{
  "question": [
    "what are the statistical approaches for qtls analysis?",
    "Create a how-to guide for GWAS analysis?",
    " Create a how-to guide for genetic sequencing",
    " Create a how-to guide for genetic sequencing.",
    " What is the significance of the length of telomeres?"
  ],
  "answer": [
    "The statistical approaches for QTLs (Quantitative Trait Loci) analysis mentioned in the text include regression analysis, permutation tests, Pearson's correlation, and analysis of variance (ANOVA). QTL mapping analysis was performed using the WebQTL module of GeneNetwork. The GEMMA method was also used for QTL mapping on all chromosomes. Additionally, quantitative trait association for SNPs was performed using a series of ANOVA tests.",
    "1. Carefully select the populations for the study, ensuring a large number of cases.\n2. Employ centralized SNP genotyping, data coordination, and control centers for quality control checks and standardized annotation.\n3. Conduct SNP-level association tests using methods like the likelihood ratio test (LRT) to obtain SNP level summary statistics.\n4. Perform a gene-level GWAS on the summary statistics using a hierarchically structured prior that incorporates the SNP-gene hierarchical structure.\n5. Use methods like meta-analysis to combine the results of multiple surveys and replication studies on promising variants.\n6. Incorporate existing information about the SNPs into the analysis, such as prior information about linkage or association evidence.\n7. Use tools like ePheWAS for applications in human cohorts.\n8. Share GWAS results to enable further understanding and analyses by other researchers.\n9. Deposit data in a public repository for wider scientific community access.\n10. Follow up on SNPs that merit further replication analysis.\n11. Use post-GWAS tools to make biological sense of the statistical genetic associations.\n12. Finally, report summarization and visualization of the GWAS results.",
    "1. Choose the appropriate sequencing method: Depending on the specific requirements of your project, you may choose from various sequencing methods such as whole-genome sequencing, exome sequencing, or targeted sequencing.\n\n2. Prepare the sample: Extract the DNA or RNA from your sample. The quality and quantity of the extracted genetic material are crucial for successful sequencing.\n\n3. Library preparation: Prepare a sequencing library. This involves fragmenting the DNA or RNA into smaller pieces, then attaching specific adapters to the ends of these fragments.\n\n4. Sequencing: Use a sequencing platform to read the genetic information. Platforms like Illumina generate massive amounts of short reads.\n\n5. Data analysis: Analyze the sequencing data. This involves aligning the reads to a reference genome, identifying variants, and interpreting the results. Bioinformatics tools and databases such as UCSC Genome Browser, Ensembl, GenBank, MUSCLE, MAFFT, and PRANK can be used for these steps.\n\n6. Validation: Validate the findings using other techniques such as Sanger sequencing or qPCR.\n\n7. Report: Compile the results into a comprehensive report, including methods, findings, and interpretations.\n\nRemember, each step requires careful planning and execution, and the specific details may vary depending on the sequencing method and the research question.",
    "1. Start with an initial linkage or association study to identify potential genes of interest.\n2. Use bioinformatics tools to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if a gene is identified to play a role in a disease model. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Present an overview of genome structure and content to provide a context for subsequent discussions.\n6. Use DNA sequencing and other genome technologies to gather information about the mutations and functional abnormalities found in multiple samples from many major types of diseases.\n7. Analyze the sequence data using advanced technologies and bioinformatics.\n8. Finally, interpret the results of the genetic experiment, which can be a challenge on any level.",
    "The length of telomeres is significant as it is associated with aging, age-related diseases, and mortality. Shorter telomeres are generally considered a sign of advanced age and have been linked to higher risks of heart disease, infection-related death, and other diseases. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also potentially influence life expectancy, with some studies suggesting that longer telomeres decrease the risk of dying."
