set of Ensembl genes should therefore be a much more accurate reection of reality than ab initio predictions alone, but it is clear that some novel genes are missed (Hogenesch et al. , 2001). Of the many novel genes that are detected, some are", + "supported by a signicant BLAST match to one or more expressed sequences or proteins. Ensembl also identies the positions of known human genes from public sequence database entries, usually using GENEWISE to predict their exon structures. The total set of Ensembl genes should therefore be a much more accurate reection of reality than ab initio predictions alone, but it is clear that some novel genes are missed (Hogenesch et al. , 2001). Of the many novel genes that are detected, some are", + "Ostell/Spidey/ SSAHA at Sanger Institute http://www.sanger.ac.uk/Software/analysis/SSAHA/ human and mouse genomes, where there are large full-length cDNA collections to guide the hunt for genes, Ensembl should be very reliable. From the beginning, many genomic features other than predicted genes were included in Ensembl: different repeat classes, cytological bands, CpG island predic- tions, tRNA gene predictions, expressed sequence clusters from the UniGene database", + "Ostell/Spidey/ SSAHA at Sanger Institute http://www.sanger.ac.uk/Software/analysis/SSAHA/ human and mouse genomes, where there are large full-length cDNA collections to guide the hunt for genes, Ensembl should be very reliable. From the beginning, many genomic features other than predicted genes were included in Ensembl: different repeat classes, cytological bands, CpG island predic- tions, tRNA gene predictions, expressed sequence clusters from the UniGene database", + "Ostell/Spidey/ SSAHA at Sanger Institute http://www.sanger.ac.uk/Software/analysis/SSAHA/ human and mouse genomes, where there are large full-length cDNA collections to guide the hunt for genes, Ensembl should be very reliable. From the beginning, many genomic features other than predicted genes were included in Ensembl: different repeat classes, cytological bands, CpG island predic- tions, tRNA gene predictions, expressed sequence clusters from the UniGene database", + "database, which aims to compile a non-redundant, curated data set representing current knowledge of known genes (Wheeler et al. , 2002; http://www.ncbi.nlm.nih. gov/entrez/query.fcgi?db=gene). Like the Ensembl protocol, many Acembly- predicted structures (the NCBI estimate 42 per cent) are incomplete. These struc- tures can be displayed alongside ab initio gene models, Ensembl-predicted genes, and matching UniGene clusters to allow users to make their own conclusions about the likeliest gene structure.", + "database, which aims to compile a non-redundant, curated data set representing current knowledge of known genes (Wheeler et al. , 2002; http://www.ncbi.nlm.nih. gov/entrez/query.fcgi?db=gene). Like the Ensembl protocol, many Acembly- predicted structures (the NCBI estimate 42 per cent) are incomplete. These struc- tures can be displayed alongside ab initio gene models, Ensembl-predicted genes, and matching UniGene clusters to allow users to make their own conclusions about the likeliest gene structure.", + "database, which aims to compile a non-redundant, curated data set representing current knowledge of known genes (Wheeler et al. , 2002; http://www.ncbi.nlm.nih. gov/entrez/query.fcgi?db=gene). Like the Ensembl protocol, many Acembly- predicted structures (the NCBI estimate 42 per cent) are incomplete. These struc- tures can be displayed alongside ab initio gene models, Ensembl-predicted genes, and matching UniGene clusters to allow users to make their own conclusions about the likeliest gene structure.", + "populations as Ensembl reects the progress of the International Haplotype Map Project (Thorisson et al. , 2005). More speculative data, such as GENSCAN-predicted exons that have not been incorporated into Ensembl-conrmed genes, may also be viewed. This means that the display can be used as a workbench for the user to develop personalized an- notation. For example, one may discover novel exons by nding GENSCAN exon predictions which coincide with good matches to a fragment of the draft mouse" + ], + "answer": "Ensembl is a genomic database that identifies the positions of known human genes from public sequence database entries, usually using tools like GENEWISE to predict their exon structures. It aims to provide a more accurate reflection of reality compared to ab initio predictions alone [1]. Ensembl also includes many genomic features other than predicted genes, such as different repeat classes, cytological bands, CpG island predictions, tRNA gene predictions, and expressed sequence clusters from the UniGene database [4].", + "question": "what is ensembl?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_18 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_18 new file mode 100644 index 0000000..f8b9214 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_18 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2012 - Identifying Gene Networks Underlying the Neurobiology of Ethanol and Alcoholism.pdf", + "2010 - Systems genetics, bioinformatics and eQTL mapping.pdf", + "2011 - Genetical genomics approaches for systems genetics.pdf", + "2012 - Functional genomics research in aquaculture principles and general approaches.pdf", + "2020 - A Multi-Omics Perspective of Quantitative Trait Loci in Precision Medicine.pdf", + "2014 - Identification of a QTL in Mus musculus for Alcohol Preference, Withdrawal, and Ap3m2 Expression Using Integrative Functional Genomics and Precision Genetics.pdf", + "2012 - Functional genomics research in aquaculture principles and general approaches.pdf", + "2020 - A platform for experimental precision medicine The extended BXD mouse family.pdf", + "2014 - Genetics of Gene Expression in CNS.pdf", + "2012 - Functional genomics research in aquaculture principles and general approaches.pdf" + ], + "extraction_id": [ + "4253d9a7-5ade-5ac3-b37d-c27ed5a71ef6", + "298ee1f5-58a9-567c-86ba-8ac5967e1718", + "19febe84-f1fa-599d-84b4-95329b3d7f3f", + "a261cf24-3fe0-5cf1-ba6d-adf91794be38", + "65a5b8cd-6cf6-5c37-95b2-8677516d01e8", + "08ca6342-74ea-5196-b5b4-b46c9ec46713", + "0c3d0cb3-d4b0-5655-8b04-285a87710636", + "a797ba45-1fd5-58c5-af8f-e81341ecb7b2", + "7cea62b5-dbd8-5447-8126-9f2bcfe8b9eb", + "0c3d0cb3-d4b0-5655-8b04-285a87710636" + ], + "document_id": [ + "c02542c0-eff8-5ec7-8f73-78f5d28d4226", + "27c922c6-e449-5f83-868a-3ad7284facc8", + "de78a01d-8d03-5afb-af5b-ce2ed2167766", + "a39b4cc1-8661-578b-a61b-b9962e45fc33", + "8503b166-b917-5efb-a356-5ba371504cc1", + "22ac294c-736f-5adb-8a0d-bd7166b578e8", + "a39b4cc1-8661-578b-a61b-b9962e45fc33", + "dd4994b9-9546-59c0-bc71-60e2617b6bcd", + "51a824c3-34c2-5be0-87a1-9f9f08b06e4a", + "a39b4cc1-8661-578b-a61b-b9962e45fc33" + ], + "id": [ + "chatcmpl-ADZAyuRH6G3gbrSvS025ayW0rNJ7F", + "33fc773a-2ee3-572d-8629-2ec1e359aca1", + "68a13597-c223-54d9-9664-604d69b97c50", + "02a79024-51ee-5bdc-9a5b-ac26a6f3e40b", + "63febc09-7871-5cfc-9f7d-1f05eed65f41", + "1ad460e0-0a45-5f5e-9d37-d40bc8c65054", + "ee54bf38-d7c6-5e1f-bc75-8951d5ae917e", + "fa1981fe-6730-59a1-b331-c6c7250b0f2c", + "7924dd91-82c7-50d5-b663-0f5390f43065", + "ec03e315-1d99-5149-945c-5c4c0f4afed9", + "a5e77dc2-9d73-5d24-a446-0df546e34d85" + ], + "contexts": [ + "traditional QTL mapping and GWASsapproaches can benefit from systems-biological approaches by filling in criticalinformation about the molecular phenotypes that stand between DNAvariation and complex disease (figure5). The incorporation of data fromhigh-throughput molecular profilingtechnologies, such as gene expressionmicroarrays, can better define a diseaseby identifying groups of genes thatrespond to or covary with disease-associated traits. Network analysis ofdisease-associated genes allows", + "knowledge of the true QTL location (Doss et al. 2005 ), which can be used to empirically estimate the power of aGWAS performed at a similar scale (Hao et al. 2008 ; Schadt et al. 2008 ). A GWAS on its own does little more than establish correlations between changes in DNA at agiven locus and changes in a disease trait of interest, with respect to populations of interest. Further, these studies on", + "genotypes. Since association studies allow for a mu ch finer mapping of the QTL than that obtained with linkage analysis, there is a trade-off to consider between power and resolution when choosing the mapping stra tegy. Genome-wide associa- tion studies (GWAS) have naturally been used to per form genetical genomics studies in humans [18, 24-27] and are emerging in m odel organisms studies using outbred populations [28]. 8.2.2 Combining studies", + "genetically also mapped to the same genomic location. In order to locate the positions of genes that are responsible for a certain trait, GWAS can be conducted. GWAS is a quan- titative approach to analyze the association of whole genome DNA polymorphisms and a phe- notypic trait, thereby localizing the genes un- derlining the trait. Genome-Wide Association Studies (GWAS) GWAS is a holistic whole-genome approach to robustly determine the association of DNA polymorphisms with correlated phenotypic", + "(PHMs) use principles of MR embedded within a Bayesian hierarchical model to detect interac-tions between regulatory elements [ 98]. Furthermore, GWAS is often integrated with the QTL analysis despite the fact that many GWAS loci are not strong eQTL loci [ 56]. GWAS-eQTL colocalization methods, including RTC [ 145], QTLMacth [ 158], Sherlock [ 159], and coloc [ 160], are based on the concept that disease-", + "association studies (GWAS) or linkage studies (Enoch 2013). QTL mapping studies historically had very low resolution,and many have been performed using populations for whichlimited genetic data exist. Publications of gene expressionstudies typically highlight a few interesting gene centered results, but the bulk of information is rejected due to concern", + "pairs that include many genes within the seg- ment. On the other hand, GWAS may point to several or even many genomic locations for the trait of interest, complicating further functional analysis. Analysis of Quantitative Trait Loci (QTL) QTL analysis reveals statistically signicant linkage between phenotypes and genotypes, thereby providing explanation for the genetic basis of variation in complex traits (Falconer and Mackay, 1996; Lynch and Walsh, 1998). In a sense, QTL analysis can be viewed as incom-", + "QTL mapping QTL mapping using GeneNetwork has been described in detail elsewhere ( Mulligan et al., 2017 ). However, in brief, quantitative trait loci (QTLs) are segments of the genome affecting a particular phenotype ( Falconer and Mackay, 1996 ). QTL mapping, identifying", + "3. Genetic Mapping Methods Several statistical approaches have been developed for genome-wide linkage analysis of traditional phenotypes. The same approaches can be used to map eQTLs. These approaches range from single marker tests ( t-test, ANOVA, and simple regression analysis) to multiple locus mapping methods. The only major difference is that eQTL studies involve tens of thousands of expression traits and require fast algorithms. Since an eQTL study tests for", + "plete GWAS analysis with limited number of markers that does not cover the entire genome. As such, if one or few QTLs are found, there may be more QTLs in the genome to be dis- covered. More importantly, in the absence of closely linked markers in the genomic regions containing signicant QTLs for the trait, the most signicant genes responsible for the trait can be missed. However, because of historical reasons such as the lack of genome-wide mark- ers, or the lack of funding, QTL analysis is still" + ], + "answer": "The primary difference between QTL mapping and GWAS lies in their approaches and resolutions for identifying genetic associations with traits. 1. **QTL Mapping**: - **Approach**: QTL mapping involves identifying segments of the genome (quantitative trait loci) that are associated with a particular phenotype. It typically uses linkage analysis to find statistically significant linkages between phenotypes and genotypes [7], [8]. - **Resolution**: Historically, QTL mapping has had low resolution, meaning it identifies larger genomic regions associated with traits rather than pinpointing specific genetic variants [6]. 2. **GWAS (Genome-Wide Association Studies)**: - **Approach**: GWAS is a holistic, whole-genome approach that analyzes the association between DNA polymorphisms and phenotypic traits across the entire genome. It establishes correlations between changes in DNA at specific loci and changes in disease traits within populations [2], [4]. - **Resolution**: GWAS allows for much finer mapping of QTLs compared to traditional linkage analysis, providing higher resolution in identifying specific genetic variants associated with traits [3]. In summary, while QTL mapping identifies broader genomic regions linked to traits with lower resolution, GWAS provides a finer, more detailed mapping of genetic associations across the entire genome [2], [3], [4], [6], [7], [8].", + "question": "What is the difference between QTL mapping and GWAS?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_19 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_19 new file mode 100644 index 0000000..56cbe4f --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_19 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2005 -Dipetrillo- Bioinformatics toolbox QTL.pdf", + "2005 - Bioinformatics toolbox for narrowing rodent quantitative trait loci .pdf", + "2008 - Gene Expression Profiling.pdf", + "2016 - Systems proteomics of liver mitochondria function.pdf", + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2005 - quantitative-trait-locus-analysis-of-aggressive-behaviours-in-mi.pdf", + "2012 - Systems genetic analysis of the effects of iron deficiency in mouse brain.pdf", + "2005 -Broadkin- quantitative-trait-locus-analysis-of-aggressive-behaviours-in-mi.pdf", + "2020 - A Multi-Omics Perspective of Quantitative Trait Loci in Precision Medicine.pdf", + "2009 - Multiscale Genomic Analysis of the Corticolimbic System_ Uncoveri (1).pdf" + ], + "extraction_id": [ + "e3adaae7-b5c1-5d35-9ba8-e082ccbb6fee", + "8311a931-a1b1-5228-bd9d-e9fcdd803ae9", + "6f6a41a6-61ef-5d73-8bce-5de9a9cc4798", + "1a46d28d-fc4a-5154-b887-3956d64959ef", + "2134720b-01d9-5e45-96bf-d1ff449d406d", + "c9fe8c31-86f9-5c59-8af8-8e81157cb99f", + "6ab990b0-4f9c-5be3-ab79-9ca6835271fa", + "cf8c1f06-37d2-5b82-bc22-6bc129759445", + "81b38987-de8d-5e87-8c09-72efc4cf781e", + "3296b30e-7dd3-576d-a2df-442406caa472" + ], + "document_id": [ + "9b089457-5804-594a-99ea-e716b65c216c", + "5d87aefe-dee5-5f25-8b46-d87b24907dcc", + "59f3b969-089b-5258-93ad-892dbc9ffa9c", + "9843107b-2d0c-5cbe-b55f-74e8090a575d", + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "0dc730ba-4ff4-52aa-a988-71075113c416", + "99fc80f0-f3c3-5766-a604-921552bb3298", + "e6027e7f-aec0-5e76-8aff-96b36389e701", + "8503b166-b917-5efb-a356-5ba371504cc1", + "3d0df5a3-7d7c-5edc-b94d-cae582f59c12" + ], + "id": [ + "chatcmpl-ADZB337KFj8E0Gaou64p4qZLCzlv7", + "3aa13846-0d67-5481-8684-e414fdf712aa", + "a917decd-be31-53bd-9b1d-03eb6ba1e082", + "11bb5512-c251-5348-a53e-88ebaa9bcbbb", + "e4d4bbfc-d6b5-51cc-bf94-10bfc7099bf8", + "7d6a48a0-e046-520c-8434-7544e20b7a6c", + "38ba1c61-a6cc-504c-a6a3-da3c1252a17d", + "7ef9df1d-b21a-597a-9e74-6eace5d0c33c", + "fed778e8-dda5-5766-886d-287c24ff3758", + "c3d5e48d-ebf6-54b9-811e-6dbe2a171b77", + "bcb46fb1-dc0a-5f38-85e4-8c6d99809d74" + ], + "contexts": [ + "candidate genes. These candidate genes must then betested for a causal link to the phenotype. A good starting point would be sequencing the cDNA of strong candidate genes to identify amino acid polymorphisms and testingfor mRNA and protein expression differences in target tissues of the original strains used to detect the QTL. Sequencing and expression studies will rene the list ofcandidate genes that can then be tested rigorously for proof of cause and effect. The nal proof of a causal gene", + "candidate genes. These candidate genes must then betested for a causal link to the phenotype. A good starting point would be sequencing the cDNA of strong candidate genes to identify amino acid polymorphisms and testingfor mRNA and protein expression differences in target tissues of the original strains used to detect the QTL. Sequencing and expression studies will rene the list ofcandidate genes that can then be tested rigorously for proof of cause and effect. The nal proof of a causal gene", + "do you identify the responsible gene within a QTL that you have identified? Generally, one starts by performing a strain survey to find two parental inbred strains that have a markedly different trait. One can now look up many different traits of inbred mice online at the Mouse Phenome Database ( http://phenome. jax.org/pub-cgi/phenome/mpdcgi?rtn=docs/home ). However, the trait you may want to study may not be present in wild type mice, so you may want to cross", + "used to test the hypothesis at locus-specific sig-nificance (LRS 12). In doing so, an additional 7 cQTLs are observed as consistent in both diets(Fig. 2I, red number). Solving QTLs: Finding the quantitative trait gene For cis-QTLs, the causal factors can be quickly identified: With few exceptions, they will be driv-en by variants within the gene itself or imme-diately adjacent. For trans-QTLs, mQTLs, and cQTLs, the identification of the causal quanti-", + "data is to find a quantitative trait locus, or QTL. A QTL (http://gn1.genenetwork.org/glossary.html#Q ) is an area on a chromosome that can contain one or many genes, that is linked to a change in phenotype. After a QTL that is responsible for the apparent variation in phenotype has been identified , one can start stu dying the genes within that locus to identify the likely causal gene . Once the data is normalized appropriately (in our case, no normalization was required) , the QTL", + "candidate genes that are expressed in tissues likely to inuence the traits of interest(Su et al 2004). These candidate genes are then sequenced in the two parental inbred strains looking for sequence dierences in coding or regulatory regions. After ne mapping the QTL interval and shortening the list of plausible candidate polymorphisms, the major challenge remains /C246 proving denitively which nucleotide polymorphism underlies the QTL. The most direct proof", + "because these strains have been genotyped at more than 14,000 markers, including single nucleotide polymorphisms (SNP). Hundreds of genes may lie within a QTL interval, so identifying the underlying genes requires complementary methods. One method is to use BXD gene expression data (a public resource at www.genenetwork.org) to screen for genes within the QTL interval whose expression correlates with the trait of interest [23].", + "candidate genes that are expressed in tissues likely to inuence the traits of interest(Su et al 2004). These candidate genes are then sequenced in the two parental inbred strains looking for sequence dierences in coding or regulatory regions. After ne mapping the QTL interval and shortening the list of plausible candidate polymorphisms, the major challenge remains /C246 proving denitively which nucleotide polymorphism underlies the QTL. The most direct proof", + "curate approaches to identify various types of QTL according to their molecular features, in par- ticular to control various confounding factors, such as dietary habit and population structure. Fine Mapping of Causal Variants and Causal Genes Despite the identi cation of large numbers of QTLs, it remains challenging to establish causal", + "to date, only a small handful of genes have been definitively identified for complex traits. Our own efforts to identify a causal gene were stymied by the compound nature of QTLs and the high gene density in Qrr1 , and in Vol8a . Furthermore, it is now becoming clear that in addition to the canonical candidate genes, there are multiple spliced variants, microRNAs, and epigenetic factors to be considered. With what appears to be an increasingly complex genom ic landscape, it is now all" + ], + "answer": "To determine which gene in your QTL is causal for the trait, you can follow these steps: 1. **Identify Candidate Genes**: Start by identifying candidate genes within the QTL interval. This can be done by looking at genes that are expressed in tissues likely to influence the traits of interest [6]. 2. **Sequence Candidate Genes**: Sequence the cDNA of strong candidate genes to identify amino acid polymorphisms. This helps in detecting sequence differences in coding or regulatory regions between the two parental inbred strains [1], [6]. 3. **Expression Studies**: Test for mRNA and protein expression differences in target tissues of the original strains used to detect the QTL. This will help refine the list of candidate genes [1]. 4. **Fine Mapping**: Perform fine mapping of the QTL interval to shorten the list of plausible candidate polymorphisms [6]. 5. **Correlation with Trait**: Use gene expression data to screen for genes within the QTL interval whose expression correlates with the trait of interest [7]. 6. **Proof of Causality**: After refining the list of candidate genes, test them rigorously for proof of cause and effect. This involves proving definitively which nucleotide polymorphism underlies the QTL [1], [6]. By following these steps, you can systematically narrow down and identify the causal gene within your QTL.", + "question": "How do I determine which gene in my QTL is causal for the trait?