Got R2R responses for all human questions.

12 files changed, 18164 insertions, 0 deletions

diff --git a/gnqa/paper2_eval/data/dataset/domain_expert_aging_1 b/gnqa/paper2_eval/data/dataset/domain_expert_aging_1
index 81d5073..1a249db 100644
--- a/gnqa/paper2_eval/data/dataset/domain_expert_aging_1
+++ b/gnqa/paper2_eval/data/dataset/domain_expert_aging_1
@@ -62,4 +62,132 @@
   ],
   "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"
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+    "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"
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+    "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?"
 }
\ No newline at end of file
diff --git a/gnqa/paper2_eval/data/dataset/domain_expert_aging_2 b/gnqa/paper2_eval/data/dataset/domain_expert_aging_2
index c4e6321..690772d 100644
--- a/gnqa/paper2_eval/data/dataset/domain_expert_aging_2
+++ b/gnqa/paper2_eval/data/dataset/domain_expert_aging_2
@@ -62,4 +62,132 @@
   ],
   "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"
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+    "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?"
+}{
+  "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"
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+    "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
index 1f78fa9..d89d819 100644
--- a/gnqa/paper2_eval/data/dataset/domain_expert_aging_3
+++ b/gnqa/paper2_eval/data/dataset/domain_expert_aging_3
@@ -62,4 +62,132 @@
   ],
   "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"
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+    "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"
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+    "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
index 09d1513..accd8c0 100644
--- a/gnqa/paper2_eval/data/dataset/domain_expert_aging_4
+++ b/gnqa/paper2_eval/data/dataset/domain_expert_aging_4
@@ -62,4 +62,132 @@
   ],
   "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": [
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+  ],
+  "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",
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+    "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
index c0b3b7f..c1c597d 100644
--- a/gnqa/paper2_eval/data/dataset/domain_expert_aging_5
+++ b/gnqa/paper2_eval/data/dataset/domain_expert_aging_5
@@ -62,4 +62,132 @@
   ],
   "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"
+  ],
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+  ],
+  "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"
+  ],
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+    "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
index 8e291e1..455da0d 100644
--- a/gnqa/paper2_eval/data/dataset/domain_expert_aging_6
+++ b/gnqa/paper2_eval/data/dataset/domain_expert_aging_6
@@ -62,4 +62,132 @@
   ],
   "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"
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+  "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"
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+    "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/gpt4o-queries.json b/gnqa/paper2_eval/data/gpt4o-queries.json
new file mode 100644
index 0000000..74c18b0
--- /dev/null
+++ b/gnqa/paper2_eval/data/gpt4o-queries.json
@@ -0,0 +1,159 @@
+[
+    {
+        "level": "domainexpert",
+        "domain": "diabetes",
+        "query": [
+            "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 Alström 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?"
+        ]
+    },
+    {
+        "level": "citizenscientist",
+        "domain": "diabetes",
+        "query": [
+            "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?"
+        ]
+    },
+    {
+        "level":"citizenscientist",
+        "domain": "aging",
+        "query": [
+            "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?"
+        ]
+    },
+    {
+        "level":"domainexpert",
+        "domain":"aging",
+        "query": [
+            "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?"
+        ]
+    },
+    {
+        "level": "domainexpert",
+        "domain": "gn",
+        "query": [
+            "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?"
+        ]
+    },
+    {
+        "level": "citizenscientist",
+        "domain": "gn",
+        "query": [
+            "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’s the difference between looking at one gene and studying a whole gene network?",
+            "How can GeneNetwork.org contribute to advancements in genetic engineering?"
+        ]
+    }
+]
diff --git a/gnqa/paper2_eval/data/responses/human/cs_aging_responses.json b/gnqa/paper2_eval/data/responses/human/cs_aging_responses.json
new file mode 100644
index 0000000..709e21e
--- /dev/null
+++ b/gnqa/paper2_eval/data/responses/human/cs_aging_responses.json
@@ -0,0 +1,2095 @@
+{
+  "1": {
+    "results": {
+      "completion": {
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+        "choices": [
+          {
+            "finish_reason": "stop",
+            "index": 0,
+            "message": {
+              "content": "Here are the studies that include rapamycin:\n\n1. **Yin L, Ye S, Chen Z, Zeng Y (2012)**: \"Rapamycin preconditioning attenuates transient focal cerebral ischemia/reperfusion injury in mice\" [1].\n2. **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].\n3. **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].\n4. **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].\n5. **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].\n6. **Wilkinson JE, Burmeister L, Brooks SV, Chan CC, Friedline S, Harrison DE, et al. (2012)**: \"Rapamycin slows aging in mice\" [7].\n7. **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].\n8. **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].\n\nThese references provide a comprehensive list of studies involving rapamycin.",
+              "role": "assistant"
+            }
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+        ],
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+        }
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+      "search_results": {
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+          {
+            "id": "57be0715-77c8-55e3-8239-56e1fa11a543",
+            "score": 0.6735478043556213,
+            "metadata": {
+              "text": "168. Yin L, Ye S, Chen Z, Zeng Y . Rapamycin preconditioning attenuates tran-\nsient focal cerebral ischemia/reperfusion injury in mice. Int J Neurosci. 2012;122:748756. doi: 10.3109/00207454.2012.721827\n 169. Spilman P, Podlutskaya N, Hart MJ, Debnath J, Gorostiza O, Bredesen \nD, Richardson A, Strong R, Galvan V . Inhibition of mTOR by rapamy-cin abolishes cognitive deficits and reduces amyloid-beta levels in a \nmouse model of Alzheimers disease. PLoS One. 2010;5:e9979. doi: \n10.1371/journal.pone.0009979",
+              "title": "2018 -  Mechanisms of Vascular Aging.pdf",
+              "version": "v0",
+              "chunk_order": 262,
+              "document_id": "659b84b6-63dd-5bb1-80ee-7478ed3c47e3",
+              "extraction_id": "3e65812c-453e-53aa-83ab-92f2ce15da29",
+              "associatedQuery": "List as many studies as you can that include rapamycin."
+            }
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+          {
+            "id": "03e62089-fef5-5ed5-bf7f-36ff595fbaea",
+            "score": 0.6702993512153625,
+            "metadata": {
+              "text": "Anisimov VN, Zabezhinski MA, Popovich IG, Piskunova TS,\nSemenchenko AV, Tyndyk ML, Yurova MN, Rosenfeld SV,Blagosklonny MV (2011b) Rapamycin increases lifespan and\ninhibits spontaneous tumorigenesis in inbred female mice. Cell\nCycle 10:42304236\nAugustine JJ, Bodziak KA, Hricik DE (2007) Use of sirolimus in\nsolid organ transplantation. Drugs 67:369391\nBannister CA, Holden SE, Jenkins-Jones S, Morgan CL, Halcox JP,",
+              "title": "2016 - The dog aging project translational geroscience in companion.pdf",
+              "version": "v0",
+              "chunk_order": 81,
+              "document_id": "e841c6bd-78b8-56e1-b3dd-e2bcc8a0f590",
+              "extraction_id": "2c1fcce1-b723-5f9f-8f66-49ed7895f2ac",
+              "associatedQuery": "List as many studies as you can that include rapamycin."
+            }
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+          {
+            "id": "fe5b60e5-ded6-5950-bc1c-72cb39e16234",
+            "score": 0.6631683707237244,
+            "metadata": {
+              "text": "ACCEPTED MANUSCRIPTACCEPTED MANUSCRIPT\nmTOR complex 2 (mTORC2), the less clearly identified  and less sensitive to rapamycin. Most information to \ndate on the r ole of mTOR has studied the insulin/nutrient signaling via the mTORC1 and significantly less in \nknown about the role of mTORC2 ( in this review, future references measure either mTORC1 or general mTOR \nactivity )[251]. Earlier this decade studies showed that decreasing TOR signaling, genetically or with rapamycin,",
+              "title": "2015 - Cellular and Molecular Biology of Aging Endothelial Cells.pdf",
+              "version": "v0",
+              "chunk_order": 106,
+              "document_id": "815d7f3e-e219-502f-aba0-57a68ae787d3",
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+            "id": "d7dcefa4-133c-594c-b8a8-38fe945c6b5c",
+            "score": 0.6623727880494137,
+            "metadata": {
+              "text": "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\nCell148, January 20, 2012 2012 Elsevier Inc. 55",
+              "title": "2012 - Aging, Rejuvenation, and Epigenetic.pdf",
+              "version": "v0",
+              "chunk_order": 139,
+              "document_id": "bde26feb-f423-51b0-89ec-6f079bfc8b17",
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+          {
+            "id": "907d7d31-04db-5f66-b390-7740142af182",
+            "score": 0.6617367267608643,
+            "metadata": {
+              "text": "96. Lamming DW, Ye L, Katajisto P, Goncalves MD, Saitoh M, Stevens DM, etal. Rapamycin- \ninduced insulin resistance is mediated by mTORC2 loss and uncoupled from longevity. \nScience. 2012;335:163843.\n 97. Tataranni T, Biondi G, Cariello M, Mangino M, Colucci G, Rutigliano M, etal. Rapamycin- \ninduced hypophosphatemia and insulin resistance are associated with mTORC2 activation \nand klotho expression. Am J Transplant. 2011;11(8):165664.",
+              "title": "2020 - Clinical Genetics and Genomics of Aging.pdf",
+              "version": "v0",
+              "chunk_order": 200,
+              "document_id": "62b635c3-040e-512a-b016-6ef295308a1e",
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+              "text": "ing these aspects in future studies on the effects of resveratrol could help to study in \ngreater depth the mechanisms of action of this compound [56].\n Rapamycin\nRapamycin is a macrolide isolated from Streptomyces hygroscopicus, a bacteria \nfrom Pascua Island (Rapa Nui). It has functions as an antibiotic, an immune sup-\npressant drug, and it is also proposed as a CRM.After the first studies, it was found \nthat rapamycin could induce the extension of the replicative life of yeast through the",
+              "title": "2020 - Clinical Genetics and Genomics of Aging.pdf",
+              "version": "v0",
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+              "document_id": "62b635c3-040e-512a-b016-6ef295308a1e",
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+          {
+            "id": "a9666b11-4567-52dd-90c8-be2238dafdcb",
+            "score": 0.6544352769851685,
+            "metadata": {
+              "text": "[257] Wilkinson JE, Burmeister L, Brooks SV, Chan CC, Friedline S, Harrison DE, et al. Rapamycin slows \naging in mi ce. Aging Cell. 2012;11:675 -82. \n[258] Selman C, Tullet JM, Wieser D, Irvine E, Lingard SJ, Choudhury AI, et al. Ribosomal protein S6 kinase 1 \nsignaling regulates mammalian life span. Science. 2009;326:140 -4. \n[259] Reihl K, Seals D, Henson G, LaRocca T, Mag erko K, Bosshardt G, et al. Dietary rapamycin selectively \nimproves arterial function in old mice. FASEB Journal. 2013;27:1194.17.",
+              "title": "2015 - Cellular and Molecular Biology of Aging Endothelial Cells.pdf",
+              "version": "v0",
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+              "document_id": "815d7f3e-e219-502f-aba0-57a68ae787d3",
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+              "associatedQuery": "List as many studies as you can that include rapamycin."
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+          {
+            "id": "729598dc-94e6-5f52-ae19-071c959c7dd2",
+            "score": 0.6458548518505792,
+            "metadata": {
+              "text": "29. Wilkinson JE, Burmeister L, Brooks SV, Chan C-C, Friedline S, Harrison DE,\net al. Rapamycin slows aging in mice. Aging Cell. 2012;11:675 82.\n30. Lamming DW, Ye L, Katajisto P, Goncalves MD, Saitoh M, Stevens DM, et al.\nRapamycin-induced insulin resistance is mediated by mTORC2 loss and\nuncoupled from longevity. Science. 2012;335:1638 43.\n31. Zampieri M, Ciccarone F, Calabrese R, Franceschi C, Brkle A, Caiafa P.\nReconfiguration of DNA methylation in aging. Mech Ageing Dev. 2015;151:60 70.",
+              "title": "2017 - Epigenetic aging signatures in mice livers.pdf",
+              "version": "v0",
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+              "document_id": "b20b11a6-1490-51b8-9218-c441a2e65ba7",
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+              "text": "files [55, 62]. Of note, rapamycin in particular appears to\ninduce additional changes u nrelated to age-associated\nchanges. While both CR and rapamycin induced these\nnon-age-related effects, this effect was much more marked\nfor rapamycin. These non age-related epigenetic changes\ninclude gains of methylation at genes, enhancers and CpG\nislands and losses of methylation at genes and enhancers.\nConceivably, such non age-related effects of rapamycin in",
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+              "text": "23\n 94. Chakrabarti P, English T, Shi J, Smas CM, Kandror KV .Mammalian target of rapamycin \ncomplex 1 suppresses lipolysis, stimulates lipogenesis, and promotes fat storage. Diabetes. \n2010;59:77581.\n 95. Miller RA, Harrison DE, Astle CM, Fernandez E, Flurkey K, Han M, et al. Rapamycin- \nmediated lifespan increase in mice is dose and sex dependent and metabolically distinct from \ndietary restriction. Aging Cell. 2014;13:46877.",
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+              "content": "Mapping gene loci that control aging in humans is difficult due to several reasons:\n\n1. **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].\n\n2. **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].\n\n3. **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].\n\n4. **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].\n\n5. **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].\n\n6. **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].\n\nThese factors collectively contribute to the difficulty in mapping gene loci that control aging in humans.",
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+              "text": "that is differentiated at hundreds of loci. Many ofthe loci that control aging in Drosophila will not have\nthe 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\npriori which group any particular locus will belong\nin. Thus, the individual mutants that increase\nDrosophila lifespan may or may not come from loci",
+              "title": "2002 - Pharmacology, Genomics, and the Evolutionary Biology.pdf",
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+              "text": "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.\nAdditional association studies with these families and repli-",
+              "title": "2001 - A genome-wide scan for linkage to human.pdf",
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+              "text": "understanding of molecular mechanisms underlyingthe human ageing process. Like other complexhuman traits, nding common variants that accountfor the entire genetic component of human lifespan\nvariability has proved difcult. If rare variants rather\nthan 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",
+              "title": "2010 - Genetics and genomics of human ageing.pdf",
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+              "text": "understanding of molecular mechanisms underlyingthe human ageing process. Like other complexhuman traits, nding common variants that accountfor the entire genetic component of human lifespan\nvariability has proved difcult. If rare variants rather\nthan 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",
+              "title": "2011 - Genetics and genomics of human ageing.pdf",
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+              "text": "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-\nescence pathways and fundamental differences in aging between\nmice and humans (Fig. 2E).\nAging-associated genes can alternatively be identified in a",
+              "title": "2023 - A transcriptome-based single-cell biological age model.pdf",
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+              "text": "Several explanations are possible for the lack of genome-\nwide 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",
+              "title": "2011 - A genome-wide association study of aging.pdf",
+              "version": "v0",
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+              "text": "discover core mechanisms of regulation.ANALYSIS OF HUMAN VARIATION IN\nTHE GENETIC CONTROL OF LONGEVITY\nHeritability studies have convincingly demonstrated that at\nleast some fraction of human lifespan is heritable. In tandem,\nlarge-scale genome-wide association studies (GWAS) have\nidentied numerous loci associated with age-related traits\n(Buniello et al., 2019). While genetic studies have functionally\nshown an inverse eect of multiple age-related, disease-",
+              "title": "2021 - Footprints in the Sand Deep Taxonomic Comparisons in Vertebrate Genomics to Unveil the Genetic Programs of Human Longevity.pdf",
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+              "text": "[12]More than 1000\nloci exhibit age-dependent changes in geneexpression (1264 genes). This is a substantialproblem, because not all of these loci will be causally\ninvolved in aging, and there are so many to sort out.\nAn additional application of gene chip technologyis to compare ies with and without a lifespanmodulating physiological treatment. Pletcher et al.",
+              "title": "2002 - Pharmacology, Genomics, and the Evolutionary Biology.pdf",
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+              "text": "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",
+              "title": "2011 - Genomics of human longevity.pdf",
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+              "text": "Kenyon, 2010; Vellai et al., 2003 ). However, in humans,\ncommon variants within genes involved in these pathways\nhave not been consistently associated with lifespan ( Chris-tensen et al., 2006; Kenyon, 2010; Kuningas et al., 2008;\nVijg and Suh, 2005 ).\nThe lack of success in the identication of genes related\nto aging in humans may be due to the complexity of the\nphenotype. One approach to investigate aging and longevity\nis to compare frequencies of genetic variants between no-",
+              "title": "2011 - A genome-wide association study of aging.pdf",
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+              "content": "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].",
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+              "text": "Cell Death\nA form of programmed cell death, apoptosis is necessary for normal cell turnover and is essential to a\nplethora of other biological processes. Apoptosis can be executed via Bcl-2 activation of caspases,\nvia 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\ncaspases, eventually leading to the death of the cell. With age, however, apoptotic activity changes.",
+              "title": "2020 - Protecting the Aging Genome.pdf",
+              "version": "v0",
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+              "text": "(during development and for maintenance of homeostasis) in multi -cellular \norganism is apoptosis, which is character ized by a  sequence of well -defined \nevents resulting in cell destruction. Dysregulation of apoptosis is responsible for \nmany physiological health problems and diseases; therefore, it is necessary to \nunderstand  the responsible signaling pathways and complex interplay of \ncellularprocesses. Results:   A combined mathematical model of apoptosis",
+              "title": "2013 - Pathways, Networks and Systems Medicine Conferences.pdf",
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+              "text": "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",
+              "title": "2012 - Pleiotropic Cellular Functions of PARP1 in Longevity.pdf",
+              "version": "v0",
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+              "document_id": "e67324c0-474b-5280-8cbc-3778c6c0e5f0",
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+            "score": 0.5777220381664026,
+            "metadata": {
+              "text": "tion of cells undergoing apoptosis.   Immunol Today   14:  131  136.   \n   82.     Platt   N,     Silva   RP,   da    Gordon   S    (1998)   Recognizing death: the \nphagocytosis of apoptotic cells.   Trends Cell Biol   8:  365  372.   \n   83.     Giles   KM,     Hart   SP,     Haslett   C,     Rossi   AG,     Dransfield   I    (2000) \n  An appetite for apoptotic cells? Controversies and challenges. \n  Br J Haematol   109:  1  12.",
+              "title": "2008 - Biotools for Determining the Genetics of Susceptibility to Infectious Diseases.pdf",
+              "version": "v0",
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+            "score": 0.5777220381664026,
+            "metadata": {
+              "text": "tion of cells undergoing apoptosis.   Immunol Today   14:  131  136.   \n   82.     Platt   N,     Silva   RP,   da    Gordon   S    (1998)   Recognizing death: the \nphagocytosis of apoptotic cells.   Trends Cell Biol   8:  365  372.   \n   83.     Giles   KM,     Hart   SP,     Haslett   C,     Rossi   AG,     Dransfield   I    (2000) \n  An appetite for apoptotic cells? Controversies and challenges. \n  Br J Haematol   109:  1  12.",
+              "title": "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",
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+              "text": "the induc-tion of apoptosis.",
+              "title": "1999 - The NOD mouse model of type 1 diabetes.pdf",
+              "version": "v0",
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+              "document_id": "cc139813-6771-5434-b948-381291c86509",
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+              "text": "to cancer , b ut probably not rele v ant to the i ntrinsic aging process i n yeast.\nApoptosis\nCell suicide, or apoptosis, i s a well-studied biological phenomenon in multicellular\nor g anisms t hat allo ws specic cells to be remo v e d during t he de v e lopment of com-\nple 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-",
+              "title": "2012 - Genome-Wide Analysis of Yeast Aging.pdf",
+              "version": "v0",
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+              "text": "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",
+              "title": "2005 -Liang- GENETIC REGULATION OF HEMATOPOIETIC STEM CELL NUMBERS IN MICE.pdf",
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+            "id": "3a15c325-3b6e-54ff-a58a-0e61631ed073",
+            "score": 0.5636914632504316,
+            "metadata": {
+              "text": "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",
+              "title": "2005 - GENETIC REGULATION OF HEMATOPOIETIC STEM CELL NUMBERS IN MICE.pdf",
+              "version": "v0",
+              "chunk_order": 89,
+              "document_id": "815a8b4a-6902-5bb9-87e0-563e1ba7a38a",
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+            "id": "fe4906aa-37b1-5514-962c-1e8dc5b2fb13",
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+              "text": "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),",
+              "title": "2006 - Molecular pathogenesis of thyroid cancer the significance.pdf",
+              "version": "v0",
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+              "content": "Several genes are involved in the aging process, as identified in the provided context:\n\n1. **APOE**: This gene is involved in lipoprotein metabolism and is one of the candidate genes related to longevity [2].\n2. **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].\n3. **Genes involved in DNA repair and chromatin remodeling**: These genes are down-regulated during aging, indicating their involvement in the aging process [3], [7].\n4. **Genes inducing apoptosis**: These genes are also associated with the aging process [4].\n5. **Werners syndrome gene**: Mutations in this gene result in segmental progeroid syndromes, which are related to aging [5].\n6. **Genes in the insulin/insulin-like signaling pathway**: These genes are critical in pathways previously related to aging [5].\n7. **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].\n\nThese genes collectively contribute to various aspects of the aging process, including genomic stability, cellular senescence, and response to oxidative stress.",
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+              "text": "OTHER AGING RELATED GENES",
+              "title": "2010 - Do different neurons age differently Direct genome-wide analysis of aging in single identified cholinergic neurons.pdf",
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+              "text": "ation of the process of aging.\nStudies revealed from 300 to 750 genes related to longev-\nity that are critically involved in a variety of life activities,\nsuch as growth and developme nt, energy metabolism, oxi-\ndative stress, genomic stability maintenance, and neurocog-\nnition [ 4]. These candidate genes include mainly APOE, a\ngene involved in lipoprotein metabolism [ 5,6]. Others are\nthose involved in cell cycle regulation, cell growth and\nsignal transduction, the maintenance of genome stability,",
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+              "text": "down-regulated during aging were genes involved in DNA repair and chromatin remodelling.\n55 While these studies revealed thousands of age-regulated genes, \nthe 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 \ncloser. \nAdding the dimension of epigenetics",
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+              "text": "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.",
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+              "text": "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  \net \n  \nal \n., 2005a). The",
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+              "text": "genes driving cellular senescence, and perform various integrative analyses. Genes inducing cellular senescence\ntend to be overexpressed with age in human tissues and are significantly overrepresented in anti-longevity and\ntumor-suppressor genes, while genes inhibiting cellular senescence overlap with pro-longevity and oncogenes.\nFurthermore, cellular senescence genes are strongly conserved in mammals but not in invertebrates. We also build",
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+              "text": "those down-regulated during aging were genes involved in\nDNA repair and chromatin remodelling (Chambers et al.\n2007b ). While these studies revealed thousands of age-\nregulated genes, the ultimate causes of these expressionperturbations remain unknown. Analyzing age-dependent\ngene expression changes using multidimensional genetical\ngenomics could bring the identification of genes causingthe age-induced alterations and thereby future therapeutic\nintervention strategies one step closer.",
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+              "text": "lar signatures of mammalian aging. Some of the genes",
+              "title": "2012 - Genome-Environment Interactions That Modulate.pdf",
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+              "text": "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,",
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+              "text": "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.\nAGING GENES AS TARGETS FOR DRUG DISCOVERY 91",
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+              "content": "The aging process is caused by several factors, including:\n\n1. **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].\n2. **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].\n3. **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].\n4. **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].\n5. **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].\n6. **Accumulated Defects in Function**: Progressive changes in a cell or organism lead to accumulated defects in function, resulting in system failure and death [8].\n7. **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].\n\nThese factors collectively contribute to the aging process and the associated decline in physiological functions.",
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+              "text": "in the aging process.",
+              "title": "2012 - Genome-Environment Interactions That Modulate.pdf",
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+              "text": "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",
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+              "text": "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,",
+              "title": "2007 - Genome Dynamics and Transcriptional Deregulation.pdf",
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+              "text": "this process between proteins and other macromolecules responsible for ageing, \nwhile the theory of free radicals suggests that ageing is the result of inadequate pro-\ntection against cell and tissue damage by free radicals and oxidative stress through-\nout life. Finally, the wear-and-tear theory poses that the cumulative damage that \neventually leads to ageing and death is, in fact, the result of the continuous function-\ning of vital processes, during which stochastic errors gradually arise.",
+              "title": "2020 - Clinical Genetics and Genomics of Aging.pdf",
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+              "text": "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\ncause of aging, since it is a consistent response of a sizable fraction of the cell\npopulation. 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.",
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+              "title": "2001 - The genetics of aging.pdf",
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+              "text": "INTRODUCTION \n \nThe aging process represents progressive changes in a \ncell 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 \naccumulated damage to DNA.  Such damage may result     \nwww.impactaging.com AGING, January 2009, Vol. 1. No 1\n Review",
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+              "text": "that induce complex molecular changes and, in turn, a\ndeterioration of cellular structures and function. These\nchanges are major causes of age-related diseases like\ncancer or cardiovascular disorders [1, 2]. The main mo-\nlecular adaptations occurring during aging are loss ofgenomic stability due to reduced DNA repair capacities\n[3], loss of proliferative potential caused by increased\nsenescence [1, 4], and age-related alterations in the\nDNA-methylation patterns that affect cellular plasticity",
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+              "text": "cause in turn metabolic and cognitive alterations, resulting in increasing vulnerabil-\nity to environmental challenge and a growing risk for disease and death [1]. Since \naging comprises the greatest risk factor for a variety of chronic diseases, includ-\ning cancer, cardiovascular disorders, and neurodegenerative diseases [2], one of the \ngoals of biomedical research is to decipher the molecular mechanism underlying \naging, which in turn might facilitate the development of treatments aimed at delay-",
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+              "content": "Several genes are involved in the aging process, as identified in various studies:\n\n1. **APOE**: This gene is involved in lipoprotein metabolism and is critically related to longevity [4].\n2. **Superoxide dismutase**: A free-radical scavenger gene that has been shown to increase lifespan in model organisms [8].\n3. **Methuselah**: A potential G protein-coupled receptor gene in *Drosophila melanogaster* associated with increased lifespan [8].\n4. **p66shc**: An oxidative stress-response gene involved in aging [8].\n5. **Insulin/IGF1/GH pathway genes**: These genes are part of a pathway that affects longevity across different model organisms [5].\n6. **Genes involved in DNA repair and chromatin remodeling**: These genes are down-regulated during aging [7].\n7. **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].\n\nAdditionally, 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].",
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+              "title": "2010 - Do different neurons age differently Direct genome-wide analysis of aging in single identified cholinergic neurons.pdf",
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+              "text": "ation of the process of aging.\nStudies revealed from 300 to 750 genes related to longev-\nity that are critically involved in a variety of life activities,\nsuch as growth and developme nt, energy metabolism, oxi-\ndative stress, genomic stability maintenance, and neurocog-\nnition [ 4]. These candidate genes include mainly APOE, a\ngene involved in lipoprotein metabolism [ 5,6]. Others are\nthose involved in cell cycle regulation, cell growth and\nsignal transduction, the maintenance of genome stability,",
+              "title": "2020 - Whole-genome sequencing of Chinese.pdf",
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+              "text": "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",
+              "title": "2012 - Genome-Environment Interactions That Modulate.pdf",
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+              "text": "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  \net \n  \nal \n., 2005a). The",
+              "title": "2009 - The Human Ageing Genomic Resources online.pdf",
+              "version": "v0",
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+              "text": "down-regulated during aging were genes involved in DNA repair and chromatin remodelling.\n55 While these studies revealed thousands of age-regulated genes, \nthe 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 \ncloser. \nAdding the dimension of epigenetics",
+              "title": "2011 - EXPLOITING NATURAL AND INDUCED GENETIC VARIATION TO STUDY HEMATOPOIESIS.pdf",
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+              "text": "Aging is a biological process universal to eukaryotic organ-\nisms, and its underlying mechanisms are under intensive\nstudy. 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\ngenes include superoxide dismutase , a free-radical scavenger;\nmethuselah , a potential G protein-coupled receptor, in Drosoph-\nila melanogaster ; and p66\nshc, an oxidative stress-response gene, in",
+              "title": "2000 - Genome-wide study of aging and oxidative stress.pdf",
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+            "id": "bf2cd208-273f-5848-b243-df8b95ea7833",
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+              "text": "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.",
+              "title": "2007 - Temporal and spatial transcriptional profiles.pdf",
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+              "text": "5. Jiang CH, Tsien JZ, Schultz PG, Hu Y (2001) The effects of aging on gene\nexpression in the hypothalamus and cortex of mice. Proc Natl Acad Sci U S A\n98: 19301934.\n6. Lu T, Pan Y, Kao SY, Li C, Kohane I, et al. (2004) Gene regulation and DNA\ndamage in the ageing human brain. Nature 429: 883891.\n7. Fraser HB, Khaitovich P, Plotkin JB, Paabo S, Eisen MB (2005) Aging and gene\nexpression in the primate brain. PLoS Biol 3: e274.\n8. Zahn JM, Poosala S, Owen AB, Ingram DK, Lustig A, et al. (2007) AGEMAP: a",
+              "title": "2008 - Evolution of the Aging Brain Transcriptome and Synaptic.pdf",
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+              "content": "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:\n\n1. **APOE**: Involved in lipoprotein metabolism [2].\n2. **Superoxide dismutase**: A free-radical scavenger [8].\n3. **Methuselah**: A potential G protein-coupled receptor in *Drosophila melanogaster* [8].\n4. **p66shc**: An oxidative stress-response gene [8].\n5. **Genes involved in DNA repair and chromatin remodeling**: These genes are down-regulated during aging [3], [10].\n6. **Genes involved in cell cycle regulation, cell growth, and signal transduction**: These genes help maintain genome stability [2].\n7. **Genes involved in inducing apoptosis**: These genes are associated with damage response [5].\n8. **Genes in the insulin/insulin-like signaling pathway**: Critical in pathways previously related to aging [4].\n\nThese genes collectively contribute to processes such as growth and development, energy metabolism, oxidative stress response, genomic stability maintenance, and neurocognition [2].",
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+              "text": "OTHER AGING RELATED GENES",
+              "title": "2010 - Do different neurons age differently Direct genome-wide analysis of aging in single identified cholinergic neurons.pdf",
+              "version": "v0",
+              "chunk_order": 73,
+              "document_id": "153b070f-0291-5ed4-ad33-edea5e3fa8f7",
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+              "text": "ation of the process of aging.\nStudies revealed from 300 to 750 genes related to longev-\nity that are critically involved in a variety of life activities,\nsuch as growth and developme nt, energy metabolism, oxi-\ndative stress, genomic stability maintenance, and neurocog-\nnition [ 4]. These candidate genes include mainly APOE, a\ngene involved in lipoprotein metabolism [ 5,6]. Others are\nthose involved in cell cycle regulation, cell growth and\nsignal transduction, the maintenance of genome stability,",
+              "title": "2020 - Whole-genome sequencing of Chinese.pdf",
+              "version": "v0",
+              "chunk_order": 4,
+              "document_id": "9ac921c7-3991-579b-bd53-7966b91e3aae",
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+            "id": "8f8848f4-d5fb-5f8c-a6b1-0f965f2abbc6",
+            "score": 0.7322750091552734,
+            "metadata": {
+              "text": "down-regulated during aging were genes involved in DNA repair and chromatin remodelling.\n55 While these studies revealed thousands of age-regulated genes, \nthe 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 \ncloser. \nAdding the dimension of epigenetics",
+              "title": "2011 - EXPLOITING NATURAL AND INDUCED GENETIC VARIATION TO STUDY HEMATOPOIESIS.pdf",
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+            "id": "8fd5ab85-67ed-55e6-bbfa-09436c4fdbfb",
+            "score": 0.7254693508148193,
+            "metadata": {
+              "text": "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  \net \n  \nal \n., 2005a). The",
+              "title": "2009 - The Human Ageing Genomic Resources online.pdf",
+              "version": "v0",
+              "chunk_order": 18,
+              "document_id": "e43cd3b6-ad8e-5422-ba7c-ceb6e66cc529",
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+            "id": "b58deffd-3cd3-5b7b-893d-b9cfc880830b",
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+              "text": "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.",
+              "title": "2000 - Genome-wide study of aging and oxidative stress.pdf",
+              "version": "v0",
+              "chunk_order": 6,
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+            "score": 0.7204392719006986,
+            "metadata": {
+              "text": "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,",
+              "title": "2012 - Genome-Environment Interactions That Modulate.pdf",
+              "version": "v0",
+              "chunk_order": 114,
+              "document_id": "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec",
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+            "id": "3c369292-4b9c-5156-a80f-4b3301026f30",
+            "score": 0.7204358577728271,
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+              "text": "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.\nAGING GENES AS TARGETS FOR DRUG DISCOVERY 91",
+              "title": "2012 - Genome-Environment Interactions That Modulate.pdf",
+              "version": "v0",
+              "chunk_order": 67,
+              "document_id": "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec",
+              "extraction_id": "a01ca925-4ccf-5863-a162-7bd4c754fe89",
+              "associatedQuery": "what genes are involved in  the aging process"
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+            "id": "43abb9e9-5ffb-58d8-b5b9-251c50c1283d",
+            "score": 0.7201110935244294,
+            "metadata": {
+              "text": "Aging is a biological process universal to eukaryotic organ-\nisms, and its underlying mechanisms are under intensive\nstudy. 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\ngenes include superoxide dismutase , a free-radical scavenger;\nmethuselah , a potential G protein-coupled receptor, in Drosoph-\nila melanogaster ; and p66\nshc, an oxidative stress-response gene, in",
+              "title": "2000 - Genome-wide study of aging and oxidative stress.pdf",
+              "version": "v0",
+              "chunk_order": 3,
+              "document_id": "3fc2266c-d677-54f9-b3a2-5129eedf214a",
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+            "id": "b284606e-a2db-5151-9d30-b591493b984d",
+            "score": 0.7184746861457825,
+            "metadata": {
+              "text": "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-\nlular level that report changes in rate of migration, dieren-\ntiation and neurogenesis with aging ( Seki & Arai, 1995;",
+              "title": "2008 - Genome-wide analysis of aging and learning-related genes.pdf",
+              "version": "v0",
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+            "score": 0.7166516949742034,
+            "metadata": {
+              "text": "those down-regulated during aging were genes involved in\nDNA repair and chromatin remodelling (Chambers et al.\n2007b ). While these studies revealed thousands of age-\nregulated genes, the ultimate causes of these expressionperturbations remain unknown. Analyzing age-dependent\ngene expression changes using multidimensional genetical\ngenomics could bring the identification of genes causingthe age-induced alterations and thereby future therapeutic\nintervention strategies one step closer.",
+              "title": "2008 - Combining transcriptional profiling and genetic linkage analysis to uncover gene networks operating in hematopoietic stem cells and their progeny.pdf",
+              "version": "v0",
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+              "content": "The genotypes related to Alzheimer's disease (AD) and dementia that have commonalities with those for aging include several key genetic factors:\n\n1. **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].\n\n2. **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].\n\n3. **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].\n\n4. **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].\n\nThese genotypes highlight the genetic commonalities between Alzheimer's disease, dementia, and the aging process, emphasizing the multifactorial nature of these conditions.",
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+            }
+          }
+        ],
+        "created": 1727798140,
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+            "score": 0.7288241386413574,
+            "metadata": {
+              "text": "Introduction\nAlzheimers disease (AD), a devastating neurodegen-\nerative disease, is the most common form of dementiaamong the elderly. Genetically, AD is a complex and\nmultifactorial disease with the possible involvement of\nmultiple genes. The rare early-onset form of the diseaseusually follows an autosomal-dominant inheritance\npattern and to date three genes have been identified:\namyloid precursor protein ( APP) and presenilin 1 and\n2(PSEN1 andPSEN2 ). The common late-onset form of",
+              "title": "2012 - Genome-wide association analysis of age-at-onset.pdf",
+              "version": "v0",
+              "chunk_order": 6,
+              "document_id": "9874359e-5f5d-5e6a-9844-cd9a1d2cae24",
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+              "associatedQuery": "Describe the genotypes related to Alzheimers and dementia which have commonalities with those for aging."
+            }
+          },
+          {
+            "id": "4014c984-d6d9-5eb2-a25e-9e9fe15d1b41",
+            "score": 0.7282055020332336,
+            "metadata": {
+              "text": "Background\nAge-related neurological diseases such as stroke and\ndementia represent a substantial population burden, and\none in three persons will develop either stroke or demen-\ntia in their lifetime [1]. Twin studies suggest that 3778%\nof the variance in the age of onset of Alzheimer's disease\n(AD), the most common cause of dementia in the elderly,\ncan be attributed to additive genetic effects [2,3]. Con-\nversely, cognitively healthy aging also has a substantial",
+              "title": "2007 - Genetic correlates of brain aging on MRI and cognitive test measures a genome-wide association and linkage analysis in the Framingham study.pdf",
+              "version": "v0",
+              "chunk_order": 11,
+              "document_id": "56b25b5a-fc9e-5d61-8502-1c110466ba16",
+              "extraction_id": "b545e588-2876-5928-9c01-710c1371b44e",
+              "associatedQuery": "Describe the genotypes related to Alzheimers and dementia which have commonalities with those for aging."
+            }
+          },
+          {
+            "id": "cd8f4d4a-2b1a-542f-a3f3-364a83fb10fb",
+            "score": 0.7260361909866333,
+            "metadata": {
+              "text": "cognitive status in Alzheimer's disease. Neurobiol. Aging  1996 , 17: \n921-933. \n[3]  Ertekin-Taner, N. Genetics of Alzheimer's disease: a centennial \nreview. Neurol. Clin.  2007 , 25: 611-667. \n[4]  Bernardi, L., Tomaino, C., Anfossi, M., Gallo, M., Geracitano, S., \nPuccio, G., Colao, R., Frangipane, F., Mirabelli, M., Smirne, N., \nGiovanni Maletta, R., Bruni, A.C. Late onset familial Alzheimer's \ndisease: novel presen ilin 2 mutation and PS1 E 318G polymor-\nphism. J. Neurol.  2008 , 255: 604-606.",
+              "title": "2009 - MicroRNA Implications for Alzheimer Disease and other Human CNS.pdf",
+              "version": "v0",
+              "chunk_order": 112,
+              "document_id": "70d08119-d16d-5e9b-89ed-ec8547be125a",
+              "extraction_id": "4b383c2a-f0de-5420-af8d-07060b8874f3",
+              "associatedQuery": "Describe the genotypes related to Alzheimers and dementia which have commonalities with those for aging."
+            }
+          },
+          {
+            "id": "f24834c0-1862-5d9f-bdb6-2af38505aa5c",
+            "score": 0.72203528881073,
+            "metadata": {
+              "text": "Keywords: alzheimers disease; genomics; GWAS; genetic risk factors; epigenetic modication; aging\n1. Introduction\nAlzheimers disease (AD) is the most common cause of dementia, accounting for approximately\n6080% of dementia cases, followed by vascular dementia (approximately 10%), Lewy Body or\nParkinsons disease-related dementia, and alcohol-mediated dementia [ 1]. Mild cognitive impairment,\none of the representative early symptoms of AD, makes this disease distinguishable from other types",
+              "title": "2018 - Genomics New Light on Alzheimer?s.pdf",
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+          {
+            "id": "064a3510-9a3d-5b93-b848-69478e02e013",
+            "score": 0.713762640953064,
+            "metadata": {
+              "text": "14. Heyman A, Wilkinson WE, Hurwitz BJ, Schmechel D, Sigmon AH, et al. (1983)\nAlzheimers disease: genetic aspects and associated clinical disorders. AnnNeurol 14: 507515.\n15. Farrer LA, Myers RH, Connor L, Cupples LA, Growdon JH (1991) Segregation\nanalysis reveals evidence of a major gene for Alzheimer disease. Am J HumGenet 48: 10261033.\n16. Duara R, Lopez-Alberola RF, Barker WW, Loewenstein DA, Zatinsky M, et al.\n(1993) A comparison of familial and sporadic Alzheimers disease. Neurology 43:\n13771384.",
+              "title": "2012 - Mitochondrial Genomic Analysis of Late Onset.pdf",
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+              "chunk_order": 86,
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+            "id": "794ac337-0c08-52ca-9603-fe840fa343af",
+            "score": 0.7122418880462646,
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+              "text": "(2016).\n 3. DeTure, M. A. & Dickson, D. W . The neuropathological diagnosis of Alzheimers disease. Mol. Neurodegener. 14, 32 (2019).\n 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).\n 5. Gatz, M. et al. Role of genes and environments for explaining Alzheimer disease. Arch. Gen. Psychiatry 63, 168174 (2006).",
+              "title": "2021 - A genome-wide association study with 1,126,563.pdf",
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+            "id": "fc44e06f-a727-5544-ad7c-6ba3632552b7",
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+              "text": "Lett 379(3):199204.\nAvramopoulos D. 2009. Genetics of Alzheimers disease: Recent advances.\nGenome Med 1(3):34.\nBachman DL, Wolf PA, Linn R, Knoefel JE, Cobb J, Belanger A, DAgostino\nRB, White LR. 1992. Prevalence of dementia and probable seniledementia of the Alzheimer type in the Framingham study. Neurology42(1):115119.\nBarral S, Cheng R, Reitz C, Vardarajan B, Lee J, Kunkle B, Beecham G,",
+              "title": "2017 - Genomic Variants, Genes, and Pathways.pdf",
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+              "text": "[11] and the\nexclusion 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 \n[12]. In addition, most studies attempt-\ning 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",
+              "title": "2003 - The application of functional genomics.pdf",
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+            "id": "784e7626-1d9c-521b-84f3-965965435366",
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+              "text": "associated with Alzheimers disease neuropathology. J. Alzheimers Dis. 60, 10351043 (2017).\n63. Gottesman, R. F. etal. Association between midlife vascular risk factors and estimated brain amyloid \ndeposition. JAMA 317, 14431450 (2017).\n64. Moran, C. etal. T ype 2 diabetes mellitus and \nbiomarkers of neurodegeneration. Neurology 85, \n11231130 (2015).\n65. Vemuri, P . etal. Age, vascular health, and Alzheimer disease biomarkers in an elderly sample. Ann. Neurol.  \n82, 706718 (2017).",
+              "title": "2018 - Cognitive decline and dementia in diabetes mellitus.pdf",
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+            "id": "dac0ab78-d01f-5f95-a129-559cbe6791ec",
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+              "text": "Introduction\nAlzheimers disease (AD), the most common form\nof dementia, is highly heritable (heritability of up\nto 76%) but genetically complex.1Neuropatho-\nlogically, the disease is characterized by extracellular\nsenile plaques containing b-amyloid (A b) and intra-\ncellular neurofibrillary tangles containing hyperpho-sphorylated tau protein.\n1Before 2009, four genes had\nbeen definitively implicated in its aetiology. Muta-\ntions of the amyloid precursor protein (APP) gene",
+              "title": "2012 - Genome-wide association study of Alzheimer?s disease.pdf",
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+              "content": "The genotypes related to Alzheimer's disease (AD) and dementia that have commonalities with those for aging include several key genetic factors:\n\n1. **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].\n\n2. **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].\n\n3. **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].\n\n4. **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].\n\n5. **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].\n\nIn 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].",
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+              "text": "Background\nAge-related neurological diseases such as stroke and\ndementia represent a substantial population burden, and\none in three persons will develop either stroke or demen-\ntia in their lifetime [1]. Twin studies suggest that 3778%\nof the variance in the age of onset of Alzheimer's disease\n(AD), the most common cause of dementia in the elderly,\ncan be attributed to additive genetic effects [2,3]. Con-\nversely, cognitively healthy aging also has a substantial",
+              "title": "2007 - Genetic correlates of brain aging on MRI and cognitive test measures a genome-wide association and linkage analysis in the Framingham study.pdf",
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+              "text": "cognitive status in Alzheimer's disease. Neurobiol. Aging  1996 , 17: \n921-933. \n[3]  Ertekin-Taner, N. Genetics of Alzheimer's disease: a centennial \nreview. Neurol. Clin.  2007 , 25: 611-667. \n[4]  Bernardi, L., Tomaino, C., Anfossi, M., Gallo, M., Geracitano, S., \nPuccio, G., Colao, R., Frangipane, F., Mirabelli, M., Smirne, N., \nGiovanni Maletta, R., Bruni, A.C. Late onset familial Alzheimer's \ndisease: novel presen ilin 2 mutation and PS1 E 318G polymor-\nphism. J. Neurol.  2008 , 255: 604-606.",
+              "title": "2009 - MicroRNA Implications for Alzheimer Disease and other Human CNS.pdf",
+              "version": "v0",
+              "chunk_order": 112,
+              "document_id": "70d08119-d16d-5e9b-89ed-ec8547be125a",
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+              "associatedQuery": "Describe the genotypes related to Alzheimer's and dementia which have commonalities with those for aging."
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+            "id": "6ac3f90f-ec8a-55c7-a3f7-d597d9d7cb2e",
+            "score": 0.7231204509735107,
+            "metadata": {
+              "text": "Introduction\nAlzheimers disease (AD), a devastating neurodegen-\nerative disease, is the most common form of dementiaamong the elderly. Genetically, AD is a complex and\nmultifactorial disease with the possible involvement of\nmultiple genes. The rare early-onset form of the diseaseusually follows an autosomal-dominant inheritance\npattern and to date three genes have been identified:\namyloid precursor protein ( APP) and presenilin 1 and\n2(PSEN1 andPSEN2 ). The common late-onset form of",
+              "title": "2012 - Genome-wide association analysis of age-at-onset.pdf",
+              "version": "v0",
+              "chunk_order": 6,
+              "document_id": "9874359e-5f5d-5e6a-9844-cd9a1d2cae24",
+              "extraction_id": "2a2e5ce1-cc56-579c-bf79-f9057f4c9671",
+              "associatedQuery": "Describe the genotypes related to Alzheimer's and dementia which have commonalities with those for aging."
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+            "id": "5e6b9b4b-1e03-585a-af52-18a054e1e603",
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+              "text": "[11] and the\nexclusion 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 \n[12]. In addition, most studies attempt-\ning 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",
+              "title": "2003 - The application of functional genomics.pdf",
+              "version": "v0",
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+            "id": "784e7626-1d9c-521b-84f3-965965435366",
+            "score": 0.7059235572814941,
+            "metadata": {
+              "text": "associated with Alzheimers disease neuropathology. J. Alzheimers Dis. 60, 10351043 (2017).\n63. Gottesman, R. F. etal. Association between midlife vascular risk factors and estimated brain amyloid \ndeposition. JAMA 317, 14431450 (2017).\n64. Moran, C. etal. T ype 2 diabetes mellitus and \nbiomarkers of neurodegeneration. Neurology 85, \n11231130 (2015).\n65. Vemuri, P . etal. Age, vascular health, and Alzheimer disease biomarkers in an elderly sample. Ann. Neurol.  \n82, 706718 (2017).",
+              "title": "2018 - Cognitive decline and dementia in diabetes mellitus.pdf",
+              "version": "v0",
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+              "associatedQuery": "Describe the genotypes related to Alzheimer's and dementia which have commonalities with those for aging."
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+          {
+            "id": "dac0ab78-d01f-5f95-a129-559cbe6791ec",
+            "score": 0.7049078941345215,
+            "metadata": {
+              "text": "Introduction\nAlzheimers disease (AD), the most common form\nof dementia, is highly heritable (heritability of up\nto 76%) but genetically complex.1Neuropatho-\nlogically, the disease is characterized by extracellular\nsenile plaques containing b-amyloid (A b) and intra-\ncellular neurofibrillary tangles containing hyperpho-sphorylated tau protein.\n1Before 2009, four genes had\nbeen definitively implicated in its aetiology. Muta-\ntions of the amyloid precursor protein (APP) gene",
+              "title": "2012 - Genome-wide association study of Alzheimer?s disease.pdf",
+              "version": "v0",
+              "chunk_order": 11,
+              "document_id": "7e7a8526-ff6e-5c83-ae72-e45509e3b788",
+              "extraction_id": "1f9039f1-91e9-538b-b709-a1880cf47007",
+              "associatedQuery": "Describe the genotypes related to Alzheimer's and dementia which have commonalities with those for aging."
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+            "id": "f24834c0-1862-5d9f-bdb6-2af38505aa5c",
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+              "text": "Keywords: alzheimers disease; genomics; GWAS; genetic risk factors; epigenetic modication; aging\n1. Introduction\nAlzheimers disease (AD) is the most common cause of dementia, accounting for approximately\n6080% of dementia cases, followed by vascular dementia (approximately 10%), Lewy Body or\nParkinsons disease-related dementia, and alcohol-mediated dementia [ 1]. Mild cognitive impairment,\none of the representative early symptoms of AD, makes this disease distinguishable from other types",
+              "title": "2018 - Genomics New Light on Alzheimer?s.pdf",
+              "version": "v0",
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+            "id": "064a3510-9a3d-5b93-b848-69478e02e013",
+            "score": 0.7009342908859253,
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+              "text": "14. Heyman A, Wilkinson WE, Hurwitz BJ, Schmechel D, Sigmon AH, et al. (1983)\nAlzheimers disease: genetic aspects and associated clinical disorders. AnnNeurol 14: 507515.\n15. Farrer LA, Myers RH, Connor L, Cupples LA, Growdon JH (1991) Segregation\nanalysis reveals evidence of a major gene for Alzheimer disease. Am J HumGenet 48: 10261033.\n16. Duara R, Lopez-Alberola RF, Barker WW, Loewenstein DA, Zatinsky M, et al.\n(1993) A comparison of familial and sporadic Alzheimers disease. Neurology 43:\n13771384.",
+              "title": "2012 - Mitochondrial Genomic Analysis of Late Onset.pdf",
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+              "text": "disease. Nat. Genet. ,19, 321 322.\n7. Bergem, A.L., Engedal, K. and Kringlen, E. (1997) The role of heredity in\nlate-onset Alzheimer disease and vascular dementia. A twin study. Arch.\nGen. Psychiat. ,54, 264 270.\n8. Payami, H., Grimslid, H., Oken, B., Camicioli, R., Sexton, G., Dame, A.,\nHowieson, 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.",
+              "title": "2003 - Results of a high-resolution genome screen.pdf",
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+            "id": "fc44e06f-a727-5544-ad7c-6ba3632552b7",
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+              "text": "Lett 379(3):199204.\nAvramopoulos D. 2009. Genetics of Alzheimers disease: Recent advances.\nGenome Med 1(3):34.\nBachman DL, Wolf PA, Linn R, Knoefel JE, Cobb J, Belanger A, DAgostino\nRB, White LR. 1992. Prevalence of dementia and probable seniledementia of the Alzheimer type in the Framingham study. Neurology42(1):115119.\nBarral S, Cheng R, Reitz C, Vardarajan B, Lee J, Kunkle B, Beecham G,",
+              "title": "2017 - Genomic Variants, Genes, and Pathways.pdf",
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+              "content": "Genetic factors play a significant role in influencing aging in humans. Here are some key points based on the provided context:\n\n1. **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].\n\n2. **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].\n\n3. **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.\n\n4. **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].\n\n5. **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].\n\nIn 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].",
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+              "text": "Recent developments on the genetics of aging can be seen as several streams\nof effort. In general, humans show a relatively modest ( <50%) heritability of",
+              "title": "2001 - The genetics of aging.pdf",
+              "version": "v0",
+              "chunk_order": 13,
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+              "text": "effect  genetic  variants  on  human  longevity.  Aging  2,  612620.\nYu,  C.E.,  Seltman,  H.,  Peskind,  E.R.,  Galloway,  N.,  Zhou,  P.X.,  Rosenthal,  E.,  Wijsman,\nE.M.,  Tsuang,  D.W.,  Devlin,  B.,  Schellenberg,  G.D.,  2007.  Comprehensive  analysis\nof  APOE  and  selected  proximate  markers  for  late-onset  Alzheimers  disease:\npatterns  of  linkage  disequilibrium  and  disease/marker  association.  Genomics",
+              "title": "2011 - A genome-wide association study confirms APOE as the major gene influencing.pdf",
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+              "associatedQuery": "What genetic factors influence aging in humans?"
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+            "score": 0.6992318838605849,
+            "metadata": {
+              "text": "It is undisputed that genetic factors influence aging. In a remarkable",
+              "title": "2009 - The Human Ageing Genomic Resources online.pdf",
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+              "text": "males: what are the molecular and evolutionary causes? Aging Cell. 2007;6:225233. doi:10.1111/j.1474-9726.2007.00279.x\n 63. Benayoun BA, Pollina EA, Brunet A. Epigenetic regulation of ageing: link-\ning environmental inputs to genomic stability. Nat Rev Mol Cell Biol. 2015;16:593610. doi:10.1038/nrm4048\n 64. Sen P, Shah PP, Nativio R, Berger SL. Epigenetic mechanisms of longevity \nand aging. Cell. 2016;166:822839. doi:10.1016/j.cell.2016.07.050",
+              "title": "2018 - Sex Differences in Aging Genomic Instability.pdf",
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+              "text": "Genet  1998, 81:92-97.\n3. Pedersen NL, Posner SF, Gatz M: Multiple-threshold models for\ngenetic influences on age of  onset for Alzheimer disease:\nfindings in Swedish twins.   Am J Med Genet  2001, 105:724-728.\n4. Gudmundsson H, Gudbjartsson DF, Frigge M, Gulcher JR, Stefansson\nK: Inheritance of human longevity in Iceland.   Eur J Hum Genet\n2000, 8:743-749.\n5. Flossmann E, Schulz UG, Rothwell PM: Systematic review of\nmethods and results of studie s of the genetic epidemiology",
+              "title": "2007 - Genetic correlates of brain aging on MRI and cognitive test measures a genome-wide association and linkage analysis in the Framingham study.pdf",
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+              "text": "population dynamics on the genetic architecture of human longevity. Aging (Albany NY). 2018;10(8):1947 63.\n68. Bellenguez C, Kucukali F, Jansen I, Andrade V, Morenau-Grau S, Amin N, et al. Large meta-analysis of genome-wide\nassociation studies expands knowledge of the genetic etiology of Alzheimer disease and highlights potential\ntranslational opportunities. medRxiv. 2020.\n69. Kojima T, Shimazui T, Hinotsu S, Joraku A, Oikawa T, Kawai K, et al. Decreased expression of CXXC4 promotes a",
+              "title": "2021 - Genome-wide association studies identify.pdf",
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+              "text": "discover core mechanisms of regulation.ANALYSIS OF HUMAN VARIATION IN\nTHE GENETIC CONTROL OF LONGEVITY\nHeritability studies have convincingly demonstrated that at\nleast some fraction of human lifespan is heritable. In tandem,\nlarge-scale genome-wide association studies (GWAS) have\nidentied numerous loci associated with age-related traits\n(Buniello et al., 2019). While genetic studies have functionally\nshown an inverse eect of multiple age-related, disease-",
+              "title": "2021 - Footprints in the Sand Deep Taxonomic Comparisons in Vertebrate Genomics to Unveil the Genetic Programs of Human Longevity.pdf",
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+              "text": "than in healthy elderly patients [71].\n Concluding Remarks\nThe study of the human aging process is complex and multifactorial, where genetic \nand environmental variables are key players in its development. That is why we sug-\ngest a series of different biomarkers which include hormonal, inflammatory, and \noxidative stress biomarkers. However, it is possible that other biomarkers such as \nDNA damage, telomere length determination, DNA repair mechanisms and p53",
+              "title": "2020 - Clinical Genetics and Genomics of Aging.pdf",
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+            "metadata": {
+              "text": "Clinical Genetics and Genomics of Aging",
+              "title": "2020 - Clinical Genetics and Genomics of Aging.pdf",
+              "version": "v0",
+              "chunk_order": 1,
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+              "text": "standing the cause and mechanisms of aging is imperative in\nassisting to suppress age-related diseases and promote healthylongevity. It is well-known that aging is influenced by a combin-\nation of genetic and environmental factors. Previous twin stud-\nies have shown that the genetic contribution to general human\nlongevity is about 2030% [ 4,5], whereas environmental factors\nin human aging and longevity still account for the largest effect.\nEpigenetic factors influence the regulation of gene expres-",
+              "title": "2016 - Progress on the role of DNA methylation in aging.pdf",
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+              "content": "Several genes have been associated with aging. Here are some key points from the provided context:\n\n1. **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].\n\n2. **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].\n\n3. **Human Longevity-Associated Genes**: Dozens of genes have been associated with human longevity, although only a handful have shown consistent effects across populations [4].\n\n4. **GenAge Data Set**: A curated list of human genes associated with aging in different model systems is available from the GenAge data set [6].\n\n5. **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].\n\nThese references collectively highlight the involvement of various genes and pathways in the aging process.",
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+            "id": "8fd5ab85-67ed-55e6-bbfa-09436c4fdbfb",
+            "score": 0.7669302364033334,
+            "metadata": {
+              "text": "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  \net \n  \nal \n., 2005a). The",
+              "title": "2009 - The Human Ageing Genomic Resources online.pdf",
+              "version": "v0",
+              "chunk_order": 18,
+              "document_id": "e43cd3b6-ad8e-5422-ba7c-ceb6e66cc529",
+              "extraction_id": "52c67b46-63f2-54ae-a78e-e9d54a55f6e4",
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+          {
+            "id": "413f8f54-b5cc-5089-9f5c-d9e3b8bcf594",
+            "score": 0.7600773858931796,
+            "metadata": {
+              "text": "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,",
+              "title": "2012 - Genome-Environment Interactions That Modulate.pdf",
+              "version": "v0",
+              "chunk_order": 114,
+              "document_id": "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec",
+              "extraction_id": "d59d7882-333d-5576-86ab-3cfa6354b946",
+              "associatedQuery": "what genes are associated with aging?"
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+          {
+            "id": "6f04401a-b938-5a60-8b69-d37f9086748c",
+            "score": 0.758702300748306,
+            "metadata": {
+              "text": "OTHER AGING RELATED GENES",
+              "title": "2010 - Do different neurons age differently Direct genome-wide analysis of aging in single identified cholinergic neurons.pdf",
+              "version": "v0",
+              "chunk_order": 73,
+              "document_id": "153b070f-0291-5ed4-ad33-edea5e3fa8f7",
+              "extraction_id": "81c68113-aa96-5af3-b4fc-5898fa20e379",
+              "associatedQuery": "what genes are associated with aging?"
+            }
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+          {
+            "id": "786d2756-4c4d-5ac0-8d3d-63f914d51664",
+            "score": 0.7552609881292021,
+            "metadata": {
+              "text": "In addition to aging- and CR-related genes, another\nsource 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.\nMany longevity-associated genes are related to spe-",
+              "title": "2012 - Genome-Environment Interactions That Modulate.pdf",
+              "version": "v0",
+              "chunk_order": 119,
+              "document_id": "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec",
+              "extraction_id": "d59d7882-333d-5576-86ab-3cfa6354b946",
+              "associatedQuery": "what genes are associated with aging?"
