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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].",
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              "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",
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              "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",
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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",
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              "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].",
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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",
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              "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",
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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",
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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",
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              "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",
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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",
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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.",
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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",
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              "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).",
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              "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",
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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-",
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              "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",
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              "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",
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              "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",
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              "text": "were extracted from unthawed, frozen faecal subsamples (150 mg) after pretreatment\nof the weighed subsamples with 1.5 ml RNAlater ICE (LifeTechnologies) overnight.The faeces-RNAlater ICE mixture was homogenized by bead-beating, as previously\ndescribed\n53. Differential centrifugation and extraction using the All-In-One kit\n(Norgen Biotek) to recover DNA and proteins were carried out as previously\ndescribed53. DNA fractions were supplemented with DNA extracted from 200 mg",
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              "text": "DNA was then extracted destructively by grinding the frozen tissue with a sterile pestle, using aDNeasy Blood and TissueKit (Qiagen, Valencia, CA, USA) and following the manufacturer s\nprotocol, except the DNA was eluted in 130 L ddH\n2O instead of the supplied buffer. We ran\n10L of each extract for 60 min at 100 volt on 1.5% agarose SB (sodium borate) gels, to estimate\nsize of the genomic DNA.\nFrom a pool of 60 successful extractions (12 extractions produced no quantifiable DNA), we",
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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",
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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.",
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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-",
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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-",
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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",
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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",
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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,",
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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]",
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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": "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": "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": "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",
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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",
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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).",
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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",
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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",
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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": "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",
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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": "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",
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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).",
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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",
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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",
              "title": "2003 - Haplotypes and the systematic analysis of genetic variation in genes and genomes.pdf",
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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]",
              "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": "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",
              "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": "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-",
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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": "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",
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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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              "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",
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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": "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",
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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": "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",
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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-",
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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",
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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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              "text": "maternal lineage membership with those who weredirectly genotyped. Based on these pedigree (matrilineal) relation-",
              "title": "2012 - Mitochondrial Genomic Analysis of Late Onset.pdf",
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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 -",
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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": "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",
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              "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",
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              "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": "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",
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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",
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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",
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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.",
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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.",
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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",
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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.",
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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.",
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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.",
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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.",
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              "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].",
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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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              "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",
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              "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",
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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",
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              "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,",
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              "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.",
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              "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",
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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",
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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].",
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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",
              "title": "2005 -Dipetrillo- Bioinformatics toolbox QTL.pdf",
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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]",
              "title": "2020 - A Multi-Omics Perspective of Quantitative Trait Loci in Precision Medicine.pdf",
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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",
              "title": "2005 -Dipetrillo- Bioinformatics toolbox QTL.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": "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",
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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.",
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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-",
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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",
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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",
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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",
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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;",
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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).",
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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": "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": "### 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": "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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              "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",
              "title": "2008 - Telomeres and Aging.pdf",
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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",
              "title": "2018 - Sex Differences in Aging Genomic Instability.pdf",
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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].",
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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",
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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).",
              "title": "2016 - Coming of age ten years of next.pdf",
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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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              "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": "### 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": "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": "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": "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": "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. 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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              "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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              "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.",
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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",
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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",
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              "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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              "text": "oxidoreductase MitochondriaF29C4.2 IV Cytochrome",
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              "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-",
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              "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",
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              "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",
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              "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",
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              "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",
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              "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",
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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",
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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",
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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",
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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",
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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.",
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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.",
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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",
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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",
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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.",
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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.",
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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.",
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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",
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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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              "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",
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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",
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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 - Experimental_Evolution.pdf",
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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",
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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",
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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",
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              "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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              "text": "I na d d i t i o n ,i ts h o u l db en o t e dt h a tt h ee f -\nfect of temperature on sex determination has\na genetic basis itself and an interaction be-\ntween families and temperature effect has been\nreported in several species (Schultz, 1993; Van-\ndeputte et al., 2007). Finally, other environ-\nmental effects such as pH, hypoxia, and so-\ncial factors have claimed to be involved on sex\ndetermination (reviewed by Guerrero-Est evez\nand Moreno-Mendoza, 2010). All the informa-",
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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",
              "title": "2008 - Rutter_s child and adolescent psychiatry-Blackwell Pub (2008).pdf",
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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",
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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",
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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",
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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",
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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",
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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",
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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",
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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",
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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)",
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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",
              "title": "2012 - Genetic architecture supports mosaic brain evolution and independent brain\u2013body size regulation_.pdf",
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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.",
              "title": "2009 - Age-associated cognitive decline.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",
              "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",
              "title": "2022 - 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": "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.",
              "title": "2008 - Rutter_s child and adolescent psychiatry-Blackwell Pub (2008).pdf",
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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": "for cognitive processes such as learning,memory, and emotions.",
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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": "pin-releasing hormone (CRH), adrenocorticotropic hormone (ACTH), and \nglucocorticoids (GC), which are also called stress hormones. These hormones con-\ntribute to the regulation of immune responses and can also affect neuronal survival, \nneurogenesis, synaptic plasticity, and behavioral responses  [  1,   2  ] . The HPA axis is \na three-tiered biological system that begins at the highest level with the release of \nCRH from the hypothalamic paraventricular nucleus (PVN). CRH-expressing neu-",
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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",
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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 &",
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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-",
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              "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).",
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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",
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              "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",
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              "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",
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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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              "text": "that corticosterone importantly amplies the SD induced changes",
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              "text": "be used to predict corticosteroid response [200]. George etal.",
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              "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",
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              "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,",
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              "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",
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              "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",
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              "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",
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              "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",
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              "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",
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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",
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}