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_2 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_2 new file mode 100644 index 0000000..46bb673 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_2 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Shared (epi)genomic background connecting neurodegenerative diseases.pdf", + "2018 - DNA methylation in the APOE genomic.pdf", + "2016 - Te-Mata-Ira-Genome-Research-Guidelines.pdf", + "2015 - Maternal diabetes, gestational diabetes and the role of epigenetics in their long term effects on offspring.pdf", + "2011 - Molecular Genomic Research Designs.pdf", + "2022 - Genomics and Epigenomics of Gestational Diabetes Mellitus Understanding the Molecular Pathways of the Disease Pathogenesis.pdf", + "2012 - Systems Biology Approaches to Nutrition.pdf", + "2012 - Aging, Rejuvenation, and Epigenetic.pdf", + "2008 - Genetic Effects on Environmental Vulnerability to Disease Novartis Foundation Symposium 293.pdf", + "2011 - EXPLOITING NATURAL AND INDUCED GENETIC VARIATION TO STUDY HEMATOPOIESIS.pdf" + ], + "extraction_id": [ + "8963fcd1-8685-5518-9dd4-cb6d7075fe56", + "f8846e53-c9c0-5feb-8616-f2adcbf139eb", + "05ecf103-b037-5216-93f5-329714fc422c", + "746af210-6a0f-5814-80b6-8a3147246af2", + "66dfdd26-c34d-58b7-bc9b-fddd291c80c4", + "0072a2f8-0a81-5327-bfc9-24ed9886ef28", + "2f188d05-2160-5e55-b7b7-e18adebcfb12", + "9c1c1db0-57cf-5fae-bedd-f7fc61e8e6cb", + "eb19a2ea-02e9-5b7b-b493-2ed13c25a0e2", + "83da0679-fd33-562c-a3a7-1d7d4c5b79ed" + ], + "document_id": [ + "3a7a3370-8de6-5d16-aac8-ba62336c7397", + "34b623d2-af48-5fc7-8e9f-e83b5f7a799a", + "86047c9b-e1f6-5c2d-b1d2-5becf4cb0957", + "3e92bd8e-fbf7-5bc4-9395-0a6dd0b0934e", + "ced08e27-8655-59a4-bf63-0ba746f139b7", + "f2353e3e-a250-5543-9906-d7d675c10eca", + "6955478b-950d-5d29-b24c-3a5ca656f3ae", + "bde26feb-f423-51b0-89ec-6f079bfc8b17", + "5d65e407-34e5-5c1c-b394-989b7a09b57d", + "6f250b15-61b3-57ed-8900-5aa4a173fa8c" + ], + "id": [ + "chatcmpl-ADZ9QxtSzyI2BzaSwoHdkiSzca6zm", + "bc59df3b-f204-5bf4-8915-9d172cdc040f", + "bb94a5a9-2c25-5952-940d-05e102f2f8e5", + "9b4ddd27-ffbd-5c10-beae-e808c75e7fa5", + "8530798b-380a-5511-a61c-bcb75004a2f1", + "de68ac40-3950-53e5-b13e-7459026f02a9", + "d96d8aca-6024-5f5b-80bc-e1e018a8ceed", + "4e952f12-2c91-54fd-9662-4200ed92cad8", + "6030ef44-f93f-5637-8f09-2ab6cd06d180", + "1aacc908-4ed2-54ee-bb8f-5f8e000d4ae3", + "e3cf7319-1be5-5c01-b462-559ef450d72c" + ], + "contexts": [ + "to regulate lifetime and aging processes. In fact, epigenetics modulate gene expression without altering the DNA sequence. This is possible by means of different kinds of epigenetic modifications, including DNA methylation and histone modifications (which might affect gene transcription), and noncoding (nc)RNAs (which might change gene expression at the post-transcriptional level)[59]. Given the crucial role of epigenetics in the modulation of gene expression, its alteration can contribute to", + "can regulate gene expression while the underlying DNA sequence remains the same. The epigenome is influenced both by underlying genetic variants as well as by environ- mental factors including the social environment, health behaviors, and environmental pollutants [ 11]. Methylation of CpG dinucleotides, the best understood epigenetic mechanism, is also dynamic over the life course. It is well established that epigenomic patterns of DNA methylation change with age [ 12]. A recent study in lymphocytes", + "Epigenetics Changes arising from alterations in gene expression levels that are caused by reversible chemical modification of DNA, but not changes to the DNA sequence passed on from parents to offspring.", + "Epigenetic changes refer to heritable changes in gene expression which do not involve changes in DNA sequences. Several epigenetic mechanisms have been found to regulate gene expression. Whilst the most studied mechanism relates to DNA methylation, other changes, including histone modi cations and non-coding RNAs, also play an important role, and can be transmitted from one generation to the next. DNA methylation involves the addition of methyl groups to DNA, mainly at CpG sites, which converts cytosine", + "EPIGENETIC STUDIES An epigenetic mechanism is a biochemical alteration to the DNA molecule that does not change the sequence of the DNA but does in uence gene expression. Epigenetics is often de ned as the study of mitotically and/or meiotically heri- table changes in gene function that cannot be explained by changes in DNA sequence (Russo, Martienssen, & Riggs, 1996, p. 1). The epigenetic/epigenomic approach shares many advantages and disad-", + "ity and expression of genes without changing their DNA sequence [ 4]. These modications are: DNA methylation, histone modications, and ncRNAs including miRNA [4]. The en- vironment and lifestyle can induce epigenetic changes, such as pollution, tobacco smoking, obesity, lack of physical activity, and alcohol consumption [ 108]. Furthermore, exposure to such environmental factors can have a buttery effect: epigenetic modications may", + "epigenetics is the study of mitotically heritable alterations in gene expression potential that are not caused by changes in DNA sequence (Jaenisch and Bird, 2003 ). Hence, rather than encompassing all of developmental biology, modern epigenetics is focused on understanding the spe-ci c molecular mechanisms that convey cellular memory. Within the nucleus, the mammalian genome is wrapped", + "gene expression can also occur by trans-epigenetics ( Bonasio et al., 2010 ), in which proteins and RNAs inuence gene expres-sion and repression. Stable transcription factor networks are anexample of trans -epigenetics ( Young, 2011 ). Clearly, enzymes that modify DNA and histones (methyltransferases, demethy-lases, acetyltransferases, deacetylases) are central epigeneticregulatory mechanisms ( Rando and Chang, 2009 ). The essence of epigenetics is not only the establishment, but", + "pay attention to epigenetic effects on gene expressionmeaning changes that are heritable but that do not involve any change in DNA sequence (see Rutter 2006). Three key points are relevant. First, genes only have effects when they are expressed. Many genes are expressed in only some body tissues and only at certain phases in development. Second, there are multiple inherited DNA elements that do not code for proteins but yet which have important effects through their in uence on gene expression. We need to", + "genetics of gene expression (i.e. regular genetical genomics) and the genetics of epigenetics could be studied simultaneously, thus revealing genes that directly or indirectly affect epigenetic gene states. An additional issue that could be addressed by such an approach is to estimate the percentage of variation in gene expression that can be explained by different epigenetic conformations." + ], + "answer": "Epigenetics influences gene expression without changing the underlying DNA sequence through several mechanisms. These include: 1. **DNA Methylation**: This involves the addition of methyl groups to DNA, primarily at CpG sites, which can affect gene transcription by converting cytosine to 5-methylcytosine, thereby repressing gene activity [1], [4]. 2. **Histone Modifications**: Chemical modifications to histone proteins, such as methylation, acetylation, phosphorylation, and ubiquitination, can alter the chromatin structure, making genes more or less accessible for transcription [1], [6]. 3. **Non-coding RNAs (ncRNAs)**: These RNAs, including microRNAs (miRNAs), can regulate gene expression at the post-transcriptional level by binding to messenger RNAs (mRNAs) and preventing their translation into proteins [1], [6]. These epigenetic modifications are influenced by both genetic variants and environmental factors such as pollution, tobacco smoking, obesity, lack of physical activity, and alcohol consumption [2], [6]. These changes are heritable and can be passed from one generation to the next without altering the DNA sequence itself [4], [5].", + "question": "How does epigenetics inluence gene expression without changing the underlying DNA sequence?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_20 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_20 new file mode 100644 index 0000000..7055f47 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_20 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2007 - Quantitative genetics of age-related retinal degeneration a second F1 intercross between the AJ and C57BL6 strains.pdf", + "2015 - Basic Concepts and Potential Applications of Genetics and Genomics for Cardiovascular and Stroke Clinicians.pdf", + "2007 - Prenatal nicotine exposure alters gene expression in a sexually dimorphic manner.pdf", + "2009 - Experimental_Evolution.pdf", + "2009 - Garland_and_Rose_Experimental_Evolution.pdf", + "2009 - Experimental_Evolution.pdf", + "2009 - Garland_and_Rose_Experimental_Evolution.pdf", + "2022 - Genetic and genomic architecture in eight strains of the laboratory opossum.pdf", + "2012 - Needs Analysis of Genetics and Genomics in Communication Sciences and Disorders.pdf", + "2017 - Primer in Genetics and Genomics, Article 1 DNA, Genes, and Chromosomes.pdf" + ], + "extraction_id": [ + "749877a1-0114-5bcd-8a5b-3b944012f5c9", + "34fa36d0-0b64-5c70-8645-ba3576d9262c", + "061d1490-4ce6-5f60-bdf8-15e8d863baf6", + "29e674a2-7ec9-5e00-9db3-308b112e439f", + "2f77d356-4cca-595c-912a-099efcc8b797", + "29e674a2-7ec9-5e00-9db3-308b112e439f", + "2f77d356-4cca-595c-912a-099efcc8b797", + "5afcc18d-5385-5d5e-8683-dd38f86131e7", + "10a507d1-60ca-5dae-9e49-4a6bace53668", + "89acea57-5c8a-55a6-90cf-ad11e5d527b6" + ], + "document_id": [ + "f41cf6ad-273a-571a-866e-46b3dd407731", + "8610e699-218a-50e6-8d1d-ef689623266f", + "036efa18-a4b0-51bf-99d6-7c65193ccfed", + "34821353-1b74-5ee2-ac39-66dd46f145bf", + "496faa7f-9623-5ab7-9816-7c3755abb3aa", + "34821353-1b74-5ee2-ac39-66dd46f145bf", + "496faa7f-9623-5ab7-9816-7c3755abb3aa", + "f09eaa22-afb8-5bf7-90d3-4703056c18c5", + "c8a76cb1-506d-57e4-a18e-548e777898e2", + "b30c111b-1ca2-5f0a-93f3-862aa733fcad" + ], + "id": [ + "chatcmpl-ADZBAENbLHFzwNSyDkvHF2ndPXSYM", + "45fd59f1-baa6-54b9-bfd6-9ba7ad122b86", + "e761426e-5f1d-5add-be86-bd6060d75ca7", + "748b07c1-c80f-5a4f-b295-9726493a698f", + "4e99669a-96cc-5269-a463-ff13337c56c3", + "9c00e371-7349-5ff0-8469-ffd95dd58e57", + "3cf13ae8-6c1c-5ddb-a719-81340d1c8ef6", + "27608ea2-c234-56f5-ad58-01fb67362130", + "c171e03f-4baf-5a0c-b961-401be867d691", + "e625cca4-7b62-5adf-b94e-1fdecc8e143c", + "03b1323c-d449-55fe-966e-d4925246b013" + ], + "contexts": [ + "that accounts for the significant difference. One explanationis a contribution of the Y chromosome from the B strain. Sincethe cross was non-reciprocal all F2 mice carried the B strain Ychromosome. Thus, males carrying Chr X B QTL alleles andthe B Y chromosome differ in two ways from females carry-ing Chr X A alleles (or AB but B alleles are recessive) and noY chromosome, but in only one way from males carrying ChrX A/J QTL alleles because they share the B Y chromosome.However, pursuit of the identity of", + "women comprises 2 X chromosomes and in men 1 X and 1 Y chromosome (Figure 2). For each chromosome pair, 1 chro- mosome was inherited from the mother and 1 from the father. The full set of chromosomes is collectively called the genome. The human genome is largely contained within the nucleus of each cell, where it is separated from the rest of the cell functions. However, a small amount of DNA exists outside the nucleus in the mitochondria and is considered to be part of the human genome.", + "betweenmalesandfemalesisthesexchromosomes.MaleshaveanXYgenotypeand femaleshaveanXXgenotype.TheXisamuchlargerchromosome,165.5x106bpsvs. 16.0x106bps,withapproximately30timesmoregenesthantheYchromosome.To compensateforthelargernumberofgenes,andtoensurefemalesdonothaveover expressionofgenesresidingontheXchromosome,oneoftheXchromosomesis inactivated(7).TheXinactivationoccursearlyindevelopmentandisarandomprocess. Onlyasmallportionoftheinactivatedchromosomeretainstranscriptionalability.This", + "mammals. Instead of a dominant gene for maleness on the Y chromosome, it is the ratioof X chromosomes to autosomes that determines gender. The 2:2 ratio of XX femalesand the 1:2 ratio in XY males produce different ratios of regulatory proteins encoded byX-linked and autosomal genes. Those regulatory genes in turn cause transcripts of theregulatory Sex-lethal (Sxl) gene to be spliced differently in males and females, which be-", + "mammals. Instead of a dominant gene for maleness on the Y chromosome, it is the ratioof X chromosomes to autosomes that determines gender. The 2:2 ratio of XX femalesand the 1:2 ratio in XY males produce different ratios of regulatory proteins encoded byX-linked and autosomal genes. Those regulatory genes in turn cause transcripts of theregulatory Sex-lethal (Sxl) gene to be spliced differently in males and females, which be-", + "gins the process of sexual differentiation. A fly with two X chromosomes can thereforecarry a Y and still be a fertile female, leading to a paradoxical sex chromosome system inwhich males inherit X chromosomes from their fathers (figure 16.13). Rice and Chippindale (2001) used a combination of these genetic techniques to test", + "gins the process of sexual differentiation. A fly with two X chromosomes can thereforecarry a Y and still be a fertile female, leading to a paradoxical sex chromosome system inwhich males inherit X chromosomes from their fathers (figure 16.13). Rice and Chippindale (2001) used a combination of these genetic techniques to test", + "ity on the X chromosome compared to the other five strains(Figure 2B ). Compared to females, males had a deficiency of heterozygous X-linked SNP loci ( Supplementary Figure S2 ), which was expected because males are hemizygous. The resid-ual X-linked heterozygous SNPs in males could be due to mis-assembled autosomal contigs on the X chromosome, multiplecopies on the X, or homology between X and autosomalsequences. Chromosome XAutosomesProportion of SNP lociHomozygous SNPs Heterozygous SNPs", + "sex chromosome Y chromosome: One of the two sex chromosomes, X and Y. See also; X chromosome, sex chromosome", + "one Y chromosome. Human chromosomes are typically displayed pictorially in a karyotype, as shown in Figure 9, arranged according to length and position of the centromere (i.e., the most con-stricted area of a chromosome). The ends of the chromosomesare called telomeres. Most human karyotypes look identicalbecause they are constructed from cells arrested in the phaseof the cell cycle when chromosomes are most condensed. During this phase of the cell cyc le, allelic differences cannot be detected." + ], + "answer": "The query appears to be based on a misunderstanding. Males do not have two Y chromosomes; they have one X and one Y chromosome, while females have two X chromosomes. This is clearly explained in the provided context: - Males have an XY genotype, meaning they have one X chromosome and one Y chromosome [3]. - Females have an XX genotype, meaning they have two X chromosomes [3]. Additionally, it is noted that for each chromosome pair, one chromosome is inherited from the mother and one from the father [2]. Therefore, males inherit their Y chromosome from their father and their X chromosome from their mother, while females inherit one X chromosome from each parent.", + "question": "Why do males have two Y chromosomes and females only one?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_21 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_21 new file mode 100644 index 0000000..2e96cf2 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_21 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2009 - eQTL analysis in mice and rats.pdf", + "2015 - Genetic Control of Survival and Weight Loss during Pneumonic Burk.pdf", + "2015 -Emery- Genetic Control of Survival and Weight Loss during Pneumonic Burk.pdf", + "2005 - quantitative-trait-analysis-in-the-investigation-of-function-and.pdf", + "2006 - From_gene_to_behavior_and_back_again_new.pdf", + "2005 - Gene Expression Differences in Mice.pdf", + "2008 - Using gene expression databases for classical trait QTL candidate gene discovery in the BXD recombinant inbred genetic reference population Mouse forebrain weight.pdf", + "2005 - quantitative-trait-locus-analysis-of-aggressive-behaviours-in-mi.pdf", + "2005 -Broadkin- quantitative-trait-locus-analysis-of-aggressive-behaviours-in-mi.pdf", + "2005 -Knott- Regression based QTL mapping.pdf" + ], + "extraction_id": [ + "71981bfb-284e-50ad-854e-2055c07f77a7", + "615ee0cd-5960-57e5-b4e6-56e4b8020a1b", + "268a23e8-f528-5b59-89f2-188331e0a03c", + "0a895880-91c0-5079-b258-73926b38430f", + "64c0287d-aeea-52eb-a074-e9591c5593ae", + "2ee9945a-e33c-5303-84f6-6bb4fec529ea", + "dbf6a85f-6ae5-54da-87e4-8c2c70c2b37d", + "9de93371-6239-53c2-b42c-71f615a0614b", + "0a5c759e-8dab-55f1-ac59-e8211ec683b8", + "a4a2e963-3b9b-576e-885a-d5e757a6ce8c" + ], + "document_id": [ + "8d67ea90-f7b1-5bb8-937c-4a9eceddff43", + "ae1025b0-1410-51ae-9be2-26fa2e9d5808", + "a9aceace-bf48-5472-b54c-59a458a84c62", + "dac1c73c-0b5f-5a54-bb12-7e8b654009c0", + "7a088b36-11b7-5379-bfe5-ce571e11de07", + "47abbcce-503c-552f-a02e-bf2f31fd1d8a", + "d2dc6644-2feb-5d2b-8ec7-436fc9e449b6", + "0dc730ba-4ff4-52aa-a988-71075113c416", + "e6027e7f-aec0-5e76-8aff-96b36389e701", + "cd41c63b-e5c2-5040-bbc5-ab20925b7d17" + ], + "id": [ + "chatcmpl-ADZBER3gC3GniJPKr4d0S0Jc8x850", + "73540700-b5cf-5838-852b-b281ca086140", + "374c456a-d1db-5b4a-8713-97abe4162d77", + "b9d52798-0235-5018-bccd-560565d16cc3", + "b660d882-1cb0-5150-ae76-8eb3ccb88a58", + "fef212bc-631b-591d-b8e3-d1523da0507d", + "60643722-3d4e-571c-97e9-3b5c67670ca0", + "e9424ae3-c15b-5b96-aa5f-fe0865f4b2fd", + "c8f17022-aeae-5242-9082-d6d1eee4c4bf", + "1b2de424-be9f-572d-bd62-dc2ecd92192b", + "1c584e4b-db8b-5f00-ad8b-d43702b65f22" + ], + "contexts": [ + "While most of the Y chromosome does not undergo recombination, the recombination rate of the X chromosomeis slower than that of the autosomes. This has important consequences on the detection of significant QTLs. For a comprehensive view of these issues, see(43). 9.Probe hybridization artifacts When several probes are available for the same gene, it is not uncommon to observe a difference in the mapping results", + "8 QTL Mapping Allelic variation exists among natural populations and inbred strains, and this is reflective of the segregation of quantitative tr ait loci (QTLs) [96]. QTLs are stretches of DNA that are closely linked to genes that underlie a phenotype of interest. QTL analysis has been proven to be an invaluable tool to help unravel heritable traits, by enabling researchers to map different quantitative traits back to the genomic location involved in the regulation of these phenotypes.", + "8 QTL Mapping Allelic variation exists among natural populations and inbred strains, and this is reflective of the segregation of quantitative tr ait loci (QTLs) [96]. QTLs are stretches of DNA that are closely linked to genes that underlie a phenotype of interest. QTL analysis has been proven to be an invaluable tool to help unravel heritable traits, by enabling researchers to map different quantitative traits back to the genomic location involved in the regulation of these phenotypes.", + "genetic background. Gene identification of QTL should be distinguished from identification of the quanti- tative trait nucleotide (QTN). The latter is a daunting task, since SNPs are so frequent. Final proof for a QTN in mice would require placing a genomic segment containing theputative QTN from a donor mouse strain on the background of another strain using homologous recombination and reproducing the phenotype of the donor strain.", + "The basic pr emise of QTL an alysis is simple (Ph illips and Belknap, 2002 ) . First, one must meas ure a speci c phen otype within a popul ation. Next, the population must be genotyped at a hundred or more marker loci186 Boehm II et al.", + "verify the difference, and the data were then ana-lyzed by the QTL detection method of Belknap et al.(1997) based on allele frequency differences betweenthe two lines. When a difference was confirmed,individual genotypes and individual behavioral re-sponses to MA were used to estimate the position ofthe bQTL using the interval mapping methods as implemented in R/qtl (Broman et al. 2003). The lat-", + "X axis depicts 19 autosomes and X chromoso me. The Y axis is the likelihood ratio statistic from a single QTL model. Two QTLs, on chromosomes 1 and 11, are significant at a mu ltiple test corrected permut ation threshold as shown. Chromosome 1 and 11 likeli hood ratio statistic plots Figure 2 Chromosome 1 and 11 likelih ood ratio statistic plots . Interval mapping plots of chromosomes 1 and 11, showing more detail of Figure 1. 2 LOD support inte rvals are shown in Mb on the X axis.", + "genes underlying QTLs in animals and plants (see for example Shirley et al 2004,Korstanje & Paigen 2002, Fridman et al 2004). I should also point out, though, that even in a single QTL region isolated in a congenic strain, it is possible that there is more than one allele that aects the phenotype. So, you have a fair pointabout the challenges and complexities of QTL analysis. Koolhaas: There are dierent questions underlying both approaches. The QTL", + "genes underlying QTLs in animals and plants (see for example Shirley et al 2004,Korstanje & Paigen 2002, Fridman et al 2004). I should also point out, though, that even in a single QTL region isolated in a congenic strain, it is possible that there is more than one allele that aects the phenotype. So, you have a fair pointabout the challenges and complexities of QTL analysis. Koolhaas: There are dierent questions underlying both approaches. The QTL", + "model at the QTL assumes that the original lines arexed for different alleles although genes can besegregating elsewhere. Hence, it is possible to combineinformation about the QTL across families. The assumption of xation at the QTL can be tested by" + ], + "answer": "To differentiate between X and Y DNA with respect to DNA tracing and determining quantitative trait loci (QTLs), consider the following points: 1. **Recombination Rates**: - The Y chromosome largely does not undergo recombination, which means it is passed down relatively unchanged from father to son. This makes it useful for tracing paternal lineage [1]. - The X chromosome, on the other hand, does undergo recombination, but at a slower rate compared to autosomes. This slower recombination rate has significant implications for the detection of QTLs on the X chromosome [1]. 2. **QTL Mapping**: - QTLs are stretches of DNA closely linked to genes that underlie specific phenotypes. The process of QTL mapping involves identifying these regions to understand the genetic basis of heritable traits [2]. - The detection of QTLs on the X chromosome may be influenced by its slower recombination rate, which can affect the resolution and accuracy of QTL mapping compared to autosomes [1]. In summary, the primary differences between X and Y DNA in the context of DNA tracing and QTL determination lie in their recombination behaviors and how these behaviors impact the detection and mapping of QTLs. The Y chromosome's lack of recombination makes it a stable marker for paternal lineage tracing, while the X chromosome's slower recombination rate affects the detection of significant QTLs [1], [2].", + "question": "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_22 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_22 new file mode 100644 index 0000000..abd1498 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_22 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2008 - Study Design and Statistical Issues.pdf", + "2009 - Opening Up the Conversation on Genetics.pdf", + "2015 - Constraint and divergence of global gene expression in the mammalian embryo.pdf", + "2015 - Constraint and divergence of global gene expression in the mammalian embryo.pdf", + "2015 - Basic Concepts and Potential Applications of Genetics and Genomics for Cardiovascular and Stroke Clinicians.pdf", + "2008 - Genotype-phenotype relationships and the patterning of complex traits as exemplified in the mammalian dentition.pdf", + "2019 - Sexual Dimorphism in the Age of Genomics How, When, Where.pdf", + "2007 - Promoting_Student_Scientific_Literacy_of_Molecular Genetics and Genomics.pdf", + "2015 - Basic Concepts and Potential Applications of Genetics and Genomics for Cardiovascular and Stroke Clinicians.pdf", + "2019 - The influence of paternal diet on sncRNA-mediated epigenetic.pdf" + ], + "extraction_id": [ + "06bf0605-388a-592c-96ad-3a53bb36362c", + "51dbd5e2-fde6-5097-aa05-fcf57d3ca6b1", + "261c4af7-f63d-51ac-b164-0d9e7a64bff9", + "261c4af7-f63d-51ac-b164-0d9e7a64bff9", + "8a1ce8fa-b5f4-5942-b7b1-14a8a7887710", + "5aab3e60-b8b0-52ad-b4d3-817cf012cfa5", + "e22bb6fb-bec4-5c4c-8690-c96d0b8d13d4", + "67369433-749b-5d6a-b5ef-3f0afe78b767", + "206b8810-b7c1-5195-a10f-4e291864b77c", + "84335575-34d7-56b6-aa06-5a8ac13d637a" + ], + "document_id": [ + "c3bd9cf0-f768-55c4-be94-96590d7acc21", + "b62a8f54-c2f5-5bbb-9324-af80f7537167", + "3d9005f1-8f71-5d39-8749-4ebeab962cab", + "3d9005f1-8f71-5d39-8749-4ebeab962cab", + "8610e699-218a-50e6-8d1d-ef689623266f", + "f6e866b8-b233-5862-bfb8-9949d0dabb97", + "3f8c03b0-4235-5774-9d26-e43d55c1001b", + "755f34c4-cc06-5275-a744-16d48162b012", + "8610e699-218a-50e6-8d1d-ef689623266f", + "dfcbd6e6-f60d-5eb7-867b-34ec78415e82" + ], + "id": [ + "chatcmpl-ADZBKlm7c8ZE1GCPmwcK7X0HEcljf", + "1e151ad5-59d9-598d-97ba-90ba0e64c4cb", + "60ad1512-b0c0-59cd-ace4-c146e2c04b52", + "a66b8b00-d51c-575b-b6ac-fa445c4ca715", + "df4c6108-740d-5bcf-99e6-dbda74f7e41a", + "4472740a-d22d-5bb1-98e3-e91332cbb303", + "47b9142f-98a3-5a45-8eaa-d327c9cc055d", + "0158f264-120f-5942-ad55-ef5fde1f188a", + "8e3fdc2c-0962-5854-83e7-a60ab05cf6de", + "6c8dfaa1-a96f-5f1c-8b5a-870acfd46f5f", + "be93ee68-72ae-5015-a3f0-19e7bf24827a" + ], + "contexts": [ + "phenomena such as mutations and gene conversion events) occur in relevant meioses leading up to the formation of the gametes (i.e., egg and sperm) which are combined during fertilization and the formation of zygotes. Thus, individuals inherit a patch- work of chromosomal segments from maternal and paternal chromosomes.", + "the egg and the sperm. Such a process would result in genetic changes that will be copied into every cell of the future adult, including reproductive cells (Stock & Campbell, 2000), opening the door to irreversibly alter the human species. Inevitably, signifi cant self-disclosure and discussion challenges await families", + "a fertilized egg is a complicated process that relies on controlling: which genes are active; whenthese genes activate; and for how long they are active. In broad terms, there are four ways that thiscontrol can be achieved: First, inside the sperm or egg, genes can be marked with small chemical tags that flag these genes", + "to be activated (or remain inactive) after fertilization, depending on whether the modification wasmade by the father (in the sperm) or the mother (in the egg); this process is known as imprinting. Second, the mother can alter the gene activity in her offspring via the placenta; this process is known as maternal effect. Third, instructions encoded within the embryos DNA can directly control if, andwhen, a nearby gene becomes activated; this is known as cis-regulation. Finally, similar instructions", + "(Figures 8 and 9). Two gametes (egg and sperm) ultimately join into a single cell, the zygote, which has the full comple-ment of 23 chromosome pairs restored. If all goes well, the zygote gives rise to a live offspring. The Mendel Laws: Segregation and Independent Assortment Both of the Mendel laws pertain directly to the process of meiosis. The first Mendel law, the law of segregation, states that each parent passes a randomly selected allele for a given", + "the subset of that genetic information that is active. But how does the differentiation process begin? The key insight in resolving this conundrum came from fly genetics and was the realization that the egg is not a homogenous sack of protoplasm. The maternally-derived genes active in the fertilized egg are asymmetrically distributed such that at the first cell division each daughter cell receives a different complement of factors. Development continues as a", + "sex chromosome effects. (B)Soon after fertilization, male and female cells have sex-specic transcriptomes, epigenomes, and phenotypes (for example, male embryos grow faster than female embryos). At implantation, lineage determination begins and gene expression differences are reduced. Epigenetic marks, however, are less constrained and some are maintained, affecting gene expression, and phenotype later in development. Once specic lineages are established, differences in", + "genes. An altered gene may be passed on to every cell that develops from it. The resulting features my help, harm, or have little or no effect on the offsprings success in its environment. (AAAS, pg. 109, 5B:9-12#4 ) 6. Heritable material: The information passed from parents to offspring is coded in DNA molecules (AAAS, pg 108, 5B:9-12#3) 7. Mutagens: Gene mutations can be caused by such things as radiation and chemicals. When they occur in sex cells, the mutations can be passed onto offspring; if they", + "or father (sperm cell). Each gamete has a set of 23 unpaired chromosomes. Two human gametes (egg and sperm) combine to create a cell (zygote) that contains the full human genome of 23 paired chromosomes.Genetic Information Nondiscrimination Act (GINA) US federal legislation that makes it unlawful to discriminate against individuals on the basis of their genetic profiles in regard to health insurance and employment. These protections are intended to encourage Americans to take advantage of", + "spermatozoa: more than the sum of its parts? DNA, histones, pro - tamines and epigenetics. Reproduction 139:287301 Nilsson EE, Sadler-Riggleman I, Skinner MK (2018) Environmentally induced epigenetic transgenerational inheritance of disease. Envi-ron Epigenet 4:dvy016Pembrey M, Saffery R, Bygren LO, Network in Epigenetic Epide-" + ], + "answer": "Once a sperm combines with an egg, the traits passed onto the resulting lifeform are determined by several factors: 1. **Chromosomal Segments from Parents**: Individuals inherit a patchwork of chromosomal segments from both maternal and paternal chromosomes [1]. 