+            }
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+            "id": "0ae63c75-df5f-59b0-9561-30d5115f0f74",
+            "score": 0.7522995023548913,
+            "metadata": {
+              "text": "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;\ngenes affecting multiple, even if not all, age-related",
+              "title": "2012 - Human Ageing Genomic Resources Integrated.pdf",
+              "version": "v0",
+              "chunk_order": 20,
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+            "id": "f2fbfb29-0a51-5f94-8b67-d47ab4de68bd",
+            "score": 0.7424599085772106,
+            "metadata": {
+              "text": "Pleiotropies and Aging-Related Genesets\nTo study genes that have been previously related to aging, a\nlist of curated human genes associated with aging in different\nmodel systems was obtained from the GenAge data set ( de\nMagalh ~aes et al. 2005 ). We used gene ontology (GO) anno-",
+              "title": "2018 - Biological Processes Modulating Longevity across Primates.pdf",
+              "version": "v0",
+              "chunk_order": 138,
+              "document_id": "930103c1-e98e-524c-aa68-233a45dc6726",
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+            "id": "fd6cfc2c-76b1-5620-a68c-fb37db9b6f78",
+            "score": 0.7399801766884235,
+            "metadata": {
+              "text": "aging in human muscle reveals a common aging signa-ture. PLoS Genet. 2, e115. ( doi:10.1371/journal.pgen.\n0020115 )\n64 Lener, T ., Moll, P . R., Rinnerthaler, M., Bauer, J.,\nAberger, F. & Richter, K. 2006 Expression proling ofaging in the human skin. Exp. Gerontol. 41, 387397.\n(doi:10.1016/j.exger.2006.01.012 )\n65 Kim, S. K. 2008 Genome-wide views of aging gene net-\nworks . Molecular Biology of Aging Monograph 9. Cold\nSpring Harbor, CT: Cold Spring Harbor LaboratoryPress.",
+              "title": "2011 - Genetics and genomics of human ageing.pdf",
+              "version": "v0",
+              "chunk_order": 110,
+              "document_id": "08eee102-d627-5f1b-84c7-603c38981adf",
+              "extraction_id": "07a34581-749c-5556-bdea-806b2c9c7915",
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+          {
+            "id": "df45a752-e866-54bb-ab49-daff9a702eef",
+            "score": 0.7399801766884235,
+            "metadata": {
+              "text": "aging in human muscle reveals a common aging signa-ture. PLoS Genet. 2, e115. ( doi:10.1371/journal.pgen.\n0020115 )\n64 Lener, T ., Moll, P . R., Rinnerthaler, M., Bauer, J.,\nAberger, F. & Richter, K. 2006 Expression proling ofaging in the human skin. Exp. Gerontol. 41, 387397.\n(doi:10.1016/j.exger.2006.01.012 )\n65 Kim, S. K. 2008 Genome-wide views of aging gene net-\nworks . Molecular Biology of Aging Monograph 9. Cold\nSpring Harbor, CT: Cold Spring Harbor LaboratoryPress.",
+              "title": "2010 - Genetics and genomics of human ageing.pdf",
+              "version": "v0",
+              "chunk_order": 110,
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+            "id": "66f72bdc-d38b-5c7a-afdd-4c7549ce2131",
+            "score": 0.7388778484536512,
+            "metadata": {
+              "text": "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).\nWe show that the fundamenta l changes in genes and proc-",
+              "title": "2008 - Estrogen, not intrinsic aging, is the major regulator of delayed.pdf",
+              "version": "v0",
+              "chunk_order": 14,
+              "document_id": "04a3d8f1-64c1-5e25-ab0a-3eb749c06c92",
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+            "id": "d53018ae-0881-5ef4-9c49-48623e8aa342",
+            "score": 0.738076642866336,
+            "metadata": {
+              "text": "on model organisms [3] or have been confined to specificaging-associated disorders such as progeria syndromes\n[4]. A study of postmortem human brain tissue from 30\nindividuals aged 26 to 106 years [5] showed that approxi-\nmately 4% of approximately 11,000 genes analyzed show\na significant age-related expression change (1.5-fold or\nmore) in individuals aged >40 years. These genes were\nreported to play central roles in synaptic plasticity, vesi-\ncular transport, and mitoch ondrial function. Another",
+              "title": "2013 - Gene expression changes with age in skin.pdf",
+              "version": "v0",
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+            "message": {
+              "content": "Several genes have been associated with aging in humans according to the provided context:\n\n1. **GenAge Data Set**: This data set includes genes that may regulate aging in humans or are considerably associated with the human aging phenotype [2].\n2. **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].\n3. **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].\n\nThese 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.",
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+            "metadata": {
+              "text": "In addition to aging- and CR-related genes, another\nsource 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.\nMany longevity-associated genes are related to spe-",
+              "title": "2012 - Genome-Environment Interactions That Modulate.pdf",
+              "version": "v0",
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+            "score": 0.7540779485083268,
+            "metadata": {
+              "text": "GenAge features a data set of genes that may regulate agingin humans or that at least appear to be considerably associated\nwith the human aging phenotype. This data set includes\northologues  derived from established databases, mainly In-Paranoid (OBrien  \net \n  \nal \n., 2005) but also HomoloGene (http://",
+              "title": "2009 - The Human Ageing Genomic Resources online.pdf",
+              "version": "v0",
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+            "id": "6f04401a-b938-5a60-8b69-d37f9086748c",
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+            "metadata": {
+              "text": "OTHER AGING RELATED GENES",
+              "title": "2010 - Do different neurons age differently Direct genome-wide analysis of aging in single identified cholinergic neurons.pdf",
+              "version": "v0",
+              "chunk_order": 73,
+              "document_id": "153b070f-0291-5ed4-ad33-edea5e3fa8f7",
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+          {
+            "id": "06e319e1-b054-5f33-9b40-ee892f507736",
+            "score": 0.7499399334228092,
+            "metadata": {
+              "text": "processes in human longevity and aging. Ten of the 22\nsuggestive 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],\nBIN2, GRIA1), were previously related to the regulation of\nneuronal excitability and plasticity (KCNQ4 [Van Eyken et\nal., 2006], LMO4 [Joshi et al., 2009; Leuba et al., 2004],",
+              "title": "2011 - A genome-wide association study of aging.pdf",
+              "version": "v0",
+              "chunk_order": 92,
+              "document_id": "8e9c1150-1047-54a2-bf85-1cc5000a6811",
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+            "id": "9defe0af-80a1-56da-90df-551fd55baa13",
+            "score": 0.7440464053126346,
+            "metadata": {
+              "text": "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.\nFig. 1 From the main page of the Human Ageing",
+              "title": "2009 - The Human Ageing Genomic Resources online.pdf",
+              "version": "v0",
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+              "text": "Pleiotropies and Aging-Related Genesets\nTo study genes that have been previously related to aging, a\nlist of curated human genes associated with aging in different\nmodel systems was obtained from the GenAge data set ( de\nMagalh ~aes et al. 2005 ). We used gene ontology (GO) anno-",
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+              "text": "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  \net \n  \nal \n., 2005a). The",
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+              "text": "shown that genes associated with aging and/or longevity inmodel organisms are evolutionary conserved in terms of havingmore homologues than predicted by chance (Budovsky  \net \n  \nal \n.,2007, 2008) and exhibiting slower molecular evolution rates (de\nMagalhes & Church, 2007). Therefore, it is now clear that atleast some genes identified in model organisms may be relevantto human aging.\nTo allow researchers to focus specifically on human aging,",
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+              "text": "aging in human muscle reveals a common aging signa-ture. PLoS Genet. 2, e115. ( doi:10.1371/journal.pgen.\n0020115 )\n64 Lener, T ., Moll, P . R., Rinnerthaler, M., Bauer, J.,\nAberger, F. & Richter, K. 2006 Expression proling ofaging in the human skin. Exp. Gerontol. 41, 387397.\n(doi:10.1016/j.exger.2006.01.012 )\n65 Kim, S. K. 2008 Genome-wide views of aging gene net-\nworks . Molecular Biology of Aging Monograph 9. Cold\nSpring Harbor, CT: Cold Spring Harbor LaboratoryPress.",
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+              "content": "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].\n\nIn 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.",
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+              "text": "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  \net \n  \nal \n. (2007) used GenAge to construct a modular\nnetwork 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  \net \n  \nal",
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+              "text": "[111], and for generation of networks based on known gene \ninteractions 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-\ncation by regression (NIR) [119] has been used to identify",
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+              "text": "networks can be built using protein interaction and gene\nco-expression data. A previous paper used protein-\nprotein interactions to build genetic networks identifying\npotential longevity genes along with links between genes\nand aging-related diseases [ 30]. Here, we present the\nnetwork of proteins and genes co-expressed with the\nCellAge senescence genes. Assaying the networks, we\nfind links between senescence and immune system func-\ntions and find genes highly connected to CellAge genes",
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+              "text": "GeneNetwork have reinvigorated it, including the addition  of data from  10 species, multi -omics \nanalysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for \npredictive medicine and systems genetics, which is constantly being maintained and improved.   \nHere, we give a brief overview of the process  for carrying out some of the most common \nfunctions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small",
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+              "text": "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  \net \n  \nal \n., 2007). Similar works are made",
+              "title": "2009 - The Human Ageing Genomic Resources online.pdf",
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+              "text": "2009, with over 400 genes added in the current update (Ta-ble1), includingmiRNAs for thefirst time.\nGenAge has proven a valuable resource for ageing re-\nsearch, as evidence by many publications. A systems levelanalysis of the GenAge human genes database identified a\nrobust group of ageing-specific network characteristics, re-\nvealingageinggenesasnetworkhubs( 11).Moreover,inan\nanalysis of genes in the ageing human brain, 54 genes with\nsustained, consistent expression and 23 genes with DNA",
+              "title": "2018 - Human Ageing Genomic Resources new and updated.pdf",
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+              "text": "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",
+              "title": "2009 - The Human Ageing Genomic Resources online.pdf",
+              "version": "v0",
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+              "text": "et \n  \nal \n., 2007). In a sense, GenAge\noffers 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\nMain reason for selectionNumber \nof genes\nEvidence directly linking the gene product to aging in humans 3",
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+              "text": "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\nincludes, since 2008, a list of genes from model organisms\nbased on genetic manipulation experiments (2).\nCurrently, the database is divided into three main",
+              "title": "2012 - Human Ageing Genomic Resources Integrated.pdf",
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+              "text": "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",
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diff --git a/gnqa/paper2_eval/data/responses/human/cs_diabetes_responses.json b/gnqa/paper2_eval/data/responses/human/cs_diabetes_responses.json
new file mode 100644
index 0000000..67394e9
--- /dev/null
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+              "content": "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:\n\n1. **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].\n\n2. **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].\n\n3. **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].\n\n4. **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].\n\n5. **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].\n\n6. **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].\n\nThese 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.",
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+              "text": "interindividual variation in responses to antidiabetic\ntreatment and may provide the foundation for future\ngenotype-based treatment standards. Pharmacogenetics\nand Genomics 25:475 484 Copyright  2015\nWolters Kluwer Health, Inc. All rights reserved.\nPharmacogenetics and Genomics 2015, 25:475 484\nKeywords: antidiabetic treatment, diabetes type 2, disease progression,\ngenotype, pharmacogenetics\naSection of Metabolic Genetics, Novo Nordisk Research Foundation Center for",
+              "title": "2015 - Pharmacogenetics and individual responses to treatment of hyperglycemia.pdf",
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+              "text": "treatment guidelines. Yet, the interindividual response to\ntherapy and slope of disease progression varies markedly\namong patients with type 2 diabetes. Gene gene,\ngene environment, and gene treatment interactions may\nexplain some of the variation in disease progression.\nSeveral genetic variants have been suggested to beassociated with response to antidiabetic drugs. Some are\npresent in drug receptors or drug metabolizers ( OCT genes,\nKCNJ11 ,ABCC8 , and CYP2C9 ). Numerous type 2 diabetes",
+              "title": "2015 - Pharmacogenetics and individual responses to treatment of hyperglycemia.pdf",
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+              "document_id": "46081466-a50f-59d8-893d-8b8883b38507",
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+              "text": "mic control in the majority of insulin-treated \npatients. Diabet Med . 2009;26(4):437441.\n 20. Pearson ER, et al. Sensitivity to sulphonylureas \nin patients with hepatocyte nuclear factor-1alpha \ngene mutations: evidence for pharmacogenetics \nin diabetes. Diabet Med . 2000;17(7):543545.\n 21. Pearson ER, et al. Genetic cause of hypergly-\ncaemia and response to treatment in diabetes. \nLancet . 2003;362(9392):12751281.\n 22. Fantasia KL, Steenkamp DW. Optimal glycemic",
+              "title": "2021 -  Monogenic diabetes a gateway to precision medicine.pdf",
+              "version": "v0",
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+              "text": "When considering etiological varia-\ntion, 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\nprocesses. These processes, such asb-cell dysfunction, lipodystrophy, or obe-\nsity, could respond differently to drugsthat act on these pathways, such assulfonylureas, glucagon-like peptide 1 re-\nceptor agonist (GLP-1RA), DPP4i, and\nthiazolidinediones.",
+              "title": "2020 - Precision Medicine in Diabetes.pdf",
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+              "text": "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 \nthe biological mechanism of metformin.\nAs with other complex traits, glycaemic response to \nmetformin 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.\n3 Pharmacogenetic",
+              "title": "2014 - Heritability of variation in glycaemic response to metformin.pdf",
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+            "id": "9ced327e-3feb-5b7b-a938-30ad544113e2",
+            "score": 0.6816630363464355,
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+              "text": "Pharmacogenetics and individual responses to treatment of\nhyperglycemia in type 2 diabetes\nLine Engelbrechtsena, Ehm Anderssona, Soeren Roepstorffb,\nTorben Hansenaand Henrik Vestergaarda\nThe aim of this study was to summarize current knowledge\nand provide perspectives on the relationships between\nhuman genetic variants, type 2 diabetes, antidiabetic\ntreatment, and disease progression. Type 2 diabetes is a\ncomplex disease with clear-cut diagnostic criteria and",
+              "title": "2015 - Pharmacogenetics and individual responses to treatment of hyperglycemia.pdf",
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+              "text": "Genomics. 2010; 20:3844. [PubMed: 19898263]\n168. Jablonski KA, McAteer JB, de Bakker PI, Franks PW, Pollin TI, et al. Common variants in 40\ngenes assessed for diabetes incidence and response to metformin and lifestyle intervention in the\ndiabetes prevention program. Diabetes. 2010; 59:26722681. [PubMed: 20682687]\n169. Wolford JK, Yeatts KA, Dhanjal SK, Black MH, Xiang AH, et al. Sequence variation in PPARG\nmay underlie differential response to troglitazone. Diabetes. 2005; 54:33193325. [PubMed:\n16249460]",
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+              "text": "10.1007/s00125-017-4227-1.\n 42. Hattersley AT, et al. Precision diabetes: learning from monogenic diabetes. Diabetologia. 2017;60:769777. doi: 10.1007/s00125-017-4226-2.\n 43. Florez JC. The pharmacogenetics of metformin. Diabetologia. \n2017;60:16481655. doi: 10.1007/s00125-017-4335-y.\n 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.\n 45. Zhou K, et al. Variation in the glucose transporter gene SLC2A2 is associ-",
+              "title": "2018 - Human Genetics of Obesity and Type 2 Diabetes Mellitus.pdf",
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+              "text": "typically based on efficacy, yet favorable respon ses to such therapeutics are oftentimes \nvariable and difficult to pred ict. Characterization of drug  response is expected to \nsubstantially enhance our ability to provide patients with the most effective treatment \nstrategy given their indivi dual backgrounds, yet pharmacogenetic study of diabetes \nmedications is still in its infancy. To date, major pharmacogenetic studies have focused on",
+              "title": "2010 - Pharmacogenetics of Anti-Diabetes Drugs.pdf",
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+              "text": "treatment or adverse effects  and dosing of medications \nare not likely to be adversely affected by environmental \nexposures and tend to have large effect sizes [95]. There\nfore, some of the variability in response or dosing could \nbe due to genetic variation. Pharmacogenetics in the area \nof diabetes is still in its infancy, although there have been \nstudies examining KCNJ11  and sulfonylurea therapy for \nboth rare [96,97] and common [98,99] variants and res",
+              "title": "2011 - Inherited destiny Genetics and gestational diabetes mellitus.pdf",
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+              "content": "Protective genetic factors against diabetes in elderly populations can be understood through several key insights from the provided context:\n\n1. **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].\n\n2. **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].\n\n3. **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].\n\n4. **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].\n\nIn 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.",
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+              "text": "Genetic factors appear to play a role in determining an individuals risk of developing diabetes. It is hoped",
+              "title": "2008 - Glossary of Genetics Genomics Terms.pdf",
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+              "text": "ger, will develop diabetes because the prevalence of diabetes increases\nwith age. In order to circumvent this problem, age was adjusted for in2 K. Ramya et al. / Gene xxx (2013) xxx xxx\nPlease cite this article as: Ramya, K., et al., Genetic association of ADIPOQ gene variants with type 2 diabetes, obesity and serum adiponectin levels\nin south Indian population, Gene (2013), http://dx.doi.org/10.1016/j.gene.2013.09.012",
+              "title": "2013 - Genetic association of ADIPOQ gene variants with type 2 diabetes, obesity.pdf",
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+              "text": "elderly population. PLoS One 9: e100548. doi: 10.1371/journal.pone.0100548 PMID: 24959828\n23. Strawbridge RJ, Dupuis J, Prokopenko I, Barker A, Ahlqvist E, Rybin D, et al. (2011) Genome-wide\nassociation identifies nine common variants associated with fasting proinsulin levels and provides new\ninsights into the pathophysiology of type 2 diabetes. Diabetes 60: 2624 2634. doi: 10.2337/db11-0415\nPMID: 21873549",
+              "title": "2015 - The Association of Type 2 Diabetes Loci.pdf",
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+              "text": "information for diabetes risk prediction - differences according to sex, age, family history and\nobesity. PloS One 8(5):e64307. doi: 10.1371/journal.pone.0064307\nNeel JV (1962) Diabetes mellitus: a thrifty genotype rendered detrimental by progress? Am J\nHum Genet 14:353362\nNeel JV (1999) The thrifty genotype in 1998. Nutr Rev 57(5 Pt 2):S2S9\nPalmer ND, McDonough CW, Hicks PJ, Roh BH, Wing MR, An SS, Hester JM, Cooke JN,",
+              "title": "2016 - Genome-Wide Association Studies of Type 2 Diabetes.pdf",
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+              "text": "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 ). \n In a few patients, genetic mutations appear to be associ-\nated with T2D (Roche  et al.  ,  2005 ; American Diabetes \nAssociation,  2010a ). For example, recent work using the DPP data has led to the identi  cation of 27 single nucle-",
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+              "text": "early-onset diabetes in some pedigrees, but it also maybe observed in individuals who retain normal glucose\ntolerance into late adulthood and beyond ( ). Studying\n individuals from  HNF A-MODY families,\nLango Allen et al. () found that a -SNP T Dr s P S\nwas signi cantly associated with earlier age of diabetes\ndiagnosis, with each additional risk allele accelerating\ndiagnosis by ~ months.\nClinical application of predictive scores",
+              "title": "2019 - Genetic Risk Scores for Diabetes Diagnosis.pdf",
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+              "text": "12. de Miguel-Yanes JM, Shrader P, Pencina MJ, Fox CS, Manning AK, et al. 2011. Genetic risk reclassi-\ncation for type 2 diabetes by age below or above 50 years using 40 type 2 diabetes risk single nucleotide\npolymorphisms. Diabetes Care 34:12125\n13. Dempe A, Scherag A, Hein R, Beckmann L, Chang-Claude J, Schafer H. 2008. Gene-environment\ninteractions for complex traits: denitions, methodological requirements and challenges. Eur. J. Hum.\nGenet. 16:116472",
+              "title": "2012 - Gene-Environment Interactions in the Development of Type 2 Diabetes.pdf",
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+              "text": "diabetes risk genes predicts impaired glucose tolerance in female andobese individuals. PLoS One . 2012;7:e38224 .\n74. Stevens JW, Khunti K, Harvey R, et al. Preventing the progression\nto type 2 diabetes mellitus in adults at high risk: a systematic review\nand 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\n18jJ Epidemiol 2018;28(1):3-18",
+              "title": "2018 - Quantitative Relationship Between Cumulative Risk Alleles Based.pdf",
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+              "text": "and protects against oxidative stress-induced insulin-deficient \ndiabetes. PLoS One  2014; 9: e87941 [PMID: 24498408 DOI: \n10.1371/journal.pone.0087941]\n23 Maahs DM , West NA, Lawrence JM, Mayer-Davis EJ. Epidemiology \nof type 1 diabetes. Endocrinol Metab Clin North Am  2010; 39: \n481-497 [PMID: 20723815 DOI: 10.1016/j.ecl.2010.05.011]\n24 Daneman D . Type 1 diabetes. Lancet  2006; 367: 847-858 [PMID: \n16530579 DOI: 10.1016/S0140-6736(06)68341-4]",
+              "title": "2015 - Diabetes mellitus The epidemic of the century.pdf",
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+              "text": "Sosenko JM, Skyler JS, Krischer JP , Greenbaum CJ, Mahon J,\nRafkin LE, Cuthbertson D, Cowie C, Herold K, Eisen-barth G, et al. 2010. Glucose excursions between states\nof glycemia with progression to type 1 diabetes in the\ndiabetes prevention trial-type 1 (DPT-1). Diabetes 59:\n23862389.\nSteck AK, Armstrong TK, Babu SR, Eisenbarth GS. 2011.\nType 1 Diabetes Genetics Consortium. Stepwise or linear\ndecrease in penetrance of type 1 diabetes with lower-risk\nHLA genotypes over the past 40 years. Diabetes 60:",
+              "title": "2012 - The Pathogenesis and Natural History of Type 1 diabetes.pdf",
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+              "content": "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.\n\nFurthermore, 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.\n\nThe 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].\n\nIn 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].",
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+              "text": "demonstrate that lifestyle modi  cation comprising higher levels \nof PA and prudent food consumption may be e  ective in obesity \nand T2DM prevention. The positive e  ect of lifestyle on body \nweight seems somewhat transient, whereas the e  ect on T2DM \nis sustained for longer periods. Furthermore, lifestyle modi  ca-\ntion appears to have an e  ect on diabetes risk independently of \nbody weight and even of weight loss.\n     Lifestyle and Genetics in Obesity and Type 2 Diabetes",
+              "title": "2011 - Lifestyle and Genetics in Obesity and type 2 Diabetes.pdf",
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+              "text": "suggested to attenuate its negative e  ect on metabolic pro  le, \nbody weight, and diabetes risk (   Franks et al., 2007   ;    Kilpelainen et al., 2008   ;    Lindi et al., 2002   ;    Ruchat et al., 2010   ) (     \n       Table 1   ). \nThe notion that lifestyle modi  cation can eliminate the increased \nrisk for development of T2DM in subjects with genetic suscepti-bility is also supported by  ndings of    Barwell et al. (2008)    who",
+              "title": "2011 - Lifestyle and Genetics in Obesity and type 2 Diabetes.pdf",
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+              "text": "M., Bray, G. A. et al (2006). Effect of weight loss withlifestyle intervention on risk of diabetes. Diabetes\nCare, 29 , 21022107.\nHerder, C., Peltonen, M., Koenig, W., Sutfels, K.,\nLindstrom, J. et al (2009). Anti-inammatory effect oflifestyle changes in the Finnish Diabetes PreventionStudy. Diabetologia, 52 , 433442.\nHung, J., McQuillan, B. M., Thompson, P . L., and Beilby,",
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+              "text": "22\n       Medications for Diabetes \nPrevention \n Even in the most successful of the randomized \ncontrolled trials, the risk reduction for incident diabetes following lifestyle intervention was ~60 % [ \n48  51 ]. That raises the argument as to",
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+              "text": "SRT2104 extend the life span of obese mice and protect against age-\nrelated changes in multiple tissues ( 215).\nThe antidiabetic drug metformin also induces effects similar to CR\n(216). Diabetes is considered an age-associated disease, and disturbances\nin insulin signaling and carbohydrate homeostasis may essentially lead toother age-related complications, including cancer, if untreated. Along\nwith its antidiabetic properties, metformin supplementation has been",
+              "title": "2016 - Epigenetics and aging.pdf",
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+              "text": "74\nThe 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.\n75,76\nBoth a reduction in macronutrient intake and exercise cause areduction in inflammation.\nReferences\n1. Reaven GM. Banting lecture 1988. Role of insulin resistance in human\ndisease. Diabetes . 1988;37:15951607.",
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+              "text": "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   ).\n    Evidence from randomized controlled trails\n  The e  cacy of lifestyle changes in obesity and T2DM prevention",
+              "title": "2011 - Lifestyle and Genetics in Obesity and type 2 Diabetes.pdf",
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+              "text": "extends lifespan. Cell Rep. 20, 451463 (2017). [PubMed: 28700945] \n64. Barzilai N & Ferrucci L Insulin resistance and aging: A cause or a protective response? J. \nGerontol. Ser. A 67, 13291331 (2012).\n65. Holmes MV , Ala-Korpela M & Smith GD Mendelian randomization in cardiometabolic disease: \nchallenges in evaluating causality. Nat. Rev. Cardiol. 14, 577590 (2017). [PubMed: 28569269] \n66. Holmes MVet al.Mendelian randomization of blood lipids for coronary heart disease. Eur. Heart J.",
+              "title": "2021 - Gene-by-environment modulation of lifespan and weight gain in the murine BXD family.pdf",
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+              "text": "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\n71. Crandall J, Schade D, Ma Y, et al.; DiabetesPrevention Program Research Group. The in-uence of age on the effects of lifestyle mod-",
+              "title": "2020 - Precision Medicine in Diabetes.pdf",
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+              "text": "diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. N Engl J Med 2001; 344: 134350.\n114 Knowler WC, Barrett-Connor E, Fowler SE, et al. Reduction in \nthe incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med 2002; 346: 393403.\n115 Ramachandran A, Snehalatha C, Mary S, Mukesh B, Bhaskar AD,",
+              "title": "2011 - Type 2 diabetes across generations from pathophysiology to prevention and management.pdf",
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+              "content": "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:\n\n1. **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].\n\n2. **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].\n\n3. **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].\n\n4. **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].\n\nIn 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].",
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+              "text": "Longitudinal Study of Aging. The natural history of progression from normalglucose tolerance to type 2 diabetes in the Baltimore Longitudinal Study\nof Aging. Diabetes 2003; 52:1475 1484.\n22 Hornbak M, Allin KH, Jensen ML, Lau CJ, Witte D, Jrgensen ME ,e ta l .A\ncombined analysis of 48 type 2 diabetes genetic risk variants shows nodiscriminative value to predict time to first prescription of a glucose lowering\ndrug in Danish patients with screen detected type 2 diabetes. PLoS One\n2014; 9:e104837.",
+              "title": "2015 - Pharmacogenetics and individual responses to treatment of hyperglycemia.pdf",
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+              "text": "A set of currently known alleles increasing the risk for coronary\nartery disease, cancer, and type 2 diabetes as identi ed by genome-\nwide association studies was tested for compatibility with human\nlongevity. Here, we show that nonagenarian siblings from long-\nlived families and singletons older than 85 y of age from the general\npopulation carry the same number of disease risk alleles as young\ncontrols. Longevity in this study population is not compromised by",
+              "title": "2010 - Genome-wide association study (GWAS)-identified disease risk alleles do not compromisehuman longevity.pdf",
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+              "text": "52561.x )\n17 Atzmon, G., Schechter, C., Greiner, W ., Davidson, D.,\nRennert, G. & Barzilai, N. 2004 Clinical phenotype of\nfamilies with longevity. J. Am. Geriatr. Soc. 52, 274\n277. ( doi:10.1111/j.1532-5415.2004.52068.x )\n18 Rozing, M. P . et al. 2009 Human insulin/IGF-1 and\nfamilial longevity at middle age. Aging (Albany NY )1,\n714722.\n19 Rozing, M. P . et al. 2010 Favorable glucose tolerance\nand lower prevalence of metabolic syndrome in",
+              "title": "2011 - Genomics of human longevity.pdf",
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+              "text": "extends lifespan. Cell Rep. 20, 451463 (2017). [PubMed: 28700945] \n64. Barzilai N & Ferrucci L Insulin resistance and aging: A cause or a protective response? J. \nGerontol. Ser. A 67, 13291331 (2012).\n65. Holmes MV , Ala-Korpela M & Smith GD Mendelian randomization in cardiometabolic disease: \nchallenges in evaluating causality. Nat. Rev. Cardiol. 14, 577590 (2017). [PubMed: 28569269] \n66. Holmes MVet al.Mendelian randomization of blood lipids for coronary heart disease. Eur. Heart J.",
+              "title": "2021 - Gene-by-environment modulation of lifespan and weight gain in the murine BXD family.pdf",
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+              "text": "et al., 2012 ), possibly due to the indirect and/or a mixed relation-\nship between individual genetic disease risk loci and exceptional\nlongevity (as discussed by Fortney et al., 2015 ) versus the poten-\ntially more direct relationship between aging in the absence of\ndisease and overall genetic disease risk.\nOn the other hand, no difference in genetic risk is observed for\ntype 2 diabetes genetic risk and cancer. Some of these ndings\n(type 2 diabetes, colon, and lung cancer) can be explained by the",
+              "title": "2016 - Whole-Genome Sequencing of a Healthy Aging Cohort.pdf",
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+              "text": "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\n 6. Sebastiani P, Nussbaum L, Andersen SL, Black MJ, Perls TT. Increasing \nsibling relative risk of survival to older and older ages and the importance",
+              "title": "2017 - Four Genome-Wide Association Studies Identify New.pdf",
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+              "text": "Genetic factors appear to play a role in determining an individuals risk of developing diabetes. It is hoped",
+              "title": "2008 - Glossary of Genetics Genomics Terms.pdf",
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+              "text": "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",
+              "title": "2019 - Bioinformatic prediction of critical genes and pathways.pdf",
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+              "text": "offspring without diabetes mellitus of nonagenariansiblings: the Leiden Longevity Study. J. Am. Geriatr.\nSoc. 58, 564569. ( doi:10.1111/j.1532-5415.2010.\n02725.x )\n20 Suh, Y . et al. 2008 Functionally signicant insulin-like\ngrowth factor I receptor mutations in centenarians.Proc. Natl Acad. Sci. USA 105, 34383442. ( doi:10.\n1073/pnas.0705467105 )\n21 Heijmans, B. T ., Beekman, M., Houwing-Duistermaat,\nJ. J., Cobain, M. R., Powell, J., Blauw, G. J., van der",
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+              "text": "early-onset diabetes in some pedigrees, but it also maybe observed in individuals who retain normal glucose\ntolerance into late adulthood and beyond ( ). Studying\n individuals from  HNF A-MODY families,\nLango Allen et al. () found that a -SNP T Dr s P S\nwas signi cantly associated with earlier age of diabetes\ndiagnosis, with each additional risk allele accelerating\ndiagnosis by ~ months.\nClinical application of predictive scores",
+              "title": "2019 - Genetic Risk Scores for Diabetes Diagnosis.pdf",
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+              "content": "Diabetes is classified into several types based on different criteria. The main types of diabetes are:\n\n1. **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].\n\n2. **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].\n\n3. **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].\n\n4. **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].\n\nAdditionally, 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].",
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+              "text": "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 [ \n83 ]. Scientists have \ndivided diabetes into three different types: Type 1 F. Assah and J.C. Mbanya",
+              "title": "2017 - diabetes-mellitus-in-developing-countries-and-underserved-commun-2017.pdf",
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+              "text": "Type 1 and type 2 diabetes are the two main types, with type 2 diabetesaccounting for the majority ( >85%) of total diabetes prevalence. Both",
+              "title": "2010 - Epidemiology of diabetes.pdf",
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+              "text": "classical classification of diabetes as proposed by the \nAmerican Diabetes Association (ADA) in 1997 as type \n1, type 2, other types, and gestational diabetes mellitus \n(GDM) is still the most accepted classification and \nadopted by ADA[1]. Wilkin[8] proposed the accelerator \nhypothesis that argues type 1 and type 2 diabetes \nare the same disorder of insulin resistance set against \ndifferent genetic backgrounds[9]. The difference bet -\nween the two types relies on the tempo, the faster",
+              "title": "2015 - Diabetes mellitus The epidemic of the century.pdf",
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+              "text": "41\ndiabetes mellitus (formerly insulin- dependent \ndiabetes 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), \nthe third type, is de  ned as any degree of glucose",
+              "title": "2017 - diabetes-mellitus-in-developing-countries-and-underserved-commun-2017.pdf",
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+              "text": "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\ngenerally classi ed into monogenic diabetes (maturity onset diabetes\nof the young [MODY], neonatal diabetes, mitochondrial diabetes[54,55] , syndromes of insulin resistance) [56], type 1 diabetes (T1D)\nand type 2 diabetes (T2D). The metabolic syndrome is a combination of",
+              "title": "2016 - Dissecting diabetes metabolic disease.pdf",
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+              "text": "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",
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+              "text": "2. Classification of Diabetes\nOn the basis of insulin deficiency, diabetes can be classifiedintothefollowingtypesasfollows.2.1. Insulin Dependent Diabetes Mellitus (IDDM). It is also\nknown as juvenile onset diabetes or type 1 diabetes, which\naccounts for 510% of the patients, resulting from cellular-mediated autoimmune destruction of the pancreatic cells.\nThediseasecanaffectpeopleofallagesbutusuallyoccursin\nchildrenoryoungadults.Regularsupplyofinsulininjections",
+              "title": "2015 - Recent Trends in Therapeutic Approaches for Diabetes Management  A Comprehensive Updat.pdf",
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+              "text": "2 JournalofDiabetesResearch\nType I diabetes \nIDDM\nType II diabetes \nNIDDM \nGestational \ndiabetesPancreas\nIslet of Langerhans-glucagon\nbeta cells: insulin\nGenomic mutationsadministration for \nsurvival\nsugar levels\nInsulin \nresistance\nDefective insulin \nproduction\nIncreased \nmortalityY ounger \npopulationGlobal \npandemicHuman body \nand diabetes \npregnancy, it needs\ncomplete care and \nglucose monitorin g\nglycemic status\nindividual level\nidentification/development of \nlead moleculesRegular insulin\nExercise",
+              "title": "2015 - Recent Trends in Therapeutic Approaches for Diabetes Management  A Comprehensive Updat.pdf",
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+              "text": "However, there are two major clinical types, type 1  diabetes (T1D) and type 2 \ndiabetes (T2D), according to the etiopathology of t he disorder.  T2D appears to be the",
+              "title": "2017 - Machine Learning and Data Mining Methods in Diabetes Research.pdf",
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+              "text": "SIDD Severe insulin-deficient diabetes\nSIRD Severe insulin-resistant diabetes\nIntroduction\nIn 2018, a ground-breaking study identified five novel subtypes\nof adult-onset diabetes: severe autoimmune diabetes (SAID,\nincluding type 1 diabetes and latent autoimmune diabetes in\nadults [LADA]) and four subtypes of type 2 diabetes (severe\ninsulin-deficient diabetes [SIDD], severe insulin-resistant diabetes\n[SIRD], mild obesity-related diabetes [MOD] and mild age-",
+              "title": "2023 - Childhood adiposity and novel subtypes of adult-onset diabetes a Mendelian randomisation and genome-wide genetic correlation study.pdf",
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+              "content": "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:\n\n1. Type 1 diabetes\n2. Type 2 diabetes\n3. Other types of diabetes\n4. Gestational diabetes mellitus (GDM)\n5. Monogenic diabetes (e.g., MODY)\n\nIn summary, there are at least five main types of diabetes, with additional subtypes identified in recent studies.",
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+              "text": "Type 1 and type 2 diabetes are the two main types, with type 2 diabetesaccounting for the majority ( >85%) of total diabetes prevalence. Both",
+              "title": "2010 - Epidemiology of diabetes.pdf",
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+              "text": "classical classification of diabetes as proposed by the \nAmerican Diabetes Association (ADA) in 1997 as type \n1, type 2, other types, and gestational diabetes mellitus \n(GDM) is still the most accepted classification and \nadopted by ADA[1]. Wilkin[8] proposed the accelerator \nhypothesis that argues type 1 and type 2 diabetes \nare the same disorder of insulin resistance set against \ndifferent genetic backgrounds[9]. The difference bet -\nween the two types relies on the tempo, the faster",
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+              "text": "41\ndiabetes mellitus (formerly insulin- dependent \ndiabetes 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), \nthe third type, is de  ned as any degree of glucose",
+              "title": "2017 - diabetes-mellitus-in-developing-countries-and-underserved-commun-2017.pdf",
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+              "text": "SIDD Severe insulin-deficient diabetes\nSIRD Severe insulin-resistant diabetes\nIntroduction\nIn 2018, a ground-breaking study identified five novel subtypes\nof adult-onset diabetes: severe autoimmune diabetes (SAID,\nincluding type 1 diabetes and latent autoimmune diabetes in\nadults [LADA]) and four subtypes of type 2 diabetes (severe\ninsulin-deficient diabetes [SIDD], severe insulin-resistant diabetes\n[SIRD], mild obesity-related diabetes [MOD] and mild age-",
+              "title": "2023 - Childhood adiposity and novel subtypes of adult-onset diabetes a Mendelian randomisation and genome-wide genetic correlation study.pdf",
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+              "text": "7 American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care  37(Suppl. 1), S81S90 \n(2014).\n8 Daneman D. Type 1 diabetes. Lancet  367(9513), 847858 \n(2006).\n9 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).\n\t Describes\tthe\tpathophysiology\tof\tType\t2\tdiabetes\t(T2D)\tin \t\ndetail\twith\tprospective\tof\t -cell\tdysfunction\tand\tpotential",
+              "title": "2015 - Genetics, genomics and personalized medicine in Type 2 Diabetes.pdf",
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+              "text": "However, there are two major clinical types, type 1  diabetes (T1D) and type 2 \ndiabetes (T2D), according to the etiopathology of t he disorder.  T2D appears to be the",
+              "title": "2017 - Machine Learning and Data Mining Methods in Diabetes Research.pdf",
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+              "text": "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\n 112 (883%) patients were considered \nto have type 2 diabetes (appendix).\nTo classify patients into novel diabetes subgroups, first",
+              "title": "2018 - Novel subgroups of adult-onset diabetes and their association.pdf",
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+              "text": "4 \n monogenic diabetes not only provides opportunities for etiology- based treatment of the \nminority of individuals with highly penetrant variants, but also informs broader \nunderstanding of diabetes etiology. Types of monogenic diabetes  \nMaturity onset diabetes of the young (MODY)  \nMODY comprises most  monogenic diabetes cases, with classical characteristics",
+              "title": "2021 - Genomic Medicine in Diabetes Improving the Diagnostic Rate of Monogenic Diabetes.pdf",
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+              "text": "19\nRACIALIZED ETIOLOGIES OF DIABETES\nDiabetes is not one disease but many. More than 90 percent of all diabetics",
+              "title": "2007 - Bioethnic Conscription Genes, Race.pdf",
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+              "text": "with young-onset diabetes. Diabetologia 55:1265 1272\n13. Schwartz SS, Epstein S, Corkey BE, Grant SF, Gavin JR 3rd,\nAguilar RB (2016) The time is right for a new classification system\nfor diabetes: rationale and implications of the -cell-centric classi-\nfication schema. Diabetes Care 39:179 186\n14. Gale EAM (2006) Declassifying diabetes. Diabetologia 49:1989 \n1995\n15. V oight BF, Scott LJ, Steinthorsdottir V et al (2010) Twelve type 2",
+              "title": "2017 - Painting a new picture of personalised medicine for diabetes.pdf",
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+              "content": "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].",
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+              "text": "The biological processes linking aging and disease risk are poorly understood. Still, aging is considered to date as \none of the main factors responsible for several complex diseases including cancer, cardiovascular diseases, and diabetes.\nParticularly, type 2 diabetes (T2D) has become very prevalent all over the world, with a projected increas-\ning growth rate for the years ahead\n1. The pathophysiological mechanism that underlines diabetic complications",
+              "title": "2017 - Regular exercise participation improves genomic stability in diabetic patients an exploratory study to analyse telomere length and DNA damage.pdf",
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+              "text": "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",
+              "title": "2009 - Antidiabetic drug metformin (GlucophageR) increasesbiogenesis of Alzheimer\u2019s amyloid peptides viaup-regulatingBACE1transcription.pdf",
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+              "text": "striking similarities to people with respect to age-associ-\nated increases in risk for several diseases, the relative risk\nfor individual diseases is not always shared. For example,although the prevalence of type II diabetes in older dogs\nincreases with age, it is still much lower than the current\nprevalence of type II diabetes in people, and the mostcommon form of diabetes in dogs resembles type I diabetes\nin people (Nelson and Reusch 2014 ). Whether this reects",
+              "title": "2016 - The dog aging project translational geroscience in companion.pdf",
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+              "text": "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.\n28",
+              "title": "2018 - Type 2 Diabetes in adolescents and young adults.pdf",
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+              "text": "COMMENT\nIn 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-",
+              "title": "2004 - Diabetes Mellitus and Risk of Alzheimer Disease and Decline in Cognitive Function.pdf",
+              "version": "v0",
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+              "text": "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 ). \n In a few patients, genetic mutations appear to be associ-\nated with T2D (Roche  et al.  ,  2005 ; American Diabetes \nAssociation,  2010a ). For example, recent work using the DPP data has led to the identi  cation of 27 single nucle-",
+              "title": "2012 - Systems Biology Approaches to Nutrition.pdf",
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+            "id": "19faf41b-7716-5244-a9c3-196c2e5cd477",
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+              "text": "et al., 2012 ), possibly due to the indirect and/or a mixed relation-\nship between individual genetic disease risk loci and exceptional\nlongevity (as discussed by Fortney et al., 2015 ) versus the poten-\ntially more direct relationship between aging in the absence of\ndisease and overall genetic disease risk.\nOn the other hand, no difference in genetic risk is observed for\ntype 2 diabetes genetic risk and cancer. Some of these ndings\n(type 2 diabetes, colon, and lung cancer) can be explained by the",
+              "title": "2016 - Whole-Genome Sequencing of a Healthy Aging Cohort.pdf",
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+              "text": "equal number of adults over 18 are thought to develop the disease,although incidence in older people receives less media/research\nattention. In this review, we discuss our current understanding of\nthe 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.",
+              "title": "2010 - Genetics, pathogenesis and clinical interventions in type\u20091 diabetes.pdf",
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+              "text": "associated with maturity onset diabetes of the young and early onset-age of \ntype 2 diabetes. J. Diabetes Complications 26, 343347 (2012).\n19. Langenberg, C. et al. Design and cohort description of the InterAct Project: \n \nan examination of the interaction of genetic and lifestyle factors on the incidence of type 2 diabetes in the EPIC Study. Diabetologia 54, 22722282 \n(2011).",
+              "title": "2016 - The genetic architecture of type 2 diabetes.pdf",
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+              "text": "in the precipitation of diabetes. Saturated fatty acids drive the apoptosis  and senescence of beta cells27,41, with increased oxidative stress42 and \nendoplasmic reticulum stress41. As increased body mass index is asso -\nciated with earlier onset of T1D43, it is possible that dietary fat is acting \nas a sensitizer similar to insHEL, in effect lowering the threshold for \nautoimmune stress to precipitate clinical diabetes.\nThe male-specific susceptibility to diabetes in this model is in sharp",
+              "title": "2016 - Genetic predisposition for beta cell fragility underlies type 1 and type 2 diabetes.pdf",
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+              "content": "Genetics plays a significant role in influencing the emergence of diabetes. Several key points highlight this influence:\n\n1. **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].\n\n2. **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.\n\n3. **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].\n\n4. **Familial Clustering**: There is evidence of familial clustering of diabetes, indicating a genetic predisposition to the disease and its complications [7].\n\n5. **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].\n\n6. **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].\n\nIn summary, genetics significantly influences the emergence of diabetes by predisposing individuals to the disease, which is then often triggered or exacerbated by environmental factors.",
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+              "text": "Genetic factors appear to play a role in determining an individuals risk of developing diabetes. It is hoped",
+              "title": "2008 - Glossary of Genetics Genomics Terms.pdf",
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+              "text": "the diabetes epidemic, and its predilection\nfor certain ethnic groups, are unknown.\nHowever, interactions between genetic pre-disposition and environmental triggers (or\naccelerants) are generally presumed to un-\nderlie the etiology of diabetes (3 5) (Fig. 1).\nThe best known environmental risk factors\nare dietary habits, physical inactivity, and\nobesity; interventions that ameliorate theserisk factors prevent the development oftype 2 diabetes (6,7).\nBy contrast, knowledge of the genetic",
+              "title": "2012 - Predicting Diabetes Our Relentless Quest for Genomic Nuggets.pdf",
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+              "text": "increases the risk of type  2 diabetes. Such a strong environmental component to a dis -\nease should perhaps have deterred geneticists \nfrom studying the disorder. However, there \nare many obese people who do not suffer \nfrom diabetes and many non-obese people \nwho do, showing that obesity is not the only \nfactor involved in the aetiology of type\n \n2 \ndiabetes (FIG. 1).\nIn the past 10 years, geneticists have \ndevoted a large amount of effort to finding type\n \n2 diabetes genes. These efforts have",
+              "title": "2007 - Genome\u2013wide association studies provide new insights into type 2 diabetes aetiology..pdf",
+              "version": "v0",
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+              "text": "future diabetes, however, is not possible on a genetic basis alone. For \nexample, the concordance rate for identical twins is < 50%, indicating that \neither environmental or developmental events (such as T cell development) \naffect the progression of diabetes. \nThe ability of serologic studies to identify individuals at risk for \ndiabetes in the general population is under investigation. Among relatives of \npatients with diabetes, serologic markers can identify patients at high risk.3",
+              "title": "2004 - Diabetes Genes b.pdf",
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+              "text": "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.\nCitation: 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\nONE 8(1): e53522. doi:10.1371/journal.pone.0053522",
+              "title": "2013 - Systems Biology Approach Reveals Genome to Phenome Correlation in Type 2 Diabetes.pdf",
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+              "text": "Genetic factors\nType 2 diabetes has a strong genetic component and most Asian patients have a   rst-degree relative with diabetes.\n48,49 Much progress has been made in our \nunderstanding 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, \nsuch as those seen in maturity-onset diabetes of young people,",
+              "title": "2010 - Diabetes in Asia.pdf",
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+              "text": "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",
+              "title": "2004 - Diabetic nephropathy Linking histology, cell biology.pdf",
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+            "id": "44725666-366f-5123-92dd-ab2cf29e88c1",
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+              "text": "GENETIC MODELS OF DIABETES \nClassically, genetic models of diabetes and obesity have been produced in \ntwo ways. One is serendipitous observation of a spontaneously arising \nextreme phenotype, followed by selective breeding to fix the trait. The \nresulting model will often be monogenic, i.e. due to a single mutation. The \nother approach is by repeated selective breeding of initially normal\nappearing members of a genetically diverse ( outbred) population that are at",
+              "title": "2004 - Diabetes Genes a.pdf",
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+              "text": "36 Herder C, Roden M. Genetics of type 2 diabetes: pathophysiologic \nand clinical relevance. Eur J Clin Invest 2011; 41: 67992.\n37 Dabelea D, Hanson RL, Lindsay RS, et al. Intrauterine exposure \nto diabetes conveys risks for type 2 diabetes and obesity: a study of discordant sibships. Diabetes 2000; 49: 220811.\n38 Voight BF, Scott LJ, Steinthorsdottir V, et al. Twelve type 2 diabetes \nsusceptibility loci identi  ed through large-scale association analysis. Nat Genet 2010; 42: 57989.",
+              "title": "2011 - Type 2 diabetes across generations from pathophysiology to prevention and management.pdf",
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+              "text": "Environmental influences interact with genetic factors to determine \nsusceptibility to type 2 diabetes by affecting either insulin action, insulin \nsecretion or both. The prevalence of type 2 diabetes has increased markedly \nin populations that have rapidly adopted a Western lifestyle (for example the \nPima Indians) and in many populations that have migrated to regions with a \nmore affluent lifestyle compared to their native country (see Chapter IV.2).",
+              "title": "2004 - Diabetes Genes a.pdf",
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+              "content": "Several genes have been associated with diabetes, both type 1 and type 2, according to the provided context:\n\n1. **Type 1 Diabetes:**\n   - **Insulin gene minisatellite locus (IDDM2)**: Susceptibility to type 1 diabetes is determined by tandem repeat variation at this locus [2].\n   - **CTLA-4 gene region of chromosome 2q33**: This gene region is linked to and associated with type 1 diabetes [4].\n\n2. **Type 2 Diabetes:**\n   - **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].\n   - **Common gene variants**: Several genome-wide association studies (GWAS) have linked common gene variants with increased risk of type 2 diabetes, including:\n     - **CDKAL1, CDKN2A, CDKN2B**: Associated with reduced insulin secretion via reduced beta-cell mass [3].\n     - **MTNR1B, TCF7L2, KCNJ11**: Associated with beta-cell dysfunction [3].\n     - **FTO**: Associated with increased insulin resistance related to obesity [3].\n     - **IRS1, PPARG**: Associated with increased insulin resistance unrelated to obesity [3].\n   - **Other genes**: Additional genes associated with type 2 diabetes include:\n     - **TCF7L2, KCNJ11, PPARG, CDKAL1, IGF2BP2, FTO, HHEX, SLC30A8, WFS1** [5].\n     - **JAZF1, CDC123/CAMK1D, TSPAN8/LGR5, THADA, ADAMTS9, NOTCH2** [5].\n   - **Genes related to beta-cell dysfunction**: Many type 2 diabetes genes appear to be related to beta-cell dysfunction [6].\n   - **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].\n   - **Genes associated with recessive diseases that include diabetes**: **WFS1, NEUROG3, EIF2AK3, GLIS3, RFX6, SLC19A2** [7].\n   - **Genes with heterozygous mutations causing diabetes mellitus**: **PAX6, GATA6, PPARG** [7].\n   - **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].\n\nThese references provide a comprehensive list of genes associated with both type 1 and type 2 diabetes.",
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+              "text": "gene are associated with NIDDM in Caucasians. Diabetes 1996 , 45, 825-831. \n46.  Tarasov, A.I.; Nicolson, T.J. ; Riveline, J.P.; Taneja, T.K. ; Baldwin, S.A.; Baldwin, J.M.; \nCharpentier, G.; Gautier, J.F. ; Froguel, P.; Vaxillaire, M.; et al.  A rare mutation in ABCC8/SUR1 \nleading to altered ATP-sensitive K+ channel activ ity and beta-cell glucose sensing is associated \nwith type 2 diabetes in adults. Diabetes 2008 , 57, 1595-1604.",
+              "title": "2010 - Pharmacogenetics of Anti-Diabetes Drugs.pdf",
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+              "chunk_order": 184,
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+              "text": "gene is associated with insulin-dependent diabetes mellitus. Diabetes\n33:176 183, 1984\n6. Bennett ST, Lucassen AM, Gough SCL, Powell EE, Undlien DE, Pritchard\nLE, Merriman ME, Kawaguchi Y, Drons eld MJ, Pociot F, Nerup J,\nBouzekri N, Cambon-Thomasen A, R nningen KS, Barnett AH, Bain SC,\nTodd JA: Susceptibility to human type 1 diabetes at IDDM2 is determinedby tandem repeat variation at the insulin gene minisatellite locus. Nat\nGenet 9:284 292, 1995",
+              "title": "2002 - Genetic Effects on Age-Dependent Onset and Islet Cell.pdf",
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+              "text": "of Diabetes \n Results of several genome-wide association stud-\nies (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) [ \n11 ]. While most of the early studies",
+              "title": "2017 - diabetes-mellitus-in-developing-countries-and-underserved-commun-2017.pdf",
+              "version": "v0",
+              "chunk_order": 2429,
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+              "text": "gene is associated with insulin-dependent diabetes mellitus. Diabetes\n33:176 183, 1984\n3. Nistico L, Buzzetti R, Pritchard L, Van der Auwera B, Giovannini C, Bosi E,\nLarrad 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,\n1996",
+              "title": "2004 - Interaction and Association Analysis of a Type 1 Diabetes Susceptibility Locus.pdf",
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+              "text": "ly associated with type 2 diabetes: TCF7L2, KCNJ11,  \nand PPARG .\n5-7 However, in 2007, a number of novel \ngenetic variants ( CDKAL1, IGF2BP2,  the locus on \nchromosome 9 close to CDKN2A/CDKN2B, FTO, \nHHEX, SLC30A8,  and WFS1)8-14 were shown to in -\ncrease susceptibility to type 2 diabetes in repro -\nducible studies. Furthermore, a recent meta-analy -\nsis identified six novel variants ( JAZF1, CDC123/\nCAMK1D, TSPAN8/LGR5, THADA, ADAMTS9, and NOTCH2 ) that are associated with type 2 dia -\nbetes.\n15",
+              "title": "2008 - Clinical Risk Factors, DNA Variants.pdf",
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+              "text": "date gene approaches now have identified /H1101140 genes as-\nsociated 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\n/H9252-cell dysfunction,",
+              "title": "2011 - Obesity and Type 2 Diabetes What Can Be Unified.pdf",
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+              "text": "HNF1A ,HNF4A ,HNF1B ,INS,NEUROD1 ,PDX1 ,PAX4 ,\nABCC8 ,KCNJ11 ,KLF11 ,CEL, and BLK), 6 genes associ-\nated with recessive diseases that include diabetes as a\nphenotype ( WFS1 ,NEUROG3 ,EIF2AK3 ,GLIS3 ,RFX6 ,\nandSLC19A2 ), and 3 genes in which heterozygous mu-\ntations have been shown to cause diabetes mellitus\n(PAX6 ,GATA6 , and PPARG ). Our primary objectives\nwere to (1) identify subjects with potentially undiag-\nnosed monogenic diabetes, (2) compare and contrast the",
+              "title": "2017 - Spectrum of mutations in monogenic diabetes genes identified from high-throughput DNA sequencing of 6888 individuals.pdf",
+              "version": "v0",
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+          {
+            "id": "b4efc562-0077-5428-be43-f3eeafeb6847",
+            "score": 0.7354490099445952,
+            "metadata": {
+              "text": "4. ORahilly S. Human genetics illumi -\nnates the paths to metabolic disease. Na -\nture 2009;462:307-14.\n5. McCarthy MI. Growing evidence for \ndiabetes susceptibility genes from genome scan data. Curr Diab Rep 2003;3:159-67.\n6. Hattersley AT, McCarthy MI. What \nmakes a good genetic association study? Lancet 2005;366:1315-23.\n7. Altshuler D, Hirschhorn JN, Klanne -\nmark M, et al. The common PPARgamma Pro12Ala polymorphism is associated with decreased risk of type 2 diabetes. Nat Genet 2000;26:76-80.",
+              "title": "2010 - Genomics, Type 2 Diabetes, and Obesity.pdf",
+              "version": "v0",
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+              "document_id": "3d629777-f1b6-5450-94ef-56736e5a4e10",
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+            "id": "d184bcc3-8c38-5969-859a-22db976fec35",
+            "score": 0.7351778964751335,
+            "metadata": {
+              "text": "genes including interlukin-6 ( IL-6), tumor necrosis \nfactor- and IL-10 genes were found to be associated \nwith greater risk of developing type 2 diabetes[171], in \naddition to genetic variants in the genes for IL12B , \nIL23R  and IL23A  genes[172]. In a study involving the \nhormone sensitive lipase re sponsible for lipolysis \nin adipose tissues, a deletion null mutation, which \nresulted in the absence of the protein from adipocytes, \nwas reported to be associated with diabetes[173]. Nine",
+              "title": "2015 - Diabetes mellitus The epidemic of the century.pdf",
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+              "text": "2 diabetes[144,149,150], however, not all of these genes \nshowed consistent and reproducible association with \nthe disease[151]. Genome wide association studies \n(GWAS) in various populations identified 70 loci \nassociated with type 2 diabetes and revealed positive \nlinkage of many mutations and SNPs that influence \nthe expression and physiological impact of the related \nproteins and risk to develop type 2 diabetes. One study \ninvolved several thousand type 2 diabetes patients and",
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+              "content": "Diabetes can be caused by different factors depending on the type. \n\n1. **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].\n\n2. **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].\n\n3. **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].\n\nIn summary, diabetes is caused by a combination of genetic, autoimmune, and environmental factors that affect insulin production and action [1], [3], [8], [10].",
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+              "text": "two broad etiopathogenetic groups. In one group (type I diabetes), the cause \nis an absolute deficiency of insulin secretion. Individuals at increased risk of \ndeveloping this type of diabetes can often be identified by serological \nevidence of an autoimmune process of the pancreatic islets and by genetic \nmarkers. In the second and more prevalent group (type 2 diabetes), the cause \nis a combination of resistance to insulin action with inadequate \ncompensatory insulin secretory response.",
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+              "text": "Diabetes mellitus.  Type1 diabetes mellitus (T1DM) and \nT2DM have different causes, but both ultimately lead to \npancreatic -cell dysfunction. Damaging the pancreas \nchemically or mechanically can induce experimental \ndiabetes mellitus. Pancreatic damage can be achieved by \nsurgically removing parts of or all of the pancreatic tissue \n(pancreatectomy) to reduce or fully ablate endogenous \ninsulin production282. The benefit of this method is the \nlack of toxic adverse effects (compared with diabetogenic",
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+              "text": "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",
+              "title": "2012 - Systems Biology Approaches to Nutrition.pdf",
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+              "text": "diabetes but a rare cause of diabetes diag -\nnosed in childhood or adulthood. Diabetes . \n2008;57(4):10341042.\n 152. Molven A, et al. Mutations in the insulin gene can \ncause MODY and autoantibody-negative type 1 \ndiabetes. Diabetes . 2008;57(4):11311135.\n 153. Gloyn AL, et al. Mutations in the genes encoding \nthe pancreatic beta-cell KATP channel subunits \nKir6.2 (KCNJ11) and SUR1 (ABCC8) in diabe -\ntes mellitus and hyperinsulinism. Hum Mutat. \n2006;27(3):220231.",
+              "title": "2021 -  Monogenic diabetes a gateway to precision medicine.pdf",
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+              "text": "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",
+              "title": "2003 - A functional polymorphism in the promoterenhancer region of the FOXP3Scurfin gene associated with type 1 diabetes.pdf",
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+              "text": "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 [ \n83 ]. Scientists have \ndivided diabetes into three different types: Type 1 F. Assah and J.C. Mbanya",
+              "title": "2017 - diabetes-mellitus-in-developing-countries-and-underserved-commun-2017.pdf",
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+              "text": "(Fig. 1), indicating that insulin resistance and insulin secretory\ndefect played a cooperative role in the development and exac-\nerbation of diabetes, even though neither was strong enough\nalone to cause overt diabetes. From another point of view,\neven if genetically determined insulin resistance itself might\nnot be sufficient for the development of diabetes, insulin resis-\ntance results in diabetes if pancreatic /H9252 cell function is im-\npaired genetically (this study) or nongenetically. Development",
+              "title": "1997 - Development of non-insulin-dependent diabetes mellitus.pdf",
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+              "text": "tors, and other environmental factors that trigger isletautoimmunity and/or type 1 diabetes.\nType 2 Diabetes\nType 2 diabetes develops when b-cells fail to secrete suf-\ncient insulin to keep up with demand, usually in the\ncontext of increased insulin resistance. A minority of peo-\nple diagnosed with type 2 diabetes also have evidence ofislet autoimmunity (57,58). Obesity is a major risk factor\nfor type 2 diabetes (59,60) with complex genetic and en-\nvironmental etiology.",
+              "title": "2017 - Differentiation of Diabetes by Pathophysiology.pdf",
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+              "text": "have environmental (islet-injuring drugs or a particular diet) and/or genetic \n(monogenic or polygenic) causes. We have grouped the models by cause \nand type of diabetes. While this grouping is reasonable and instructive, it \ncan over-emphasize distinctions. For example, it is believed that beta cell \nfailure (and/or poor islet regeneration) contributes to type 2 diabetes, but in \ntheir pure, severe form these processes cause type I diabetes. \nMODELS OF INSULIN-DEFICIENT DIABETES",
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+              "text": "Diabetes mellitus comprises a heterogenous group\nof disorders that have been classified as either\ninsulin-dependent (IDDM) or non-insulin-depend-\nent (NIDDM).1 Their causes are poorly understood\nbut appear to involve some form of interaction between ge-\nnetic and environmental factors.2-4 Some of the environmen-\ntal factors that can contribute to IDDM include viral infections\nand chemicals, while obesity is a common predisposing fac-\ntor for NIDDM. Genes that confer susceptibility or can cause",
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+              "content": "Yes, cycling, as a form of physical activity, can reduce the risk of diabetes. According to the context provided:\n\n- 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].\n- Additionally, other studies have shown that exercise, including activities like cycling, can prevent the development of Type 2 diabetes in high-risk groups [5].\n\nThus, engaging in regular cycling can contribute to a reduced risk of developing diabetes.",
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+              "text": "2 diabetes suggest that regular exercise might play an important role in \ndecreasing the very high incidence of premature coronary artery disease. \nAlthough there are no randomized controlled trials assessing reduction \nin cardiovascular events induced by physical activity in type 2 diabetes, \navailable evidence is consistent with the concept that physical activity may play \nan important role in reducing cardiovascular risk in type 2 diabetes. 44 Large",
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+              "text": "tern of weight change impact health. For example, in the DiabetesPrevention Program (DPP; described in more detail later), both\nshort- and intermediate-term weight loss were associated with\nreduced diabetes risk and intermediate cardiometabolic risk factor\nlevels, whereas weight cycling (defined as number of 5 lb [2.25 kg]\nweight cycles) raised diabetes risk, fasting glucose levels, insulinresistance, and systolic blood pressure. Initial (baseline to 1 month)",
+              "title": "2016 - NIH working group report using genomic information to guide weight management  From universal.pdf",
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+              "text": "sclerosis Risk in Communities (ARIC) study, the highestquartile of leisure activity (primarily cycling and walking)had a 34% lower odds of developing hypertension over\n6 years compared to the least active [ 107]. Thus, physical\nactivity reduces the risk of developing diabetes and hyper-\ntension. The mechanism involves changes in body weight\nand glucose tolerance, as well as other factors [ 107].\nThe effect of obesity susceptibility genes on the onset of",
+              "title": "2012 - Diabetes and Hypertension Is There a Common Metabolic Pathway.pdf",
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+              "text": "exercise can reduce the incidence of type 2 diabetes. Tuomilehto and \ncoworkers demonstrated that the individuals on a consistent diet and exercise \nprogram had 10% incidence of diabetes during 4 years of follow-up \ncompared to 22% for patients in the control group, who met only once a year \nwith the dietician and the physician.40 A six-year randomized trial conducted \nby Pan and colleagues demonstrated that exercise resulted in 46% reduction",
+              "title": "2004 - Diabetes Genes a.pdf",
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+              "text": "Exercise \nExercise has been shown to prevent development of Type 2 \ndiabetes in high-risk groups. A number of studies have looked at the effect \nof insulin on delaying the onset of diabetes. In a study of 5990 male \nalumni from an American university followed over 10 years, 202 pts (3.3 \npercent) developed Type 2 diabetes mellitus. The relative risk was lower \nin patients who exercised regularly even when adjusted for obesity, \nhypertension, and a family history of diabetes. The benefit was greatest in",
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+              "text": "nonrandomized studies of both men and women with type 2 diabetes and \nimpaired glucose tolerance have found that physical activity is associated with \na decreased risk for cardiovascular disease. It also appears that the amount of \nphysical activity is inversely associated with coronary events.5354 \nRISK OF EXERCISE IN PATIENTS WITH DIABETES \nThe risks associated with exercise can be divided into metabolic, \nvascular, neurologic and musculoskeletal (Table 4).",
+              "title": "2004 - Diabetes Genes a.pdf",
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+              "text": "74\nThe 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.\n75,76\nBoth a reduction in macronutrient intake and exercise cause areduction in inflammation.\nReferences\n1. Reaven GM. Banting lecture 1988. Role of insulin resistance in human\ndisease. Diabetes . 1988;37:15951607.",
+              "title": "2005 - Metabolic Syndrome A Comprehensive Perspective Based  on Interactions Between Obesity Diabetes and Inflammation.pdf",
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+              "text": "physical training on carbohydrate metabolism and associated cardiovascular risk factors in patients with diabetes. Diabetes Rev. 1995;3:378407.  \n   23.    Rogers MA, Yamamoto C, King DS, Harberg JM, \nEnsani AA, Holloszy JO. Improvement in glucose tolerance after one week of exercise in patients with mild NIDDM. Diabetes Care. 1988;11:6138.  \n   24.    Eriksson KF, Lindgarde F. Prevention of type 2 dia-\nbetes mellitus by diet and physical exercise. Diabetologia. 1991;34:8918.",
+              "title": "2017 - diabetes-mellitus-in-developing-countries-and-underserved-commun-2017.pdf",
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+              "text": "migrant and other observational studie!f86970 and prospective studies in subjects \nat high risk for developing type 2 diabetes.717273 Recently, large interventional \ntrials have reinforced the benefits of exercise in reducing the risk for type 2 \ndiabetes. These include the Malmo study from Sweden45, the Da Quing study \nfrom China74 and the recently concluded Finnish Diabetes Prevention Study.75 \nThese prospective but not randomized studies show a reduction in the risk of \n560",
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+              "text": "reduce systolic blood pressure, reduce total cholesterol, raise HDL cholesterol, and improve endothelial function in overweight patients with young-onset type 2 diabetes.\n47 \nHowever, 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.\n47,121 Additionally, \nreviews49,121,122 of the limited number of studies done to",
+              "title": "2018 - Type 2 Diabetes in adolescents and young adults.pdf",
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+              "content": "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.",
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+              "text": "Diabetes (GoKinD) study: a genetics collection \navailable for identifying genetic susceptibility \nfactors for diabetic nephropathy in type1 \ndiabetes. J. Am. Soc. Nephrol.  17, 17821790 \n(2006).\n137. Scott, R.A. etal. Large-scale association \nanalyses identify new loci influencing glycaemic \ntraits and provide insight into the underlying \nbiological pathways. Nat. Genet.  44, 9911005 \n(2012).\nAuthor contributions\nAll authors researched the data for the article,",
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+              "text": "identifying genetic susceptibility factors for diabetic nephropathy in type 1\ndiabetes. J Am Soc Nephrol 17: 17821790.\n44. Manolio TA, Rodriguez LL, Brooks L, Abecasis G, Ballinger D, et al. (2007)\nNew models of collaboration in genome-wide association studies: the Genetic\nAssociation Information Network. Nat Genet 39: 10451051.\n45. Mailman MD, Feolo M, Jin Y, Kimura M, Tryka K, et al. (2007) The NCBI\ndbGaP database of genotypes and phenotypes. Nat Genet 39: 11811186.",
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+              "text": "in Diabetes (GoKinD) study: a genetics collection availablefor identifying genetic susceptibility factors for diabeticnephropathy in type 1 diabetes. J Am Soc Nephrol 2006;\n177: 1782 1790.\n10. Pezzolesi MG, Poznik GD, Mychaleckyj JC, et al. Genome-\nwide association scan for diabetic nephropathysusceptibility genes in type 1 diabetes. Diabetes 2009; 586:\n14031410.\n11. Paterson AD, Lopes-Virella MF, Waggott D, et al.",
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+              "text": "beta cell function, insulin mode of action, glucose  metabolism and/or other risk \nfactors.  It is a fact that advances in genotyping technology, over the past few years, \nhave facilitated rapid progress in large-scale gene tic studies.   Identification of a large \nnumber of novel genetic variants increasing suscept ibility diabetes and related traits \nopened up opportunities, not existing thus far, to associate this genetic information",
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+              "text": "DISCUSSION\nThe findings of previous epidemiological and family studies\nsuggest that diabetic nephropathy results from an interaction\nbetween metabolic abnormalities that are typical of poorlycontrolled IDDM and predisposing genetic factors (4,5). Thenature of the genetic factors, however, has remained un-\nknown (22).\nUsing a candidate gene approach, we have found in this",
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+              "text": "PLoS Genetics | www.plosgenetics.org June 2007 | Volume 3 | Issue 6 | e96 0963\nType 2 Diabetes Network-Based Analysis",
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+              "text": "High-Density Single Nucleotide Polymorphism\nGenome-Wide Linkage Scan for Susceptibility Genes forDiabetic Nephropathy in Type 1 Diabetes\nDiscordant Sibpair Approach\nJohn J. Rogus,1,2G. David Poznik,1Marcus G. Pezzolesi,1,2Adam M. Smiles,1Jonathon Dunn,1\nWilliam Walker,1Krzysztof Wanic,1,2Dariusz Moczulski,1,2,3Luis Canani,1,2,4Shinichi Araki,1,2,5\nYuichiro Makita,1,2,6James H. Warram,1and Andrzej S. Krolewski1,2\nOBJECTIVE Epidemiological and family studies have demon-",
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+              "content": "GeneNetwork offers several specific tools that are particularly useful for diabetes research:\n\n1. **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].\n\n2. **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].\n\n3. **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].\n\n4. **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].\n\n5. **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].\n\nThese 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].",
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+              "text": "GeneNetwork have reinvigorated it, including the addition  of data from  10 species, multi -omics \nanalysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for \npredictive medicine and systems genetics, which is constantly being maintained and improved.   \nHere, we give a brief overview of the process  for carrying out some of the most common \nfunctions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small",
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+              "text": "GeneNetwork http://www.genenetwork.org is anexample of a bioinformatics tool that can be used to\nexplore systems genetics data.\nThe importance of defining biological networks and\npredicting molecular interactions has been emphasized\nby several reports [1,2]. Such studies emphasize that\nwhen knowledge about DNA variation within popula-\ntions is interfaced with data on gene expression, protein\ninteractions and DNA-protein binding, biological\nnetworks can be constructed that are predictive of the",
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+              "text": "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\nare two such examples of possible tools. For this study, we",
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+              "text": "subnetworks \n GeneNetwork (www.genenetwork.org) is a depository of data-\nsets and tools for use in complex systems biology approaches in \norder to generate or predict higher order gene function ( 23, 24 ).",
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+              "text": "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.",
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+              "text": "including correlation and network analysis to compare associations \nbetween tissues and between other rodent or human data sets[32]\nMany 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",
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+              "text": "including correlation and network analysis to compare associations \nbetween tissues and between other rodent or human data sets[32]\nMany 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",
+              "title": "2017 - GeneNetwork a toolbox for systems genetics.pdf",
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+              "text": "data are entered into GeneNetwork after they have been shepherded through a system like\nPhenoGen that has extensive capabilities for normalization and quality control. A\ncomparison of the brain gene expression datasets and some of the tools for data analysis\navailable on PhenoGen and GeneNetwork is shown in Table 3, and more detailed\ninformation on features provided by each site is outlined in the Supplementary DiscussionHoffman et al. Page 5\nAddict Biol . Author manuscript; available in PMC 2012 July 1.",
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+              "text": "results in applying the method  to type 2 diabetes mellitus suggest it may hold \npromise as a useful research tool for complex diseases .  Further details on the \nmethodol ogy is available from the following paper: Liu M, Liberzon A, Kong \nSW, Lai WR, Park PJ et al (2007) Network -based analysis of affected biological \nprocesses in type 2 diabetes models.  PLoS Genet 3(6):e96.  \ndoi:10.1371/journal.pgen.0030096.",
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+              "content": "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]. \n\nInsulin 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].",
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+              "text": "Figure 3. Schematics view of insulin regulation. Elevated glucose level by either food intake or liver glycogenolysis is sensed by islet and leads\nto insulin secretion to the bloodstream. The increased insulin stimulates peripheral tissues to absorb glucose, and as a consequence, the glucose le vel",
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+              "text": "(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",
+              "title": "2014 - Pathophysiology and treatment of type 2 diabetes.pdf",
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+              "text": "Insulin Action \n In healthy, normal individuals, blood glucose concentra-\ntion 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",
+              "title": "2012 - Systems Biology Approaches to Nutrition.pdf",
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+              "text": "from the gut, glucose production by the liver, and glucose extraction from the blood into the cells and tissues. \n Insulin plays a central role in the regulation of blood",
+              "title": "2012 - Systems Biology Approaches to Nutrition.pdf",
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+              "text": "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.",
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+              "text": "and suppression ofglucose production are regulated by insulin.",
+              "title": "2000 - Pathophysiology and Pharmacological Treatment.pdf",
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+              "text": "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",
+              "title": "2012 - Systems Biology Approaches to Nutrition.pdf",
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+              "text": "insulin suppresses both hepatic and renal glucose release, 3031 and stimulates \nglucose uptake exogenous insulin administration causes systemic glucose \nutilization to exceed systemic glucose release so that plasma glucose \nconcentrations decrease. \nAs the plasma glucose levels decrease there is a characteristic \nhierarchy of responses (Figure 1 ). Reduction of insulin secretion, the first in \nthe cascade of hypoglycemia counterregulation, 2 derepresses glucose",
+              "title": "2004 - Diabetes Genes a.pdf",
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+              "text": "Counter-regulatory hormones antagonize the glucose lowering action \nof insulin, and act to raise the blood glucose level. Glucagon, a potent \ncounter-regulatory hormone inhibited by insulin, is secreted from pancreatic \nalpha cells when cells perceive low glucose. In diabetes, pancreatic insulin \nlevels are reduced and glucagon is chronically elevated. In DKA, in addition \nto low insulin action, there is the cellular perception of low glucose , which",
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+              "content": "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.",
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+              "text": "The biological processes linking aging and disease risk are poorly understood. Still, aging is considered to date as \none of the main factors responsible for several complex diseases including cancer, cardiovascular diseases, and diabetes.\nParticularly, type 2 diabetes (T2D) has become very prevalent all over the world, with a projected increas-\ning growth rate for the years ahead\n1. The pathophysiological mechanism that underlines diabetic complications",
+              "title": "2017 - Regular exercise participation improves genomic stability in diabetic patients an exploratory study to analyse telomere length and DNA damage.pdf",
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+              "text": "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\nassociation between age at menopause and type 2 diabetes are\ncontradictory, with a few studies reporting no association and\nsome other reporting higher odds of having type 2 diabetes with\nearly onset of menopause [ 57]. Recently, a nested case cohort\nstudy reported that an increased risk of type 2 diabetes is associ-",
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+              "text": "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.\n18 In adolescents with type 2 diabetes, as in \nlater onset type 2 diabetes, the initial deterioration in -cell function is characterised by loss of first-phase nutrient-stimulated insulin secretion.",
+              "title": "2018 - Type 2 Diabetes in adolescents and young adults.pdf",
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+              "text": "anincreased risk of developing type 2 diabetes (T2D) later in their",
+              "title": "2012 - Meta-Analysis of the Relationship between Common.pdf",
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+              "text": "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",
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+              "text": "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\ndiabetes and greatly diminished b-cell function but also\nlikely includes individuals who have slowly progressingautoimmune diabetes with adult onset (LADA) or otherambiguous forms of diabetes.\nAge. Data from randomized controlled trials in people\nwith type 2 diabetes under the age of 18 years or over the\nage of 65 years are scarce. Bene cial effects of tight",
+              "title": "2017 - Differentiation of Diabetes by Pathophysiology.pdf",
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+              "text": "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.\n28",
+              "title": "2018 - Type 2 Diabetes in adolescents and young adults.pdf",
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+              "text": "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 \ntype 2 diabetes presenting in middle and later life.\n106 In \nHong Kong, where 20% of type 2 diabetes diagnosed since \n1995 occurs in people aged 40 years or younger, a 7-year prospective study\n107 showed that when adjusted for age,",
+              "title": "2018 - Type 2 Diabetes in adolescents and young adults.pdf",
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+              "text": "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,",
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+              "text": "pathophysiology of type 2 diabetes. Diabetes 60(10):26242634.\ndoi:10.2337/db11-0415Aging Clin Exp Res\n123",
+              "title": "2015 - A Chromosome 13 locus is associated with male-specific mortality in mice.pdf",
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+              "content": "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:\n\n1. **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].\n\n2. **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.\n\n3. **Cornerstone of Treatment**: Lifestyle modification, including exercise, nutrition, and behavioral changes, is considered the cornerstone for both preventing and treating type 2 diabetes [7].\n\nIn 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].",
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+              "text": "of Type 2 Diabetes \n The lifestyle intervention using physical exercise \nand modi  cation of nutrition is ef  cient in pre-\nventing type 2 diabetes in patients with impaired glucose tolerance [ \n99 ]. Clinical trials con  rm \nthat lifestyle interventions (dietary modi  cation \nand increased physical activity) reduce the risk of progressing from impaired glucose tolerance to type 2 diabetes [ \n105 ]. Assessing T2D risk accord-\ning to FINDRISK scale [ 106 ] is quite common in",
+              "title": "2017 - diabetes-mellitus-in-developing-countries-and-underserved-commun-2017.pdf",
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+              "text": "Major clinical trials have demonstrated that diet and lifestyle modifications are \neffective in preventing T2DM in high-risk individuals.\n T2DM management strategies including lifestyle modifications, social support and \nensuring medication adherence are key to reducing the incidence of diabetes \nmellitus complications.\nREVIEWS\nNATURE REVIEWS | ENDOCRINOLOGY  VOLUME 14 | FEBRUARY 2018 | 89",
+              "title": "2018 - Global aetiology and epidemiology of type 2 diabetes mellitus and its complications.pdf",
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+              "text": "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 \nthe exception of the thiazolidinedione anti diabetics.\n163175",
+              "title": "2014 - Pathophysiology and treatment of type 2 diabetes.pdf",
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+              "text": "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",
+              "title": "2011 - Lifestyle and Genetics in Obesity and type 2 Diabetes.pdf",
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+              "text": "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",
+              "title": "2010 - Interactions of Dietary Whole-Grain Intake.pdf",
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+              "text": "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",
+              "title": "2008 - Public Health Genomics Approach to Type 2 Diabetes.pdf",
+              "version": "v0",
+              "chunk_order": 36,
+              "document_id": "47186d35-9c05-5b0a-b8cd-21d2e0e688d8",
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+            "id": "e6e5b010-d608-5a19-ae74-d571499fbb7b",
+            "score": 0.6680524945259094,
+            "metadata": {
+              "text": "Lifestyle modification including exercise, nutrition and\nbehavioral changes is the cornerstone to prevent and treat\ntype 2 diabetes. Oral antidiabetic medication either as\nsingle agent or combination therapy is frequently required\nto maintain metabolic control, as assessed by monitoring ofglycated hemoglobin A\n1C(HbA 1C) levels. Eventually, asignificant proportion of patients with type 2 diabetes require\nthe exogenous administration of insulin [40].",
+              "title": "2009 - Zinc and Diabetes - clinical links and molecular mechanisms.pdf",
+              "version": "v0",
+              "chunk_order": 17,
+              "document_id": "72ab8458-928b-56b9-9547-1ba4b59dfab9",
+              "extraction_id": "ef8e6aa1-b7e0-5988-b9fb-a339317f9a66",
+              "associatedQuery": "Can lifestyle changes reverse type 2 diabetes?"