  ],
  "contexts": [
    [
      "For the observed difference in total variance of SS and LL, 0.93 and1.17 to be significant, the standard errors of the estimates need to be at most 0.06,a 2.5-fold reduction, implying a required sample size of 2.5  2.5 = 6.25 times oursample size or 5150 MZT. Plate 1 presents graphically the individual estimates of the items ai and bi foreach item i (using the model which includes known PLE). The x-axis representsthe normally distributed trait, liability to depression and the y-axis is the probability of endorsement of an item.",
      "False-positive rate and correct null distributionTo investigate the properties of the LT statistic under the null we computed the mean value in the simulations above when c = 0.0.As seen in Table 2 this has the correct value of 1.00.In addition it has the correct median, with l GC ~1.00, 5.00% of tests with P-value,0.05and 1.00% of tests with P-value,0.01.We applied Kolmogorov-Smirnov test [31] to determine if the LT statistic differed significantly from a x 2 (1 dof) distribution.The two-tailed K-S test of the full distribution was not significant (Pvalue = 0.34), nor was the K-S test restricted to the tail where the LT statistic had x 2 .3.84 (P-value = 0.21).In order to further investigate the extreme tail of the distribution we ran 10 8 tests under the null and verified that 98 of the 10 8 tests (10 26 ) had a Pvalue,10 26 .The LT statistic is a score test when the parameters are estimated correctly and will therefore have the correct null distribution.We investigated the properties of the LT statistic when the parameters were severely mis-estimated and found no inflation (see Text S1 in File S1).Furthermore, since the LT statistic is an ATT test between g and the posterior mean of the residual of the liability E(eDz,t), it will not have an inflated false- positive rate provided that E(eDz,t) does not have heavy tails or extreme heteroscedasticity [32].E(eDz,t) is the area under the tail of a normal distribution and will therefore not have these properties provided that the clinical covariate does not.",
      "Statistical AnalysesCategorical variables and continuous variables were tested and compared for significant differences using ttests.Quantitative trait association for SNPs was performed using a series of analysis of variance (ANOVA) tests.Hardy-Weinberg equilibrium (HWE) was assessed using the  2 goodness-of-fit statistic at a significance level of <0.05.To test all SNPs for genotypic association, adjusted odds ratios (ORs) and 95% confidence intervals (CI) were computed using Vassar statistic (http://vassarstats.net/) and SNPStats (http://bioinfo.iconcologia.net/SNPstats)softwares.All analyses were adjusted using linear regression.",
      "statistical analysis after QC procedures (Supplementary data, Table S2).",
      "Climate Correlations and Q X statistics for all six phenotypes in the global analysis.",
      "3.2).Quantitative data are presented as mean  standard error mean (SEM).Differences between two groups were analyzed by the student's t-test or Wilcoxon rank sum test.Differences among multiple groups were analyzed by the ANOVA.P values of multiple tests were adjusted by the Bonferroni method in the meta-analysis and were adjusted by the false discovery rate (FDR) in the differential Prior pairwise F ST values.D: Posterior pairwise F ST values.E: Blood eQTL analysis of rs3743121 (n = 30 in the TT group; n = 59 in the CT group; n = 62 in the CC group; expression levels were calculated as log 2 (fold of change, compared with TT group), *P < 0.05).Differential expression analysis of AQR in the skeletal muscle of rhesus macaques with metabolic syndrome (CTR: normal controls, MS: rhesus macaques with metabolic syndrome; n = 3 per group, data are presented as mean  SEM, *P < 0.05; expression levels were calculated as fold of change compared with CTR group).",
      "Data analysis and QTL mapping analysisDescriptive statistics like mean, standard deviation (SD), maximum and minimum trait value, coefficient of variation (CV%), analysis of variance (ANOVA) and heritability for each seed germination-related trait, and correlations among pairs of traits were calculated using the SPSS17.0software (http://www.spss.com).",