2. **Mendelian Laws**: The Mendel laws of segregation and independent assortment play a crucial role. The law of segregation states that each parent passes a randomly selected allele for a given trait to their offspring [5]. 3. **Gene Activation and Regulation**: The process involves controlling which genes are active, when they activate, and for how long. This control can be achieved through mechanisms such as imprinting, maternal effect, cis-regulation, and other instructions encoded within the embryo's DNA [3], [4]. 4. **Epigenetic Marks**: Epigenetic marks, which are chemical modifications to DNA or histones, can affect gene expression and phenotype later in development [7]. These combined genetic and epigenetic factors determine how traits are passed onto and expressed in the resulting lifeform.", + "question": "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_23 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_23 new file mode 100644 index 0000000..f1c323c --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_23 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2012 - Using Genome-Wide Expression Profiling to Define Gene Networks Relevant to the Study of Complex Traits From RNA Integrity to Network Topology.pdf", + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2020 - Phylogenetic tree building.pdf", + "2011 - Peroxisomal L-bifunctional enzyme (Ehhadh) is essential for the production of medium-chain dicarboxylic acids.pdf", + "2018 - Invited review Genetic and genomic_ xmltexbreak_ mouse models for livestock research.pdf", + "2013 - Pathogenesis and reversal of liver fibrosis Effects of genes and environment.pdf", + "2022 - Systems genetics in the rat HXBBXH family identifies Tti2 as a pleiotropic quantitative trait gene for adult hippocampal neurogenesis and serum glucose.pdf", + "2022 -Senko- System Genetics in the Rat HXB\uf022BXH Family.pdf", + "2022 -Senko- Hippocampal neurogenesis serum glucose.pdf" + ], + "extraction_id": [ + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "3e0c2a06-e6de-5888-a360-a2c483d9f744", + "f7e3761d-1baa-573a-9cbd-4070a400c42e", + "e697c9f2-c175-5e85-9a7a-03bf5ef921b7", + "a8b40857-7ae8-512a-9817-bea1ae3345ba", + "6983f2dd-b440-5696-92a1-84f4c332834b", + "71f1aefb-6e32-5add-804e-6fbaa39ca720", + "c6be3dd3-f076-54bd-b1fb-04678962a817", + "065807db-909e-5654-8dd7-5652f07be29c", + "7fe8fbaa-b950-5553-940b-317f306efb4b" + ], + "document_id": [ + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "1eb6f5b7-a3bc-5455-91f0-6f2eb37be861", + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "7310a5bc-2fc1-5fa0-ad1d-f3411830062b", + "ac61753e-bcb2-55c3-804b-e821e3d1a4ad", + "5b167564-85a2-5886-b800-37932c3143a9", + "73724c99-98df-53b2-a378-29c8b4faa171", + "e6323aba-6fec-500b-99e3-a41c2e7f17ff", + "c67a6829-954a-5202-85fb-7524b03fab28", + "bac2ab98-4317-59ed-99ef-deda8c22786d" + ], + "id": [ + "chatcmpl-ADZBPsS9vmK32TYjsc1YUUJCtSSmn", + "c63cfaee-749e-547b-9c0a-086266f10670", + "312eae52-ede7-5c13-8974-fce0126426cf", + "2ae780e5-9549-50c0-a260-d7ef774f7956", + "6a443d81-33ed-524c-9f11-318f1013a214", + "8b8a24da-a175-5cb8-91bd-8966fca5d344", + "d5c42ccf-569f-5a50-bd49-6b45097a3d00", + "f5c218f0-1280-55f8-912b-b32b833e93a3", + "ac4f8148-e6da-5d16-9e61-3a1aff2f9c81", + "69a916ea-abe7-5637-81fe-ee5e38f9c68b", + "03d0618c-8ed8-5984-a4eb-e743daf4f1a7" + ], + "contexts": [ + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "addition to this, GeneNetwork can be used to study correlations between traits and to perform data mining in genomic regions containing candidates for quantitative trait genes (Hoffman et al., 2011). All datasets in GeneNetwork are linked to a materials and methods information page that summarizes experimental details relating to the dataset. Databases within GeneNetwork include the transcriptome database, the BXD published", + "publication, and links to the dataset database and to the published paper (4C). There is also an option to add this trait to your collection by pressing the Add button (4D), or to view this trait in an ear lier version of GeneNetwork, GN1 (4E).", + "Bayesian inference of species networks from multilocus sequence data. Mol. Biol. Evol. 35, 504517 (2018). 167. Flouri, T ., Jiao, X., Rannala, B. & Yang, Z. A Bayesian implementation of the multispecies coalescent model with introgression for phylogenomic analysis. Mol. Biol. Evol. 37, 12111223 (2020). 168. Kubatko, L. in Handbook of Statistical Genomics (eds Balding, D., Moltke, I. & Marioni, J.) 219245 (Wiley, 2019). 169. Rannala, B., Edwards, S., Leach, A. D. & Yang, Z.", + "subnetworks GeneNetwork (www.genenetwork.org) is a depository of data- sets and tools for use in complex systems biology approaches in order to generate or predict higher order gene function ( 23, 24 ).", + "on different cross types, such as F 2crosses (B6BTBRF2, B6D2F2, BH/HB F2, CastB6/B6Cast F2, B6JxB6N F2), butalso on more complex outbred crosses such as the HS, the CC, and the Hybrid Mouse Diversity Panel. Recently, data from other species has also been integrated into GeneNet- work (human, rat, monkey, fruit ies, and others) to facilitate the translational research of results into other species. To this end, GeneNetwork provides many tools for the analysis of", + "GeneNetwork (www.genenetwork.org). The web -based software further allows extraction of sets of", + "Phenotypes Database attheGeneNetwork (www.", + "Phenotypes Database attheGeneNetwork (www.", + "Phenotypes Database attheGeneNetwork (www." + ], + "answer": "The provided context does not include specific instructions on how to add a new species to the GeneNetwork database. However, it does mention that data from multiple species have been integrated into GeneNetwork [1], [6]. This suggests that the process is possible, but the exact steps are not detailed in the given excerpts. To add a new species to the GeneNetwork database, you may need to refer to the official GeneNetwork website (www.genenetwork.org) [5], [7], or contact their support team for detailed guidance. Additionally, reviewing the materials and methods information page linked to datasets in GeneNetwork [2] might provide further insights into the process.", + "question": "How can I add a new species to the GeneNetwork database?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_24 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_24 new file mode 100644 index 0000000..e6e78ff --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_24 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2014 - The genetic basis of obesity-associated type 2 diabetes (diabesity) in polygenic mouse models.pdf", + "2006 - Quantitative Trait Loci on Chromosome 8q24.pdf", + "2017 - Genomic regulation of type 2 diabetes endophenotypes Contribution.pdf", + "2015 - A Chromosome 13 locus is associated with male-specific mortality in mice.pdf", + "2008 - Meta-Analysis Approach identifies Candidate Genes and associated Molecular Networks for Type-2 Diabetes Mellitus.pdf", + "2016 - The genetic architecture of type 2 diabetes.pdf", + "1998 - Genetic dissection of ``OLETF_, a rat model for non-insulin-dependent diabetes mellitus.pdf", + "2015 - Transcript Expression Data from Human.pdf", + "2004 - Interaction and Association Analysis of a Type 1 Diabetes Susceptibility Locus.pdf", + "2001 - Genetic Analysis of a New Mouse Model for Non-InsulinDependent Diabetes.pdf" + ], + "extraction_id": [ + "1ab308e3-565f-5d14-86bc-2909dd9a1de0", + "d35d2e8c-0e2f-5be4-a902-18d5c857746d", + "9dfc060c-bf5e-5958-b446-cfc12a4f85c5", + "cc39ccbe-150c-5d7e-8b6b-f6c98738cb95", + "309adb8f-fa42-5806-9e50-95742ba90857", + "8b8b572d-68f5-5470-b5ed-ec5c6219dd5e", + "c29fe565-1167-5821-8715-559cb48f2090", + "b9d039d0-8982-52c6-ba45-be2e2eeda7d5", + "b7586c99-af71-5f11-8fed-fd8395c783b6", + "4cc0bd43-c6a8-55fb-8300-d2228636c89d" + ], + "document_id": [ + "1459a93f-3052-5cea-ba83-caf266ef9b86", + "8c5ffeac-5108-5b03-acd0-57aa09469af5", + "fef1ae33-b3af-50ea-909c-f1b57f7fe981", + "ad8f2626-87fb-520e-8cef-ee9a9cc3ab0b", + "4060609b-1464-55fa-93cd-fefaf2cac900", + "d7e2a9de-46f1-5191-9cb0-dd68eb9f365a", + "0f04bb9f-6d45-5511-a05c-a09f8ee9a5e9", + "2b30d4f3-9ec3-574f-9a36-709b0e09c3f2", + "4246f8d0-69e8-56cf-9674-d379467dfb61", + "c6086f32-0a3a-5a92-9e5b-4d2fa7fbbc93" + ], + "id": [ + "chatcmpl-ADZBUB9zekyDDARKA9rzHsVGglrzJ", + "313e590c-40a4-5adb-a2d8-18577f465b30", + "f5220a71-d1bc-50ae-933a-2b92bab0c4ae", + "569bb9be-0b57-535a-ab0c-206d85f1dd4a", + "f3de711d-7dff-5b13-89c1-720bb6be9e12", + "6299defb-19e0-5f6d-aaea-44b36cdece6e", + "807bf364-408f-50c9-bacd-b9da438a1703", + "410c1b39-1d2a-5954-ac2c-9bf4ad38aa58", + "0ea7a0f3-5fdd-5d9f-8f53-4620492867f7", + "517a8a37-697b-500b-a5e8-7eff80fc0f79", + "af834bd3-8462-5159-99e8-59a2fc1f09c9" + ], + "contexts": [ + "genes that are responsible for obesity-associated diabetes. By the generation of subcongenic lines of a QTL, if pos- sible starting with chromosome substitution strains, thensmall critical regions that harbor the gene(s) in question can be identied with certainty. Sequence analysis and mRNA proling together with gene targeting in-vitro andin-vivo may lead to a solid chain of evidence linking sequence differences with altered molecular, cellular, and", + "tensive nondiabetic families, the QTLs on chromosomes 8q24 and 7q11, which are located in regions previouslyidentied as harboring type 2 diabetesassociated genes,may govern insulin sensitivity and insulin secretion in thepresence of insulin resistance before development of overttype 2 diabetes. Follow-up ne-scale mapping aroundthese loci and well-designed candidate gene studies, inparticular, are strongly encouraged. ACKNOWLEDGMENTS", + "studies used the QTL approach for statistical analysis of genotypes and phenotypes measured in the crosses. The concept of genetic dissection of diabetes into quantitative endophenotypes was introduced and resulted in the detection of genetic loci responsible for the control of fasting glycemia [39,42] , fasting insulinemia [39,43] , glucose tolerance [39,41,42] , insulin secretion induced by glucose or arginine [39], body weight [39,41,44] , adiposity [39], b-", + "indicating that risk factors exist on both genetic back- grounds [ 29]. QTL mapping studies indicate that these murine metabolic traits have a complex genetic architec- ture that is not dominated by any single allele [ 2931], much like humans [ 32,33]. Prior work identied candidate genes on Chr 13 that might underlie diabetes-related traits, including RASA1, Nnt, andPSK1. RASA1 show strong sequence differences between B6 and D2 strains [ 34]. Rasche et al. [ 35] reported that", + "genetic background [4]. Linkage analyses have shown that several quantitative trait loci interact with each other and with the environment to elicit obesity syndromes that are potentially diabetic. Several recent genome-wide associa- tion studies have identified novel candidate genes for T2DM but the effect of these variants on disease suscepti- bility is generally low, with odds ratios mostly around 1.5 [5-11]. Multiple studies on the transcriptome level have been per-", + "(2011). 7. Steinthorsdottir, V. et al. Identification of low-frequency and rare sequence variants associated with elevated or reduced risk of type 2 diabetes. Nat. Genet. 46, 294298 (2014).8. Ma, R. C. et al. Genome-wide association study in a Chinese population identifies a susceptibility locus for type 2 diabetes at 7q32 near PAX4. Diabetologia 56, 12911305 (2013). 9. Huyghe, J. R. et al. Exome array analysis identifies new loci and low-frequency", + "nificant QTL, strongly associated with body weight (Galli et al.1996; Gauguier et al. 1996). Moreover, Gauguier and colleagues(1996) mapped a QTL linked to postprandial insulin secretion intheregionofChr4wherewedetectedasuggestiveQTL.DifferentNIDDM models (obese OLETF rats and lean GK rats) may carryalleles conferring NIDDM susceptibility in the same genes. Thecombined results imply the possibility of common genetic factorsunderlyingNIDDMinhumans,notwithstandingthehighdegreeofgenetic heterogeneity in human", + "data indicates that variants regulating islet gene transcription influence type 2 diabetes(T2D) predisposition and glucose homeostasis. However, the specific genes through whichthese regulatory variants act remain poorly characterized. We generated expression quanti-tative trait locus (eQTL) data in 118 human islet samples using RNA-sequencing and high-density genotyping. We identified fourteen loci at which cis-exon-eQTL signals overlapped", + "linkage analysis assists in the identication of possiblegene-gene interactions and that 5q11-q13 and 7q32together constitute a signicant susceptibility factorfor type 1 diabetes. Diabetes 53:15841591, 2004Type 1 diabetes is a common multifactorial dis- ease characterized by autoimmune destructionof the insulin-producing /H9252-cells in the endocrine pancreas, resulting in deranged metabolic ho-", + "model for common forms of NIDDM in humans associ-ated with obesity. This study identies the location of amajor QTL and additional independent QTLs contrib-uting to development of hyperglycemia in TH malemice. We have also elucidated genegene interactionsbetween QTLs in the development of NIDDM, detect-ing new QTLs that reveal their signicant effects onlywhen they interact with other QTLs. This complexinheritance pattern associated with genegene inter-actions may be of prime importance in" + ], + "answer": "In QTL (Quantitative Trait Loci) analyses, several genes have been identified as being associated with diabetes. Specifically: 1. **RASA1, Nnt, and PSK1** on chromosome 13 have been identified as candidate genes underlying diabetes-related traits [4]. 2. **PAX4** near the locus 7q32 has been identified in a genome-wide association study in a Chinese population as a susceptibility locus for type 2 diabetes [6]. 3. **Genes on chromosomes 8q24 and 7q11** have been associated with insulin sensitivity and insulin secretion, which are critical factors in the development of type 2 diabetes [2]. 4. **Genes on 5q11-q13 and 7q32** have been identified as significant susceptibility factors for type 1 diabetes [9]. These genes and loci are typically associated with diabetes in QTL analyses, highlighting the complex genetic architecture of the disease.", + "question": "which genes are typically associated with diabetes in QTL analyses?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_25 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_25 new file mode 100644 index 0000000..e1d1cea --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_25 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2010 - Candidate Gene and Genome-Wide Association Studies in Behavioral Medicine.pdf", + "2009 - Pathomechanisms of Type 2 Diabetes Genes.pdf", + "2014 - Dorothy Hodgkin Lecture 2014 Understanding genes identified by genome\u2010wide association.pdf", + "2015 - Type 2 Diabetes Mellitus and the Association of Candidate Genes.pdf", + "2012 - Type 2 Diabetes Genetics Beyond GWAS.pdf", + "2015 - Diabetes mellitus The epidemic of the century.pdf", + "2013 - TCF7L2 gene polymorphisms and type 2 diabetes association with diabetic retinopathy and cardiovascular autonomic neuropathy.pdf", + "2007 - A German genome-wide linkage scan for type 2 diabetes supports the existence of a metabolic syndrome locus on chromosome 1p36.13 and a type 2 diabetes locus on chromosome 16p12.pdf", + "2015 - Diabetes mellitus The epidemic of the century.pdf", + "2013 - TCF7L2 gene polymorphisms and type 2 diabetes association with diabetic retinopathy and cardiovascular autonomic neuropathy.pdf" + ], + "extraction_id": [ + "f7bbf3dd-a50c-59b5-9d67-721d4d14e744", + "eff1d167-9689-5c26-9a12-c66714696d86", + "86253f12-bb43-5236-bfb1-df5dff759f6d", + "5ffb710d-ca19-5415-bbb6-34b3f85bf47f", + "a3a875fa-e55b-52d0-b9bf-72b96330c393", + "36f9d4f2-293e-53e3-8b4b-12571af6669a", + "cdb798b2-60ec-5821-8fe5-3de463595e4d", + "198c5f2d-fc43-5744-9cd8-4222c8fa8ab8", + "7cfe9f29-a0ee-56d3-be3b-1b238a43bc07", + "b1e38adc-ccd8-5607-a43c-f3e4a4f5eafc" + ], + "document_id": [ + "17637a6f-804e-50e4-9cf5-37318e17f15c", + "cf8ec75c-8ffe-5baa-830d-ac7a4a5964bd", + "11d0cb98-a00f-53f1-92e3-e1be17002c02", + "3236fdee-e304-5b88-921f-52e52dc5afa3", + "d59a38d7-889b-51b5-b896-c305c82a2169", + "e114dd28-fd39-56df-bdeb-8806474a6c10", + "89080fac-5530-5601-9822-9e1ba6defb29", + "f8a85060-6303-5228-ba89-7ee8701bda9f", + "e114dd28-fd39-56df-bdeb-8806474a6c10", + "89080fac-5530-5601-9822-9e1ba6defb29" + ], + "id": [ + "chatcmpl-ADZBZNwu6nCnZNX1afjr9dVRs9c7K", + "0cc14865-601a-5668-9dfa-b5131c05ce91", + "3e678e3c-ad2c-5884-9c88-7f4d54c914bd", + "5d936c2c-faf7-5b0f-92e1-c3f8f43b3011", + "263f6b22-d314-5653-bbef-3f0e3e09839b", + "1eb3a215-002b-528b-a954-bb9e2419ea6f", + "6617e15c-ab52-596c-b628-60ec5a7001e7", + "b3f5855d-c61f-5bd7-bc12-508198ec48bd", + "05e76af5-c67b-50ca-a06a-a603d6d4b35e", + "ed5d8e9e-859e-5256-a7b5-468c1f7837a2", + "506f1dde-50cc-5f4d-b090-842a48d96f04" + ], + "contexts": [ + "T. I., de Bakker, P . I. et al (2006). TCF7L2", + "single nucleotide polymorphisms in TCF7L2 are reproduc-ibly associated with type 2 diabetes and reduce the insulinresponse to glucose in nondiabetic individuals. Diabetes55:28902895 135. Cauchi S, Meyre D, Dina C, Choquet H, Samson C, Gallina S, Balkau B, Charpentier G, Pattou F, StetsyukV, Scharfmann R, Staels B, Fru hbeck G, Froguel P 2006 Transcription factor TCF7L2 genetic study in the Frenchpopulation: expression in human /H9252-cells and adipose tissue", + "rs7903146 and rs12255372 in intron 3 of the TCF7L2 gene [20], associated with a ~45% increase in Type 2 diabetes risk per allele. As such, the TCF7L2 locus presently repre- sents the strongest known genetic determinant of Type 2diabetes. Risk allele carriers show impaired insulin produc-tion [21] and b-cell dysfunction in vitro [22]. TCF7L2 (previously referred to as TCF-4) is a high-mobility group box-containing transcription factor involved in Wingless-type MMTV integration site (Wnt)", + "et al. Variant of transcription factor 7-like 2 (TCF7L2) gene confers risk of type 2 diabetes. Nat Genet . 2006;38:320-23. Sladek R, Rocheleau G, Rung J, Dina C, Shen L, Serre D, et al. A genome- [9] wide association study identifies novel risk loci for type 2 diabetes. Nature . 2007;445:881-85. Kirchhoff K, Machicao F, Haupt A, Schafer SA, Tschritter O, Staiger H, et al. [10] Polymorphisms in the TCF7L2, CDKAL1 and SLC30A8 genes are associated", + "transcription factor 7-like 2 ( TCF7L2 ) gene confers risk of type 2 diabetes. Nat Genet. 2006; 38:320323. [PubMed: 16415884] 172. Gloyn AL, Noordam K, Willemsen MA, Ellard S, Lam WW, et al. Insights into the biochemical and genetic basis of glucokinase activation from naturally occurring hypoglycemia mutations. Diabetes. 2003; 52:24332440. [PubMed: 12941786] 173. Pearson ER, Donnelly LA, Kimber C, Whitley A, Doney AS, et al. Variation in TCF7L2", + "L. Mechanisms by which common variants in the TCF7L2 gene increase risk of type 2 diabetes. J Clin Invest 2007; 117: 2155-2163 [PMID: 17671651 DOI: 10.1172/JCI30706] 164 Gloyn AL , Braun M, Rorsman P. Type 2 diabetes susceptibility gene TCF7L2 and its role in beta-cell function. Diabetes 2009; 58: 800-802 [PMID: 19336690 DOI: 10.2337/db09-0099] 165 da Silva Xavier G , Loder MK, McDonald A, Tarasov AI, Carzaniga R, Kronenberger K, Barg S, Rutter GA. TCF7L2 regulates late", + "tion. Although the disease progression results from aninterplay of environmental factors and genetic predisposi- tion, in recent years TCF7L2 gene has been considered the strongest genetic determinant for the risk of developingT2DM [ 24,19,20]. The gene encodes a transcription factor of the canonical Wnt signaling pathway, expressed in several tissues, known to have developmental roles indetermining cell fate, survival, proliferation and movement [9]. Wnt signaling plays an important role also in B-cell", + "transcription factor 7-like 2 (TCF7L2) gene confers risk of type 2diabetes. Nat Genet 38:320 3231422 Diabetologia (2007) 50:1418 1422", + "genes which also play a significant role in the risk and pathogenesis of the disease[158,159]. The association of TCF7L2 gene variants with type 2 diabetes and its mechanism of action received special attention by several investigators[161,162]. Over expression of the protein was shown to decrease the sensitivity of beta islet cells to secrete insulin[163,164] and was more precisely involved in the regulation of secretary granule fusion that constitute a late event in insulin secretion", + "Muggeo M, Stoico V, Negri C, Pignatti PF, Bonora E, Bonadonna RC (2011) Variants and haplotypes of TCF7L2 are associatedwithb-cell function in patients with newly diagnosed type 2 diabetes: the Verona Newly Diagnosed Type 2 Diabetes Study (VNDS) 1. J Clin Endocrinol Metab 96(2):E389E393 13. Grundy SM, Cleeman JI, Merz CN, Brewer HB Jr, Clark LT, Hunninghake DB, Pasternak RC, Smith SC Jr, Stone NJ, National Heart, Lung, and Blood Institute, American College of Cardiol-" + ], + "answer": "The gene TCF7L2 is primarily involved in Type 2 diabetes. This association is well-documented across multiple sources: 1. Single nucleotide polymorphisms in TCF7L2 are reproducibly associated with Type 2 diabetes and reduce the insulin response to glucose in nondiabetic individuals [2]. 2. The TCF7L2 locus represents the strongest known genetic determinant of Type 2 diabetes, with risk allele carriers showing impaired insulin production and beta-cell dysfunction [3]. 3. Variants of the TCF7L2 gene confer risk of Type 2 diabetes [4], [5]. 4. TCF7L2 is considered the strongest genetic determinant for the risk of developing Type 2 diabetes [7]. 5. Overexpression of TCF7L2 protein decreases the sensitivity of beta islet cells to secrete insulin, which is involved in the regulation of insulin secretion [9]. Thus, the gene TCF7L2 is significantly involved in Type 2 diabetes.", + "question": "In which diseases is the gene TCF7L2 involved?