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+            "id": "2dc0e0fa-b061-5c09-8af3-02a44811042e",
+            "score": 0.6664275129778687,
+            "metadata": {
+              "text": "diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. N Engl J Med 2001; 344: 134350.\n114 Knowler WC, Barrett-Connor E, Fowler SE, et al. Reduction in \nthe incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med 2002; 346: 393403.\n115 Ramachandran A, Snehalatha C, Mary S, Mukesh B, Bhaskar AD,",
+              "title": "2011 - Type 2 diabetes across generations from pathophysiology to prevention and management.pdf",
+              "version": "v0",
+              "chunk_order": 181,
+              "document_id": "0f49b102-1d7e-5702-af30-35e5f2ed93a6",
+              "extraction_id": "de689016-3a4c-53b2-b3bf-a25ccbcbbb02",
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+            "id": "0e465787-e5b0-5f33-88cf-9bd1d0624f68",
+            "score": 0.6654229362435027,
+            "metadata": {
+              "text": "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\ntrials 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",
+              "title": "2007 - Physical activity modifies the effect of SNPs in the SLC2A2 (GLUT2).pdf",
+              "version": "v0",
+              "chunk_order": 9,
+              "document_id": "6f5ced46-b777-563a-b644-432f4e7e2644",
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+            "score": 0.6643173892592609,
+            "metadata": {
+              "text": "demonstrate that lifestyle modi  cation comprising higher levels \nof PA and prudent food consumption may be e  ective in obesity \nand T2DM prevention. The positive e  ect of lifestyle on body \nweight seems somewhat transient, whereas the e  ect on T2DM \nis sustained for longer periods. Furthermore, lifestyle modi  ca-\ntion appears to have an e  ect on diabetes risk independently of \nbody weight and even of weight loss.\n     Lifestyle and Genetics in Obesity and Type 2 Diabetes",
+              "title": "2011 - Lifestyle and Genetics in Obesity and type 2 Diabetes.pdf",
+              "version": "v0",
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+              "document_id": "a16d3328-039c-530a-bfe5-f6f80ecf2ad0",
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+      }
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+}
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diff --git a/gnqa/paper2_eval/data/responses/human/cs_gn_responses.json b/gnqa/paper2_eval/data/responses/human/cs_gn_responses.json
new file mode 100644
index 0000000..867d46f
--- /dev/null
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@@ -0,0 +1,5154 @@
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+              "content": "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].",
+              "role": "assistant"
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+            "score": 0.6928697641414275,
+            "metadata": {
+              "text": "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.\nFunding US National Institutes of Health.\nCopyright  2019 Elsevier Ltd. All rights reserved.\nIntroduction\nAsthma is the most prevalent chronic respiratory disease \nworldwide.1 The diagnosis of asthma is based on the",
+              "title": "2019 - Shared and distinct genetic risk factors for childhood-onset.pdf",
+              "version": "v0",
+              "chunk_order": 8,
+              "document_id": "6f5caedc-c2b4-54e4-8bc6-8b78cab6bb0e",
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+            "metadata": {
+              "text": "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",
+              "title": "2008 - Genetic Effects on Environmental Vulnerability to Disease Novartis Foundation Symposium 293.pdf",
+              "version": "v0",
+              "chunk_order": 1295,
+              "document_id": "5d65e407-34e5-5c1c-b394-989b7a09b57d",
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+              "text": "living all represent risk factors for asthma, while early farm exposures and breastfeeding confer\nprotective effects. Such observations have been assimilated into the hygiene hypothesis, rst set out\nin 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\nour understanding of microbial factors in asthma has been a revolution of a different kind. Only",
+              "title": "2018 - The Genetics and Genomics of Asthma.pdf",
+              "version": "v0",
+              "chunk_order": 127,
+              "document_id": "47cf2a47-d8d2-583b-8c12-2a7cfbe92e5e",
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+            "id": "b64a52ee-d2e8-50a7-a101-b255cd905180",
+            "score": 0.6543693542480469,
+            "metadata": {
+              "text": "tobacco smoke exposure and with early-onset asthma\n(before age 4) [49/C15/C15]. Further studies of preschool asth-\nmatics have shown the 17q21 variants are associated with\nan almost two-fold increased risk of developing recurrent\nwheeze, asthma, asthma exacerbations and bronchial\nhyper-responsiveness, but are not associated with eczema,\nrhinitis or allergic sensitization, indicating that they are\nspecic determinants of nonatopic asthma in children [47].",
+              "title": "2010 - Recent advances in the genetics and genomics of asthma.pdf",
+              "version": "v0",
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+              "text": "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.\n46,47 In fact, childhood \nonset-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,\n41 atopic",
+              "title": "2019 - Shared and distinct genetic risk factors for childhood-onset.pdf",
+              "version": "v0",
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+            "metadata": {
+              "text": "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",
+              "title": "2008 - Genetic Effects on Environmental Vulnerability to Disease Novartis Foundation Symposium 293.pdf",
+              "version": "v0",
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+              "text": "[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.\n [12] de NijsSB, VenekampLN, BelEH. Adult-onset asthma: is it really different? Eur Respir Rev 2013;22(127):44.\n [13] RackemannFM. Intrinsic asthma. J Allergy 1940;11(2):14762.\n [14] JarvisD, NewsonR, LotvallJ, HastanD, TomassenP, KeilT, etal. Asthma in adults and its as -",
+              "title": "2019 - Leveraging genomics to uncover.pdf",
+              "version": "v0",
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+              "text": "GG19CH10_Cookson ARI 26 July 2018 9:47\nEpigenetic Features of Asthma: Within the Lung\nA study of the epigenome in primary airway epithelial cells from 74 asthmatic and 41 non-asthmatic\nadults (111) revealed a regulatory locus on chromosome 17q1221 (the same locus identied by\nasthma 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",
+              "title": "2018 - The Genetics and Genomics of Asthma.pdf",
+              "version": "v0",
+              "chunk_order": 105,
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+            "metadata": {
+              "text": "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.\nAttempts to map the genetic architecture of asthma have identied a broad spectrum of potential\ncontributory 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",
+              "title": "2018 - The Genetics and Genomics of Asthma.pdf",
+              "version": "v0",
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+              "text": "49 \nVariants 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.\n33,46\nBy contrast, the enrichment for genes highly expressed",
+              "title": "2019 - Shared and distinct genetic risk factors for childhood-onset.pdf",
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+              "content": "To extract DNA from flora or fauna, one can follow these general steps:\n\n1. **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].\n\n2. **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].\n\n3. **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].\n\n4. **Specific Protocols for Different Organisms**: Different organisms may require specific protocols. For example, insects may need hypochlorite treatment before extraction to reduce contamination [3].\n\n5. **High Purity Requirements**: If a high degree of purity is required, DNA can be subjected to density gradient centrifugation [1].\n\nBy following these steps, one can effectively extract DNA from various types of flora and fauna.",
+              "role": "assistant"
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+        "created": 1727797327,
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+              "text": "by shearing. A flow diagram summarizing the extraction of DNA is given in \nFig. 1.2. The above-described procedure is suitable for total cellular DNA. \nIf the DNA from a specific organelle or viral particle is needed, it is best to \nisolate the organelle or virus before extracting its DNA, because the recovery \nof a particular type of DNA from a mixture is usually rather difficult. Where \na high degree of purity is required, DNA may be subjected to density gradient",
+              "title": "2008 - Gene Expression Profiling.pdf",
+              "version": "v0",
+              "chunk_order": 76,
+              "document_id": "59f3b969-089b-5258-93ad-892dbc9ffa9c",
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+            "id": "045c27b0-dad8-56f1-8772-ae9d0da11c8a",
+            "score": 0.6141914955263449,
+            "metadata": {
+              "text": "2017 Nature America, Inc., part of Springer Nature. All rights reserved.\nnature medicine doi:10.1038/nm.434564. Salonen, A. et al.  Comparative analysis of fecal DNA extraction methods with \nphylogenetic microarray: effective recovery of bacterial and archaeal DNA using \nmechanical cell lysis. J. Microbiol. Methods  81, 127134 (2010).\n65. Murphy, N.R. & Hellwig, R.J. Improved nucleic acid organic extraction through use \nof a unique gel barrier material. Biotechniques  21, 934936, 938939 (1996).",
+              "title": "2017 - Metformin alters the gut microbiome of individuals with treatment-naive type 2 diabetes, contributing to the therapeutic effects of the drug.pdf",
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+              "associatedQuery": "How would one extract the DNA, from say, flora or fauna?"
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+            "id": "61393b99-58f3-5f1d-899d-809166e88442",
+            "score": 0.609249734327214,
+            "metadata": {
+              "text": "is the suitable preparation of the DNA template with a high level of purity \nand free from contaminating DNA (14). Different procedures are used for \nDNA extraction with specific protocol for mammals, plants, fungi, bacteria, \nprotozoan, helminthes, insects, and others. In specific cases, such as insects, \ncontamination can be reduced by hypochlorite treatment before extraction \nto avoid contact with foreign DNA (15). DNA preparation includes the",
+              "title": "2008 - Gene Expression Profiling.pdf",
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+              "chunk_order": 864,
+              "document_id": "59f3b969-089b-5258-93ad-892dbc9ffa9c",
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+              "associatedQuery": "How would one extract the DNA, from say, flora or fauna?"
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+              "text": "this method is well suited for larger scale investigations of museum insect phylogenomics. We\ndid 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\nwhether sufficient input DNA can also be obtained from smaller dried insect specimens. None-",
+              "title": "2016 - Sequence Capture and Phylogenetic Utility.pdf",
+              "version": "v0",
+              "chunk_order": 120,
+              "document_id": "6232f392-169a-50c5-b8c9-a250f3d840cc",
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+            "score": 0.5837758605547017,
+            "metadata": {
+              "text": "usually requires that it be isolated and purified to a certain degree. DNA is \nusually recovered from cells by methods that include cell rupture but that \nprevent the DNA from fragmenting by mechanical shearing. This is gener-\nally undertaken in the presence of EDTA, which chelates the magnesium ions \nneeded as cofactors for enzymes that degrade DNA, termed DNase. Ideally, \ncell walls, if present, should be digested enzymatically (e.g., lysozyme in the",
+              "title": "2008 - Gene Expression Profiling.pdf",
+              "version": "v0",
+              "chunk_order": 71,
+              "document_id": "59f3b969-089b-5258-93ad-892dbc9ffa9c",
+              "extraction_id": "3aa1db4d-6c18-53ab-8859-676d34d2b2ae",
+              "associatedQuery": "How would one extract the DNA, from say, flora or fauna?"
+            }
+          },
+          {
+            "id": "559fdf4f-5d14-5277-ba7b-a367d4795ed2",
+            "score": 0.5787351382532644,
+            "metadata": {
+              "text": "DNA and then using a gene probe representing a protein or enzyme from \none of the organisms. In this way, it is possible to search for related genes in \ndifferent species. This technique is generally termed Zoo blotting. A similar \nprocess of nucleic acid blotting can be used to transfer RNA separated by gel \nelectrophoresis onto membranes similar to that used in Southern blotting. This \nprocess, termed Northern blotting , allows the identification of specific mRNA",
+              "title": "2008 - Gene Expression Profiling.pdf",
+              "version": "v0",
+              "chunk_order": 97,
+              "document_id": "59f3b969-089b-5258-93ad-892dbc9ffa9c",
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+              "associatedQuery": "How would one extract the DNA, from say, flora or fauna?"
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+          {
+            "id": "3252d040-7281-54ca-a478-46a30b6d84f6",
+            "score": 0.566219500753939,
+            "metadata": {
+              "text": "6. Staats M, Erkens RH, van de Vossenberg B, Wieringa JJ, Kraaijeveld K, Stielow B, et al. Genomic trea-\nsure troves: complete genome sequencing of herbarium and insect museum specimens. PLOS ONE.\n2013; 8:e69189. doi: 10.1371/journal.pone.0069189 PMID: 23922691\n7. Burrell AS, Disotell TR, Bergey CM. The use of museum specimens with high-throughput DNA\nsequencers. J Hum Evol. 2015; 79:35 44. doi: 10.1016/j.jhevol.2014.10.015 PMID: 25532801",
+              "title": "2016 - Sequence Capture and Phylogenetic Utility.pdf",
+              "version": "v0",
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+              "text": "genetics and genomics, article 1DNA, genes, and chromosomes.\nBiological Research for Nursing ,19, 717.\nDueker, N. D., & Pericak-Vance, M. A. (2014). Analysis of genetic\nlinkage data for Mendelian traits. Current Protocols in Human\nGenetics ,83, 1.4.11.4.31.\nFu, M. R., Conley, Y. P., Axelrod, D., Guth, A. A., Yu, G., Fletcher, J.,\n& Zagzag, D. (2016). Precision assessment of heterogeneity of\nlymphedema phenotype, genotypes and risk prediction. Breast ,\n29, 231240.",
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+              "title": "2018 - Identification of non-HLA genes associated with development of islet autoimmunity and type.pdf",
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+              "text": "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.",
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+              "content": "The process of finding a genetic marker followed by a quantitative trait loci (QTL) involves several steps:\n\n1. **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].\n\n2. **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].\n\n3. **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].\n\n4. **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].\n\n5. **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].\n\n6. **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].\n\nBy following these steps, researchers can effectively identify genetic markers and subsequently map quantitative trait loci, providing insights into the genetic basis of complex traits.",
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+              "text": "(although quite demanding) process offollowing the trait across multiple generations by \ntracing its coinheritance with genetic markers (a technique referred to as linkage \nmapping). Finding loci responsible for variability in a quantitative trait (quantitative trait \nlocus mapping, or QTL mapping) is much more difficult, as there are many more sources \nof variation to capture. lnbred mouse strains are the optimum starting point for QTL",
+              "title": "2006 - Positional cloning of genes contributing to variability in nociceptive and analgesic phenotypes.pdf",
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+              "text": "Genetic linkage analysis can be used to identify regions of the genome that\ncontain 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-",
+              "title": "2007 - QTL Mapping in Aging Systems.pdf",
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+              "text": "quantitative trait loci in crosses between outbred linesusing least squares. Genetics 136, 11951207.\nHaseman, J. K. & Elston, R. C. 1972 The investigation of\nlinkage between a quantitative trait and a marker locus.Behav. Genet. 2, 319.\nHenshall, J. M. & Goddard, M. E. 1999 Multiple trait\nmapping of quantitative trait loci after selective genotypingusing logistic regression. Genetics 151, 885894.\nJansen, R. C. 1993 Interval mapping of multiple quantitative\ntrait loci. Genetics 135, 205211.",
+              "title": "2005 -Knott- Regression based QTL mapping.pdf",
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+              "text": "quantitative trait loci in crosses between outbred linesusing least squares. Genetics 136, 11951207.\nHaseman, J. K. & Elston, R. C. 1972 The investigation of\nlinkage between a quantitative trait and a marker locus.Behav. Genet. 2, 319.\nHenshall, J. M. & Goddard, M. E. 1999 Multiple trait\nmapping of quantitative trait loci after selective genotypingusing logistic regression. Genetics 151, 885894.\nJansen, R. C. 1993 Interval mapping of multiple quantitative\ntrait loci. Genetics 135, 205211.",
+              "title": "2005 - Regression-based quantitative trait loci mapping robust, efficient and effective.pdf",
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+              "text": "Keywords: quantitative trait loci mapping; regression; structured outbred populations\n1. HISTORY\nThe idea of using markers associated with a trait of\ninterest, for example, to predict the performance of\nindividuals in the trait, is not new. Initially, however,\nthe markers used were not identied at the molecular\nlevel but rather through the phenotype, for example,\ncoat colour or by the use of simple biochemicalprocedures such as blood groups. An early implemen-",
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+              "text": "Keywords: quantitative trait loci mapping; regression; structured outbred populations\n1. HISTORY\nThe idea of using markers associated with a trait of\ninterest, for example, to predict the performance of\nindividuals in the trait, is not new. Initially, however,\nthe markers used were not identied at the molecular\nlevel but rather through the phenotype, for example,\ncoat colour or by the use of simple biochemicalprocedures such as blood groups. An early implemen-",
+              "title": "2005 -Knott- Regression based QTL mapping.pdf",
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+              "text": "tions between markers and phenotype. Once allelic effects at each locus are\nidentified, different techniques can be used to position precise loci (i.e., QTL)\ninfluencing the trait. These techniques include marker regression (30), interval\nmapping (31), and multiple mapping strategies (32). Marker regression locates\nQTL with respect to all markers simultaneously by regression onto the marker\nmeans. It also estimates the additive (and dominance) effects, tests their signif-",
+              "title": "2007 - Using quantitative trait loci analysis to select plants for altered radionuclide accumulation.pdf",
+              "version": "v0",
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+              "text": "successful in identifying genes for simple traits.  Quantitative trait mapping and genome wide \nassociation studies identify chromosomal regions referred to as quantitative trait loci (QTLs) that \nare statistically associated with the trait.  Usually there are several such associations, each on the \norder of megabases (Mb) in length containing the usual diversity of single nucleotide \npolymorphisms (SNPs), one to two thousand per Mb, and there has been little success identifying",
+              "title": "2008 - Genotype-phenotype relationships and the patterning of complex traits as exemplified in the mammalian dentition.pdf",
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+              "text": "markers reveal potential gene locations regulating the trait of interest as known as quant itative \ntrait loci  (QTL s). Historically, this approach has been successful in identifying genes that are \nresponsible for rare, monogenic bone diseases. More recently, much denser maps of SNP s allow \nresearchers  to perform genome -wide linkage analysis for  complex trait s like bone phenotypes . \nHowever, several difficulties preventing the discovery of causal genes include genetic",
+              "title": "2019 - Novel Genetic Loci Control L5 Vertebral Trabecular Bone and the Response to Low Calcium Intake in Growing BXD Recombinant Inbred Mice.pdf",
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+              "text": "Quantitative Trait Locus (QTL) analysis, which links \nphenotype to loci on chromosomes that likely had an \nimpact on the phenotype.  Students then are able to sift \nthrough a list of genes in the region(s) of the chromosome \nidentified by the QT L analysis and find a candidate gene \nthat has relatively high expression in the brain region of \ninterest.  Once such a candidate gene is identified, \nstudents can find out more information about the gene,",
+              "title": "2012 - Teaching Neuroinformatics with an Emphasis on Quantitative Locus Anlaysis.pdf",
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+              "content": "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].",
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+              "text": "Genes 2018 ,9, 615 18 of 20\n97. McFarlane, R.J.; Humphrey, T.C. A role for recombination in centromere function. Trends Genet. 2010 ,26,\n209213. [CrossRef]\n98. Talbert, P .B.; Henikoff, S. Centromeres convert but dont cross. PLoS Biol. 2010 ,8, e1000326. [CrossRef]\n99. Durfy, S.J.; Willard, H.F. Concerted Evolution of Primate Alpha Satellite DNA Evidence for an Ancestral\nSequence Shared by Gorilla and Human X Chromosome Satellite. J. Mol. Biol. 1990 ,216, 555566.\n[CrossRef]",
+              "title": "2018 - Repetitive Fragile Sites Centromere Satellite DNA.pdf",
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+              "text": "4.1. Recombination and Repair at Centromeres: Errors in Copying and Mending Highly Repetitive DNA\nWhy are centromeres so cold?, asked Andy Choo in his review of centromeres [ 96]. He was\nreferring to centromere DNA as being cold to recombination. While maternal and paternal\nchromosomes suffer multiple DNA double-stranded breaks (DSBs) to induce recombination and\nexchange of genetic information by crossing over during meiosis, centromere loci are refractory",
+              "title": "2018 - Repetitive Fragile Sites Centromere Satellite DNA.pdf",
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+              "text": "exacerbates centromere rearrangements [ 54], indicating that there may be active mechanisms to\nsuppress centromeric recombination and these may, at least in part, involve core centromeric proteins.\nCentromere alpha-satellite DNA is estimated to represent between 3% and 10% of the human\ngenome [ 101], reviewed in [ 19]. During each round of replication, unperturbed cells suffer over 40 DNA\nDSBs [ 102], of which at least half are repaired by homologous recombination (HR) in S-phase and G2,",
+              "title": "2018 - Repetitive Fragile Sites Centromere Satellite DNA.pdf",
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+              "text": "347357 (1998).\n 31. Baudat, F. et al. PRDM9 is a major determinant of meiotic recombination hotspots in humans and mice. Science  327, 836840 (2010).\n 32. Kong, A. et al. Recombination rate and reproductive success in humans. Nat.Genet.  36, 12031206 (2004).\n 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).",
+              "title": "2018 - Germline de novo mutation clusters arise.pdf",
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+              "text": "to this process. This led to the assumption that centromeres do not undergo recombination and\nthat the repetitive arrays are maintained as stable. However, this clashed with the notion that\ncentromeres very origin stems from recombination to create the repetitive array, where multiple short-\nand long-range recombination events may be responsible for the generation and reiteration of blocks of\nhighly homogenized alpha-satellite DNA throughout the centromere [ 97,98]. Furthermore, in addition",
+              "title": "2018 - Repetitive Fragile Sites Centromere Satellite DNA.pdf",
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+              "text": "of these DSBs through recombination-dependent pathways, such as homologous recombination (HR), \nmay disrupt centromere integrity in several ways: (1) Crossover between sister chromatids will lead \nto sister chromatid exchange (SCE), which has been  reported at human cent romeres. (2) Search for \nthe homologous sequence may erroneously identify an  identical or nearly identical sequence within \nthe same chromatid downstream or upstream of the break site. Recombination between these two",
+              "title": "2018 - Repetitive Fragile Sites Centromere Satellite DNA.pdf",
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+              "text": "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, \n101109 (2012).\n 27. Alexandrov, L. B. et al. Signatures of mutational processes in human cancer. Nature  500, 415421 (2013).",
+              "title": "2018 - Germline de novo mutation clusters arise.pdf",
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+              "text": "to chromosome-specic alpha-satellites, certain centromeric sequences are shared by all chromosomes,\nevidence that formation of these arrays is dominated by interchromosomal exchanges [ 8,98100].\nThis invites new questions about the stability of centromere DNA outside of meiosis. Indeed, our\nrecent analysis has shown that centromeres can undergo recombination during a single round\nof cell division in primary human cells [ 54]. Depletion of CENP-A and other CCAN proteins",
+              "title": "2018 - Repetitive Fragile Sites Centromere Satellite DNA.pdf",
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+              "text": "shown to play a role in DNA repair (reviewed in [ 125]), and in vitro experiments show that this\nhybridization may facilitate DSB repair by bridging the broken DNA fragments in a Rad52-dependent\nmanner during recombination [126].\nCentromeres have been suggested [ 127,128], but not proven, to behave like fragile sites of the\nhuman genome. Common fragile sites are described as genomic loci where ongoing replication collides",
+              "title": "2018 - Repetitive Fragile Sites Centromere Satellite DNA.pdf",
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+              "text": "Cell Biol. 2016 ,17, 1629. [CrossRef] [PubMed]\n54. Giunta, S.; Funabiki, H. Integrity of the human centromere DNA repeats is protected by CENP-A, CENP-C,\nand CENP-T. Proc. Natl. Acad. Sci. USA 2017 ,114, 19281933. [CrossRef] [PubMed]\n55. Giunta, S. Centromere Chromosome Orientation Fluorescent in situ Hybridization (Cen-CO-FISH) Detects\nSister Chromatid Exchange at the Centromere in Human Cells. Bio-Protocol 2018 ,8. [CrossRef]",
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+              "content": "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:\n\n1. **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].\n\n2. **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].\n\n3. **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].\n\n4. **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].\n\n5. **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].\n\nIn 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.",
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+              "text": "4.1. Recombination and Repair at Centromeres: Errors in Copying and Mending Highly Repetitive DNA\nWhy are centromeres so cold?, asked Andy Choo in his review of centromeres [ 96]. He was\nreferring to centromere DNA as being cold to recombination. While maternal and paternal\nchromosomes suffer multiple DNA double-stranded breaks (DSBs) to induce recombination and\nexchange of genetic information by crossing over during meiosis, centromere loci are refractory",
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+              "text": "Genes 2018 ,9, 615 18 of 20\n97. McFarlane, R.J.; Humphrey, T.C. A role for recombination in centromere function. Trends Genet. 2010 ,26,\n209213. [CrossRef]\n98. Talbert, P .B.; Henikoff, S. Centromeres convert but dont cross. PLoS Biol. 2010 ,8, e1000326. [CrossRef]\n99. Durfy, S.J.; Willard, H.F. Concerted Evolution of Primate Alpha Satellite DNA Evidence for an Ancestral\nSequence Shared by Gorilla and Human X Chromosome Satellite. J. Mol. Biol. 1990 ,216, 555566.\n[CrossRef]",
+              "title": "2018 - Repetitive Fragile Sites Centromere Satellite DNA.pdf",
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+              "text": "of these DSBs through recombination-dependent pathways, such as homologous recombination (HR), \nmay disrupt centromere integrity in several ways: (1) Crossover between sister chromatids will lead \nto sister chromatid exchange (SCE), which has been  reported at human cent romeres. (2) Search for \nthe homologous sequence may erroneously identify an  identical or nearly identical sequence within \nthe same chromatid downstream or upstream of the break site. Recombination between these two",
+              "title": "2018 - Repetitive Fragile Sites Centromere Satellite DNA.pdf",
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+              "text": "exacerbates centromere rearrangements [ 54], indicating that there may be active mechanisms to\nsuppress centromeric recombination and these may, at least in part, involve core centromeric proteins.\nCentromere alpha-satellite DNA is estimated to represent between 3% and 10% of the human\ngenome [ 101], reviewed in [ 19]. During each round of replication, unperturbed cells suffer over 40 DNA\nDSBs [ 102], of which at least half are repaired by homologous recombination (HR) in S-phase and G2,",
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+              "text": "to this process. This led to the assumption that centromeres do not undergo recombination and\nthat the repetitive arrays are maintained as stable. However, this clashed with the notion that\ncentromeres very origin stems from recombination to create the repetitive array, where multiple short-\nand long-range recombination events may be responsible for the generation and reiteration of blocks of\nhighly homogenized alpha-satellite DNA throughout the centromere [ 97,98]. Furthermore, in addition",
+              "title": "2018 - Repetitive Fragile Sites Centromere Satellite DNA.pdf",
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+              "text": "347357 (1998).\n 31. Baudat, F. et al. PRDM9 is a major determinant of meiotic recombination hotspots in humans and mice. Science  327, 836840 (2010).\n 32. Kong, A. et al. Recombination rate and reproductive success in humans. Nat.Genet.  36, 12031206 (2004).\n 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).",
+              "title": "2018 - Germline de novo mutation clusters arise.pdf",
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+              "text": "shown to play a role in DNA repair (reviewed in [ 125]), and in vitro experiments show that this\nhybridization may facilitate DSB repair by bridging the broken DNA fragments in a Rad52-dependent\nmanner during recombination [126].\nCentromeres have been suggested [ 127,128], but not proven, to behave like fragile sites of the\nhuman genome. Common fragile sites are described as genomic loci where ongoing replication collides",
+              "title": "2018 - Repetitive Fragile Sites Centromere Satellite DNA.pdf",
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+              "text": "to chromosome-specic alpha-satellites, certain centromeric sequences are shared by all chromosomes,\nevidence that formation of these arrays is dominated by interchromosomal exchanges [ 8,98100].\nThis invites new questions about the stability of centromere DNA outside of meiosis. Indeed, our\nrecent analysis has shown that centromeres can undergo recombination during a single round\nof cell division in primary human cells [ 54]. Depletion of CENP-A and other CCAN proteins",
+              "title": "2018 - Repetitive Fragile Sites Centromere Satellite DNA.pdf",
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+            "metadata": {
+              "text": "Studying the direct link between re combination and sister chromatid\ndynamics with combined live cell imaging and genomics will likely yieldimportant insight into the impact that centromeric and telomeric cross-\novers have on chromosome segregation.Reconstructing the bivalent con guration\nfrom MeioMaps: recombination and its linkwith chromosome segregation\nThe combined assessment of haplotypes that are determined by\nrecombination also allowed the rst direct correlations between",
+              "title": "2017 - Human female meiosis revised new.pdf",
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+              "text": "Cell Biol. 2016 ,17, 1629. [CrossRef] [PubMed]\n54. Giunta, S.; Funabiki, H. Integrity of the human centromere DNA repeats is protected by CENP-A, CENP-C,\nand CENP-T. Proc. Natl. Acad. Sci. USA 2017 ,114, 19281933. [CrossRef] [PubMed]\n55. Giunta, S. Centromere Chromosome Orientation Fluorescent in situ Hybridization (Cen-CO-FISH) Detects\nSister Chromatid Exchange at the Centromere in Human Cells. Bio-Protocol 2018 ,8. [CrossRef]",
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+              "content": "Recombination in the human genome is a complex process with several key aspects highlighted in the provided context:\n\n1. **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.\n\n2. **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].\n\n3. **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].\n\n4. **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].\n\n5. **Influence on Genetic Variation**: Recombination hotspots are a general feature of the human genome and play a major role in shaping genetic variation [5].\n\n6. **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].\n\nThese points collectively highlight the importance of recombination in shaping the human genome, influencing genetic diversity, and having evolutionary consequences.",
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+              "text": "347357 (1998).\n 31. Baudat, F. et al. PRDM9 is a major determinant of meiotic recombination hotspots in humans and mice. Science  327, 836840 (2010).\n 32. Kong, A. et al. Recombination rate and reproductive success in humans. Nat.Genet.  36, 12031206 (2004).\n 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).",
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+              "text": "Genet  39: 977983\n33 Myers S et al. (2005) A fine-scale map of recombination \nrates and hotspots across the human genome. \nScience  310: 321324REVIEW\nNature.indt   1 Nature.indt   1 28/11/07   9:46:50 am 28/11/07   9:46:50 am",
+              "title": "2008 - Mechanisms of Disease genetic insights into the etiology of type 2 diabetes and obesity.pdf",
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+              "text": "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, \n101109 (2012).\n 27. Alexandrov, L. B. et al. Signatures of mutational processes in human cancer. Nature  500, 415421 (2013).",
+              "title": "2018 - Germline de novo mutation clusters arise.pdf",
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+              "text": "D.R., and Donnelly, P. (2004). The ne-scale structure ofrecombination rate variation in the human genome. Science\n304, 581584.\n33. Winckler, W., Myers, S.R., Richter, D.J., Onofrio, R.C., McDo-\nnald, G.J., Bontrop, R.E., McVean, G.A., Gabriel, S.B., Reich,\nD., Donnelly, P., et al. (2005). Comparison of ne-scale recom-\nbination rates in humans and chimpanzees. Science 308,\n107111.\n1192 The American Journal of Human Genetics 82, 11851192, May 2008",
+              "title": "2008 - Loci Related to Metabolic-Syndrome Pathways Including LEPR.pdf",
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+              "text": "www.pharmaco-genomics.com 569REVIEW\n48. Reich DE, Schaffner SF , Daly MJ  et al. : \nHuman chromosome sequence variation and the influence of gene history, mutation \nand recombination. Nat. Genet.  32, 135-142 \n(2002). \n The authors provide evidence that \nrecombination hot spots may represent a \ngeneral feature of the human genome and play a major role in shaping genetic \nvariation in humans.\n49. Wall JD, Pritchard JK: Haplotype blocks \nand linkage disequilibrium in the human",
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+              "text": "Genes 2018 ,9, 615 18 of 20\n97. McFarlane, R.J.; Humphrey, T.C. A role for recombination in centromere function. Trends Genet. 2010 ,26,\n209213. [CrossRef]\n98. Talbert, P .B.; Henikoff, S. Centromeres convert but dont cross. PLoS Biol. 2010 ,8, e1000326. [CrossRef]\n99. Durfy, S.J.; Willard, H.F. Concerted Evolution of Primate Alpha Satellite DNA Evidence for an Ancestral\nSequence Shared by Gorilla and Human X Chromosome Satellite. J. Mol. Biol. 1990 ,216, 555566.\n[CrossRef]",
+              "title": "2018 - Repetitive Fragile Sites Centromere Satellite DNA.pdf",
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+              "text": "Variations on a theme: cataloguing human \nDNA sequence variation. Science  278, 1580-\n1581 (1997).\n37. Jeffreys AJ, Kauppi L, Neumann R: Intensely punctate meiotic recombination in the class II region of the major \nhistocompatibility complex. Nat. Genet.  29, \n217-222 (2001).\n38. Chakravarti A, Buetow KH, Antonarakis SE  \net al.: Nonuniform recombination within \nthe human beta-globin gene cluster. Am. J. \nHum. Genet.  36, 1239-1258 (1984).\n39. Smith RA, Ho PJ, Clegg JB, Kidd, JR,",
+              "title": "2003 - Haplotypes and the systematic analysis of genetic variation in genes and genomes.pdf",
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+              "text": "genome. Nat. Rev. Genet.  4, 587-597 \n(2003). \n Important review, including discussion of \nthe recently proposed haplotype-block \nmodel of LD.\n50. Nachman MW: Variation in recombination \nrate across the genome: evidence and \nimplications. Curr. Opin. Genet. Dev.  12, \n657-663 (2002).\n51. Kong A, Gudbjartsson DF , Sainz J  et al. : A \nhigh-resolution recombination map of the human genome. Nat. Genet.  31, 241-247 \n(2002).\n52. Sabeti PC, Reich DE, Higgins JM  et al. :",
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+              "text": "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",
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+              "text": "31. Fu Q, et al. (2015) An early modern human from Romania with a recent Neanderthal\nancestor. Nature 524(7564):216 219.\n32. Baudat F, et al. (2010) PRDM9 is a major determinant of meiotic recombination\nhotspots in humans and mice. Science 327(5967):836 840.\n33. Lesecque Y, Glmin S, Lartillot N, Mouchiroud D, Duret L (2014) The red queen model\nof recombination hotspots evolution in the light of archaic and modern human ge-\nnomes. PLoS Genet 10(11):e1004790.",
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+              "content": "### How to Guide for Genetic Sequencing\n\nGenetic 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.\n\n#### Step 1: Sample Preparation\n1. **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.\n2. **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.\n\n#### Step 2: Library Preparation\n1. **Fragment DNA**: Break the DNA into smaller fragments. This can be done using mechanical shearing or enzymatic digestion.\n2. **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.\n\n#### Step 3: Amplification\n1. **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.\n\n#### Step 4: Sequencing\n1. **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].\n2. **Load the Sample**: Load the prepared DNA library onto the sequencing platform. Each platform has specific protocols for loading samples.\n3. **Run the Sequencer**: Initiate the sequencing run. The platform will read the DNA fragments and generate raw sequence data.\n\n#### Step 5: Data Analysis\n1. **Base Calling**: Use base-calling software to convert raw data into nucleotide sequences. Tools like Phred are commonly used for this purpose [7].\n2. **Sequence Assembly**: Assemble the short DNA sequences into longer contiguous sequences (contigs) using assemblers like Phrap or CAP3 [7].\n3. **Quality Control**: Assess the quality of the assembled sequences. Tools like consed or GAP4 can help in finishing sequencing projects and ensuring accuracy [7].\n\n#### Step 6: Interpretation\n1. **Variant Calling**: Identify genetic variants such as single nucleotide polymorphisms (SNPs) and insertions/deletions (indels) using variant calling software.\n2. **Annotation**: Annotate the identified variants to understand their potential impact on genes and biological functions.\n3. **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].\n\n#### Additional Resources\n- For more detailed protocols and information on specific sequencing technologies, refer to resources from companies like 10X Genomics, Illumina, and Pacific Biosciences [1].\n- For historical context and advancements in sequencing technologies, see references such as Heather and Chain (2016) and Shendure and Ji (2008) [2].\n\nBy following these steps, you can successfully perform genetic sequencing and analyze the resulting data to gain insights into the genetic makeup of your sample.",
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+              "text": "FURTHER INFORMATION\n10X Genomics: http://www.10xgenomics.com\n454 Sequencing: http://www.454.com\nAdvances in Genome Biology and Technology (AGBT): \nhttp://www.agbt.org\nBGISEQ500: http://seq500.com/en/portal/Sequencer.shtml\nIllumina: http://www.illumina.com\nIon Torrent: https://www.thermofisher.com/us/en/home/\nbrands/ion-torrent.html\nOxford Nanopore Technologies: https://www.nanoporetech.\ncom\nPacific Biosciences: http://www.pacb.com\nPersonal Genome Project: http://www.personalgenomes.org",
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+              "text": "36. Sequencing, H.G. Finishing the euchromatic sequence of the human genome. Nature 2004 ,431, 931945.\n37. Heather, J.M.; Chain, B. The sequence of sequencers: The history of sequencing DNA. Genomics 2016 ,107,\n18. [CrossRef]\n38. Rothberg, J.M.; Leamon, J.H. The development and impact of 454 sequencing. Nat. Biotechnol. 2008 ,26,\n11171124. [CrossRef] [PubMed]\n39. Shendure, J.; Ji, H. Next-generation DNA sequencing. Nat. Biotechnol. 2008 ,26, 11351145. [CrossRef]\n[PubMed]",
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+              "text": "sequencing. Genome Res. 20, 11651173 (2010).\n64. English,A.C. etal.  Assessing structural variation in a \npersonal genome-towards a human reference diploid \ngenome. BMC Genomics 16, 286 (2015).\n65. Carneiro,M.O. etal.  Pacific Biosciences sequencing \ntechnology for genotyping and variation discovery in \nhuman data. BMC Genomics 13, 375 (2012).\n66. Quail,M.A. etal.  A tale of three next generation \nsequencing platforms: comparison of Ion T orrent, \nPacific Biosciences and Illumina MiSeq sequencers.",
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+              "text": "22. Karow, J. Qiagen launches GeneReader NGS System \natAMP; presents performance evaluation by broad. \nGenomeWeb  [online], https:// www.genomeweb.com/\nmolecular-diagnostics/qiagen-launches-genereader-\nngs-system-amp-presents-performance-evaluation  \n(4Nov 2015).\n23. Smith,D.R. & McKernan,K. Methods of producing \nand sequencing modified polynucleotides . US Patent \n8058030 (2011).\n24. Margulies,M. etal.  Genome sequencing in \nmicrofabricated high-density picolitre reactors. Nature \n437, 376380 (2005).",
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+              "text": "160. Glenn,T .C. Field guide to next-generation DNA \nsequencers. Mol. Ecol. Resour. 11, 759769 (2011).\n161. Karow,J. At AGBT , 10X Genomics launches GemCode \nplatform; shipments slated for Q2 as firm battles IP \nlawsuits.  GenomeWeb  [online], https://www.\ngenomeweb.com/sample-prep/agbt-10x-genomics-\nlaunches-gemcode-platform-shipments-slated-q2-firm-\nbattles-ip-lawsuits  (2Mar 2015).\nCompeting interests statement\nThe authors declare competing interests: see Web version  for \ndetails.\nFURTHER INFORMATION",
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+              "text": "sequencing. Bioinformatics 31, 20402042 (2015).\n46. Qiagen.  Oncology insights enabled by knowledge base-\nguided panel design and the seamless workflow of the \nGeneReader NGS system  Press Release. Qiagen  \n[online], http://www.genereaderngs.com/PROM-9192-\n001_1100403_WP_GeneReader_NGS_0116_NA.pdf  \n(2016).\n47. Forgetta,V. etal.  Sequencing of the Dutch elm disease \nfungus genome using the Roche/454 GS-FLX Titanium \nSystem in a comparison of multiple genomics core",
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+              "text": "DNA), and provide the means to link sequences containing applications. First, \nbase- callers like Phred (4,5) extract raw sequences from raw data. There are \nalso contig assemblers like Phrap (University of Washington, http://bozeman.\nmbt.washington.edu/phrap.docs/phrap.html ) or CAP3 (6) that assemble frag-\nments to contigs and packages like consed (7) or GAP4 (8), which are used to \nfinish sequencing projects. These programs are not explained in detail here.",
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+              "text": "sequencing data to solutions from the genotyping array data.\niv\n \n \n \n \n \n \n \nPREVIEW",
+              "title": "2014 - Computational tools to aid the design and development of a genetic reference population.pdf",
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+              "text": "11 BIOINFORMATIC CHALLENGES FOR GENOMIC MEDICINE \n \n \nProcessing and managing of high-throughput sequence data \n \n High throughput sequencing offers severa l advantages relative to array-based \ngenotyping or expression assays. First, unlike genotyping arrays, whole genome sequencing is not limited to interrogating onl y known sequence variants. Similarly, RNA-\nsequencing (RNA-seq) enables expression quanti fication of novel transcripts that are not",
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+              "text": "11 BIOINFORMATIC CHALLENGES FOR GENOMIC MEDICINE \n \n \nProcessing and managing of high-throughput sequence data \n \n High throughput sequencing offers severa l advantages relative to array-based \ngenotyping or expression assays. First, unlike genotyping arrays, whole genome sequencing is not limited to interrogating onl y known sequence variants. Similarly, RNA-\nsequencing (RNA-seq) enables expression quanti fication of novel transcripts that are not",
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+              "content": "The length of telomeres is significant for several reasons:\n\n1. **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].\n\n2. **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].\n\n3. **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].\n\n4. **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].\n\n5. **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].\n\nIn summary, telomere length is crucial for maintaining chromosome integrity, regulating cellular aging, and potentially serving as a biomarker for disease progression and risk assessment.",
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+              "text": "Telomeres are arrays of linked nucleotide hexamer repeats that are found at the \nends of chromosomes in a vast clade of organisms [14]. While the sequence of \nthese telomeric repeats can vary between organisms, their biological function is \nhighly conserved, which is to limit damage inflicted on genes during the replica-\ntion of chromosomes. Telomere length is progressively shortened with each round \nof genomic replication, unless it is restored through the action of a ribonucleo-",
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+              "text": "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\n8, \ndifferent studies have shown that T2D individuals have shorter leukocyte telomeres than non-T2D individuals9, 10  \nthat may be associated with disease progression11. Indeed, the decreased antioxidant capacity described in patients",
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+              "text": "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",
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+              "text": "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  \net al \n., 1997; Hande  \net al \n., 1999), causing",
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+              "text": "age telomere length through accumulation of several short telo-\nmeres (Londono-Vallejo et al., 2001; Martens et al., 2000) is \nresponsible for senescence or whether a speci  c chromosome \narm limits the replication potential of human cells (Hemann et al., 2001). Individual chromosome arms were shown to have \nlarge variations in their length (Lansdorp et al., 1996; Benn, \n1997; Londono-Vallejo et al., 2001), and chromosome 17p seemed to be equipped with especially short telomeres in hu-",
+              "title": "2006 - Sex-specific telomere length profiles.pdf",
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+              "text": "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",
+              "title": "2018 - Sex Differences in Aging Genomic Instability.pdf",
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+              "text": "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",
+              "title": "2008 - Telomeres and Aging.pdf",
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+              "text": "telomeres, the repetitive sequence at the end of linear chromosomes, has garnered much attention for\nits relation to aging. Telomere repeats serve as an internal clock for cycling cells because each round of\nreplication results in the loss of telomeric DNA in the absence of active telomerase (reviewed in [66]).\nEventually, this loss over cellular generations culminates in telomere crisis and a permanent state of",
+              "title": "2018 - Repetitive Fragile Sites Centromere Satellite DNA.pdf",
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+              "text": "a pivotal role in maintenance of genomic integrity and func-tion (Moyzis et al., 1988; McElligott and Wellinger, 1997; van \nSteensel and de Lange, 1997). \n It is generally accepted that telomeres shorten during DNA \nreplication both in vitro and in vivo. In individuals, short telomeres are considered to be a sign of advanced age. Cawthon \nand coworkers (2003) showed that telomere shortening in hu-",
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+              "text": "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\nstudied, 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",
+              "title": "2010 - Roles of Werner syndrome protein in protection of genome integrity.pdf",
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+              "content": "Once a sperm combines with an egg, the traits passed on to the resulting lifeform are determined by several factors:\n\n1. **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].\n\n2. **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:\n   - **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].\n   - **Maternal Effect**: The mother can alter gene activity in her offspring via the placenta [3].\n   - **Cis-Regulation**: Instructions encoded within the embryo's DNA can directly control if and when a nearby gene becomes activated [3].\n\n3. **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].\n\n4. **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].\n\nThese combined factors ensure that the resulting lifeform inherits a unique set of traits from both parents, influenced by genetic, epigenetic, and environmental factors.",
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+              "text": "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",
+              "title": "2009 - Opening Up the Conversation on Genetics.pdf",
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+              "text": "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:\nFirst, inside the sperm or egg, genes can be marked with small chemical tags that flag these genes",
+              "title": "2015 - Constraint and divergence of global gene expression in the mammalian embryo.pdf",
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+              "text": "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.\nSecond, the mother can alter the gene activity in her offspring via the placenta; this process is known\nas 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",
+              "title": "2015 - Constraint and divergence of global gene expression in the mammalian embryo.pdf",
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+              "text": "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",
+              "title": "2007 - Promoting_Student_Scientific_Literacy_of_Molecular Genetics and Genomics.pdf",
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+              "text": "sex chromosome effects. (B)Soon after fertilization, male and female cells have sex-specic transcriptomes, epigenomes, and phenotypes (for example, male\nembryos grow faster than female embryos). At implantation, lineage determination begins and gene expression differences are reduced. Epigenetic marks, however,\nare less constrained and some are maintained, affecting gene expression, and phenotype later in development. Once specic lineages are established, differences in",
+              "title": "2019 - Sexual Dimorphism in the Age of Genomics How, When, Where.pdf",
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+              "text": "phenomena such as mutations and gene conversion events) occur in relevant meioses \nleading up to the formation of the gametes (i.e., egg and sperm) which are combined \nduring fertilization and the formation of zygotes. Thus, individuals inherit a patch-\nwork of chromosomal segments from maternal and paternal chromosomes.",
+              "title": "2008 -  Study Design and Statistical Issues.pdf",
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+              "text": "(Figures 8 and 9). Two gametes (egg and sperm) ultimately \njoin 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.\nThe Mendel Laws: Segregation and Independent \nAssortment\nBoth of the Mendel laws pertain directly to the process of \nmeiosis. The first Mendel law, the law of segregation, states \nthat each parent passes a randomly selected allele for a given",
+              "title": "2015 - Basic Concepts and Potential Applications of Genetics and Genomics for Cardiovascular and Stroke Clinicians.pdf",
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+              "text": "the subset of that genetic information that is active.  But how does the differentiation process \nbegin?  The key insight in resolving this conundrum came from fly genetics and was the \nrealization that the egg is not a homogenous sack of protoplasm.  The maternally-derived genes \nactive in the fertilized egg are asymmetrically distributed such that at the first cell division each \ndaughter cell receives a different complement of factors.  Development continues as a",
+              "title": "2008 - Genotype-phenotype relationships and the patterning of complex traits as exemplified in the mammalian dentition.pdf",
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+              "text": "spermatozoa: more than the sum of its parts? DNA, histones, pro -\ntamines and epigenetics. Reproduction 139:287301\nNilsson EE, Sadler-Riggleman I, Skinner MK (2018) Environmentally \ninduced epigenetic transgenerational inheritance of disease. Envi-ron Epigenet 4:dvy016Pembrey M, Saffery R, Bygren LO, Network in Epigenetic Epide-",
+              "title": "2019 - The influence of paternal diet on sncRNA-mediated epigenetic.pdf",
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+              "text": "mediated through the transmission of epigenetic  information through the paternal sperm cells \n[6,80,81]. \n4.1. Persistence of Maternal Exposure to A dverse Environmental Conditions along Generations \nIn some cases, developmentally programmed traits may simply be the result of persistent or \nreplicated exposure during critical periods of deve lopment, generation after generation. It has been \nsuggested that the history of seve re socio-political disruptions and economic disadvantage suffered",
+              "title": "2019 - Mother or Father who is in the front line.pdf",
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+              "content": "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].",
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+              "text": "variation with cultural practices around lineage. In certain societies, individuals place greater\nimportance on (and have greater knowledge about) one side of the family than another\n(unilineal descent). Thus, individuals in patrilineal groups trace relationships through males\nonly so that your fathers brothers children are members of your family, but not your fathers\nsisters (Kottak, 2007 ). They are members of their husbands group or family. Efforts to create",
+              "title": "2009 - When Family Means More (or Less) Than Genetics.pdf",
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+            "metadata": {
+              "text": "maternal lineage membership with those who weredirectly genotyped. Based on these pedigree (matrilineal) relation-",
+              "title": "2012 - Mitochondrial Genomic Analysis of Late Onset.pdf",
+              "version": "v0",
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+              "text": "in three-generation families, and read pair tracing DNMs with phased variants.\nIn the former approach, we determined the parent of origin as in our previous \nanalysis4. For example, if an offspring of the proband was a carrier of the DNM \nallele and had haplotype sharing to paternal chromosome of the proband, we \nassigned the mutation to the father. Meanwhile, if the offspring was not a DNM \nallele carrier, we would assign it to the maternal germline. We restricted the haplo -",
+              "title": "2017 - Parental influence on human germline de novo.pdf",
+              "version": "v0",
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+              "text": "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",
+              "title": "2020 - Mitonuclear genomics and aging.pdf",
+              "version": "v0",
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+          {
+            "id": "612366c9-fcdc-5081-bc6d-47cd39922eeb",
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+              "text": "c) Mitochondrial DNA (maternal line testing) markers:\nmitochondrial DNA or mtDNA haploid is the\nmaternally inherited mitochondrial genome\n(mtDNA) [ 44]. All children inherit mtDNA from\ntheir mother, with no admixture from the father.\nLike Y-line DNA, mtDNA is passed intact from one\ngeneration to the next but through maternal line.\nMitochondrial DNA does not follow any surname.\nIn fact, the surname changes in every generation\nwhen women marry. Polymorphisms of mtDNA",
+              "title": "2015 - Self-reported race or ethnicity in the age of genomic.pdf",
+              "version": "v0",
+              "chunk_order": 42,
+              "document_id": "51ff0b84-193b-525a-b686-f29a423fcef9",
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+            "score": 0.5930852293968201,
+            "metadata": {
+              "text": "a family pedigree may be hampered if the participant is not familiar with her mothers relatives,\nbut her mothers brothers children (her cousins) may be able to supplement her overall family\nhistory. Knowledge about the cultural system of unilineal descent avoids assuming the\nuniversality of bilateral descent. Cultural beliefs such as these also have implications in the\nconduct of genetic research in terms of confidentiality and autonomy (Benkendorf et al.,",
+              "title": "2009 - When Family Means More (or Less) Than Genetics.pdf",
+              "version": "v0",
+              "chunk_order": 23,
+              "document_id": "7ba44399-3765-5ef5-9fdd-119b62117f66",
+              "extraction_id": "baf15552-4198-5701-9175-c3fd31b2068e",
+              "associatedQuery": "Why is genetic tracing matrilineal rather than patrilineal? "
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+            "id": "db1fe67a-3d0c-549f-a54a-74ea0fa44d11",
+            "score": 0.5844577899252061,
+            "metadata": {
+              "text": "225 three-generation families using haplotype sharing (Fig. 1c and \nMethods), 80.4% were found to be of paternal origin (Extended Data \nFig. 1). Figure 1e shows a strong relationship between the number of \npaternal DNMs and the fathers age at conception (1.47 per year, 95% \nCI 1.341.59) and a weaker impact of the mothers age on the number \nof maternal DNMs (0.37 per year, 95% CI 0.300.45).\nThe parental origin of all DNMs was also assessed by read pair",
+              "title": "2017 - Parental influence on human germline de novo.pdf",
+              "version": "v0",
+              "chunk_order": 14,
+              "document_id": "7c8bee23-b142-5fce-be77-6910277a136f",
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+            "id": "74484e0c-c862-5091-9fb5-957453a069af",
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+              "text": "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\ncoalescent analysis of Icelandic matrilineal and patrilineal genealogies: Evidence for a\nfaster evolutionary rate of mtDNA lineages than Y chromosomes. Am J Hum Genet 72(6):\n1370 1388.\n23. Amster G, Sella G (2015) Life history effects on the molecular clock of autosomes and\nsex chromosomes. Proc Natl Acad Sci USA 113(6):1588 1593.",
+              "title": "2016 - A genetic method for dating ancient genomes provides.pdf",
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+              "text": "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",
+              "title": "1996 - IDDM2-VNTR-encoded Susceptibility to Type 1 Diabetes.pdf",
+              "version": "v0",
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+              "text": "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",
+              "title": "2012 - Mitochondrial Genomic Analysis of Late Onset.pdf",
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+              "content": "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:\n\n1. **Recombination Rates**:\n   - 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].\n   - 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].\n\n2. **QTL Mapping**:\n   - 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].\n   - 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].\n\nIn 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.",
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+              "text": "While most of the Y chromosome does not undergo\nrecombination, the recombination rate of the X chromosomeis slower than that of the autosomes. This has important\nconsequences on the detection of significant QTLs. For a\ncomprehensive view of these issues, see(43).\n9.Probe hybridization artifacts\nWhen several probes are available for the same gene, it is\nnot uncommon to observe a difference in the mapping results",
+              "title": "2009 - eQTL analysis in mice and rats.pdf",
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+              "text": "8 QTL Mapping  \n \nAllelic variation exists among natural populations and inbred strains, and this is \nreflective of the segregation of quantitative tr ait loci (QTLs) [96]. QTLs are stretches of \nDNA 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 \nresearchers to map different quantitative traits back to the genomic location involved in the regulation of these phenotypes.",
+              "title": "2015 - Genetic Control of Survival and Weight Loss during Pneumonic Burk.pdf",
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+              "text": "8 QTL Mapping  \n \nAllelic variation exists among natural populations and inbred strains, and this is \nreflective of the segregation of quantitative tr ait loci (QTLs) [96]. QTLs are stretches of \nDNA 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 \nresearchers to map different quantitative traits back to the genomic location involved in the regulation of these phenotypes.",
+              "title": "2015 -Emery- Genetic Control of Survival and Weight Loss during Pneumonic Burk.pdf",
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+              "text": "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,\nthat even in a single QTL region isolated in a congenic strain, it is possible that\nthere is more than one allele that aects the phenotype. So, you have a fair pointabout the challenges and complexities of QTL analysis.\nKoolhaas: There are dierent questions underlying both approaches. The QTL",
+              "title": "2005 - quantitative-trait-locus-analysis-of-aggressive-behaviours-in-mi.pdf",
+              "version": "v0",
+              "chunk_order": 86,
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+            "id": "1b2de424-be9f-572d-bd62-dc2ecd92192b",
+            "score": 0.6700757351675676,
+            "metadata": {
+              "text": "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,\nthat even in a single QTL region isolated in a congenic strain, it is possible that\nthere is more than one allele that aects the phenotype. So, you have a fair pointabout the challenges and complexities of QTL analysis.\nKoolhaas: There are dierent questions underlying both approaches. The QTL",
+              "title": "2005 -Broadkin- quantitative-trait-locus-analysis-of-aggressive-behaviours-in-mi.pdf",
+              "version": "v0",
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+            "id": "fef212bc-631b-591d-b8e3-d1523da0507d",
+            "score": 0.6696171962758585,
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+              "text": "The basic  pr emise  of QTL  an alysis  is simple  (Ph illips  and Belknap,\n2002 ) . First,  one must  meas  ure a speci  c phen  otype  within  a popul  ation.\nNext, the population must be genotyped at a hundred or more marker loci186 Boehm II et al.",
+              "title": "2006 - From_gene_to_behavior_and_back_again_new.pdf",
+              "version": "v0",
+              "chunk_order": 129,
+              "document_id": "7a088b36-11b7-5379-bfe5-ce571e11de07",
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+            "id": "f72795a1-66c3-5a98-84bc-b085e8008073",
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+              "text": "through analysis of line crosses, quantitative trait loci (QTL) mapping, and verification\nof 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.",
+              "title": "2009 - Experimental_Evolution.pdf",
+              "version": "v0",
+              "chunk_order": 2219,
+              "document_id": "34821353-1b74-5ee2-ac39-66dd46f145bf",
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+            "id": "31a32dc5-81ac-52ba-a463-c61e293f21e5",
+            "score": 0.6656335193674039,
+            "metadata": {
+              "text": "through analysis of line crosses, quantitative trait loci (QTL) mapping, and verification\nof 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.",
+              "title": "2009 - Garland_and_Rose_Experimental_Evolution.pdf",
+              "version": "v0",
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+              "document_id": "496faa7f-9623-5ab7-9816-7c3755abb3aa",
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+              "text": "genetic background.\nGene identification of QTL should be distinguished from identification of the quanti-\ntative trait nucleotide (QTN). The latter is a daunting task, since SNPs are so frequent.\nFinal 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\nhomologous recombination and reproducing the phenotype of the donor strain.",
+              "title": "2005 - quantitative-trait-analysis-in-the-investigation-of-function-and.pdf",
+              "version": "v0",
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+              "document_id": "dac1c73c-0b5f-5a54-bb12-7e8b654009c0",
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+            "metadata": {
+              "text": "because these strains have been genotyped at more than 14,000 markers, including single\nnucleotide polymorphisms (SNP). Hundreds of genes may lie within a QTL interval, so\nidentifying the underlying genes requires complementary methods. One method is to use\nBXD gene expression data (a public resource at www.genenetwork.org) to screen for genes\nwithin the QTL interval whose expression correlates with the trait of interest [23].",
+              "title": "2012 - Systems genetic analysis of the effects of iron deficiency in mouse brain.pdf",
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+              "content": "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].",
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+            "id": "a2ffc857-6d79-5889-8344-cae8f1ca5e32",
+            "score": 0.7282648844561063,
+            "metadata": {
+              "text": "QTL Mapping\nGeneNetwork ( www.genenetwork.org ) variants data set comprising about",
+              "title": "2016 - A genetic screen identifies hypothalamic Fgf15 as a regulator of glucagon secretion.pdf",
+              "version": "v0",
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+            "score": 0.720731692270537,
+            "metadata": {
+              "text": "Bioinformatics\nAll 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.\nQTL mapping\nThe QTL mapping module of GeneNetwork was used to identify",
+              "title": "2015 - Systems genetic analysis of hippocampal neuroanatomy and spatial learning in mice.pdf",
+              "version": "v0",
+              "chunk_order": 38,
+              "document_id": "8708ead5-20bc-5d41-82db-61a807eb3f90",
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+            "score": 0.7149678042550216,
+            "metadata": {
+              "text": "the database is that each data collection is associated with a protocol which describes how \nthe data were generated. The project also provides online analysis tools to allow \nidentification of correlations within its data set.\nGeneNetwork ( http://www.genenetwork.org ), encompassing WebQTL, is a database of \ngenotypes and complex phenotypes ranging from gene expression to behaviour in standard \ninbred strains, and six panels of mouse recombinant inbred strains including the two largest",
+              "title": "2007 - Integration of mouse phenome data resources.pdf",
+              "version": "v0",
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+          {
+            "id": "51757b6b-0492-5077-ba69-90a2ddf3da9d",
+            "score": 0.710901158398322,
+            "metadata": {
+              "text": "QTL/interval analysis \nQTL  mapping  was conducted  using  publically  available  software  \non GeneNetwork  (http://www .genenetwork .org/webqtl /main .py). \nOne  important  feature  of the GeneNetwork  is WebQTL , which  is the \nleading  GeneNetwork  module , and has been  optimized  for on-line \nanalysis  of traits  that are controlled  by combinations  of allelic  variants  \nand environmental  factors  [15]. A simple  graphical  user interface",
+              "title": "2016 - Genetic Regulation of Gelsolin in Lung in Mouse Model and its Potential.pdf",
+              "version": "v0",
+              "chunk_order": 21,
+              "document_id": "ec8452c0-1c16-54e6-9b9f-3e741a8c7340",
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+          {
+            "id": "dae9312b-c464-5fb7-bbc1-06ba2998e462",
+            "score": 0.7083333308498079,
+            "metadata": {
+              "text": "WebQTL is the primary module in the Gene-\nNetwork 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\nthe HXB rat strains, and allows for phenotypic traits,",
+              "title": "2005 -Integrated gene expression profiling and linkage analysis in the rat.pdf",