      "Statistical AnalysisThe impact of delivery methods on knowledge and interpretation of their genomic risk and associations to categorical participant characteristics was assessed using  2 tests.These tests were used to analyze associations between categorical variables.Logistic regression was used to model the association between comprehension and the covariates of interest.Since only one participant was found to be of below adequate health literacy, this variable was not included in any further analysis.Change in agreement between patient responses to questions of risk was assessed using Kappa statistics and McNemar's test.Change in perceived risk after testing (1-week follow-up) was evaluated using Wilcoxon signed rank test and Spearman correlation coefficient.Two-sided p values are reported for all tests using a Type I error level of 0.05.",
      "Empirical threshold significance values for pairwise interactions were determined using 100 permutationsof the data set. Statistical analyses. Weighted least-squares analysis (WLS) was performed toanalyze the TTD data of 14 inbred strains of 167/169 male and female mice(approximately 99% mortality). Preliminary investigations showed that the distribution of TTDs approximated normality, based on the Shapiro Wilk test. Within-strain variances were found to be heterogeneous, based on Levenes test. The WLS methodology, combined with a normality assumption, has the optimum power to detect differences between means of strains and susceptibilitygroups, compared to nonparametric methods.The same methodology (WLS) was used toanalyze organ CFU values, after a loge transformation was applied to approximate normality. Geometric means were calculated to estimate the medians ofeach strain and of sexes within each strain. Sex-specific analyses of TTDs and logeCFU determinations were also performed using WLS, with ANOVA models tocompare strain means by sex. Comparisons of between-strain to within-strainvariability were obtained by calculating the ratios of the variance between strainsto the residual variance, or the average variance within strains. These werecalculated for all TTD data and for each sex.",
      "After applying the ShapiroWilk test to assess the normality of our data, an unpaired t-test was applied to analyse the THmeasurement in different areas. Quantitative trait locus mappingThe QTL mapping was done with http://gn2.genenetwork.org/. The dataset containingdopamine measurements of dorsal striata of 32 CC strains were located with search terms(Species: Mouse (mm10); Group: CC Family; Type: Phenotypes; Dataset: CC Phenotypes)and navigated to Record CCF_10001 and CCF_10002. The QTL mapping was done withGEMMA on all chromosomes, MAF >= 0.05 with LOCO method.",
      "Fig. 1. (a) Quantile-quantile (Q-Q) plot after meta-analysis for time to death. (b) Quantile-quantile (Q-Q) plot after meta-analysis for time to event.",
      "Yet another approach 34 relies on combining univariate test statistics 35 either by forming an omnibus test or a linear combination of test statistics.With such approaches, however, only part of the information contained in the actual profiles is utilized when the data is reduced to univariate statistics and their covariance.",
      "A correlationcoefficient between the two measurements was highly significant (r = 0.99) indicating that technical error at thislevel of the analysis contributes little to case variation orstrain variation. A paired t-test confirmed that the difference between the first and second estimations was not significant (t < 1, NS). http://www.biomedcentral.com/1471-2202/10/44AnalysisData were analyzed using standard ANOVA and multipleregression techniques (JMP, SAS Institute, Cary, NC). QTLanalysis was performed using the WebQTL module ofGeneNetwork (GN, http://www.genenetwork.org).",
      "STATISTICAL ANALYSISQuantitative variables were expressed as median with first and third quartiles into brackets.Raw observation counts in taxa summary plots were normalized by calculating relative abundance.Qualitative variables were compared using the Fisher's exact test and quantitative variable using Mann-Whitney's test.A P value less than 0.05 was considered statistically significant.Statistical analyses were performed using SPSS software (version 18.0; IBM Corp., Armonk, NY).",
      "It is readily apparent from Figure 1that the values for norm reported here differ between thetwo analysis methods, resulting in different suggestive loci inthe subsequent QTL analysis. Such a difference between thetwo calculation methods raises the question of whether onemethod is indeed more accurate, or whether the two methodscapture different aspects of the mechanical response of thecorneoscleral shell in response to changes in IOP. We furtherinvestigated this difference by performing Bland-Altman analysis(Supplementary Figure 7).",