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_26 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_26 new file mode 100644 index 0000000..1ac619c --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_26 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2008 - Study Design and Statistical Issues.pdf", + "2009 - Opening Up the Conversation on Genetics.pdf", + "2015 - Constraint and divergence of global gene expression in the mammalian embryo.pdf", + "2015 - Basic Concepts and Potential Applications of Genetics and Genomics for Cardiovascular and Stroke Clinicians.pdf", + "2015 - Constraint and divergence of global gene expression in the mammalian embryo.pdf", + "2008 - Genotype-phenotype relationships and the patterning of complex traits as exemplified in the mammalian dentition.pdf", + "2007 - Promoting_Student_Scientific_Literacy_of_Molecular Genetics and Genomics.pdf", + "2019 - Sexual Dimorphism in the Age of Genomics How, When, Where.pdf", + "2015 - Basic Concepts and Potential Applications of Genetics and Genomics for Cardiovascular and Stroke Clinicians.pdf", + "2008 - Genotype-phenotype relationships and the patterning of complex traits as exemplified in the mammalian dentition.pdf" + ], + "extraction_id": [ + "06bf0605-388a-592c-96ad-3a53bb36362c", + "51dbd5e2-fde6-5097-aa05-fcf57d3ca6b1", + "261c4af7-f63d-51ac-b164-0d9e7a64bff9", + "8a1ce8fa-b5f4-5942-b7b1-14a8a7887710", + "261c4af7-f63d-51ac-b164-0d9e7a64bff9", + "5aab3e60-b8b0-52ad-b4d3-817cf012cfa5", + "67369433-749b-5d6a-b5ef-3f0afe78b767", + "e22bb6fb-bec4-5c4c-8690-c96d0b8d13d4", + "206b8810-b7c1-5195-a10f-4e291864b77c", + "5aab3e60-b8b0-52ad-b4d3-817cf012cfa5" + ], + "document_id": [ + "c3bd9cf0-f768-55c4-be94-96590d7acc21", + "b62a8f54-c2f5-5bbb-9324-af80f7537167", + "3d9005f1-8f71-5d39-8749-4ebeab962cab", + "8610e699-218a-50e6-8d1d-ef689623266f", + "3d9005f1-8f71-5d39-8749-4ebeab962cab", + "f6e866b8-b233-5862-bfb8-9949d0dabb97", + "755f34c4-cc06-5275-a744-16d48162b012", + "3f8c03b0-4235-5774-9d26-e43d55c1001b", + "8610e699-218a-50e6-8d1d-ef689623266f", + "f6e866b8-b233-5862-bfb8-9949d0dabb97" + ], + "id": [ + "chatcmpl-ADZBd1UUBp1PO5G8iYcYCk8wkpCF7", + "1e151ad5-59d9-598d-97ba-90ba0e64c4cb", + "60ad1512-b0c0-59cd-ace4-c146e2c04b52", + "a66b8b00-d51c-575b-b6ac-fa445c4ca715", + "4472740a-d22d-5bb1-98e3-e91332cbb303", + "df4c6108-740d-5bcf-99e6-dbda74f7e41a", + "47b9142f-98a3-5a45-8eaa-d327c9cc055d", + "8e3fdc2c-0962-5854-83e7-a60ab05cf6de", + "0158f264-120f-5942-ad55-ef5fde1f188a", + "6c8dfaa1-a96f-5f1c-8b5a-870acfd46f5f", + "c3562719-52f3-50de-8e92-82f64e5c5b05" + ], + "contexts": [ + "phenomena such as mutations and gene conversion events) occur in relevant meioses leading up to the formation of the gametes (i.e., egg and sperm) which are combined during fertilization and the formation of zygotes. Thus, individuals inherit a patch- work of chromosomal segments from maternal and paternal chromosomes.", + "the egg and the sperm. Such a process would result in genetic changes that will be copied into every cell of the future adult, including reproductive cells (Stock & Campbell, 2000), opening the door to irreversibly alter the human species. Inevitably, signifi cant self-disclosure and discussion challenges await families", + "a fertilized egg is a complicated process that relies on controlling: which genes are active; whenthese genes activate; and for how long they are active. In broad terms, there are four ways that thiscontrol can be achieved: First, inside the sperm or egg, genes can be marked with small chemical tags that flag these genes", + "(Figures 8 and 9). Two gametes (egg and sperm) ultimately join into a single cell, the zygote, which has the full comple-ment of 23 chromosome pairs restored. If all goes well, the zygote gives rise to a live offspring. The Mendel Laws: Segregation and Independent Assortment Both of the Mendel laws pertain directly to the process of meiosis. The first Mendel law, the law of segregation, states that each parent passes a randomly selected allele for a given", + "to be activated (or remain inactive) after fertilization, depending on whether the modification wasmade by the father (in the sperm) or the mother (in the egg); this process is known as imprinting. Second, the mother can alter the gene activity in her offspring via the placenta; this process is known as maternal effect. Third, instructions encoded within the embryos DNA can directly control if, andwhen, a nearby gene becomes activated; this is known as cis-regulation. Finally, similar instructions", + "the subset of that genetic information that is active. But how does the differentiation process begin? The key insight in resolving this conundrum came from fly genetics and was the realization that the egg is not a homogenous sack of protoplasm. The maternally-derived genes active in the fertilized egg are asymmetrically distributed such that at the first cell division each daughter cell receives a different complement of factors. Development continues as a", + "genes. An altered gene may be passed on to every cell that develops from it. The resulting features my help, harm, or have little or no effect on the offsprings success in its environment. (AAAS, pg. 109, 5B:9-12#4 ) 6. Heritable material: The information passed from parents to offspring is coded in DNA molecules (AAAS, pg 108, 5B:9-12#3) 7. Mutagens: Gene mutations can be caused by such things as radiation and chemicals. When they occur in sex cells, the mutations can be passed onto offspring; if they", + "sex chromosome effects. (B)Soon after fertilization, male and female cells have sex-specic transcriptomes, epigenomes, and phenotypes (for example, male embryos grow faster than female embryos). At implantation, lineage determination begins and gene expression differences are reduced. Epigenetic marks, however, are less constrained and some are maintained, affecting gene expression, and phenotype later in development. Once specic lineages are established, differences in", + "or father (sperm cell). Each gamete has a set of 23 unpaired chromosomes. Two human gametes (egg and sperm) combine to create a cell (zygote) that contains the full human genome of 23 paired chromosomes.Genetic Information Nondiscrimination Act (GINA) US federal legislation that makes it unlawful to discriminate against individuals on the basis of their genetic profiles in regard to health insurance and employment. These protections are intended to encourage Americans to take advantage of", + "Proponents of the evo-devo view rightly point out that evolution occurs through changes in the development of traits, which may or may not have changes in DNA as their root cause. The processes that produce traits occur during development and involve more than just genes. All animals begin life as a fertilized egg, a single cell containing mitochondria and other organelles, and enough maternally derived RNA and proteins to kick start development and" + ], + "answer": "Once a sperm combines with an egg, the traits passed onto the resulting lifeform are determined by several factors: 1. **Chromosomal Segments from Parents**: Individuals inherit a patchwork of chromosomal segments from both maternal and paternal chromosomes [1]. 2. **Mendelian Laws**: The Mendel laws of segregation and independent assortment play a crucial role. The law of segregation states that each parent passes a randomly selected allele for a given trait to their offspring [4]. 3. **Gene Activation and Regulation**: The process involves controlling which genes are active, when they activate, and for how long. This control can be achieved through mechanisms such as imprinting, maternal effect, cis-regulation, and other instructions encoded within the embryo's DNA [3], [5]. 4. **Epigenetic Marks**: Soon after fertilization, epigenetic marks can affect gene expression and phenotype later in development [8]. 5. **Heritable Material**: The information passed from parents to offspring is coded in DNA molecules [7]. These factors collectively determine how traits are inherited and expressed in the resulting lifeform.", + "question": "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_27 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_27 new file mode 100644 index 0000000..a0c741b --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_27 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2015 -Ghorbani- Biochemical Pathways and System Bio Analysis.pdf", + "2015 - Bioinformatics Methods for Biochemical Pathways and System Biology Analysis_.pdf", + "2015_GN_Diabets_notheses.pdf", + "2011 - A Role for the MS Analysis of Nucleic Acids.pdf", + "2008 - Gene Expression Profiling.pdf", + "2012 - Genome-Wide Analysis of Yeast Aging.pdf", + "2009 - Next generation synthetic gene networks.pdf", + "2017 - Mutation and catastrophe in the aging genome.pdf", + "2008 - Gene Expression Profiling.pdf", + "2009 - Next generation synthetic gene networks.pdf" + ], + "extraction_id": [ + "583e1b6c-5a64-5b10-aee0-9f25132cb1af", + "9166f54b-f72e-5028-a048-0a8c45e1d27e", + "c9653e9d-2bb8-5bfe-9c06-25286ee85b1b", + "24143fb1-ad8f-5ddc-a935-3ed4677445c9", + "02d772b3-ae4b-502c-a77e-1a4fdb261c30", + "168583fa-6b49-52c4-9a65-7463c95456a6", + "38e443bd-610e-5a1d-9f32-082e808d016a", + "b2844c41-6a10-5249-947a-811cbbe61bef", + "d78c044d-7554-54c2-a2b7-ae37e2e9b2cd", + "38e443bd-610e-5a1d-9f32-082e808d016a" + ], + "document_id": [ + "222be20a-d320-540b-8b4f-888665acb158", + "9aa0b70d-ee1f-544b-bdac-f6a40c11e890", + "1744d9bf-29f9-52e3-a7c9-62a916999cda", + "2fa837a9-c3fb-5ee7-8e3d-58e4d8ec9ed9", + "59f3b969-089b-5258-93ad-892dbc9ffa9c", + "cf24db9a-e013-5780-8b0f-369c56143f29", + "0d620c5e-a9ae-5b19-851b-37e40292ab8d", + "7ae205a2-e002-5e8b-bbf1-ea96ab599b37", + "59f3b969-089b-5258-93ad-892dbc9ffa9c", + "0d620c5e-a9ae-5b19-851b-37e40292ab8d" + ], + "id": [ + "chatcmpl-ADZBi7XfGRRcyH1pOOCv70ZYOQgEu", + "b5e2c368-1437-591e-b886-e7aa879d8b1a", + "64fff1dc-a484-5138-9158-390bc27843ec", + "9f9d3ff1-8e26-53ab-b154-b72ca90e22ab", + "4b66fb22-87ff-5df3-99b4-6bdaea507bb5", + "870151f4-373d-50a7-8511-3a9a64f78514", + "de5d6db7-46da-5a27-bee4-48867524092d", + "296fc75a-e72d-5e72-a96f-8dd5fedbd709", + "b2b5baf2-4a44-5ecf-8c27-4789a878039f", + "98cdcaa8-1595-5269-9019-69f381738c58", + "d414d811-e98b-54e7-ad01-b4f185511dd9" + ], + "contexts": [ + "promoters ,regulatory proteins and their binding sites, ribosomal binding sites terminators ,et. RegulonDB contains both documentation and prediction objects. In addition it is linked with Swiss -prot, with microarray databases for analysis and visualization of microarray experiments.[5] WIT The WIT (What Is There) (http://wit.mcs.anl.gov/WIT2/) is a comparable computational system for analysis of sequenced genomes and generation of metabolic", + "promoters ,regulatory proteins and their binding sites, ribosomal binding sites terminators ,et. RegulonDB contains both documentation and prediction objects. In addition it is linked with Swiss -prot, with microarray databases for analysis and visualization of microarray experiments.[5] WIT The WIT (What Is There) (http://wit.mcs.anl.gov/WIT2/) is a comparable computational system for analysis of sequenced genomes and generation of metabolic", + "promoters ,regulatory proteins and their binding sites, ribosomal binding sites terminators ,et. RegulonDB contains both documentation and prediction objects. In addition it is linked with Swiss -prot, with microarray databases for analysis and visualization of microarray experiments.[5] WIT The WIT (What Is There) (http://wit.mcs.anl.gov/WIT2/) is a comparable computational system for analysis of sequenced genomes and generation of metabolic", + "173. Griffey, R. H.; Greig, M. J.; Haoyun, A.; Sasmor, H.; Manalili, S. Targeted Site-Specific Gas-Phase Cleavage of Oligoribonucleotides. Application in Mass Spectrometry-Based Identification of Ligand Binding Sites. J. Am. Chem. Soc. 1999, 121, 474475. 174. Hanson, C. L.; Fucini, P.; Ilag, L. L.; Nierhaus, K. H.; Robinson, C. V. Dissociation of Intact Escherichia coli Ribosomes in a Mass Spectrome- terEvidence for Conformational Change in a Ribosome Elongation", + "or chloramphenicol Immobilized targetDissociation of ribosome and release of mRNA5Poly(AAA)3 mRNA Isolation of mRNART-PCRdsDNA Mutagenesis by error-prone PCR Fig. 35.5. Schematic presentation of a ribosome display round. The gene of interest is transcribed from dsDNA into mRNA and translated into proteins by in vitro techniques. The ribosomes remain tethered to the mRNA by either cold shock or chloramphenicol. This step ensures that the genotype remains coupled to the phenotype. The proteins are", + "270 G.L. Sutphin e t a l. gene (Hinneb usch 2005 ). The m echanism of re gulation i s t hought to in v o lv e r el- ati v e a v a ilability of the l ar ge and small r ibosome s ub units. Specically , w hen 60Sribosomal sub unit l e v els a re lo w , ternary comple x e s containing initiation f actors and 40S ribosomal sub units are p roposed to more frequently scan through the", + "then used to develop synthetic gene networks with defined outputs, without significant post-hoc adjustments 22,4751. Alternatively, syn- thetic ribosome binding site (RBS) sequences can be used to optimize protein expression levels. Recently, Salis et al. 52 have developed a thermodynamic model for predicting the relative translational ini -", + "Philips, R.M., 2017 How Many Ribosomes Are in a Cell? [WWW Document]. URL http:// book.bionumbers.org/how-many-ribosomes-are-in-a-cell/ ((accessed 7.24.16) n.d.). R Core Team, 2014. R: a Language and Environment for Statistical Computing. R Founda- tion for Statistical Computing, Vienna, Austria. Sigurdson, A.J., Ha, M., Hauptmann, M., Bhatti, P., Sram, R.J., Beskid, O., Tawn, E.J.,", + "structure, and to find sites that are likely to be cleaved or modified; interac- tion or catalytic mechanisms can be simulated. Bioinformatic resources on the WWW range from the determination of the molecular weight to complex threading and three-dimensional (3D) prediction algorithms. A huge list of tools can be found on the ExPASy proteomic tools homepage (65). Because of the great variety of programs available, several of these single tools have", + "tiation rates for a protein with different upstream RBS sequences, a model that can also be used to rationally forward-engineer RBS sequences to give desired protein expression. In addition, protein degradation can be controlled by tagging proteins with degradation-targeting peptides that impart different degradation dynamics 53. By automating the construction and characterization of biomo-" + ], + "answer": "A ribosomal binding site (RBS) is a specific sequence of nucleotides in messenger RNA (mRNA) that is crucial for the initiation of protein synthesis. At a high level, you can think of it as a \"landing pad\" for the ribosome, which is the molecular machine that reads the mRNA and translates it into a protein. Here's a simple analogy: Imagine the ribosome as a train and the mRNA as a track. The ribosomal binding site is like a train station where the train (ribosome) stops to pick up passengers (the instructions for making a protein). Without this station, the train wouldn't know where to start its journey. In more technical terms, the RBS helps the ribosome to correctly position itself on the mRNA so that it can start translating the genetic code into a protein at the right spot. This process is essential for the proper expression of genes and the production of proteins that perform various functions in the cell [1], [2], [3].", + "question": "Can you explain what a ribosomal binding site at a high level and make it accessable to a non-expert?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_28 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_28 new file mode 100644 index 0000000..89b7324 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_28 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2008 - Study Design and Statistical Issues.pdf", + "2009 - Opening Up the Conversation on Genetics.pdf", + "2015 - Constraint and divergence of global gene expression in the mammalian embryo.pdf", + "2015 - Basic Concepts and Potential Applications of Genetics and Genomics for Cardiovascular and Stroke Clinicians.pdf", + "2015 - Constraint and divergence of global gene expression in the mammalian embryo.pdf", + "2008 - Genotype-phenotype relationships and the patterning of complex traits as exemplified in the mammalian dentition.pdf", + "2007 - Promoting_Student_Scientific_Literacy_of_Molecular Genetics and Genomics.pdf", + "2019 - Sexual Dimorphism in the Age of Genomics How, When, Where.pdf", + "2015 - Basic Concepts and Potential Applications of Genetics and Genomics for Cardiovascular and Stroke Clinicians.pdf", + "2008 - Genotype-phenotype relationships and the patterning of complex traits as exemplified in the mammalian dentition.pdf" + ], + "extraction_id": [ + "06bf0605-388a-592c-96ad-3a53bb36362c", + "51dbd5e2-fde6-5097-aa05-fcf57d3ca6b1", + "261c4af7-f63d-51ac-b164-0d9e7a64bff9", + "8a1ce8fa-b5f4-5942-b7b1-14a8a7887710", + "261c4af7-f63d-51ac-b164-0d9e7a64bff9", + "5aab3e60-b8b0-52ad-b4d3-817cf012cfa5", + "67369433-749b-5d6a-b5ef-3f0afe78b767", + "e22bb6fb-bec4-5c4c-8690-c96d0b8d13d4", + "206b8810-b7c1-5195-a10f-4e291864b77c", + "5aab3e60-b8b0-52ad-b4d3-817cf012cfa5" + ], + "document_id": [ + "c3bd9cf0-f768-55c4-be94-96590d7acc21", + "b62a8f54-c2f5-5bbb-9324-af80f7537167", + "3d9005f1-8f71-5d39-8749-4ebeab962cab", + "8610e699-218a-50e6-8d1d-ef689623266f", + "3d9005f1-8f71-5d39-8749-4ebeab962cab", + "f6e866b8-b233-5862-bfb8-9949d0dabb97", + "755f34c4-cc06-5275-a744-16d48162b012", + "3f8c03b0-4235-5774-9d26-e43d55c1001b", + "8610e699-218a-50e6-8d1d-ef689623266f", + "f6e866b8-b233-5862-bfb8-9949d0dabb97" + ], + "id": [ + "chatcmpl-ADZBnwLDpOz1ruhxgc8K7GIp36KrG", + "1e151ad5-59d9-598d-97ba-90ba0e64c4cb", + "60ad1512-b0c0-59cd-ace4-c146e2c04b52", + "a66b8b00-d51c-575b-b6ac-fa445c4ca715", + "4472740a-d22d-5bb1-98e3-e91332cbb303", + "df4c6108-740d-5bcf-99e6-dbda74f7e41a", + "47b9142f-98a3-5a45-8eaa-d327c9cc055d", + "8e3fdc2c-0962-5854-83e7-a60ab05cf6de", + "0158f264-120f-5942-ad55-ef5fde1f188a", + "6c8dfaa1-a96f-5f1c-8b5a-870acfd46f5f", + "c3562719-52f3-50de-8e92-82f64e5c5b05" + ], + "contexts": [ + "phenomena such as mutations and gene conversion events) occur in relevant meioses leading up to the formation of the gametes (i.e., egg and sperm) which are combined during fertilization and the formation of zygotes. Thus, individuals inherit a patch- work of chromosomal segments from maternal and paternal chromosomes.", + "the egg and the sperm. Such a process would result in genetic changes that will be copied into every cell of the future adult, including reproductive cells (Stock & Campbell, 2000), opening the door to irreversibly alter the human species. Inevitably, signifi cant self-disclosure and discussion challenges await families", + "a fertilized egg is a complicated process that relies on controlling: which genes are active; whenthese genes activate; and for how long they are active. In broad terms, there are four ways that thiscontrol can be achieved: First, inside the sperm or egg, genes can be marked with small chemical tags that flag these genes", + "(Figures 8 and 9). Two gametes (egg and sperm) ultimately join into a single cell, the zygote, which has the full comple-ment of 23 chromosome pairs restored. If all goes well, the zygote gives rise to a live offspring. The Mendel Laws: Segregation and Independent Assortment Both of the Mendel laws pertain directly to the process of meiosis. The first Mendel law, the law of segregation, states that each parent passes a randomly selected allele for a given", + "to be activated (or remain inactive) after fertilization, depending on whether the modification wasmade by the father (in the sperm) or the mother (in the egg); this process is known as imprinting. Second, the mother can alter the gene activity in her offspring via the placenta; this process is known as maternal effect. Third, instructions encoded within the embryos DNA can directly control if, andwhen, a nearby gene becomes activated; this is known as cis-regulation. Finally, similar instructions", + "the subset of that genetic information that is active. But how does the differentiation process begin? The key insight in resolving this conundrum came from fly genetics and was the realization that the egg is not a homogenous sack of protoplasm. The maternally-derived genes active in the fertilized egg are asymmetrically distributed such that at the first cell division each daughter cell receives a different complement of factors. Development continues as a", + "genes. An altered gene may be passed on to every cell that develops from it. The resulting features my help, harm, or have little or no effect on the offsprings success in its environment. (AAAS, pg. 109, 5B:9-12#4 ) 6. Heritable material: The information passed from parents to offspring is coded in DNA molecules (AAAS, pg 108, 5B:9-12#3) 7. Mutagens: Gene mutations can be caused by such things as radiation and chemicals. When they occur in sex cells, the mutations can be passed onto offspring; if they", + "sex chromosome effects. (B)Soon after fertilization, male and female cells have sex-specic transcriptomes, epigenomes, and phenotypes (for example, male embryos grow faster than female embryos). At implantation, lineage determination begins and gene expression differences are reduced. Epigenetic marks, however, are less constrained and some are maintained, affecting gene expression, and phenotype later in development. Once specic lineages are established, differences in", + "or father (sperm cell). Each gamete has a set of 23 unpaired chromosomes. Two human gametes (egg and sperm) combine to create a cell (zygote) that contains the full human genome of 23 paired chromosomes.Genetic Information Nondiscrimination Act (GINA) US federal legislation that makes it unlawful to discriminate against individuals on the basis of their genetic profiles in regard to health insurance and employment. These protections are intended to encourage Americans to take advantage of", + "Proponents of the evo-devo view rightly point out that evolution occurs through changes in the development of traits, which may or may not have changes in DNA as their root cause. The processes that produce traits occur during development and involve more than just genes. All animals begin life as a fertilized egg, a single cell containing mitochondria and other organelles, and enough maternally derived RNA and proteins to kick start development and" + ], + "answer": "Once a sperm combines with an egg, the traits passed onto the resulting lifeform are determined by several factors: 1. **Chromosomal Segments**: Individuals inherit a patchwork of chromosomal segments from maternal and paternal chromosomes due to phenomena such as mutations and gene conversion events during meiosis [1]. 2. **Mendelian Laws**: The Mendel laws of segregation and independent assortment play a crucial role. The law of segregation states that each parent passes a randomly selected allele for a given trait to their offspring [4]. 3. **Gene Activation and Regulation**: The process of trait determination also involves controlling which genes are active, when they activate, and for how long. This control can be achieved through mechanisms such as imprinting, maternal effect, and cis-regulation [3], [5]. 4. **Epigenetic Marks**: Epigenetic marks, which are chemical modifications to DNA that do not change the DNA sequence but affect gene expression, also play a role. These marks can be inherited and influence gene expression and phenotype later in development [8]. 5. **Heritable Material**: The information passed from parents to offspring is coded in DNA molecules, and any mutations in these genes can be inherited by the offspring [7]. In summary, the combination of chromosomal inheritance, Mendelian genetic laws, gene activation and regulation mechanisms, epigenetic marks, and the heritable DNA material all contribute to how traits are passed onto the resulting lifeform after fertilization.", + "question": "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_29 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_29 new file mode 100644 index 0000000..9216e36 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_29 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2016 - Coming of age ten years of next.pdf", + "2016 - Coming of age ten years of next.pdf", + "2016 - Coming of age ten years of next.pdf", + "2016 - Coming of age ten years of next.pdf", + "2019 - Genomic Analysis in the Age.pdf", + "2016 - Coming of age ten years of next.pdf", + "2016 - Coming of age ten years of next.pdf", + "2016 - Coming of age ten years of next.pdf", + "2018 - Effects of Genetic Background on Susceptibility and the Acceleration of Hearing Loss in Mice.pdf", + "2012 - Functional genomics research in aquaculture principles and general approaches.pdf" + ], + "extraction_id": [ + "c91e328e-4a01-5952-85b8-d7b5b47237c5", + "c91e328e-4a01-5952-85b8-d7b5b47237c5", + "5da5fc5d-1fe6-58f0-9141-72b9b2996fff", + "7fb68eb5-75cc-5db7-a182-d0ea055d49fe", + "06285eb9-37a8-5f76-a6d4-69cab398f2c0", + "97796d0a-1595-5cc9-a0db-c4186788ad07", + "97796d0a-1595-5cc9-a0db-c4186788ad07", + "97796d0a-1595-5cc9-a0db-c4186788ad07", + "a97b6b0b-d841-5cd3-a79f-f6d283b8337c", + "e5aa10c8-8b26-517f-9725-cb809cb4a37a" + ], + "document_id": [ + "9dd6e4e9-d136-507b-b628-68c8e1461bd0", + "9dd6e4e9-d136-507b-b628-68c8e1461bd0", + "9dd6e4e9-d136-507b-b628-68c8e1461bd0", + "9dd6e4e9-d136-507b-b628-68c8e1461bd0", + "f50c4d62-acab-5024-8ec7-526fffbfbf25", + "9dd6e4e9-d136-507b-b628-68c8e1461bd0", + "9dd6e4e9-d136-507b-b628-68c8e1461bd0", + "9dd6e4e9-d136-507b-b628-68c8e1461bd0", + "0567de5c-e886-5660-82de-8b80d2b580a9", + "a39b4cc1-8661-578b-a61b-b9962e45fc33" + ], + "id": [ + "chatcmpl-ADZBtpMuJOymoi8ODiNQwPGHnYpg6", + "a2d9c614-903d-513a-ad88-5a40f3534988", + "aa1d9f58-486c-522f-9981-5ce7e943b87f", + "47703589-35f9-5cff-8e62-ed299caa3356", + "5bd5b104-1b21-536e-90b2-2179bd152858", + "f49954d4-5769-5b9d-b06c-9f0050ab9e81", + "aec521d0-0c70-59bc-b457-6d801e8a7ab7", + "7445eff9-43fa-5328-84b7-5db7f16197e2", + "76137d35-eb92-5512-bbff-fa90de8e445c", + "63009249-a23b-5b5f-b9aa-34dc63c88218", + "c80d766b-4629-5a42-b3c2-877aa3f5af7c" + ], + "contexts": [ + "for sequencing on existing short-read instrumentation, after which data are split by barcode and reassembled with the knowledge that fragments sharing barcodes Barcodes A series of known bases addedto a template molecule either through ligation or amplification. After sequencing, these barcodes can be used to identify which sample a particular read is derived from. Figure 5 | Real-time and synthetic long-read sequencing approaches.", + "sequence 2D read. Synthetic long-reads. Unlike true sequencing platforms, synthetic long-read technology relies on a system of barcoding to associate fragments that are sequenced on existing short-read sequencers61. These approaches par - tition large DNA fragments into either microtitre wells or an emulsion such that very few molecules exist in each partition. Within each partition the template frag - ments are sheared and barcoded. This approach allows", + "sequencing. This platform is used by the Illumina suite of platforms. 