+              "version": "v0",
+              "chunk_order": 64,
+              "document_id": "7b3a7517-2967-5693-b4e8-8423a9fa432b",
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+            "metadata": {
+              "text": "67. As described above, loci are identified in GeneNet work by the computation of a \nlikelihood ratio statistic score and significance  was determined using at least 5,000 \npermutations of the phenotype data.  \nUpdated QTL mapping methods , such as  R/qtl 2 66,146, Multiple QTL mapping  64, \nGEMMA  156 and pyLMM  63, have been implimented on t he GeneNetwork2 site 46.",
+              "title": "2019 - The expanded BXD family of mice A cohort for experimental systems genetics and precision medicine.pdf",
+              "version": "v0",
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+              "document_id": "8df14e3b-644f-5a18-94a6-5ff5a1eae053",
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+            "score": 0.6970214012835739,
+            "metadata": {
+              "text": "genetic mapping, and correlation of quantitative traits such as gene expression data and behavioral parameters (Wang  et al, 2003) . GeneNetwork employs \ngenotype 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",
+              "title": "2018 - Molecular Brain Adaptations to Ethanol_ Role of Glycogen Synthase (2).pdf",
+              "version": "v0",
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+            "score": 0.6965950312259787,
+            "metadata": {
+              "text": "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\nthe relationship between gene expressions and QTL inrecombinant in bred mice [ 3].\nComparing QTL and microarray data is not completely",
+              "title": "2008 -Han- Comparing Quantitative Trait Loci.pdf",
+              "version": "v0",
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+            "id": "bd69b879-f1fe-57ee-8b36-b621708bdcc3",
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+              "text": "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\nthe relationship between gene expressions and QTL inrecombinant in bred mice [ 3].\nComparing QTL and microarray data is not completely",
+              "title": "2008 - Comparing Quantitative Trait Loci.pdf",
+              "version": "v0",
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+              "text": "the database entries. Once the resulting record set of the\nquery is returned, it can be further restricted by selecting\nrelevant records based on attached annotations before for-\nwarding it for further analysis.\nTo map genetic loci associated with mRNA abundance or\ntrait phenotypes, any one of the three QTL mapping func-\ntions currently employed by GeneNetwork's WebQTL\nmodule can be used. These are 1. interval mapping, 2. sin-\ngle-marker regression, or 3. composite mapping [29,30].",
+              "title": "2008 - Towards systems genetic analyses in barley Integration of phenotypic, expression and genotype data into GeneNetwork.pdf",
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+              "content": "The bioinformatics tools for QTLs analysis include:\n\n1. **R/qtl** - A comprehensive tool for QTL mapping [4].\n2. **QTL Cartographer** - Used for QTL mapping and analysis [4].\n3. **MapQTL** - Another tool for QTL mapping [4].\n4. **WebQTL** - An online tool optimized for the analysis of traits controlled by combinations of allelic variants and environmental factors [4], [7].\n5. **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].\n6. **CPMAtranseqtl** - Available on GitHub, supports R/Python [3].\n7. **eMap** - Available on GNU, supports R [3].\n8. **FastMap** - Available on SourceForge, supports JAVA [3].\n9. **lme4qtl** - Available on GitHub, supports R [3].\n10. **Matrix eQTL** - Available on the UNC website, supports R/Matlab [3].\n11. **Meta-eQTL** - Available on HPC MSSM, supports R/C [3].\n12. **Plink** - A library for association QTL mapping on single nucleotide polymorphisms (SNP) in natural populations [8].\n13. **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].\n\nThese tools can be used in combination with classical experimental methods to accelerate QTL gene identification [1], [2].",
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+              "text": "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.\nIntroduction\nQuantitative trait locus (QTL) analysis is a method to\nlocalize chromosomal regions harboring genetic variants\nthat affect a continuously distributed, polygenic phenotype(including many common diseases) [1]. It is particularly",
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+              "text": "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.\nIntroduction\nQuantitative trait locus (QTL) analysis is a method to\nlocalize chromosomal regions harboring genetic variants\nthat affect a continuously distributed, polygenic phenotype(including many common diseases) [1]. It is particularly",
+              "title": "2005 - Bioinformatics toolbox for narrowing rodent quantitative trait loci .pdf",
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+              "text": "Table 2. Computational Approaches for Identi cation of QTLs\nTools Link Programming\nlanguageRefs\nLinear models\nCPMAtranseqtl https://github.com/cotsapaslab/CPMAtranseqtl R/Python [ 176]\neMap www.gnu.org/software/gsl/ R\nFastMap https://sourceforge.net/projects/fastmapunix/ JAVA [ 134]\nlme4qtl https://github.com/variani/lme4qtl R[ 175]\nMatrix eQTL www.bios.unc.edu/research/genomic_software/\nMatrix_eQTLR/Matlab [ 133]\nMeta-eQTL https://haok01.u.hpc.mssm.edu/meta_eQTL/ R/C [ 177]",
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+              "text": "2012). Tools for QTL analysis have been de veloped and released for researchers such as \nR/qtl, QTL cartographer, M apQTL, and WebQTL. Recently, Wang et al. (2012) \ndeveloped a free software for QTL mapping called QTL IciMapping which constructs genetic linkage maps and QTL analysis  by simple interval mapping and inclusive \ncomposite interval mapping.  QTL IciMapping is available for segregating and inbred \n \n \n \n \n \n \n \nPREVIEW",
+              "title": "2016 - Genotyping by sequencing for identification and mapping of QTLs for bioenergy-related traits in sweet sorghum.pdf",
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+              "text": "incorrect, the analysis can separate the QTL peak into twoTable 1. Summary of bioinformatics tools for dissecting rodent QTLs\nBioinformatics tool Summary Resolution\nComparative genomics Identies regions of chromosomal synteny in QTLs that are concordant across\nspecies1020 Mb\nCombined cross analysis Recodes genotype information from multiple crosses detecting a shared QTL into\none susceptibility and one resistance genotype to combine the crosses in a singleQTL analysis1020 Mb\nInterval-specic haplotype",
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+              "text": "incorrect, the analysis can separate the QTL peak into twoTable 1. Summary of bioinformatics tools for dissecting rodent QTLs\nBioinformatics tool Summary Resolution\nComparative genomics Identies regions of chromosomal synteny in QTLs that are concordant across\nspecies1020 Mb\nCombined cross analysis Recodes genotype information from multiple crosses detecting a shared QTL into\none susceptibility and one resistance genotype to combine the crosses in a singleQTL analysis1020 Mb\nInterval-specic haplotype",
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+              "text": "QTL/interval analysis \nQTL  mapping  was conducted  using  publically  available  software  \non GeneNetwork  (http://www .genenetwork .org/webqtl /main .py). \nOne  important  feature  of the GeneNetwork  is WebQTL , which  is the \nleading  GeneNetwork  module , and has been  optimized  for on-line \nanalysis  of traits  that are controlled  by combinations  of allelic  variants  \nand environmental  factors  [15]. A simple  graphical  user interface",
+              "title": "2016 - Genetic Regulation of Gelsolin in Lung in Mouse Model and its Potential.pdf",
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+              "text": "model selection approach for mapping multiple interacting QTL [376]\nand Plink, a library for association QTL mapping on single nu cleotide\npolymorphisms (SNP) in natural populations [277].\n3.2.3 Add new analysis tools\nxQTL workbench supports exible adding of more QTL analysis s oft-\nware: any R-based, or command-line tool, can be plugged in. A ll anal-\nysis results are uploaded, stored and tracked in the xQTL workbench\ndatabase through an R-API. When new tools are added, they can b uild",
+              "title": "2009 - Detection and interpretation of expression quantitative trait loci (eQTL).pdf",
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+              "text": "717 730\n14. Delaneau, O. et al. (2017) A complete tool set for molecular\nQTL discovery and analysis. Nat. Commun. 8, 1545215. Liu, B.H. (2017) Statistical Genomics: Linkage, Mapping, and\nQTL Analysis , CRC Press\n16. Gibson, G. et al. (2015) Expression quantitative trait locus anal-\nysis for translational medicine. Genome Med. 7, 1 14\n17. Ritchie, M.D. et al. (2015) Methods of integrating data to\nuncover genotype-phenotype interactions. Nat. Rev. Genet.\n16, 185 197",
+              "title": "2020 - A Multi-Omics Perspective of Quantitative Trait Loci in Precision Medicine.pdf",
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+              "text": "236 CH 10 TOOLS FOR STATISTICAL GENETICS\nLastly, Bayesian methods allow the consideration of multiple QTLs, QTL positions\nand QTL strengths (Jansen, 1996; Satagopan et al. , 1996; Uimari et al. , 1996; Sillanpaa\nand Arjas, 1998, Borevitz et al. , 2002). Multimapper (Sillanpaa, 1998), for example,\nallows the automatic building of models of multiple QTLs within the same linkage\ngroup. It is designed to work as a companion program to QTL Cartographer (Basten",
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+              "content": "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:\n\n1. **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].\n\n2. **Composite Interval Mapping (CIM)**: This method is available in the R/qtl package and is used for QTL mapping [2].\n\n3. **Haley-Knott Regression**: Another method available in the R/qtl package, used for QTL analysis [2].\n\n4. **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].\n\n5. **Permutation Analysis**: This technique is used to establish the significance threshold for each phenotype, ensuring that the identified QTLs are statistically significant [2].\n\n6. **Additive and Interactive Covariates**: These can be added to the model to account for additional sources of variation in the data [2].\n\n7. **Regression Method**: One of the commonly used methods for linkage analysis in QTL studies [7].\n\n8. **Likelihood Method**: Another method used for linkage analysis in QTL studies [7].\n\n9. **Variance Component Method**: This method is also used for linkage analysis in QTL studies [7].\n\n10. **Bayesian Method**: A method used for linkage analysis, which can also be used to check significant thresholds using Bayesian factors [7].\n\n11. **Bootstrapping**: This method is preferred for checking confidence intervals in QTL analysis [7].\n\nThese approaches provide a comprehensive toolkit for conducting QTL analysis, allowing researchers to identify and validate QTLs effectively.",
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+              "text": "Methods   31 \nstatistical language/software R (R DEVELOPMENT CORE TEAM 2008) . The core of R/qtl is a set \nof functions that make use of the hidden Markov model (HMM) technology to calculate QTL \ngenotype probabilities, to simulate from the  joint genotype distribution and to calculate the \nmost likely sequence of underlying genotypes (all conditional on the observed marker data) \n(BROMAN  et al.  2003) . R/qtl also calculates several functio ns that are useful for a quality",
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+              "text": "A variety of analytical methodologies are available in the R/qtl package, including,\ne.g., composite interval mapping or Haley-Knott regression (see Ref. 42for discussion).\nThe scanone function in R/qtl is used to calculate log of the odds (LOD) scores. Per-\nmutation analysis (perm 1000) is used to establish the signi cance threshold for each\nphenotype ( P<.05). Additive and/or interactive covariates can be added to the model",
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+              "text": "WebQTL (Chesler et al. 2003; http://www.web-\nqtl.org/home.html), because each has some uniquecapabilities.\nR/qtl is an interactive environment for mapping\nQTLs in experimental crosses, implemented as anadd-on package for the freely available statisticallanguage/software R. Empirical significance valuesare calculated by permutation tests by comparing\nthe peak likelihood ratio statistic (LRS) obtained\nfrom 1000 permutations (Churchill and Doerge1994). The permutation test results of highly sig-",
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+              "text": "The basic  pr emise  of QTL  an alysis  is simple  (Ph illips  and Belknap,\n2002 ) . First,  one must  meas  ure a speci  c phen  otype  within  a popul  ation.\nNext, the population must be genotyped at a hundred or more marker loci186 Boehm II et al.",
+              "title": "2006 - From_gene_to_behavior_and_back_again_new.pdf",
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+              "text": "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\nxQTL uses pbs based traditional clusters and is\nmore 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\nanalysis protocols between experiments and insti-",
+              "title": "2012 - Bioinformatics tools and database resources for systems genetics analysis in mice\u2014a short review and an evaluation of future needs.pdf",
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+              "text": "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\ncomputing.\nThe interactive QTL System (TIQS) (http://eqtl\n.berlios.de) is a web application that guides its usersthrough the analysis steps needed. It maximizes the\ndistribution of computational effort (supporting trad-",
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+              "text": "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,",
+              "title": "2012 - Genetic regulation of adult hippocampal neurogenesis A systems genetics approach using BXD recombinant inbred mouse strains.pdf",
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+              "text": "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",
+              "title": "2007 - Metabolic and genomic dissection of diabetes in the Cohen rat.pdf",
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+              "text": "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",
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+              "text": "R/QTL [35] is an R package which includes many func tions for mapping, including \nan algorithm to infer missing genotype data using H idden Markov Models. Gene- \nNetwork (www.genenetwork.org [11]) also offers eQTL  analysis for user uploaded \ndata, one trait at a time, and genome-wide analysis  tools for a number of published \ndatasets. \n \n4. Alternative Illumina data pre-processing \nCompared with Affymetrix for example, Illumina is a  relatively new technology and",
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+              "content": "### How-to Guide for GWAS Analysis\n\nGenome-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:\n\n#### Step 1: Preliminary Genome-Wide Analysis and Data Preprocessing\n1. **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].\n2. **Genotyping**: Perform large-scale genotyping using commercially available chips such as Affymetrix or Illumina [9].\n3. **Data Formatting**: Prepare the GWAS genotype data by generating bed, bim, and fam files using tools like PLINK [5].\n\n#### Step 2: Identifying Gene-Set Definitions\n1. **Gene-Set Identification**: Identify gene-set definitions whose patterns need to be recognized [4].\n\n#### Step 3: Processing Genomic Data\n1. **Filtering and Identifying Gene Patterns**: Process the genomic data by filtering and identifying gene patterns [4].\n2. **Intermediate Datasets**: Generate intermediate datasets that integrate original GWAS p-values, rsID, position, and chromosome column for each SNP [8].\n\n#### Step 4: Statistical Analysis\n1. **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].\n2. **Gene-Wise P-Values**: Calculate gene-wise p-values from the raw SNP p-values using programs like VEGAS2 or KGG [1].\n\n#### Step 5: Aggregating GWAS Data\n1. **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].\n\n#### Step 6: Assessing Statistical Magnitude\n1. **Statistical Hypothesis**: Identify gene set analysis models and assess the statistical magnitude of the associations [4].\n\n#### Step 7: Constructing Gene Networks\n1. **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].\n\nBy following these steps, you can systematically perform a GWAS analysis to identify genetic variants associated with your trait or disease of interest.",
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+              "text": "1. Formatting genome wide association study (GWAS) data . For this step, a \nhuman GWAS results file is needed that contains SNP names and raw p-\nvalues for the association of each SNP with a trait of interest. Because the \nnodes of the dmGWAS network will represent genes, as opposed to SNPs, \ngene-wise p-values need to be calculated from the raw SNP p-values. This \ncan be accomplished by using programs like VEGAS2 (Versatile Gene-\nBased Association Study) [ 10] or KGG (Knowledge-based mining system",
+              "title": "2017 - INTEGRATIVE ANALYSIS OF GENETIC, GENOMIC AND PHENOTYPIC DATA FOR ETHANOL BEHAVIORS A NETWORK-BASED PIPELINE FOR IDENTIFYING MECHANISMS AND POTENTIAL DRUG TARGETS.pdf",
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+              "text": "A general outline for GWAS is provided in Figure 2. These studies usually begin\nwith thousands of individuals who are charact erized for the phenotype of interest using\ncontinuous measurements, or dichotomous classi fication as a case (affected) or control\n(unaffected). Statistical analysis, typically us ing linear or logistic regression, tests the\nassociation of each SNP against the phenotype (including relevant covariate variables) to",
+              "title": "2008 - The Environmental Genome Project Reference Polymorphisms for Drug Metabolism Genes and Genome Wide Association Studies.pdf",
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+              "text": "GWAS has also provided polygenic characteristics of diseases. Figure 1 presents a block \nof GWAS in disease prediction. There are many steps  during a gene-set analysis. They are \nshown below as Steps 1 through Step 6: \nStep 1:  Preliminary genome-wide analysis and data preproces sing; \nStep 2:  Identifying gene-set definitions whose patterns have  to be recognized;  \nStep 3:  Processing genomic data such as filtering and ident ifying gene patterns;",
+              "title": "2022 - Using Recurrent Neural Networks for Predicting Type-2 Diabetes from Genomic and Tabular Data.pdf",
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+              "text": "GWAS in disease prediction. There are many steps during a gene-set analysis. They are\nshown below as Steps 1 through Step 6:\nStep 1: Preliminary genome-wide analysis and data preprocessing;\nStep 2: Identifying gene-set denitions whose patterns have to be recognized;\nStep 3: Processing genomic data such as ltering and identifying gene patterns;\nStep 4: Identify gene set analysis models, such as identifying the statistical hypothesis;\nStep 5: Assessing the statistical magnitude;",
+              "title": "2022 - Using Recurrent Neural Networks for Predicting Type-2 Diabetes from Genomic and Tabular Data.pdf",
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+              "text": "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",
+              "title": "2013 - Genome-Wide Contribution of Genotype by Environment Interaction.pdf",
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+              "text": "7 Constructing Gene Networks to Enhance GWAS\nand GOGE Results\nAs discussed, generating a GOGE data set and performing a rst-pass analysis on\nthis 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\nstudy. However, if analysis of GOGE data stopped at the identication of SNPs\nthat associate with expression, the true v alue of these data would not be realized.",
+              "title": "2009 - Processing Large-Scale, High-Dimension Genetic and Gene Expression Data.pdf",
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+              "text": "Aggregating GWAS data into biological units\nGWAS data can be further combined into biological units using gene and network-based \napproaches.\nGene-based approaches\nThere is a high multiple testing burden in the context of a GWAS. Gene-based approaches, \nwhich aggregate across summary statistics derived from association analyses of multiple loci \nto derive p-values for association at the level of the gene, developed as one way to reduce",
+              "title": "2019 - Beyond Genome-wide Significance Integrative Approaches to the Interpretation and Extension of GWAS Findings for Alcohol Use Disorder.pdf",
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+              "text": "Steps involved inthegene-based association testwere described asbelow: 1)Generating\nintermediate datasets which integrate original GWAS Pvalues, rsID, position and chromo-\nsome column foreach SNP. Atotal of6,559,815 European-specific and 5,351,262 Asian-spe-\ncific autosomal SNPs were used forsubsequent analysis after excluding theSNPs that could\nnotberecognized byKGG and that located insexchromosomes (XorY);2)Defining asetof",
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+              "text": "248 M. J. RIEDER ET AL.\nFigure 2 An overview of GWAS. Samples with  a phenotype(s) or trait(s) of interest are identified; typically,\nthousands of samples are required to achieve appropri ate statistical power. Large-scale genotyping is carried\nout using commercially available chips (Affymetrix or  Illumina). P-values are generated from the associa-\ntion between the phenotype and genotype for each SNP tested. Highly associated SNPs will typically cluster",
+              "title": "2008 - The Environmental Genome Project Reference Polymorphisms for Drug Metabolism Genes and Genome Wide Association Studies.pdf",
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+              "text": "2006).\n40. Welter, D. et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait\nassociations. Nucleic Acids Res. 42,D1001D1006 (2014).41. Wang, X. et al. Comparing methods for performing trans-ethnic meta-analysis\nof genome-wide association studies.\nHum. Mol. Genet. 22,23032311 (2013).\n42. Purcell, S. et al. PLINK: a tool set for whole-genome association and\npopulation-based linkage analyses. Am. J. Hum. Genet. 81,559575 (2007).",
+              "title": "2015 - Genetic associations at 53 loci highlight cell types and biological pathways relevant for kidney function.pdf",
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+              "content": "### How-to Guide for Genetic Sequencing\n\nGenetic 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.\n\n#### Step 1: Sample Preparation\n1. **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.\n2. **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.\n\n#### Step 2: Library Preparation\n1. **Fragment DNA**: Break the DNA into smaller fragments. This can be done using mechanical shearing or enzymatic digestion.\n2. **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].\n\n#### Step 3: Amplification\n1. **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.\n\n#### Step 4: Sequencing\n1. **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].\n2. **Load the Sample**: Load the prepared DNA library onto the sequencing machine. Follow the manufacturer's instructions for your specific platform.\n\n#### Step 5: Data Acquisition\n1. **Run the Sequencer**: Start the sequencing run. The machine will read the nucleotide sequences of the DNA fragments and generate raw data.\n2. **Monitor the Run**: Keep an eye on the sequencing run to ensure it is proceeding correctly. Some platforms provide real-time monitoring capabilities [6].\n\n#### Step 6: Data Analysis\n1. **Base Calling**: Use base-calling software to convert raw sequencing data into nucleotide sequences. Tools like Phred are commonly used for this purpose [8].\n2. **Sequence Assembly**: Assemble the short reads into longer contiguous sequences (contigs) using assemblers like Phrap or CAP3 [8].\n3. **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].\n\n#### Step 7: Validation and Reporting\n1. **Validate Results**: Confirm the accuracy of your sequencing results using additional methods such as Sanger sequencing or quantitative PCR.\n2. **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.\n\n#### Further Information\nFor more detailed information on specific sequencing technologies and platforms, refer to the following resources:\n- [10X Genomics](http://www.10xgenomics.com)\n- [Illumina](http://www.illumina.com)\n- [Ion Torrent](https://www.thermofisher.com/us/en/home/brands/ion-torrent.html)\n- [Pacific Biosciences](http://www.pacb.com)\n- [Oxford Nanopore Technologies](https://www.nanoporetech.com) [1]\n\nBy following these steps and utilizing the referenced resources, you can successfully perform genetic sequencing and analyze the resulting data.",
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+              "text": "FURTHER INFORMATION\n10X Genomics: http://www.10xgenomics.com\n454 Sequencing: http://www.454.com\nAdvances in Genome Biology and Technology (AGBT): \nhttp://www.agbt.org\nBGISEQ500: http://seq500.com/en/portal/Sequencer.shtml\nIllumina: http://www.illumina.com\nIon Torrent: https://www.thermofisher.com/us/en/home/\nbrands/ion-torrent.html\nOxford Nanopore Technologies: https://www.nanoporetech.\ncom\nPacific Biosciences: http://www.pacb.com\nPersonal Genome Project: http://www.personalgenomes.org",
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+              "text": "22. Karow, J. Qiagen launches GeneReader NGS System \natAMP; presents performance evaluation by broad. \nGenomeWeb  [online], https:// www.genomeweb.com/\nmolecular-diagnostics/qiagen-launches-genereader-\nngs-system-amp-presents-performance-evaluation  \n(4Nov 2015).\n23. Smith,D.R. & McKernan,K. Methods of producing \nand sequencing modified polynucleotides . US Patent \n8058030 (2011).\n24. Margulies,M. etal.  Genome sequencing in \nmicrofabricated high-density picolitre reactors. Nature \n437, 376380 (2005).",
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+              "text": "36. Sequencing, H.G. Finishing the euchromatic sequence of the human genome. Nature 2004 ,431, 931945.\n37. Heather, J.M.; Chain, B. The sequence of sequencers: The history of sequencing DNA. Genomics 2016 ,107,\n18. [CrossRef]\n38. Rothberg, J.M.; Leamon, J.H. The development and impact of 454 sequencing. Nat. Biotechnol. 2008 ,26,\n11171124. [CrossRef] [PubMed]\n39. Shendure, J.; Ji, H. Next-generation DNA sequencing. Nat. Biotechnol. 2008 ,26, 11351145. [CrossRef]\n[PubMed]",
+              "title": "2020 - Precision and Personalized Medicine How Genomic.pdf",
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+              "text": "sequencing. Genome Res. 20, 11651173 (2010).\n64. English,A.C. etal.  Assessing structural variation in a \npersonal genome-towards a human reference diploid \ngenome. BMC Genomics 16, 286 (2015).\n65. Carneiro,M.O. etal.  Pacific Biosciences sequencing \ntechnology for genotyping and variation discovery in \nhuman data. BMC Genomics 13, 375 (2012).\n66. Quail,M.A. etal.  A tale of three next generation \nsequencing platforms: comparison of Ion T orrent, \nPacific Biosciences and Illumina MiSeq sequencers.",
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+              "text": "sequencing. Bioinformatics 31, 20402042 (2015).\n46. Qiagen.  Oncology insights enabled by knowledge base-\nguided panel design and the seamless workflow of the \nGeneReader NGS system  Press Release. Qiagen  \n[online], http://www.genereaderngs.com/PROM-9192-\n001_1100403_WP_GeneReader_NGS_0116_NA.pdf  \n(2016).\n47. Forgetta,V. etal.  Sequencing of the Dutch elm disease \nfungus genome using the Roche/454 GS-FLX Titanium \nSystem in a comparison of multiple genomics core",
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+              "text": "for sequencing on existing short-read instrumentation, \nafter which data are split by barcode and reassembled \nwith the knowledge that fragments sharing barcodes Barcodes\nA series of known bases \naddedto a template molecule \neither through ligation or \namplification. After \nsequencing, these barcodes \ncan be used to identify which \nsample a particular read is \nderived from.\nFigure 5 | Real-time and synthetic long-read sequencing approaches.",
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+            "id": "97f2aa12-623b-53ec-9793-5834311a37dd",
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+              "text": "160. Glenn,T .C. Field guide to next-generation DNA \nsequencers. Mol. Ecol. Resour. 11, 759769 (2011).\n161. Karow,J. At AGBT , 10X Genomics launches GemCode \nplatform; shipments slated for Q2 as firm battles IP \nlawsuits.  GenomeWeb  [online], https://www.\ngenomeweb.com/sample-prep/agbt-10x-genomics-\nlaunches-gemcode-platform-shipments-slated-q2-firm-\nbattles-ip-lawsuits  (2Mar 2015).\nCompeting interests statement\nThe authors declare competing interests: see Web version  for \ndetails.\nFURTHER INFORMATION",
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+              "text": "DNA), and provide the means to link sequences containing applications. First, \nbase- callers like Phred (4,5) extract raw sequences from raw data. There are \nalso contig assemblers like Phrap (University of Washington, http://bozeman.\nmbt.washington.edu/phrap.docs/phrap.html ) or CAP3 (6) that assemble frag-\nments to contigs and packages like consed (7) or GAP4 (8), which are used to \nfinish sequencing projects. These programs are not explained in detail here.",
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+              "text": "Nat. Biotechnol. 30, 10331036 (2012).\n111. Chrystoja,C.C. & Diamandis,E.P . Whole genome \nsequencing as a diagnostic test: challenges and \nopportunities. Clin. Chem. 60, 724733 (2014).\n112. McGuire,A.L. etal.  Point-counterpoint. Ethics and \ngenomic incidental findings. Science 340, 10471048 \n(2013).\n113. Bowers,J. etal.  Virtual terminator nucleotides for \nnext-generation DNA sequencing. Nat. Methods 6, \n593595 (2009).\n114. Heger,M. Chinas Direct Genomics unveils new",
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+              "text": "11 BIOINFORMATIC CHALLENGES FOR GENOMIC MEDICINE \n \n \nProcessing and managing of high-throughput sequence data \n \n High throughput sequencing offers severa l advantages relative to array-based \ngenotyping or expression assays. First, unlike genotyping arrays, whole genome sequencing is not limited to interrogating onl y known sequence variants. Similarly, RNA-\nsequencing (RNA-seq) enables expression quanti fication of novel transcripts that are not",
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+              "content": "### How-to Guide for Genetic Sequencing\n\nGenetic 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.\n\n#### Step 1: Sample Preparation\n1. **DNA Extraction**: Begin by extracting DNA from your sample. This can be done using various commercial kits or standard laboratory protocols.\n2. **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.\n\n#### Step 2: Library Preparation\n1. **Fragmentation**: Fragment the DNA into smaller pieces. This can be achieved through mechanical shearing, enzymatic digestion, or sonication.\n2. **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.\n3. **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.\n\n#### Step 3: Amplification and Enrichment\n1. **PCR Amplification**: Amplify the adapter-ligated DNA fragments using polymerase chain reaction (PCR). This step increases the quantity of DNA available for sequencing.\n2. **Size Selection**: Select DNA fragments of the desired size range using gel electrophoresis or magnetic beads.\n\n#### Step 4: Sequencing\n1. **Loading the Sequencer**: Load the prepared DNA library onto the sequencing platform. Popular platforms include Illumina, Ion Torrent, and Pacific Biosciences [6], [9].\n2. **Sequencing Run**: Initiate the sequencing run. The platform will read the nucleotide sequences of the DNA fragments and generate raw sequence data [1], [5].\n\n#### Step 5: Data Processing\n1. **Base Calling**: Convert raw data into nucleotide sequences using base-calling software such as Phred [10].\n2. **Sequence Assembly**: Assemble the short DNA sequences into longer contiguous sequences (contigs) using software like Phrap or CAP3 [10].\n3. **Quality Control**: Assess the quality of the assembled sequences and remove any errors or low-quality reads.\n\n#### Step 6: Data Analysis\n1. **Variant Calling**: Identify genetic variants such as single nucleotide polymorphisms (SNPs) and insertions/deletions (indels) using variant calling software.\n2. **Annotation**: Annotate the identified variants to understand their potential impact on gene function and association with diseases.\n\n#### Step 7: Interpretation and Reporting\n1. **Bioinformatics Analysis**: Use bioinformatics tools to interpret the sequencing data in the context of the research question or clinical application [3], [4].\n2. **Report Generation**: Generate a comprehensive report summarizing the findings, including identified variants and their potential implications.\n\n#### Additional Resources\n- For a detailed history and development of sequencing technologies, refer to Heather and Chain's review [1].\n- For information on specific sequencing platforms and their comparisons, see Quail et al. [6].\n- For best practices in sequencing and data management, consult Olson et al. [5].\n\nBy 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.",
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+              "text": "36. Sequencing, H.G. Finishing the euchromatic sequence of the human genome. Nature 2004 ,431, 931945.\n37. Heather, J.M.; Chain, B. The sequence of sequencers: The history of sequencing DNA. Genomics 2016 ,107,\n18. [CrossRef]\n38. Rothberg, J.M.; Leamon, J.H. The development and impact of 454 sequencing. Nat. Biotechnol. 2008 ,26,\n11171124. [CrossRef] [PubMed]\n39. Shendure, J.; Ji, H. Next-generation DNA sequencing. Nat. Biotechnol. 2008 ,26, 11351145. [CrossRef]\n[PubMed]",
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+              "text": "22. Karow, J. Qiagen launches GeneReader NGS System \natAMP; presents performance evaluation by broad. \nGenomeWeb  [online], https:// www.genomeweb.com/\nmolecular-diagnostics/qiagen-launches-genereader-\nngs-system-amp-presents-performance-evaluation  \n(4Nov 2015).\n23. Smith,D.R. & McKernan,K. Methods of producing \nand sequencing modified polynucleotides . US Patent \n8058030 (2011).\n24. Margulies,M. etal.  Genome sequencing in \nmicrofabricated high-density picolitre reactors. Nature \n437, 376380 (2005).",
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+              "text": "11 BIOINFORMATIC CHALLENGES FOR GENOMIC MEDICINE \n \n \nProcessing and managing of high-throughput sequence data \n \n High throughput sequencing offers severa l advantages relative to array-based \ngenotyping or expression assays. First, unlike genotyping arrays, whole genome sequencing is not limited to interrogating onl y known sequence variants. Similarly, RNA-\nsequencing (RNA-seq) enables expression quanti fication of novel transcripts that are not",
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+              "text": "11 BIOINFORMATIC CHALLENGES FOR GENOMIC MEDICINE \n \n \nProcessing and managing of high-throughput sequence data \n \n High throughput sequencing offers severa l advantages relative to array-based \ngenotyping or expression assays. First, unlike genotyping arrays, whole genome sequencing is not limited to interrogating onl y known sequence variants. Similarly, RNA-\nsequencing (RNA-seq) enables expression quanti fication of novel transcripts that are not",
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+              "text": "High-throughput bacterial genome sequencing: an embarrassment of choice,\naworldof opportunity.NatRevMicrobiol2012;10:599-606.\n11.CroucherNJ,DidelotX.Theapplicationof genomicstotracingbacterialpathogen\ntransmission.CurrOpinMicrobiol2015;23:62-7.\n12.ShendureJ,JiH.Next-generationDNAsequencing.NatBiotechnol2008;26:1135-\n45.\n13.MillerJR,KorenS,SuttonG.Assemblyalgorithmsfornext-generationsequencing\ndata.Genomics2010;95:315-27.\n14.OlsonND,LundSP,ColmanRE,FosterJT,SahlJW,SchuppJM,etal.Bestpractices",
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+              "text": "sequencing. Genome Res. 20, 11651173 (2010).\n64. English,A.C. etal.  Assessing structural variation in a \npersonal genome-towards a human reference diploid \ngenome. BMC Genomics 16, 286 (2015).\n65. Carneiro,M.O. etal.  Pacific Biosciences sequencing \ntechnology for genotyping and variation discovery in \nhuman data. BMC Genomics 13, 375 (2012).\n66. Quail,M.A. etal.  A tale of three next generation \nsequencing platforms: comparison of Ion T orrent, \nPacific Biosciences and Illumina MiSeq sequencers.",
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+              "text": "Nat. Biotechnol. 30, 10331036 (2012).\n111. Chrystoja,C.C. & Diamandis,E.P . Whole genome \nsequencing as a diagnostic test: challenges and \nopportunities. Clin. Chem. 60, 724733 (2014).\n112. McGuire,A.L. etal.  Point-counterpoint. Ethics and \ngenomic incidental findings. Science 340, 10471048 \n(2013).\n113. Bowers,J. etal.  Virtual terminator nucleotides for \nnext-generation DNA sequencing. Nat. Methods 6, \n593595 (2009).\n114. Heger,M. Chinas Direct Genomics unveils new",
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+              "text": "sequencing. Bioinformatics 31, 20402042 (2015).\n46. Qiagen.  Oncology insights enabled by knowledge base-\nguided panel design and the seamless workflow of the \nGeneReader NGS system  Press Release. Qiagen  \n[online], http://www.genereaderngs.com/PROM-9192-\n001_1100403_WP_GeneReader_NGS_0116_NA.pdf  \n(2016).\n47. Forgetta,V. etal.  Sequencing of the Dutch elm disease \nfungus genome using the Roche/454 GS-FLX Titanium \nSystem in a comparison of multiple genomics core",
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+              "text": "FURTHER INFORMATION\n10X Genomics: http://www.10xgenomics.com\n454 Sequencing: http://www.454.com\nAdvances in Genome Biology and Technology (AGBT): \nhttp://www.agbt.org\nBGISEQ500: http://seq500.com/en/portal/Sequencer.shtml\nIllumina: http://www.illumina.com\nIon Torrent: https://www.thermofisher.com/us/en/home/\nbrands/ion-torrent.html\nOxford Nanopore Technologies: https://www.nanoporetech.\ncom\nPacific Biosciences: http://www.pacb.com\nPersonal Genome Project: http://www.personalgenomes.org",
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+              "text": "DNA), and provide the means to link sequences containing applications. First, \nbase- callers like Phred (4,5) extract raw sequences from raw data. There are \nalso contig assemblers like Phrap (University of Washington, http://bozeman.\nmbt.washington.edu/phrap.docs/phrap.html ) or CAP3 (6) that assemble frag-\nments to contigs and packages like consed (7) or GAP4 (8), which are used to \nfinish sequencing projects. These programs are not explained in detail here.",
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+              "content": "The length of telomeres is significant for several reasons:\n\n1. **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].\n\n2. **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].\n\n3. **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].\n\n4. **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].\n\n5. **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].\n\nIn summary, the length of telomeres is crucial for maintaining chromosomal integrity, regulating cellular aging, and potentially serving as a biomarker for certain diseases.",
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+              "text": "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\n8, \ndifferent studies have shown that T2D individuals have shorter leukocyte telomeres than non-T2D individuals9, 10  \nthat may be associated with disease progression11. Indeed, the decreased antioxidant capacity described in patients",
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+              "text": "Telomeres are arrays of linked nucleotide hexamer repeats that are found at the \nends of chromosomes in a vast clade of organisms [14]. While the sequence of \nthese telomeric repeats can vary between organisms, their biological function is \nhighly conserved, which is to limit damage inflicted on genes during the replica-\ntion of chromosomes. Telomere length is progressively shortened with each round \nof genomic replication, unless it is restored through the action of a ribonucleo-",
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+              "text": "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",
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+              "text": "age telomere length through accumulation of several short telo-\nmeres (Londono-Vallejo et al., 2001; Martens et al., 2000) is \nresponsible for senescence or whether a speci  c chromosome \narm limits the replication potential of human cells (Hemann et al., 2001). Individual chromosome arms were shown to have \nlarge variations in their length (Lansdorp et al., 1996; Benn, \n1997; Londono-Vallejo et al., 2001), and chromosome 17p seemed to be equipped with especially short telomeres in hu-",
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+              "text": "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",
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+              "text": "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  \net al \n., 1997; Hande  \net al \n., 1999), causing",
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+              "text": "a pivotal role in maintenance of genomic integrity and func-tion (Moyzis et al., 1988; McElligott and Wellinger, 1997; van \nSteensel and de Lange, 1997). \n It is generally accepted that telomeres shorten during DNA \nreplication both in vitro and in vivo. In individuals, short telomeres are considered to be a sign of advanced age. Cawthon \nand coworkers (2003) showed that telomere shortening in hu-",
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+              "text": "Each cell division shortens telomeric DNA until, at a\ncritical 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)\nassociates signi cantly with vascular cell senescence,",
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+              "text": "nization may directly affect telomere attrition, resulting in accelerated replicative \nsenescence and progeroid phenotypes [180].\nTelomeres are regions constituted by tandem repeats of non-coding DNA \nsequences 5-(TTAGGG)n-3 and a protein complex called shelterin, bound to them. \nThis structure ensures the stability of the genome and protects the chromosomes \nfrom a wrong action of the DNA repair machinery [184] by allowing the formation \nof a chromatin loop called T-Loop [185].",
+              "title": "2020 - Clinical Genetics and Genomics of Aging.pdf",
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+              "content": "### How-to Guide for Genetic Sequencing\n\nGenetic 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.\n\n#### Step 1: Sample Preparation\n1. **Collect DNA Sample**: Obtain a sample containing DNA. This could be from blood, saliva, or tissue.\n2. **Extract DNA**: Use a DNA extraction kit to isolate DNA from the sample. Ensure the DNA is pure and of high quality.\n\n#### Step 2: Library Preparation\n1. **Fragment DNA**: Break the DNA into smaller fragments. This can be done using mechanical shearing or enzymatic methods.\n2. **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].\n\n#### Step 3: Amplification\n1. **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.\n\n#### Step 4: Sequencing\n1. **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].\n2. **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.\n\n#### Step 5: Data Analysis\n1. **Base Calling**: Use software to convert raw sequencing data into nucleotide sequences. Tools like Phred are commonly used for this purpose [8].\n2. **Sequence Assembly**: Assemble the short DNA reads into longer sequences (contigs). Software like Phrap or CAP3 can be used for this step [8].\n3. **Variant Calling**: Identify genetic variants by comparing the assembled sequences to a reference genome. This step is crucial for understanding genetic differences and mutations.\n\n#### Step 6: Interpretation\n1. **Bioinformatics Analysis**: Use bioinformatics tools to analyze the sequencing data. This includes identifying genes, predicting functions, and understanding genetic variations [10].\n2. **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.\n\n#### Additional Resources\n- **10X Genomics**: Offers advanced solutions for single-cell sequencing and spatial genomics [1].\n- **Illumina**: Provides a wide range of sequencing platforms and reagents [1].\n- **Pacific Biosciences**: Known for long-read sequencing technology [1], [4].\n- **Oxford Nanopore Technologies**: Offers portable and real-time sequencing solutions [1].\n\nBy following these steps and utilizing the referenced resources, you can successfully perform genetic sequencing and analyze the resulting data.",
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+              "text": "FURTHER INFORMATION\n10X Genomics: http://www.10xgenomics.com\n454 Sequencing: http://www.454.com\nAdvances in Genome Biology and Technology (AGBT): \nhttp://www.agbt.org\nBGISEQ500: http://seq500.com/en/portal/Sequencer.shtml\nIllumina: http://www.illumina.com\nIon Torrent: https://www.thermofisher.com/us/en/home/\nbrands/ion-torrent.html\nOxford Nanopore Technologies: https://www.nanoporetech.\ncom\nPacific Biosciences: http://www.pacb.com\nPersonal Genome Project: http://www.personalgenomes.org",
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+              "text": "22. Karow, J. Qiagen launches GeneReader NGS System \natAMP; presents performance evaluation by broad. \nGenomeWeb  [online], https:// www.genomeweb.com/\nmolecular-diagnostics/qiagen-launches-genereader-\nngs-system-amp-presents-performance-evaluation  \n(4Nov 2015).\n23. Smith,D.R. & McKernan,K. Methods of producing \nand sequencing modified polynucleotides . US Patent \n8058030 (2011).\n24. Margulies,M. etal.  Genome sequencing in \nmicrofabricated high-density picolitre reactors. Nature \n437, 376380 (2005).",
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+              "text": "36. Sequencing, H.G. Finishing the euchromatic sequence of the human genome. Nature 2004 ,431, 931945.\n37. Heather, J.M.; Chain, B. The sequence of sequencers: The history of sequencing DNA. Genomics 2016 ,107,\n18. [CrossRef]\n38. Rothberg, J.M.; Leamon, J.H. The development and impact of 454 sequencing. Nat. Biotechnol. 2008 ,26,\n11171124. [CrossRef] [PubMed]\n39. Shendure, J.; Ji, H. Next-generation DNA sequencing. Nat. Biotechnol. 2008 ,26, 11351145. [CrossRef]\n[PubMed]",
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+              "text": "sequencing. Genome Res. 20, 11651173 (2010).\n64. English,A.C. etal.  Assessing structural variation in a \npersonal genome-towards a human reference diploid \ngenome. BMC Genomics 16, 286 (2015).\n65. Carneiro,M.O. etal.  Pacific Biosciences sequencing \ntechnology for genotyping and variation discovery in \nhuman data. BMC Genomics 13, 375 (2012).\n66. Quail,M.A. etal.  A tale of three next generation \nsequencing platforms: comparison of Ion T orrent, \nPacific Biosciences and Illumina MiSeq sequencers.",
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+              "text": "sequencing. Bioinformatics 31, 20402042 (2015).\n46. Qiagen.  Oncology insights enabled by knowledge base-\nguided panel design and the seamless workflow of the \nGeneReader NGS system  Press Release. Qiagen  \n[online], http://www.genereaderngs.com/PROM-9192-\n001_1100403_WP_GeneReader_NGS_0116_NA.pdf  \n(2016).\n47. Forgetta,V. etal.  Sequencing of the Dutch elm disease \nfungus genome using the Roche/454 GS-FLX Titanium \nSystem in a comparison of multiple genomics core",
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+              "text": "for sequencing on existing short-read instrumentation, \nafter which data are split by barcode and reassembled \nwith the knowledge that fragments sharing barcodes Barcodes\nA series of known bases \naddedto a template molecule \neither through ligation or \namplification. After \nsequencing, these barcodes \ncan be used to identify which \nsample a particular read is \nderived from.\nFigure 5 | Real-time and synthetic long-read sequencing approaches.",
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+              "text": "160. Glenn,T .C. Field guide to next-generation DNA \nsequencers. Mol. Ecol. Resour. 11, 759769 (2011).\n161. Karow,J. At AGBT , 10X Genomics launches GemCode \nplatform; shipments slated for Q2 as firm battles IP \nlawsuits.  GenomeWeb  [online], https://www.\ngenomeweb.com/sample-prep/agbt-10x-genomics-\nlaunches-gemcode-platform-shipments-slated-q2-firm-\nbattles-ip-lawsuits  (2Mar 2015).\nCompeting interests statement\nThe authors declare competing interests: see Web version  for \ndetails.\nFURTHER INFORMATION",
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+              "text": "DNA), and provide the means to link sequences containing applications. First, \nbase- callers like Phred (4,5) extract raw sequences from raw data. There are \nalso contig assemblers like Phrap (University of Washington, http://bozeman.\nmbt.washington.edu/phrap.docs/phrap.html ) or CAP3 (6) that assemble frag-\nments to contigs and packages like consed (7) or GAP4 (8), which are used to \nfinish sequencing projects. These programs are not explained in detail here.",
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+              "text": "Nat. Biotechnol. 30, 10331036 (2012).\n111. Chrystoja,C.C. & Diamandis,E.P . Whole genome \nsequencing as a diagnostic test: challenges and \nopportunities. Clin. Chem. 60, 724733 (2014).\n112. McGuire,A.L. etal.  Point-counterpoint. Ethics and \ngenomic incidental findings. Science 340, 10471048 \n(2013).\n113. Bowers,J. etal.  Virtual terminator nucleotides for \nnext-generation DNA sequencing. Nat. Methods 6, \n593595 (2009).\n114. Heger,M. Chinas Direct Genomics unveils new",
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+              "content": "### Guide to Genetic Sequencing\n\nGenetic 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.\n\n#### 1. Introduction to Genetic Sequencing\nGenetic 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.\n\n#### 2. Types of Sequencing Technologies\nThere are several sequencing technologies available, each with its own advantages and applications:\n\n- **Sanger Sequencing**: The first-generation sequencing method, known for its high accuracy but lower throughput.\n- **Next-Generation Sequencing (NGS)**: Includes various platforms that allow for high-throughput sequencing, enabling the sequencing of millions of fragments simultaneously [9].\n\n#### 3. Next-Generation Sequencing Platforms\nSeveral NGS platforms are widely used in the field:\n\n- **Illumina**: Known for its high accuracy and throughput, suitable for a wide range of applications [2].\n- **Ion Torrent**: Uses semiconductor technology for sequencing, offering a cost-effective solution [2].\n- **Pacific Biosciences (PacBio)**: Provides long-read sequencing, which is useful for identifying structural variations and complex regions in the genome [3], [2].\n- **Oxford Nanopore Technologies**: Offers portable sequencing devices with real-time data analysis capabilities [2].\n- **10X Genomics**: Known for its GemCode platform, which provides linked-read sequencing for better genome assembly [1], [2].\n\n#### 4. Steps in Genetic Sequencing\nThe sequencing process generally involves the following steps:\n\n1. **Sample Preparation**: DNA is extracted from the sample and prepared for sequencing. This may involve fragmentation, adapter ligation, and amplification [5].\n2. **Library Construction**: The prepared DNA fragments are converted into a library suitable for the sequencing platform being used [5].\n3. **Sequencing**: The library is loaded onto the sequencing platform, where the DNA fragments are sequenced in parallel [9].\n4. **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].\n5. **Interpretation**: The processed data is interpreted to identify genetic variants, structural variations, and other genomic features [3].\n\n#### 5. Applications of Genetic Sequencing\nGenetic sequencing has a wide range of applications, including:\n\n- **Medical Diagnostics**: Identifying genetic mutations associated with diseases.\n- **Genomic Research**: Studying genetic variations and their implications.\n- **Personal Genomics**: Providing individuals with information about their genetic makeup [2].\n\n#### 6. Future Directions\nThe 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].\n\n#### 7. Further Information\nFor more detailed information on specific sequencing platforms and technologies, you can visit the following resources:\n\n- **Illumina**: [http://www.illumina.com](http://www.illumina.com)\n- **Ion Torrent**: [https://www.thermofisher.com/us/en/home/brands/ion-torrent.html](https://www.thermofisher.com/us/en/home/brands/ion-torrent.html)\n- **Pacific Biosciences**: [http://www.pacb.com](http://www.pacb.com)\n- **Oxford Nanopore Technologies**: [https://www.nanoporetech.com](https://www.nanoporetech.com)\n- **10X Genomics**: [http://www.10xgenomics.com](http://www.10xgenomics.com)\n\nBy understanding the basics of genetic sequencing and the available technologies, you can better appreciate the power and potential of this transformative field in genomics.",
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+              "text": "FURTHER INFORMATION\n10X Genomics: http://www.10xgenomics.com\n454 Sequencing: http://www.454.com\nAdvances in Genome Biology and Technology (AGBT): \nhttp://www.agbt.org\nBGISEQ500: http://seq500.com/en/portal/Sequencer.shtml\nIllumina: http://www.illumina.com\nIon Torrent: https://www.thermofisher.com/us/en/home/\nbrands/ion-torrent.html\nOxford Nanopore Technologies: https://www.nanoporetech.\ncom\nPacific Biosciences: http://www.pacb.com\nPersonal Genome Project: http://www.personalgenomes.org",
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+              "text": "36. Sequencing, H.G. Finishing the euchromatic sequence of the human genome. Nature 2004 ,431, 931945.\n37. Heather, J.M.; Chain, B. The sequence of sequencers: The history of sequencing DNA. Genomics 2016 ,107,\n18. [CrossRef]\n38. Rothberg, J.M.; Leamon, J.H. The development and impact of 454 sequencing. Nat. Biotechnol. 2008 ,26,\n11171124. [CrossRef] [PubMed]\n39. Shendure, J.; Ji, H. Next-generation DNA sequencing. Nat. Biotechnol. 2008 ,26, 11351145. [CrossRef]\n[PubMed]",
+              "title": "2020 - Precision and Personalized Medicine How Genomic.pdf",
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+              "text": "sequencing. Bioinformatics 31, 20402042 (2015).\n46. Qiagen.  Oncology insights enabled by knowledge base-\nguided panel design and the seamless workflow of the \nGeneReader NGS system  Press Release. Qiagen  \n[online], http://www.genereaderngs.com/PROM-9192-\n001_1100403_WP_GeneReader_NGS_0116_NA.pdf  \n(2016).\n47. Forgetta,V. etal.  Sequencing of the Dutch elm disease \nfungus genome using the Roche/454 GS-FLX Titanium \nSystem in a comparison of multiple genomics core",
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+              "text": "Conventional sequencing Next-generation sequencing\nSequencing  Subcloning in vectors, amplification in hosts\nfor every single DNA fragment Direct DNA fragment sequencing\n Sequencing of 100 fragments in parallel  Optional PCR amplification\n Parallel sequencing of millions\nof small fragments\nYield 1 /H11003105bp/sequencing run /H110221/H110031011bp/sequencing run\nComputational requirements Moderate High\nCost per megabase High LowAccuracy High HighFuture directions   Direct sequencing of DNA\nmolecules",
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+              "text": "Nature Reviews | GeneticsCleavage agent\nSingle-base-encoded probes\nA 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\nProbe with known base at n+1a  SOLiD \n     (Thermo Fisher)\nb  Complete Genomics\n     (BGI)\nPaired-end sequencingSequencing is performed for both the left and right sides of the adapterTTG AG TC CC GA CT TATA\nA",
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+              "content": "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].",
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+            "id": "53aa581f-06d1-52b3-b847-08ea3d95a980",
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+            "metadata": {
+              "text": "Deregulated lipid metabolism (dyslipidemia) that manifests as hypercholesterolemia, \nhypertriglyceridemia, low high -density -lipoprotein (HDL) cholesterol levels or a \ncombination of those is an established risk factor for CHD among other established risk \nfactors. The liver is of major importance in maintaining whole- body lipid metabolic",
+              "title": "2011 - Genetics and Genomics of Cholesterol and Polyunsaturated Fatty Acid Metabolism in Relation to Coronary Heart Disease Risk.pdf",
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+              "text": "23   Atherogenic dyslipidemia, manifested by raised triglycerides and low \nconcentrations of HDL cholesterol. There could be p resent other lipoprotein abnormalities \nas well, e.g., increased lipoproteins, elevated apo lipoprotein B, small LDL and HDL \nparticles. All of these abnormalities have been imp licated as being atherogenic (Kolovou et \nal., 2005; Ginsberget al., 2000). \n Elevated blood pressure  strongly associates with obesity and commonly occu rs in \ninsulin-resistant persons.",
+              "title": "2011 - Analysis of cognitive functions in recombinant inbred strains of rats produced by crossbreeding of SHR and BN Lx. lines.pdf",
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+              "text": "plasma TGisdetermined bythelevel ofVLDL-TG (the balance between synthesis and clear-\nance ofVLDL-TG), and thesynthesis ofVLDL-TG isassociated with total fatmass and liver\nfat[59]. Thus, thelarge amount offatmass inobese patients leads toincreasing synthesis of\nVLDL-TG, buttheclearance ofVLDL-TG remains unchanged. Hypertriglyceridemia isaprin-\ncipal characteristic ofdyslipidemia and islinked tomany other types ofdyslipidemia such as",
+              "title": "2018 - Multivariate analysis of genomics data to identify potential pleiotropic genes.pdf",
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+            "score": 0.5646401643753052,
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+              "text": "Dyslipidemia status\nNormolipidemia 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)\nOverall 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",
+              "title": "2008 - The Common P446L Polymorphism in GCKR Inversely.pdf",
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+            "id": "60c771fb-a2fa-5f19-a13c-e4086864bcd5",
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+            "metadata": {
+              "text": "The most characteristic lipoprotein abnormality in patients with \ndiabetes, especially type 2, is elevated triglyceride, i.e. VLDL, reduced HDL, \nand smaller dense LDL. This lipoprotein profile is sometimes referred to as \ndiabetic dyslipidemia. Moreover, in conjunction with obesity, and insulin \nresistance this lipoprotein profile constitutes part of the \"polymetabolic \nsyndrome\". The primary lipoprotein abnormality is hypertriglyceridemia .",
+              "title": "2004 - Diabetes Genes a.pdf",
+              "version": "v0",
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+            "metadata": {
+              "text": "Hyperlipidemia 63 (23%) 100 (38%) < 0.001c\nDiabetes 66 (24%) 106 (40%) < 0.001c\nTC (mmol/L) 4.36  0.55 4.37  1.07 0.832b,d\nTG (mmol/L) 1.01 (0.77~1.28) 1.35 (1.00~1.92) < 0.001d,e\nHDL-C (mmol/L) 1.26 (1.13~1.42) 1.10 (0.94~1.34) < 0.001d,e\nLDL-C (mmol/L) 2.57  0.36 2.43  0.88 0.017b,d\nFBG (mmol/L) 4.71 (4.35~5.15) 5.84 (5.31~6.87) < 0.001e\nPBLs counts (109/L) 5.30 (4.60~6.29) 6.58 (5.33~7.92) < 0.001e\nPBLs classifications\n(PBMCs %)40.31  8.11 34.48  10.16 < 0.001b",
+              "title": "2018 - Genomic 5-mC contents in peripheral.pdf",
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+            "score": 0.5394852322186601,
+            "metadata": {
+              "text": "lipid traits as (lipid follow-up lipid baseline ) / lipid baseline .\nDyslipidemia/abnormal lipid levels were defined\naccording to the thresholds used in clinical practice\nguidelines [ 19]: (1) TC 5.1 mmol/l; TG 1.1 mmol/l;\nand LDL-C 3.4 mmol/l in children; (2) TC 5.1 mmol/l;\nTG1.4 mmol/l; and LDL-C 3.4 mmol/l in adolescents;\n(3) TC 5.2 mmol/l; TG 1.7 or 1.97 mmol/l; and LDL-\nC1.8 or 2.6 mmol/l in adults or patients with T2D.\nIn the two cohorts of adult women, cIMT was mea-",
+              "title": "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",
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+              "text": "dyslipidemia. It also lowered in ammatory biomarkers (CRP and PAI - 1) associated",
+              "title": "2012 - Systems Biology Approaches to Nutrition.pdf",
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+              "text": "usually associated with reduced HDL cholesterol and small dense LDL. \nBiliary \ncholesterol \n+ \nBile acids \nBlood vessel \nFigure 3. HDL metabolism: HDL production requires addition of lipid \nto small, nascent particles. This lipid arrives via hydrolysis of VLDL and \nchylomicrons with transfer of surface lipids (phospholipid PL, and free \ncholesterol, FC) via the actions of phospholipid transfer protein (PL TP). A \nsecond pathway is via effiux of cellular free cholesterol (FC), a process",
+              "title": "2004 - Diabetes Genes a.pdf",
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+            "id": "cba6153e-0a7f-540c-897b-40cbf9284ea9",
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+              "text": "shift in the composition of the lipoprotein particle from one de  ned as VLDL to",
+              "title": "2012 - Systems Biology Approaches to Nutrition.pdf",
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+        "choices": [
+          {
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+            "message": {
+              "content": "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].",
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+            }
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+        ],
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+        "vector_search_results": [
+          {
+            "id": "7bab87cd-7bd1-525e-98fe-14e9c51f617a",
+            "score": 0.567793395054735,
+            "metadata": {
+              "text": "oxidoreductase MitochondriaF29C4.2 IV Cytochrome",
+              "title": "2007 - Functional genomic approach to identify novel genes.pdf",
+              "version": "v0",
+              "chunk_order": 57,
+              "document_id": "6d78d3b0-1754-5f4c-8dbc-c972c68d21cd",
+              "extraction_id": "3aebacd5-b198-5144-8fe3-34ac09f6e1e8",
+              "associatedQuery": "What is cytochrome?"
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+          {
+            "id": "a6bd961a-ed80-5266-8519-4399a1c7a6ba",
+            "score": 0.561569230603255,
+            "metadata": {
+              "text": "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-\nbrane and plays an important role in biochemical synthesis of ATP . It functions to catalyze electrons to trans-",
+              "title": "2017 - Gene-based genome-wide association study identified 19p13.3 for lean body mass.pdf",
+              "version": "v0",
+              "chunk_order": 40,
+              "document_id": "970711f0-abe2-5129-8374-b7bb24c58b9e",
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+            "score": 0.5515203475952148,
+            "metadata": {
+              "text": "Chapter 36 Directed Protein Evolution  653\n3.1.9. SHIPREC\nCytochromes are proteins that contain heme groups and are responsible for \nthe transport of electrons. P450 is a family of membrane-bound cytochromes \nwith an absorption maximum of 450 nm when complexed with CO. One of the \nmajor roles of the cytochrome P450 system is the detoxification of harmful \nsubstances.\nSieber et al. (23) produced hybrids of two cytochromes, which share only",
+              "title": "2008 - Gene Expression Profiling.pdf",
+              "version": "v0",
+              "chunk_order": 4188,
+              "document_id": "59f3b969-089b-5258-93ad-892dbc9ffa9c",
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+          {
+            "id": "cf86caa8-56c6-58f5-8233-e1de07f1ef37",
+            "score": 0.5502725511626878,
+            "metadata": {
+              "text": "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",
+              "title": "2007 - Functional genomic approach to identify novel genes.pdf",
+              "version": "v0",
+              "chunk_order": 59,
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+              "extraction_id": "3aebacd5-b198-5144-8fe3-34ac09f6e1e8",
+              "associatedQuery": "What is cytochrome?"
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+          },
+          {
+            "id": "53fee260-155f-58c5-810e-ff890292a46b",
+            "score": 0.5452050566673279,
+            "metadata": {
+              "text": "Process\n2.9 2.9 25.4 gi 149058974 rCG44669 (cytochrome c\noxidase, subunit VIIc;Cox7c)1.19 0.2121 1.35 1.42 0.05 1.30 1.26 0.0480 1.26 unclassied\n29.6 29.7 56.0 gi 149016520 rCG50966 (3-oxoacid-CoA\ntransferase 1(OXCT1/SCOT)1.12 0.3615 1.27 1.08 0.46 1.23 1.33 <0.0001 1.12 metabolism: ketone\nmetabolism\n60.9 60.9 67.6 gi 116242506 stress-70 protein,\nmitochondrial 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",
+              "title": "2012 - Quantitative proteomic analysis reveals novel mitochondrial targets.pdf",
+              "version": "v0",
+              "chunk_order": 104,
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+          {
+            "id": "7ff5d5ed-f6a9-52ab-8994-4bdb61161f4f",
+            "score": 0.539888056509976,
+            "metadata": {
+              "text": "413\nTable 2\nGene ontology\nDatabase: molecular function name: Cytochrome  c oxidase activity ID:GO:0004129\nC = 16 O = 2 E = 0.12 R = 17.06  rawP  = 0.0060  adjP  = 0.0590\nIndex User IDGene  \nsymbol Gene namesEntrez  \ngene Ensemble\n1 ILMN_2657141 Surf1 Surfeit gene 1 20930 ENSMUSG00000015790\n2 ILMN_1254971 Cox6b1 Cytochrome c oxidase, \nsubunit VIb polypeptide110323 ENSMUSG00000036751\nDatabase: molecular function Name: NADH dehydrogenase activity ID:GO:0003954",
+              "title": "2017 - Systems Genetics Analysis to Identify the Genetic Modulation of a Glaucoma-Associated Gene.pdf",
+              "version": "v0",
+              "chunk_order": 111,
+              "document_id": "67e804db-8127-5938-8d7f-a5918cdf4f86",
+              "extraction_id": "d83136ee-cf42-5167-902b-470a6e0b2d3c",
+              "associatedQuery": "What is cytochrome?"
+            }
+          },
+          {
+            "id": "cf251057-96a1-581c-b60a-63a04d845511",
+            "score": 0.5393913984298706,
+            "metadata": {
+              "text": "F42A9.5 cyp-33E2, cytochrome P450 family 13.81 (  0.49) 118 0.0010\nC47E12.2 Mitochondrial ADP/ATP carrier protein 16.00 (  0.78) 136 < 0.0001\nF21D5.8 Mitochondrial 28S ribosomal protein S33 15.95 (  0.99) 136 < 0.0001\nC33A12.1 NADH: ubiquinone oxidoreductase 16.28 (  1.05) 139 0.0003\nZK809.3 NADH: ubiquinone oxidoreductase 23.46 (  1.14) 200 < 0.0001\nY57G11C.12 nuo-3, NADH: ubiquinone oxidoreductase 20.71 (  1.18) 177 < 0.0001",
+              "title": "2007 - Functional genomic approach to identify novel genes.pdf",
+              "version": "v0",
+              "chunk_order": 99,
+              "document_id": "6d78d3b0-1754-5f4c-8dbc-c972c68d21cd",
+              "extraction_id": "47e612a2-c181-5c19-8b1c-c6aaa107e88a",
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+            }
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+              "text": "Y66A7A1 100 52 33 4 0 9.00 (  0.29) 0.0572 210\nY71H2_388.c PP2A regulatory subunit (cytochrome C oxidase subunit) 100 82 48 2 0 5.57 (  0.20) < 0.0001 130\nF54D8.2 Cytochrome c oxidase subunit Vla 100 70 41 22 3 5.62 (  0.27) < 0.0001 131\nF56D2.1 Mitochondrial processing peptidase 100 55 17 3 0 4.46 (  0.20) 0.4303 104\nK04G7.4 Nuo-4, NADH: ubiquinone oxidoreductase 100 78 55 4 0 5.06 (  0.23) < 0.0001 118\nT20H4.5 Ubiquinone Fe-S protein 100 99 89 45 2 7.58 (  0.18) < 0.0001 177",
+              "title": "2007 - Functional genomic approach to identify novel genes.pdf",
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+              "text": "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 \nIII). The mutated cytochrome b  loses the ability to accept incoming",
+              "title": "2014 - Evidence for the presence of somatic mitochondrial DNA.pdf",
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+              "text": "c \n oxidase polypeptide Mitochondria\nK08F11.4 year-1 IV Tyrosyl-tRNA synthetase, predicted to be mitochondrial MitochondriaE04A4.7 IV Cytochrome  \nc \nMitochondria",
+              "title": "2007 - Functional genomic approach to identify novel genes.pdf",
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+              "content": "To differentiate between X and Y DNA with respect to DNA tracing and determining quantitative trait loci (QTLs), consider the following points:\n\n1. **Recombination Rates**:\n   - 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].\n   - 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].\n\n2. **QTL Mapping**:\n   - 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].\n   - 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].\n\nIn 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].",
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+              "text": "While most of the Y chromosome does not undergo\nrecombination, the recombination rate of the X chromosomeis slower than that of the autosomes. This has important\nconsequences on the detection of significant QTLs. For a\ncomprehensive view of these issues, see(43).\n9.Probe hybridization artifacts\nWhen several probes are available for the same gene, it is\nnot uncommon to observe a difference in the mapping results",
+              "title": "2009 - eQTL analysis in mice and rats.pdf",
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+              "text": "8 QTL Mapping  \n \nAllelic variation exists among natural populations and inbred strains, and this is \nreflective of the segregation of quantitative tr ait loci (QTLs) [96]. QTLs are stretches of \nDNA 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 \nresearchers to map different quantitative traits back to the genomic location involved in the regulation of these phenotypes.",
+              "title": "2015 - Genetic Control of Survival and Weight Loss during Pneumonic Burk.pdf",
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+              "text": "8 QTL Mapping  \n \nAllelic variation exists among natural populations and inbred strains, and this is \nreflective of the segregation of quantitative tr ait loci (QTLs) [96]. QTLs are stretches of \nDNA 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 \nresearchers to map different quantitative traits back to the genomic location involved in the regulation of these phenotypes.",
+              "title": "2015 -Emery- Genetic Control of Survival and Weight Loss during Pneumonic Burk.pdf",
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+            "metadata": {
+              "text": "The basic  pr emise  of QTL  an alysis  is simple  (Ph illips  and Belknap,\n2002 ) . First,  one must  meas  ure a speci  c phen  otype  within  a popul  ation.\nNext, the population must be genotyped at a hundred or more marker loci186 Boehm II et al.",
+              "title": "2006 - From_gene_to_behavior_and_back_again_new.pdf",
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+            "metadata": {
+              "text": "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,\nthat even in a single QTL region isolated in a congenic strain, it is possible that\nthere is more than one allele that aects the phenotype. So, you have a fair pointabout the challenges and complexities of QTL analysis.\nKoolhaas: There are dierent questions underlying both approaches. The QTL",
+              "title": "2005 - quantitative-trait-locus-analysis-of-aggressive-behaviours-in-mi.pdf",
+              "version": "v0",
+              "chunk_order": 86,
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+              "associatedQuery": "How does one tell the difference between X and Y DNA, with respect  to DNA tracing and determining QTLs?"