36. Dohm,J.C., Lottaz,C., Borodina,T . & Himmelbauer,H. Substantial biases in ultra-short read data sets from high-throughput DNA sequencing. Nucleic Acids Res. 36, e105 (2008). 37. Nakamura,K. etal. Sequence-specific error profile ofIllumina sequencers. Nucleic Acids Res. 39, e90 (2011). 38. Minoche,A.E., Dohm,J.C. & Himmelbauer,H. Evaluation of genomic high-throughput sequencing data generated on Illumina HiSeq and genome", + "Comparison of short-read platforms. Individual short- read sequencing platforms vary with respect to through - put, cost, error profile and read structure (TABLE1 ). Despite the existence of several NGS technology pro - viders, NGS research is increasingly being conducted within the Illumina suite of instruments21. Although this implies high confidence in their data, it also raises concerns about systemic biases derived from using a single sequencing approach2628. As a consequence, new", + "short-read sequencing. arXiv, arXiv:1203.3907v2, https://arxiv.org/abs/ 12073907 . Garrison, E., Sire n, J., Novak, A.M., Hickey, G., Eizenga, J.M., Dawson, E.T., Jones, W., Garg, S., Markello, C., Lin, M.F., et al. (2018). Variation graph toolkit improves read mapping by representing genetic variation in the reference. Nat. Biotechnol. 36, 875879 . Giambartolomei, C., Vukcevic, D., Schadt, E.E., Franke, L., Hingorani, A.D.,", + "or transcriptomic structure53. Long-read sequencing Overview. It has become apparent that genomes are highly complex with many long repetitive elements, copy number alterations and structural variations that are relevant to evolution, adaptation and disease5456. However, many of these complex elements are so long that short-read paired-end technologies are insufficient to resolve them. Long-read sequencing delivers reads in excess of several kilobases, allowing for the resolution of", + "these large structural features. Such long reads can span complex or repetitive regions with a single continuous read, thus eliminating ambiguity in the positions or size of genomic elements. Long reads can also be useful for transcriptomic research, as they are capable of span - ning entire mRNA transcripts, allowing researchers to identify the precise connectivity of exons and discern geneisoforms. Currently, there are two main types of long-read tech -", + "nologies: single-molecule real-time sequencing approaches and synthetic approaches that rely on existing short- read technologies to construct long reads insilico . The single-molecule approaches differ from short-read approaches in that they do not rely on a clonal popula - tion of amplified DNA fragments to generate detectable Figure 2 | Sequencing by ligation methods. a | SOLiD sequencing. Following cluster generation or bead deposition onto a slide, fragments are sequenced by ligation, in", + "Tools for alignment-free analyses of sequencing data The vast majority of next-generation sequencing experiments in mouse have read alignment to a reference genome as their first step. However, the primary data from any sequencing experiment are the reads themselves. Recognition that the raw reads are information-rich has led to the development of alignment-free algorithms for error correction (among many others, Chaisson and Pevzner 2008 ), abundance estimation ( Patro et al. 2014 ), and de novo", + "(right). Sequencing adaptors (depicted by short red bars and short purple bars) are subsequently ligated to each cDNA fragment (green lines) and short sequence reads (single end or paired ends) from each cDNA are generated using high-throughput sequencing technology. The resulting sequence reads [short lines beneath the genome sequence with three genes shown (fat blue bars)] are aligned with the reference genome to" + ], + "answer": "Sure! Let's break down the difference between short-read and long-read sequencing in a way that's easy to understand. ### Short-Read Sequencing: 1. **Read Length**: Short-read sequencing generates small fragments of DNA, typically around 50-300 base pairs long [4]. 2. **Technology**: This method is commonly used by platforms like Illumina [3]. 3. **Advantages**: - **High Throughput**: It can produce a large amount of data quickly and is relatively cost-effective [4]. - **Accuracy**: Generally, short-read sequencing has a lower error rate per base compared to long-read sequencing [4]. 4. **Limitations**: - **Complex Regions**: It struggles to accurately sequence complex regions of the genome, such as those with long repetitive elements or structural variations [6]. - **Assembly**: Because the reads are short, assembling them into a complete genome can be challenging and may leave gaps or errors [6]. ### Long-Read Sequencing: 1. **Read Length**: Long-read sequencing produces much longer fragments, often several kilobases (thousands of base pairs) in length [6]. 2. **Technology**: There are two main types: - **Single-Molecule Real-Time Sequencing**: This method reads long DNA fragments directly without needing to amplify them [8]. - **Synthetic Long-Reads**: This method uses short-read technology to create long reads in a computational process [2]. 3. **Advantages**: - **Complex Regions**: It can span complex or repetitive regions in a single read, making it easier to resolve these areas accurately [7]. - **Transcriptomics**: Long reads can cover entire mRNA transcripts, helping researchers understand gene structures and variations better [7]. 4. **Limitations**: - **Cost and Throughput**: Long-read sequencing is generally more expensive and produces less data per run compared to short-read sequencing [4]. - **Error Rate**: The error rate per base can be higher than that of short-read sequencing, although this is improving with new technologies [4]. In summary, short-read sequencing is like reading a book by looking at many small snippets of text, which is fast and accurate but can be tricky if the text is very repetitive or complex. Long-read sequencing, on the other hand, is like reading longer passages at a time, which helps to understand the context better but might be slower and more expensive.", + "question": "Can you explain the difference between sequencing with short reads vs long reads? Please make you answer accessible to a non-expert." +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_3 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_3 new file mode 100644 index 0000000..9e65677 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_3 @@ -0,0 +1,65 @@ +{ + "titles": [ + "1998 - Neurodegeneration and Aging Role.pdf", + "1998 - Neurodegeneration and Aging Role.pdf", + "1998 - Neurodegeneration and Aging Role.pdf", + "2015 - Basic Concepts and Potential Applications of Genetics and Genomics for Cardiovascular and Stroke Clinicians.pdf", + "2020 - Mitonuclear genomics and aging.pdf", + "2020 - Mitonuclear genomics and aging.pdf", + "2002 - Genomic Medicine - A Primer.pdf", + "2001 - Mitochondrial genome instability in human cancers.pdf", + "2005 - The mitochondrial genome in human adaptive radiation and disease.pdf", + "2015 - Altered Levels of Mitochondrial DNA.pdf" + ], + "extraction_id": [ + "ceaf66e9-9822-5f7e-84b7-c687982f63e1", + "ceaf66e9-9822-5f7e-84b7-c687982f63e1", + "ceaf66e9-9822-5f7e-84b7-c687982f63e1", + "fa1dc2c0-8cc1-53e1-ad3e-8037506ec897", + "9c7f0bf0-7180-587e-a852-1187f18c2aea", + "472c8adc-54e7-5c27-a7b8-882b7e49cd2b", + "8e4ad64b-5541-52aa-bcd0-d61a8add4662", + "e79c57f4-e46b-5d8a-b9f3-2ee45c27349f", + "2f3c7ffe-45b9-5437-89cf-5fb7bbadc3d5", + "f85318e1-8e21-512e-8805-ade90e5232d0" + ], + "document_id": [ + "694c44f3-6025-5a2c-9c72-d9c5f16c8b85", + "694c44f3-6025-5a2c-9c72-d9c5f16c8b85", + "694c44f3-6025-5a2c-9c72-d9c5f16c8b85", + "8610e699-218a-50e6-8d1d-ef689623266f", + "e05fdc09-c8d8-5134-a1fd-bf07a1564981", + "e05fdc09-c8d8-5134-a1fd-bf07a1564981", + "a55c57bb-56e9-5218-ae80-66ee7aa93ae5", + "1a8d11d4-c469-5c5c-80a6-3800615dac43", + "c157cd40-1e5f-5ddb-b9cf-7927d8f2ee7b", + "ee28bbbd-66e4-5d9e-abcb-41cd89fcae22" + ], + "id": [ + "chatcmpl-ADZ9W7mSuiYhzAdBeFHuooWOpJA78", + "bdba9fea-2d0e-534e-9f32-688e499ec4dc", + "bbf1b1b5-873d-51cb-ada1-000a02458e32", + "b452f309-162e-5611-87c2-6907fd854ebf", + "a2eaf128-38b1-592d-b340-79ff1333c36e", + "e134edd5-f5f0-54d7-bc05-991f3d930de4", + "99a2cfc1-5a54-53af-b2a4-4c274e1d5ef1", + "a7a9acf4-c7c3-5b14-b40f-6667f32f74ab", + "6baf04ee-578e-5c53-9751-46241804e085", + "2570a50b-982d-5a2d-b5c1-c10e2fbcaed2", + "9c8c7e6b-7701-5a5e-a3e9-0b22eb29c099" + ], + "contexts": [ + "drial DNA sequence variation seems impossible withoutan understanding of some important differences betweennuclear and mitochondrial genetics (Table I). Mitochon-drial DNA replicates autonomously and is inherited viathe cytoplasm of the parent cell with the individualmitochondrion being the segregating unit (Attardi et al.,1995). Thus, in the case of mitochondrial mutations bothmutated as well as normal mitochondria may be presentwithin the same cell. This situation has been termedheteroplasmy and can", + "cMitochondria are semiautonomous organelles; possess their own replication-, transcription- and translation system cExclusively maternal inheritance of mitochondrial DNA cMitotic segregation of mitochondrial DNAcan lead to hetero- plasmy, i.e., the proportion of genetically different populations ofmitochondria differs between generations of mitotically activecells cApproximately tenfold higher mutation rate compared with nuclear", + "DIFFERENCES BETWEEN MITOCHONDRIAL AND NUCLEAR GENETICS Arealisticassessmentoftherelevanceofmitochon-", + "In the fifth mode of inheritance, the disease mutation lies not on a chromosome in the nucleus but rather in mitochondrial DNA outside the nucleus. Mitochondria are inherited exclu- sively from an offsprings mother; because of this phenome- non, the mutation and thus the disease can be passed only from a mother to her offspring. This is maternal inheritance, also known as extranuclear inheritance (Figure 11). Representative disorders include various mitochondrial myopathies.", + "The regulation of the mitochondrial genome also reflects its prokaryotic ancestry. While nuclear DNA undergoes replication during cell division, mtDNA replication occurs independently of cell cycle. The majority of the compo-nents for mtDNA replication are imported nuclear-encoded proteins, including the catalytic subunit of mtDNA poly -", + "Unlike the nuclear genome, which requires both paternal and maternal contributions, mtDNA is inherited solely from the maternal lineage. It is unclear what advantage a uniparental mtDNA transmission confers, but one possibil-ity is to minimize the number of distinct genomes to maxi-mize the efficiency of a multi-genomic system (Hill etal. 2019). In fact, humans have developed complex, redundant mechanisms to ensure uniparental inheritance of mtDNA (DeLuca and OFarrell 2012; Rojansky etal. 2016). Paternal", + "mitochondria and sperm are not, mitochondrial DNAis usually inherited from the mother. Therefore, mito-chondrial genes and diseases due to DNA-sequencevariants in them are transmitted in a matrilineal pat-tern that is distinctly different from the pattern of in-heritance of nuclear genes. MONOGENIC CONDITIONS Over the course of the 20th century, a combination", + "2. Mitochondrial DNA structure and properties Mitochondrial genomes (mt-genomes) are short circular molecules that, with the exception of viruses,represent the most economically packed forms ofDNA in the whole biosphere. The human mt-genomeis only 16,569bp long [9]; within this extension, wend the coding sequences for seven subunits of theNADH-ubiquitone reductase (respiratory complex I),the apocytochrome bof the ubiquitone cytochrome creductase (respiratory complex III), three subunits", + "Abstract The human mitochondrial genome consists of approximately 1500 genes, 37 encoded by the maternally inherited mitochondrial DNA (mtDNA) and the remainder encoded in the nuclear DNA (nDNA). The mtDNA is present in thousands of copies per cell and encodes", + "(mtDNA). MtDNA carries important genetic information concerning cellular metabolismand the generation of energy. It has been suggested that mitochondria and mtDNA could be of significance during early embryo development. Our work confirms this hypothesis. Specif- ically, our findings implicate mitochondria and their genome in female reproductive agingand the generation of embryonic chromosome abnormalities. Importantly, we describe a di-" + ], + "answer": "Mitochondrial DNA (mtDNA) plays a unique role in heredity and differs significantly from nuclear DNA (nDNA) in several ways: 1. **Inheritance Pattern**: Mitochondrial DNA is inherited exclusively from the mother, a phenomenon known as maternal or extranuclear inheritance [4], [6], [7]. This contrasts with nuclear DNA, which is inherited from both parents. 2. **Replication and Segregation**: mtDNA replicates autonomously and independently of the cell cycle, unlike nuclear DNA which replicates during cell division [1], [5]. Mitochondria are semiautonomous organelles with their own replication, transcription, and translation systems [2]. 3. **Genetic Variation and Mutation Rate**: Mitochondrial DNA has a higher mutation rate compared to nuclear DNA, approximately tenfold higher [2]. This can lead to a condition called heteroplasmy, where both mutated and normal mitochondria coexist within the same cell [1], [2]. 4. **Structure and Size**: The mitochondrial genome is a short circular molecule, significantly smaller than the nuclear genome. The human mitochondrial genome is only 16,569 base pairs long and encodes 37 genes, whereas the nuclear genome consists of approximately 1500 genes related to mitochondrial function [8], [9]. 5. **Functional Role**: mtDNA carries essential genetic information for cellular metabolism and energy generation, which is crucial for the functioning of the cell [10]. This is distinct from the broader range of functions encoded by nuclear DNA. In summary, mitochondrial DNA is maternally inherited, replicates independently, has a higher mutation rate, and is structurally distinct from nuclear DNA. These differences underscore the unique role of mtDNA in heredity and cellular function.", + "question": "Describe the role of mitochondrial DNA in heredity and how it differs from nuclear DNA." +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_30 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_30 new file mode 100644 index 0000000..f277081 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_30 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2021 - Plant Pan-Genomics.pdf", + "2018 - Effects of Genetic Background on Susceptibility and the Acceleration of Hearing Loss in Mice.pdf", + "2019 - Genomic Analysis in the Age.pdf", + "2023 - Clinical, technical, and environmental biases.pdf", + "2021 - Plant Pan-Genomics.pdf", + "2011 - The Reference Human Genome High Risk of Type 1 Diabetes and Other Disorder.pdf", + "2015 - Informatics resources for the Collaborative Cross and related mouse populations.pdf", + "2021 - Human Molecular Genetics and Genomics.pdf", + "2009 - Detection and interpretation of expression quantitative trait loci (eQTL).pdf", + "2017 - Post-genomic behavioral genetics From revolution to routine.pdf" + ], + "extraction_id": [ + "b75d8a8c-6c3a-5fce-92ee-46ae61aceb95", + "bcae5dd7-f775-5634-801b-76a71c99b2f4", + "70f829cc-2b89-593f-9995-f3e1d369acd4", + "7b399dda-fb0e-5111-929c-78fa82a74636", + "73f80ca8-2f2c-5ff4-9b65-2eeae1fd0b02", + "de94e095-34e7-537c-8c85-531bb17f4735", + "ffe01714-be5b-5aaa-889b-b83e97fc022c", + "35967ed4-335d-5b3a-b66f-97f3073a292d", + "8cc88dd8-4985-57f5-93db-4bbf171f938b", + "022e1268-76b1-590b-b73e-a096d4719c72" + ], + "document_id": [ + "3b346320-36f0-593c-bb36-c40cc6e23715", + "0567de5c-e886-5660-82de-8b80d2b580a9", + "f50c4d62-acab-5024-8ec7-526fffbfbf25", + "6a81e435-bd17-558d-850a-44ee3dbab5bd", + "3b346320-36f0-593c-bb36-c40cc6e23715", + "05e764f5-4ae8-51b7-89f0-987c79f6ed8f", + "889af7dc-d665-59a8-8b32-d3a65a831c70", + "68e362a5-e580-5a4d-8d41-6a138c873ede", + "ef974b09-4ea2-5382-85e5-c2169f440fda", + "cf1fdd6b-e926-5e84-a6b1-a5e92abbd2f3" + ], + "id": [ + "chatcmpl-ADZC0hJis0QrHtORi8K0UBB4TqKH0", + "66e86865-9c57-5ee7-883c-7bd1044fa708", + "83a31bf6-bd31-5a7b-ad2b-0f4223aa085a", + "21c0b3f1-a901-5a49-88ff-38963651d6cd", + "c43cf59c-5359-50cb-b9ee-73e74e3e1bd7", + "13a284d7-ff1c-5933-bce0-a69bbcee02cc", + "872237a6-b34e-57b4-bc4f-9967f8908796", + "940a31fb-adfd-558c-9c9d-39cb8d1ecee6", + "edcd5595-3b69-5ebe-b24f-a0c611f79606", + "16f7648c-92d7-5128-ae30-2a19ec89e04c", + "14cd9387-ac3c-52f9-81c3-c535925aeea8" + ], + "contexts": [ + "When reliable prior knowledge exists about the variant composition in a pan-genome (typi- cally obtained via read-to-reference mapping), there are computational tools that can transform a linear reference sequence and a set of variant calls into graphs (18).This approach bypasses the computationallyexpensiveall-versus-allalignmentstepalongwiththeuncertaintiesofsubsequent graph construction, but the trade-off is increased reference bias and a potentially incomplete", + "(Karolchik et al. 2014 )] and Ensembl ( Flicek et al. 2013 ). Use of a single haploid reference sequence as an anchor for all studies of genetic variation in mouse offers many practical advantages. But the dependency on a reference genome requires several assumptions about the nature of genetic variation which may be violated in practicethe strongest of which is that of genomic collinearity (i.e., conserved marker order) between strains. We consider the", + "for at least 500 ancestrally diverse humans. This resource willalso provide a set of highly accurate genomes that can be used as a benchmarking dataset to improve short-read analysis tools. Even more importantly, these genomes allow completelynew designs for more effective short-read analysis strategiesthat overcome many of the limitations described above. Transitioning to a pan-genome reference will require develop-", + "2018;562(7726):203-209. http://doi.org/10.1038/s41586-018-0579-z 110. Li R, Li Y, Zheng H, et al. Building the sequence map of the human pan-genome. Nat Biotechnol . 2010;28(1):57-63. http://doi.org/10. 1038/nbt.1596 111. Vernikos G, Medini D, Riley DR, Tettelin H. Ten years of pan- genome analyses. Curr Opin Microbiol . 2015;23:148-154. http:// doi.org/10.1016/j.mib.2014.11.016 112. Miga KH, Wang T. The need for a human pangenome reference sequence. Annu Rev Genomics Hum Genet . 2021;22:81-102. http://", + "Whilemostpan-genomesconstructedtodateareprimarilygene-basedbecauseoftherelative easeofcomparingandcategorizingdiscreteunitsdefinedbytranscriptionandtranslation,theim- portanceofnoncodingandrepetitivesequencesisunquestionable.Itwouldthereforebeextremely powerfultodefineacomprehensivesequence-basedpan-genomethatincludesinformationabout therelativepositionofallsequences.Unfortunately,interpretingnoncodingsequencevariationischallenging.Indeed,evenforclassesofnoncodingsequencesofknownimportance,e.g.,promot-", + "assessment will improve our understanding of the reference to better assemble and interpret future genome sequences. We have previously developed a method to assess the risk of a patient for 55 diseases using a quantitative human disease -SNP association database, and showed that we could suggest useful and clinical relevant information using his personal genome sequence (16). Here, we queried the reference genome sequence against our databa se and identified 3,556 disease -susceptib ility", + "The shortcomings of a single, linear reference genome per species are well appreciated, and richer reference datastructures are an active area of research (Church et al. 2015 ). An alternative is de novo assembly of the genomes of commonly used strains. The Sanger Mouse GenomesProject is using a combination of long-insert jumping libraries and optical mapping to build de novo assemblies", + "undertake comprehensive and powerful explorations rather than being confined to testing hypoth - eses focused on candidate path - ways. With the completion of the first reference sequence of the human genome,3 attention shifted from searching for genes to dis - covering their functions. System - atic genetic mapping in families and populations helped scientists pinpoint the genetic variants that contribute to human disease.", + "points, nding statistical associations, modeling and run ning predic- tors, or constructing and pruning networks of detected rela tions. In the following paragraphs I will explore these opportunities in detail. 1.4.1 Population reference genomes Genomes are relatively similar between individuals, there fore, instead of assembling the complete sequence for each person, we only de termine points of DNA variation compared to a reference genome. Subs equently,", + "having a reference genome for a related specie s certainly makes the process easier. The availability of long-read sequences vastly improves our ability to assemble new genomes, and new technologies, such as PacBio and Nanopore, are now able to give reads between 100-1000 kilobases, an order of m agnitude longer than current Illumina sequencing (Shendure et al. 2017). Combining these new technologies with traditional short read NGS will greatly improve our ability" + ], + "answer": "Using a pangenome-based reference can be more useful than using a single linear reference for several reasons: 1. **Capturing Genetic Diversity**: A single linear reference genome represents just one version of a species' DNA, which might not include all the genetic variations found in different individuals. A pangenome, on the other hand, includes multiple versions of genes and sequences from various individuals, capturing a broader spectrum of genetic diversity [3]. 2. **Reducing Reference Bias**: When using a single linear reference, there's a risk of reference bias, which means that genetic variations not present in the reference genome might be missed or misinterpreted. A pangenome reduces this bias by including multiple genetic variants, making it more likely to accurately represent the genetic makeup of different individuals [1]. 3. **Improving Disease Research**: By including a wider range of genetic variations, a pangenome can help researchers better understand the genetic basis of diseases. This comprehensive approach allows for more accurate identification of disease-associated genetic variants, which can lead to better diagnostics and treatments [6]. 4. **Enhanced Analysis Tools**: The use of a pangenome can improve the performance of genetic analysis tools. For example, it can provide a more accurate benchmark for evaluating new sequencing technologies and analysis methods, leading to more reliable results [3]. 