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+          {
+            "id": "1b2de424-be9f-572d-bd62-dc2ecd92192b",
+            "score": 0.6653512914583546,
+            "metadata": {
+              "text": "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,\nthat even in a single QTL region isolated in a congenic strain, it is possible that\nthere is more than one allele that aects the phenotype. So, you have a fair pointabout the challenges and complexities of QTL analysis.\nKoolhaas: There are dierent questions underlying both approaches. The QTL",
+              "title": "2005 -Broadkin- quantitative-trait-locus-analysis-of-aggressive-behaviours-in-mi.pdf",
+              "version": "v0",
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+            "id": "f72795a1-66c3-5a98-84bc-b085e8008073",
+            "score": 0.6629081174244741,
+            "metadata": {
+              "text": "through analysis of line crosses, quantitative trait loci (QTL) mapping, and verification\nof 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.",
+              "title": "2009 - Experimental_Evolution.pdf",
+              "version": "v0",
+              "chunk_order": 2219,
+              "document_id": "34821353-1b74-5ee2-ac39-66dd46f145bf",
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+            "id": "31a32dc5-81ac-52ba-a463-c61e293f21e5",
+            "score": 0.6629027329145245,
+            "metadata": {
+              "text": "through analysis of line crosses, quantitative trait loci (QTL) mapping, and verification\nof 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.",
+              "title": "2009 - Garland_and_Rose_Experimental_Evolution.pdf",
+              "version": "v0",
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+            "metadata": {
+              "text": "genetic background.\nGene identification of QTL should be distinguished from identification of the quanti-\ntative trait nucleotide (QTN). The latter is a daunting task, since SNPs are so frequent.\nFinal 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\nhomologous recombination and reproducing the phenotype of the donor strain.",
+              "title": "2005 - quantitative-trait-analysis-in-the-investigation-of-function-and.pdf",
+              "version": "v0",
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+              "document_id": "dac1c73c-0b5f-5a54-bb12-7e8b654009c0",
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+            "id": "985378d7-e164-581b-ac1c-97bbcda9c06f",
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+            "metadata": {
+              "text": "measuring correlations between genetic markers and phenotypic \ntraits in a population. Individuals are scored for their  phenotype      for \na particular trait, and their genotype at a marker. If there is a differ-\nence in mean phenotype between those individuals with one geno-\ntype at a particular locus compared with the other, than we can infer \nthat there is a QTL linked to that marker [ 40 ,  153 ]. 2.3  Analysis and QTL \nMappingDavid G. Ashbrook and Reinmar Hager",
+              "title": "2016 - Social interactions and indirect genetic effects on complex juvenile and adult traits.pdf",
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+              "content": "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:\n\n1. **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].\n\n2. **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].\n\n3. **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].\n\n4. **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].\n\n5. **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].\n\nIn 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].",
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+              "text": "ferentiation in animals reared at male- and\nfemale-producing temperatures (Fernandino\net al., 2011).\nFrom a pure experimental point of view,\nthere are several potential sources of environ-\nmental inuences that need to be under con-\ntrol in order to avoid confounding results when\nstudying gene expression levels (Hodgins-Davis\nand Townsend, 2009; Table 8.3). One of them is\neffect of the developmental environment, typi-\ncally in the range of weeks to years. Size is pos-",
+              "title": "2012 - Functional genomics research in aquaculture principles and general approaches.pdf",
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+            "metadata": {
+              "text": "the fertilization rate (Table 1). There was an interaction between the two factors (strain and",
+              "title": "2017 - Identification of quantitative trait loci associated with the susceptibility of mouse spermatozoa to cryopreservation.pdf",
+              "version": "v0",
+              "chunk_order": 40,
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+              "text": "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.",
+              "title": "2009 - Garland_and_Rose_Experimental_Evolution.pdf",
+              "version": "v0",
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+          {
+            "id": "833bb510-694a-5646-960e-66b5e38ef609",
+            "score": 0.5670874883856624,
+            "metadata": {
+              "text": "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.",
+              "title": "2009 - Experimental_Evolution.pdf",
+              "version": "v0",
+              "chunk_order": 2389,
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+          {
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+              "text": "environment interactions, particularly the contribution of environmen-\ntal factors in utero (Burmeister, McInnis, & Zllner, 2008; Henriksen,\nNordgaard, & Jansson, 2017), and these limitations in turn hinder the\ndevelopment of a mechanistic understanding of aetiology. Here, we\ndissect the impact of gene prenatal environmental interactions on\ncocaine responsiveness of adult male and female mice from the BXD\nrecombinant inbred panel.\nEarly life stressors, including prenatal stress (PNS), are important",
+              "title": "2019 - Discovery of early life stress interacting and sex-specific quantitative trait loci impacting cocaine responsiveness.pdf",
+              "version": "v0",
+              "chunk_order": 10,
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+            "id": "5db85ce4-25a9-5aba-b5ca-ab394fb0394f",
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+              "text": "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\nmethylation, in mediating environmentally-induced reg-\nulation 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.",
+              "title": "2011 - Evidence for widespread changes in promoter.pdf",
+              "version": "v0",
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+              "text": "as well as the intrinsic fertilizing ability of the strain. Therefore, the results of the QTL analysis \nbased on the fertilization rates of frozen thawed spermatozoa might have reflected the 220 \ncumulative effect of these two factors. T o exclude the possible background strain effects, we \ncalculated the ratio of the fertilization rate of frozen thawed spermatozoa per that of fresh \nspermatozoa in individual male mice (designated here as relative fertilization rate ). As shown",
+              "title": "2017 - Identification of quantitative trait loci associated with the susceptibility of mouse spermatozoa to cryopreservation.pdf",
+              "version": "v0",
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+            "score": 0.5544293092021143,
+            "metadata": {
+              "text": "male ; Relative fertilization  rate (%) = (Fertilization rate with f rozen spermatozoa  \n(%)/Fertilization rate with f resh spermatozoa  (%))  100  (n = 6 for each strain) .  \n \nFig. 2. Genome -wide interval mapping for suggestive QTLs affecting the fertilization rate 515 \nusing frozen thawed spermatozoa.  (A) Mapping based on the actual fertilization rates. (B) \nMapping based on the relative fertilization rates. Critical intervals were selected based on peak",
+              "title": "2017 - Identification of quantitative trait loci associated with the susceptibility of mouse spermatozoa to cryopreservation.pdf",
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+              "text": "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.\nA ROLE FOR EPIGENETICS IN THE LINK BETWEEN MATERNAL\nCARE AND BEHAVIORAL OUTCOMES IN ANIMAL MODELS",
+              "title": "2011 - Using animal models to disentangle the role of genetic, epigenetic, and environmental influences on behavioral outcomes associated with maternal anxiety and depression.pdf",
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+              "content": "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:\n\n1. **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].\n\n2. **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].\n\n3. **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].\n\nIn 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.",
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+              "text": "economic status of a population, for example childhood nutrition status and the disease \nenvironment etc.21 Rare are the stud ies that unveil the relation between height decline \nand bone loss. A study performed by Galloway et al. on 1,024 subjects (735 women and \n289 men) evaluated the correlation between height decline and bone loss with ageing. \nTheir findings show that bone mine ral density (BMD) plays the largest role in \ndetermining annual height reduction.22",
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+              "text": "economic status of a population, for example childhood nutrition status and the disease \nenvironment etc.21 Rare are the stud ies that unveil the relation between height decline \nand bone loss. A study performed by Galloway et al. on 1,024 subjects (735 women and \n289 men) evaluated the correlation between height decline and bone loss with ageing. \nTheir findings show that bone mine ral density (BMD) plays the largest role in \ndetermining annual height reduction.22",
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+              "text": "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",
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+              "text": "Dietary factor s deserve special attention as an  environmental factor that interacts with \ngenetics because we are exposed to our diet  every day and we  can modify it to our own benefit. \nThe findings from several  Ca intervention trials in children and adolescents demonstrated that \nthere is a large  variability in the acquisition of bone mass , despite the control of  age range and \npubertal maturation  of part icipants.(28) Weaver et al.(102) conducted a 3 -week long, controlled",
+              "title": "2019 - Novel Genetic Loci Control L5 Vertebral Trabecular Bone and the Response to Low Calcium Intake in Growing BXD Recombinant Inbred Mice.pdf",
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+              "text": "rapidly than Paleolithic people andreaching both maximal adult height\nandsexual maturity earlier. Wehave earlier speculated thatcompression\nofthegrowth history predisposes tohigher blood pressure during adoles-\ncence andincreases theriskofhypertension inadulthood [57] .\nArecent interesting series ofstudies byBarker andcolleagues hasfor-\nwarded theargument thatsome fraction ofthepredisposition tohyperten-\nsionandNIDDM maybeprogrammed inutero bylowbirth weight. Several",
+              "title": "1998 - Type II Diabetes, Essential Hypertension, and Obesity as Syndromes of Impaired Genetic Homeostasis The Thrifty Genotype Hypothesis Enters the 21st Century.pdf",
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+              "text": "diets are likely to vary in composition by batch, season and \nvendor. Variability in non-nutritive dietary components, \nsuch as soluble fibre content and plant- derived phyto-\nestrogens, affects the progression of DIO and metabolic  \ndisease, even affecting behavioural traits151,152.\nAnother consideration is that humans consume \n~30% of their daily calories from fat. This fat intake is \nremarkably consistent across age and BMI153 and lower \nthan the 40% to 60% calories from fat used in many",
+              "title": "2018 - Animal models of obesity.pdf",
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+              "text": "several factors such as age, nutritional status, overall health and geographic location, all of which in  uence the diet of",
+              "title": "2012 - Systems Biology Approaches to Nutrition.pdf",
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+              "text": "4  Hypertension   November 2020\nestimated the relative influence of genetic and environmental \nfactors 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.\nContributions to the total phenotypic variances of SBP and",
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+              "text": "individuals. Augmentation index was in reverse correlation with height, in addition it \nwas observed that taller participants had less prevalence of hypertension and use of \nantihypertensive drugs suggesting th e beneficial role of height in estimating \ncardiovascular risks (159). In a study done on patients with end stage renal disease \naugmentation index wa s found to negatively correlate with body height, and it was",
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+              "text": "individuals. Augmentation index was in reverse correlation with height, in addition it \nwas observed that taller participants had less prevalence of hypertension and use of \nantihypertensive drugs suggesting th e beneficial role of height in estimating \ncardiovascular risks (159). In a study done on patients with end stage renal disease \naugmentation index wa s found to negatively correlate with body height, and it was",
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+              "content": "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].",
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+              "text": "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 .\nDownloaded from http://ahajournals.org by on July 10, 2023",
+              "title": "2015 - Basic Concepts and Potential Applications of Genetics and Genomics for Cardiovascular and Stroke Clinicians.pdf",
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+              "text": "FIGURE 3. Telomere arrays of chicken and human chromosomes:\nthe chicken genome contains more telomere sequence than the human",
+              "title": "2003 - Telomeres in the chicken genome stability and chromosome ends.pdf",
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+              "text": "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 \ncell division, producing 4 gametes, each of which has 1 set \nof chromosomes (for humans, 23 unpaired chromosomes). Recombination occurs during meiosis.\nMendelian diseaseSame as monogenic disease. Named",
+              "title": "2015 - Basic Concepts and Potential Applications of Genetics and Genomics for Cardiovascular and Stroke Clinicians.pdf",
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+              "text": "some set. Therefore, chromosome morphology sup-ports the designation of two separate genera [5]. \nSex Chromosomes  \nSeveral studies have revealed high degrees of \nhomology among autosomal chromosomes of bovids with similar banding patterns and gene order among the chromosome arms of ca ttle, river buffalo, sheep, \nand goats [14, 15]. Bovid sex chromosomes, unlike the highly similar autosomal chromosomes, share a slightly more complex rearrangement of sequences",
+              "title": "2010 - Water Buffalo Genome Science Comes of Age.pdf",
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+              "text": "14\n Mice share an anatomy, physiology, and genome that is similar, though not \nidentical, to humans (May a nd Lutjen-Drecoll 2002; Smith 2002; Emes, Goodstadt et al. \n2003; Huang, Winter et al. 2004). Mice and hum ans also share a su sceptibility to many \nsimilar diseases. As an experimental genetic platform for vertebrates, tools for studying \nand manipulating the mouse genome are near ly, if not completely, unparalleled",
+              "title": "2009 - Genetic pathways of Lyst and exfoliation syndrome.pdf",
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+              "text": "DELANY ET AL. 920\nTABLE 1. Cytogenetic and telomere characteristics of vertebrate animal species (in vivo)\nOrganism Terminal\nreference 2n/no. of telomere Telomere\n(maximum longevity) Telomeres array sizes shortening\nRainbow trout 5860/116120 20 kb Unknown\nOncohynchus mykiss\nLejnine et al., 1995(20 yr)\nAfrican clawed toad 36/72 1050 kb No\nXenopus laevisBassham et al., 1998(15 yr)\nLaboratory mouse 40/80 50150 kb No\nMus musculusKipling and Cooke, 1990(2 yr)\nWild mouse 40/80 525 kb Yes",
+              "title": "2003 - Telomeres in the chicken genome stability and chromosome ends.pdf",
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+              "text": "A human has 23 pairs of chromosomes, i.e. 46 in total. In each pair one\nchromosome has been inherited from the mother and the other from the father.\nThe chromosomes in a pair are said to be homologous. They have the same\ngenes at the same loci, but they may have different variants, different so called\nalleles, of the gene. Recall the eye color example from standard high school\ntexts on genetics. We inherit one eye color allele from each parent, either a",
+              "title": "2005 - Numerical Algorithms for Mapping of Multiple Quantitative Trait Loci in Experimental Populations.pdf",
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+              "text": "A human has 23 pairs of chromosomes, i.e. 46 in total. In each pair one\nchromosome has been inherited from the mother and the other from the father.\nThe chromosomes in a pair are said to be homologous. They have the same\ngenes at the same loci, but they may have different variants, different so called\nalleles, of the gene. Recall the eye color example from standard high school\ntexts on genetics. We inherit one eye color allele from each parent, either a",
+              "title": "2005 -Ljungberg- Numerical algos for Multi QTL.pdf",
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+              "text": "and zebra sh (http://www.alliancegenome.org, last access:\n3 January 2018).\n3 The mouse as a model animal for livestock\nresearch\nMice are mammals, sharing 92 to 95 % of protein cod-\ning genes with humans and other mammalian livestock\nspecies, such as cattle (Elsik et al., 2009), pigs (Humphray\net al., 2007), sheep (Iannuzzi et al., 1999), and goats\n(Schibler et al., 1998). The mouse genome is structured\ninto 19 autosomes and the sex chromosomes. The mouse",
+              "title": "2018 - Invited review Genetic and genomic_ xmltexbreak_ mouse models for livestock research.pdf",
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+              "text": "Figure 3: Comparison of human and baboon chromosomes. (A)\nConservation of microsatellite marker order for orthologs human 12and baboon 11. (B) C hromosome inversion between orthologs hu-\nman 4 and baboon 5. The y-axis indicates chromosome length incentimorgans. Microsatellite markers identi ed in human have\nidenti cation numbers that begin with D,and microsatellite\nmarkers identi ed in baboon have identi cation numbers that begin\nswith Pha.\nFigure 2: Papio hamadryas anubis (Olive baboon)",
+              "title": "2013 - Baboons as a Model to Study Genetics and Epigenetics of Human Disease.pdf",
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+              "content": "The proper functioning of the brain is ensured by several factors:\n\n1. **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].\n\n2. **Energy Metabolism**: Energy metabolism plays a crucial role in the fate of adult neural stem cells, which is essential for maintaining brain function [8].\n\n3. **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].\n\nThese factors collectively ensure that the brain works effectively.",
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+              "text": "ARTICLE\n nATuRE C ommunICATIons | 3:1079 | DoI: 10.1038/ncomms2086 | www.nature.com/naturecommunications\n 2012 Macmillan Publishers Limited. All rights reserved.Received 8 may 2012 | Accepted 23 Aug 2012 | Published 25 sep 2012 DOI: 10.1038/ncomms2086\nThe mammalian brain consists of distinct parts that fulfil different functions. Finlay and  \nDarlington have argued that evolution of the mammalian brain is constrained by",
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+              "text": "ARTICLE\n nATuRE C ommunICATIons | 3:1079 | DoI: 10.1038/ncomms2086 | www.nature.com/naturecommunications\n 2012 Macmillan Publishers Limited. All rights reserved.Received 8 may 2012 | Accepted 23 Aug 2012 | Published 25 sep 2012 DOI: 10.1038/ncomms2086\nThe mammalian brain consists of distinct parts that fulfil different functions. Finlay and  \nDarlington have argued that evolution of the mammalian brain is constrained by",
+              "title": "2012 - Genetic architecture supports mosaic brain evolution and independent brain\u2013body size regulation_(1).pdf",
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+              "text": "Daniel H. Geschwind, Michael J. Hawrylycz, Matthew W. State, Stephan J. Sanders,\nPatrick F. Sullivan, Mark B. Gerstein , Ed S. Lein , James A. Knowles , Nenad Sestan \nINTRODUCTION: The brain is responsible\nfor cognition, behavior, and much of what\nmakes us uniquely human. The development\nof the brain is a highly complex process, and\nthis process is reliant on precise regulation of\nmolecular and cellular events grounded in the\nspatiotemporal regulation of the transcrip-",
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+              "text": "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.",
+              "title": "2003 - Imaging genomics.pdf",
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+              "text": "brain to prevent theapoptosis of irreplaceable neurons, even in the",
+              "title": "2008 - The Aging Brain.pdf",
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+              "text": "Funding\nFunding from the BBSRC, EPSRC, ESRC and MRC is gratefully\nacknowledged.\nReferences\n1 Brayne C (2007) The elephant in the room: healthy brains in later life, epidemiology and\npublic health. Nat Rev Neurosci ,8, 233239.\n2 Gow J, Gilhooly M (2003) Risk Factors for Dementia and Cognitive Decline . Glasgow: NHS\nHealth Scotland.\n3 House of Lords (2005) Ageing: scientific aspects. London: The Stationery Office.\n4 Stern PC, Carstensen LL (2000) The Aging Mind. Washington, DC: National Academy Press.",
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+              "text": "1124 the brain. Nature Reviews Neuroscience. Nat Rev Neurosci; 2012. pp. 225239. \n1125 doi:10.1038/nrn3209\n1126 75. van Praag X, Fleshner M, Schwartz MW, Mattson MP. Exercise, energy intake, \n1127 glucose homeostasis, and the brain. J Neurosci. 2014;34: 1513915149. \n1128 doi:10.1523/JNEUROSCI.2814-14.2014\n1129 76. Rafalski VA, Brunet A. Energy metabolism in adult neural stem cell fate. Progress in \n1130 Neurobiology. Prog Neurobiol; 2011. pp. 182203. \n1131 doi:10.1016/j.pneurobio.2010.10.007",
+              "title": "2021 - System genetics in the rat HXBBXH family identifies Tti2 as a pleiotropic quantitative trait gene for adult hippocampal neurogenesis and serum glucose.pdf",
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+              "text": "1124 the brain. Nature Reviews Neuroscience. Nat Rev Neurosci; 2012. pp. 225239. \n1125 doi:10.1038/nrn3209\n1126 75. van Praag X, Fleshner M, Schwartz MW, Mattson MP. Exercise, energy intake, \n1127 glucose homeostasis, and the brain. J Neurosci. 2014;34: 1513915149. \n1128 doi:10.1523/JNEUROSCI.2814-14.2014\n1129 76. Rafalski VA, Brunet A. Energy metabolism in adult neural stem cell fate. Progress in \n1130 Neurobiology. Prog Neurobiol; 2011. pp. 182203. \n1131 doi:10.1016/j.pneurobio.2010.10.007",
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+              "text": "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.",
+              "title": "2011 - A genome-wide association study of aging.pdf",
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+              "text": "of complex traits. It has been said that The brain is the chief architect, \norchestrator and driver of behavior; behavior, in turn, is the principal function of \nthe brain (Gomez -Marin et al., 2014, p. 1455) , and therefore to understand one \nwe need to understand the other. The brain and the behaviours that it causes are \nhighly complex traits influen ced by many factors including genes (Hager et al., \n2012; Hitzemann et al., 2013; McCarroll and Hyman, 2013) , environment (Carola",
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+              "content": "Our brains maintain emotions through a complex interplay of various brain regions and neural circuits. Key areas involved include:\n\n1. **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].\n\n2. **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].\n\n3. **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].\n\n4. **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].\n\n5. **Nucleus Accumbens and Ventral Striatum**: These areas participate in reward responses and positive emotional states [10].\n\nOverall, 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.",
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+              "text": "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.",
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+              "text": "Importantly, regions of the brain responsible for emotional regulation, executive \nfunctioning, and their consequential behavioral outcomes are sensitive to \nin  ammation  [  22  ] . The extended limbic system, primitively responsible for fear and \npleasure responses, stress, memory, and learning, has been shown to be modulated \nby immune signaling. Early work established that there is a high density of IL-1 \nreceptors in the dentate gyrus and pyramidal cell layer of the hippocampus, the",
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+              "text": "the midbrain structures are implicated in cardiacresponses to social stress (Wager et al, 2009 ).\nIt is now evident that these same brain regions\nare involved in emotion regulation. Furthermore,\nthe circuitry involved in physical pain and plea-sure appears to be activated by positive and\nnegative socially induced emotion (Takahashi\net al, 2009 ). The possibility therefore arises that\npositive well-being may be embodied in the acti-\nvation of neural circuitry in a reciprocal fashion",
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+              "text": "723732.\nEtkin, A., Egner, T., Peraza, D. M., Kandel, E. R., and\nHirsch, J. (2006). Resolving emotional conict: a rolefor the rostral anterior cingulate cortex in modulatingactivity in the amygdala. Neuron, 51 , 871882.\nFales, C. L., Barch, D. M., Rundle, M. M., Mintun, M. A.,\nSnyder, A. Z. et al (2008). Altered emotional inter-ference processing in affective and cognitive-controlbrain circuitry in major depression. Biol Psychiatry,\n63, 377384.\nFanselow, M. S. (2000). Contextual fear, gestalt mem-",
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+              "text": "expression of emotional behavior. Sensory inputs with emotional components are \ntransmitted to the amygdala where they are processed and fu rther relayed to other regions \nto modulate autonomic and behavioral responses, and to form emotional memories \n(LeDoux, 2000; Rosen, 2004). As a neural substrate of emotionality, many \nneuropsychiatric disorders have been associated with structural changes i n the amygdala. \nIndividuals with genetically predisposed susceptibility to anxiety and depression have",
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+              "text": "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.",
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+              "text": "et al, 1995 ; Scher et al, 2005 ), (2) are less\neasily distracted from negative emotion process-\ning (Ellenbogen et al, 2002 ; Lyubomirsky et al,\n1998 ; Siegle et al, 2002 ; Wenzlaff and Bates,\n1998 ), (3) show heightened stress hormone lev-\nels such as cortisol that may have deleterious\neffects on the brain (Sapolsky, 2000 ), and (4)",
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+              "text": "et al, 2000 ). Once activated, the amygdala sets in\nmotion a cascade of responses to threat via pro-jections to the hypothalamus and prefrontal cor-tex (LeDoux, 1996 ). A neural region that is criti-\ncal for regulating responses to emotional stimuli\nis the ventrolateral prefrontal cortex (VLPFC;Hariri et al, 2002 ). Studies have shown that\nthe labeling of negative affective states activates\nthe right VLPFC and that increased activity inright VLPFC is associated with decreased activ-",
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+              "text": "tially participates in negative emotional states,although it also participates in positive emo-\ntional states (Zald, 2003 ). The amygdala orches-\ntrates the somatomotor, visceral, and cognitiveresponses to threats by virtue of its connections\nwith cortical brain structures above and hypotha-\nlamic and brainstem structures below it (LeDouxet al, 1990 ). The nucleus accumbens and ventral\nstriatum participate in reward responses and pos-\nitive emotional states. Other structures that are",
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+              "text": "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  \n    63.    Deak T et al (2005) Stress-induced increases in hypothalamic IL-1: a systematic analysis of \nmultiple stressor paradigms. Brain Res Bull 64:541556  \n    64.    Hennessy MB et al (2004) Responses of guinea pig pups during isolation in a novel",
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+              "text": "stressful events. In rats and mice, the secretion\nof hypothalamicpituitaryadrenal hormones istypically greater, and increased HPA activity\noften persists into adulthood (Koehl et al, 1999 ).\nBasal levels of adrenal hormones are more typ-ically reported to be normal in primates, but\nthere may be alterations in the diurnal hormone\nrhythm or an altered negative feedback, whichresults in protracted cortisol responses once acti-vated. Many effects of prenatal stress on brain",
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+              "text": "Y in depression and stress. Brain Research 1314, 194 205.\nMozhui, K., Karlsson, R.M., Kash, T.L., Ihne, J., Norcross, M., Patel, S., Farrell, M.R., Hill,\nE.E., Graybeal, C., Martin, K.P., Camp, M., Fitzgerald, P.J., Ciobanu, D.C., Sprengel,\nR., Mishina, M., Wellman, C.L., Winder, D.G., Williams, R.W., Holmes, A., 2010.\nStrain differences in stress responsivity are associated with divergent amygdala\ngene expression and glutamate-mediated neuronal excitability. The Journal of",
+              "title": "2014 - Genetic regulatory network analysis reveals that low density lipoprotein receptor-related protein 11 is involved in stress responses in mice.pdf",
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+              "text": "Neurobiology of Learning and Memory 185 (2021) 107509\n21.Introduction \nJames McGaugh was one of the first neuroscientists to point to the \nimportant influence of stress hormones on memory consolidation \n(McGaugh, Gold, Van Buskirk, & Haycock, 1975 ). He and others \nconsidered that hormones released by stressful experiences could \nenhance memory consolidation, indicating particularly the hormones \nepinephrine and glucocorticoids as memory modulators (McGaugh &",
+              "title": "2021 - Prefrontal cortex VAMP1 gene network moderates the effect of the early environment on cognitive flexibility in children.pdf",
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+              "text": "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 (\nhpA) axis and the autonomic \nnervous (ANS) system. Under chronic stress, the adrenal glands of mammals (including humans) release the steroid hormone cortisol. Cortisol acts by increas\n-",
+              "title": "2015 - Great Is Their Sin.pdf",
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+            "score": 0.6268789955043497,
+            "metadata": {
+              "text": "55:485494. \nHerman JP, Ostrander MM, Mueller NK, Figueiredo H (2005). Limbic system \nmechanisms of stress regulation: hypothalamo -pituitary -adrenocortical axis. Prog \nNeuropsychopharmacol Biol Psychiatry 29:1201 1213.  \nHerry C, Bach DR, Esposito F, Di Salle F, P errig WJ, Scheffler K et al. (2007). \nProcessing of temporal unpredictability in human and animal amygdala. J Neurosci \n27:5958 5966.  \nHitzemann R, Malmanger B, Cooper S, Coulombe S, Reed C, Demarest K et al. (2002).",
+              "title": "2009 - Multiscale Genomic Analysis of the Corticolimbic System_ Uncoveri (1).pdf",
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+              "text": "after restraint stress. Acute stress (like acute ethanol) activates the\nHPA axis and increases brain and circulating levels of GABAergic\nneuroactive steroids [1] as well as corticosterone, the major\ncorticosteroid synthesized in rodents from DOC. GABAergic\nneuroactive steroids have anxiolytic properties when administered\nsystemically [54,55]. Thus, we might have predicted that those\nstrains with higher basal DOC levels would have been less",
+              "title": "2011 - Genetic Analysis of the Neurosteroid Deoxycorticosterone and Its Relation to Alcohol Phenotypes Identification of QTLs and Downstream Gene Regulation.pdf",
+              "version": "v0",
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+            "id": "869496a0-2bff-569f-ba3a-03294ebf2e98",
+            "score": 0.6251489110335386,
+            "metadata": {
+              "text": "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)-\n3,21-dihydroxypregnan-20-one (tetrahydrodeoxycorticosterone,THDOC). These steroids are all elevated following acute stress[1] or ethanol administration in rats, and their elevation is blunted",
+              "title": "2011 - Genetic Analysis of the Neurosteroid Deoxycorticosterone and Its Relation to Alcohol Phenotypes Identification of QTLs and Downstream Gene Regulation.pdf",
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+            "metadata": {
+              "text": "plasticity and epigenetic regulation as a consequence of stress. Neuropharmacology\n62, 3 12.\nMcEwen, B.S., Nasca, C., Gray, J.D., 2016. Stress e ects on neuronal structure: hippo-\ncampus, amygdala, and prefrontal cortex. Neuropsychopharmacology 41, 3 .\nMozhui, K., Lu, L., Armstrong, W.E., Williams, R.W., 2012. Sex-speci c modulation of\ngene expression networks in murine hypothalamus. Front. Neurosci. 6, 63 .\nNavarro, V.M., 2013. Interactions between kisspeptins and neurokinin B. In: Kisspeptin",
+              "title": "2019 - Exploring the involvement of Tac2 in the mouse hippocampal stress response through gene networking.pdf",
+              "version": "v0",
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+              "content": "Corticosterone has several uses and effects as indicated in the provided context:\n\n1. **Amplification of Stress-Induced Changes**: Corticosterone importantly amplifies stress-induced changes [1].\n2. **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].\n3. **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].\n4. **Reduction of Delayed-Type Hypersensitivity Response**: Corticosterone dramatically reduces the delayed-type hypersensitivity response [6].\n5. **Measurement in Research**: Corticosterone levels can be measured in plasma samples using specific enzyme immunoassay kits, which is useful in various research settings [8].\n\nThese points highlight the diverse roles and applications of corticosterone in both physiological and research contexts.",
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+            }
+          }
+        ],
+        "created": 1727797578,
+        "model": "gpt-4o-2024-05-13",
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+            "id": "bf56c010-06d1-598e-81cf-2a2603f0a883",
+            "score": 0.6560750603675842,
+            "metadata": {
+              "text": "that corticosterone importantly amplies the SD induced changes",
+              "title": "2015 - Genetic dissection of sleep homeostasis.pdf",
+              "version": "v0",
+              "chunk_order": 191,
+              "document_id": "ed971d1f-e77e-566b-b549-81cd0038834a",
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+            "id": "76804170-ccb4-5e86-b9ba-533264556893",
+            "score": 0.6132207377885937,
+            "metadata": {
+              "text": "be used to predict corticosteroid response [200]. George etal.",
+              "title": "2019 - Leveraging genomics to uncover.pdf",
+              "version": "v0",
+              "chunk_order": 221,
+              "document_id": "5da46d3b-fa82-57f6-b3e5-c82784347881",
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+            "id": "63c085a5-ad08-5f28-b3be-3e62b7739183",
+            "score": 0.5948904994195413,
+            "metadata": {
+              "text": "we do not wish to dispute this viewpoint, it is interesting to note that anti-\nin  ammatory actions of CORT are most pronounced at high and supraphysiological \nconcentrations, whereas lower concentrations of CORT appear to have some \nimmune-potentiating effects (e.g.,  [  6  ] ). Whether these low-dose facilitation effects \nrelate more directly to the timing of CORT injection relative to cytokine measure-\nments, or represent differential tissue sensitivity to glucocorticoids, remains to be",
+              "title": "2013 - Neural-Immune Interactions in Brain Function and Alcohol Related Disorders.pdf",
+              "version": "v0",
+              "chunk_order": 1131,
+              "document_id": "78271275-3409-5fc7-bbdd-53c484178e0b",
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+            "id": "74ffa8aa-80dc-5e94-a373-c1af483d63f4",
+            "score": 0.5867375381745386,
+            "metadata": {
+              "text": "cortisol to the less bioactive cortisone (Seckl,1997 ). While the protection afforded by this bar-\nrier enzyme can be overwhelmed when cortisol\nlevels get very high, it likely functions effec-\ntively when cortisol remains within the normalrange (Campbell and Murphy, 1997 ). There is\nnow considerable interest in what types of events\nor other hormones might lower 11-HSD2 andthereby reduce the buffering benets it affords.\nOn example is elevated catecholamine levels,",
+              "title": "2010 - Candidate Gene and Genome-Wide Association Studies in Behavioral Medicine.pdf",
+              "version": "v0",
+              "chunk_order": 4617,
+              "document_id": "17637a6f-804e-50e4-9cf5-37318e17f15c",
+              "extraction_id": "f9be673c-af23-5d15-9087-37e818cf1a68",
+              "associatedQuery": "what is the use of corticosterone?"
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+          },
+          {
+            "id": "05e15635-52ee-5d80-9696-15cea22fb7e4",
+            "score": 0.575142400716019,
+            "metadata": {
+              "text": "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",
+              "title": "2012 - Genetic regulation of adult hippocampal neurogenesis A systems genetics approach using BXD recombinant inbred mouse strains.pdf",
+              "version": "v0",
+              "chunk_order": 118,
+              "document_id": "c54da858-9620-588e-8e41-76a960af2ff6",
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+              "associatedQuery": "what is the use of corticosterone?"
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+          {
+            "id": "5ccf3333-4675-577f-bfce-5d5e72fd7c3f",
+            "score": 0.5715612173080444,
+            "metadata": {
+              "text": "corticosterone dramatically reduce the delayed-type hypersensitivity response (Dhabhar andMcEwen, 1997 ,1999 ). Sorrells and Sapolsky\n(2007 ) have provided a thought provoking recent\nreview, contrasting the well-established anti-inammatory aspect of glucocorticoids, with the\nmounting evidence for their pro-inammatory\neffects both in the periphery and in the brain fol-lowing chronic exposure. This pattern of results\ndemonstrates that the acute stress response has",
+              "title": "2010 - Candidate Gene and Genome-Wide Association Studies in Behavioral Medicine.pdf",
+              "version": "v0",
+              "chunk_order": 5507,
+              "document_id": "17637a6f-804e-50e4-9cf5-37318e17f15c",
+              "extraction_id": "59789bd0-1ee6-51da-b2a1-94f847ff6c63",
+              "associatedQuery": "what is the use of corticosterone?"
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+          {
+            "id": "6d2d21e3-a1c5-5a11-a7ca-7fc643cf8b36",
+            "score": 0.5678959062068437,
+            "metadata": {
+              "text": "mature babies in order to stimulate lung maturation. As\nillustrated 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,",
+              "title": "2010 - Candidate Gene and Genome-Wide Association Studies in Behavioral Medicine.pdf",
+              "version": "v0",
+              "chunk_order": 4627,
+              "document_id": "17637a6f-804e-50e4-9cf5-37318e17f15c",
+              "extraction_id": "32902b1c-3a3a-5f5b-b651-a6fd0fa653a9",
+              "associatedQuery": "what is the use of corticosterone?"
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+            "id": "8f5142d0-8efa-5fe8-b7bf-46dea42ec444",
+            "score": 0.5626591444015503,
+            "metadata": {
+              "text": "first session. Approximately 50 microliters was collected into lithium heparin-coated tubes \nand then centrifuged for collection of plasma. Samples were stored at 80 degrees until \nready for processing.\nPlasma corticosterone concentration was measured with the use of the DetectX CORT \nEnzyme Immunoassay kit (ArborAssays K014-H5, Ann Arbor, MI, USA). Room \ntemperature plasma samples were diluted 1:450 in assay buffer and processed according to",
+              "title": "2019 - Strain differences in maternal neuroendocrine and behavioral responses to stress and the relation to offspring cocaine responsiveness..pdf",
+              "version": "v0",
+              "chunk_order": 28,
+              "document_id": "d29d8018-09a1-53d4-8f07-9dd110c79b39",
+              "extraction_id": "29253383-31a5-5fe1-8160-9d6091273a4d",
+              "associatedQuery": "what is the use of corticosterone?"
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+            "id": "7b2a0384-586f-582f-93da-8fd64dc76095",
+            "score": 0.5621330142021179,
+            "metadata": {
+              "text": "corticosterone in their drinking water as sole liquid source for seven days prior to i.p. treatment with\n4 mg /kg diisopropylurophosphate. Data are mean consumption per day s.e.m.\n4.2. Gene Expression in Response to Treatments\n4.2.1. IL1b\nWe observed wide variability in the e ect of CORT on expression of Il1b (Figure 2). ANOVA\nrevealed a signicant main e ect for strain (F 32,122 =3.61, p<0.001). The main e ects for sex and",
+              "title": "2020 - Modeling the Genetic Basis of Individual Differences in Susceptibility to Gulf War Illness.pdf",
+              "version": "v0",
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+            "score": 0.5606993436813354,
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+              "text": "cortisol.\nSupport has emerged for the basic tenets of\nthis hypothesis in studies of both humans and\nanimals (Avitsur et al, 2001 ; Miller and Chen,\n2006 ; Miller et al, 2002 ,2009 ; Rohleder et al,\n2001 ; Stark et al, 2001 ). Most of this work has\nmade use of an ex vivo assay system, where\nleukocytes are stimulated with a bacterial prod-\nuct (endotoxin) in the presence of varying levelsof cortisol, which is expected to suppress their\nability to synthesize inammatory cytokines. In",
+              "title": "2010 - Candidate Gene and Genome-Wide Association Studies in Behavioral Medicine.pdf",
+              "version": "v0",
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+        "kg_search_results": null
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+    }
+  }
+}
\ No newline at end of file
diff --git a/gnqa/paper2_eval/data/responses/human/de_diabetes_responses.json b/gnqa/paper2_eval/data/responses/human/de_diabetes_responses.json
new file mode 100644
index 0000000..4479348
--- /dev/null
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+              "content": "Gene expression in the liver is significantly affected by diabetes in several ways:\n\n1. **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].\n\n2. **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].\n\n3. **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].\n\n4. **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].\n\n5. **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].\n\nOverall, 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.",
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+        ],
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+              "text": "Lan H, Rabaglia ME, Stoehr JP, Nadler ST, Schueler KL et al (2003)\nGene expression proles of nondiabetic and diabetic obese mice\nsuggest a role of hepatic lipogenic capacity in diabetes\nsusceptibility. Diabetes 52:688700Theor Appl Genet (2008) 116:683690 689\n123",
+              "title": "2007 - Combining classical trait and microarray data to dissect transcriptional regulation a case study.pdf",
+              "version": "v0",
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+              "text": "Effects of high fat feeding on liver gene expression in diabetic goto-kakizaki\nrats, Gene Regul. Syst. Bio 6 (2012) 151 e168.\n[23] P.J. Kaisaki, G.W. Otto, J.F. McGouran, A. Toubal, K. Argoud, H. Waller-Evans,\nC. Finlay, S. Cald /C19erari, M.T. Bihoreau, B.M. Kessler, D. Gauguier, R. Mott, Ge-\nnetic control of differential acetylation in diabetic rats, PLoS One 9 (2014)\ne94555 .\n[24] S.P. Wilder, P.J. Kaisaki, K. Argoud, A. Salhan, J. Ragoussis, M.T. Bihoreau,",
+              "title": "2017 - Genomic regulation of type 2 diabetes endophenotypes Contribution.pdf",
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+              "text": "Figure 2. Diabetes increases the variability of gene expression levels in other experimental paradigms. ( A) Microarray data from gene",
+              "title": "2010 - Neural tube defect genes and maternal diabetes during pregnancy.pdf",
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+              "text": "also showed differential expression in the liver, where it\nregulates 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 \ncompared to controls\nP < 0.01 (DGI) P < 0.05 (DGI)\nP < 0.01 (WTCCC) 11 42\nP < 0.05 (WTCCC) 30 115\nP < 0.01 in DGI and P < 0.05 in WTCCC or \nP < 0.01 in WTCCC and P < 0.05 in DGI60",
+              "title": "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",
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+              "text": "toSHR wild type littermates. Liver, together with skeletal muscle and adipose tissue, aredeci-\nsive organs inmaintaining glucose homeostasis and, hence, thedevelopment ofinsulin resis-\ntance [75]. Functional analysis ofdifferentially expressed genes intheliver identified networks\nofgenes and potential regulators whose activation and inhibition could explain insulin resis-\ntance and dysglycemia intheheterozygous animals. Wealso recorded significant upregulation",
+              "title": "2022 - System Genetics in the Rat Family.pdf",
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+              "text": "toSHR wild type littermates. Liver, together with skeletal muscle and adipose tissue, aredeci-\nsive organs inmaintaining glucose homeostasis and, hence, thedevelopment ofinsulin resis-\ntance [75]. Functional analysis ofdifferentially expressed genes intheliver identified networks\nofgenes and potential regulators whose activation and inhibition could explain insulin resis-\ntance and dysglycemia intheheterozygous animals. Wealso recorded significant upregulation",
+              "title": "2022 - Systems genetics in the rat HXBBXH family identifies Tti2 as a pleiotropic quantitative trait gene for adult hippocampal neurogenesis and serum glucose.pdf",
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+              "text": "toSHR wild type littermates. Liver, together with skeletal muscle and adipose tissue, aredeci-\nsive organs inmaintaining glucose homeostasis and, hence, thedevelopment ofinsulin resis-\ntance [75]. Functional analysis ofdifferentially expressed genes intheliver identified networks\nofgenes and potential regulators whose activation and inhibition could explain insulin resis-\ntance and dysglycemia intheheterozygous animals. Wealso recorded significant upregulation",
+              "title": "2022 -Senko- Hippocampal neurogenesis serum glucose.pdf",
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+              "text": "toSHR wild type littermates. Liver, together with skeletal muscle and adipose tissue, aredeci-\nsive organs inmaintaining glucose homeostasis and, hence, thedevelopment ofinsulin resis-\ntance [75]. Functional analysis ofdifferentially expressed genes intheliver identified networks\nofgenes and potential regulators whose activation and inhibition could explain insulin resis-\ntance and dysglycemia intheheterozygous animals. Wealso recorded significant upregulation",
+              "title": "2022 -Senko- System Genetics in the Rat HXB\uf022BXH Family.pdf",
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+              "text": "mRNA in diabetic liver. Biochem Biophys Res Commun 290: 903-908, 2002. 712\n42. Watson PJ, Fairall L, and Schwabe JW . Nuclear hormone receptor co-repressors: 713\nstructure and function. Mol Cell Endocrinol 348: 440-449, 2012. 714\n43. Yamashita H, Takenoshita M, Sakurai M, Bruick RK, Henzel WJ, Sh illinglaw 715\nW, Arnot D, and Uyeda K . A glucose-responsive transcr iption factor that regulates 716\ncarbohydrate metabolism in the liver. Proc Natl Acad Sci U S A 98: 9116-9121, 2001. 717",
+              "title": "2020 - ChREBP downregulates SNAT2 amino acid transporter expression through interactions with SMRT in response to a high-carbohydrate diet.pdf",
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+              "text": "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",
+              "title": "2015 - Targeted Allelic Expression.pdf",
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+              "content": "Yes, the genes SH2B3, IFIH1, and ERBB3 are related to diabetes:\n\n1. **IFIH1**: This gene is implicated in antiviral responses and has been found to protect against type 1 diabetes [1], [3], [5], [8].\n2. **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].\n3. **SH2B3**: This gene is also considered to have a role in immune signaling related to type 1 diabetes susceptibility [4].\n\nThus, all three genes, SH2B3, IFIH1, and ERBB3, are related to diabetes.",
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+              "text": "associated with increased fasting plasma glucose levels and type2 diabetes risk. Nat Genet. 2009;41(1):89 94.\n23. Rees M, Wincovitch S, Schultz J, Waterstradt R, Beer N, Baltrusch\nS, et al. Cellular characterisation of the GCKR P446L variant\nassociated with type 2 diabe tes risk. Diabetologia. 2012;55\n(1):114 22.\n24. Nejentsev S, Walker N, Riches D, Egholm M, Todd J, et al. Rare\nvariants of IFIH1 , a gene implicated in antiviral responses, protect\nagainst type 1 diabetes. Science. 2009;324(5925):387 9.",
+              "title": "2012 - Recent Developments in the Genetic and Genomic Basis of Type 2 Diabetes.pdf",
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+              "text": "HLAlinked genes in juvenile diabetes mellitus. \nBr.Med. J. 3, 133135 (1975).\n52. Erlich,H.A.  etal.  Next generation sequencing reveals \nthe association of DRB3*02:02 with type 1 diabetes. \nDiabetes  62, 26182622 (2013).\n53. CaillatZucman,S.  etal.  Agedependent HLA genetic \nheterogeneity of type1 insulindependent diabetes \nmellitus. J.Clin. Invest. 90, 22422250 (1992).\n54. Cucca,F.  etal.  The distribution of DR4 haplotypes \ninSardinia suggests a primary association of typeI",
+              "title": "2017 - Type 1 diabetes mellitus.pdf",
+              "version": "v0",
+              "chunk_order": 187,
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+            "id": "f0c9d05b-7999-5cb7-bb48-0666cf74aec0",
+            "score": 0.6240234375,
+            "metadata": {
+              "text": "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\n58. Nejentsev S, Walker N, Riches D, Egholm M, Todd JA. Rare variants of\nIFIH1, a gene implicated in antiviral responses, protect against type 1diabetes. Science 2009;324:387389\n59. Altshuler D, Daly M. Guilt beyond a reasonable doubt. Nat Genet 2007;39:\n813 815",
+              "title": "2010 - Genetics of Type 1 Diabetes What\u2019s Next.pdf",
+              "version": "v0",
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+            "id": "55dca975-78ec-594d-8a30-a0849b683089",
+            "score": 0.6116858903076022,
+            "metadata": {
+              "text": "because of their presumed roles in immune signalling, considered\nto be a major feature of T1D-susceptibility. These include ERBB3\n(receptor tyrosine-protein kinase erbB-3 precursor) at 12q13 and\nSH2B3/LNK (SH2B adaptor protein 3), TRAFD1 (TRAF-type zinc\nfinger domain containing 1) and PTPN11 (protein tyrosine phos-\nphatase, non-receptor type 11) at 12q24. For these signal regions in",
+              "title": "2018 - Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls.pdf",
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+          {
+            "id": "126bf287-0f5e-52a9-abac-ad59ad3ea153",
+            "score": 0.6115562088225023,
+            "metadata": {
+              "text": "Nejentsev S, Walker N, Riches D, Egholm M, Todd JA (2009) Rare\nvariants of IFIH1, a gene implicated in antiviral responses,\nprotect against type 1 diabetes. Science 324:387389\nNicolson TJ, Bellomo EA, Wijesekara N, Loder MK, Baldwin JM,\nGyulkhandanyan AV, Koshkin V, Tarasov AI, Carzaniga R,\nKronenberger K, Taneja TK, da Silva Xavier G, Libert S,",
+              "title": "2011 - Type 2 diabetes and obesity genomics and the clinic.pdf",
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+              "text": "7 \n (Wellcome Trust Case Control Consortium 2007) .  Separate work that examined liver gene \nexpression in a smaller cohort of human samples with and without Type I diabetes found \nthat ERBB3  did not have a cis -eQTL but that a flanking gene, R PS26, did.  Since the disease \nphenotype and RPS26  both had QTLs in the same location, this suggested the RPS26  was a \nstronger candidate than ERBB3 .  The authors then used mouse liver and adipose expression",
+              "title": "2010 - Genome-wide analysis of transcriptional regulation in the murine liver.pdf",
+              "version": "v0",
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+              "document_id": "8a115c1b-662c-5062-b77f-bbde0532bbe9",
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+            "id": "ceb7bd13-b917-566f-8e17-40dd523afd42",
+            "score": 0.6101589202880859,
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+              "text": "models.  A genome wide association study in a large human population proposed the \nreceptor typrosine kinase ERBB3  as the best candidate gene near a QTL for Type I diabetes",
+              "title": "2010 - Genome-wide analysis of transcriptional regulation in the murine liver.pdf",
+              "version": "v0",
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+            "id": "226e2873-a0bf-554d-9576-7fca5f2ffc0f",
+            "score": 0.6101525658278139,
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+              "text": "61. Nejentsev S, Walker N, Riches D, Egholm M, Todd JA (2009) Rare variants of IFIH1, a gene implicated\nin antiviral responses, protect against type 1 diabetes. Science 324: 387 389. doi: 10.1126/science.\n1167728 PMID: 19264985\n62. Nica AC, Ongen H, Irminger JC, Bosco D, Berney T, et al. (2013) Cell-type, allelic, and genetic signa-\ntures in the human pancreatic beta cell transcriptome. Genome Res 23: 1554 1562. doi: 10.1101/gr.\n150706.112 PMID: 23716500",
+              "title": "2015 - Transcript Expression Data from Human.pdf",
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+            "score": 0.6081522340683808,
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+              "text": "gene is associated with insulin-dependent diabetes mellitus. Diabetes\n33:176 183, 1984\n3. Nistico L, Buzzetti R, Pritchard L, Van der Auwera B, Giovannini C, Bosi E,\nLarrad 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,\n1996",
+              "title": "2004 - Interaction and Association Analysis of a Type 1 Diabetes Susceptibility Locus.pdf",
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+              "text": "One of these genes associated with type 2 diabetes is the \ninsulin receptor substrate 1 (IRS1, OMIM association num-ber, 147545) (Alharbi, Khan, Abotalib, & AlHakeem, 2014; Alharbi, Khan, Munshi et al., 2014; Brender et al., 2013; \nBrunetti, 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; \nMuntinghe et al., 2009).\nInsulin initiates a wide range of growth and metabolic ef-",
+              "title": "2019 - IRS1\u2010 rs10498210 GA and CCR5\u201059029 AG polymorphisms in patients with type 2 diabetes in Kurdistan.pdf",
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+              "content": "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]. \n\nBy 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].\n\nMoreover, 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]. \n\nOverall, 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.",
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+              "text": "understood. It seems that interactions between multiple genes and environmental factors may play a role. \nOne of these factors is dietary factors. There is evidence supporting the role  of nutrient- gene interactions  \nin DM pathophysiology  [5]. Thus, a greater understanding of potential gene -nutrient interactions may \nbe relevant for DM prevention and treatment. \nNutrigenetics and nutrigenomics are defined as the science of the effects of genetic variation on",
+              "title": "2014 - Nutrigenetics and Nutrigenomics Insights into Diabetes Etiopathogenesis.pdf",
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+              "text": "nutrition  [12] . The identi  cation of gene variants that contribute \nboth to variation in fetal growth and to the susceptibility to T2DM, however, suggests that this metabolic   programming   could also be partly genetically determined  [13] . \n These complex interactions between genes and environment \ncomplicate the task of identifying any single genetic susceptibility factor for T2DM. Three general approaches have been adopted",
+              "title": "2010 - The Genetics of Type 2 Diabetes.pdf",
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+              "text": "Nutrients 2014, 6 5340 \n \nHowever, while the a pplication of these technologies is becoming more accessible, analysis of the \ncomplex large data sets that are generated  presents multiple challenges.  \nThe aim of the present review was to provide insights regarding the role of nutrient -gene interactions \nin DM pathogenesis, prevention and treatment. In addition, we explored how an individuals genetic \nmakeup can affect nutrient metabolism and the response to nutrient intake, potentially leading to DM.",
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+              "text": "Nutrients 2014, 6 5343 \n \n3. Gene -Nutrient or Dietary Patter n Interactions in T he Development of T2DM  \nRecently, several studies have d emonstrated the  significant effects of genotype by environment \ninteractions on T2D M [48,49] . However, further clarification of the role of these interactions at the \ngenome -wide level could help predict disease risk more accurately and facilitate the development of",
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+              "text": "in nutritional epidemiology: applications, needs and \nnew horizons .Hum Genet 125, 507525.\nKaput, J., Noble, J., Hatipoglu, B., et al. ( 2007) Application\nof nutrigenomic concepts to type 2 diabetes melli-tus.Nutr Metab Cardiovasc Dis 17,89103.\nOrdovas, J.M., Kaput, J., and Corella, D. ( 2007) Nutrition\nin the genomics era: cardiovascular disease risk and \nthe Mediterranean diet .Mol Nutr Food Res 51,\n12931299.\nvan Ommen, B., El-Sohemy , A., Hesketh, J., et al . ( 2010)",
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+              "text": "dietary patterns according to genetic variations, the role of gene -nutrient interactions, gene -\ndiet-phenotype interactions and epigenetic modifications caused by nutrients; these studies \nwill 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",
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+              "text": "Abstract:  Diabetes mellitus (DM) is considered a global pandemic, and the incidence of \nDM continues to grow worldwide. Nutrients and dietary patterns are central issues in the \nprevention, development and treatment of this disease. The pathogenesis of DM is not \ncomp letely understood, but nutrient -gene interactions at different levels, genetic predisposition \nand dietary factors appear to be involved. Nutritional genomics studies generally focus on",
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+              "text": "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 \nepigenomics 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",
+              "title": "2014 - Nutrigenetics and Nutrigenomics Insights into Diabetes Etiopathogenesis.pdf",
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+              "text": "It was previously report ed that food intake is a key component that affects the incidence of DM. Thus, \nthe 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; \n(ii) through their metabolites and ( iii) through signal tran sduction molecules (Figure 1).",
+              "title": "2014 - Nutrigenetics and Nutrigenomics Insights into Diabetes Etiopathogenesis.pdf",
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+              "text": "Nutrients 2014, 6 5347 \n \n3.4. Importance of Genotype by Macronutrient Interactions for T2DM -Related Traits  \nRecently, using genome -wide complex trait anal ysis, the genome -environment contribution of  \n14 dietary factors (glycemic load, total energy, protein, total fat, SF A, MUFA, PUFA, n- 3 PUFA,  \nn-6 PUFA, n-3:n-6 PUFA, carbohydrate, alcohol intake, trans fat and fiber) to the total phenotypic \nvariance of 4 T2DM -related traits (fasting glucose, fasting insulin, HOMA -IR and HOMA of  cell",
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+              "content": "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].",
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+              "text": "Abstract:  Diabetes mellitus (DM) is considered a global pandemic, and the incidence of \nDM continues to grow worldwide. Nutrients and dietary patterns are central issues in the \nprevention, development and treatment of this disease. The pathogenesis of DM is not \ncomp letely understood, but nutrient -gene interactions at different levels, genetic predisposition \nand dietary factors appear to be involved. Nutritional genomics studies generally focus on",
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+              "text": "ABSTRACT \n \nGenomics has contributed to a better understanding of many disorders including diabetes. The \nfollowing article looks at the ethical, social and legal consequences of genomic medicine and \npredictive genetic testing for diabetes. This is currently a field in its nascent stage and developing \nrapidly all over the world. The various ethical facets of genomic medicine in diabetes like its effects",
+              "title": "2018 - Ethical_Social_and_Legal_Consequences.pdf",
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+              "text": "Nutrients 2014, 6 5340 \n \nHowever, while the a pplication of these technologies is becoming more accessible, analysis of the \ncomplex large data sets that are generated  presents multiple challenges.  \nThe aim of the present review was to provide insights regarding the role of nutrient -gene interactions \nin DM pathogenesis, prevention and treatment. In addition, we explored how an individuals genetic \nmakeup can affect nutrient metabolism and the response to nutrient intake, potentially leading to DM.",
+              "title": "2014 - Nutrigenetics and Nutrigenomics Insights into Diabetes Etiopathogenesis.pdf",
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+              "text": "in nutritional epidemiology: applications, needs and \nnew horizons .Hum Genet 125, 507525.\nKaput, J., Noble, J., Hatipoglu, B., et al. ( 2007) Application\nof nutrigenomic concepts to type 2 diabetes melli-tus.Nutr Metab Cardiovasc Dis 17,89103.\nOrdovas, J.M., Kaput, J., and Corella, D. ( 2007) Nutrition\nin the genomics era: cardiovascular disease risk and \nthe Mediterranean diet .Mol Nutr Food Res 51,\n12931299.\nvan Ommen, B., El-Sohemy , A., Hesketh, J., et al . ( 2010)",
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+              "text": "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",
+              "title": "2007 - Bioethnic Conscription Genes, Race.pdf",
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+              "text": "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 \nepigenomics 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",
+              "title": "2014 - Nutrigenetics and Nutrigenomics Insights into Diabetes Etiopathogenesis.pdf",
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+              "text": "understood. It seems that interactions between multiple genes and environmental factors may play a role. \nOne of these factors is dietary factors. There is evidence supporting the role  of nutrient- gene interactions  \nin DM pathophysiology  [5]. Thus, a greater understanding of potential gene -nutrient interactions may \nbe relevant for DM prevention and treatment. \nNutrigenetics and nutrigenomics are defined as the science of the effects of genetic variation on",
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+              "text": "Nutrients 2014, 6 5343 \n \n3. Gene -Nutrient or Dietary Patter n Interactions in T he Development of T2DM  \nRecently, several studies have d emonstrated the  significant effects of genotype by environment \ninteractions on T2D M [48,49] . However, further clarification of the role of these interactions at the \ngenome -wide level could help predict disease risk more accurately and facilitate the development of",
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+              "text": "nutrition  [12] . The identi  cation of gene variants that contribute \nboth to variation in fetal growth and to the susceptibility to T2DM, however, suggests that this metabolic   programming   could also be partly genetically determined  [13] . \n These complex interactions between genes and environment \ncomplicate the task of identifying any single genetic susceptibility factor for T2DM. Three general approaches have been adopted",
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+              "content": "Yes, the gene TCF7L2 is involved in diabetes, specifically type 2 diabetes. Multiple sources in the provided context confirm this association:\n\n1. Single nucleotide polymorphisms in TCF7L2 are reproducibly associated with type 2 diabetes and reduce the insulin response to glucose in nondiabetic individuals [1].\n2. Common variants in the TCF7L2 gene increase the risk of type 2 diabetes [2].\n3. The TCF7L2 gene confers risk of type 2 diabetes [3], [4].\n4. 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].\n5. Overexpression of TCF7L2 protein decreases the sensitivity of beta islet cells to secrete insulin, which is involved in the regulation of insulin secretion [6].\n6. TCF7L2 risk alleles result in overexpression in pancreatic cells, causing reduced nutrient-induced insulin secretion and a predisposition to type 2 diabetes [9].\n7. The predominant effect of TCF7L2 dysfunction on type 2 diabetes development is mediated through impairment of insulin secretion [10].\n\nThese references collectively support the involvement of TCF7L2 in the pathogenesis and risk of type 2 diabetes.",
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+              "text": "single nucleotide polymorphisms in TCF7L2 are reproduc-ibly associated with type 2 diabetes and reduce the insulinresponse to glucose in nondiabetic individuals. Diabetes55:28902895\n135. Cauchi S, Meyre D, Dina C, Choquet H, Samson C,\nGallina S, Balkau B, Charpentier G, Pattou F, StetsyukV, Scharfmann R, Staels B, Fru  hbeck G, Froguel P 2006\nTranscription factor TCF7L2 genetic study in the Frenchpopulation: expression in human\n/H9252-cells and adipose tissue",
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+              "text": "L. Mechanisms by which common variants in the TCF7L2 gene \nincrease risk of type 2 diabetes. J Clin Invest  2007; 117: 2155-2163 \n[PMID: 17671651 DOI: 10.1172/JCI30706]\n164 Gloyn AL , Braun M, Rorsman P. Type 2 diabetes susceptibility \ngene TCF7L2 and its role in beta-cell function. Diabetes  2009; 58: \n800-802 [PMID: 19336690 DOI: 10.2337/db09-0099]\n165 da Silva Xavier G , Loder MK, McDonald A, Tarasov AI, Carzaniga \nR, Kronenberger K, Barg S, Rutter GA. TCF7L2 regulates late",
+              "title": "2015 - Diabetes mellitus The epidemic of the century.pdf",
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+              "text": "transcription factor 7-like 2 ( TCF7L2 ) gene confers risk of type 2 diabetes. Nat Genet. 2006;\n38:320323. [PubMed: 16415884]\n172. Gloyn AL, Noordam K, Willemsen MA, Ellard S, Lam WW, et al. Insights into the biochemical\nand genetic basis of glucokinase activation from naturally occurring hypoglycemia mutations.\nDiabetes. 2003; 52:24332440. [PubMed: 12941786]\n173. Pearson ER, Donnelly LA, Kimber C, Whitley A, Doney AS, et al. Variation in TCF7L2",
+              "title": "2012 - Type 2 Diabetes Genetics Beyond GWAS.pdf",
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+              "text": "2 (TCF7L2 ) gene confers risk of Type 2 \ndiabetes. Nat. Genet.  38(3), 320323 \n(2006).\n143Florez JC, Jablonski KA, Bayley N et al.  \nTCF7L2 polymorphisms and progression to diabetes in the Diabetes Prevention Program. N. Engl. J. Med.  355(3), \n241250 (2006).\n144Damcott CM, Pollin TI, Reinhart LJ et al.  \nPolymorphisms in the transcription factor 7-like 2 ( TCF7L2 ) gene are associated with",
+              "title": "2007 - Recent development in pharmacogenomics from candidate genes to genome-wide association studies.pdf",
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+              "text": "rs7903146 and rs12255372 in intron 3 of the TCF7L2 gene\n[20], associated with a ~45% increase in Type 2 diabetes\nrisk per allele. As such, the TCF7L2 locus presently repre-\nsents the strongest known genetic determinant of Type 2diabetes. Risk allele carriers show impaired insulin produc-tion [21] and b-cell dysfunction in vitro [22].\nTCF7L2 (previously referred to as TCF-4) is a\nhigh-mobility group box-containing transcription factor\ninvolved in Wingless-type MMTV integration site (Wnt)",
+              "title": "2014  - Dorothy Hodgkin Lecture 2014 Understanding genes identified by genome\u2010wide association.pdf",
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+              "text": "genes which also play a significant role in the risk and \npathogenesis of the disease[158,159]. The association \nof TCF7L2  gene variants with type 2 diabetes and \nits mechanism of action received special attention \nby several investigators[161,162]. Over expression of the protein was shown to decrease the sensitivity of \nbeta islet cells to secrete insulin[163,164] and was more \nprecisely involved in the regulation of secretary granule \nfusion that constitute a late event in insulin secretion",
+              "title": "2015 - Diabetes mellitus The epidemic of the century.pdf",
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+              "text": "et al. Variant of transcription factor 7-like 2 (TCF7L2) gene confers risk of type 2 \ndiabetes. Nat Genet . 2006;38:320-23. \n Sladek R, Rocheleau G, Rung J, Dina C, Shen L, Serre D, et al. A genome- [9]\nwide association study identifies novel risk loci for type 2 diabetes. Nature . \n2007;445:881-85.\n Kirchhoff K, Machicao F, Haupt A, Schafer SA, Tschritter O, Staiger H, et al. [10]\nPolymorphisms in the TCF7L2, CDKAL1 and SLC30A8 genes are associated",
+              "title": "2015 - Type 2 Diabetes Mellitus and the Association of Candidate Genes.pdf",
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+              "text": "transcription factor 7-like 2 (TCF7L2) gene confers risk of type 2diabetes. Nat Genet 38:320 3231422 Diabetologia (2007) 50:1418 1422",
+              "title": "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",
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+              "text": "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, \nand it has been observed that TCF7L2  risk alleles result in the overexpression of TCF7L2  in  \npancreatic  cells. This overexpression causes reduced nutrient -induced insulin secretion, which results \nin a direct predisposition to T2DM as well as an indirect predisp osition via an increase in hepatic glucose",
+              "title": "2014 - Nutrigenetics and Nutrigenomics Insights into Diabetes Etiopathogenesis.pdf",
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+              "text": "diabetes. The gene seems to be widely expressed [ 18] and\nthe transcription factor product is known to be involved in\nthe Wnt signalling cascade. Current evidence strongly\nsupports the idea that the predominant effect of TCF7L2\ndysfunction on type 2 diabetes development is mediated\nthrough impairment of insulin secretion [ 11,1517,20], a\nfinding that would be consistent, for example, with theknown effects of other (non-homologous) TCF genes\n(TCF1 [also known as HNF1A ] and TCF2 [also known as",
+              "title": "2007 - TCF7L2 the biggest story in diabetes genetics since HLA.pdf",
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+              "content": "Yes, the genes SH2B3, IFIH1, and ERBB3 are related to diabetes:\n\n1. **IFIH1**: Rare variants of IFIH1, a gene implicated in antiviral responses, protect against type 1 diabetes [1], [3], [5], [6].\n\n2. **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].\n\n3. **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].\n\nThus, all three genes, SH2B3, IFIH1, and ERBB3, are related to diabetes.",
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+              "text": "associated with increased fasting plasma glucose levels and type2 diabetes risk. Nat Genet. 2009;41(1):89 94.\n23. Rees M, Wincovitch S, Schultz J, Waterstradt R, Beer N, Baltrusch\nS, et al. Cellular characterisation of the GCKR P446L variant\nassociated with type 2 diabe tes risk. Diabetologia. 2012;55\n(1):114 22.\n24. Nejentsev S, Walker N, Riches D, Egholm M, Todd J, et al. Rare\nvariants of IFIH1 , a gene implicated in antiviral responses, protect\nagainst type 1 diabetes. Science. 2009;324(5925):387 9.",
+              "title": "2012 - Recent Developments in the Genetic and Genomic Basis of Type 2 Diabetes.pdf",
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+              "text": "HLAlinked genes in juvenile diabetes mellitus. \nBr.Med. J. 3, 133135 (1975).\n52. Erlich,H.A.  etal.  Next generation sequencing reveals \nthe association of DRB3*02:02 with type 1 diabetes. \nDiabetes  62, 26182622 (2013).\n53. CaillatZucman,S.  etal.  Agedependent HLA genetic \nheterogeneity of type1 insulindependent diabetes \nmellitus. J.Clin. Invest. 90, 22422250 (1992).\n54. Cucca,F.  etal.  The distribution of DR4 haplotypes \ninSardinia suggests a primary association of typeI",
+              "title": "2017 - Type 1 diabetes mellitus.pdf",
+              "version": "v0",
+              "chunk_order": 187,
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+            "id": "f0c9d05b-7999-5cb7-bb48-0666cf74aec0",
+            "score": 0.622846782207489,
+            "metadata": {
+              "text": "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\n58. Nejentsev S, Walker N, Riches D, Egholm M, Todd JA. Rare variants of\nIFIH1, a gene implicated in antiviral responses, protect against type 1diabetes. Science 2009;324:387389\n59. Altshuler D, Daly M. Guilt beyond a reasonable doubt. Nat Genet 2007;39:\n813 815",
+              "title": "2010 - Genetics of Type 1 Diabetes What\u2019s Next.pdf",
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+            "id": "55dca975-78ec-594d-8a30-a0849b683089",
+            "score": 0.618482608153898,
+            "metadata": {
+              "text": "because of their presumed roles in immune signalling, considered\nto be a major feature of T1D-susceptibility. These include ERBB3\n(receptor tyrosine-protein kinase erbB-3 precursor) at 12q13 and\nSH2B3/LNK (SH2B adaptor protein 3), TRAFD1 (TRAF-type zinc\nfinger domain containing 1) and PTPN11 (protein tyrosine phos-\nphatase, non-receptor type 11) at 12q24. For these signal regions in",
+              "title": "2018 - Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls.pdf",
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+            "id": "126bf287-0f5e-52a9-abac-ad59ad3ea153",
+            "score": 0.6160884270720748,
+            "metadata": {
+              "text": "Nejentsev S, Walker N, Riches D, Egholm M, Todd JA (2009) Rare\nvariants of IFIH1, a gene implicated in antiviral responses,\nprotect against type 1 diabetes. Science 324:387389\nNicolson TJ, Bellomo EA, Wijesekara N, Loder MK, Baldwin JM,\nGyulkhandanyan AV, Koshkin V, Tarasov AI, Carzaniga R,\nKronenberger K, Taneja TK, da Silva Xavier G, Libert S,",
+              "title": "2011 - Type 2 diabetes and obesity genomics and the clinic.pdf",
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+            "id": "226e2873-a0bf-554d-9576-7fca5f2ffc0f",
+            "score": 0.6138047210094509,
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+              "text": "61. Nejentsev S, Walker N, Riches D, Egholm M, Todd JA (2009) Rare variants of IFIH1, a gene implicated\nin antiviral responses, protect against type 1 diabetes. Science 324: 387 389. doi: 10.1126/science.\n1167728 PMID: 19264985\n62. Nica AC, Ongen H, Irminger JC, Bosco D, Berney T, et al. (2013) Cell-type, allelic, and genetic signa-\ntures in the human pancreatic beta cell transcriptome. Genome Res 23: 1554 1562. doi: 10.1101/gr.\n150706.112 PMID: 23716500",
+              "title": "2015 - Transcript Expression Data from Human.pdf",
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+              "text": "7 \n (Wellcome Trust Case Control Consortium 2007) .  Separate work that examined liver gene \nexpression in a smaller cohort of human samples with and without Type I diabetes found \nthat ERBB3  did not have a cis -eQTL but that a flanking gene, R PS26, did.  Since the disease \nphenotype and RPS26  both had QTLs in the same location, this suggested the RPS26  was a \nstronger candidate than ERBB3 .  The authors then used mouse liver and adipose expression",
+              "title": "2010 - Genome-wide analysis of transcriptional regulation in the murine liver.pdf",
+              "version": "v0",
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+            "id": "ceb7bd13-b917-566f-8e17-40dd523afd42",
+            "score": 0.6118218898773193,
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+              "text": "models.  A genome wide association study in a large human population proposed the \nreceptor typrosine kinase ERBB3  as the best candidate gene near a QTL for Type I diabetes",
+              "title": "2010 - Genome-wide analysis of transcriptional regulation in the murine liver.pdf",
+              "version": "v0",
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+            "id": "487d6a88-44ef-520e-a910-5b4b89416880",
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+              "text": "and 16p13.2 (near TMEM114 ) have not previously been\nimplicated in b-cell function, type 2 diabetes susceptibility,\nor related phenotypes. However, in publically available\ngene expression data from the MuTHER consortium,\nrs4148941 acts as eQTL for CHST3 in lymphoblast cell\nlines ( P=5310251) and SPOCK2 in both adipose tissue\n(P=1310221) and lymphoblast cell line ( P=331024)\n(22). Given the additional trend toward association with\nGLP-1 RA treatment response in diabetic patients, further",
+              "title": "2013 - The CTRB12 Locus Affects Diabetes Susceptibility.pdf",
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+              "text": "IGFBP1, and IGFBP3. The IGF pathway is nowsuspected to play a role in diabetes because of observedassociations with IGF2BP2 (2729).",
+              "title": "2009 - Genome-Wide Linkage Scan in Gullah-Speaking African American Families.pdf",
+              "version": "v0",
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+              "content": "To use GeneNetwork to find genes related to diabetes in humans, you can follow these steps:\n\n1. **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].\n\n2. **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].\n\n3. **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].\n\n4. **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].\n\nBy following these steps, you can effectively utilize GeneNetwork to identify and analyze genes related to diabetes in humans.",
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+            "id": "e81d17bd-858c-52b7-8c02-2076e59afe20",