5. **Overcoming Limitations of Single References**: A single reference genome assumes that the order and structure of genes are the same across all individuals, which is not always true. This assumption can lead to errors in genetic studies. A pangenome, by including multiple sequences, can account for these structural variations and provide a more accurate representation of the genome [2]. In summary, a pangenome-based reference is more useful because it captures the genetic diversity of a species, reduces reference bias, improves disease research, enhances analysis tools, and overcomes the limitations of using a single linear reference. This makes it a more comprehensive and accurate tool for genetic studies.", + "question": "Can you explain why using a pangenome-based reference might be more useful than simply using a single linear reference? Please make you answer accessible to a non-expert." +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_31 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_31 new file mode 100644 index 0000000..82a1daf --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_31 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2016 - The Genomics of Type 1 Diabetes.pdf", + "2015 - Quantitative and logic modelling of molecular and gene networks.pdf", + "2016 - The Genomics of Type 1 Diabetes.pdf", + "2013 - Genetic and Genomic Approaches to Understanding Macrophage Identity and Function.pdf", + "2016 - The Genomics of Type 1 Diabetes.pdf", + "2010 - The Role of Epigenetics in the Pathology of Diabetic Complications.pdf", + "2018 - Molecular Brain Adaptations to Ethanol_ Role of Glycogen Synthase (2).pdf", + "2011 - The age of the \u201come\u201d Genome, transcriptome and proteome data set collection and analysis.pdf", + "2009 - Detection and interpretation of expression quantitative trait loci (eQTL).pdf", + "2005 - Part I Previous Research Track Record.pdf" + ], + "extraction_id": [ + "24a08eeb-d72d-5ff6-97e3-d5f07795db7a", + "2ba86c45-9754-5300-8052-8b9c2765ecbc", + "cc42c6bf-d890-5a83-9598-b1a518f097b1", + "4c2afa3b-cf31-58ba-8ae8-2bf609f25dbc", + "24a08eeb-d72d-5ff6-97e3-d5f07795db7a", + "5c84ddde-f0cc-51fc-927c-0d5a11de0c07", + "ce57e949-0eeb-575a-9d67-5e21d08a47a2", + "772a0ef3-9be9-5b05-ad99-01c5c06347a0", + "a744cab4-ff8a-59e2-a789-ef7911c0a9ea", + "0853c5ab-3d98-565c-ba1f-50e5bd91d14c" + ], + "document_id": [ + "4933cdc2-7d36-5181-87c9-63b58498839f", + "8db6a373-be03-5653-beaf-1b2ae1d98c31", + "4933cdc2-7d36-5181-87c9-63b58498839f", + "1526d201-2f4e-5e6c-b2c8-8c825e741401", + "4933cdc2-7d36-5181-87c9-63b58498839f", + "766edfd5-4756-51bf-b636-c94b041d030c", + "cc2690a9-5a87-5f09-87d5-115a6a6b8349", + "ca99ed69-ee09-5717-95ed-c26eefb5e42d", + "ef974b09-4ea2-5382-85e5-c2169f440fda", + "1875d68b-adeb-5f91-8a67-91d881906238" + ], + "id": [ + "chatcmpl-ADZCAQL2r34gNZf3z3cSmtRoVHASI", + "1b7834f6-6ffa-5ebe-8b53-e590f43a13e0", + "93732536-96d1-5b06-a73b-06387cdfc139", + "4c7d9d92-5685-5560-8ec3-5c0df73a7a4f", + "6baacb55-1480-592e-9b6b-87c3e3980e19", + "8bb2457a-69e7-5e4a-9cc3-f51b8db47a89", + "8ddad342-ceca-56a1-afd6-6a3218436c1c", + "49fe2fc3-a95f-5de1-a261-bf596e48e6ff", + "0423eecb-319c-598b-a1d6-2ca3ecee84ce", + "b46d80d5-c968-5541-b658-d9a2dcd86e37", + "b9320635-76da-5a0c-a1fc-ea3a11cc7068" + ], + "contexts": [ + "al., 2012 ; Hindhorff, 2009; Barrett et al ., 2007 ). Recent efforts by the Encyclopedia of DNA elements (ENCODE) consortium, to characterise the human genome, have revealed that most of the non -coding part of the genome is not inactive but is associated with different forms of regulatory activity (ENCODE, 2012 ; Thurman, 2012 ). One important regulatory process that takes place within the genome is the (in-) activation of gene expression through the interaction", + "network of transcriptional regulators. Nature 403, 335338 (2000). 18. Gardner,T ., Cantor,C. & Collins,J. Construction of a genetic toggle switch in Escherichia coli. Nature 403, 339342 (2000). 19. Kauffman,S.A. Metabolic stability and epigenesis in randomly constructed genetic nets. J.Theor. Biol. 22, 437467 (1969). 20. Thomas,R. Boolean formalization of genetic control circuits. J.Theor. Biol. 42, 563585 (1973). REVIEWS NATURE REVIEWS | GENETICS ADV ANCE ONLINE PUBLICATION | 11", + "25 2.8 REGULATION OF GENE EXPRESSION Apart from the protein coding sequences, there are other biologically relevant nucleic acid sequences that play other important roles in the genome such as regulation of gene expression and maintenance of the chromatin structure (Pique -Regis et al., 2011). Regu lation of gene expression involves a process that leads to increase or decrease in the production of specific", + "expression is regulated at many levels, but gene transcription represents an essential and, in many cases, dominant point of control. Protein-coding genes are transcribed from promoters, which represent genomic regions that recruit basal transcrip- tion factors and RNA polymerase II. Physiological levels of gene expression and responses to internal and external signals require the actions of additional sequence-specific transcrip- tion factors that recruit nucleosome-remodeling complexes,", + "regulatory elements and variants thereof that may affect gene expression particularly through the binding of transcription factors (TFs) to DNA. The suggestion that the genetic determinants of complex diseases are perh aps better sought in problems associated with gene regulation is due to findings that many of the disease associated variants occur in non -coding DNA sequences within the genome (ENCODE, 2012; Schuab et", + "through multiple cell divisions at the transcriptio nal and epigenetic level need to be more 204 carefully examined and have evolved as an exciting area of research. 205 206 Epigenetics and transcriptional regulation 207 Regulation of gene expression relies on the ac cessibility of DNA to various transcription 208 factors, co-activators/co-repressors, and the transcriptional machinery. DNA is first wrapped 209", + "post-translationally, translationally, transcriptionally, or epigenetically (Lempradl et al, 2015; Zong et al, 2017) . It seems likely that these different layers of regulation can operate cooperatively on different time- scales . More permanent adaptations might be expected following persistent regulation on a more transient levelfor example, lowered transcriptional activity of a gene might follow a period of low functional activity of its protein. Elucidating the means of such", + "important component in the regulation of gene expression with between 10 and 20% of the transcriptome being regulated by DNA variation. 2. Technologies The study of DNA and its downstream effects is very much a technology driven process. Most of the rst screens looking at DNA changes in disease involved looking at segregation in fam- ilies because there were no reasonable technologies at the time", + "the cytosine and adenine nucleotides[31]. In addition, the c hromosomal structure of DNA can be decondensated by histone acetylatio n (trans- fer of acetyl groups to DNA organizational elements), makin g it more accessible for transcription[87]. The transcriptional ex pression of genes is further regulated by genetic variants themselves[7]. Fi nally, proteins form a complex network of interactions[265] that, in turn, a lso regulate gene expression[331].", + "eterogeneity and common, small effect genetic variants will be assessed. h D (c) Regulatory Signals: Co-regulation of genes via shared transcriptional networks provides the basis for context-dependent gene expression, an understanding of which is vital to the understanding of disease etiology and disease progression. In particular, transcription factors (TF) and their transcription factor binding sites (TFBS) provide a key component in the understanding of how co-regulation is achieved." + ], + "answer": "Not all genetic regulation is done solely through DNA elements like promoters, repressors, and activators. There are other forms of genetic regulation that play crucial roles in controlling gene expression. Here are some key points to consider: 1. **Non-Coding DNA**: Recent research has shown that much of the non-coding part of the genome, which does not code for proteins, is still active in regulating gene expression. This includes various regulatory activities that are not directly related to the traditional DNA elements like promoters and repressors [1]. 2. **Chromatin Structure**: The structure of chromatin, which is the complex of DNA and proteins in the cell nucleus, plays a significant role in gene regulation. For example, histone acetylation, which involves adding acetyl groups to histone proteins, can decondense chromosomal structure and make DNA more accessible for transcription [9]. 3. **Epigenetic Regulation**: Epigenetics involves changes in gene expression that do not alter the DNA sequence itself. This can include modifications like DNA methylation and histone modification, which affect how tightly DNA is wound around histones and thus its accessibility for transcription [6], [7]. 4. **Post-Transcriptional Regulation**: After DNA is transcribed into RNA, there are additional layers of regulation. This includes processes like RNA splicing, editing, and degradation, which can influence how much of the RNA is available to be translated into protein [7]. 5. **Translational and Post-Translational Regulation**: Even after RNA is translated into protein, there are mechanisms that regulate the activity, stability, and localization of proteins. These include modifications like phosphorylation and ubiquitination, which can alter protein function and lifespan [7]. In summary, while DNA elements like promoters, repressors, and activators are important for genetic regulation, there are multiple other layers of regulation involving chromatin structure, epigenetic modifications, and post-transcriptional and post-translational processes that also play critical roles in controlling gene expression.", + "question": "Is all genetic regulation done through DNA (e.g., prompters, repressors, activators) or are there other forms of genetic regulation? Please make you answer accessible to a non-expert." +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_32 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_32 new file mode 100644 index 0000000..ee973b3 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_32 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2015 - Linking traits based on their shared molecular mechanisms.pdf", + "2015_GN_Diabets_notheses.pdf", + "2015 -Oren- Linking traits molecular mechanisms.pdf", + "2008 - Rutter_s child and adolescent psychiatry-Blackwell Pub (2008).pdf", + "2004 - Combining QTL and Microarray Data.pdf", + "2015 - Linking traits based on their shared molecular mechanisms.pdf", + "2015_GN_Diabets_notheses.pdf", + "2015 -Oren- Linking traits molecular mechanisms.pdf", + "2012 - Needs Analysis of Genetics and Genomics in Communication Sciences and Disorders.pdf", + "2007 - An Informatics Approach to Systems Neurogenetics.pdf" + ], + "extraction_id": [ + "d54f1d09-1606-5cc3-92b7-e68d46a9f705", + "089366c5-7d36-5621-b463-6d1ad16d98cd", + "d1b6294a-3096-5385-8ae1-cf229e122f83", + "248fc08f-6f5e-5367-97dd-bdab0ca49699", + "d0be4c6e-9821-53e5-a770-3a68a06d4d84", + "6060c5cc-b34f-57ee-89e3-266cc660aa7e", + "0f901755-7d06-5722-8d57-49b7da4bc35f", + "65feb87b-411c-5835-9bb4-d6b14115f86d", + "2a443a7a-f71c-5a52-adda-085fa9199b40", + "6e361ba5-ecb0-51e6-b178-6f244743383f" + ], + "document_id": [ + "162160c3-1395-58da-98bd-b8450ec923d6", + "1744d9bf-29f9-52e3-a7c9-62a916999cda", + "ebf5e07f-4b24-5955-a2a6-fc8b9d5b5904", + "59daba11-206e-5bbc-8833-9d1b661532b5", + "5aeba67e-2338-5add-b8ee-ea304020834b", + "162160c3-1395-58da-98bd-b8450ec923d6", + "1744d9bf-29f9-52e3-a7c9-62a916999cda", + "ebf5e07f-4b24-5955-a2a6-fc8b9d5b5904", + "c8a76cb1-506d-57e4-a18e-548e777898e2", + "4d0692d8-74ef-58a3-82ec-d198b1d1c758" + ], + "id": [ + "chatcmpl-ADZCLib2EWT6WxKcrpOOYTqqeufBy", + "0c2422d5-9509-58b6-9ae2-615d4ee0ddee", + "359c496d-0870-5f54-82bd-3ce31e12863f", + "8e339342-5a42-5332-8c7d-3cea19e5f11b", + "bfe0accf-3aa8-5d95-97ce-6761e88c526a", + "edf0249b-5a8b-5050-b8fb-56a8304cbb23", + "651a0790-ec4f-5615-8aaa-90293e45ae42", + "b457363b-69ea-5b9e-9a48-06ae89034def", + "59e00799-df5e-52c0-882d-5c1eefc74e8b", + "1a01fece-3b4b-5b36-b994-e0fe945bdbf9", + "aa0a5df1-8084-579d-9d31-40d3bc9bee4d" + ], + "contexts": [ + "3, 4 and 5 suggest previously unknown connections between traits. We next characterized pairs of traits within each group of traits (trait pairs) to show that the quality of these pairs is not lower than in existing methods. We focused on three main properties of trait pairs: the correlation among traits in a pair; the correlation between a trait pair and the", + "3, 4 and 5 suggest previously unknown connections between traits. We next characterized pairs of traits within each group of traits (trait pairs) to show that the quality of these pairs is not lower than in existing methods. We focused on three main properties of trait pairs: the correlation among traits in a pair; the correlation between a trait pair and the", + "3, 4 and 5 suggest previously unknown connections between traits. We next characterized pairs of traits within each group of traits (trait pairs) to show that the quality of these pairs is not lower than in existing methods. We focused on three main properties of trait pairs: the correlation among traits in a pair; the correlation between a trait pair and the", + "taxonomy of traits is that it allows researchers to turn theirattention to the ways temperament and personality traitsexpress themselves in daily life and to the fundamental pro-cesses underlying variations in these traits. In this section, we rst describe the traits and then review some of the mostinteresting current work on the psychological and evolutionaryunderpinnings of each trait. A more detailed description of thecomponents of these traits is found in Caspi and Shiner (2006).Because relatively less", + "ditions and related totraits ofinter est,often bycomparing two groups differing forthetrait. Darvasi (2003) states that thereisanundeclar eddispute among resear chers who study complex traits :::Onone side areclassical geneticists :::ontheother areproponents ofgene expr ession analysis :::.Darvasi goes ontooutline thepossible advantages ofcombining these techniques over and above either technique alone. Inaddition tobetter correlating ge-", + "three types of high-order organization of traits. (i) Groups of tightly related traits that share thesame transcripts mechanisms (modules 1, 2, 6, 7, 8, e.g., Figure 3 ). (ii) Groups of distinct traits that share the same transcripts mechanism, but not necessarily high correlations among them (modules 3, 4, 5, e.g., Figure 4 ). (iii) Different groups commonly have overlapping traits, but typically differ in their underlying mechanisms ( Figure 2B ).", + "three types of high-order organization of traits. (i) Groups of tightly related traits that share thesame transcripts mechanisms (modules 1, 2, 6, 7, 8, e.g., Figure 3 ). (ii) Groups of distinct traits that share the same transcripts mechanism, but not necessarily high correlations among them (modules 3, 4, 5, e.g., Figure 4 ). (iii) Different groups commonly have overlapping traits, but typically differ in their underlying mechanisms ( Figure 2B ).", + "three types of high-order organization of traits. (i) Groups of tightly related traits that share thesame transcripts mechanisms (modules 1, 2, 6, 7, 8, e.g., Figure 3 ). (ii) Groups of distinct traits that share the same transcripts mechanism, but not necessarily high correlations among them (modules 3, 4, 5, e.g., Figure 4 ). (iii) Different groups commonly have overlapping traits, but typically differ in their underlying mechanisms ( Figure 2B ).", + "of varying effect sizes (small to moderate), interact with each other across time to manifest as individual genotypic and phenotypic traits. These traits contribute to normal variation in human behavior. Yet, these trait variants also increase the susceptibility of a disorder or a condition for many others.", + "action will open a Correlation Plot page in which you can examine the relationship between the two traits. Look for linearity and outliers. 3.3.1. Selection and Saving Multiple Traits The list of traits on the Correlation Results page represents traits that may be related in some way. You may want to select a group of them for further analysis. For example, use the checkboxes to the left of each entry to check entries 1, 9, 10, 14, 16, 18, traits related to brain size. Click the Add to collection" + ], + "answer": "The different relationships between traits can be categorized into several types based on the provided context: 1. **Correlation Among Traits in a Pair**: This refers to how traits within a pair are related to each other in terms of their correlation [1], [2], [3]. 2. **Correlation Between a Trait Pair and Other Factors**: This involves examining how a pair of traits correlates with other external factors or conditions [1], [2], [3]. 3. **High-Order Organization of Traits**: - **Groups of Tightly Related Traits**: These are traits that share the same transcript mechanisms and are highly correlated with each other (modules 1, 2, 6, 7, 8) [6], [7], [8]. - **Groups of Distinct Traits with Shared Mechanisms**: These traits share the same transcript mechanisms but do not necessarily have high correlations among themselves (modules 3, 4, 5) [6], [7], [8]. - **Overlapping Traits in Different Groups**: Different groups of traits may have overlapping traits but typically differ in their underlying mechanisms [6], [7], [8]. These relationships highlight the complexity and interconnectedness of traits, showing that they can be related through direct correlations, shared mechanisms, or overlapping characteristics.", + "question": "What are the different relationship between traits?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_33 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_33 new file mode 100644 index 0000000..f781f08 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_33 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2014 - A Population Genetic Signal of Polygenic Adaptation.pdf", + "2021 - Correlational selection in the age of genomics.pdf", + "2012 - Functional genomics research in aquaculture principles and general approaches.pdf", + "2011 - Genetical genomics approaches for systems genetics.pdf", + "2020 - A Multi-Omics Perspective of Quantitative Trait Loci in Precision Medicine.pdf", + "2010 - Systems genetics, bioinformatics and eQTL mapping.pdf", + "2022 -Chunduri- Drugs Animal Models.pdf", + "2022 - New Insights on Gene by Environmental Effects of Drugs of Abuse in Animal Models Using GN.pdf", + "2022 - New Insights on Gene by Environmental Effects of Drugs of Abuse in Animal Models Using GeneNetwork.pdf", + "2016 - Mouse genome-wide association and systems genetics identifies Lhfp as a regulator of bone mass.pdf" + ], + "extraction_id": [ + "c28f56f2-4e3d-5c8c-afcc-c6ac1dc43074", + "2aea6aad-eaf7-5e30-b505-4c08b47a8e98", + "0c3d0cb3-d4b0-5655-8b04-285a87710636", + "ea23303c-d909-5bda-9a48-8c78fb60cf8c", + "2d44caa2-d625-5252-87a1-a9691af99e36", + "298ee1f5-58a9-567c-86ba-8ac5967e1718", + "3df0d755-b4aa-5635-a223-3bc6d454a196", + "d01794ca-a660-5319-af06-8f0b9ee8e060", + "407f64ca-3b4b-57b8-954c-b5a58132d458", + "2cecc2f8-8211-544f-88e7-23e270d34f63" + ], + "document_id": [ + "5760b25c-236b-527d-98d6-563a85888727", + "5449975c-261a-5e45-a979-04fad61cefd8", + "a39b4cc1-8661-578b-a61b-b9962e45fc33", + "de78a01d-8d03-5afb-af5b-ce2ed2167766", + "8503b166-b917-5efb-a356-5ba371504cc1", + "27c922c6-e449-5f83-868a-3ad7284facc8", + "9cfa4f4c-37ce-5c0f-9da6-3bbb075fdc45", + "6f5d0c5b-0bbb-5eca-9e3e-73c3b0675472", + "d71efa0d-5de8-549c-964d-489ef6b73a1f", + "a554412b-b074-5bcd-9617-06ea69647b8a" + ], + "id": [ + "chatcmpl-ADZCTjdARUSr934Zl60dbSl3iWvA2", + "75b9b0fa-38e8-5674-8000-ae14f26a1780", + "1641cea6-8773-516e-b08e-fad820ebfdb9", + "fa1981fe-6730-59a1-b331-c6c7250b0f2c", + "72b37b21-1d41-55bd-b835-f0bd267a3970", + "108483cf-404b-5a9c-bf1f-be58ebf6d16d", + "68a13597-c223-54d9-9664-604d69b97c50", + "ed4ddc1b-45f9-5d9c-8969-e881d96edc4e", + "73b8e482-b204-5da6-b92d-f090efb622f1", + "60a84952-41ed-57ee-b689-6da313793843", + "616a41e7-df46-54d5-979e-1654973aa642" + ], + "contexts": [ + "ST, see [40,120122]). Such tools may also offer a way of incorporating GxE interactions, as multiple GWAS for the same trait in different environments can be treated as correlatedtraits [123]. As association data for a greater variety of populations, species, and traits becomes available, we view the methods described outhere as a productive way forward in developing a quantitativeframework to explore the genetic and phenotypic basis of local adaptation. Materials and Methods", + "has been achieved by quantitative trait loci mapping, admixture mapping and GW AS131, which have limited power to detect small-effect-size genes. Newer approaches map pleiotropy by simultaneously associating genomic loci with multiple traits 54 and can also detect epistatic interactions using machine learning algorithms 132.Detecting the genomic signatures of correlational selectionCorrelational selection could potentially be inferred from signatures of selective sweeps at loci under strong selection", + "pairs that include many genes within the seg- ment. On the other hand, GWAS may point to several or even many genomic locations for the trait of interest, complicating further functional analysis. Analysis of Quantitative Trait Loci (QTL) QTL analysis reveals statistically signicant linkage between phenotypes and genotypes, thereby providing explanation for the genetic basis of variation in complex traits (Falconer and Mackay, 1996; Lynch and Walsh, 1998). In a sense, QTL analysis can be viewed as incom-", + "studies. There are many possible causal networks even in a simple syst em consisting of a genomic locus (QTL) and two traits, T1 and T2 ( Figure 1 ). Causal inference in GWLS and GWAS involves, in its simplest form, the i dentification of pairs of traits with a common QTL (QTL-trait-trait triads) and dete rmining whether the QTL directly affects each of two traits (independent), or if the QTL affects only one trait", + "tions by matching patterns of expression QTL and GWAS. Am. J. Hum. Genet. 92, 92 160. Giambartolomei, C. et al. (2014) Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10, e1004383 161. Porcu, E. et al. (2019) Mendelian randomization integrating GWAS and eQTL data reveals genetic determinants of com-plex and clinical traits. Nat. Commun. 10, 3300 162. Zhu, Z. et al. (2016) Integration of summary data from GWAS", + "knowledge of the true QTL location (Doss et al. 2005 ), which can be used to empirically estimate the power of aGWAS performed at a similar scale (Hao et al. 2008 ; Schadt et al. 2008 ). A GWAS on its own does little more than establish correlations between changes in DNA at agiven locus and changes in a disease trait of interest, with respect to populations of interest. Further, these studies on", + "Another method to identify candidate genes is to leverage data generated in another population or species. Phenome-wide association studies (PheWAS) take a gene or variant of interest and nd all reported associations in GWAS datasets. A number of these GWAS tools exist, using either different methods, or different human cohorts (https://atlas.ctglab. nl/PheWAS, http://pheweb.sph.umich.edu/, accessed on 2 February 2022). Mouse QTL mapping has high power but low precision (i.e., we can detect a QTL, but", + "Another method to identify candidate genes is to leverage data generated in another population or species. Phenome-wide association studies (PheWAS) take a gene or variant of interest and nd all reported associations in GWAS datasets. A number of these GWAS tools exist, using either different methods, or different human cohorts (https://atlas.ctglab. nl/PheWAS, http://pheweb.sph.umich.edu/, accessed on 2 February 2022). Mouse QTL mapping has high power but low precision (i.e., we can detect a QTL, but", + "Another method to identify candidate genes is to leverage data generated in another population or species. Phenome-wide association studies (PheWAS) take a gene or variant of interest and nd all reported associations in GWAS datasets. A number of these GWAS tools exist, using either different methods, or different human cohorts (https://atlas.ctglab. nl/PheWAS, http://pheweb.sph.umich.edu/, accessed on 2 February 2022). Mouse QTL mapping has high power but low precision (i.e., we can detect a QTL, but", + "narrow regions ofthegenome harboring trait associated genetic variants. Itisstill, however, a challenge toidentify causal genes and several approaches have been developed that canassist inbridging thisgap. Specifically, systems genetics approaches involving theintegration of other types of-omics data have proven useful [25]. Two systems genetics approaches for informing GWAS areexpression quantitative trait loci(eQTL) discovery and co-expression" + ], + "answer": "Yes, the landscape of QTL (Quantitative Trait Loci) and GWAS (Genome-Wide Association Studies) hits can be used to find relationships between traits. This can be achieved through several methods: 1. **Correlated Traits in Different Environments**: Multiple GWAS for the same trait in different environments can be treated as correlated traits, which helps in exploring the genetic and phenotypic basis of local adaptation [1]. 2. **Mapping Pleiotropy**: Newer approaches map pleiotropy by simultaneously associating genomic loci with multiple traits, which can reveal relationships between traits [2]. 3. **QTL-Trait-Trait Triads**: Causal inference in GWAS and QTL studies involves identifying pairs of traits with a common QTL and determining whether the QTL directly affects each of the two traits independently or if it affects only one trait, which then influences the other [4]. 4. **Colocalization and Integration of Data**: Methods such as Bayesian tests for colocalization between pairs of genetic association studies using summary statistics, and Mendelian randomization integrating GWAS and eQTL data, can reveal genetic determinants of complex and clinical traits, thereby identifying relationships between traits [5]. These methods collectively demonstrate that the landscape of QTL and GWAS hits can indeed be used to find relationships between traits.", + "question": "Can landscape of QTL and GWAS hits be used to find relationships between traits?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_4 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_4 new file mode 100644 index 0000000..3fa0eae --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_4 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2009 - Opening Up the Conversation on Genetics.pdf", + "2009 - Opening Up the Conversation on Genetics.pdf", + "2013 - ACMG recommendations for reporting of incidental findings.pdf", + "2008 - Genetic and Genomic Healthcare Ethical Issues of Importance to Nurses.pdf", + "2009 - From Genetics to Genomics Ethics, Policy, and Parental.pdf", + "2009 - Opening Up the Conversation on Genetics.pdf", + "2020 - Informed Consent for Genetic and Genomics Research.pdf", + "2008 - Canada Public Health Genomics.pdf", + "2018 - Ethical_Social_and_Legal_Consequences.pdf", + "2009 - From Genetics to Genomics Ethics, Policy, and Parental.pdf" + ], + "extraction_id": [ + "92f0e5a8-a086-5236-962d-9b11584c65f4", + "92f0e5a8-a086-5236-962d-9b11584c65f4", + "1c2f1490-e98b-5ff8-8af0-edda9d5f3993", + "b7cdc9f8-d2b3-5ba3-a15f-6ef8d0c4f398", + "fd6ec3a0-cd63-5a6c-915b-7560fee0206e", + "93dd42f5-abb6-5a66-975f-1c1056c57173", + "c6ea7f08-9005-547c-b7a2-8011d7b67221", + "94557cbe-8255-53c3-8f56-5ea21b1075c6", + "8c1a83eb-78c2-56ba-b572-53753ea5bf2d", + "a7b4b523-5ec0-5212-80d7-2796d2799e44" + ], + "document_id": [ + "b62a8f54-c2f5-5bbb-9324-af80f7537167", + "b62a8f54-c2f5-5bbb-9324-af80f7537167", + "ef8364d6-d413-5150-8ad5-034a62bf787e", + "5e5322c1-f77d-5f71-8b1d-1a6eb0b9744a", + "4e65d14b-5c3f-547f-909d-d07064e59a47", + "b62a8f54-c2f5-5bbb-9324-af80f7537167", + "05a8ff6b-0de7-53af-a403-79eb2a9e9b33", + "ed94a341-3cf7-529a-827b-3ef681ce72c4", + "3fb80410-0b56-5c01-b3d6-9388b6029a77", + "4e65d14b-5c3f-547f-909d-d07064e59a47" + ], + "id": [ + "chatcmpl-ADZ9d7iHwaIjxfj8twd8wFGyZBbWb", + "45c7b32c-b032-5031-9e74-9a50fb63543f", + "da82c453-a630-5708-8b8a-93c6cd5e4ce4", + "9ebb7f76-4039-541f-b5d6-0bf16b3b1f35", + "9700655c-04a3-58ed-af01-79629c936f2d", + "96371003-643f-5d58-ba56-73dadd8fe812", + "f536f809-6b9a-5602-8915-5a47210a361b", + "838f60f9-1253-58a5-9cf6-36568f0cf07f", + "fcd27256-ac2e-5fbb-bf71-5357836e5e02", + "1f807579-9f8c-53e6-a35d-8d426024b71a", + "73dcb25d-3f7a-50ec-a0a4-b27669015092" + ], + "contexts": [ + "1999) raises practical and ethical issues of access to resulting opportunities and creates family communication challenges. Currently, prenatal testing for chromosomal diseases has become increasingly common (Moyer et al., 1999). Options such as pre-implantation genetic diagnosis (PGD) can identify over 1,250 disease-related mutations creating an opportunity for parents to select unaffected embryos for implantation in the womb (R. M. Green, 2008). Test results provide potential parents with information", + "undergo prenatal testing have determined that partners base their decision upon several factors, including, but not limited to: parental beliefs about abor-tion, attitudes regarding disability and their perceptions of the usefulness of having the information revealed by genetic tests (Moyer et al., 1999, p. 522). Abortion beliefs constitute a key issue in the decision-making process. Even though a majority of parents receiving abnormal prenatal test results terminate their pregnancies (Redlinger-Grosse,", + "Hum Genet 1995;57:12331241. 24. Committee on Bioethics. Ethical and policy issues in genetic testing and screening of children. Pediatrics 2013;131:620622. 25. Ross LF, Saal HM, David KL, Anderson RR. Technical report: ethical and policy issues in genetic testing and screening of children. Genet Med 2013;15: 234245. 26. Wilfond B, Ross LF. From genetics to genomics: ethics, policy, and parental decision-making. J Pediatr Psychol 2009;34:639647.", + "Informed Consent and Genetic Testing Genetic testing is increasingly used across the life continuum for screening, diagnosis, and de termining the best treatment of diseases. Obstetric and pediat ric nurses have traditionally been involved in the genetic testing process with prenatal screening for genetic conditions such as spina bifida and Down syndrome, and newborn screening for genetic conditions such", + "Objective Ethical evaluation of genetic testing in children is traditionally based on balancing clinical benefits and risks. However, this focus can be inconsistent with the general practice of respecting parentaldecision-making about their childrens health care. We argue that respect for parental decision-making should play a larger role in shaping pediatric genetic testing practices, and play a similar role regarding decisions", + "prenatal decisions. Further research needs to investigate how different families engage in such discussions and decision-making pro-cesses, especially as prenatal testing becomes more common and better able to predict or prevent a wider range of genetic conditions.", + "all of the complex ethical and legal issues rel- evant to genetic testing would disappear if there were effective preventions or treatments available for genetic conditions. The ability to predict future disease in conjunction with a limited ability to do much about it has im- portant social and psychological implications that must be addressed in conducting genetic research. One final factor worth consideration in un- derstandingthesensitivitytogeneticmedicine", + "Newborn screening by tandem mass spec-trometry: ethical and social issues. Can J Public Health 2007; 98: 284286. 65 Belle-Isle L: Genetic testing for late onset dis- eases: a population and public health per-spective. Health Policy Res Bull 2001; 1: 11 12. 66 Williams-Jones B: Private genetic testing in Canada: a summary. Health Law Rev 2001; 9: 1013. 67 Begleiter ML: Training for genetic counsel- lors. Nat Rev Genet 2002; 3: 557561. 68 Carroll JC, Reid AJ, Woodward CA, Per-", + "Although risk-based genetic testing for common diseases raise similar ethical issues to more traditional genetic testing for rare diseases, new challenges are raised due to the type of information revealed and access to tests. With thoughtful deliberation with health professionals, patients and families, test developers and laboratories, insurers and other stakeholders, these issues can be addressed to ensure the safe and appropriate use of these promising new clinical applications. REFERENCES", + "against testing, parents should generally be given final decision-making authority. Ethical Considerations in Developing Policy for Comprehensive Genomic Testing In the near future, genomic testing is likely to become more accessible and will provide both information aboutthe risks of common conditions such as heart disease, diabetes, and hypertension as well as predictions aboutindividual responses to specific pharmaceuticals and other medical therapies (Aspinall & Hamermesh, 2007)." + ], + "answer": "The ethical considerations surrounding prenatal genetic testing and the selective termination of pregnancies based on genetic factors are multifaceted and complex. Key considerations include: 1. **Parental Beliefs and Attitudes**: Decisions about prenatal testing and subsequent actions, such as termination, are heavily influenced by parental beliefs about abortion, attitudes regarding disability, and their perceptions of the usefulness of the information revealed by genetic tests [2]. These personal beliefs and attitudes play a crucial role in shaping the ethical landscape of prenatal genetic testing. 2. **Informed Consent**: Ensuring that parents are fully informed about the implications of genetic testing is critical. This includes understanding the potential outcomes, the accuracy of the tests, and the possible decisions they may face based on the results [4]. Informed consent is a cornerstone of ethical medical practice, particularly in the context of genetic testing. 3. **Respect for Parental Decision-Making**: Ethical considerations must balance clinical benefits and risks with respect for parental decision-making. Parents should generally have the final authority in making decisions about their children's health care, including whether to undergo genetic testing and how to respond to the results [5]. 4. **Social and Psychological Implications**: The ability to predict future diseases through genetic testing, coupled with limited options for prevention or treatment, has significant social and psychological implications. These must be addressed to ensure that parents are not unduly burdened by the information provided by genetic tests [7]. 5. **Access and Equity**: Ethical issues also arise from the accessibility of genetic testing and the opportunities it creates. There are concerns about equitable access to these technologies and the potential for disparities in who can benefit from them [1]. 6. **Family Communication Challenges**: Genetic testing results can create communication challenges within families, as they navigate the complex information and make decisions that affect their future [1]. In summary, the ethical considerations surrounding prenatal genetic testing and selective termination involve respecting parental beliefs and decision-making, ensuring informed consent, addressing social and psychological impacts, and promoting equitable access to genetic testing technologies. These considerations must be carefully balanced to navigate the ethical complexities of prenatal genetic testing.", + "question": "What are the ethical considerations surrounding prenatal genetic testing and the selective termination of pregnancies based on genetic factors?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_5 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_5 new file mode 100644 index 0000000..3407a58 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_5 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Precision and Personalized Medicine How Genomic.pdf", + "2016 - Coming of age ten years of next.pdf", + "2015 -Pandey- Functional Analysis of Genomic Variation and Impact on Molecular.pdf", + "2015 - Functional Analysis of Genomic Variation and Impact on Molecular and Higher Order Phenotypes.pdf", + "2017 - Infection control in the new age of genomic epidemiology.pdf", + "2016 - Coming of age ten years of next.pdf", + "2016 - Coming of age ten years of next.pdf", + "2016 - Coming of age ten years of next.pdf", + "2016 - Coming of age ten years of next.pdf", + "2008 - Gene Expression Profiling.pdf" + ], + "extraction_id": [ + "de09f30d-e9ba-5379-8c7a-85b2cd2ed6c8", + "5da5fc5d-1fe6-58f0-9141-72b9b2996fff", + "f4e989e5-c3d0-5d5c-b8c3-95894a14630b", + "fa426831-7c04-56c1-a191-1ebbc35342ed", + "04f06fb6-b2ff-57d4-bac0-de5cf4782ff3", + "4cdf13c0-c505-5ff9-9a6e-b10e5d1c8819", + "4cdf13c0-c505-5ff9-9a6e-b10e5d1c8819", + "5da5fc5d-1fe6-58f0-9141-72b9b2996fff", + "cad1dcca-621d-5003-ba3a-81950819bc52", + "c14d1c74-a14a-5037-8d3f-f32a60faa9a5" + ], + "document_id": [ + "cd11028a-933b-52a0-9534-c173323056ef", + "9dd6e4e9-d136-507b-b628-68c8e1461bd0", + "93381e23-494b-5bc2-9d09-ef315506601f", + "263d327b-f5db-54e4-a215-b3f8a51cd7d6", + "8f028916-b990-5e95-b2a6-e69f451cc291", + "9dd6e4e9-d136-507b-b628-68c8e1461bd0", + "9dd6e4e9-d136-507b-b628-68c8e1461bd0", + "9dd6e4e9-d136-507b-b628-68c8e1461bd0", + "9dd6e4e9-d136-507b-b628-68c8e1461bd0", + "59f3b969-089b-5258-93ad-892dbc9ffa9c" + ], + "id": [ + "chatcmpl-ADZ9nZqF5q344Dbv0zjyqijEIpDdi", + "8477a774-dddb-5541-b8d7-d51a7e56b0af", + "13a6b6f9-4a9a-5eb9-ac79-d986d9e613f0", + "d1158643-3625-5855-a03d-eec4ac96eb4d", + "cdf2b80f-1509-50a2-9cb2-a36dd6f3f2cc", + "f8ae01ae-cea8-5b8b-95c0-7147055de596", + "d2540614-9397-5e3e-8b5f-ad328ca973b2", + "199e1929-dc7c-58d4-8c8d-1c931e658e9c", + "1e324977-2ca5-5062-8a09-7659d516e899", + "98010acc-fd11-5d33-bced-626ef29f2896", + "3e782f01-a06e-51b6-ac8a-0e0a56939d08" + ], + "contexts": [ + "36. Sequencing, H.G. Finishing the euchromatic sequence of the human genome. Nature 2004 ,431, 931945. 37. Heather, J.M.; Chain, B. The sequence of sequencers: The history of sequencing DNA. Genomics 2016 ,107, 18. [CrossRef] 38. Rothberg, J.M.; Leamon, J.H. The development and impact of 454 sequencing. Nat. Biotechnol. 2008 ,26, 11171124. [CrossRef] [PubMed] 39. Shendure, J.; Ji, H. Next-generation DNA sequencing. Nat. Biotechnol. 2008 ,26, 11351145. [CrossRef] [PubMed]", + "22. Karow, J. Qiagen launches GeneReader NGS System atAMP; presents performance evaluation by broad. GenomeWeb [online], https:// www.genomeweb.com/ molecular-diagnostics/qiagen-launches-genereader- ngs-system-amp-presents-performance-evaluation (4Nov 2015). 23. Smith,D.R. & McKernan,K. Methods of producing and sequencing modified polynucleotides . US Patent 8058030 (2011). 24. Margulies,M. etal. Genome sequencing in microfabricated high-density picolitre reactors. Nature 437, 376380 (2005).", + "11 BIOINFORMATIC CHALLENGES FOR GENOMIC MEDICINE Processing and managing of high-throughput sequence data High throughput sequencing offers severa l advantages relative to array-based genotyping or expression assays. First, unlike genotyping arrays, whole genome sequencing is not limited to interrogating onl y known sequence variants. Similarly, RNA- sequencing (RNA-seq) enables expression quanti fication of novel transcripts that are not", + "11 BIOINFORMATIC CHALLENGES FOR GENOMIC MEDICINE Processing and managing of high-throughput sequence data High throughput sequencing offers severa l advantages relative to array-based genotyping or expression assays. First, unlike genotyping arrays, whole genome sequencing is not limited to interrogating onl y known sequence variants. Similarly, RNA- sequencing (RNA-seq) enables expression quanti fication of novel transcripts that are not", + "High-throughput bacterial genome sequencing: an embarrassment of choice, aworldof opportunity.NatRevMicrobiol2012;10:599-606. 11.CroucherNJ,DidelotX.Theapplicationof genomicstotracingbacterialpathogen transmission.CurrOpinMicrobiol2015;23:62-7. 12.ShendureJ,JiH.Next-generationDNAsequencing.NatBiotechnol2008;26:1135- 45. 13.MillerJR,KorenS,SuttonG.Assemblyalgorithmsfornext-generationsequencing data.Genomics2010;95:315-27. 14.OlsonND,LundSP,ColmanRE,FosterJT,SahlJW,SchuppJM,etal.Bestpractices", + "sequencing. Genome Res. 20, 11651173 (2010). 64. English,A.C. etal. Assessing structural variation in a personal genome-towards a human reference diploid genome. BMC Genomics 16, 286 (2015). 65. Carneiro,M.O. etal. Pacific Biosciences sequencing technology for genotyping and variation discovery in human data. BMC Genomics 13, 375 (2012). 66. Quail,M.A. etal. A tale of three next generation sequencing platforms: comparison of Ion T orrent, Pacific Biosciences and Illumina MiSeq sequencers.", + "Nat. Biotechnol. 30, 10331036 (2012). 111. Chrystoja,C.C. & Diamandis,E.P . Whole genome sequencing as a diagnostic test: challenges and opportunities. Clin. Chem. 60, 724733 (2014). 112. McGuire,A.L. etal. Point-counterpoint. Ethics and genomic incidental findings. Science 340, 10471048 (2013). 113. Bowers,J. etal. Virtual terminator nucleotides for next-generation DNA sequencing. Nat. Methods 6, 593595 (2009). 114. Heger,M. Chinas Direct Genomics unveils new", + "sequencing. Bioinformatics 31, 20402042 (2015). 46. Qiagen. Oncology insights enabled by knowledge base- guided panel design and the seamless workflow of the GeneReader NGS system Press Release. Qiagen [online], http://www.genereaderngs.com/PROM-9192- 001_1100403_WP_GeneReader_NGS_0116_NA.pdf (2016). 47. Forgetta,V. etal. Sequencing of the Dutch elm disease fungus genome using the Roche/454 GS-FLX Titanium System in a comparison of multiple genomics core", + "FURTHER INFORMATION 10X Genomics: http://www.10xgenomics.com 454 Sequencing: http://www.454.com Advances in Genome Biology and Technology (AGBT): http://www.agbt.org BGISEQ500: http://seq500.com/en/portal/Sequencer.shtml Illumina: http://www.illumina.com Ion Torrent: https://www.thermofisher.com/us/en/home/ brands/ion-torrent.html Oxford Nanopore Technologies: https://www.nanoporetech. com Pacific Biosciences: http://www.pacb.com Personal Genome Project: http://www.personalgenomes.org", + "DNA), and provide the means to link sequences containing applications. First, base- callers like Phred (4,5) extract raw sequences from raw data. There are also contig assemblers like Phrap (University of Washington, http://bozeman. mbt.washington.edu/phrap.docs/phrap.html ) or CAP3 (6) that assemble frag- ments to contigs and packages like consed (7) or GAP4 (8), which are used to finish sequencing projects. These programs are not explained in detail here." + ], + "answer": "### How-to Guide for Genetic Sequencing Genetic sequencing is a powerful tool used to determine the order of nucleotides in DNA. This guide will walk you through the basic steps involved in genetic sequencing, referencing key resources for further information. #### Step 1: Sample Preparation 1. **Collect DNA Sample**: Obtain a sample from the organism or tissue of interest. This could be blood, saliva, or tissue biopsy. 2. **Extract DNA**: Use a DNA extraction kit to isolate DNA from the sample. This involves cell lysis, removal of proteins and other contaminants, and purification of DNA. #### Step 2: Library Preparation 1. **Fragment DNA**: Break the DNA into smaller fragments. This can be done using mechanical shearing or enzymatic digestion. 2. **Add Adapters**: Ligate specific adapter sequences to the ends of the DNA fragments. These adapters are necessary for the fragments to bind to the sequencing platform. #### Step 3: Amplification 1. **PCR Amplification**: Amplify the DNA fragments using Polymerase Chain Reaction (PCR). This step increases the quantity of DNA to ensure there is enough material for sequencing. #### Step 4: Sequencing 1. **Choose a Sequencing Platform**: Select an appropriate sequencing technology based on your needs. Common platforms include Illumina, Ion Torrent, and Pacific Biosciences [6], [9]. 2. **Load DNA onto the Sequencer**: Follow the manufacturer's instructions to load your prepared DNA library onto the sequencing machine. 3. **Run the Sequencer**: Initiate the sequencing run. The machine will read the nucleotide sequences of the DNA fragments and generate raw sequence data. #### Step 5: Data Analysis 1. **Base Calling**: Use software like Phred to convert raw data into nucleotide sequences [10]. 2. **Sequence Assembly**: Assemble the short DNA sequences into longer contiguous sequences (contigs) using tools like Phrap or CAP3 [10]. 3. **Variant Calling**: Identify genetic variants by comparing the assembled sequences to a reference genome. This can be done using various bioinformatics tools. #### Step 6: Interpretation 1. **Annotation**: Annotate the sequences to identify genes, regulatory elements, and other functional regions. 2. **Data Management**: Store and manage the high-throughput sequence data efficiently. This is crucial for handling large datasets generated by next-generation sequencing technologies [3], [4]. #### Additional Resources - **Next-Generation Sequencing (NGS)**: For a detailed understanding of NGS technologies and their applications, refer to Shendure and Ji's review on next-generation DNA sequencing [1], [5]. - **Sequencing Platforms**: For comparisons of different sequencing platforms, see the work by Quail et al. [6]. - **Bioinformatics Tools**: Explore various bioinformatics tools and software for sequence analysis and data management [10]. By following these steps and utilizing the referenced resources, you can successfully perform genetic sequencing and analyze the resulting data.", + "question": "Create a how-to guide for genetic sequencing." +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_6 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_6 new file mode 100644 index 0000000..ca72887 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_6 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2017 - Machine Learning and Data Mining Methods in Diabetes Research.pdf", + "2018 - Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls.pdf", + "2011 - Type 1 Diabetes Etiology, Immunology.pdf", + "2011 - Type 1 Diabetes Etiology, Immunology.pdf", + "2015 -precision-medicine-for-managing-diabetes.pdf", + "2016 - The Genomics of Type 1 Diabetes.pdf", + "2001 - The genetics of type 2 diabetes.pdf", + "2007 - Localization of type 1 diabetes susceptibility to the MHC Class 1 Genes.pdf", + "2019 - (Epi)genomic heterogeneity of pancreatic islet function and failure in type 2 diabetes.pdf", + "2010 - A recombination hotspot leads to sequence variability.pdf" + ], + "extraction_id": [ + "46f1cae6-a01f-5445-b20f-0eadf892f8bf", + "43eecb5d-aca2-5c3e-9351-afbef000a795", + "682b7a19-c6f3-5773-8286-c027adef1fd3", + "69694cc4-e333-599c-9046-17a192ef3080", + "f53ccf4e-f47f-5b44-8b41-f7068efc8be3", + "e1274c5c-c854-52b0-83d9-72487111ba34", + "737e4fe2-91ba-50c5-8f64-1149944fb60c", + "92a54171-9f94-51ea-83cb-11698b1f0c21", + "7a2a9981-4096-5049-a717-3e69eb609777", + "7cf0ebfd-7231-540b-b44f-9c94316fdf80" + ], + "document_id": [ + "e2dcbb80-5ad7-5441-b170-9b46607445b0", + "af63c74d-a204-5f9f-9a32-3451b112e5ba", + "3c9823cd-3615-53b6-96c8-b7d2123d3eb0", + "3c9823cd-3615-53b6-96c8-b7d2123d3eb0", + "80949bab-d085-5f61-b98a-4bee043bc4e2", + "4933cdc2-7d36-5181-87c9-63b58498839f", + "8ab06972-1c6c-5d68-a270-65fb0af0917b", + "3887995f-fa61-5472-b0a2-90b7b39592c2", + "b9bc63a5-e366-5685-bd7a-4732a8eeffb7", + "72115ac1-f66b-58c5-9a6f-2230ec7eacb4" + ], + "id": [ + "chatcmpl-ADZ9vHPpqfR4t9mEA9x34UA73YZmX", + "abf69b53-da1e-5d4a-b957-e528cf986a22", + "2308bc87-b4e4-5e68-80f0-877bfd340377", + "bad4e085-d889-5a45-a5a4-f943a33bf72a", + "027471f3-0ccd-5b0d-b5d6-d8027ee07326", + "263dc0cb-dfa0-5ee2-b927-f9a196294d46", + "a76c839e-ec94-5fdb-b5b9-a3bd6eff1315", + "d8447ac5-d246-5cca-9336-693710b17f7a", + "4658d1c8-e096-54d3-8e93-4bf95a6ca114", + "84259ad2-080b-5f5c-82f3-0fe9a88500f4", + "ac8cfb6b-42cd-5c42-a4a3-b525790a22b1" + ], + "contexts": [ + "are involved in the development of the disease [127 ]. There is evidence that more than twenty regions of the genome are involved in t he genetic susceptibility to T1D. The genes most strongly associated with T1D are loc ated in the HLA region of chromosome 6 [128]. Similar to T1D, T2D has a stro ng genetic component. To date, more than 50 candidate genes for T2D have been inve stigated in various populations worldwide. Candidate genes are selected due to the ir interference with pancreatic", + "pre-existing statistical support for a role in T1D-susceptibility: these are the major histocompatibility complex (MHC), the genes encod- ing insulin, CTLA-4 (cytotoxic T-lymphocyte associated 4) and PTPN22 (protein tyrosine phosphatase, non-receptor type 22), and the regions around the interleukin 2 receptor alpha ( IL2RA/CD25 ) and interferon-induced helicase 1 genes ( IFIH1 /MDA5)94. However, these signals can explain only part of the familial aggregation of T1D.", + "C. The Insulin Gene A lesser genetic predisposition to T1D is conferred by the IDDM2 locus on chromosome 11 containing the insu-lin gene region. A polymorphic region located 5 =of the insulin gene was rst reported in 1984 to be associatedwith T1D in caucasoids (39). Now established as a pri- TYPE 1 DIABETES: FROM CAUSE TO CURE 81 Physiol Rev VOL 91 JANUARY 2011 www.prv.org Downloaded from journals.physiology.org/journal/physrev (041.090.188.152) on July 14, 2023.", + "ception of the insulin gene (434). The genetic susceptibil-ity component of T1D allows some targeting of primarypreventive care to family members of diagnosed T1Dpatients, but there is no complete inheritance of the dis-ease. Nevertheless, the risk for developing T1D comparedwith people with no family history is /H110111015 times greater. Although /H1101170% of individuals with T1D carry", + "Genes signifying increased risk for both type 1 and type 2 dia-betes have been identified. Genomewide association studies have identified over 50 loci associated with an increased genetic risk of type 1 diabetes. Several T1D candidate genes for increased risk of developing type 1 diabetes have been sug-gested or identified within these regions, but the molecular basis by which they contribute to islet cell inflammation and beta cell destruction is not fully understood. 12 Also, several", + "14 carried out on large cohorts including collections of families with affected sibling pairs (Pociot et al., 2010). These studies have provided evidence for over forty T1D susceptibility regions , but the exact mechanisms by which the variation found in these regions confer susceptibility to T1D is still not clear (Noble and Erlich, 2012). The most important genes contributing to T1D susceptibility are located in the MHC class II region , also referred to as t he Human Leukocyte", + "The ultimate proof of an inherited contribution to disease pathogenesis comes from the identication of susceptibility genes. As described below, an increasing number of T2D susceptibility genes have been discovered in the past decade, especially,but not exclusively, in monogenic subtypes. Collectively, these probably account for294 A. L. Gloyn and M. I. McCarthy", + "loci contribute to Type 1 Diabetes (T1D) susceptibility and age at T1D onset. Hum. Immunol. 66,301313 (2005). 9. Aly, T. A. et al. Extreme genetic risk for type 1A diabetes. Proc. Natl Acad. Sci. USA 103, 14074 14079 (2006). 10. Noble, J. A. et al. The HLA class I A locus affects susceptibility to type 1 diabetes. Hum. Immunol. 63,657664 (2002). 