+            "score": 0.7071822285652161,
+            "metadata": {
+              "text": "9.    Ehm MG, Karnoub MC, Sakul H, Gottschalk K, \nHolt 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.  \n   10.    McCarthy M, Zeggini E. Genome-wide association \nstudies in type 2 diabetes. Curr Diab Rep. 2009;9:16471.  \n   11.    Hivert MF, Jablonski KA, Perreault L, Saxena R,",
+              "title": "2017 - diabetes-mellitus-in-developing-countries-and-underserved-commun-2017.pdf",
+              "version": "v0",
+              "chunk_order": 292,
+              "document_id": "8a9451b9-d7e8-5417-b6a5-5fd1b791cc4d",
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+          {
+            "id": "18817608-0557-5acb-a091-9bc4d3640f7e",
+            "score": 0.6993173360824585,
+            "metadata": {
+              "text": "that from orthologous genes of yeast, worm, and fly. The resulting\nHumanNet gene network can be accessed through a web interface\n(http://www.functionalnet.org/humannet). Using this interface,\nresearchers can easily search the network using a set of seedTable 1. Selected top-ranked Crohns disease and type 2 diabetes genes for which\nnetwork data added support to GWAS evidence, measured as an increase in odds\n(prior =1.7 for each)\nCrohns disease",
+              "title": "2011 - Prioritizing candidate disease genes by network-based boosting of genome-wide association data.pdf",
+              "version": "v0",
+              "chunk_order": 100,
+              "document_id": "db0aa4b3-66ec-5d51-be72-2a1289db944a",
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+            "score": 0.696830153465271,
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+              "text": "twins. Diabetologia 30, 763768 (1987).\n3. Neel, J. V. in The Genetics of Diabetes Mellitus   \n(eds W. Creutzfeldt, J. Kbberling, & J. V. Neel) 1-11 (Springer, 1976).\n4. International HapMap Consortium, etal. A second generation human haplotype map of over 3.1 million \nSNPs. Nature 449, 851861 (2007).\n5. Sabeti, P . C. etal. Genome-wide detection and \ncharacterization of positive selection in human \npopulations. Nature 449, 913918 (2007).\n6. Genomes Project, C. etal. A global reference",
+              "title": "2020 - Insights into pancreatic islet cell dysfunction from type 2 diabetes mellitus genetics..pdf",
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+              "text": "Genome Biology  2007, 8:R253Open Access2007Bergholdtet al.Volume 8, Issue 11, Article R253Research\nIntegrative analysis for finding genes and networks involved in \ndiabetes and other complex diseases\nRegine Bergholdt*, Zenia M Strling, Kasper Lage, E Olof Karlberg, \nPll  lason, Mogens Aalund, Jrn Nerup*, Sren Brunak, \nChristopher T Workman and Flemming Pociot*\nAddresses: *Steno Diabetes Center, Niels Steensensvej 2, DK-2820 Gentofte, Denmark. Center for Biological Sequence Analysis, Technical",
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+              "text": "77. Bergholdt R, Brorsson C, Lage K, Nielsen JH, Brunak S, Pociot F.\nExpression proling of human genetic and protein interaction networks intype 1 diabetes. PLoS One 2009;4:e6250\n78. Bergholdt R, Storling ZM, Lage K, Karlberg EO, Olason PI, Aalund M,\nNerup 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\n79. Oresic M, Simell S, Sysi-Aho M, Na nto -Salonen K, Seppa nen-Laakso T,",
+              "title": "2010 - Genetics of Type 1 Diabetes What\u2019s Next.pdf",
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+              "text": "31. Saxena, R. et al. Genome-wide association analysis identies loci for type 2\ndiabetes and triglyceride levels. Science 316, 13311336 (2007).\n32. Franke, L. et al. Reconstruction of a functional human gene network, with an\napplication for prioritizing positional candidate genes. Am. J. Hum. Genet. 78,\n10111025 (2006).\n33. Su, Z., Marchini, J. & Donnelly, P. HAPGEN2: simulation of multiple disease\nSNPs. Bioinformatics 27,23042305 (2011).",
+              "title": "2015 - Biological interpretation of genome-wide association studies using predicted gene functions.pdf",
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+              "text": "Genetic exploration of GDM is in its initial stage. The genetics of GDM, \nfocusing 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. \n SEARCH STRATEGIES \n A systematic literature search using PubMed was performed to identify stud-",
+              "title": "2011 - Shared Genomics of Type 2 and Gestational Diabetes Mellitus.pdf",
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+              "text": "Human Molecular Genetics 16(1): 3649, 2007). The DiabetesGenetics Initiative (DGI) study was used for the analysis, as we had\naccess to genotype data in this study. The unadjusted gene p-value,\nP\nBestSNP\ng is the association p-value of the best regional SNP for gene\ng(y-axis in A). Phenotype permutation analysis was used as the gold\nstandard to test goodness of gene score correction as it corrects forall confounders without requiring a priori knowledge of the\nconfounders ( P\nGene",
+              "title": "2010 - Common Inherited Variation in Mitochondrial Genes.pdf",
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+            "metadata": {
+              "text": "version 2.0: users manual. PGL tech rep 2. Population Ge-netics Laboratory, Department of Genetics, Southwest Foun-dation for Biomedical Research, San Antonio\nElbein SC (1997) The genetics of human noninsulin-dependent\n(type 2) diabetes mellitus. J Nutr 127:1891S1896S\nElbein S, Hoffman M, Leppert M, Hasstedt S (1997) Linkage\nof fasting glucose in relatives of an NIDDM sib pair tomarkers on chromosome 9p. Diabetes 57 Suppl 1:51A\nElston RC (1998) Methods of linkage analysisand the as-",
+              "title": "1999 - Linkage of Type 2 Diabetes Mellitus and of Age at Onset to a Genetic Location.pdf",
+              "version": "v0",
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+            "score": 0.6732332311044109,
+            "metadata": {
+              "text": "Diabetes Study (DDS): a platform for chronic disease research.Glob Health Epidemiol Genom 1:e2. https://doi.org/10.1017/\ngheg.2015.3\n17. Genomes Project C, Auton A, Brooks LD et al (2015) A global\nreference for human genetic variation. Nature 526:68 74\n18. Howie BN, Donnelly P, Marchini J (2009) A flexible and accurate\ngenotype imputation method for the next generation of genome-wide association studies. PLoS Genet 5(6):e1000529. https://doi.\norg/10.1371/journal.pgen.1000529",
+              "title": "2019 - Genome-wide association study of type 2 diabetes in Africa.pdf",
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+              "content": "To use the GeneNetwork tool to find genes related to diabetes in humans, you can follow these steps:\n\n1. **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].\n\n2. **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].\n\n3. **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].\n\n4. **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].\n\nBy following these steps, you can effectively use the GeneNetwork tool to identify and analyze genes related to diabetes in humans.",
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+              "text": "that from orthologous genes of yeast, worm, and fly. The resulting\nHumanNet gene network can be accessed through a web interface\n(http://www.functionalnet.org/humannet). Using this interface,\nresearchers can easily search the network using a set of seedTable 1. Selected top-ranked Crohns disease and type 2 diabetes genes for which\nnetwork data added support to GWAS evidence, measured as an increase in odds\n(prior =1.7 for each)\nCrohns disease",
+              "title": "2011 - Prioritizing candidate disease genes by network-based boosting of genome-wide association data.pdf",
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+              "text": "Genome Biology  2007, 8:R253Open Access2007Bergholdtet al.Volume 8, Issue 11, Article R253Research\nIntegrative analysis for finding genes and networks involved in \ndiabetes and other complex diseases\nRegine Bergholdt*, Zenia M Strling, Kasper Lage, E Olof Karlberg, \nPll  lason, Mogens Aalund, Jrn Nerup*, Sren Brunak, \nChristopher T Workman and Flemming Pociot*\nAddresses: *Steno Diabetes Center, Niels Steensensvej 2, DK-2820 Gentofte, Denmark. Center for Biological Sequence Analysis, Technical",
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+              "text": "9.    Ehm MG, Karnoub MC, Sakul H, Gottschalk K, \nHolt 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.  \n   10.    McCarthy M, Zeggini E. Genome-wide association \nstudies in type 2 diabetes. Curr Diab Rep. 2009;9:16471.  \n   11.    Hivert MF, Jablonski KA, Perreault L, Saxena R,",
+              "title": "2017 - diabetes-mellitus-in-developing-countries-and-underserved-commun-2017.pdf",
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+              "text": "77. Bergholdt R, Brorsson C, Lage K, Nielsen JH, Brunak S, Pociot F.\nExpression proling of human genetic and protein interaction networks intype 1 diabetes. PLoS One 2009;4:e6250\n78. Bergholdt R, Storling ZM, Lage K, Karlberg EO, Olason PI, Aalund M,\nNerup 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\n79. Oresic M, Simell S, Sysi-Aho M, Na nto -Salonen K, Seppa nen-Laakso T,",
+              "title": "2010 - Genetics of Type 1 Diabetes What\u2019s Next.pdf",
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+            "id": "b3455bcd-494e-5288-93ae-2fd761dd4157",
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+              "text": "31. Saxena, R. et al. Genome-wide association analysis identies loci for type 2\ndiabetes and triglyceride levels. Science 316, 13311336 (2007).\n32. Franke, L. et al. Reconstruction of a functional human gene network, with an\napplication for prioritizing positional candidate genes. Am. J. Hum. Genet. 78,\n10111025 (2006).\n33. Su, Z., Marchini, J. & Donnelly, P. HAPGEN2: simulation of multiple disease\nSNPs. Bioinformatics 27,23042305 (2011).",
+              "title": "2015 - Biological interpretation of genome-wide association studies using predicted gene functions.pdf",
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+              "text": "Page 16 of 21 Tohetal. BMC Biology           (2022) 20:245 \nIdentification ofdiabeteslinked genes bytext mining\nWe used four techniques to derive a set of genes associ -\nated with type 2 diabetes and with diet-induced diabe -\ntes. First, we compiled an expert-curated gene-disease \nassociation database from standard resources, the Com -\nparative Toxicogenomics Database [35] and PharmGKB \n[36]. The result gave 277 genes associated with type 2 \ndiabetes, but none associated with diet-induced dia -",
+              "title": "2022 - A haplotype-resolved genome assembly of the Nile rat facilitates exploration of the genetic basis of diabetes.pdf",
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+              "text": "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\ndiabetes meta-analysis of 4549 cases and 5579 controls (Zeggini et al. 2008). As for Crohns disease, consideration of the network boosts performance\nacross a wide range of parameter values. Notably, consideration of the network strongly implicates the genes CTNNB1 and BACH2 in type 2 diabetes;",
+              "title": "2011 - Prioritizing candidate disease genes by network-based boosting of genome-wide association data.pdf",
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+              "text": "twins. Diabetologia 30, 763768 (1987).\n3. Neel, J. V. in The Genetics of Diabetes Mellitus   \n(eds W. Creutzfeldt, J. Kbberling, & J. V. Neel) 1-11 (Springer, 1976).\n4. International HapMap Consortium, etal. A second generation human haplotype map of over 3.1 million \nSNPs. Nature 449, 851861 (2007).\n5. Sabeti, P . C. etal. Genome-wide detection and \ncharacterization of positive selection in human \npopulations. Nature 449, 913918 (2007).\n6. Genomes Project, C. etal. A global reference",
+              "title": "2020 - Insights into pancreatic islet cell dysfunction from type 2 diabetes mellitus genetics..pdf",
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+              "text": "type 1 diabetes genome scan data, and a high -confidence human protein interaction network.\nResulting networks were ranked by the significance  of the enrichment of proteins from interacting\nregions. We identified a number of new prot ein network modules and novel candidate genes/\nproteins for type 1 diabetes. We propose this type of integrative analysis as a general method for\nthe elucidation of genes and networks involv ed in diabetes and other complex diseases.\nBackground",
+              "title": "2007 - Integrative analysis for finding genes and networks involved in diabetes and other complex diseases.pdf",
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+              "text": "gene prioritization are explained in detail in the Appendix,\nSupplemental Digital Content 1 , http://links.lww.com/A1049.\nIn addition, the complete list of the training genes, including both the\nGene HGNC symbol, and gene name are shown in the Appendix ,\nSupplemental Digital Content 1 , http://links.lww.com/A1049.\nMoreover, from the freely available site http:// www.broad.mit.edu/\ndiabetes/, we downloaded the results of the GWA study in 3000\nScandinavian individuals about the genetic variants that inu-",
+              "title": "2009 - Gene prioritization based on biological plausibility over genome wide association studies renders new loci associated with type 2 diabetes.pdf",
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+              "content": "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:\n\n1. **Physical Activity**: Errors in measuring physical activity can lead to inadequate control of this confounder in epidemiological analyses [1].\n2. **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].\n3. **Environmental Effects**: These include varying duration of disease, differing access to healthcare, heterogeneity in prescription, and variation in adherence to treatment regimes [5].\n4. **Adiposity**: Adjusting for adiposity and other confounding factors is crucial in studies examining the onset and complications of diabetes [9].\n\nThese factors need to be carefully controlled for in studies to ensure accurate and reliable results.",
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+              "text": "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.\n5 \nAlthough results from clinical trials6,7 have shown no e ect of vitamin D supplementation on the incidence of \ntype 2 diabetes, these   ndings require cautious \ninterpretation because of issues with doses, combination treatment with calcium, compliance, and generalisability.\n3",
+              "title": "2015 - Association between circulating 25-hydroxyvitamin D.pdf",
+              "version": "v0",
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+              "text": "common (confounding factors) that are the real causes of diabetes. In\nthis study, the researchers use Mendelian randomization to examine\nwhether increased blood CRP causes diabetes. Some variants of CRP (the\ngene that encodes CRP) increase the amount of CRP in the blood.\nBecause these variants are inherited randomly, there is no likelihood ofconfounding factors, and an association between these variants and the\ndevelopment of insulin resistance and diabetes indicates, therefore, that",
+              "title": "2008 - Inflammation, Insulin Resistance and Diabetes.pdf",
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+              "text": "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",
+              "title": "2019 - Adiponectin Levels and Risk of Type 2 Diabetes.pdf",
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+              "text": "diabetes are related to impaired glucose counterregulation and \nhypoglycemia unawareness, one should also keep in mind that \nhypoglycemia can be multifactorial and be the result of several unrelated \ndiseases. These include liver disease, malnutrition, sepsis, burns, total \nparenteral nutrition, malignancy and administration of certain medications \nknown to reduce plasma glucose concentrations (Table 1).27 \nIn principle, the same risk factors for hypoglycemia apply to",
+              "title": "2004 - Diabetes Genes a.pdf",
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+              "text": "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",
+              "title": "2001 - A Gene Conferring Susceptibility to Type 2 Diabetes.pdf",
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+              "text": "of diabetes remains one of the great challenges in human genetics. \nDiabetes is a result of complex interactions between genetic and \nnon-genetic (including environmental) factors. Although diabetes and its \nrelated traits have been shown to cluster within families, their .transmission \ndoes not follow a Mendelian fashion, except for some rare syndromes such \nas MODY. Diabetes could be the result of few common variants with a \nrelatively large effect, such as HLA alleles at the MHC locus and VNTR",
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+              "text": "predisposing to diabetes through effects on insulin sensitivity, however, may be more dif  cult to track down because of strong",
+              "title": "2010 - The Genetics of Type 2 Diabetes.pdf",
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+              "text": "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",
+              "title": "2005  - Animal models of diabetes mellitus.pdf",
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+              "text": "adjustment for adiposity and other confounding factors [4\n10]. Preventing or delaying onset of diabetes and its compli-\ncations is an important therapeutic aim, and there is interest in\ninammatory effectors including CRP as drug targets [11,12].\nIt is therefore highly desirable to establish which mediators in\nthe inammatory cascade are causal for diabetes.\nMendelian randomization involves comparison of pheno-\ntype and genotype effects in observational studies [13]. If the",
+              "title": "2008 - Inflammation, Insulin Resistance and Diabetes.pdf",
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+              "text": "adjusting for sex, diabetes duration, HbA1c, and smoking, assuming either additive or dominant effects of the polymorphisms.N. VIONNET AND ASSOCIATES\nDIABETES, 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",
+              "title": "2006 - Analysis of 14 Candidate Genes for Diabetic Nephropathy.pdf",
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+              "content": "The immune system is intricately related to diabetes, particularly Type 1 Diabetes (T1D), through several mechanisms:\n\n1. **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].\n\n2. **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].\n\n3. **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].\n\n4. **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].\n\n5. **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].\n\nIn 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.",
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+              "text": "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].\nConclusion",
+              "title": "2004 - A New Look at Viruses in Type 1 Diabetes.pdf",
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+              "text": "ished immune responsiveness, a well-characterized feature of diabetes ( Shanmugam et al., 2003 ;\nMowat and Baum, 1971 ).\nFurther, we considered that the genetic component of an individuals response to glucose may\ninfluence their susceptibility to diabetic complications like retinopathy. Cell lines from individuals\nwith diabetes with and without retinopathy reveal differences in the response to glucose at a molec-",
+              "title": "2020 - Integration of genomics and transcriptomics predicts diabetic retinopathy susceptibility genes.pdf",
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+              "text": "diabetes. ISME J. 5,8291 (2011).\n30. Brown, C. T. et al. Gut microbiome metagenomics analysis suggests a\nfunctional model for the development of autoimmunity for type 1 diabetes.PLoS ONE 6,e25792 (2011).\n31. Endesfelder, D. et al. Compromised gut microbiota networks in children with\nanti-islet cell autoimmunity. Diabetes 63,2006 2014 (2014).\n32. Kostic, A. D. et al. The dynamics of the human infant gut microbiome in\ndevelopment and in progression toward type 1 diabetes. Cell Host Microbe 17,\n260273 (2015).",
+              "title": "2016 - Integrated multi-omics of the human gut microbiome in a case study of familial type 1 diabetes.pdf",
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+              "text": "+T cells related to diabetes-associated",
+              "title": "2003 - A functional polymorphism in the promoterenhancer region of the FOXP3Scurfin gene associated with type 1 diabetes.pdf",
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+              "text": "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",
+              "title": "2005 - Pathway analysis of coronary atherosclerosis.pdf",
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+              "text": "same or related viruses might complete the process of\nimmune-mediated b-cell destruction. Alternatively, chil-\ndren genetically predisposed to develop autoimmunediabetes might have an altered immune system that is\nmore likely to respond to viral exposures with strongly\ndetectable antibody levels against certain viral antigens.If so, the detectable levels of antibodies to multiple viral\nantigens in diabetic patients would not indicate a causal",
+              "title": "2003 -Genetic epidemiology of type 1 diabetes.pdf",
+              "version": "v0",
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+              "text": "with -cell autoimmunity and those without. Diabetes 62, 12381244 (2013).\n 9. Mario, E. et al. Gut microbial metabolites limit the frequency of autoimmune \nT cells and protect against type 1 diabetes. Nat. Immunol. 18, 552562 \n(2017).\n 10. Needell, J. C. & Zipris, D. The role of the intestinal microbiome in type 1 diabetes pathogenesis. Curr. Diab. Rep. 16, 89 (2016).\n 11. Davis-Richardson, A. G. et al. Bacteroides dorei dominates gut microbiome prior",
+              "title": "2018 - The human gut microbiome in early-onset type 1 diabetes from the TEDDY study.pdf",
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+              "text": "141. Filippi CM, Estes EA, Oldham JE, von Herrath MG. Immuno-\nregulatory mechanisms triggered by viral infections protect fromtype 1 diabetes in mice. J Clin Invest 119: 15151523, 2009.\n142. Filippi CM, von Herrath MG. Viral trigger for type 1 diabetes:\npros and cons. Diabetes 57: 28632871, 2008.\n143. Flohe SB, Wasmuth HE, Kerad JB, Beales PE, Pozzilli P. A\nwheat-based, diabetes-promoting diet induces a Th1-type cytokinebias in the gut of NOD mice. Cytokine 21: 149154, 2003.",
+              "title": "2011 - Type 1 Diabetes Etiology, Immunology.pdf",
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+              "text": "12451252 (2008).\n77. Hofer,J.  etal.  Elevated proportions of recent thymic \nemigrants in children and adolescents with type1 \ndiabetes. Rejuvenation Res. 12, 311320 (2009).\n78. Wong,F.S. How does Bcell tolerance contribute to \nthe protective effects of diabetes following induced \nmixed chimerism in autoimmune diabetes? Diabetes \n63, 18551857 (2014).\n79. Roep,B.O. & Peakman,M. Antigen targets of type1 \ndiabetes autoimmunity. Cold Spring Harb. Perspect. \nMed.  2, a007781 (2012).",
+              "title": "2017 - Type 1 diabetes mellitus.pdf",
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+              "text": "Immune Hypothesis: Evidence supporting an immune pathogenesis \nis strongest for diabetic autonomic neuropathy. Autonomic ganglia heavily \ninfiltrated by lymphocytes, plasma cells, and macrophages were found at \nautopsy in five type 1 diabetics with symptomatic autonomic neuropathy. \nStriking cervical sympathetic ganglia atrophy was reported in another with \nsevere sensory and autonomic neuropathy.32 \nAutoimmune pathogenesis may be involved in proximal diabetic",
+              "title": "2004 - Diabetes Genes a.pdf",
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+              "content": "The genomic variants associated with immune system components and diabetes include:\n\n1. **SLC30A8 (rs13266634)**: This variant is associated with an increased risk of Type 2 Diabetes Mellitus (T2DM) [1].\n2. **6q23/TNFAIP3**: Identified as a susceptibility locus for Type 1 Diabetes (T1D) [2], [3].\n3. **Pancreatic islet enhancer clusters**: These clusters are enriched in T2DM risk-associated variants [4].\n4. **Regulatory variants affecting monocyte gene expression**: These variants are conditioned by innate immune activity [4].\n5. **>60 loci**: Genome-wide association studies have identified over 60 loci that confer genetic susceptibility to T1D [5], [7].\n6. **CCR5-del32 mutation**: This mutation in the chemokine receptor CCR5 is a modifying pathogenetic factor in T1D [9].\n7. **CCR2 and CCR5 polymorphisms**: These polymorphisms are found in children with insulin-dependent diabetes mellitus [9].\n8. **Novel insights linking immune and metabolic diabetes**: Identified through the first genome-wide association study of latent autoimmune diabetes in adults [10].\n\nThese variants highlight the complex interplay between genetic factors influencing both the immune system and diabetes.",
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+              "text": "Imran Ali Khan et al., Genetic Variants in Indian Diabetes Patients  www.jcdr.net\nJournal of Clinical and Diagnostic Research. 2015 Nov, Vol-9(11): GC01-GC05 44of the pancreas and islets during embryonic growth [3]. Genetic \nvariants in this gene are associated with increased risk of T2DM in a \nvariety of study populations [28,29].\n In the first published GWAS for T2DM, SLC30A8 (rs13266634) was \nrevealed to be associated with diabetes (OR, 1.26; p = 5.0  10-7).",
+              "title": "2015 - Type 2 Diabetes Mellitus and the Association of Candidate Genes.pdf",
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+              "text": "diabetes and celiac disease. N Engl J Med 2008; 359: 27672777.\n11 Fung E, Smyth DJ, Howson JM, Cooper JD, Walker NM,\nStevens H et al. Analysis of 17 autoimmune disease-associated\nvariants in type 1 diabetes identifies 6q23/TNFAIP3 as asusceptibility locus. Genes Immun 2008; 10: 188191.\n12 Cooper JD, Smyth DJ, Smiles AM, Plagnol V, Walker NM,\nAllen JE et al. Meta-analysis of genome-wide association study\ndata identifies additional type 1 diabetes risk loci. Nat Genet\n2008; 40: 13991401.",
+              "title": "2009 - Analysis of 19 genes for association with type I diabetes in the Type I Diabetes Genetics Consortium families..pdf",
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+              "text": "10. Smyth, D.J. et al. Shared and distinct genetic variants in type 1 diabetes and celiac\ndisease. N. Engl. J. Med. 359, 27672777 (2008).\n11. Fung, E. et al. Analysis of 17 autoimmune disease-associated variants in type 1\ndiabetes identies 6q23/TNFAIP3 as a susceptibility locus. Genes Immun. 10,\n188191 (2009).\n12. Cooper, J.D. et al. Meta-analysis of genome-wide association study data identies\nadditional type 1 diabetes risk loci. Nat. Genet. 40, 13991401 (2008).",
+              "title": "2009 - Genome-wide association study and meta-analysis find that over 40 loci affect risk of type 1 diabetes.pdf",
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+              "text": "14. Pasquali L, Gaulton KJ, Rodriguez-Segui SA, Mularoni L, Miguel-Escalada I, et al. (2014) Pancreatic\nislet enhancer clusters enriched in type 2 diabetes risk-associated variants. Nat Genet 46: 136 143.\ndoi:10.1038/ng.2870 PMID: 24413736\n15. Fairfax BP, Humburg P, Makino S, Naranbhai V, Wong D, et al. (2014) Innate immune activity condi-\ntions the effect of regulatory variants upon monocyte gene expression. Science 343: 1246949. doi: 10.\n1126/science.1246949 PMID: 24604202",
+              "title": "2015 - Transcript Expression Data from Human.pdf",
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+              "text": "The Journal of Immunology\nSystematic Evaluation of Genes and Genetic Variants\nAssociated with Type 1 Diabetes Susceptibility\nRamesh Ram,*,Munish Mehta,*,Quang T. Nguyen,*,Irma Larma,*,\nBernhard O. Boehm,,xFlemming Pociot,{Patrick Concannon,,#and Grant Morahan*,\nGenome-wide association studies have found >60 loci that confer genetic susceptibility to type 1 diabetes (T1D). Many of these are",
+              "title": "2016 - Systematic Evaluation of Genes and Genetic Variants Associated with Type 1 Diabetes Susceptibility.pdf",
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+              "text": "disease and type II diabetes. Genes Immun.  10, 654658 (2009).\n41. Hindorff, L.A. et al. Potential etiologic and functional implications of genome-wide \nassociation loci for human diseases and traits. Proc. Natl. Acad. Sci. USA  106, \n93629367 (2009).\n42. Nicolson, T.J. et al.  Insulin storage and glucose homeostasis in mice null for the \ngranule zinc transporter ZnT8 and studies of the type 2 diabetes-associated variants. \nDiabetes  58, 20702083 (2009).",
+              "title": "2010 - Twelve type 2 diabetes susceptibility loci identified.pdf",
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+              "text": "The composition and activity of the human immune system is under genetic control, and people \nwith certain changes in their genes are more susceptible than others to develop type 1 diabetes. \nPrevious studies have identified around 60 locations in the human DNA (known as loci) associated \nwith the condition, but it remains unclear how these loci influence the immune system and whether \ndiabetes will emerge.\nChu, Janssen, Koenen et al. explored how variations in genetic information can influence the",
+              "title": "2022 - A genome-wide functional genomics approach uncovers genetic determinants of immune phenotypes in type 1 diabetes.pdf",
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+              "text": "mellitus-associated genetic variants contribute to overlapping\nimmune regulatory networks. Front Genet 2018; 9:535.\n13 Syreeni A, Sandholm N, Cao J et al. Genetic determinants of\nglycated hemoglobin in type 1 diabetes. Diabetes 2019; 68:\n858 67.\n14 Sidore C, Busonero F, Maschio A et al. Genome sequencing\nelucidates Sardinian genetic architecture and augmentsGenes affecting type 1 diabetes diagnosis age / A. Syreeni et al .",
+              "title": "2021- Genome\u2010wide search for genes affecting the age at diagnosis of type 1.pdf",
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+              "text": "Genetic Variants in Type 1 Diabetes and Celiac Disease\nn engl j med 359;26 www.nejm.org december 25, 2008 2777Kalev I, Oselin K, Prlist P, et al. CC-26. \nchemokine receptor CCR5-del32 mutation \nas a modifying pathogenetic factor in type I diabetes. J Diabetes Complications 2003;17:387-91.\nSzalai C, Csszr A, Czinner A, et al. \n27. \nChemokine receptor CCR2 and CCR5 polymorphisms in children with insulin-dependent diabetes mellitus. Pediatr Res 1999;46:82-4.\nYang B, Houlberg K, Millward A, De -\n28.",
+              "title": "2008 - Shared and Distinct Genetic Variants in Type 1 Diabetes.pdf",
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+              "text": "13(1):2337. https://doi.org/10.1038/s41467-022-29932-y\n5. Burgess S, Butterworth A, Thompson SG (2013) Mendelian\nrandomization analysis with multiple genetic variants using\nsummarized data. Genet Epidemiol 37(7):658 665. https://doi.\norg/10.1002/gepi.21758\n6. Cousminer DL, Ahlqvist E, Mishra R et al (2018) First genome-\nwide association study of latent autoimmune diabetes in adults\nreveals novel insights linking immune and metabolic diabetes.\nDiabetes Care 41(11):2396 2403. https://doi.org/10.2337/dc18-",
+              "title": "2023 - Childhood adiposity and novel subtypes of adult-onset diabetes a Mendelian randomisation and genome-wide genetic correlation study.pdf",
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+              "content": "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.",
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+              "text": "allows the detection of systemic metabolic imbalances, thereby providing a disease specific picture of human physiology.\ndoi:10.1371/journal.pone.0013953.g003Metabolomics of Diabetes\nPLoS ONE | www.plosone.org 9 November 2010 | Volume 5 | Issue 11 | e13953",
+              "title": "2010 - Metabolic Footprint of Diabetes A Multiplatform.pdf",
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+              "text": "Metabolomics studies allow metabolites involved in disease\nmechanisms to be discovered by monitoring metabolite level\nchanges in predisposed individuals compared with healthy\nones (Shaham et al, 2008; Newgard et al, 2009; Zhao et al,\n2010; Pietilainen et al, 2011; Rhee et al, 2011; Wang et al,2 0 1 1 ;\nCheng et al, 2012; Goek et al, 2012). Altered metabolite levels\nmay serve as diagnostic biomarkers and enable preventive\naction. Previous cross-sectional metabolomics studies of T2D",
+              "title": "2012 - Novel biomarkers for pre\u2010diabetes identified by metabolomics.pdf",
+              "version": "v0",
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+              "text": "doi:10.1371/journal.pone.0013953.t006Metabolomics of Diabetes\nPLoS ONE | www.plosone.org 8 November 2010 | Volume 5 | Issue 11 | e13953",
+              "title": "2010 - Metabolic Footprint of Diabetes A Multiplatform.pdf",
+              "version": "v0",
+              "chunk_order": 114,
+              "document_id": "b199607e-293e-56e8-88c8-e0716d1ee9eb",
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+            "id": "ead10261-182f-5ab1-9af0-ce8a17677d4a",
+            "score": 0.696293830871582,
+            "metadata": {
+              "text": "monitoring and preventing progression to costly co-morbidities.\nThe principal concept of metabolomics being able to find some\nmetabolites differing in a control and a type 2 diabetic group is\nestablished. It is not our goal here to show this once again. The\nquestions we ask are rather How well are different approaches\nsuited to attain this goal? and What are optimal settings under\nwhich such studies can be successful?. Others have already\ninvestigated these questions before [16,17,18]. However, we",
+              "title": "2010 - Metabolic Footprint of Diabetes A Multiplatform.pdf",
+              "version": "v0",
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+            "id": "024eea85-c974-51fc-8def-89db09ba56b0",
+            "score": 0.6954019872964023,
+            "metadata": {
+              "text": "H, Raftery D, Nair KS. Quantitative me-tabolomics by H-NMR and LC-MS/MSconrms altered metabolic pathways in\ndiabetes. PLoS ONE 2010;5:e10538\n2. Li LO, Hu YF, Wang L, Mitchell M, Berger\nA, Coleman RA. Early hepatic insulin re-sistance in mice: a metabolomics analysis.Mol Endocrinol 2010;24:657 666\n3. Bain JR, Stevens RD, Wenner BR, Ilkayeva\nO, Muoio DM, Newgard CB. Metabolomicsapplied to diabetes research: moving frominformation to knowledge. Diabetes 2009;\n58:2429 2443",
+              "title": "2011 - Emerging Applications of Metabolomic.pdf",
+              "version": "v0",
+              "chunk_order": 77,
+              "document_id": "10c69e6a-3771-5cc6-a915-a31556dec650",
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+              "associatedQuery": "What is the role of the immune system in the metabolomics of diabetes and associated conditions?"
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+          {
+            "id": "cef34be2-673e-553f-9c92-1ecef8edec4f",
+            "score": 0.6943854300494623,
+            "metadata": {
+              "text": "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).\n71 Roberts LD, Koulman A, Griffin JL. Towards metabolic biomarkers of insulin resistance and Type 2 diabetes: progress from the metabolome. Lancet Diabetes Endocrinol.  \n2(1), 6575 (2014). \n\t Illustrates\tpotential\tmetabolic\tbio-markers\twhich\tmay\tbe\t\nused\tto\tdetect\tpeople\tat-risk\tfor\tT2D/insulin\tresistance,",
+              "title": "2015 - Genetics, genomics and personalized medicine in Type 2 Diabetes.pdf",
+              "version": "v0",
+              "chunk_order": 160,
+              "document_id": "d8b85c3e-62f3-5e67-99b0-d0a2f225aff0",
+              "extraction_id": "92a2a3c7-ed41-5394-b716-fdbf5c198a86",
+              "associatedQuery": "What is the role of the immune system in the metabolomics of diabetes and associated conditions?"
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+          {
+            "id": "5c7dc6d7-800e-5c77-ac61-bd8e3086754c",
+            "score": 0.6911985278129578,
+            "metadata": {
+              "text": "Serum or plasma concentrations of sugars and sugar metabo-\nlites (e.g., glucose, mannose, desoxyhexose, and 1,5-anhy-droglucoitol), ketone bodies ( -hydroxybutyrate),  lipids \n(e.g., phosphatidyl-cholines and nonesterified fatty acids), \nbranched-chain amino acids, and other metabolites were found \nto be associated with insulin resistance or diabetes status (see \nSupplementary Data  online for full references). A proof-of-\nconcept multi-platform, metabolome-wide study based on the",
+              "title": "2011 - The eMERGE Network A consortium of biorepositories linked to electronic medical records data for conducting genomic studies.pdf",
+              "version": "v0",
+              "chunk_order": 159,
+              "document_id": "3a174301-2941-578f-8ed6-f16d88fd2230",
+              "extraction_id": "a35d4e2a-ce04-536d-b88a-8f273aa03f40",
+              "associatedQuery": "What is the role of the immune system in the metabolomics of diabetes and associated conditions?"
+            }
+          },
+          {
+            "id": "3b9547ce-8316-5256-a68b-256058b3ee79",
+            "score": 0.6911985278129578,
+            "metadata": {
+              "text": "Serum or plasma concentrations of sugars and sugar metabo-\nlites (e.g., glucose, mannose, desoxyhexose, and 1,5-anhy-droglucoitol), ketone bodies ( -hydroxybutyrate),  lipids \n(e.g., phosphatidyl-cholines and nonesterified fatty acids), \nbranched-chain amino acids, and other metabolites were found \nto be associated with insulin resistance or diabetes status (see \nSupplementary Data  online for full references). A proof-of-\nconcept multi-platform, metabolome-wide study based on the",
+              "title": "2011 - Biomarkers for the Prediction of Type 2 Diabetes.pdf",
+              "version": "v0",
+              "chunk_order": 159,
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+              "associatedQuery": "What is the role of the immune system in the metabolomics of diabetes and associated conditions?"
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+              "text": "Conclusions/Significance: Our study depicts the promising potential of metabolomics in diabetes research by identification\nof a series of known and also novel, deregulated metabolites that associate with diabetes. Key observations include\nperturbations of metabolic pathways linked to kidney dysfunction (3-indoxyl sulfate), lipid metabolism (glyceropho-",
+              "title": "2010 - Metabolic Footprint of Diabetes A Multiplatform.pdf",
+              "version": "v0",
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+            "score": 0.6782652735710144,
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+              "text": "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",
+              "title": "2009 - Metabolomics Applied to Diabetes Research.pdf",
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+              "content": "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:\n\n1. **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].\n\n2. **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].\n\n3. **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].\n\n4. **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].\n\n5. **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].\n\n6. **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].\n\n7. **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].\n\nIn 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.",
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+              "text": "'&'.+*\n.%(\"'.+ *\n$$*\n!\n\f\r\n\t\f\u000b\n'&'.+*\n.%(\"'.+ *\n$$*\t\u000b\nr\nFigure 2. Impact of type 1 diabetes (T1D) genome- wide association studies (GWAS) single- nucleotide polymorphisms (SNPs) on immune phenotypes. \n(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",
+              "title": "2022 - A genome-wide functional genomics approach uncovers genetic determinants of immune phenotypes in type 1 diabetes.pdf",
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+              "text": "diseases, including T2D. Many of the module-QTL locioverlap with GWAS hits for immune-related pheno-\ntypes, suggesting that the modules described here might\nbe of importance in the context of inflammatory dis-\neases. Similar analyses should be performed for co-\nexpression modules in other more T2D-relevant tissues\nto provide further insight into the causal networks\nunderlying T2D aetiology. Similarly, network rewiring in\nT2D might be more strongly detectable in other tissues",
+              "title": "2020 - Whole blood co-expression modules associate with metabolic traits and type 2 diabetes an IMI-DIRECT study.pdf",
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+            }
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+              "text": "(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\ninvolved in immune functions. Another interesting application of",
+              "title": "2020 - Polygenic inheritance, GWAS, polygenic risk scores,and the search for functional variants.pdf",
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+              "text": "Research article      \nGenetics and Genomics | Medicine\nChu, Janssen, Koenen etal. eLife 2022;11:e73709. DOI: https://doi.org/10.7554/eLife.73709  9 of 17Genetic regulation of immune phenotypes in T1D\nTo further explore potential genetic regulation of immune phenotypes on the whole- genome level, \nwe performed QTL mapping in 300DM. This identified nine genome- wide significant QTLs (p- value \n< 5  108) associated with immune- cell proportion, including four associated with T cell subpopu-",
+              "title": "2022 - A genome-wide functional genomics approach uncovers genetic determinants of immune phenotypes in type 1 diabetes.pdf",
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+            "id": "50474cf9-286a-50eb-b344-2837cc7c43a6",
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+              "text": "studies (r2> 0.8) and performed a chi- square test on clinical status by using PLINK 1.9. Samples in \n300DM were taken as cases and samples in 500FG as controls.\nImpact of T1D GWAS loci on immune phenotypes\nTo detect the impact of T1D GWAS loci on immune- cell populations, we grouped all traits into four \ncategories (B cells, T cells, monocytes, and NK cells), and counted the number of suggestive associ-\nations (p- value < 0.05) between the 63 top SNPs from T1D GWAS loci and immune- cell traits. 1000",
+              "title": "2022 - A genome-wide functional genomics approach uncovers genetic determinants of immune phenotypes in type 1 diabetes.pdf",
+              "version": "v0",
+              "chunk_order": 42,
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+              "associatedQuery": "Can the landscape of QTL and GWAS hits be used to dissect the role of immune system in diabetes and complications?"
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+            "id": "5f2de1ce-56f7-501d-a0e0-54991c1324f7",
+            "score": 0.677860417621086,
+            "metadata": {
+              "text": "In the present study, we interrogated GWAS data sets on\nCD, 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\nstatistical power by limiting the number of hypotheses",
+              "title": "2010 - Comparative genetic analysis of inflammatory.pdf",
+              "version": "v0",
+              "chunk_order": 16,
+              "document_id": "ab74ea2b-684a-5f6f-b77b-f3dbd4de86e8",
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+          {
+            "id": "82f7da2f-7abf-59e1-b259-46a01b375f1c",
+            "score": 0.6774772203720472,
+            "metadata": {
+              "text": "Conclusions\nA major challenge is to translate GWAS ndings intocausal variants and target genes. The Immunochipeffort has greatly contributed to our understanding\nof disease mechanisms by identifying pathways, which\ncould not be linked to diabetes by existing hypotheticalmodels. Diabetes is probably a much more diverse\ndisease than the current subdivision into T1DM\nand T2D implies and a more precise subdivisioninto subgroups may also pave the way for a more",
+              "title": "2016 - Effects of the genome on immune regulation in type 1 diabetes.pdf",
+              "version": "v0",
+              "chunk_order": 39,
+              "document_id": "9fcfc0c3-80b5-515c-9263-a1a17cfa9a4c",
+              "extraction_id": "a744412e-5003-5732-9a73-f1f5267aa715",
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+            }
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+          {
+            "id": "acc9b87c-583b-5ba6-bc6f-b833d2e8d2cb",
+            "score": 0.6764135553590336,
+            "metadata": {
+              "text": "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\nautomatically determine the particular gene(s) in a speci c locus that\nare mechanistically associated with disease pathogenesis, or elucidate\nthe manner in which disease gene(s) interact ( Zhong\n et al., 2010). The\ndiculty associated with ascribing functional impacts to SNPs is partly\nexplained by the fact that most disease-associated SNPs identi ed by",
+              "title": "2018 - The genetic architecture of type 1 diabetes mellitus.pdf",
+              "version": "v0",
+              "chunk_order": 75,
+              "document_id": "341261db-b38a-5bd2-8d8d-fc04a0b3da30",
+              "extraction_id": "5b8b3673-7fd4-5989-9982-a6d5ea374c8d",
+              "associatedQuery": "Can the landscape of QTL and GWAS hits be used to dissect the role of immune system in diabetes and complications?"
+            }
+          },
+          {
+            "id": "9b1cf5ca-d793-5c2a-a2db-c88f44ac6ec4",
+            "score": 0.675408123360613,
+            "metadata": {
+              "text": "(Supplementary file 1C).\nWe next investigated whether these genetic risk loci for T1D affect immune parameters and func-\ntion. The quantile- quantile plot of the association of the 63 T1D GWAS loci with different cell types \nand cytokines illustrates an inflated deviation from an expected uniform distribution (Figure 2A, \nFigure2figure supplement 1). We further tested whether this deviation can be explained by chance",
+              "title": "2022 - A genome-wide functional genomics approach uncovers genetic determinants of immune phenotypes in type 1 diabetes.pdf",
+              "version": "v0",
+              "chunk_order": 86,
+              "document_id": "368e0215-393e-5bec-a87c-e976adaa3ca5",
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+              "associatedQuery": "Can the landscape of QTL and GWAS hits be used to dissect the role of immune system in diabetes and complications?"
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+          },
+          {
+            "id": "ce911802-af16-57a4-90e8-e3257a9ee7af",
+            "score": 0.673246820375514,
+            "metadata": {
+              "text": "Fadason et al. demonstrated that functionally relevant type 2 diabetes-\nassociated SNPs are spatially linked with speci c changes in the ex-\npression levels of genes within disease-associated tissues ( Fadason\net al., 2017 ). Similarly, a study demonstrated that integrating chro-\nmatin interactions with GWAS analyses is important in elucidatingcausal genes that modulate regulatory networks in autoimmune dis-\neases ( McGovern et al., 2016). As such, the spatial organization of DNA",
+              "title": "2018 - The genetic architecture of type 1 diabetes mellitus.pdf",
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+          }
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+}
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diff --git a/gnqa/paper2_eval/data/responses/human/de_gn_responses.json b/gnqa/paper2_eval/data/responses/human/de_gn_responses.json
new file mode 100644
index 0000000..6e5f9f6
--- /dev/null
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+              "content": "### Potential Benefits of CRISPR-Cas9:\n\n1. **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].\n2. **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].\n3. **Functional Genomics**: CRISPR is set to make a significant impact on genomic screening and scientific discovery, with recent demonstrations showing great promise [2].\n4. **Targeted Gene Regulation and Epigenome Modifications**: CRISPR-Cas9 has been linked to various effector proteins to enable targeted gene regulation and epigenome modifications [3].\n5. **Precision Medicine**: CRISPR-Cas9 holds significant promise for the development of new disease models and precision-targeted therapies, particularly in molecular medicine [4].\n6. **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].\n\n### Potential Risks of CRISPR-Cas9:\n\n1. **Off-Target Effects**: CRISPR-Cas9 is known to generate off-target alterations, which can result in unwanted mutations and potentially cytotoxic effects [6].\n2. **Technical Challenges**: There are several technical challenges that need to be addressed to maximize the benefits of CRISPR technology [2].\n3. **Long-Term Safety**: While early trial results are promising, the long-term safety of CRISPR-engineered cells is yet to be determined [1].\n4. **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].\n\nIn 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.",
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+            "metadata": {
+              "text": "neered nucleases, CRISPR-Cas9 \ntools have accelerated the pace of \ngenomic research by permitting \nhighly efficient knockouts or \nedits of virtually any gene in cells \nor model organisms. Multiple CRISPR-Cas9based clinical trials \nare in progress or are expected \nto begin soon. Although Cas9-\nengineered cells havent yet dem -\nonstrated efficacy at scale, early \ntrial results suggest that such \ncells are stable and dont cause \nacute adverse reactions in humans. \nLong-term safety is yet to be de -",
+              "title": "2021 - Human Molecular Genetics and Genomics.pdf",
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+            "id": "2fbabecd-22c1-5570-8f38-bc934d463710",
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+            "metadata": {
+              "text": "stageissetforCRISPRtomakeanenormousimpactongenomic\nscreening and thus scientic discovery in the coming years, and\nrecent demonstrations of this system have shown great promise\n(Shalem etal., 2015 ).However,a number of technical challenges\nmust be addressed in order to maximize the benet of this\ntechnology. In this review, we will discuss current applications\nof CRISPR in functional genomics and provide a perspective on\nfuturedevelopmentsinthisarea.\nCRISPR/Cas9 Genome Editing",
+              "title": "2015 - A new age in functional genomics using CRISPR Cas9 in arrayed library screening.pdf",
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+              "text": "heralding the age of genome editing. Furthermore, Cas9 or guide RNAs have been linked to various effector proteins to enable targeted gene regulation\n12,13 and epigenome modifications14,15. \nIt is worth noting, however, that many of these feats had been demonstrated previously using other nucleases or DNA-binding proteins\n1,16. In this Perspective, I shed light on early genome \nediting platforms that laid the groundwork for the widespread use of CRISPRCas9 in research and medicine (Fig. 1 ).",
+              "title": "2016 - Genome editing comes of age.pdf",
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+              "text": "CRISPR/CAS9 HOLDS SIGNIFICANT\nPROMISE FOR THE DEVELOPMENT OFNEW AD MODELS AND PRECISIONTARGETED AD THERAPY\nClustered regularly interspaced short palindromic\nrepeat (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-",
+              "title": "2018 - Neuro-Immuno-Gene- and GenomeEditing-Therapy for Alzheimer\u2019s.pdf",
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+            "score": 0.690676212310791,
+            "metadata": {
+              "text": "81.\nApplications for CRISPRCas9 beyond genome editing",
+              "title": "2016 - Genome editing comes of age.pdf",
+              "version": "v0",
+              "chunk_order": 55,
+              "document_id": "4078087a-c2a4-5c58-95b5-4ae243794800",
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+              "text": "cline- or Tet-regulated Cas9 system. Current CRISPR/Cas systems arefrom Streptococcus pyogenes ,Streptococcus thermophilus ,Neisseria\nmeningitides and Treponema denticola .2.5. Caveats of advanced genome editing tools\nOff-target effects . The DNA-binding domains of ZFNs and TALENs\nneed to be very speci c for the target site to avoid off-target cleavage,\nwhich results in unwanted mutations and potentially cytotoxic effects\n[27]. CRISPR/Cas9 is also known to generate off-target alterations,",
+              "title": "2016 - Dissecting diabetes metabolic disease.pdf",
+              "version": "v0",
+              "chunk_order": 29,
+              "document_id": "eee2f79d-e093-52fb-871a-798fd859235e",
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+            "id": "6ba3cf43-be4d-561f-ad84-f79921cab37e",
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+              "text": "on transcriptional interfere nce (CRISPRi) and activation\n(CRISPRa) have also harnessed Cas9-based technologies for\nuse in genome-wide studies ( 59,174). In addition, recent\nimprovements 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).\nnCas9\nThe CRISPR-Cas9 system can tolerate certain mismatches to the\nDNA target since the required gRNAs are short. A disadvantage,",
+              "title": "2020 - Functional Genomics in Pancreatic \u03b2 Cells Recent Advances in Gene Deletion and Genome Editing Technologies for Diabetes Research.pdf",
+              "version": "v0",
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+              "text": "CRISPR-Cas9 can be used to in -\nduce genome edits by creating \ntargeted DNA breaks that trigger \nsite-specific DNA repair. In next-\ngeneration formats, it can also \ncontrol the transcriptional output \nof genes or alter genome se -\nquences using a process of nu -\ncleotide base editing \nthat does not require \nrepair of DNA breaks. \nAs these technolo -\ngies continue to mature, it will \nbecome increasingly possible to \nalter cellular genomes efficiently \nand accurately.\nComing on the heels of engi -",
+              "title": "2021 - Human Molecular Genetics and Genomics.pdf",
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+              "text": "S.P . Raikwar et al. / Alzheimers Disease: New Therapeutic Horizons 333\ngene editing efciency of the CRISPR/Cas9 systems.",
+              "title": "2018 - Neuro-Immuno-Gene- and GenomeEditing-Therapy for Alzheimer\u2019s.pdf",
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+              "text": "13. Kleinstiver BP, etal. High-fidelity CRISPRCas9 nucleases with no detectable genome-wide \noff-target effects. Nature. 2016;529:4905.\n 14. Brane A, Tollefsbol T.Targeting telomeres and telomerase: studies in aging and disease uti-\nlizing CRISPR/Cas9 technology. Cells. 2019;8:186.\n 15. Wang H, etal. One-step generation of mice carrying mutations in multiple genes by CRISPR/\nCas-mediated genome engineering. Cell. 2013;153:9108.",
+              "title": "2020 - Clinical Genetics and Genomics of Aging.pdf",
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+              "content": "Epigenetics influences gene expression without changing the underlying DNA sequence through several mechanisms. These include:\n\n1. **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].\n2. **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].\n3. **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].\n\nThese 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].",
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+              "text": "to regulate lifetime and aging processes. In fact, epigenetics modulate gene expression\nwithout altering the DNA sequence. This is possible by means of different kinds of\nepigenetic modifications, including DNA methylation and histone modifications\n(which might affect gene transcription), and noncoding (nc)RNAs (which might\nchange gene expression at the post-transcriptional level)[59]. Given the crucial role of\nepigenetics in the modulation of gene expression, its alteration can contribute to",
+              "title": "2020 - Shared (epi)genomic background connecting neurodegenerative diseases.pdf",
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+              "text": "can regulate gene expression while the underlying DNA\nsequence remains the same. The epigenome is influenced\nboth by underlying genetic variants as well as by environ-\nmental factors including the social environment, health\nbehaviors, and environmental pollutants [ 11]. Methylation\nof CpG dinucleotides, the best understood epigenetic\nmechanism, is also dynamic over the life course. It is well\nestablished that epigenomic patterns of DNA methylation\nchange with age [ 12]. A recent study in lymphocytes",
+              "title": "2018 - DNA methylation in the APOE genomic.pdf",
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+              "text": "Epigenetics Changes arising from alterations in gene expression \nlevels that are caused by reversible chemical \nmodification of DNA, but not changes to the DNA \nsequence passed on from parents to offspring.",
+              "title": "2016 - Te-Mata-Ira-Genome-Research-Guidelines.pdf",
+              "version": "v0",
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+              "text": "Epigenetic changes refer to heritable changes in gene expression\nwhich do not involve changes in DNA sequences. Several epigenetic\nmechanisms have been found to regulate gene expression. Whilst\nthe most studied mechanism relates to DNA methylation, other\nchanges, including histone modi cations and non-coding RNAs,\nalso play an important role, and can be transmitted from one\ngeneration to the next. DNA methylation involves the addition of\nmethyl groups to DNA, mainly at CpG sites, which converts cytosine",
+              "title": "2015 - Maternal diabetes, gestational diabetes and the role of epigenetics in their long term effects on offspring.pdf",
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+              "text": "EPIGENETIC STUDIES \n An epigenetic mechanism is a biochemical alteration to the DNA molecule that \ndoes not change the sequence of the DNA but does in  uence gene expression. \n Epigenetics   is often de  ned as the study of mitotically and/or meiotically heri-\ntable changes in gene function that cannot be explained by changes in DNA sequence (Russo, Martienssen, & Riggs, 1996, p. 1). \n The epigenetic/epigenomic approach shares many advantages and disad-",
+              "title": "2011 - Molecular Genomic Research Designs.pdf",
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+              "text": "ity and expression of genes without changing their DNA sequence [ 4]. These modications\nare: DNA methylation, histone modications, and ncRNAs including miRNA [4]. The en-\nvironment and lifestyle can induce epigenetic changes, such as pollution, tobacco smoking,\nobesity, lack of physical activity, and alcohol consumption [ 108]. Furthermore, exposure\nto such environmental factors can have a buttery effect: epigenetic modications may",
+              "title": "2022 - Genomics and Epigenomics of Gestational Diabetes Mellitus Understanding the Molecular Pathways of the Disease Pathogenesis.pdf",
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+              "text": "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. \n Within the nucleus, the mammalian genome is wrapped",
+              "title": "2012 - Systems Biology Approaches to Nutrition.pdf",
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+              "text": "gene expression can also occur by  trans-epigenetics ( Bonasio\net 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\nthat modify DNA and histones (methyltransferases, demethy-lases, acetyltransferases, deacetylases) are central epigeneticregulatory mechanisms ( Rando and Chang, 2009 ).\nThe essence of epigenetics is not only the establishment, but",
+              "title": "2012 - Aging, Rejuvenation, and Epigenetic.pdf",
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+              "text": "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",
+              "title": "2008 - Genetic Effects on Environmental Vulnerability to Disease Novartis Foundation Symposium 293.pdf",
+              "version": "v0",
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+              "text": "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.",
+              "title": "2011 - EXPLOITING NATURAL AND INDUCED GENETIC VARIATION TO STUDY HEMATOPOIESIS.pdf",
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+              "content": "Mitochondrial DNA (mtDNA) plays a unique role in heredity and differs significantly from nuclear DNA (nDNA) in several ways:\n\n1. **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.\n\n2. **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].\n\n3. **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].\n\n4. **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].\n\n5. **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.\n\nIn 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.",
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+              "text": "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",
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+              "text": "cMitochondria are semiautonomous organelles; possess their own\nreplication-, transcription- and translation system\ncExclusively maternal inheritance of mitochondrial DNA\ncMitotic segregation of mitochondrial DNAcan lead to hetero-\nplasmy, i.e., the proportion of genetically different populations ofmitochondria differs between generations of mitotically activecells\ncApproximately tenfold higher mutation rate compared with nuclear",
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+              "text": "DIFFERENCES BETWEEN MITOCHONDRIAL\nAND NUCLEAR GENETICS\nArealisticassessmentoftherelevanceofmitochon-",
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+              "text": "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-\nsively from an offsprings mother; because of this phenome-\nnon, 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.",
+              "title": "2015 - Basic Concepts and Potential Applications of Genetics and Genomics for Cardiovascular and Stroke Clinicians.pdf",
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+              "text": "The regulation of the mitochondrial genome also reflects \nits 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 -",
+              "title": "2020 - Mitonuclear genomics and aging.pdf",
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+              "text": "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",
+              "title": "2020 - Mitonuclear genomics and aging.pdf",
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+              "text": "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. \nMONOGENIC CONDITIONS \nOver the course of the 20th century, a combination",
+              "title": "2002 - Genomic Medicine - A Primer.pdf",
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+              "text": "2. Mitochondrial DNA structure and properties\nMitochondrial genomes (mt-genomes) are short\ncircular 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\ncreductase (respiratory complex III), three subunits",
+              "title": "2001 - Mitochondrial genome instability in human cancers.pdf",
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+              "text": "Abstract\nThe human mitochondrial genome consists of approximately 1500 genes, 37 encoded by the maternally inherited mitochondrial DNA\n(mtDNA) and the remainder encoded in the nuclear DNA (nDNA). The mtDNA is present in thousands of copies per cell and encodes",
+              "title": "2005 - The mitochondrial genome in human adaptive radiation and disease.pdf",
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+              "text": "(mtDNA). MtDNA carries important genetic information concerning cellular metabolismand the generation of energy. It has been suggested that mitochondria and mtDNA could be\nof significance during early embryo development. Our work confirms this hypothesis. Specif-\nically, our findings implicate mitochondria and their genome in female reproductive agingand the generation of embryonic chromosome abnormalities. Importantly, we describe a di-",
+              "title": "2015 - Altered Levels of Mitochondrial DNA.pdf",
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+              "content": "The ethical considerations surrounding prenatal genetic testing and the selective termination of pregnancies based on genetic factors are multifaceted and complex. Key considerations include:\n\n1. **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.\n\n2. **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.\n\n3. **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].\n\n4. **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].\n\n5. **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].\n\n6. **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].\n\nIn 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.",
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+              "text": "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",
+              "title": "2009 - Opening Up the Conversation on Genetics.pdf",
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+              "text": "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,",
+              "title": "2009 - Opening Up the Conversation on Genetics.pdf",
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+              "text": "Hum Genet 1995;57:12331241.\n24. Committee on Bioethics. Ethical and policy issues in genetic testing and \nscreening of children. Pediatrics 2013;131:620622.\n25. 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:  \n234245.\n26. Wilfond B, Ross LF. From genetics to genomics: ethics, policy, and parental decision-making. J Pediatr Psychol 2009;34:639647.",
+              "title": "2013 - ACMG recommendations for reporting of incidental findings.pdf",
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+              "text": "Informed Consent and Genetic Testing   \nGenetic testing is increasingly used across the life continuum \nfor screening, diagnosis, and de termining the best treatment \nof diseases. Obstetric and pediat ric nurses have traditionally \nbeen involved in the genetic testing process with prenatal \nscreening for genetic conditions such as spina bifida and Down \nsyndrome, and newborn screening for genetic conditions such",
+              "title": "2008 - Genetic and Genomic Healthcare Ethical Issues of Importance to Nurses.pdf",
+              "version": "v0",
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+              "text": "Objective Ethical evaluation of genetic testing in children is traditionally based on balancing clinical\nbenefits 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\nshould play a larger role in shaping pediatric genetic testing practices, and play a similar role regarding decisions",
+              "title": "2009 - From Genetics to Genomics Ethics, Policy, and Parental.pdf",
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+              "text": "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.",
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+              "text": "all of the complex ethical and legal issues rel-\nevant to genetic testing would disappear if\nthere were effective preventions or treatments\navailable for genetic conditions. The ability\nto predict future disease in conjunction with\na limited ability to do much about it has im-\nportant social and psychological implications\nthat must be addressed in conducting genetic\nresearch.\nOne final factor worth consideration in un-\nderstandingthesensitivitytogeneticmedicine",
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+              "text": "Newborn screening by tandem mass spec-trometry: ethical and social issues. Can J Public Health 2007; \n  98:   284286. \n 65 Belle-Isle L: Genetic testing for late onset dis-\neases: a population and public health per-spective. Health Policy Res Bull 2001; \n  1:   11\n12. \n 66 Williams-Jones B: Private genetic testing in \nCanada: a summary. Health Law Rev 2001;   9:  \n 1013. \n 67 Begleiter ML: Training for genetic counsel-\nlors. Nat Rev Genet 2002;   3:   557561. \n 68 Carroll JC, Reid AJ, Woodward CA, Per-",
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+              "text": "Although risk-based genetic testing for common diseases raise similar ethical issues to more \ntraditional genetic testing for rare diseases, new challenges are raised due to the type of information \nrevealed and access to tests. With thoughtful deliberation with health professionals, patients and \nfamilies, test developers and laboratories, insurers and other stakeholders, these issues can be \naddressed to ensure the safe and appropriate use of these promising new clinical applications. \n  \nREFERENCES",
+              "title": "2018 - Ethical_Social_and_Legal_Consequences.pdf",
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+              "text": "against testing, parents should generally be given final\ndecision-making authority.\nEthical Considerations in Developing Policy\nfor Comprehensive Genomic Testing\nIn the near future, genomic testing is likely to become\nmore accessible and will provide both information aboutthe risks of common conditions such as heart disease,\ndiabetes, and hypertension as well as predictions aboutindividual responses to specific pharmaceuticals and\nother medical therapies (Aspinall & Hamermesh, 2007).",
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+              "content": "### How-to Guide for Genetic Sequencing\n\nGenetic 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.\n\n#### Step 1: Sample Preparation\n1. **Collect DNA Sample**: Obtain a sample from the organism or tissue of interest. This could be blood, saliva, or tissue biopsy.\n2. **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.\n\n#### Step 2: Library Preparation\n1. **Fragment DNA**: Break the DNA into smaller fragments. This can be done using mechanical shearing or enzymatic digestion.\n2. **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.\n\n#### Step 3: Amplification\n1. **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.\n\n#### Step 4: Sequencing\n1. **Choose a Sequencing Platform**: Select an appropriate sequencing technology based on your needs. Common platforms include Illumina, Ion Torrent, and Pacific Biosciences [6], [9].\n2. **Load DNA onto the Sequencer**: Follow the manufacturer's instructions to load your prepared DNA library onto the sequencing machine.\n3. **Run the Sequencer**: Initiate the sequencing run. The machine will read the nucleotide sequences of the DNA fragments and generate raw sequence data.\n\n#### Step 5: Data Analysis\n1. **Base Calling**: Use software like Phred to convert raw data into nucleotide sequences [10].\n2. **Sequence Assembly**: Assemble the short DNA sequences into longer contiguous sequences (contigs) using tools like Phrap or CAP3 [10].\n3. **Variant Calling**: Identify genetic variants by comparing the assembled sequences to a reference genome. This can be done using various bioinformatics tools.\n\n#### Step 6: Interpretation\n1. **Annotation**: Annotate the sequences to identify genes, regulatory elements, and other functional regions.\n2. **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].\n\n#### Additional Resources\n- **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].\n- **Sequencing Platforms**: For comparisons of different sequencing platforms, see the work by Quail et al. [6].\n- **Bioinformatics Tools**: Explore various bioinformatics tools and software for sequence analysis and data management [10].\n\nBy following these steps and utilizing the referenced resources, you can successfully perform genetic sequencing and analyze the resulting data.",
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+              "text": "36. Sequencing, H.G. Finishing the euchromatic sequence of the human genome. Nature 2004 ,431, 931945.\n37. Heather, J.M.; Chain, B. The sequence of sequencers: The history of sequencing DNA. Genomics 2016 ,107,\n18. [CrossRef]\n38. Rothberg, J.M.; Leamon, J.H. The development and impact of 454 sequencing. Nat. Biotechnol. 2008 ,26,\n11171124. [CrossRef] [PubMed]\n39. Shendure, J.; Ji, H. Next-generation DNA sequencing. Nat. Biotechnol. 2008 ,26, 11351145. [CrossRef]\n[PubMed]",
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+              "text": "22. Karow, J. Qiagen launches GeneReader NGS System \natAMP; presents performance evaluation by broad. \nGenomeWeb  [online], https:// www.genomeweb.com/\nmolecular-diagnostics/qiagen-launches-genereader-\nngs-system-amp-presents-performance-evaluation  \n(4Nov 2015).\n23. Smith,D.R. & McKernan,K. Methods of producing \nand sequencing modified polynucleotides . US Patent \n8058030 (2011).\n24. Margulies,M. etal.  Genome sequencing in \nmicrofabricated high-density picolitre reactors. Nature \n437, 376380 (2005).",
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+              "text": "11 BIOINFORMATIC CHALLENGES FOR GENOMIC MEDICINE \n \n \nProcessing and managing of high-throughput sequence data \n \n High throughput sequencing offers severa l advantages relative to array-based \ngenotyping or expression assays. First, unlike genotyping arrays, whole genome sequencing is not limited to interrogating onl y known sequence variants. Similarly, RNA-\nsequencing (RNA-seq) enables expression quanti fication of novel transcripts that are not",
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+              "text": "11 BIOINFORMATIC CHALLENGES FOR GENOMIC MEDICINE \n \n \nProcessing and managing of high-throughput sequence data \n \n High throughput sequencing offers severa l advantages relative to array-based \ngenotyping or expression assays. First, unlike genotyping arrays, whole genome sequencing is not limited to interrogating onl y known sequence variants. Similarly, RNA-\nsequencing (RNA-seq) enables expression quanti fication of novel transcripts that are not",
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+              "text": "High-throughput bacterial genome sequencing: an embarrassment of choice,\naworldof opportunity.NatRevMicrobiol2012;10:599-606.\n11.CroucherNJ,DidelotX.Theapplicationof genomicstotracingbacterialpathogen\ntransmission.CurrOpinMicrobiol2015;23:62-7.\n12.ShendureJ,JiH.Next-generationDNAsequencing.NatBiotechnol2008;26:1135-\n45.\n13.MillerJR,KorenS,SuttonG.Assemblyalgorithmsfornext-generationsequencing\ndata.Genomics2010;95:315-27.\n14.OlsonND,LundSP,ColmanRE,FosterJT,SahlJW,SchuppJM,etal.Bestpractices",
+              "title": "2017 - Infection control in the new age of genomic epidemiology.pdf",
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+              "text": "sequencing. Genome Res. 20, 11651173 (2010).\n64. English,A.C. etal.  Assessing structural variation in a \npersonal genome-towards a human reference diploid \ngenome. BMC Genomics 16, 286 (2015).\n65. Carneiro,M.O. etal.  Pacific Biosciences sequencing \ntechnology for genotyping and variation discovery in \nhuman data. BMC Genomics 13, 375 (2012).\n66. Quail,M.A. etal.  A tale of three next generation \nsequencing platforms: comparison of Ion T orrent, \nPacific Biosciences and Illumina MiSeq sequencers.",
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+              "text": "Nat. Biotechnol. 30, 10331036 (2012).\n111. Chrystoja,C.C. & Diamandis,E.P . Whole genome \nsequencing as a diagnostic test: challenges and \nopportunities. Clin. Chem. 60, 724733 (2014).\n112. McGuire,A.L. etal.  Point-counterpoint. Ethics and \ngenomic incidental findings. Science 340, 10471048 \n(2013).\n113. Bowers,J. etal.  Virtual terminator nucleotides for \nnext-generation DNA sequencing. Nat. Methods 6, \n593595 (2009).\n114. Heger,M. Chinas Direct Genomics unveils new",
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+              "text": "sequencing. Bioinformatics 31, 20402042 (2015).\n46. Qiagen.  Oncology insights enabled by knowledge base-\nguided panel design and the seamless workflow of the \nGeneReader NGS system  Press Release. Qiagen  \n[online], http://www.genereaderngs.com/PROM-9192-\n001_1100403_WP_GeneReader_NGS_0116_NA.pdf  \n(2016).\n47. Forgetta,V. etal.  Sequencing of the Dutch elm disease \nfungus genome using the Roche/454 GS-FLX Titanium \nSystem in a comparison of multiple genomics core",
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+              "text": "FURTHER INFORMATION\n10X Genomics: http://www.10xgenomics.com\n454 Sequencing: http://www.454.com\nAdvances in Genome Biology and Technology (AGBT): \nhttp://www.agbt.org\nBGISEQ500: http://seq500.com/en/portal/Sequencer.shtml\nIllumina: http://www.illumina.com\nIon Torrent: https://www.thermofisher.com/us/en/home/\nbrands/ion-torrent.html\nOxford Nanopore Technologies: https://www.nanoporetech.\ncom\nPacific Biosciences: http://www.pacb.com\nPersonal Genome Project: http://www.personalgenomes.org",
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+              "text": "DNA), and provide the means to link sequences containing applications. First, \nbase- callers like Phred (4,5) extract raw sequences from raw data. There are \nalso contig assemblers like Phrap (University of Washington, http://bozeman.\nmbt.washington.edu/phrap.docs/phrap.html ) or CAP3 (6) that assemble frag-\nments to contigs and packages like consed (7) or GAP4 (8), which are used to \nfinish sequencing projects. These programs are not explained in detail here.",
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+              "content": "The genes that give a predisposition to developing Type 1 Diabetes (T1D) include:\n\n1. **Major Histocompatibility Complex (MHC)** genes located in the HLA region of chromosome 6 [1], [2], [6].\n2. **Insulin gene (INS)** located in the IDDM2 locus on chromosome 11 [3].\n3. **CTLA-4 (Cytotoxic T-Lymphocyte Associated 4)** [2].\n4. **PTPN22 (Protein Tyrosine Phosphatase, Non-Receptor Type 22)** [2].\n5. **IL2RA/CD25 (Interleukin 2 Receptor Alpha)** [2].\n6. **IFIH1/MDA5 (Interferon-Induced Helicase 1)** [2].\n\nThese genes are strongly associated with T1D and contribute to the genetic susceptibility to the disease.",
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+              "text": "are involved in the development of the disease [127 ].  There is evidence that more \nthan twenty regions of the genome are involved in t he genetic susceptibility to T1D.  \nThe genes most strongly associated with T1D are loc ated in the HLA region of \nchromosome 6 [128].  Similar to T1D, T2D has a stro ng genetic component.  To date, \nmore than 50 candidate genes for T2D have been inve stigated in various populations \nworldwide.  Candidate genes are selected due to the ir interference with pancreatic",
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+              "text": "pre-existing statistical support for a role in T1D-susceptibility: these\nare the major histocompatibility complex (MHC), the genes encod-\ning insulin, CTLA-4 (cytotoxic T-lymphocyte associated 4) and\nPTPN22 (protein tyrosine phosphatase, non-receptor type 22), and\nthe regions around the interleukin 2 receptor alpha ( IL2RA/CD25 )\nand interferon-induced helicase 1 genes ( IFIH1 /MDA5)94. However,\nthese signals can explain only part of the familial aggregation of T1D.",
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+              "text": "C. The Insulin Gene\nA lesser genetic predisposition to T1D is conferred by\nthe IDDM2 locus on chromosome 11 containing the insu-lin gene region. A polymorphic region located 5 =of the\ninsulin gene was rst reported in 1984 to be associatedwith T1D in caucasoids (39). Now established as a pri-\nTYPE 1 DIABETES: FROM CAUSE TO CURE 81\nPhysiol Rev VOL 91 JANUARY 2011 www.prv.org\nDownloaded from journals.physiology.org/journal/physrev (041.090.188.152) on July 14, 2023.",
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+              "text": "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\ngreater. Although /H1101170% of individuals with T1D carry",
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+              "text": "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.\n12 Also, several",
+              "title": "2015 -precision-medicine-for-managing-diabetes.pdf",
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+              "text": "14 \n carried out on large cohorts including collections of families with  affected sibling pairs (Pociot  \net al., 2010). These studies have provided evidence for over forty T1D susceptibility regions , \nbut the  exact mechanisms by which the variation found in these regions  confer susceptibility to \nT1D is still not clear (Noble and Erlich, 2012). The most important genes contributing to T1D \nsusceptibility are located in the MHC class II region , also  referred to as t he Human Leukocyte",
+              "title": "2016 - The Genomics of Type 1 Diabetes.pdf",