11. Honeyman, M. C., Harrison, L. C., Drummond, B., Colman, P. G. & Tait, B. D. Analysis of families at risk for insulin-dependent diabetes mellitus reveals that", + "failure linked to T2D genetic risk and pathophysiology. Single celltranscriptome analysis of human islet cells indicate that multiplemonogenic diabetes genes are highly expressed in beta cells (e.g., PDX1, PAX4, INS, HNF1A, andGCK)[27]. However, other non-beta cell types express genes mutated in monogenic diabetes (such as PAX6 and RFX6 ), congenital hyperinsulinemia ( HADH, UCP2 ) and those implicated as T2D GWAS target/effector genes [28].", + "chain promoter (Serreze and Leiter 2001). This observation, alongwith human genetic studies, suggests that increased T1D risk in humans may also result from the combination of rare and common variants within the human population (Concannon et al. 2009b). Despite the identification of several Iddgenes to date, this limited collection does not fully explain T1D pathogenesis or the underlying genetic architecture for T1D risk. One of the many Idd" + ], + "answer": "The genes that give a predisposition to developing Type 1 Diabetes (T1D) include: 1. **Major Histocompatibility Complex (MHC)** genes located in the HLA region of chromosome 6 [1], [2], [6]. 2. **Insulin gene (INS)** located in the IDDM2 locus on chromosome 11 [3]. 3. **CTLA-4 (Cytotoxic T-Lymphocyte Associated 4)** [2]. 4. **PTPN22 (Protein Tyrosine Phosphatase, Non-Receptor Type 22)** [2]. 5. **IL2RA/CD25 (Interleukin 2 Receptor Alpha)** [2]. 6. **IFIH1/MDA5 (Interferon-Induced Helicase 1)** [2]. These genes are strongly associated with T1D and contribute to the genetic susceptibility to the disease.", + "question": "Which genes give a predisposition to developing T1D?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_7 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_7 new file mode 100644 index 0000000..17c628d --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_7 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2007 - Bioinformatics_for_Genetices_MAZEN_SAEED.pdf", + "2007 - Bioinformatics_for_Geneticists.pdf", + "003 -Barnes- Bioinformatics_for_Geneticists.pdf", + "2007 - Bioinformatics_for_Geneticists.pdf", + "003 -Barnes- Bioinformatics_for_Geneticists.pdf", + "2007 - Bioinformatics_for_Genetices_MAZEN_SAEED.pdf", + "2007 - Bioinformatics_for_Genetices_MAZEN_SAEED.pdf", + "003 -Barnes- Bioinformatics_for_Geneticists.pdf", + "2007 - Bioinformatics_for_Geneticists.pdf", + "2021 - Old data and friends improve with age Advancements with the updated tools.pdf" + ], + "extraction_id": [ + "4ef84d22-b428-5386-bbc0-39dbd364d3d7", + "7524bdfa-63f8-57c1-b5fe-1edcf11c275e", + "c8e9c4b7-19c6-5426-83a2-6f8628b68ceb", + "9c89683f-aca5-57f9-b28d-62e9eb64377b", + "a3ae6875-b0fc-5a4e-866f-4fee99c7d2a2", + "bb247bfe-333b-553a-94e6-2dc1b13b4723", + "bb247bfe-333b-553a-94e6-2dc1b13b4723", + "a3ae6875-b0fc-5a4e-866f-4fee99c7d2a2", + "9c89683f-aca5-57f9-b28d-62e9eb64377b", + "92339404-3864-5d8d-8731-8f8d0e9ac24c" + ], + "document_id": [ + "139463d1-c63c-5c51-bf9c-9ccc356768e0", + "4ea8e1a8-e113-5f02-ad78-880b9c51a101", + "045edae8-468b-5725-be06-8cb4b8f6a92b", + "4ea8e1a8-e113-5f02-ad78-880b9c51a101", + "045edae8-468b-5725-be06-8cb4b8f6a92b", + "139463d1-c63c-5c51-bf9c-9ccc356768e0", + "139463d1-c63c-5c51-bf9c-9ccc356768e0", + "045edae8-468b-5725-be06-8cb4b8f6a92b", + "4ea8e1a8-e113-5f02-ad78-880b9c51a101", + "55cb2c81-b699-54df-96ab-2bf0b888031e" + ], + "id": [ + "chatcmpl-ADZA23VOOb8blXNwMegY44QCzuw8S", + "c36215f6-2230-58ef-b3eb-44d1799ba5c2", + "89a578c7-5961-5b88-9a6d-f338216702c3", + "81e589eb-aa51-5f2a-966f-31928fb31943", + "20738786-99f6-573d-963e-377782eeb7a6", + "ae4d4109-66f7-59be-92f5-dc10c9dc2dd6", + "7e15e9b2-c731-5ab0-85c0-b6b432623220", + "f0c00edb-f07d-5975-a16b-16a072d0f2d4", + "1bf9bb72-ebaa-51d1-82ce-aae2f16dd92b", + "e2e526cb-0ac3-51ff-a1c5-43ff032b5558", + "28386b6b-e00f-5c0f-91d2-6fd031e9433a" + ], + "contexts": [ + "supported by a signicant BLAST match to one or more expressed sequences or proteins. Ensembl also identies the positions of known human genes from public sequence database entries, usually using GENEWISE to predict their exon structures. The total set of Ensembl genes should therefore be a much more accurate reection of reality than ab initio predictions alone, but it is clear that some novel genes are missed (Hogenesch et al. , 2001). Of the many novel genes that are detected, some are", + "supported by a signicant BLAST match to one or more expressed sequences or proteins. Ensembl also identies the positions of known human genes from public sequence database entries, usually using GENEWISE to predict their exon structures. The total set of Ensembl genes should therefore be a much more accurate reection of reality than ab initio predictions alone, but it is clear that some novel genes are missed (Hogenesch et al. , 2001). Of the many novel genes that are detected, some are", + "supported by a signicant BLAST match to one or more expressed sequences or proteins. Ensembl also identies the positions of known human genes from public sequence database entries, usually using GENEWISE to predict their exon structures. The total set of Ensembl genes should therefore be a much more accurate reection of reality than ab initio predictions alone, but it is clear that some novel genes are missed (Hogenesch et al. , 2001). Of the many novel genes that are detected, some are", + "populations as Ensembl reects the progress of the International Haplotype Map Project (Thorisson et al. , 2005). More speculative data, such as GENSCAN-predicted exons that have not been incorporated into Ensembl-conrmed genes, may also be viewed. This means that the display can be used as a workbench for the user to develop personalized an- notation. For example, one may discover novel exons by nding GENSCAN exon predictions which coincide with good matches to a fragment of the draft mouse", + "populations as Ensembl reects the progress of the International Haplotype Map Project (Thorisson et al. , 2005). More speculative data, such as GENSCAN-predicted exons that have not been incorporated into Ensembl-conrmed genes, may also be viewed. This means that the display can be used as a workbench for the user to develop personalized an- notation. For example, one may discover novel exons by nding GENSCAN exon predictions which coincide with good matches to a fragment of the draft mouse", + "populations as Ensembl reects the progress of the International Haplotype Map Project (Thorisson et al. , 2005). More speculative data, such as GENSCAN-predicted exons that have not been incorporated into Ensembl-conrmed genes, may also be viewed. This means that the display can be used as a workbench for the user to develop personalized an- notation. For example, one may discover novel exons by nding GENSCAN exon predictions which coincide with good matches to a fragment of the draft mouse", + "Ostell/Spidey/ SSAHA at Sanger Institute http://www.sanger.ac.uk/Software/analysis/SSAHA/ human and mouse genomes, where there are large full-length cDNA collections to guide the hunt for genes, Ensembl should be very reliable. From the beginning, many genomic features other than predicted genes were included in Ensembl: different repeat classes, cytological bands, CpG island predic- tions, tRNA gene predictions, expressed sequence clusters from the UniGene database", + "Ostell/Spidey/ SSAHA at Sanger Institute http://www.sanger.ac.uk/Software/analysis/SSAHA/ human and mouse genomes, where there are large full-length cDNA collections to guide the hunt for genes, Ensembl should be very reliable. From the beginning, many genomic features other than predicted genes were included in Ensembl: different repeat classes, cytological bands, CpG island predic- tions, tRNA gene predictions, expressed sequence clusters from the UniGene database", + "Ostell/Spidey/ SSAHA at Sanger Institute http://www.sanger.ac.uk/Software/analysis/SSAHA/ human and mouse genomes, where there are large full-length cDNA collections to guide the hunt for genes, Ensembl should be very reliable. From the beginning, many genomic features other than predicted genes were included in Ensembl: different repeat classes, cytological bands, CpG island predic- tions, tRNA gene predictions, expressed sequence clusters from the UniGene database", + "comprehensive, powerful, flexible and interactive gene set enrichment analysis toolkit. Nucleic Acids Research ,45(W1), W130W137. [44] Zhang, B., Kirov, S., & Snoddy, J. (2005). WebGestalt: an integrated system for exploring gene sets in various biological contexts. Nucleic Acids Research ,33(Web Server issue), W741-8. [45] McLaren, W., Gil, L., Hunt, S. E., Riat, H. S., Ritchie, G. R. S., Thormann, A., Flicek, P ., et al. (2016). The ensembl variant effect predictor. Genome Biology ,17(1), 122." + ], + "answer": "Ensembl is a comprehensive genomic database that identifies the positions of known human genes from public sequence database entries, often using tools like GENEWISE to predict their exon structures. It aims to provide a more accurate reflection of reality compared to ab initio predictions alone [1]. Additionally, Ensembl includes various genomic features such as different repeat classes, cytological bands, CpG island predictions, tRNA gene predictions, and expressed sequence clusters from the UniGene database [7]. It also reflects the progress of projects like the International Haplotype Map Project and allows users to view speculative data and develop personalized annotations [4].", + "question": "What is ensembl" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_8 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_8 new file mode 100644 index 0000000..817666f --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_8 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2009 - Use of rat genomics for investigating the metabolic syndrome.pdf", + "2007 - The 20th International Mammalian Genome Conference Meeting Report.pdf", + "2009 - Use of rat genomics for investigating the metabolic syndrome.pdf", + "2009 - Use of rat genomics for investigating the metabolic syndrome.pdf", + "2007 - The 20th International Mammalian Genome Conference Meeting Report.pdf", + "2018 - Reproducibility and replicability of rodent phenotyping in preclinical studies.pdf", + "2014 - An evolutionarily conserved role for the aryl hydrocarbon receptor in the regulation of movement.pdf", + "2021 - Characterizing modifier genes of cardiac fibrosis phenotype in hypertrophic cardiomyopathy.pdf", + "2009 - Prioritizing genes for follow-up from genome wide association studies using information on gene expression in tissues relevant for type 2 diabetes mellitus.pdf", + "1999 - Functional Genomics and Rat Models.pdf" + ], + "extraction_id": [ + "29832535-60a1-5d5f-9909-6b38160bb183", + "b846ba66-5f3b-5ff4-bf49-4324909d52c5", + "9de9e2d1-114a-5fa2-ae3f-e646f59ee116", + "6027b20e-d480-5485-874b-62cbe06c9c57", + "b846ba66-5f3b-5ff4-bf49-4324909d52c5", + "6af0332a-a004-5933-91e1-fb3fcd42fc2d", + "10934f40-1148-5e89-a06d-01909c6807e7", + "31573012-679a-513b-a878-882723f39855", + "9d081a37-83c4-52f5-9ed1-43a05a44a62c", + "2a252f5b-a6a1-54bd-bc0a-c25642002243" + ], + "document_id": [ + "b06c0e90-1be1-5ba1-ad60-02b238070d07", + "d8b5b643-b7e7-5534-81fa-ee2e3679102d", + "b06c0e90-1be1-5ba1-ad60-02b238070d07", + "b06c0e90-1be1-5ba1-ad60-02b238070d07", + "d8b5b643-b7e7-5534-81fa-ee2e3679102d", + "2c03b37f-8c92-5fee-b19d-c582df5edb13", + "6a49b34d-b451-5b28-9e66-34c37b3ace6e", + "b29bc6c1-384d-5d91-bc0e-d6907116871c", + "4b1a56e7-6821-5504-b6da-27dcdf57c6a5", + "dd8b0499-f6d2-5202-8093-1a36d99796de" + ], + "id": [ + "chatcmpl-ADZA6mykgNrrlE5Rh6Pwwt7u5tbjM", + "9a5513d0-5aeb-5c7e-9343-1794cee269d1", + "e47b58b3-214c-55a7-8a82-ea5d3b3e91db", + "ddc43bd2-6e83-5e79-9f3e-682a77398eeb", + "b35435ab-72c5-50c2-ab3d-df1f6c9fc445", + "74508b6c-cbb0-56ea-8acb-47a1c271e820", + "54b6e5a7-49e5-5e2a-9c35-86c10f671cd8", + "c9ca3828-4dcd-554c-97ef-5af644093f54", + "27781fa3-a3bd-5d17-9e77-b039ec04126b", + "5c92f513-fea8-51fa-8432-929553dc9e32", + "976a6422-6743-5d92-b368-3712cd13d3d2" + ], + "contexts": [ + "417 Use of Rat Genomics for Investigating the Metabolic Syndrome and phenotypic traits are available to the scientific community in databases, such as Ensembl ( http://www.ensembl.or g), the Rat Genome Database ( http://www.rgd.mcw.ed u), eQTL Explorer ( http://www. web.bioinformatics.ic.ac.uk/eqtlexplore r) or GeneNetwork ( http://www.genenetwork.or g). Additional online rat genetic resources have been recently reviewed by Twigger et al. (11).", + "Howard Jacob (Medical College of Wisconsin) discussed the Rat Genome Database disease portals, a platform for genetic and genomic research. Thereare 845 strains of rats, 573 of which are inbred,including substrains. Historically, biologists usingthe rat as a model have been disease focused,studying diseases, related phenotypes, pathways, and biological processes. The Rat Genome Database", + "10. Consortium STAR, Saar K, Beck A, Bihoreau MT, Birney E, Brocklebank D, Chen Y et al (2008) SNP and haplotype mapping for genetic analysis in the rat. Nat Genet 40:560566 11. Twigger SN, Pruitt KD, Fernndez-Surez XM, Karolchik D, Worley KC, Maglott DR et al (2008) What everybody should know about the rat genome and its online resources. Nat Genet 40:523527 12. Butcher LM, Beck S (2008) Future impact of integrated high-throughput methylome anal- yses on human health and disease. J Genet", + "for linkage analyses using new methods of efficient genotyping based on genechip microarrays (10). In addition, over 800,000 ESTs and 5,000 annotated rat gene sequences are available for functional analyses of candidate genes. Development of new methodologies for high throughput phenotyping, such as expres- sion profiling, are becoming routinely used. Most of these genetic 2. Recent Advances in Rat Genetics and Genomics", + "serves as a repository of all rat QTLs related to thedisease area as well as associated mouse and humanQTLs, strains used as disease models, phenotypedata, related references, expression data, genome-wide views of disease genes, and QLS via GViewer,comparative maps of disease-related regions, cus-tomization of data sets and download options, and analysis and visualization of function and cellular localization makeup of gene sets (http://www.rgd.mcw.edu/). ENU mutagenesis is now being done with rats.", + "3. Can data sharing in rodent phenotyping help with replicability? Laboratory mice and rats are the main mammalian models currently used for high-throughput genomic and behavior genetic research, and are employed primarily to explore and test gene function. This is con- sidered by some to be the great challenge facing biologists today (Collins et al., 2007 ). Rodent models are used extensively as part of preclinical development and testing of treatments for disease in hu-", + "Bioinformatics and Statistical Analysis R was used for basic analysis of phenotypic data. GeneNetwork (www.genenetwork.org) was used for correlation and genetic analyses. The original phenotypes published in this paper and all microarray data generated in these cohorts are available for public analysis or download using the GeneNetwork database (Species: Mouse, Group: BXD, Type: Adipose mRNA, Liver mRNA, or Muscle mRNA, then select the EPFL datasets). The three", + "[23]. Shimoyama M, De Pons J, Hayman GT, Laulederkind SJ, Liu W, Nigam R, Petri V , Smith JR, Tutaj M, Wang S-J, The Rat Genome Database 2015: genomic, phenotypic and environmental variations and disease, Nucleic acids research 43(D1) (2014) D743D750. [PubMed: 25355511] [24]. Dickinson ME, Flenniken AM, Ji X, Teboul L, Wong MD, White JK, Meehan TF, Weninger WJ, Westerberg H, Adissu H, High-throughput discovery of novel developmental phenotypes, Nature 537(7621) (2016) 508. [PubMed: 27626380]", + "database (dbSNP) build 130 to identify genes located inthe vicinity of selected SNPs. Homologues of the genes formouse and rat were identified using the NCBI's Homolo-Gene release 64. We included only those genes that wereevolutionarily conserved in three different species namelyhuman, mouse and rat. Analysis of microarray data", + "(data not shown). Therefore, it seems logical to position the rat field so themechanistic, disease-based research canbe integrated into the awesome power ofthe human and mouse genome projects. Progress of the Rat Genome Project Recognizing the usefulness of the rat as amodel system, NIH, led by the NationalHeart, Lung, and Blood Institute(NHLBI), has funded the Rat GenomeProject (RGP), the Rat Expressed Se-quence Tag (RGP EST) Project, and the Rat" + ], + "answer": "You can use the Rat Genome Database (RGD) for genetic, genomics, phenotype, and disease-related data generated from rat research. The RGD is specifically mentioned as a platform for genetic and genomic research, including disease portals and phenotypic data [2], [5]. Additionally, it serves as a repository for various types of data such as QTLs, strains used as disease models, expression data, and more [5]. The database can be accessed at http://www.rgd.mcw.edu [1], [5].", + "question": "Which database can I use for genetic, genomics, phenotype, and disease-related data generated from rat research?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_9 b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_9 new file mode 100644 index 0000000..cab698c --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/human/intermediate_files/human_de_gn_9 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2010 - Integrated genomic approaches to identification of candidate genes underlying metabolic and cardiovascular phenotypes in the spontaneously hypertensive rat.pdf", + "2015 - Multipronged approach to identify and validate a novel upstream regulator of Sncg.pdf", + "The FEBS Journal - 2015 - Chintalapudi - Multipronged approach to identify and validate a novel upstream regulator of Sncg.pdf", + "2007 - Bioinformatics_for_Geneticists.pdf", + "003 -Barnes- Bioinformatics_for_Geneticists.pdf", + "2007 - Bioinformatics_for_Genetices_MAZEN_SAEED.pdf", + "2018 - Genetic Networks Activated by Blast Injury to the Eye.pdf", + "2010 - Identification of a Chr 11 quantitative trait locus that modulates proliferation in the rostral migratory stream of the adult mouse brain_.pdf", + "2011 - Genetic Regulatory Network Analysis for Rpe65 in the Eye of BXD Mice.pdf", + "2013 - Effects of Glaucoma on Chrna6 Expression in the Retina.pdf" + ], + "extraction_id": [ + "bb30622c-7f00-5ee4-928d-6f4f6f9f9e3d", + "ad4bf6de-f811-5ebc-82be-5fbd3aa1ba03", + "184d5422-8e35-57ca-b542-3bcfbd821b5a", + "af7722e9-a91e-533e-9403-e54ff59ffd73", + "9f3fd618-f56f-538a-b955-c7205a7c8107", + "d528a008-6931-562b-831c-f3c6dd925fac", + "c02d0625-3478-52d4-8339-78b2df351668", + "5eb43710-e1c0-5955-a34c-fb4b7204f421", + "86c6e14a-66bf-5a33-bcb6-750fbf259c87", + "1b9b34cc-e87b-53c2-aab5-2913d1e6fd25" + ], + "document_id": [ + "ec54d632-be36-5d11-8437-2233e07049a0", + "803e3b96-d4ed-5f1f-b788-eb7564d4f6b4", + "734e6a57-5d63-5e10-b01d-1ccc04618c8a", + "4ea8e1a8-e113-5f02-ad78-880b9c51a101", + "045edae8-468b-5725-be06-8cb4b8f6a92b", + "139463d1-c63c-5c51-bf9c-9ccc356768e0", + "57e3820f-7a5d-51f1-a0c6-ecfbdf546005", + "0b7c325a-0be0-54a2-9c8f-d4607d0f7151", + "44e70f2d-3bda-563c-ae0b-83833b98529b", + "3d3e03db-a961-5668-bd69-44039142fb87" + ], + "id": [ + "chatcmpl-ADZAAr5I5AVyGWpvGliJuDqL5V4HD", + "0b23b976-d97e-56ea-bb4d-6372a12cd48e", + "d71e9649-f56e-5376-9b97-79d450e932de", + "96700d1c-5c9b-545d-bec5-338a5aa8ea19", + "cbf58283-6ec3-5fc4-8a1e-73b1f0aa27f3", + "4a7fc44c-82d5-5808-a864-2dd4dd1ce33f", + "5008de52-b46c-5eb4-b033-66bdccda49a1", + "0db6fb13-b666-586a-bfe6-63b31e44ec5d", + "6d6bf436-2af4-5f8b-bf8b-81de331d2ad7", + "602fed11-6848-5916-89cd-67189890f37c", + "2c979a43-4536-5171-9f11-2c620a117551" + ], + "contexts": [ + "were identied using the RGD (68). This resource provides infor-mation regarding the physiological trait studied, strain combina-tion used, associated linkage statistics, and the genomic coordi-nates of the pQTL region. For pQTL regions identied from RGD,the original data (Supplementary Table S3) were examined, and the99% condence interval [within the 2 logarithm of the odds (LOD)drop from the peak of linkage] was estimated. Cis-eQTLs were", + "RGCs. The discovery of this relationship may help inguiding studies that explore the disease mechanismsassociated with altered protein transport and foldingin RGCs. In glaucoma, the identication and conr-mation of these two proteins in RGC health and dis-ease holds great promise for the development ofmolecular targets to slow or reverse RGC damage, which, in turn, will preserve vision. Experimental procedures Human donor eyes Human donor eyes were collected in accordance with the", + "RGCs. The discovery of this relationship may help inguiding studies that explore the disease mechanismsassociated with altered protein transport and foldingin RGCs. In glaucoma, the identication and conr-mation of these two proteins in RGC health and dis-ease holds great promise for the development ofmolecular targets to slow or reverse RGC damage, which, in turn, will preserve vision. Experimental procedures Human donor eyes Human donor eyes were collected in accordance with the", + "(http://www.cbil.upenn.edu/PaGE/). All microarray platforms and image-analysis software are supported. In addition, RAD is being used for CGH, ChIP , and SAGE data. RAD can produce MAGE-ML les for export of data to other databases or software packages. RAD is part of a more general Genomics Unied Schema, which provides a platform to integrate gene and transcript data from a variety of organisms. Advantages RAD is a scalable, Web-accessible database that can accommodate data from sev-", + "(http://www.cbil.upenn.edu/PaGE/). All microarray platforms and image-analysis software are supported. In addition, RAD is being used for CGH, ChIP , and SAGE data. RAD can produce MAGE-ML les for export of data to other databases or software packages. RAD is part of a more general Genomics Unied Schema, which provides a platform to integrate gene and transcript data from a variety of organisms. Advantages RAD is a scalable, Web-accessible database that can accommodate data from sev-", + "(http://www.cbil.upenn.edu/PaGE/). All microarray platforms and image-analysis software are supported. In addition, RAD is being used for CGH, ChIP , and SAGE data. RAD can produce MAGE-ML les for export of data to other databases or software packages. RAD is part of a more general Genomics Unied Schema, which provides a platform to integrate gene and transcript data from a variety of organisms. Advantages RAD is a scalable, Web-accessible database that can accommodate data from sev-", + "differentiallysusceptibletodeath,withalpha-RGCsandintrinsicallyphotosensitiveRGCs (ipRGCs) being less sensitive to cell death than other RGC subtypes in a mouse model of glaucoma. Keywo rds: retinal ganglion cells, gene regulatory networks, transcription factors, recombinant inbred strain, subtypes INTRODUCTION Theretinalganglioncell(RGC)isthenaloutputneuronoftheretina,projectingthroughtheoptic nerve to the brain, where it targets a number of functionally distinct areas: for visual perception,", + "AG18245 (DG), NIAAA U01AA014425 (LL), and P20 DA021131 (RW). We thank Derek Rains, Gurjit Rai, Meifen Lu, Richard Cushing, Erich Brauer, and Alan Weatherford for their invaluable technical assistance. Abbreviations BrdU bromodeoxyuridine CV cresyl violet GF growth fraction LOD likelihood of the odds LRS likelihood ratio statistic NSCs neural stem cells OB olfactory bulb DG dentate gyrus QTL quantitative trait locus RI recombinant inbred RMS rostral migratory stream SGZ subgranular zone", + "Rdh10, Lrat,) whose biology functions are directly associated w ith the metabolism of retinoid. RGR (retinal G protein-coupled receptor, protein of Rgr ) is a protein that structurally resembles visual pigments and other G protein-coupled recepto rs. Light isomerizes 11- cis - into all-trans - retinal, triggering a conformational transition of the opsin molecule that initiates phototransduction . After bleaching all- trans -retinal leaves the opsin, and light sensitivity mu st be restored by", + "GeneNetwork system, we were able to define robust expression covariance signatures for RGCs and confirmed membership of Chrna6 within the RGC cell type of the retina using new array data sets and RT-PCR tracking through a progressive RGC loss mouse line. Chrna6 can be added as reliable biomarker for RGCs and RGC loss secondary to glaucoma. It is important to note that in addition to providing evidence for Chrna6 expression as a" + ], + "answer": "RGD refers to a resource that provides information regarding physiological traits studied, strain combinations used, associated linkage statistics, and the genomic coordinates of the pQTL (protein Quantitative Trait Loci) region. For pQTL regions identified from RGD, the original data were examined, and the 99% confidence interval within the 2 logarithm of the odds (LOD) drop from the peak of linkage was estimated [1].", + "question": "What is RGD?" +}
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