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+              "text": "The ultimate proof of an inherited contribution to disease pathogenesis comes\nfrom the identication of susceptibility genes. As described below, an increasing\nnumber 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",
+              "title": "2001 - The genetics of type 2 diabetes.pdf",
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+              "text": "loci contribute to Type 1 Diabetes (T1D) susceptibility and age at T1D onset. Hum.\nImmunol. 66,301313 (2005).\n9. Aly, T. A. et al. Extreme genetic risk for type 1A diabetes. Proc. Natl Acad. Sci. USA\n103, 14074 14079 (2006).\n10. Noble, J. A. et al. The HLA class I A locus affects susceptibility to type 1 diabetes.\nHum. Immunol. 63,657664 (2002).\n11. Honeyman, M. C., Harrison, L. C., Drummond, B., Colman, P. G. & Tait, B. D.\nAnalysis of families at risk for insulin-dependent diabetes mellitus reveals that",
+              "title": "2007 - Localization of type 1 diabetes susceptibility to the MHC Class 1 Genes.pdf",
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+              "text": "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.,\nPDX1, PAX4, INS, HNF1A, andGCK)[27]. However, other non-beta cell\ntypes express genes mutated in monogenic diabetes (such as PAX6\nand RFX6 ), congenital hyperinsulinemia ( HADH, UCP2 ) and those\nimplicated as T2D GWAS target/effector genes [28].",
+              "title": "2019 - (Epi)genomic heterogeneity of pancreatic islet function and failure in type 2 diabetes.pdf",
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+              "text": "chain promoter (Serreze and Leiter 2001). This observation, alongwith human genetic studies, suggests that increased T1D risk in\nhumans may also result from the combination of rare and common\nvariants within the human population (Concannon et al. 2009b).\nDespite the identification of several Iddgenes to date, this\nlimited collection does not fully explain T1D pathogenesis or the\nunderlying genetic architecture for T1D risk. One of the many Idd",
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+              "text": "comprehensive, powerful, flexible and interactive gene set enrichment analysis toolkit. Nucleic\nAcids Research ,45(W1), W130W137.\n[44] Zhang, B., Kirov, S., & Snoddy, J. (2005). WebGestalt: an integrated system for exploring\ngene sets in various biological contexts. Nucleic Acids Research ,33(Web Server issue), W741-8.\n[45] McLaren, W., Gil, L., Hunt, S. E., Riat, H. S., Ritchie, G. R. S., Thormann, A., Flicek, P ., et al.\n(2016). The ensembl variant effect predictor. Genome Biology ,17(1), 122.",
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+              "content": "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].",
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+              "text": "417 Use of Rat Genomics for Investigating the Metabolic Syndrome\nand phenotypic traits are available to the scientific community \nin databases, such as Ensembl ( http://www.ensembl.or g), the \nRat Genome Database ( http://www.rgd.mcw.ed u), eQTL \nExplorer ( http://www. web.bioinformatics.ic.ac.uk/eqtlexplore r) \nor GeneNetwork ( http://www.genenetwork.or g). Additional \nonline rat genetic resources have been recently reviewed by \nTwigger et  al. (11).",
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+              "text": "Howard Jacob (Medical College of Wisconsin)\ndiscussed the Rat Genome Database disease portals,\na 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\nbiological processes. The Rat Genome Database",
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+              "text": "10. Consortium STAR, Saar K, Beck A, Bihoreau \nMT, Birney E, Brocklebank D, Chen Y et  al \n(2008) SNP and haplotype mapping for \ngenetic analysis in the rat. Nat Genet \n40:560566\n 11. Twigger SN, Pruitt KD, Fernndez-Surez \nXM, Karolchik D, Worley KC, Maglott DR \net al (2008) What everybody should know about the rat genome and its online resources. \nNat Genet 40:523527\n 12. Butcher LM, Beck S (2008) Future impact of \nintegrated high-throughput methylome anal-\nyses on human health and disease. J Genet",
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+              "text": "3. Can data sharing in rodent phenotyping help with replicability?\nLaboratory mice and rats are the main mammalian models currently\nused for high-throughput genomic and behavior genetic research, and\nare employed primarily to explore and test gene function. This is con-\nsidered by some to be the great challenge facing biologists today \n(Collins et al., 2007 ). Rodent models are used extensively as part of\npreclinical development and testing of treatments for disease in hu-",
+              "title": "2018 - Reproducibility and replicability of rodent phenotyping in preclinical studies.pdf",
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+              "text": "Bioinformatics and Statistical Analysis\nR was used for basic analysis of phenotypic data. GeneNetwork\n(www.genenetwork.org) was used for correlation and genetic\nanalyses. The original phenotypes published in this paper and all\nmicroarray data generated in these cohorts are available for public\nanalysis or download using the GeneNetwork database (Species:\nMouse, Group: BXD, Type: Adipose mRNA, Liver mRNA, or\nMuscle mRNA, then select the EPFL datasets). The three",
+              "title": "2014 - An evolutionarily conserved role for the aryl hydrocarbon receptor in the regulation of movement.pdf",
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+              "text": "[23]. Shimoyama M, De Pons J, Hayman GT, Laulederkind SJ, Liu W, Nigam R, Petri V , Smith JR, \nTutaj M, Wang S-J, The Rat Genome Database 2015: genomic, phenotypic and environmental \nvariations and disease, Nucleic acids research 43(D1) (2014) D743D750. [PubMed: 25355511] \n[24]. Dickinson ME, Flenniken AM, Ji X, Teboul L, Wong MD, White JK, Meehan TF, Weninger WJ, \nWesterberg H, Adissu H, High-throughput discovery of novel developmental phenotypes, Nature \n537(7621) (2016) 508. [PubMed: 27626380]",
+              "title": "2021 - Characterizing modifier genes of cardiac fibrosis phenotype in hypertrophic cardiomyopathy.pdf",
+              "version": "v0",
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+            "id": "5c92f513-fea8-51fa-8432-929553dc9e32",
+            "score": 0.6662621696669513,
+            "metadata": {
+              "text": "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.\nAnalysis of microarray data",
+              "title": "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",
+              "version": "v0",
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+              "text": "(data not shown). Therefore, it seems\nlogical to position the rat field so themechanistic, disease-based research canbe integrated into the awesome power ofthe human and mouse genome projects.\nProgress of the Rat Genome Project\nRecognizing 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\nEST) Project, and the Rat",
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+              "content": "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].",
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+              "text": "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",
+              "title": "2010 - Integrated genomic approaches to identification of candidate genes underlying metabolic and cardiovascular phenotypes in the spontaneously hypertensive rat.pdf",
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+              "text": "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,\nwhich, in turn, will preserve vision.\nExperimental procedures\nHuman donor eyes\nHuman donor eyes were collected in accordance with the",
+              "title": "2015 - Multipronged approach to identify and validate a novel upstream regulator of Sncg.pdf",
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+              "text": "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,\nwhich, in turn, will preserve vision.\nExperimental procedures\nHuman donor eyes\nHuman donor eyes were collected in accordance with the",
+              "title": "The FEBS Journal - 2015 - Chintalapudi - Multipronged approach to identify and validate a novel upstream regulator of Sncg.pdf",
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+              "text": "(http://www.cbil.upenn.edu/PaGE/). All microarray platforms and image-analysis\nsoftware are supported. In addition, RAD is being used for CGH, ChIP , and SAGE\ndata. RAD can produce MAGE-ML les for export of data to other databases or\nsoftware packages. RAD is part of a more general Genomics Unied Schema, which\nprovides a platform to integrate gene and transcript data from a variety of organisms.\nAdvantages\nRAD is a scalable, Web-accessible database that can accommodate data from sev-",
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+              "text": "(http://www.cbil.upenn.edu/PaGE/). All microarray platforms and image-analysis\nsoftware are supported. In addition, RAD is being used for CGH, ChIP , and SAGE\ndata. RAD can produce MAGE-ML les for export of data to other databases or\nsoftware packages. RAD is part of a more general Genomics Unied Schema, which\nprovides a platform to integrate gene and transcript data from a variety of organisms.\nAdvantages\nRAD is a scalable, Web-accessible database that can accommodate data from sev-",
+              "title": "003 -Barnes- Bioinformatics_for_Geneticists.pdf",
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+            "metadata": {
+              "text": "(http://www.cbil.upenn.edu/PaGE/). All microarray platforms and image-analysis\nsoftware are supported. In addition, RAD is being used for CGH, ChIP , and SAGE\ndata. RAD can produce MAGE-ML les for export of data to other databases or\nsoftware packages. RAD is part of a more general Genomics Unied Schema, which\nprovides a platform to integrate gene and transcript data from a variety of organisms.\nAdvantages\nRAD is a scalable, Web-accessible database that can accommodate data from sev-",
+              "title": "2007 - Bioinformatics_for_Genetices_MAZEN_SAEED.pdf",
+              "version": "v0",
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+              "text": "differentiallysusceptibletodeath,withalpha-RGCsandintrinsicallyphotosensitiveRGCs\n(ipRGCs) being less sensitive to cell death than other RGC subtypes in a mouse model\nof glaucoma.\nKeywo\nrds: retinal ganglion cells, gene regulatory networks, transcription factors, recombinant inbred strain,\nsubtypes\nINTRODUCTION\nTheretinalganglioncell(RGC)isthenaloutputneuronoftheretina,projectingthroughtheoptic\nnerve to the brain, where it targets a number of functionally distinct areas: for visual perception,",
+              "title": "2018 - Genetic Networks Activated by Blast Injury to the Eye.pdf",
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+              "text": "AG18245 (DG), NIAAA U01AA014425 (LL), and P20 DA021131 (RW). We thank Derek Rains, Gurjit Rai,\nMeifen Lu, Richard Cushing, Erich Brauer, and Alan Weatherford for their invaluable technical assistance.\nAbbreviations\nBrdU bromodeoxyuridine\nCV cresyl violet\nGF growth fraction\nLOD likelihood of the odds\nLRS likelihood ratio statistic\nNSCs neural stem cells\nOB olfactory bulb\nDG dentate gyrus\nQTL quantitative trait locus\nRI recombinant inbred\nRMS rostral migratory stream\nSGZ subgranular zone",
+              "title": "2010 - Identification of a Chr 11 quantitative trait locus that modulates proliferation in the rostral migratory stream of the adult mouse brain_.pdf",
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+              "text": "Rdh10, Lrat,) whose biology functions are directly associated w ith the metabolism of  retinoid. \nRGR (retinal G protein-coupled receptor, protein of  Rgr ) is a protein that structurally resembles \nvisual pigments and other G protein-coupled recepto rs. Light isomerizes 11- cis - into all-trans -\nretinal, triggering a conformational transition of the opsin molecule that initiates phototransduction . \nAfter bleaching all- trans -retinal leaves the opsin, and light sensitivity mu st be restored by",
+              "title": "2011 - Genetic Regulatory Network Analysis for Rpe65 in the Eye of BXD Mice.pdf",
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+              "text": "GeneNetwork system, we were able to define robust expression covariance signatures for\nRGCs and confirmed membership of Chrna6 within the RGC cell type of the retina using\nnew array data sets and RT-PCR tracking through a progressive RGC loss mouse line.\nChrna6  can be added as reliable biomarker for RGCs and RGC loss secondary to glaucoma.\nIt is important to note that in addition to providing evidence for Chrna6 expression as a",
+              "title": "2013 - Effects of Glaucoma on Chrna6 Expression in the Retina.pdf",
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+              "content": "For pathway analyses, you can use the following resources:\n\n1. **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].\n\n2. **Reactome**: A cross-referenced, manually curated, and peer-reviewed pathway database that provides enhanced pathway visualization [2], [3], [5], [7], [9], [10].\n\n3. **WikiPathways**: A multifaceted pathway database that bridges metabolomics to other omics research [2], [3], [9], [10].\n\n4. **HumanCyc**: Primarily a database of metabolic pathways [4].\n\n5. **Pathway Commons**: A web resource for biological pathway data [6].\n\n6. **BioCarta**: Provides pathway maps [8].\n\n7. **BioGRID**: A genetic and protein interaction database [8].\n\n8. **DAVID (Database for Annotation, Visualization, and Integrated Discovery)**: Offers gene ontology and pathway analysis [8].\n\n9. **WebGestalt**: Provides gene ontology and pathway analysis [8].\n\nThese 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].",
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+              "text": "[3] and KEGG [4] all allow a list of genes to be crossed\nwith biological functions and genetic networks, including\nmetabolic, signalling or other regulation pathways. Basic\nstatistical analysis (e.g., [5,6]) can then determine\nwhether a pathway is over-represented in the list, and\nwhether it is over-activated or under-activated. However,\none can argue that introducing information on the path-\nway at this point in the analysis process sacrifices some\nstatistical power to the simplicity of the approach. For",
+              "title": "2007 - Classification of microarray data using gene networks.pdf",
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+            "id": "6ab69fa3-2dcf-50dc-bd36-283407a39451",
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+            "metadata": {
+              "text": "Sidiropoulos, K., Viteri, G., Sevilla, C., Jupe, S., Webber, M., Orlic -Milacic, M., et al. (2017). \nReactome enhanced pathway visualization. Bioinformatics  33, 3461 3467. \ndoi:10.1093/bioinformatics/btx441.  \nSlenter, D. N., Kutmon, M., Hanspers, K., Riutta, A., Windsor, J., Nunes, N., et al. (2018). \nWikiPathways: a multifaceted pathway database bri dging metabolomics to other omics \nresearch. Nucleic Acids Res.  46, D661 D667. doi:10.1093/nar/gkx1064.",
+              "title": "2020 - Gene network a continuously updated tool for systems genetics analyses.pdf",
+              "version": "v0",
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+            "id": "02b60e7c-25ee-5583-822d-a0a4799f4eeb",
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+            "metadata": {
+              "text": "Sidiropoulos, K., Viteri, G., Sevilla, C., Jupe, S., Webber, M., Orlic -Milacic, M., et al. (2017). \nReactome enhanced pathway visualization. Bioinformatics  33, 3461 3467. \ndoi:10.1093/bioinformatics/btx441.  \nSlenter, D. N., Kutmon, M., Hanspers, K., Riutta, A., Windsor, J., Nunes, N., et al. (2018). \nWikiPathways: a multifaceted pathway database bri dging metabolomics to other omics \nresearch. Nucleic Acids Res.  46, D661 D667. doi:10.1093/nar/gkx1064.",
+              "title": "2020 - Gene network a completely updated tool for systems genetics analyses.pdf",
+              "version": "v0",
+              "chunk_order": 151,
+              "document_id": "128224f1-3545-52c3-93cb-77c3cf4ec70a",
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+            "id": "7dbba72c-bf76-5431-aa01-9c828355bed8",
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+              "text": "analysis, we restrict the analysis to curated, peer-reviewedpathways based on experimental evidence, and pathways inferred\nvia gene homology. We draw candidate pathways from the\ncollections listed in Figure 6 (see also Supplementary Materials).\nKEGG [146] and HumanCyc [147] are primarily databases of\nmetabolic pathways, and are unlikely to be relevant to someJoint Analysis of Variants and Pathways in Disease\nPLOS Genetics | www.plosgenetics.org 11 October 2013 | Volume 9 | Issue 10 | e1003770",
+              "title": "2013 - Integrated Enrichment Analysis of Variants.pdf",
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+            "id": "3539d21a-cc75-54dc-aca3-2d936893481b",
+            "score": 0.6798771023750305,
+            "metadata": {
+              "text": "textual interface, also linking out to the original articles.\nAnalysing participating pathways is an important aspect\nof any gene s functional analysis strategy. In this view,\nREACTOME (http://www.reactome.org) [13] is a cross\nreferenced, manually curated and peer reviewed pathway\ndatabase. LitInspector (http://www.litinspector.org) [14]and NetPath (http://www.netpath.org/index.html) [15]\nallow one to access curated signal transduction related lit-",
+              "title": "2013 - Candidate gene association studies a comprehensive guide to useful in silicotools.pdf",
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+            "id": "0c72f387-9074-592d-a87e-7643c2f37d0c",
+            "score": 0.6796749830245972,
+            "metadata": {
+              "text": "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: \n 10.1093/nar/gkq1039      \n    6.    Baker EJ, Jay JJ, Bubier JA, Langston MA, \nChesler EJ (2012) GeneWeaver: a web-based system for integrative functional genomics. Nucleic Acids Res 40(Database issue):D1067D1076. doi: \n 10.1093/nar/gkr968      \n     7.    Bubier JA, Phillips CA, Langston MA, Baker",
+              "title": "2017 - Integrative functional genomics for systems genetics in GeneWeaver. org.pdf",
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+              "text": "67. Krmer, A., Green, J., Pollard, J. Jr. & Tugendreich, S. Causal analysis approaches in ingenuity pathway analysis. Bioinformatics  \n30, 523530 (2014).\n68. Jassal, B. et al. The reactome pathway knowledgebase. Nucleic Acids Res. 48, D498D503 (2020).\n69. Okonechnikov, K., Conesa, A. & Garca-Alcalde, F. Qualimap 2: advanced multi-sample quality control for high-throughput sequencing data. Bioinformatics 32, 292294 (2016).",
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+              "text": "Biocarta\n   pathway  maps  www.biocarta.com\nBioGRID\n   genetic  and  protein  interaction  database  thebiogrid.org\nAnalysisPLINK  processing  and  QC  of  genetic  data  sets  pngu.mgh.harvard.edu/ purcell/plink\nBioconductor\n   processing  and  QC  of  expression  data  sets  www.bioconductor.org\nDAVID\n  gene  ontology,  pathway  analysis  david.abcc.ncifcrf.gov\nWebGestalt\n   gene  ontology,  pathway  analysis  bioinfo.vanderbilt.edu/webgestalt\nSage",
+              "title": "2011 - The age of the \u201come\u201d Genome, transcriptome and proteome data set collection and analysis.pdf",
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+              "text": "2004; Gene Ontology Consortium, 2015; The Gene Ontology Consortium, 2019) , KEGG \npathways  (Kanehisa and Goto, 2000; Kanehisa et al., 2012) , Panther pathways  (Mi et al., 2019a, \n2019b) , Reactome pathways  (Sidiropoulos et al., 2017; Jassal et al., 2020) , and Wikipathway \npathways  (Pico et al., 2008; Slenter et al., 2018)  (Figure 31). As many different annotations as \nwanted can be chosen by clicking on the + icon ( Figure 31). Also note, that the user can",
+              "title": "2020 - Gene network a continuously updated tool for systems genetics analyses.pdf",
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+              "text": "2004; Gene Ontology Consortium, 2015; The Gene Ontology Consortium, 2019) , KEGG \npathways  (Kanehisa and Goto, 2000; Kanehisa et al., 2012) , Panther pathways  (Mi et al., 2019a, \n2019b) , Reactome pathways  (Sidiropoulos et al., 2017; Jassal et al., 2020) , and Wikipathway \npathways  (Pico et al., 2008; Slenter et al., 2018)  (Figure 31). As many different annotations as \nwanted can be chosen by clicking on the + icon ( Figure 31). Also note, that the user can",
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+              "content": "Once a sperm combines with an egg, the traits passed onto the resulting lifeform are determined by several genetic and epigenetic factors:\n\n1. **Chromosomal Segments**: Individuals inherit a patchwork of chromosomal segments from maternal and paternal chromosomes, which are combined during fertilization to form the zygote [2].\n   \n2. **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].\n\n3. **Gene Activation and Regulation**: The process of gene activation and regulation is complex and involves several mechanisms:\n   - **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].\n   - **Maternal Effect**: The mother can alter gene activity in her offspring via the placenta [4].\n   - **Cis-regulation**: Instructions encoded within the embryo's DNA can directly control if and when a nearby gene becomes activated [4].\n\n4. **Epigenetic Marks**: Epigenetic marks, which are less constrained and can be maintained, affect gene expression and phenotype later in development [6].\n\nThese factors collectively determine how traits are passed onto the resulting lifeform after the sperm combines with the egg.",
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+              "text": "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",
+              "title": "2009 - Opening Up the Conversation on Genetics.pdf",
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+              "text": "phenomena such as mutations and gene conversion events) occur in relevant meioses \nleading up to the formation of the gametes (i.e., egg and sperm) which are combined \nduring fertilization and the formation of zygotes. Thus, individuals inherit a patch-\nwork of chromosomal segments from maternal and paternal chromosomes.",
+              "title": "2008 -  Study Design and Statistical Issues.pdf",
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+              "text": "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:\nFirst, inside the sperm or egg, genes can be marked with small chemical tags that flag these genes",
+              "title": "2015 - Constraint and divergence of global gene expression in the mammalian embryo.pdf",
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+              "text": "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.\nSecond, the mother can alter the gene activity in her offspring via the placenta; this process is known\nas 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",
+              "title": "2015 - Constraint and divergence of global gene expression in the mammalian embryo.pdf",
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+              "text": "(Figures 8 and 9). Two gametes (egg and sperm) ultimately \njoin 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.\nThe Mendel Laws: Segregation and Independent \nAssortment\nBoth of the Mendel laws pertain directly to the process of \nmeiosis. The first Mendel law, the law of segregation, states \nthat each parent passes a randomly selected allele for a given",
+              "title": "2015 - Basic Concepts and Potential Applications of Genetics and Genomics for Cardiovascular and Stroke Clinicians.pdf",
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+              "text": "sex chromosome effects. (B)Soon after fertilization, male and female cells have sex-specic transcriptomes, epigenomes, and phenotypes (for example, male\nembryos grow faster than female embryos). At implantation, lineage determination begins and gene expression differences are reduced. Epigenetic marks, however,\nare less constrained and some are maintained, affecting gene expression, and phenotype later in development. Once specic lineages are established, differences in",
+              "title": "2019 - Sexual Dimorphism in the Age of Genomics How, When, Where.pdf",
+              "version": "v0",
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+              "text": "the subset of that genetic information that is active.  But how does the differentiation process \nbegin?  The key insight in resolving this conundrum came from fly genetics and was the \nrealization that the egg is not a homogenous sack of protoplasm.  The maternally-derived genes \nactive in the fertilized egg are asymmetrically distributed such that at the first cell division each \ndaughter cell receives a different complement of factors.  Development continues as a",
+              "title": "2008 - Genotype-phenotype relationships and the patterning of complex traits as exemplified in the mammalian dentition.pdf",
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+              "text": "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",
+              "title": "2007 - Promoting_Student_Scientific_Literacy_of_Molecular Genetics and Genomics.pdf",
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+            "metadata": {
+              "text": "or father (sperm cell). Each gamete has a set of 23 unpaired chromosomes. Two human gametes (egg and sperm) combine \nto create a cell (zygote) that contains the full human genome \nof 23 paired chromosomes.Genetic Information Nondiscrimination Act (GINA)\nUS federal legislation that makes it unlawful to discriminate \nagainst individuals on the basis of their genetic profiles in regard to health insurance and employment. These protections \nare intended to encourage Americans to take advantage of",
+              "title": "2015 - Basic Concepts and Potential Applications of Genetics and Genomics for Cardiovascular and Stroke Clinicians.pdf",
+              "version": "v0",
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+              "text": "spermatozoa: more than the sum of its parts? DNA, histones, pro -\ntamines and epigenetics. Reproduction 139:287301\nNilsson EE, Sadler-Riggleman I, Skinner MK (2018) Environmentally \ninduced epigenetic transgenerational inheritance of disease. Envi-ron Epigenet 4:dvy016Pembrey M, Saffery R, Bygren LO, Network in Epigenetic Epide-",
+              "title": "2019 - The influence of paternal diet on sncRNA-mediated epigenetic.pdf",
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+              "content": "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].",
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+              "text": "variation with cultural practices around lineage. In certain societies, individuals place greater\nimportance on (and have greater knowledge about) one side of the family than another\n(unilineal descent). Thus, individuals in patrilineal groups trace relationships through males\nonly so that your fathers brothers children are members of your family, but not your fathers\nsisters (Kottak, 2007 ). They are members of their husbands group or family. Efforts to create",
+              "title": "2009 - When Family Means More (or Less) Than Genetics.pdf",
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+            "metadata": {
+              "text": "maternal lineage membership with those who weredirectly genotyped. Based on these pedigree (matrilineal) relation-",
+              "title": "2012 - Mitochondrial Genomic Analysis of Late Onset.pdf",
+              "version": "v0",
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+              "text": "in three-generation families, and read pair tracing DNMs with phased variants.\nIn the former approach, we determined the parent of origin as in our previous \nanalysis4. For example, if an offspring of the proband was a carrier of the DNM \nallele and had haplotype sharing to paternal chromosome of the proband, we \nassigned the mutation to the father. Meanwhile, if the offspring was not a DNM \nallele carrier, we would assign it to the maternal germline. We restricted the haplo -",
+              "title": "2017 - Parental influence on human germline de novo.pdf",
+              "version": "v0",
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+              "text": "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",
+              "title": "2020 - Mitonuclear genomics and aging.pdf",
+              "version": "v0",
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+            "id": "612366c9-fcdc-5081-bc6d-47cd39922eeb",
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+              "text": "c) Mitochondrial DNA (maternal line testing) markers:\nmitochondrial DNA or mtDNA haploid is the\nmaternally inherited mitochondrial genome\n(mtDNA) [ 44]. All children inherit mtDNA from\ntheir mother, with no admixture from the father.\nLike Y-line DNA, mtDNA is passed intact from one\ngeneration to the next but through maternal line.\nMitochondrial DNA does not follow any surname.\nIn fact, the surname changes in every generation\nwhen women marry. Polymorphisms of mtDNA",
+              "title": "2015 - Self-reported race or ethnicity in the age of genomic.pdf",
+              "version": "v0",
+              "chunk_order": 42,
+              "document_id": "51ff0b84-193b-525a-b686-f29a423fcef9",
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+            "metadata": {
+              "text": "a family pedigree may be hampered if the participant is not familiar with her mothers relatives,\nbut her mothers brothers children (her cousins) may be able to supplement her overall family\nhistory. Knowledge about the cultural system of unilineal descent avoids assuming the\nuniversality of bilateral descent. Cultural beliefs such as these also have implications in the\nconduct of genetic research in terms of confidentiality and autonomy (Benkendorf et al.,",
+              "title": "2009 - When Family Means More (or Less) Than Genetics.pdf",
+              "version": "v0",
+              "chunk_order": 23,
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+            "id": "db1fe67a-3d0c-549f-a54a-74ea0fa44d11",
+            "score": 0.5768319884090416,
+            "metadata": {
+              "text": "225 three-generation families using haplotype sharing (Fig. 1c and \nMethods), 80.4% were found to be of paternal origin (Extended Data \nFig. 1). Figure 1e shows a strong relationship between the number of \npaternal DNMs and the fathers age at conception (1.47 per year, 95% \nCI 1.341.59) and a weaker impact of the mothers age on the number \nof maternal DNMs (0.37 per year, 95% CI 0.300.45).\nThe parental origin of all DNMs was also assessed by read pair",
+              "title": "2017 - Parental influence on human germline de novo.pdf",
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+              "text": "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",
+              "title": "1996 - IDDM2-VNTR-encoded Susceptibility to Type 1 Diabetes.pdf",
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+              "text": "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",
+              "title": "2012 - Mitochondrial Genomic Analysis of Late Onset.pdf",
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+              "text": "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\ncoalescent analysis of Icelandic matrilineal and patrilineal genealogies: Evidence for a\nfaster evolutionary rate of mtDNA lineages than Y chromosomes. Am J Hum Genet 72(6):\n1370 1388.\n23. Amster G, Sella G (2015) Life history effects on the molecular clock of autosomes and\nsex chromosomes. Proc Natl Acad Sci USA 113(6):1588 1593.",
+              "title": "2016 - A genetic method for dating ancient genomes provides.pdf",
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+              "content": "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:\n\n1. **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.\n\n2. **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.\n\n3. **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].\n\n4. **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.\n\n5. **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].\n\n6. **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].\n\nBy 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.",
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+              "text": "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.\nTAAGACCG\nAT\nT\nCTGGCCCGTGGC. . . .\n. . .. .\nATTCTGGCTAAGACCG. . . . . . . .\nFigure 2.1: A DNA chain consists of two strands of complementary nucleotides. When\nDNA is replicated, two double chains identical to the original one are created.",
+              "title": "2005 - Numerical Algorithms for Mapping of Multiple Quantitative Trait Loci in Experimental Populations.pdf",
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+              "text": "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.\nTAAGACCG\nAT\nT\nCTGGCCCGTGGC. . . .\n. . .. .\nATTCTGGCTAAGACCG. . . . . . . .\nFigure 2.1: A DNA chain consists of two strands of complementary nucleotides. When\nDNA is replicated, two double chains identical to the original one are created.",
+              "title": "2005 -Ljungberg- Numerical algos for Multi QTL.pdf",
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+              "text": "The mechanism to maintain the rDNA copy number\nThe gene amplication mechanism that counteracts\nrecombination-mediated loss of rDNA copies is well\nstudied in budding yeast [ 6,11]. During the S phase of the\ncell cycle, replication starts from replication origins, and isinhibited at the replication fork barrier site (RFB) by the\nfunction of the fork blocking protein, Fob1 (Fig. 3)[12].\nThis inhibition works as a recombinational hotspot toinduce amplication for copy number recovery as follow;",
+              "title": "2011 - Regulation of ribosomal RNA gene copy number and its role.pdf",
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+              "text": "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\n14. \nFinally, some single repair proteins directly revert certain injuries,\nsuch as O6-methylguanine methyltransferase, which removes \nO6-methyl guanine. This highly mutagenic lesion permits base",
+              "title": "2001 - Genome maintenance mechanisms.pdf",
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+            "id": "440ffc7a-2810-5245-bc20-9284d6861472",
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+              "text": "Replication",
+              "title": "2008 - Rutter_s child and adolescent psychiatry-Blackwell Pub (2008).pdf",
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+              "text": "genotoxic agents and to guarantee faithfulchromosome duplication and transmission to\nthe offspring. In addition to DNA damage\nrepair, cells monitor replication to minimize er-rors of DNA synthesis. In eukaryotes, cell-cycle\ncheckpoints guarantee coordination of DNA\nsynthesis and DNA repair with cell division.Genome instability is mainly due to sporadic\nreplication or repair errors but can also take\nplace in response to developmental or environ-mental signals, as occurs in meiosis, and antigen",
+              "title": "2013 - Causes of Genome Instability.pdf",
+              "version": "v0",
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+              "text": "This section will explain how cells normally divide. It will also desc ribe how an unexpected change in \nthe structure of DNA can sometimes cause harm to th e body. New tools to study genetic variations of \ncommon diseases and to identify genetic variatio ns common to specific diseases will also be \npresented. \nCell Division  \nHumans grow and develop as a result of a process called cell \ndivision. There are two types of cell division  mitosis and meiosis.",
+              "title": "2009 - Basic Genetics and Genomics A Primer for Nurses.pdf",
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+              "text": "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",
+              "title": "2007 - Promoting_Student_Scientific_Literacy_of_Molecular Genetics and Genomics.pdf",
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+              "text": "When a replication fork encounters a DNA\nadduct, cells induce DNA damage toler-ance mechanisms that allow completion of\nreplication. Adducts can be bypassed by\npostreplicative repair via translesion poly-merases (either faithful or error-prone) or\nvia error-free template switching using the\nsister chromatid (64, 105). Postreplicativerepair guarantees genome stability by allowing\ncompletion of replication (albeit at the expense",
+              "title": "2013 - Causes of Genome Instability.pdf",
+              "version": "v0",
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+              "text": "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",
+              "title": "2007 - DNA replication stress, genome instability and aging.pdf",
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+              "content": "### Potential Benefits of CRISPR-Cas9:\n\n1. **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].\n   \n2. **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].\n\n3. **Functional Genomics**: CRISPR-Cas9 is set to make a significant impact on genomic screening and scientific discovery, with recent demonstrations showing great promise [2].\n\n4. **Targeted Gene Regulation and Epigenome Modifications**: CRISPR-Cas9 has been linked to various effector proteins to enable targeted gene regulation and epigenome modifications [3].\n\n5. **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].\n\n6. **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].\n\n### Potential Risks of CRISPR-Cas9:\n\n1. **Off-Target Effects**: CRISPR-Cas9 is known to generate off-target alterations, which can result in unwanted mutations and potentially cytotoxic effects [4].\n\n2. **Technical Challenges**: There are several technical challenges that need to be addressed to maximize the benefits of CRISPR-Cas9 technology [2].\n\n3. **Long-Term Safety**: While early trial results are promising, the long-term safety of CRISPR-Cas9-engineered cells is yet to be determined [1].\n\n4. **Mismatch Tolerance**: The CRISPR-Cas9 system can tolerate certain mismatches to the DNA target, which could potentially lead to unintended edits [8].\n\nBy considering these benefits and risks, researchers and clinicians can better navigate the development and application of CRISPR-Cas9 technologies.",
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+              "text": "neered nucleases, CRISPR-Cas9 \ntools have accelerated the pace of \ngenomic research by permitting \nhighly efficient knockouts or \nedits of virtually any gene in cells \nor model organisms. Multiple CRISPR-Cas9based clinical trials \nare in progress or are expected \nto begin soon. Although Cas9-\nengineered cells havent yet dem -\nonstrated efficacy at scale, early \ntrial results suggest that such \ncells are stable and dont cause \nacute adverse reactions in humans. \nLong-term safety is yet to be de -",
+              "title": "2021 - Human Molecular Genetics and Genomics.pdf",
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+              "text": "stageissetforCRISPRtomakeanenormousimpactongenomic\nscreening and thus scientic discovery in the coming years, and\nrecent demonstrations of this system have shown great promise\n(Shalem etal., 2015 ).However,a number of technical challenges\nmust be addressed in order to maximize the benet of this\ntechnology. In this review, we will discuss current applications\nof CRISPR in functional genomics and provide a perspective on\nfuturedevelopmentsinthisarea.\nCRISPR/Cas9 Genome Editing",
+              "title": "2015 - A new age in functional genomics using CRISPR Cas9 in arrayed library screening.pdf",
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+              "text": "heralding the age of genome editing. Furthermore, Cas9 or guide RNAs have been linked to various effector proteins to enable targeted gene regulation\n12,13 and epigenome modifications14,15. \nIt is worth noting, however, that many of these feats had been demonstrated previously using other nucleases or DNA-binding proteins\n1,16. In this Perspective, I shed light on early genome \nediting platforms that laid the groundwork for the widespread use of CRISPRCas9 in research and medicine (Fig. 1 ).",
+              "title": "2016 - Genome editing comes of age.pdf",
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+              "text": "cline- or Tet-regulated Cas9 system. Current CRISPR/Cas systems arefrom Streptococcus pyogenes ,Streptococcus thermophilus ,Neisseria\nmeningitides and Treponema denticola .2.5. Caveats of advanced genome editing tools\nOff-target effects . The DNA-binding domains of ZFNs and TALENs\nneed to be very speci c for the target site to avoid off-target cleavage,\nwhich results in unwanted mutations and potentially cytotoxic effects\n[27]. CRISPR/Cas9 is also known to generate off-target alterations,",
+              "title": "2016 - Dissecting diabetes metabolic disease.pdf",
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+              "text": "CRISPR/CAS9 HOLDS SIGNIFICANT\nPROMISE FOR THE DEVELOPMENT OFNEW AD MODELS AND PRECISIONTARGETED AD THERAPY\nClustered regularly interspaced short palindromic\nrepeat (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-",
+              "title": "2018 - Neuro-Immuno-Gene- and GenomeEditing-Therapy for Alzheimer\u2019s.pdf",
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+              "text": "81.\nApplications for CRISPRCas9 beyond genome editing",
+              "title": "2016 - Genome editing comes of age.pdf",
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+              "text": "CRISPR-Cas9 can be used to in -\nduce genome edits by creating \ntargeted DNA breaks that trigger \nsite-specific DNA repair. In next-\ngeneration formats, it can also \ncontrol the transcriptional output \nof genes or alter genome se -\nquences using a process of nu -\ncleotide base editing \nthat does not require \nrepair of DNA breaks. \nAs these technolo -\ngies continue to mature, it will \nbecome increasingly possible to \nalter cellular genomes efficiently \nand accurately.\nComing on the heels of engi -",
+              "title": "2021 - Human Molecular Genetics and Genomics.pdf",
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+              "text": "on transcriptional interfere nce (CRISPRi) and activation\n(CRISPRa) have also harnessed Cas9-based technologies for\nuse in genome-wide studies ( 59,174). In addition, recent\nimprovements 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).\nnCas9\nThe CRISPR-Cas9 system can tolerate certain mismatches to the\nDNA target since the required gRNAs are short. A disadvantage,",
+              "title": "2020 - Functional Genomics in Pancreatic \u03b2 Cells Recent Advances in Gene Deletion and Genome Editing Technologies for Diabetes Research.pdf",
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+              "text": "13. Kleinstiver BP, etal. High-fidelity CRISPRCas9 nucleases with no detectable genome-wide \noff-target effects. Nature. 2016;529:4905.\n 14. Brane A, Tollefsbol T.Targeting telomeres and telomerase: studies in aging and disease uti-\nlizing CRISPR/Cas9 technology. Cells. 2019;8:186.\n 15. Wang H, etal. One-step generation of mice carrying mutations in multiple genes by CRISPR/\nCas-mediated genome engineering. Cell. 2013;153:9108.",
+              "title": "2020 - Clinical Genetics and Genomics of Aging.pdf",
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+              "text": "Since its discovery, CRISPR-Cas technology has ignited a biological revolu-\ntion by providing a highly versatile platform that allows fast and efficient genome \nediting in an ever-growing list of organisms. In this chapter we will first describe \nthe most recent advances in the development and application of the CRISPR-Cas \nplatform in biomedical research. Then we will discuss the most recent and notable \nbasic research applications of this technology in the study of the molecular causes",
+              "title": "2020 - Clinical Genetics and Genomics of Aging.pdf",
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+              "content": "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:\n\n1. **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].\n\n2. **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.\n\n3. **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].\n\nIn 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.",
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+              "text": "While most of the Y chromosome does not undergo\nrecombination, the recombination rate of the X chromosomeis slower than that of the autosomes. This has important\nconsequences on the detection of significant QTLs. For a\ncomprehensive view of these issues, see(43).\n9.Probe hybridization artifacts\nWhen several probes are available for the same gene, it is\nnot uncommon to observe a difference in the mapping results",
+              "title": "2009 - eQTL analysis in mice and rats.pdf",
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+              "text": "8 QTL Mapping  \n \nAllelic variation exists among natural populations and inbred strains, and this is \nreflective of the segregation of quantitative tr ait loci (QTLs) [96]. QTLs are stretches of \nDNA 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 \nresearchers to map different quantitative traits back to the genomic location involved in the regulation of these phenotypes.",
+              "title": "2015 - Genetic Control of Survival and Weight Loss during Pneumonic Burk.pdf",
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+              "text": "8 QTL Mapping  \n \nAllelic variation exists among natural populations and inbred strains, and this is \nreflective of the segregation of quantitative tr ait loci (QTLs) [96]. QTLs are stretches of \nDNA 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 \nresearchers to map different quantitative traits back to the genomic location involved in the regulation of these phenotypes.",
+              "title": "2015 -Emery- Genetic Control of Survival and Weight Loss during Pneumonic Burk.pdf",
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+              "text": "The basic  pr emise  of QTL  an alysis  is simple  (Ph illips  and Belknap,\n2002 ) . First,  one must  meas  ure a speci  c phen  otype  within  a popul  ation.\nNext, the population must be genotyped at a hundred or more marker loci186 Boehm II et al.",
+              "title": "2006 - From_gene_to_behavior_and_back_again_new.pdf",
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+              "text": "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,\nthat even in a single QTL region isolated in a congenic strain, it is possible that\nthere is more than one allele that aects the phenotype. So, you have a fair pointabout the challenges and complexities of QTL analysis.\nKoolhaas: There are dierent questions underlying both approaches. The QTL",
+              "title": "2005 - quantitative-trait-locus-analysis-of-aggressive-behaviours-in-mi.pdf",
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+              "text": "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,\nthat even in a single QTL region isolated in a congenic strain, it is possible that\nthere is more than one allele that aects the phenotype. So, you have a fair pointabout the challenges and complexities of QTL analysis.\nKoolhaas: There are dierent questions underlying both approaches. The QTL",
+              "title": "2005 -Broadkin- quantitative-trait-locus-analysis-of-aggressive-behaviours-in-mi.pdf",
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+              "text": "through analysis of line crosses, quantitative trait loci (QTL) mapping, and verification\nof 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.",
+              "title": "2009 - Experimental_Evolution.pdf",
+              "version": "v0",
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+            "id": "31a32dc5-81ac-52ba-a463-c61e293f21e5",
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+              "text": "through analysis of line crosses, quantitative trait loci (QTL) mapping, and verification\nof 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.",
+              "title": "2009 - Garland_and_Rose_Experimental_Evolution.pdf",
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+              "text": "genetic background.\nGene identification of QTL should be distinguished from identification of the quanti-\ntative trait nucleotide (QTN). The latter is a daunting task, since SNPs are so frequent.\nFinal 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\nhomologous recombination and reproducing the phenotype of the donor strain.",
+              "title": "2005 - quantitative-trait-analysis-in-the-investigation-of-function-and.pdf",
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+              "text": "measuring correlations between genetic markers and phenotypic \ntraits in a population. Individuals are scored for their  phenotype      for \na particular trait, and their genotype at a marker. If there is a differ-\nence in mean phenotype between those individuals with one geno-\ntype at a particular locus compared with the other, than we can infer \nthat there is a QTL linked to that marker [ 40 ,  153 ]. 2.3  Analysis and QTL \nMappingDavid G. Ashbrook and Reinmar Hager",
+              "title": "2016 - Social interactions and indirect genetic effects on complex juvenile and adult traits.pdf",
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+              "content": "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.",
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+              "text": "for people to exchange data easily over the Web. Two other notable developments are\nBioMart and GBrowse. The BioMart project (http://www.biomart.org/), originally a\nspin-off from Ensembl, offers a generic data management system that allows complex\nsearches of biological data such as sequence annotation. The GBrowse project (Stein\net al. , 2002; http://www.gmod.org/) has produced a generic genome browser that can\nbe customized to organize, display and query a new genome scale data set. These",
+              "title": "003 -Barnes- Bioinformatics_for_Geneticists.pdf",
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+              "text": "for people to exchange data easily over the Web. Two other notable developments are\nBioMart and GBrowse. The BioMart project (http://www.biomart.org/), originally a\nspin-off from Ensembl, offers a generic data management system that allows complex\nsearches of biological data such as sequence annotation. The GBrowse project (Stein\net al. , 2002; http://www.gmod.org/) has produced a generic genome browser that can\nbe customized to organize, display and query a new genome scale data set. These",
+              "title": "2007 - Bioinformatics_for_Genetices_MAZEN_SAEED.pdf",
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+              "associatedQuery": "For text and biological resources, do you mean add some books (on biology stuff) or/and web resources (as ensembl) on your system?"
+            }
+          },
+          {
+            "id": "8473f1fc-d615-54de-92de-a3faf5e2045e",
+            "score": 0.5922028775336083,
+            "metadata": {
+              "text": "for people to exchange data easily over the Web. Two other notable developments are\nBioMart and GBrowse. The BioMart project (http://www.biomart.org/), originally a\nspin-off from Ensembl, offers a generic data management system that allows complex\nsearches of biological data such as sequence annotation. The GBrowse project (Stein\net al. , 2002; http://www.gmod.org/) has produced a generic genome browser that can\nbe customized to organize, display and query a new genome scale data set. These",
+              "title": "2007 - Bioinformatics_for_Geneticists.pdf",
+              "version": "v0",
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+            }
+          },
+          {
+            "id": "4741caf7-1306-52e8-874c-fa200f067978",
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+              "text": "(http://ensembl.org/ ) and the National Center for Biotechnology Information\n(NCBI) (http://www.ncbi.nlm.nih.gov/ ) all provide portals to the most current, and\narchived public assemblies. These sites also provide means of searching the assem-\nblies, such as BLAST (Altschul et al. , 1997), BLAT (Kent, 2002) and SSAHA (Ning\net al. , 2001) as well as precomputed annotation for the genome assemblies that can\nbe readily incorporated into comparative genomic analyses.",
+              "title": "2007 - Bioinformatics_for_Genetices_MAZEN_SAEED.pdf",
+              "version": "v0",
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+              "associatedQuery": "For text and biological resources, do you mean add some books (on biology stuff) or/and web resources (as ensembl) on your system?"
+            }
+          },
+          {
+            "id": "f74f9aa9-3464-58e8-a0f6-e3e38efa3c40",
+            "score": 0.5872336276034349,
+            "metadata": {
+              "text": "(http://ensembl.org/ ) and the National Center for Biotechnology Information\n(NCBI) (http://www.ncbi.nlm.nih.gov/ ) all provide portals to the most current, and\narchived public assemblies. These sites also provide means of searching the assem-\nblies, such as BLAST (Altschul et al. , 1997), BLAT (Kent, 2002) and SSAHA (Ning\net al. , 2001) as well as precomputed annotation for the genome assemblies that can\nbe readily incorporated into comparative genomic analyses.",
+              "title": "003 -Barnes- Bioinformatics_for_Geneticists.pdf",
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+              "associatedQuery": "For text and biological resources, do you mean add some books (on biology stuff) or/and web resources (as ensembl) on your system?"
+            }
+          },
+          {
+            "id": "4121b591-0dda-5347-9833-23e3d9c6d8fe",
+            "score": 0.5872336276034349,
+            "metadata": {
+              "text": "(http://ensembl.org/ ) and the National Center for Biotechnology Information\n(NCBI) (http://www.ncbi.nlm.nih.gov/ ) all provide portals to the most current, and\narchived public assemblies. These sites also provide means of searching the assem-\nblies, such as BLAST (Altschul et al. , 1997), BLAT (Kent, 2002) and SSAHA (Ning\net al. , 2001) as well as precomputed annotation for the genome assemblies that can\nbe readily incorporated into comparative genomic analyses.",
+              "title": "2007 - Bioinformatics_for_Geneticists.pdf",
+              "version": "v0",
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+              "associatedQuery": "For text and biological resources, do you mean add some books (on biology stuff) or/and web resources (as ensembl) on your system?"
+            }
+          },
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+              "text": "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",
+              "title": "2010 - Teaching Bioinformatics and Neuroinformatics by using Free Web-Based Tools.pdf",
+              "version": "v0",
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+              "associatedQuery": "For text and biological resources, do you mean add some books (on biology stuff) or/and web resources (as ensembl) on your system?"
+            }
+          },
+          {
+            "id": "df5e9619-d45e-5958-a88d-d33ecc59387d",
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+              "text": "References\nAltman RB. Building successful biological databases. Briefings in Bioinformatics. 2004; 5:45. \n[PubMed: 15153301] \nAshburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: Tool for the \nunification of biology. The Gene Ontology Consortium. Nature Genetics. 2000; 25:2529. \n[PubMed: 10802651] \nAshish N, Ambite JL, Muslea M, Turner JA. Neuroscience data integration through mediation: an",
+              "title": "2012 - Biological Databases for Behavioral Neurobiology.pdf",
+              "version": "v0",
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+              "content": "The primary difference between QTL mapping and GWAS lies in their approaches and resolutions for identifying genetic associations with traits.\n\n1. **QTL Mapping**:\n   - **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].\n   - **Resolution**: Historically, QTL mapping has had low resolution, meaning it identifies larger genomic regions associated with traits rather than pinpointing specific genetic variants [6].\n\n2. **GWAS (Genome-Wide Association Studies)**:\n   - **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].\n   - **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].\n\nIn 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].",
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+              "text": "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",
+              "title": "2012 - Identifying Gene Networks Underlying the Neurobiology of Ethanol and Alcoholism.pdf",
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+              "text": "knowledge of the true QTL location (Doss et al. 2005 ),\nwhich can be used to empirically estimate the power of aGWAS performed at a similar scale (Hao et al. 2008 ;\nSchadt et al. 2008 ). A GWAS on its own does little more\nthan establish correlations between changes in DNA at agiven locus and changes in a disease trait of interest, with\nrespect to populations of interest. Further, these studies on",
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+              "text": "genotypes. Since association studies allow for a mu ch finer mapping of the QTL \nthan that obtained with linkage analysis, there is a trade-off to consider between \npower and resolution when choosing the mapping stra tegy. Genome-wide associa- \ntion studies (GWAS) have naturally been used to per form genetical genomics \nstudies in humans [18, 24-27] and are emerging in m odel organisms studies using \noutbred populations [28].  \n8.2.2  Combining studies",
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+              "text": "genetically also mapped to the same genomic\nlocation. In order to locate the positions of\ngenes that are responsible for a certain trait,\nGWAS can be conducted. GWAS is a quan-\ntitative approach to analyze the association of\nwhole genome DNA polymorphisms and a phe-\nnotypic trait, thereby localizing the genes un-\nderlining the trait.\nGenome-Wide Association\nStudies (GWAS)\nGWAS is a holistic whole-genome approach\nto robustly determine the association of DNA\npolymorphisms with correlated phenotypic",
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+              "text": "(PHMs) use principles of MR embedded within a Bayesian hierarchical model to detect interac-tions between regulatory elements [ 98].\nFurthermore, GWAS is often integrated with the QTL analysis despite the fact that many GWAS\nloci are not strong eQTL loci [ 56]. GWAS-eQTL colocalization methods, including RTC [ 145],\nQTLMacth [ 158], Sherlock [ 159], and coloc [ 160], are based on the concept that disease-",
+              "title": "2020 - A Multi-Omics Perspective of Quantitative Trait Loci in Precision Medicine.pdf",
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+              "text": "association studies (GWAS) or linkage studies (Enoch 2013).\nQTL 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\nresults, but the bulk of information is rejected due to concern",
+              "title": "2014 - Identification of a QTL in Mus musculus for Alcohol Preference, Withdrawal, and Ap3m2 Expression Using Integrative Functional Genomics and Precision Genetics.pdf",
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+              "text": "pairs that include many genes within the seg-\nment. On the other hand, GWAS may point to\nseveral or even many genomic locations for the\ntrait of interest, complicating further functional\nanalysis.\nAnalysis of Quantitative Trait Loci (QTL)\nQTL analysis reveals statistically signicant\nlinkage between phenotypes and genotypes,\nthereby providing explanation for the genetic\nbasis of variation in complex traits (Falconer\nand Mackay, 1996; Lynch and Walsh, 1998). In\na sense, QTL analysis can be viewed as incom-",
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+              "text": "QTL mapping\nQTL mapping using GeneNetwork has been described in detail elsewhere ( Mulligan et al., 2017 ). However, in brief, quantitative trait\nloci (QTLs) are segments of the genome affecting a particular phenotype ( Falconer and Mackay, 1996 ). QTL mapping, identifying",
+              "title": "2020 - A platform for experimental precision medicine The extended BXD mouse family.pdf",
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+              "text": "3. Genetic Mapping Methods\nSeveral statistical approaches have been developed for genome-wide linkage analysis of \ntraditional phenotypes. The same approaches can be used to map eQTLs. These approaches \nrange from single marker tests ( t-test, ANOVA, and simple regression analysis) to multiple \nlocus mapping methods. The only major difference is that eQTL studies involve tens of \nthousands of expression traits and require fast algorithms. Since an eQTL study tests for",
+              "title": "2014 - Genetics of Gene Expression in CNS.pdf",
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+              "text": "plete GWAS analysis with limited number of\nmarkers that does not cover the entire genome.\nAs such, if one or few QTLs are found, there\nmay be more QTLs in the genome to be dis-\ncovered. More importantly, in the absence of\nclosely linked markers in the genomic regions\ncontaining signicant QTLs for the trait, the\nmost signicant genes responsible for the trait\ncan be missed. However, because of historical\nreasons such as the lack of genome-wide mark-\ners, or the lack of funding, QTL analysis is still",
+              "title": "2012 - Functional genomics research in aquaculture principles and general approaches.pdf",
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+              "content": "To determine which gene in your QTL is causal for the trait, you can follow these steps:\n\n1. **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].\n\n2. **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].\n\n3. **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].\n\n4. **Fine Mapping**: Perform fine mapping of the QTL interval to shorten the list of plausible candidate polymorphisms [6].\n\n5. **Correlation with Trait**: Use gene expression data to screen for genes within the QTL interval whose expression correlates with the trait of interest [7].\n\n6. **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].\n\nBy following these steps, you can systematically narrow down and identify the causal gene within your QTL.",
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+        ],
+        "created": 1727796849,
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+              "text": "candidate genes. These candidate genes must then betested for a causal link to the phenotype. A good starting\npoint would be sequencing the cDNA of strong candidate\ngenes to identify amino acid polymorphisms and testingfor mRNA and protein expression differences in target\ntissues of the original strains used to detect the QTL.\nSequencing and expression studies will rene the list ofcandidate genes that can then be tested rigorously for\nproof of cause and effect. The nal proof of a causal gene",
+              "title": "2005 -Dipetrillo- Bioinformatics toolbox QTL.pdf",
+              "version": "v0",
+              "chunk_order": 107,
+              "document_id": "9b089457-5804-594a-99ea-e716b65c216c",
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+          {
+            "id": "a917decd-be31-53bd-9b1d-03eb6ba1e082",
+            "score": 0.7390589558517311,
+            "metadata": {
+              "text": "candidate genes. These candidate genes must then betested for a causal link to the phenotype. A good starting\npoint would be sequencing the cDNA of strong candidate\ngenes to identify amino acid polymorphisms and testingfor mRNA and protein expression differences in target\ntissues of the original strains used to detect the QTL.\nSequencing and expression studies will rene the list ofcandidate genes that can then be tested rigorously for\nproof of cause and effect. The nal proof of a causal gene",
+              "title": "2005 - Bioinformatics toolbox for narrowing rodent quantitative trait loci .pdf",
+              "version": "v0",
+              "chunk_order": 107,
+              "document_id": "5d87aefe-dee5-5f25-8b46-d87b24907dcc",
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+          {
+            "id": "11bb5512-c251-5348-a53e-88ebaa9bcbbb",
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+            "metadata": {
+              "text": "do you identify the responsible gene within a QTL that you have identified? \nGenerally, one starts by performing a strain survey to find two parental inbred \nstrains that have a markedly different trait. One can now look up many different \ntraits of inbred mice online at the Mouse Phenome Database ( http://phenome.\njax.org/pub-cgi/phenome/mpdcgi?rtn=docs/home ). However, the trait you may \nwant to study may not be present in wild type mice, so you may want to cross",
+              "title": "2008 - Gene Expression Profiling.pdf",
+              "version": "v0",
+              "chunk_order": 1636,
+              "document_id": "59f3b969-089b-5258-93ad-892dbc9ffa9c",
+              "extraction_id": "6f6a41a6-61ef-5d73-8bce-5de9a9cc4798",
+              "associatedQuery": "How do I determine which gene in my QTL is causal for the trait?"
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+          {
+            "id": "e4d4bbfc-d6b5-51cc-bf94-10bfc7099bf8",
+            "score": 0.7353833256319253,
+            "metadata": {
+              "text": "used to test the hypothesis at locus-specific sig-nificance (LRS 12). In doing so, an additional\n7 cQTLs are observed as consistent in both diets(Fig. 2I, red number).\nSolving QTLs: Finding the quantitative\ntrait gene\nFor cis-QTLs, the causal factors can be quickly\nidentified: With few exceptions, they will be driv-en by variants within the gene itself or imme-diately adjacent. For trans-QTLs, mQTLs, and\ncQTLs, the identification of the causal quanti-",
+              "title": "2016 - Systems proteomics of liver mitochondria function.pdf",
+              "version": "v0",
+              "chunk_order": 68,
+              "document_id": "9843107b-2d0c-5cbe-b55f-74e8090a575d",
+              "extraction_id": "1a46d28d-fc4a-5154-b887-3956d64959ef",
+              "associatedQuery": "How do I determine which gene in my QTL is causal for the trait?"
+            }
+          },
+          {
+            "id": "7d6a48a0-e046-520c-8434-7544e20b7a6c",
+            "score": 0.7150219891971946,
+            "metadata": {
+              "text": "data is to find a quantitative trait locus, or QTL. A QTL \n(http://gn1.genenetwork.org/glossary.html#Q ) is an area on a chromosome that can contain \none or many genes, that is linked to a change in phenotype. After a  QTL that is responsible for \nthe apparent variation  in phenotype  has been identified , one can  start stu dying the  genes \nwithin that locus  to identify  the likely causal gene .  \n \nOnce the data  is normalized appropriately  (in our case, no normalization was required) , the QTL",
+              "title": "2020 - Gene network a completely updated tool for systems genetics analyses.pdf",
+              "version": "v0",
+              "chunk_order": 40,
+              "document_id": "128224f1-3545-52c3-93cb-77c3cf4ec70a",
+              "extraction_id": "2134720b-01d9-5e45-96bf-d1ff449d406d",
+              "associatedQuery": "How do I determine which gene in my QTL is causal for the trait?"
+            }
+          },
+          {
+            "id": "38ba1c61-a6cc-504c-a6a3-da3c1252a17d",
+            "score": 0.7121017191440383,
+            "metadata": {
+              "text": "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\nstrains looking for sequence dierences in coding or regulatory regions.\nAfter ne mapping the QTL interval and shortening the list of plausible\ncandidate polymorphisms, the major challenge remains /C246 proving denitively\nwhich nucleotide polymorphism underlies the QTL. The most direct proof",
+              "title": "2005 - quantitative-trait-locus-analysis-of-aggressive-behaviours-in-mi.pdf",
+              "version": "v0",
+              "chunk_order": 17,
+              "document_id": "0dc730ba-4ff4-52aa-a988-71075113c416",
+              "extraction_id": "c9fe8c31-86f9-5c59-8af8-8e81157cb99f",
+              "associatedQuery": "How do I determine which gene in my QTL is causal for the trait?"
+            }
+          },
+          {
+            "id": "7ef9df1d-b21a-597a-9e74-6eace5d0c33c",
+            "score": 0.7115814725978502,
+            "metadata": {
+              "text": "because these strains have been genotyped at more than 14,000 markers, including single\nnucleotide polymorphisms (SNP). Hundreds of genes may lie within a QTL interval, so\nidentifying the underlying genes requires complementary methods. One method is to use\nBXD gene expression data (a public resource at www.genenetwork.org) to screen for genes\nwithin the QTL interval whose expression correlates with the trait of interest [23].",
+              "title": "2012 - Systems genetic analysis of the effects of iron deficiency in mouse brain.pdf",
+              "version": "v0",
+              "chunk_order": 14,
+              "document_id": "99fc80f0-f3c3-5766-a604-921552bb3298",
+              "extraction_id": "6ab990b0-4f9c-5be3-ab79-9ca6835271fa",
+              "associatedQuery": "How do I determine which gene in my QTL is causal for the trait?"
+            }
+          },
+          {
+            "id": "fed778e8-dda5-5766-886d-287c24ff3758",
+            "score": 0.7115150134129837,
+            "metadata": {
+              "text": "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\nstrains looking for sequence dierences in coding or regulatory regions.\nAfter ne mapping the QTL interval and shortening the list of plausible\ncandidate polymorphisms, the major challenge remains /C246 proving denitively\nwhich nucleotide polymorphism underlies the QTL. The most direct proof",
+              "title": "2005 -Broadkin- quantitative-trait-locus-analysis-of-aggressive-behaviours-in-mi.pdf",
+              "version": "v0",
+              "chunk_order": 17,
+              "document_id": "e6027e7f-aec0-5e76-8aff-96b36389e701",
+              "extraction_id": "cf8c1f06-37d2-5b82-bc22-6bc129759445",
+              "associatedQuery": "How do I determine which gene in my QTL is causal for the trait?"
+            }
+          },
+          {
+            "id": "c3d5e48d-ebf6-54b9-811e-6dbe2a171b77",
+            "score": 0.7081272813119677,
+            "metadata": {
+              "text": "curate approaches to identify various types of QTL according to their molecular features, in par-\nticular to control various confounding factors, such as dietary habit and population structure.\nFine Mapping of Causal Variants and Causal Genes\nDespite the identi cation of large numbers of QTLs, it remains challenging to establish causal",
+              "title": "2020 - A Multi-Omics Perspective of Quantitative Trait Loci in Precision Medicine.pdf",
+              "version": "v0",
+              "chunk_order": 85,
+              "document_id": "8503b166-b917-5efb-a356-5ba371504cc1",
+              "extraction_id": "81b38987-de8d-5e87-8c09-72efc4cf781e",
+              "associatedQuery": "How do I determine which gene in my QTL is causal for the trait?"
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+          },
+          {
+            "id": "bcb46fb1-dc0a-5f38-85e4-8c6d99809d74",
+            "score": 0.7047744007341388,
+            "metadata": {
+              "text": "to date, only a small handful of genes have been definitively identified for complex traits. \nOur own efforts to identify a causal gene were stymied by the compound nature of QTLs \nand the high gene density  in Qrr1 , and in Vol8a . Furthermore, it is now becoming clear \nthat in addition to the canonical candidate genes, there are multiple spliced variants, \nmicroRNAs, and epigenetic factors to be considered.  \n \nWith what appears to be an increasingly complex genom ic landscape, it is now all",
+              "title": "2009 - Multiscale Genomic Analysis of the Corticolimbic System_ Uncoveri (1).pdf",
+              "version": "v0",
+              "chunk_order": 615,
+              "document_id": "3d0df5a3-7d7c-5edc-b94d-cae582f59c12",
+              "extraction_id": "3296b30e-7dd3-576d-a2df-442406caa472",
+              "associatedQuery": "How do I determine which gene in my QTL is causal for the trait?"
+            }
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+            "message": {
+              "content": "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:\n\n- Males have an XY genotype, meaning they have one X chromosome and one Y chromosome [3].\n- Females have an XX genotype, meaning they have two X chromosomes [3].\n\nAdditionally, 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.",
+              "role": "assistant"
+            }
+          }
+        ],
+        "created": 1727796856,
+        "model": "gpt-4o-2024-05-13",
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+        "vector_search_results": [
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+            "score": 0.6319282870335419,
+            "metadata": {
+              "text": "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",
+              "title": "2007 - Quantitative genetics of age-related retinal degeneration a second F1 intercross between the AJ and C57BL6 strains.pdf",
+              "version": "v0",
+              "chunk_order": 83,
+              "document_id": "f41cf6ad-273a-571a-866e-46b3dd407731",
+              "extraction_id": "749877a1-0114-5bcd-8a5b-3b944012f5c9",
+              "associatedQuery": "Why do males have two Y chromosomes and females only one?"
+            }
+          },
+          {
+            "id": "e761426e-5f1d-5add-be86-bd6060d75ca7",
+            "score": 0.6302625724751527,
+            "metadata": {
+              "text": "women comprises 2 X chromosomes and in men 1 X and 1 Y chromosome (Figure 2). For each chromosome pair, 1 chro-\nmosome was inherited from the mother and 1 from the father. The full set of chromosomes is collectively called the genome. \nThe human genome is largely contained within the nucleus \nof each cell, where it is separated from the rest of the cell functions. However, a small amount of DNA exists outside \nthe nucleus in the mitochondria and is considered to be part of \nthe human genome.",
+              "title": "2015 - Basic Concepts and Potential Applications of Genetics and Genomics for Cardiovascular and Stroke Clinicians.pdf",
+              "version": "v0",
+              "chunk_order": 18,
+              "document_id": "8610e699-218a-50e6-8d1d-ef689623266f",
+              "extraction_id": "34fa36d0-0b64-5c70-8645-ba3576d9262c",
+              "associatedQuery": "Why do males have two Y chromosomes and females only one?"
+            }
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+          {
+            "id": "748b07c1-c80f-5a4f-b295-9726493a698f",
+            "score": 0.6297062039375305,
+            "metadata": {
+              "text": "betweenmalesandfemalesisthesexchromosomes.MaleshaveanXYgenotypeand\nfemaleshaveanXXgenotype.TheXisamuchlargerchromosome,165.5x106bpsvs.\n16.0x106bps,withapproximately30timesmoregenesthantheYchromosome.To\ncompensateforthelargernumberofgenes,andtoensurefemalesdonothaveover\nexpressionofgenesresidingontheXchromosome,oneoftheXchromosomesis\ninactivated(7).TheXinactivationoccursearlyindevelopmentandisarandomprocess.\nOnlyasmallportionoftheinactivatedchromosomeretainstranscriptionalability.This",
+              "title": "2007 - Prenatal nicotine exposure alters gene expression in a sexually dimorphic manner.pdf",
+              "version": "v0",
+              "chunk_order": 533,
+              "document_id": "036efa18-a4b0-51bf-99d6-7c65193ccfed",
+              "extraction_id": "061d1490-4ce6-5f60-bdf8-15e8d863baf6",
+              "associatedQuery": "Why do males have two Y chromosomes and females only one?"
+            }
+          },
+          {
+            "id": "4e99669a-96cc-5269-a463-ff13337c56c3",
+            "score": 0.621566891670227,
+            "metadata": {
+              "text": "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-",
+              "title": "2009 - Experimental_Evolution.pdf",
+              "version": "v0",
+              "chunk_order": 4266,
+              "document_id": "34821353-1b74-5ee2-ac39-66dd46f145bf",
+              "extraction_id": "29e674a2-7ec9-5e00-9db3-308b112e439f",
+              "associatedQuery": "Why do males have two Y chromosomes and females only one?"
+            }
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+          {
+            "id": "9c00e371-7349-5ff0-8469-ffd95dd58e57",
+            "score": 0.621566891670227,
+            "metadata": {
+              "text": "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-",
+              "title": "2009 - Garland_and_Rose_Experimental_Evolution.pdf",
+              "version": "v0",
+              "chunk_order": 4266,
+              "document_id": "496faa7f-9623-5ab7-9816-7c3755abb3aa",
+              "extraction_id": "2f77d356-4cca-595c-912a-099efcc8b797",
+              "associatedQuery": "Why do males have two Y chromosomes and females only one?"
+            }
+          },
+          {
+            "id": "3cf13ae8-6c1c-5ddb-a719-81340d1c8ef6",
+            "score": 0.6002724349579943,
+            "metadata": {
+              "text": "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). \nRice and Chippindale (2001) used a combination of these genetic techniques to test",
+              "title": "2009 - Experimental_Evolution.pdf",
+              "version": "v0",
+              "chunk_order": 4267,
+              "document_id": "34821353-1b74-5ee2-ac39-66dd46f145bf",
+              "extraction_id": "29e674a2-7ec9-5e00-9db3-308b112e439f",
+              "associatedQuery": "Why do males have two Y chromosomes and females only one?"
+            }
+          },
+          {
+            "id": "27608ea2-c234-56f5-ad58-01fb67362130",
+            "score": 0.6001714468002319,
+            "metadata": {
+              "text": "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). \nRice and Chippindale (2001) used a combination of these genetic techniques to test",
+              "title": "2009 - Garland_and_Rose_Experimental_Evolution.pdf",
+              "version": "v0",
+              "chunk_order": 4267,
+              "document_id": "496faa7f-9623-5ab7-9816-7c3755abb3aa",
+              "extraction_id": "2f77d356-4cca-595c-912a-099efcc8b797",
+              "associatedQuery": "Why do males have two Y chromosomes and females only one?"
+            }
+          },
+          {
+            "id": "c171e03f-4baf-5a0c-b961-401be867d691",
+            "score": 0.5984589696849018,
+            "metadata": {
+              "text": "ity on the X chromosome compared to the other five strains(Figure 2B ). Compared to females, males had a deficiency of\nheterozygous X-linked SNP loci ( Supplementary Figure S2 ),\nwhich 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.\nChromosome XAutosomesProportion of SNP lociHomozygous SNPs\nHeterozygous SNPs",
+              "title": "2022 - Genetic and genomic architecture in eight strains of the laboratory opossum.pdf",
+              "version": "v0",
+              "chunk_order": 62,
+              "document_id": "f09eaa22-afb8-5bf7-90d3-4703056c18c5",
+              "extraction_id": "5afcc18d-5385-5d5e-8683-dd38f86131e7",
+              "associatedQuery": "Why do males have two Y chromosomes and females only one?"
+            }
+          },
+          {
+            "id": "e625cca4-7b62-5adf-b94e-1fdecc8e143c",
+            "score": 0.5933037996292114,
+            "metadata": {
+              "text": "sex chromosome  \nY chromosome:  One of the two sex chromosomes, X and Y. See also; X chromosome, \nsex chromosome",
+              "title": "2012 - Needs Analysis of Genetics and Genomics in Communication Sciences and Disorders.pdf",
+              "version": "v0",
+              "chunk_order": 1015,
+              "document_id": "c8a76cb1-506d-57e4-a18e-548e777898e2",
+              "extraction_id": "10a507d1-60ca-5dae-9e49-4a6bace53668",
+              "associatedQuery": "Why do males have two Y chromosomes and females only one?"
+            }
+          },
+          {
+            "id": "03b1323c-d449-55fe-966e-d4925246b013",
+            "score": 0.5807909619336757,
+            "metadata": {
+              "text": "one Y chromosome.\nHuman chromosomes are typically displayed pictorially in\na karyotype, as shown in Figure 9, arranged according to\nlength 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.\nDuring this phase of the cell cyc le, allelic differences cannot\nbe detected.",
+              "title": "2017 - Primer in Genetics and Genomics, Article 1 DNA, Genes, and Chromosomes.pdf",
+              "version": "v0",
+              "chunk_order": 55,
+              "document_id": "b30c111b-1ca2-5f0a-93f3-862aa733fcad",
+              "extraction_id": "89acea57-5c8a-55a6-90cf-ad11e5d527b6",
+              "associatedQuery": "Why do males have two Y chromosomes and females only one?"
+            }
+          }
+        ],
+        "kg_search_results": null
+      }
+    }
+  },
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+            "finish_reason": "stop",
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+              "content": "To differentiate between X and Y DNA with respect to DNA tracing and determining quantitative trait loci (QTLs), consider the following points:\n\n1. **Recombination Rates**:\n   - 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].\n   - 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].\n\n2. **QTL Mapping**:\n   - 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].\n   - 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].\n\nIn 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].",
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+              "text": "While most of the Y chromosome does not undergo\nrecombination, the recombination rate of the X chromosomeis slower than that of the autosomes. This has important\nconsequences on the detection of significant QTLs. For a\ncomprehensive view of these issues, see(43).\n9.Probe hybridization artifacts\nWhen several probes are available for the same gene, it is\nnot uncommon to observe a difference in the mapping results",
+              "title": "2009 - eQTL analysis in mice and rats.pdf",
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+              "text": "8 QTL Mapping  \n \nAllelic variation exists among natural populations and inbred strains, and this is \nreflective of the segregation of quantitative tr ait loci (QTLs) [96]. QTLs are stretches of \nDNA 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 \nresearchers to map different quantitative traits back to the genomic location involved in the regulation of these phenotypes.",
+              "title": "2015 - Genetic Control of Survival and Weight Loss during Pneumonic Burk.pdf",
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+              "text": "8 QTL Mapping  \n \nAllelic variation exists among natural populations and inbred strains, and this is \nreflective of the segregation of quantitative tr ait loci (QTLs) [96]. QTLs are stretches of \nDNA 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 \nresearchers to map different quantitative traits back to the genomic location involved in the regulation of these phenotypes.",
+              "title": "2015 -Emery- Genetic Control of Survival and Weight Loss during Pneumonic Burk.pdf",
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+              "text": "genetic background.\nGene identification of QTL should be distinguished from identification of the quanti-\ntative trait nucleotide (QTN). The latter is a daunting task, since SNPs are so frequent.\nFinal 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\nhomologous recombination and reproducing the phenotype of the donor strain.",
+              "title": "2005 - quantitative-trait-analysis-in-the-investigation-of-function-and.pdf",
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+              "text": "The basic  pr emise  of QTL  an alysis  is simple  (Ph illips  and Belknap,\n2002 ) . First,  one must  meas  ure a speci  c phen  otype  within  a popul  ation.\nNext, the population must be genotyped at a hundred or more marker loci186 Boehm II et al.",
+              "title": "2006 - From_gene_to_behavior_and_back_again_new.pdf",
+              "version": "v0",
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+            "id": "60643722-3d4e-571c-97e9-3b5c67670ca0",
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+              "text": "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\nimplemented in R/qtl (Broman et al. 2003). The lat-",
+              "title": "2005 - Gene Expression Differences in Mice.pdf",
+              "version": "v0",
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+              "text": "X axis depicts 19 autosomes and X chromoso me. The Y axis is the likelihood ratio statistic from a single QTL model. Two \nQTLs, on chromosomes 1 and 11, are significant at a mu ltiple test corrected permut ation threshold as shown.\nChromosome 1 and 11 likeli hood ratio statistic plots Figure 2\nChromosome 1 and 11 likelih ood ratio statistic plots . Interval mapping plots of chromosomes 1 and 11, showing more \ndetail of Figure 1. 2 LOD support inte rvals are shown in Mb on the X axis.",
+              "title": "2008 - Using gene expression databases for classical trait QTL candidate gene discovery in the BXD recombinant inbred genetic reference population Mouse forebrain weight.pdf",
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+              "text": "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,\nthat even in a single QTL region isolated in a congenic strain, it is possible that\nthere is more than one allele that aects the phenotype. So, you have a fair pointabout the challenges and complexities of QTL analysis.\nKoolhaas: There are dierent questions underlying both approaches. The QTL",
+              "title": "2005 - quantitative-trait-locus-analysis-of-aggressive-behaviours-in-mi.pdf",
+              "version": "v0",
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+              "text": "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,\nthat even in a single QTL region isolated in a congenic strain, it is possible that\nthere is more than one allele that aects the phenotype. So, you have a fair pointabout the challenges and complexities of QTL analysis.\nKoolhaas: There are dierent questions underlying both approaches. The QTL",
+              "title": "2005 -Broadkin- quantitative-trait-locus-analysis-of-aggressive-behaviours-in-mi.pdf",
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+              "text": "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\nassumption of xation at the QTL can be tested by",
+              "title": "2005 -Knott- Regression based QTL mapping.pdf",
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+              "content": "Once a sperm combines with an egg, the traits passed onto the resulting lifeform are determined by several factors:\n\n1. **Chromosomal Segments from Parents**: Individuals inherit a patchwork of chromosomal segments from both maternal and paternal chromosomes [1].\n2. **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].\n3. **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].\n4. **Epigenetic Marks**: Epigenetic marks, which are chemical modifications to DNA or histones, can affect gene expression and phenotype later in development [7].\n\nThese combined genetic and epigenetic factors determine how traits are passed onto and expressed in the resulting lifeform.",
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+              "text": "phenomena such as mutations and gene conversion events) occur in relevant meioses \nleading up to the formation of the gametes (i.e., egg and sperm) which are combined \nduring fertilization and the formation of zygotes. Thus, individuals inherit a patch-\nwork of chromosomal segments from maternal and paternal chromosomes.",
+              "title": "2008 -  Study Design and Statistical Issues.pdf",
+              "version": "v0",
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+            "id": "60ad1512-b0c0-59cd-ace4-c146e2c04b52",
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+              "text": "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",
+              "title": "2009 - Opening Up the Conversation on Genetics.pdf",
+              "version": "v0",
+              "chunk_order": 328,
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+            "id": "a66b8b00-d51c-575b-b6ac-fa445c4ca715",
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+              "text": "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:\nFirst, inside the sperm or egg, genes can be marked with small chemical tags that flag these genes",
+              "title": "2015 - Constraint and divergence of global gene expression in the mammalian embryo.pdf",
+              "version": "v0",
+              "chunk_order": 15,
+              "document_id": "3d9005f1-8f71-5d39-8749-4ebeab962cab",
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+              "text": "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.\nSecond, the mother can alter the gene activity in her offspring via the placenta; this process is known\nas 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",
+              "title": "2015 - Constraint and divergence of global gene expression in the mammalian embryo.pdf",
+              "version": "v0",
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+            "id": "4472740a-d22d-5bb1-98e3-e91332cbb303",
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+              "text": "(Figures 8 and 9). Two gametes (egg and sperm) ultimately \njoin 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.\nThe Mendel Laws: Segregation and Independent \nAssortment\nBoth of the Mendel laws pertain directly to the process of \nmeiosis. The first Mendel law, the law of segregation, states \nthat each parent passes a randomly selected allele for a given",
+              "title": "2015 - Basic Concepts and Potential Applications of Genetics and Genomics for Cardiovascular and Stroke Clinicians.pdf",
+              "version": "v0",
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+            "id": "47b9142f-98a3-5a45-8eaa-d327c9cc055d",
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+              "text": "the subset of that genetic information that is active.  But how does the differentiation process \nbegin?  The key insight in resolving this conundrum came from fly genetics and was the \nrealization that the egg is not a homogenous sack of protoplasm.  The maternally-derived genes \nactive in the fertilized egg are asymmetrically distributed such that at the first cell division each \ndaughter cell receives a different complement of factors.  Development continues as a",
+              "title": "2008 - Genotype-phenotype relationships and the patterning of complex traits as exemplified in the mammalian dentition.pdf",
+              "version": "v0",
+              "chunk_order": 88,
+              "document_id": "f6e866b8-b233-5862-bfb8-9949d0dabb97",
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+          {
+            "id": "0158f264-120f-5942-ad55-ef5fde1f188a",
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+            "metadata": {
+              "text": "sex chromosome effects. (B)Soon after fertilization, male and female cells have sex-specic transcriptomes, epigenomes, and phenotypes (for example, male\nembryos grow faster than female embryos). At implantation, lineage determination begins and gene expression differences are reduced. Epigenetic marks, however,\nare less constrained and some are maintained, affecting gene expression, and phenotype later in development. Once specic lineages are established, differences in",
+              "title": "2019 - Sexual Dimorphism in the Age of Genomics How, When, Where.pdf",
+              "version": "v0",
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+            "id": "8e3fdc2c-0962-5854-83e7-a60ab05cf6de",
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+              "text": "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",
+              "title": "2007 - Promoting_Student_Scientific_Literacy_of_Molecular Genetics and Genomics.pdf",
+              "version": "v0",
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+              "document_id": "755f34c4-cc06-5275-a744-16d48162b012",
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+            "id": "6c8dfaa1-a96f-5f1c-8b5a-870acfd46f5f",
+            "score": 0.5889748824864766,
+            "metadata": {
+              "text": "or father (sperm cell). Each gamete has a set of 23 unpaired chromosomes. Two human gametes (egg and sperm) combine \nto create a cell (zygote) that contains the full human genome \nof 23 paired chromosomes.Genetic Information Nondiscrimination Act (GINA)\nUS federal legislation that makes it unlawful to discriminate \nagainst individuals on the basis of their genetic profiles in regard to health insurance and employment. These protections \nare intended to encourage Americans to take advantage of",
+              "title": "2015 - Basic Concepts and Potential Applications of Genetics and Genomics for Cardiovascular and Stroke Clinicians.pdf",
+              "version": "v0",
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+            "id": "be93ee68-72ae-5015-a3f0-19e7bf24827a",
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+            "metadata": {
+              "text": "spermatozoa: more than the sum of its parts? DNA, histones, pro -\ntamines and epigenetics. Reproduction 139:287301\nNilsson EE, Sadler-Riggleman I, Skinner MK (2018) Environmentally \ninduced epigenetic transgenerational inheritance of disease. Envi-ron Epigenet 4:dvy016Pembrey M, Saffery R, Bygren LO, Network in Epigenetic Epide-",
+              "title": "2019 - The influence of paternal diet on sncRNA-mediated epigenetic.pdf",
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+              "content": "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.\n\nTo 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.",
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+              "text": "GeneNetwork have reinvigorated it, including the addition  of data from  10 species, multi -omics \nanalysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for \npredictive medicine and systems genetics, which is constantly being maintained and improved.   \nHere, we give a brief overview of the process  for carrying out some of the most common \nfunctions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small",
+              "title": "2020 - Gene network a completely updated tool for systems genetics analyses.pdf",
+              "version": "v0",
+              "chunk_order": 1,
+              "document_id": "128224f1-3545-52c3-93cb-77c3cf4ec70a",
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+              "associatedQuery": "How can I add a new species to the GeneNetwork database?"
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+          },
+          {
+            "id": "312eae52-ede7-5c13-8974-fce0126426cf",
+            "score": 0.5970070842496056,
+            "metadata": {
+              "text": "addition to this, GeneNetwork can be used to study correlations between traits and to\nperform data mining in genomic regions containing candidates for quantitative trait genes\n(Hoffman et al., 2011). All datasets in GeneNetwork are linked to a materials and methods\ninformation page that summarizes experimental details relating to the dataset.\nDatabases within GeneNetwork include the transcriptome database, the BXD published",
+              "title": "2012 - Using Genome-Wide Expression Profiling to Define Gene Networks Relevant to the Study of Complex Traits From RNA Integrity to Network Topology.pdf",
+              "version": "v0",
+              "chunk_order": 98,
+              "document_id": "1eb6f5b7-a3bc-5455-91f0-6f2eb37be861",
+              "extraction_id": "3e0c2a06-e6de-5888-a360-a2c483d9f744",
+              "associatedQuery": "How can I add a new species to the GeneNetwork database?"
+            }
+          },
+          {
+            "id": "2ae780e5-9549-50c0-a260-d7ef774f7956",
+            "score": 0.5869437106285278,
+            "metadata": {
+              "text": "publication, and links to the dataset database and to the published paper (4C). There is also an option \nto add this trait to your collection by pressing the Add button (4D), or to view this trait in an ear lier \nversion of GeneNetwork, GN1 (4E).",
+              "title": "2020 - Gene network a completely updated tool for systems genetics analyses.pdf",
+              "version": "v0",
+              "chunk_order": 34,
+              "document_id": "128224f1-3545-52c3-93cb-77c3cf4ec70a",
+              "extraction_id": "f7e3761d-1baa-573a-9cbd-4070a400c42e",
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+          {
+            "id": "6a443d81-33ed-524c-9f11-318f1013a214",
+            "score": 0.5704553383317398,
+            "metadata": {
+              "text": "Bayesian inference of species networks from multilocus sequence data. Mol. Biol. Evol. 35, 504517 (2018).\n167. Flouri, T ., Jiao, X., Rannala, B. & Yang, Z. A Bayesian \nimplementation of the multispecies coalescent  \nmodel with introgression for phylogenomic analysis. Mol. Biol. Evol. 37, 12111223 (2020).\n168. Kubatko, L. in Handbook of Statistical Genomics   \n(eds Balding, D., Moltke, I. & Marioni, J.) 219245 \n(Wiley, 2019).\n169. Rannala, B., Edwards, S., Leach, A. D. & Yang, Z.",
+              "title": "2020 - Phylogenetic tree building.pdf",
+              "version": "v0",
+              "chunk_order": 222,
+              "document_id": "7310a5bc-2fc1-5fa0-ad1d-f3411830062b",
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+              "associatedQuery": "How can I add a new species to the GeneNetwork database?"
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+          {
+            "id": "8b8a24da-a175-5cb8-91bd-8966fca5d344",
+            "score": 0.567895395520469,
+            "metadata": {
+              "text": "subnetworks \n GeneNetwork (www.genenetwork.org) is a depository of data-\nsets and tools for use in complex systems biology approaches in \norder to generate or predict higher order gene function ( 23, 24 ).",
+              "title": "2011 - Peroxisomal L-bifunctional enzyme (Ehhadh) is essential for the production of medium-chain dicarboxylic acids.pdf",
+              "version": "v0",
+              "chunk_order": 29,
+              "document_id": "ac61753e-bcb2-55c3-804b-e821e3d1a4ad",
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+              "associatedQuery": "How can I add a new species to the GeneNetwork database?"
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+            "id": "d5c42ccf-569f-5a50-bd49-6b45097a3d00",
+            "score": 0.5651343296428247,
+            "metadata": {
+              "text": "on different cross types, such as F 2crosses (B6BTBRF2,\nB6D2F2, BH/HB F2, CastB6/B6Cast F2, B6JxB6N F2), butalso on more complex outbred crosses such as the HS, the\nCC, and the Hybrid Mouse Diversity Panel. Recently, data\nfrom other species has also been integrated into GeneNet-\nwork (human, rat, monkey, fruit ies, and others) to facilitate\nthe translational research of results into other species. To this\nend, GeneNetwork provides many tools for the analysis of",
+              "title": "2018 - Invited review Genetic and genomic_ xmltexbreak_ mouse models for livestock research.pdf",
+              "version": "v0",
+              "chunk_order": 81,
+              "document_id": "5b167564-85a2-5886-b800-37932c3143a9",
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+              "associatedQuery": "How can I add a new species to the GeneNetwork database?"
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+            "id": "f5c218f0-1280-55f8-912b-b32b833e93a3",
+            "score": 0.5649295371854857,
+            "metadata": {
+              "text": "GeneNetwork (www.genenetwork.org). The web -based software further allows extraction of sets of",
+              "title": "2013 - Pathogenesis and reversal of liver fibrosis Effects of genes and environment.pdf",
+              "version": "v0",
+              "chunk_order": 263,
+              "document_id": "73724c99-98df-53b2-a378-29c8b4faa171",
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+            "id": "ac4f8148-e6da-5d16-9e61-3a1aff2f9c81",
+            "score": 0.5644607467102114,
+            "metadata": {
+              "text": "Phenotypes Database attheGeneNetwork (www.",
+              "title": "2022 - Systems genetics in the rat HXBBXH family identifies Tti2 as a pleiotropic quantitative trait gene for adult hippocampal neurogenesis and serum glucose.pdf",
+              "version": "v0",
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+            "score": 0.5644607467102114,
+            "metadata": {
+              "text": "Phenotypes Database attheGeneNetwork (www.",
+              "title": "2022 -Senko- System Genetics in the Rat HXB\uf022BXH Family.pdf",
+              "version": "v0",
+              "chunk_order": 9,
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+            "id": "03d0618c-8ed8-5984-a4eb-e743daf4f1a7",
+            "score": 0.5644607467102114,
+            "metadata": {
+              "text": "Phenotypes Database attheGeneNetwork (www.",
+              "title": "2022 -Senko- Hippocampal neurogenesis serum glucose.pdf",
+              "version": "v0",
+              "chunk_order": 9,
+              "document_id": "bac2ab98-4317-59ed-99ef-deda8c22786d",
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+    "results": {
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+        "choices": [
+          {
+            "finish_reason": "stop",
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+            "message": {
+              "content": "In QTL (Quantitative Trait Loci) analyses, several genes have been identified as being associated with diabetes. Specifically:\n\n1. **RASA1, Nnt, and PSK1** on chromosome 13 have been identified as candidate genes underlying diabetes-related traits [4].\n2. **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].\n3. **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].\n4. **Genes on 5q11-q13 and 7q32** have been identified as significant susceptibility factors for type 1 diabetes [9].\n\nThese genes and loci are typically associated with diabetes in QTL analyses, highlighting the complex genetic architecture of the disease.",
+              "role": "assistant"
+            }
+          }
+        ],
+        "created": 1727796876,
+        "model": "gpt-4o-2024-05-13",
+        "object": "chat.completion",
+        "system_fingerprint": "fp_057232b607",
+        "usage": {
+          "completion_tokens": 192,
+          "prompt_tokens": 1264,
+          "total_tokens": 1456
+        }
+      },
+      "search_results": {
+        "vector_search_results": [
+          {
+            "id": "313e590c-40a4-5adb-a2d8-18577f465b30",
+            "score": 0.7695692777633667,
+            "metadata": {
+              "text": "genes that are responsible for obesity-associated diabetes.\nBy the generation of subcongenic lines of a QTL, if pos-\nsible starting with chromosome substitution strains, thensmall critical regions that harbor the gene(s) in question\ncan be identied with certainty. Sequence analysis and\nmRNA proling together with gene targeting in-vitro andin-vivo may lead to a solid chain of evidence linking\nsequence differences with altered molecular, cellular, and",
+              "title": "2014 - The genetic basis of obesity-associated type 2 diabetes (diabesity) in polygenic mouse models.pdf",
+              "version": "v0",
+              "chunk_order": 84,
+              "document_id": "1459a93f-3052-5cea-ba83-caf266ef9b86",
+              "extraction_id": "1ab308e3-565f-5d14-86bc-2909dd9a1de0",
+              "associatedQuery": "which genes are typically associated with diabetes in QTL analyses?"
+            }
+          },
+          {
+            "id": "f5220a71-d1bc-50ae-933a-2b92bab0c4ae",
+            "score": 0.7525558471679688,
+            "metadata": {
+              "text": "tensive nondiabetic families, the QTLs on chromosomes\n8q24 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.\nACKNOWLEDGMENTS",
+              "title": "2006 - Quantitative Trait Loci on Chromosome 8q24.pdf",
+              "version": "v0",
+              "chunk_order": 76,
+              "document_id": "8c5ffeac-5108-5b03-acd0-57aa09469af5",
+              "extraction_id": "d35d2e8c-0e2f-5be4-a902-18d5c857746d",
+              "associatedQuery": "which genes are typically associated with diabetes in QTL analyses?"
+            }
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+          {
+            "id": "569bb9be-0b57-535a-ab0c-206d85f1dd4a",
+            "score": 0.751487672328949,
+            "metadata": {
+              "text": "studies used the QTL approach for statistical analysis of genotypes\nand phenotypes measured in the crosses. The concept of genetic\ndissection of diabetes into quantitative endophenotypes was\nintroduced and resulted in the detection of genetic loci responsible\nfor the control of fasting glycemia [39,42] , fasting insulinemia\n[39,43] , glucose tolerance [39,41,42] , insulin secretion induced by\nglucose or arginine [39], body weight [39,41,44] , adiposity [39],\nb-",
+              "title": "2017 - Genomic regulation of type 2 diabetes endophenotypes Contribution.pdf",
+              "version": "v0",
+              "chunk_order": 41,
+              "document_id": "fef1ae33-b3af-50ea-909c-f1b57f7fe981",
+              "extraction_id": "9dfc060c-bf5e-5958-b446-cfc12a4f85c5",
+              "associatedQuery": "which genes are typically associated with diabetes in QTL analyses?"
+            }
+          },
+          {
+            "id": "f3de711d-7dff-5b13-89c1-720bb6be9e12",
+            "score": 0.7442656977255854,
+            "metadata": {
+              "text": "indicating that risk factors exist on both genetic back-\ngrounds [ 29]. QTL mapping studies indicate that these\nmurine metabolic traits have a complex genetic architec-\nture that is not dominated by any single allele [ 2931],\nmuch like humans [ 32,33].\nPrior work identied candidate genes on Chr 13 that might\nunderlie diabetes-related traits, including RASA1, Nnt, andPSK1. RASA1 show strong sequence differences between\nB6 and D2 strains [ 34]. Rasche et al. [ 35] reported that",
+              "title": "2015 - A Chromosome 13 locus is associated with male-specific mortality in mice.pdf",
+              "version": "v0",
+              "chunk_order": 52,
+              "document_id": "ad8f2626-87fb-520e-8cef-ee9a9cc3ab0b",
+              "extraction_id": "cc39ccbe-150c-5d7e-8b6b-f6c98738cb95",
+              "associatedQuery": "which genes are typically associated with diabetes in QTL analyses?"
+            }
+          },
+          {
+            "id": "6299defb-19e0-5f6d-aaea-44b36cdece6e",
+            "score": 0.7377868294715881,
+            "metadata": {
+              "text": "genetic background [4]. Linkage analyses have shown that\nseveral quantitative trait loci interact with each other and\nwith the environment to elicit obesity syndromes that are\npotentially diabetic. Several recent genome-wide associa-\ntion studies have identified novel candidate genes for\nT2DM but the effect of these variants on disease suscepti-\nbility is generally low, with odds ratios mostly around 1.5\n[5-11].\nMultiple studies on the transcriptome level have been per-",
+              "title": "2008 - Meta-Analysis Approach identifies Candidate Genes and associated Molecular Networks for Type-2 Diabetes Mellitus.pdf",
+              "version": "v0",
+              "chunk_order": 12,
+              "document_id": "4060609b-1464-55fa-93cd-fefaf2cac900",
+              "extraction_id": "309adb8f-fa42-5806-9e50-95742ba90857",
+              "associatedQuery": "which genes are typically associated with diabetes in QTL analyses?"
+            }
+          },
+          {
+            "id": "807bf364-408f-50c9-bacd-b9da438a1703",
+            "score": 0.7363013248533409,
+            "metadata": {
+              "text": "(2011).\n7. Steinthorsdottir, V. et al. Identification of low-frequency and rare sequence variants associated with elevated or reduced risk of type 2 diabetes. Nat. Genet.  \n46, 294298 (2014).8. Ma, R. C. et al. Genome-wide association study in a Chinese population \nidentifies a susceptibility locus for type 2 diabetes at 7q32 near PAX4. \nDiabetologia 56, 12911305 (2013).\n9. Huyghe, J. R. et al. Exome array analysis identifies new loci and low-frequency",
+              "title": "2016 - The genetic architecture of type 2 diabetes.pdf",
+              "version": "v0",
+              "chunk_order": 83,
+              "document_id": "d7e2a9de-46f1-5191-9cb0-dd68eb9f365a",
+              "extraction_id": "8b8b572d-68f5-5470-b5ed-ec5c6219dd5e",
+              "associatedQuery": "which genes are typically associated with diabetes in QTL analyses?"
+            }
+          },
+          {
+            "id": "410c1b39-1d2a-5954-ac2c-9bf4ad38aa58",
+            "score": 0.7304001885891086,
+            "metadata": {
+              "text": "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",
+              "title": "1998 - Genetic dissection of ``OLETF_, a rat model for non-insulin-dependent diabetes mellitus.pdf",
+              "version": "v0",
+              "chunk_order": 56,
+              "document_id": "0f04bb9f-6d45-5511-a05c-a09f8ee9a5e9",
+              "extraction_id": "c29fe565-1167-5821-8715-559cb48f2090",
+              "associatedQuery": "which genes are typically associated with diabetes in QTL analyses?"
+            }
+          },
+          {
+            "id": "0ea7a0f3-5fdd-5d9f-8f53-4620492867f7",
+            "score": 0.7302536964416504,
+            "metadata": {
+              "text": "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",
+              "title": "2015 - Transcript Expression Data from Human.pdf",
+              "version": "v0",
+              "chunk_order": 4,
+              "document_id": "2b30d4f3-9ec3-574f-9a36-709b0e09c3f2",
+              "extraction_id": "b9d039d0-8982-52c6-ba45-be2e2eeda7d5",
+              "associatedQuery": "which genes are typically associated with diabetes in QTL analyses?"
+            }
+          },
+          {
+            "id": "517a8a37-697b-500b-a5e8-7eff80fc0f79",
+            "score": 0.7275592413474086,
+            "metadata": {
+              "text": "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-\nease characterized by autoimmune destructionof the insulin-producing /H9252-cells in the endocrine\npancreas, resulting in deranged metabolic ho-",
+              "title": "2004 - Interaction and Association Analysis of a Type 1 Diabetes Susceptibility Locus.pdf",
+              "version": "v0",
+              "chunk_order": 4,
+              "document_id": "4246f8d0-69e8-56cf-9674-d379467dfb61",
+              "extraction_id": "b7586c99-af71-5f11-8fed-fd8395c783b6",
+              "associatedQuery": "which genes are typically associated with diabetes in QTL analyses?"
+            }
+          },
+          {
+            "id": "af834bd3-8462-5159-99e8-59a2fc1f09c9",
+            "score": 0.7273451089859009,
+            "metadata": {
+              "text": "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",
+              "title": "2001 - Genetic Analysis of a New Mouse Model for Non-InsulinDependent Diabetes.pdf",
+              "version": "v0",
+              "chunk_order": 115,
+              "document_id": "c6086f32-0a3a-5a92-9e5b-4d2fa7fbbc93",
+              "extraction_id": "4cc0bd43-c6a8-55fb-8300-d2228636c89d",
+              "associatedQuery": "which genes are typically associated with diabetes in QTL analyses?"
+            }
+          }
+        ],
+        "kg_search_results": null
+      }
+    }
+  },
+  "25": {
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+        "choices": [
+          {
+            "finish_reason": "stop",
+            "index": 0,
+            "message": {
+              "content": "The gene TCF7L2 is primarily involved in Type 2 diabetes. This association is well-documented across multiple sources:\n\n1. Single nucleotide polymorphisms in TCF7L2 are reproducibly associated with Type 2 diabetes and reduce the insulin response to glucose in nondiabetic individuals [2].\n2. 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].\n3. Variants of the TCF7L2 gene confer risk of Type 2 diabetes [4], [5].\n4. TCF7L2 is considered the strongest genetic determinant for the risk of developing Type 2 diabetes [7].\n5. Overexpression of TCF7L2 protein decreases the sensitivity of beta islet cells to secrete insulin, which is involved in the regulation of insulin secretion [9].\n\nThus, the gene TCF7L2 is significantly involved in Type 2 diabetes.",
+              "role": "assistant"
+            }
+          }
+        ],
+        "created": 1727796881,
+        "model": "gpt-4o-2024-05-13",
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+          "total_tokens": 1593
+        }
+      },
+      "search_results": {
+        "vector_search_results": [
+          {
+            "id": "0cc14865-601a-5668-9dfa-b5131c05ce91",
+            "score": 0.7001483649059645,
+            "metadata": {
+              "text": "T. I., de Bakker, P . I. et al (2006). TCF7L2",
+              "title": "2010 - Candidate Gene and Genome-Wide Association Studies in Behavioral Medicine.pdf",
+              "version": "v0",
+              "chunk_order": 4361,
+              "document_id": "17637a6f-804e-50e4-9cf5-37318e17f15c",
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+              "associatedQuery": "In which diseases is the gene TCF7L2 involved?"
+            }
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+          {
+            "id": "3e678e3c-ad2c-5884-9c88-7f4d54c914bd",
+            "score": 0.6975938081741333,
+            "metadata": {
+              "text": "single nucleotide polymorphisms in TCF7L2 are reproduc-ibly associated with type 2 diabetes and reduce the insulinresponse to glucose in nondiabetic individuals. Diabetes55:28902895\n135. Cauchi S, Meyre D, Dina C, Choquet H, Samson C,\nGallina S, Balkau B, Charpentier G, Pattou F, StetsyukV, Scharfmann R, Staels B, Fru  hbeck G, Froguel P 2006\nTranscription factor TCF7L2 genetic study in the Frenchpopulation: expression in human\n/H9252-cells and adipose tissue",
+              "title": "2009 - Pathomechanisms of Type 2 Diabetes Genes.pdf",
+              "version": "v0",
+              "chunk_order": 256,
+              "document_id": "cf8ec75c-8ffe-5baa-830d-ac7a4a5964bd",
+              "extraction_id": "eff1d167-9689-5c26-9a12-c66714696d86",
+              "associatedQuery": "In which diseases is the gene TCF7L2 involved?"
+            }
+          },
+          {
+            "id": "5d936c2c-faf7-5b0f-92e1-c3f8f43b3011",
+            "score": 0.6955914678817124,
+            "metadata": {
+              "text": "rs7903146 and rs12255372 in intron 3 of the TCF7L2 gene\n[20], associated with a ~45% increase in Type 2 diabetes\nrisk per allele. As such, the TCF7L2 locus presently repre-\nsents the strongest known genetic determinant of Type 2diabetes. Risk allele carriers show impaired insulin produc-tion [21] and b-cell dysfunction in vitro [22].\nTCF7L2 (previously referred to as TCF-4) is a\nhigh-mobility group box-containing transcription factor\ninvolved in Wingless-type MMTV integration site (Wnt)",
+              "title": "2014  - Dorothy Hodgkin Lecture 2014 Understanding genes identified by genome\u2010wide association.pdf",
+              "version": "v0",
+              "chunk_order": 33,
+              "document_id": "11d0cb98-a00f-53f1-92e3-e1be17002c02",
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+              "text": "et al. Variant of transcription factor 7-like 2 (TCF7L2) gene confers risk of type 2 \ndiabetes. Nat Genet . 2006;38:320-23. \n Sladek R, Rocheleau G, Rung J, Dina C, Shen L, Serre D, et al. A genome- [9]\nwide association study identifies novel risk loci for type 2 diabetes. Nature . \n2007;445:881-85.\n Kirchhoff K, Machicao F, Haupt A, Schafer SA, Tschritter O, Staiger H, et al. [10]\nPolymorphisms in the TCF7L2, CDKAL1 and SLC30A8 genes are associated",
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+              "text": "transcription factor 7-like 2 ( TCF7L2 ) gene confers risk of type 2 diabetes. Nat Genet. 2006;\n38:320323. [PubMed: 16415884]\n172. Gloyn AL, Noordam K, Willemsen MA, Ellard S, Lam WW, et al. Insights into the biochemical\nand genetic basis of glucokinase activation from naturally occurring hypoglycemia mutations.\nDiabetes. 2003; 52:24332440. [PubMed: 12941786]\n173. Pearson ER, Donnelly LA, Kimber C, Whitley A, Doney AS, et al. Variation in TCF7L2",
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+              "text": "L. Mechanisms by which common variants in the TCF7L2 gene \nincrease risk of type 2 diabetes. J Clin Invest  2007; 117: 2155-2163 \n[PMID: 17671651 DOI: 10.1172/JCI30706]\n164 Gloyn AL , Braun M, Rorsman P. Type 2 diabetes susceptibility \ngene TCF7L2 and its role in beta-cell function. Diabetes  2009; 58: \n800-802 [PMID: 19336690 DOI: 10.2337/db09-0099]\n165 da Silva Xavier G , Loder MK, McDonald A, Tarasov AI, Carzaniga \nR, Kronenberger K, Barg S, Rutter GA. TCF7L2 regulates late",
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+              "text": "tion. Although the disease progression results from aninterplay of environmental factors and genetic predisposi-\ntion, in recent years TCF7L2 gene has been considered the\nstrongest genetic determinant for the risk of developingT2DM [ 24,19,20]. The gene encodes a transcription\nfactor of the canonical Wnt signaling pathway, expressed\nin several tissues, known to have developmental roles indetermining cell fate, survival, proliferation and movement\n[9]. Wnt signaling plays an important role also in B-cell",
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+              "text": "transcription factor 7-like 2 (TCF7L2) gene confers risk of type 2diabetes. Nat Genet 38:320 3231422 Diabetologia (2007) 50:1418 1422",
+              "title": "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",
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+              "text": "genes which also play a significant role in the risk and \npathogenesis of the disease[158,159]. The association \nof TCF7L2  gene variants with type 2 diabetes and \nits mechanism of action received special attention \nby several investigators[161,162]. Over expression of the protein was shown to decrease the sensitivity of \nbeta islet cells to secrete insulin[163,164] and was more \nprecisely involved in the regulation of secretary granule \nfusion that constitute a late event in insulin secretion",
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+              "text": "Muggeo M, Stoico V, Negri C, Pignatti PF, Bonora E, Bonadonna\nRC (2011) Variants and haplotypes of TCF7L2 are associatedwithb-cell function in patients with newly diagnosed type 2\ndiabetes: the Verona Newly Diagnosed Type 2 Diabetes Study\n(VNDS) 1. J Clin Endocrinol Metab 96(2):E389E393\n13. Grundy SM, Cleeman JI, Merz CN, Brewer HB Jr, Clark LT,\nHunninghake DB, Pasternak RC, Smith SC Jr, Stone NJ, National\nHeart, Lung, and Blood Institute, American College of Cardiol-",
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+              "content": "Once a sperm combines with an egg, the traits passed onto the resulting lifeform are determined by several factors:\n\n1. **Chromosomal Segments from Parents**: Individuals inherit a patchwork of chromosomal segments from both maternal and paternal chromosomes [1].\n   \n2. **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].\n\n3. **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].\n\n4. **Epigenetic Marks**: Soon after fertilization, epigenetic marks can affect gene expression and phenotype later in development [8].\n\n5. **Heritable Material**: The information passed from parents to offspring is coded in DNA molecules [7].\n\nThese factors collectively determine how traits are inherited and expressed in the resulting lifeform.",
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+              "text": "phenomena such as mutations and gene conversion events) occur in relevant meioses \nleading up to the formation of the gametes (i.e., egg and sperm) which are combined \nduring fertilization and the formation of zygotes. Thus, individuals inherit a patch-\nwork of chromosomal segments from maternal and paternal chromosomes.",
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+              "text": "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:\nFirst, inside the sperm or egg, genes can be marked with small chemical tags that flag these genes",
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+              "text": "(Figures 8 and 9). Two gametes (egg and sperm) ultimately \njoin 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.\nThe Mendel Laws: Segregation and Independent \nAssortment\nBoth of the Mendel laws pertain directly to the process of \nmeiosis. The first Mendel law, the law of segregation, states \nthat each parent passes a randomly selected allele for a given",
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+              "text": "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.\nSecond, the mother can alter the gene activity in her offspring via the placenta; this process is known\nas 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",
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+              "text": "the subset of that genetic information that is active.  But how does the differentiation process \nbegin?  The key insight in resolving this conundrum came from fly genetics and was the \nrealization that the egg is not a homogenous sack of protoplasm.  The maternally-derived genes \nactive in the fertilized egg are asymmetrically distributed such that at the first cell division each \ndaughter cell receives a different complement of factors.  Development continues as a",
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+              "text": "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",
+              "title": "2007 - Promoting_Student_Scientific_Literacy_of_Molecular Genetics and Genomics.pdf",
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+              "text": "sex chromosome effects. (B)Soon after fertilization, male and female cells have sex-specic transcriptomes, epigenomes, and phenotypes (for example, male\nembryos grow faster than female embryos). At implantation, lineage determination begins and gene expression differences are reduced. Epigenetic marks, however,\nare less constrained and some are maintained, affecting gene expression, and phenotype later in development. Once specic lineages are established, differences in",
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+              "text": "or father (sperm cell). Each gamete has a set of 23 unpaired chromosomes. Two human gametes (egg and sperm) combine \nto create a cell (zygote) that contains the full human genome \nof 23 paired chromosomes.Genetic Information Nondiscrimination Act (GINA)\nUS federal legislation that makes it unlawful to discriminate \nagainst individuals on the basis of their genetic profiles in regard to health insurance and employment. These protections \nare intended to encourage Americans to take advantage of",
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+              "text": "173. Griffey, R. H.; Greig, M. J.; Haoyun, A.; Sasmor, H.; Manalili, S.\nTargeted Site-Specific Gas-Phase Cleavage of Oligoribonucleotides.\nApplication in Mass Spectrometry-Based Identification of Ligand\nBinding Sites. J. Am. Chem. Soc. 1999, 121, 474475.\n174. Hanson, C. L.; Fucini, P.; Ilag, L. L.; Nierhaus, K. H.; Robinson, C. V.\nDissociation of Intact Escherichia coli Ribosomes in a Mass Spectrome-\nterEvidence for Conformational Change in a Ribosome Elongation",
+              "title": "2011 - A Role for the MS Analysis of Nucleic Acids.pdf",
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+              "text": "or chloramphenicol\nImmobilized targetDissociation of ribosome\nand release of mRNA5Poly(AAA)3\nmRNA\nIsolation of mRNART-PCRdsDNA\nMutagenesis by\nerror-prone PCR\nFig. 35.5.  Schematic presentation of a ribosome display round. The gene of interest is \ntranscribed from dsDNA into mRNA and translated into proteins by in vitro techniques. \nThe ribosomes remain tethered to the mRNA by either cold shock or chloramphenicol. \nThis step ensures that the genotype remains coupled to the phenotype. The proteins are",
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+              "text": "270 G.L. Sutphin e t a l.\ngene (Hinneb usch 2005 ). The m echanism of re gulation i s t hought to in v o lv e r el-\nati 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\nand 40S ribosomal sub units are p roposed to more frequently scan through the",
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+              "text": "then used to develop synthetic gene networks with defined outputs, without significant post-hoc adjustments\n22,4751. Alternatively, syn-\nthetic ribosome binding site (RBS) sequences can be used to optimize protein expression levels. Recently, Salis et al.\n52 have developed a \nthermodynamic model for predicting the relative translational ini -",
+              "title": "2009 - Next generation synthetic gene networks.pdf",
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+              "text": "Philips, R.M., 2017 How Many Ribosomes Are in a Cell? [WWW Document]. URL http://\nbook.bionumbers.org/how-many-ribosomes-are-in-a-cell/ ((accessed 7.24.16) n.d.).\nR Core Team, 2014. R: a Language and Environment for Statistical Computing. R Founda-\ntion for Statistical Computing, Vienna, Austria.\nSigurdson, A.J., Ha, M., Hauptmann, M., Bhatti, P., Sram, R.J., Beskid, O., Tawn, E.J.,",
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+              "text": "structure, and to find sites that are likely to be cleaved or modified; interac-\ntion or catalytic mechanisms can be simulated. Bioinformatic resources on \nthe WWW range from the determination of the molecular weight to complex \nthreading and three-dimensional (3D) prediction algorithms. A huge list of \ntools can be found on the ExPASy proteomic tools homepage (65). Because \nof the great variety of programs available, several of these single tools have",
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+              "text": "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\n53.\nBy automating the construction and characterization of biomo-",
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+              "content": "Once a sperm combines with an egg, the traits passed onto the resulting lifeform are determined by several factors:\n\n1. **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].\n\n2. **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].\n\n3. **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].\n\n4. **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].\n\n5. **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].\n\nIn 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.",
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+              "text": "phenomena such as mutations and gene conversion events) occur in relevant meioses \nleading up to the formation of the gametes (i.e., egg and sperm) which are combined \nduring fertilization and the formation of zygotes. Thus, individuals inherit a patch-\nwork of chromosomal segments from maternal and paternal chromosomes.",
+              "title": "2008 -  Study Design and Statistical Issues.pdf",
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+              "text": "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",
+              "title": "2009 - Opening Up the Conversation on Genetics.pdf",
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+              "text": "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:\nFirst, inside the sperm or egg, genes can be marked with small chemical tags that flag these genes",
+              "title": "2015 - Constraint and divergence of global gene expression in the mammalian embryo.pdf",
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+              "text": "(Figures 8 and 9). Two gametes (egg and sperm) ultimately \njoin 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.\nThe Mendel Laws: Segregation and Independent \nAssortment\nBoth of the Mendel laws pertain directly to the process of \nmeiosis. The first Mendel law, the law of segregation, states \nthat each parent passes a randomly selected allele for a given",
+              "title": "2015 - Basic Concepts and Potential Applications of Genetics and Genomics for Cardiovascular and Stroke Clinicians.pdf",
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+              "text": "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.\nSecond, the mother can alter the gene activity in her offspring via the placenta; this process is known\nas 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",
+              "title": "2015 - Constraint and divergence of global gene expression in the mammalian embryo.pdf",
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+              "text": "the subset of that genetic information that is active.  But how does the differentiation process \nbegin?  The key insight in resolving this conundrum came from fly genetics and was the \nrealization that the egg is not a homogenous sack of protoplasm.  The maternally-derived genes \nactive in the fertilized egg are asymmetrically distributed such that at the first cell division each \ndaughter cell receives a different complement of factors.  Development continues as a",
+              "title": "2008 - Genotype-phenotype relationships and the patterning of complex traits as exemplified in the mammalian dentition.pdf",
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+              "text": "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",
+              "title": "2007 - Promoting_Student_Scientific_Literacy_of_Molecular Genetics and Genomics.pdf",
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+              "text": "sex chromosome effects. (B)Soon after fertilization, male and female cells have sex-specic transcriptomes, epigenomes, and phenotypes (for example, male\nembryos grow faster than female embryos). At implantation, lineage determination begins and gene expression differences are reduced. Epigenetic marks, however,\nare less constrained and some are maintained, affecting gene expression, and phenotype later in development. Once specic lineages are established, differences in",
+              "title": "2019 - Sexual Dimorphism in the Age of Genomics How, When, Where.pdf",
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+              "text": "or father (sperm cell). Each gamete has a set of 23 unpaired chromosomes. Two human gametes (egg and sperm) combine \nto create a cell (zygote) that contains the full human genome \nof 23 paired chromosomes.Genetic Information Nondiscrimination Act (GINA)\nUS federal legislation that makes it unlawful to discriminate \nagainst individuals on the basis of their genetic profiles in regard to health insurance and employment. These protections \nare intended to encourage Americans to take advantage of",
+              "title": "2015 - Basic Concepts and Potential Applications of Genetics and Genomics for Cardiovascular and Stroke Clinicians.pdf",
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+              "text": "Proponents of the evo-devo view rightly point out that evolution occurs through changes in the \ndevelopment of traits, which may or may not have changes in DNA as their root cause.  The \nprocesses that produce traits occur during development and involve more than just genes.   \nAll animals begin life as a fertilized egg, a single cell containing mitochondria and other \norganelles, and enough maternally derived RNA and proteins to kick start development and",
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+              "content": "Sure! Let's break down the difference between short-read and long-read sequencing in a way that's easy to understand.\n\n### Short-Read Sequencing:\n1. **Read Length**: Short-read sequencing generates small fragments of DNA, typically around 50-300 base pairs long [4].\n2. **Technology**: This method is commonly used by platforms like Illumina [3].\n3. **Advantages**: \n   - **High Throughput**: It can produce a large amount of data quickly and is relatively cost-effective [4].\n   - **Accuracy**: Generally, short-read sequencing has a lower error rate per base compared to long-read sequencing [4].\n4. **Limitations**:\n   - **Complex Regions**: It struggles to accurately sequence complex regions of the genome, such as those with long repetitive elements or structural variations [6].\n   - **Assembly**: Because the reads are short, assembling them into a complete genome can be challenging and may leave gaps or errors [6].\n\n### Long-Read Sequencing:\n1. **Read Length**: Long-read sequencing produces much longer fragments, often several kilobases (thousands of base pairs) in length [6].\n2. **Technology**: There are two main types:\n   - **Single-Molecule Real-Time Sequencing**: This method reads long DNA fragments directly without needing to amplify them [8].\n   - **Synthetic Long-Reads**: This method uses short-read technology to create long reads in a computational process [2].\n3. **Advantages**:\n   - **Complex Regions**: It can span complex or repetitive regions in a single read, making it easier to resolve these areas accurately [7].\n   - **Transcriptomics**: Long reads can cover entire mRNA transcripts, helping researchers understand gene structures and variations better [7].\n4. **Limitations**:\n   - **Cost and Throughput**: Long-read sequencing is generally more expensive and produces less data per run compared to short-read sequencing [4].\n   - **Error Rate**: The error rate per base can be higher than that of short-read sequencing, although this is improving with new technologies [4].\n\nIn 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.",
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+              "text": "for sequencing on existing short-read instrumentation, \nafter which data are split by barcode and reassembled \nwith the knowledge that fragments sharing barcodes Barcodes\nA series of known bases \naddedto a template molecule \neither through ligation or \namplification. After \nsequencing, these barcodes \ncan be used to identify which \nsample a particular read is \nderived from.\nFigure 5 | Real-time and synthetic long-read sequencing approaches.",
+              "title": "2016 - Coming of age ten years of next.pdf",
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+              "text": "sequence 2D read.\nSynthetic long-reads.  Unlike true sequencing platforms, \nsynthetic long-read technology relies on a system of \nbarcoding to associate fragments that are sequenced on \nexisting short-read sequencers61. These approaches par -\ntition large DNA fragments into either microtitre wells \nor an emulsion such that very few molecules exist in \neach partition. Within each partition the template frag -\nments are sheared and barcoded. This approach allows",
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+              "text": "sequencing. This platform is used by the Illumina \nsuite of platforms.\n36. Dohm,J.C., Lottaz,C., Borodina,T . & \nHimmelbauer,H. Substantial biases in ultra-short read \ndata sets from high-throughput DNA sequencing. \nNucleic Acids Res. 36, e105 (2008).\n37. Nakamura,K. etal.  Sequence-specific error profile \nofIllumina sequencers. Nucleic Acids Res. 39, e90 \n(2011).\n38. Minoche,A.E., Dohm,J.C. & Himmelbauer,H. \nEvaluation of genomic high-throughput sequencing \ndata generated on Illumina HiSeq and genome",
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+              "text": "Comparison of short-read platforms.  Individual short-\nread sequencing platforms vary with respect to through -\nput, cost, error profile and read structure (TABLE1 ). \nDespite the existence of several NGS technology pro -\nviders, NGS research is increasingly being conducted \nwithin the Illumina suite of instruments21. Although \nthis implies high confidence in their data, it also raises \nconcerns about systemic biases derived from using a \nsingle sequencing approach2628. As a consequence, new",
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+              "text": "short-read sequencing. arXiv, arXiv:1203.3907v2, https://arxiv.org/abs/\n12073907 .\nGarrison, E., Sire n, J., Novak, A.M., Hickey, G., Eizenga, J.M., Dawson, E.T.,\nJones, W., Garg, S., Markello, C., Lin, M.F., et al. (2018). Variation graph toolkit\nimproves read mapping by representing genetic variation in the reference. Nat.\nBiotechnol. 36, 875879 .\nGiambartolomei, C., Vukcevic, D., Schadt, E.E., Franke, L., Hingorani, A.D.,",
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+              "text": "or  transcriptomic structure53.\nLong-read sequencing\nOverview.  It has become apparent that genomes are \nhighly complex with many long repetitive elements, \ncopy number alterations and structural variations that \nare relevant to evolution, adaptation and disease5456. \nHowever, many of these complex elements are so long \nthat short-read paired-end technologies are insufficient \nto resolve them. Long-read sequencing delivers reads in \nexcess of several kilobases, allowing for the resolution of",
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+              "text": "these large structural features. Such long reads can span \ncomplex or repetitive regions with a single continuous \nread, thus eliminating ambiguity in the positions or size \nof genomic elements. Long reads can also be useful for \ntranscriptomic research, as they are capable of span -\nning entire mRNA transcripts, allowing researchers to \nidentify the precise connectivity of exons and discern \ngeneisoforms.\nCurrently, there are two main types of long-read tech -",
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+              "text": "nologies: single-molecule real-time sequencing  approaches \nand synthetic approaches that rely on existing short-\nread technologies to construct long reads  insilico . The \nsingle-molecule approaches differ from short-read \napproaches in that they do not rely on a clonal popula -\ntion of amplified DNA fragments to generate detectable Figure 2 | Sequencing by ligation methods. a | SOLiD sequencing. Following cluster \ngeneration or bead deposition onto a slide, fragments are sequenced by ligation, in",
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+              "text": "Tools for alignment-free analyses of sequencing data\nThe vast majority of next-generation sequencing experiments in mouse have read alignment \nto a reference genome as their first step. However, the primary data from any sequencing \nexperiment are the reads themselves. Recognition that the raw reads are information-rich has \nled to the development of alignment-free algorithms for error correction (among many \nothers, Chaisson and Pevzner 2008 ), abundance estimation ( Patro et al. 2014 ), and de novo",
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+              "text": "(right). Sequencing adaptors (depicted by short red bars and short purple bars) are subsequently ligated to\neach cDNA fragment (green lines) and short sequence reads (single end or paired ends) from each cDNA are\ngenerated using high-throughput sequencing technology. The resulting sequence reads [short lines beneath\nthe genome sequence with three genes shown (fat blue bars)] are aligned with the reference genome to",
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+              "content": "Using a pangenome-based reference can be more useful than using a single linear reference for several reasons:\n\n1. **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].\n\n2. **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].\n\n3. **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].\n\n4. **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].\n\n5. **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].\n\nIn 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.",
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+              "text": "When reliable prior knowledge exists about the variant composition in a pan-genome (typi-\ncally obtained via read-to-reference mapping), there are computational tools that can transform\na linear reference sequence and a set of variant calls into graphs (18).This approach bypasses the\ncomputationallyexpensiveall-versus-allalignmentstepalongwiththeuncertaintiesofsubsequent\ngraph construction, but the trade-off is increased reference bias and a potentially incomplete",
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+              "text": "(Karolchik et al. 2014 )] and Ensembl ( Flicek et al. 2013 ). Use of a single haploid reference \nsequence as an anchor for all studies of genetic variation in mouse offers many practical \nadvantages. But the dependency on a reference genome requires several assumptions about \nthe nature of genetic variation which may be violated in practicethe strongest of which is \nthat of genomic collinearity (i.e., conserved marker order) between strains. We consider the",
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+              "text": "for at least 500 ancestrally diverse humans. This resource willalso provide a set of highly accurate genomes that can be\nused as a benchmarking dataset to improve short-read analysis\ntools. Even more importantly, these genomes allow completelynew designs for more effective short-read analysis strategiesthat overcome many of the limitations described above.\nTransitioning to a pan-genome reference will require develop-",
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+              "text": "2018;562(7726):203-209. http://doi.org/10.1038/s41586-018-0579-z\n110. Li R, Li Y, Zheng H, et al. Building the sequence map of the human\npan-genome. Nat Biotechnol . 2010;28(1):57-63. http://doi.org/10.\n1038/nbt.1596\n111. Vernikos G, Medini D, Riley DR, Tettelin H. Ten years of pan-\ngenome analyses. Curr Opin Microbiol . 2015;23:148-154. http://\ndoi.org/10.1016/j.mib.2014.11.016\n112. Miga KH, Wang T. The need for a human pangenome reference\nsequence. Annu Rev Genomics Hum Genet . 2021;22:81-102.\nhttp://",
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+              "text": "Whilemostpan-genomesconstructedtodateareprimarilygene-basedbecauseoftherelative\neaseofcomparingandcategorizingdiscreteunitsdefinedbytranscriptionandtranslation,theim-\nportanceofnoncodingandrepetitivesequencesisunquestionable.Itwouldthereforebeextremely\npowerfultodefineacomprehensivesequence-basedpan-genomethatincludesinformationabout\ntherelativepositionofallsequences.Unfortunately,interpretingnoncodingsequencevariationischallenging.Indeed,evenforclassesofnoncodingsequencesofknownimportance,e.g.,promot-",
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+              "text": "assessment  will improve our understanding of the reference to  better assemble and interpret future \ngenome sequences.  \n \nWe have previously developed a method to assess the risk of a patient for 55 diseases using a \nquantitative human disease -SNP association database, and showed that we could suggest useful \nand clinical relevant information using his personal genome sequence  (16). Here, we queried  the \nreference genome sequence against our databa se and  identified  3,556 disease -susceptib ility",
+              "title": "2011 - The Reference Human Genome High Risk of Type 1 Diabetes and Other Disorder.pdf",
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+              "text": "The shortcomings of a single, linear reference genome\nper species are well appreciated, and richer reference datastructures are an active area of research (Church et al.\n2015 ). An alternative is de novo assembly of the genomes\nof commonly used strains. The Sanger Mouse GenomesProject is using a combination of long-insert jumping\nlibraries and optical mapping to build de novo assemblies",
+              "title": "2015 - Informatics resources for the Collaborative Cross and related mouse populations.pdf",
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+              "text": "undertake comprehensive and \npowerful explorations rather than \nbeing confined to testing hypoth -\neses focused on candidate path -\nways. With the completion of the \nfirst reference sequence of the \nhuman genome,3 attention shifted \nfrom searching for genes to dis -\ncovering their functions. System -\natic genetic mapping in families \nand populations helped scientists \npinpoint the genetic variants that \ncontribute to human disease.",
+              "title": "2021 - Human Molecular Genetics and Genomics.pdf",
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+              "text": "points, nding statistical associations, modeling and run ning predic-\ntors, or constructing and pruning networks of detected rela tions. In the\nfollowing paragraphs I will explore these opportunities in detail.\n1.4.1 Population reference genomes\nGenomes are relatively similar between individuals, there fore, instead of\nassembling the complete sequence for each person, we only de termine\npoints of DNA variation compared to a reference genome. Subs equently,",
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+              "text": "having a reference genome for a related specie s certainly makes the process easier. The \navailability of long-read sequences vastly improves our ability to assemble new genomes, and new \ntechnologies, such as PacBio  and Nanopore, are now able to give reads between 100-1000 \nkilobases, an order of m agnitude longer than current Illumina sequencing (Shendure et al.  2017). \nCombining these new technologies with traditional short read NGS will greatly improve our ability",
+              "title": "2017 - Post-genomic behavioral genetics From revolution to routine.pdf",
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+              "content": "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:\n\n1. **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].\n\n2. **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].\n\n3. **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].\n\n4. **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].\n\n5. **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].\n\nIn 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.",
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+              "text": "al., 2012 ; Hindhorff, 2009; Barrett et al ., 2007 ). Recent efforts by the Encyclopedia of DNA \nelements (ENCODE) consortium, to characterise the human genome, have revealed that most \nof the non -coding part of the genome is not inactive but is associated with  different forms of \nregulatory activity (ENCODE, 2012 ; Thurman, 2012 ). One important regulatory process that \ntakes place within the genome is the (in-) activation  of gene expression through the interaction",
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+              "text": "network of transcriptional regulators. Nature 403, \n335338 (2000).\n18. Gardner,T ., Cantor,C. & Collins,J. Construction of a \ngenetic toggle switch in Escherichia coli. Nature 403, \n339342 (2000).\n19. Kauffman,S.A. Metabolic stability and epigenesis in \nrandomly constructed genetic nets. J.Theor. Biol. 22, \n437467 (1969).\n20. Thomas,R. Boolean formalization of genetic control \ncircuits. J.Theor. Biol. 42, 563585 (1973).\nREVIEWS\nNATURE REVIEWS | GENETICS   ADV ANCE ONLINE PUBLICATION | 11",
+              "title": "2015 - Quantitative and logic modelling of molecular and gene networks.pdf",
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+              "text": "25 \n 2.8 REGULATION OF GENE EXPRESSION  \n \nApart  from the protein coding sequences, there are other biologically relevant nucleic acid \nsequences that play other important roles in the genome such as regulation of gene expression \nand maintenance of the chromatin structure (Pique -Regis  et al., 2011). Regu lation of gene \nexpression involves  a process that leads to increase or decrease in the production of specific",
+              "title": "2016 - The Genomics of Type 1 Diabetes.pdf",
+              "version": "v0",
+              "chunk_order": 177,
+              "document_id": "4933cdc2-7d36-5181-87c9-63b58498839f",
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+            "id": "6baacb55-1480-592e-9b6b-87c3e3980e19",
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+              "text": "expression is regulated at many levels, but gene transcription \nrepresents an essential and, in many cases, dominant point of control. Protein-coding genes are transcribed from promoters, \nwhich represent genomic regions that recruit basal transcrip-\ntion 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-\ntion factors that recruit nucleosome-remodeling complexes,",
+              "title": "2013 - Genetic and Genomic Approaches to Understanding Macrophage Identity and Function.pdf",
+              "version": "v0",
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+            "id": "8bb2457a-69e7-5e4a-9cc3-f51b8db47a89",
+            "score": 0.654345136362894,
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+              "text": "regulatory elements  and variants thereof that may affect gene expression particularly through \nthe binding of transcription factors (TFs) to DNA.  \nThe suggestion that the  genetic determinants of complex diseases are perh aps better sought in \nproblems associated with gene regulation is due to findings that many of the disease associated \nvariants occur in non -coding DNA sequences within the genome  (ENCODE, 2012; Schuab et",
+              "title": "2016 - The Genomics of Type 1 Diabetes.pdf",
+              "version": "v0",
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+            "id": "8ddad342-ceca-56a1-afd6-6a3218436c1c",
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+              "text": "through multiple cell divisions at the transcriptio nal and epigenetic level need to be more 204 \ncarefully examined and have evolved as an exciting area of research. 205 \n 206 \nEpigenetics and transcriptional regulation  207 \nRegulation of gene expression relies on the ac cessibility of DNA to various transcription 208 \nfactors, co-activators/co-repressors, and the transcriptional machinery. DNA is first wrapped 209",
+              "title": "2010 - The Role of Epigenetics in the Pathology of Diabetic Complications.pdf",
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+              "text": "post-translationally, translationally, transcriptionally, or epigenetically  (Lempradl  \net al, 2015; Zong  et al, 2017) . It seems likely that these different layers of \nregulation can operate cooperatively on different time- scales . More permanent  \nadaptations might be expected following persistent regulation on a more transient \nlevelfor example,  lowered transcriptional activity of a gene might follow  a \nperiod of low functional  activity of its protein. Elucidating the means of such",
+              "title": "2018 - Molecular Brain Adaptations to Ethanol_ Role of Glycogen Synthase (2).pdf",
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+              "text": "important  component in the regulation of gene expression with\nbetween  10  and  20%  of  the  transcriptome  being  regulated  by  DNA\nvariation.\n2. Technologies\nThe  study  of  DNA  and  its  downstream  effects  is  very  much\na  technology  driven  process.  Most  of  the  rst  screens  looking  at\nDNA  changes in disease involved looking at segregation in fam-\nilies  because  there  were  no  reasonable  technologies  at  the  time",
+              "title": "2011 - The age of the \u201come\u201d Genome, transcriptome and proteome data set collection and analysis.pdf",
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+            "id": "b46d80d5-c968-5541-b658-d9a2dcd86e37",
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+              "text": "the cytosine and adenine nucleotides[31]. In addition, the c hromosomal\nstructure of DNA can be decondensated by histone acetylatio n (trans-\nfer of acetyl groups to DNA organizational elements), makin g it more\naccessible for transcription[87]. The transcriptional ex pression of genes\nis further regulated by genetic variants themselves[7]. Fi nally, proteins\nform a complex network of interactions[265] that, in turn, a lso regulate\ngene expression[331].",
+              "title": "2009 - Detection and interpretation of expression quantitative trait loci (eQTL).pdf",
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+              "text": "eterogeneity and common, small effect genetic variants will be assessed.  h \nD (c) Regulatory Signals: \nCo-regulation of genes via shared transcriptional networks provides the basis for context-dependent gene \nexpression, an understanding of which is vital to the understanding of disease etiology and disease progression. In \nparticular, transcription factors (TF) and their transcription factor binding sites (TFBS) provide a key component in the understanding of how co-regulation is achieved.",
+              "title": "2005 - Part I Previous Research Track Record.pdf",
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+              "content": "The different relationships between traits can be categorized into several types based on the provided context:\n\n1. **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].\n\n2. **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].\n\n3. **High-Order Organization of Traits**:\n   - **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].\n   - **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].\n   - **Overlapping Traits in Different Groups**: Different groups of traits may have overlapping traits but typically differ in their underlying mechanisms [6], [7], [8].\n\nThese relationships highlight the complexity and interconnectedness of traits, showing that they can be related through direct correlations, shared mechanisms, or overlapping characteristics.",
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+              "text": "3, 4 and 5 suggest previously unknown connections between traits.\nWe next characterized pairs of traits within each group of traits (trait pairs) to show that the\nquality of these pairs is not lower than in existing methods. We focused on three main properties of\ntrait pairs: the correlation among traits in a pair; the correlation between a trait pair and the",
+              "title": "2015 - Linking traits based on their shared molecular mechanisms.pdf",
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+              "text": "3, 4 and 5 suggest previously unknown connections between traits.\nWe next characterized pairs of traits within each group of traits (trait pairs) to show that the\nquality of these pairs is not lower than in existing methods. We focused on three main properties of\ntrait pairs: the correlation among traits in a pair; the correlation between a trait pair and the",
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+              "text": "3, 4 and 5 suggest previously unknown connections between traits.\nWe next characterized pairs of traits within each group of traits (trait pairs) to show that the\nquality of these pairs is not lower than in existing methods. We focused on three main properties of\ntrait pairs: the correlation among traits in a pair; the correlation between a trait pair and the",
+              "title": "2015 -Oren- Linking traits molecular mechanisms.pdf",
+              "version": "v0",
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+              "text": "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",
+              "title": "2008 - Rutter_s child and adolescent psychiatry-Blackwell Pub (2008).pdf",
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+              "text": "ditions and related totraits ofinter est,often bycomparing two groups differing forthetrait.\nDarvasi (2003) states that thereisanundeclar eddispute among resear chers who study\ncomplex traits :::Onone side areclassical geneticists :::ontheother areproponents ofgene\nexpr ession analysis :::.Darvasi goes ontooutline thepossible advantages ofcombining\nthese techniques over and above either technique alone. Inaddition tobetter correlating ge-",
+              "title": "2004 - Combining QTL and Microarray Data.pdf",
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+              "text": "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\nshare the same transcripts mechanism, but not necessarily high correlations among them (modules 3,\n4, 5, e.g., Figure 4 ). (iii) Different groups commonly have overlapping traits, but typically differ in their\nunderlying mechanisms ( Figure 2B ).",
+              "title": "2015 - Linking traits based on their shared molecular mechanisms.pdf",
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+              "text": "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\nshare the same transcripts mechanism, but not necessarily high correlations among them (modules 3,\n4, 5, e.g., Figure 4 ). (iii) Different groups commonly have overlapping traits, but typically differ in their\nunderlying mechanisms ( Figure 2B ).",
+              "title": "2015_GN_Diabets_notheses.pdf",
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+              "text": "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\nshare the same transcripts mechanism, but not necessarily high correlations among them (modules 3,\n4, 5, e.g., Figure 4 ). (iii) Different groups commonly have overlapping traits, but typically differ in their\nunderlying mechanisms ( Figure 2B ).",
+              "title": "2015 -Oren- Linking traits molecular mechanisms.pdf",
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+              "text": "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.",
+              "title": "2012 - Needs Analysis of Genetics and Genomics in Communication Sciences and Disorders.pdf",
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+              "text": "action will open a Correlation Plot page in which you can examine the\nrelationship between the two traits. Look for linearity and outliers.\n3.3.1. Selection and Saving Multiple Traits\nThe list of traits on the Correlation Results page represents traits that may\nbe related in some way. You may want to select a group of them for further\nanalysis. For example, use the checkboxes to the left of each entry to check\nentries 1, 9, 10, 14, 16, 18, traits related to brain size. Click the Add to collection",
+              "title": "2007 - An Informatics Approach to Systems Neurogenetics.pdf",
+              "version": "v0",
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+              "content": "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:\n\n1. **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].\n\n2. **Mapping Pleiotropy**: Newer approaches map pleiotropy by simultaneously associating genomic loci with multiple traits, which can reveal relationships between traits [2].\n\n3. **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].\n\n4. **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].\n\nThese methods collectively demonstrate that the landscape of QTL and GWAS hits can indeed be used to find relationships between traits.",
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+              "text": "ST, see [40,120122]). Such tools may also offer a\nway of incorporating GxE interactions, as multiple GWAS for the\nsame trait in different environments can be treated as correlatedtraits [123].\nAs association data for a greater variety of populations, species,\nand 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\nadaptation.\nMaterials and Methods",
+              "title": "2014 - A Population Genetic Signal of Polygenic Adaptation.pdf",
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+              "text": "has been achieved by quantitative trait loci mapping, admixture \nmapping and GW AS131, which have limited power to detect \nsmall-effect-size genes. Newer approaches map pleiotropy by simultaneously associating genomic loci with multiple traits\n54 \nand can also detect epistatic interactions using machine learning algorithms\n132.Detecting the genomic signatures of correlational selectionCorrelational selection could potentially be inferred from \nsignatures of selective sweeps at loci under strong selection",
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+              "text": "pairs that include many genes within the seg-\nment. On the other hand, GWAS may point to\nseveral or even many genomic locations for the\ntrait of interest, complicating further functional\nanalysis.\nAnalysis of Quantitative Trait Loci (QTL)\nQTL analysis reveals statistically signicant\nlinkage between phenotypes and genotypes,\nthereby providing explanation for the genetic\nbasis of variation in complex traits (Falconer\nand Mackay, 1996; Lynch and Walsh, 1998). In\na sense, QTL analysis can be viewed as incom-",
+              "title": "2012 - Functional genomics research in aquaculture principles and general approaches.pdf",
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+            "id": "72b37b21-1d41-55bd-b835-f0bd267a3970",
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+              "text": "studies.   \nThere are  many possible causal networks even in a simple syst em consisting of \na genomic locus (QTL) and two traits, T1 and T2 ( Figure 1 ). Causal inference in \nGWLS and GWAS involves, in its simplest form, the i dentification of pairs of traits \nwith a common QTL (QTL-trait-trait triads) and dete rmining whether the QTL \ndirectly affects each of two traits (independent), or if the QTL affects only one trait",
+              "title": "2011 - Genetical genomics approaches for systems genetics.pdf",
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+            "id": "108483cf-404b-5a9c-bf1f-be58ebf6d16d",
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+              "text": "tions by matching patterns of expression QTL and GWAS.\nAm. J. Hum. Genet. 92, 92\n160. Giambartolomei, C. et al. (2014) Bayesian test for colocalisation\nbetween pairs of genetic association studies using summary\nstatistics. PLoS Genet. 10, e1004383\n161. Porcu, E. et al. (2019) Mendelian randomization integrating\nGWAS and eQTL data reveals genetic determinants of com-plex and clinical traits. Nat. Commun. 10, 3300\n162. Zhu, Z. et al. (2016) Integration of summary data from GWAS",
+              "title": "2020 - A Multi-Omics Perspective of Quantitative Trait Loci in Precision Medicine.pdf",
+              "version": "v0",
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+            "id": "68a13597-c223-54d9-9664-604d69b97c50",
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+              "text": "knowledge of the true QTL location (Doss et al. 2005 ),\nwhich can be used to empirically estimate the power of aGWAS performed at a similar scale (Hao et al. 2008 ;\nSchadt et al. 2008 ). A GWAS on its own does little more\nthan establish correlations between changes in DNA at agiven locus and changes in a disease trait of interest, with\nrespect to populations of interest. Further, these studies on",
+              "title": "2010 - Systems genetics, bioinformatics and eQTL mapping.pdf",
+              "version": "v0",
+              "chunk_order": 55,
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+              "text": "Another method to identify candidate genes is to leverage data generated in another\npopulation or species. Phenome-wide association studies (PheWAS) take a gene or variant\nof interest and nd all reported associations in GWAS datasets. A number of these GWAS\ntools exist, using either different methods, or different human cohorts (https://atlas.ctglab.\nnl/PheWAS, http://pheweb.sph.umich.edu/, accessed on 2 February 2022).\nMouse QTL mapping has high power but low precision (i.e., we can detect a QTL, but",
+              "title": "2022 -Chunduri- Drugs Animal Models.pdf",
+              "version": "v0",
+              "chunk_order": 75,
+              "document_id": "9cfa4f4c-37ce-5c0f-9da6-3bbb075fdc45",
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+            "id": "73b8e482-b204-5da6-b92d-f090efb622f1",
+            "score": 0.6630002266764248,
+            "metadata": {
+              "text": "Another method to identify candidate genes is to leverage data generated in another\npopulation or species. Phenome-wide association studies (PheWAS) take a gene or variant\nof interest and nd all reported associations in GWAS datasets. A number of these GWAS\ntools exist, using either different methods, or different human cohorts (https://atlas.ctglab.\nnl/PheWAS, http://pheweb.sph.umich.edu/, accessed on 2 February 2022).\nMouse QTL mapping has high power but low precision (i.e., we can detect a QTL, but",
+              "title": "2022 - New Insights on Gene by Environmental Effects of Drugs of Abuse in Animal Models Using GN.pdf",
+              "version": "v0",
+              "chunk_order": 75,
+              "document_id": "6f5d0c5b-0bbb-5eca-9e3e-73c3b0675472",
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+          {
+            "id": "60a84952-41ed-57ee-b689-6da313793843",
+            "score": 0.6630002266764248,
+            "metadata": {
+              "text": "Another method to identify candidate genes is to leverage data generated in another\npopulation or species. Phenome-wide association studies (PheWAS) take a gene or variant\nof interest and nd all reported associations in GWAS datasets. A number of these GWAS\ntools exist, using either different methods, or different human cohorts (https://atlas.ctglab.\nnl/PheWAS, http://pheweb.sph.umich.edu/, accessed on 2 February 2022).\nMouse QTL mapping has high power but low precision (i.e., we can detect a QTL, but",
+              "title": "2022 - New Insights on Gene by Environmental Effects of Drugs of Abuse in Animal Models Using GeneNetwork.pdf",
+              "version": "v0",
+              "chunk_order": 75,
+              "document_id": "d71efa0d-5de8-549c-964d-489ef6b73a1f",
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+          {
+            "id": "616a41e7-df46-54d5-979e-1654973aa642",
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+              "text": "narrow regions ofthegenome harboring trait associated genetic variants. Itisstill, however, a\nchallenge toidentify causal genes and several approaches have been developed that canassist\ninbridging thisgap. Specifically, systems genetics approaches involving theintegration of\nother types of-omics data have proven useful [25]. Two systems genetics approaches for\ninforming GWAS areexpression quantitative trait loci(eQTL) discovery and co-expression",
+              "title": "2016 - Mouse genome-wide association and systems genetics identifies Lhfp as a regulator of bone mass.pdf",
+              "version": "v0",
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+        ],
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+      }
+    }
+  }
+}
\ No newline at end of file