      "Scheme 2: Schematic illustration of the beam-walking test (author: Anna Zemanov;reproduced with permission)4.3 Data analysis and statisticsStatistical tests include two-way ANOVA followed by Student-Newman-Keulsmethod, regression analysis (linear regression), permutation tests, and Pearson'scorrelation. To ensure that the desired traits are mapped, caution hat to be taken because a traitmay be tightly correlated with other traits. It is not desirable to unintentionally map genesthat control motility or anxiety. Therefore there is a possibility of mapping wrong QTL orno QTL at all.",
      "QUANTIFICATION AND STATISTICAL ANALYSISStatistical details, including sample size (n), what n represents, and statistical test used can be found in the figure legends.In most cases, sample size was great enough to assume normality based on the central limit theorem, and parametric statistical tests were used.Two-sided tests were employed for more conservative calculations of significance.The threshold for statistical significance was set at p < 0.05.Unless otherwise stated, data in bar graphs are expressed as mean  standard deviation.A combination of MAT-LAB_R2019b, R (v3.6), and Prism GraphPad were used for statistical analysis.",
      "Other statistical methodsWe used Mann-Whitney tests to estimate the significance of non-normally distributed continuous variables across two groups and analysis of variance to estimate the significance of normally distributed variables across three or more groups.To ensure that low variation in methylation is not fallaciously responsible for observed associations, we ran the top two highly significant T2D CpG sites against 1000 simulated phenotypes, incorporating the same transformations and using the same covariates as for other polygenic analyses.To quantify the agreement between microarray and pyrosequencing techniques, we used the Bland-Altman procedure and estimated the regression-based limits of agreement.These analyses were done using the Stata 12.0 software package.",
      "Statistical analysis of T/C dataStatistical tests were performed using MATLAB 6.1 software (The MathWorks, Inc.).Comparative statistics was done with Wilcoxon rank sum test.Values of P !0.05 were considered statistically signifi cant.The gender-specifi c relationship between telomere size and age was estimated by linear regression, the correlations among variables were assessed by Pearson's correlation coeffi cient."
    ],
    [
      "DiscussionWe proposed in this paper an integrative approach, iGWAS, that is able to analyze multiplatform genomic data under the family-based design.The model can be presented as a causal diagram (Fig. 1), which was set up based on the central dogma of molecular biology that DNA can be transcribed to mRNA expression and mRNA can then be translated to be protein to affect the phenotypic trait such as disease risk.The mediation diagram provides an intuitive illustration of our hypothesis.The iGWAS approach is integrative in different aspects.The model not only integrates different types of genomic data, i.e., SNP and gene expression data, but also incorporates different types of genetic/genomic association studies to delineate clinical outcome rather than perform a GWAS, an expression microarray study, and an eQTL study separately.Moreover, the iGWAS approach integrates biological knowledge into the computational model, as illustrated in the causal mediation diagram.",
      "Further work is needed to determine how best to modify the tests proposed herein to deal with GWAS performed in structured populations.",
      "GWAS and meta-analysisAt the discovery stage, genotyping data of the in-house Beijing dataset were obtained using the Affymetrix Genome-Wide Human SNP Array 5.0 (ThermoFisher, USA).Genome wide association study was performed using the PLINK v1.07 software (Purcell et al., 2007).",
      "However, given that much ofthe variance is driven by societal, lifestyle and behavioralinfluences - and in addition there are also problemsrelated to DSM-based diagnostic criteria (Miller 2010) larger sample sizes for GWA analysis, inclusion of endophenotypes and CFG approaches are warranted. Havingalready massive transcriptomic, genetic and phenotypicdatasets available a Bayesian-like integration strategy canbe applied where multiple independent lines of geneticand genomic evidence is used, each by itself lackingsufficient discriminatory power, but combined leads tothe identification of high probability candidate genes orgene clusters.",
      "Statistical analysisThe general statistical methods for linkage and GWA analyses are described in the Overview Methods [19].For diabetes-related quantitative traits we used additive GEE and FBAT models, testing associations between SNP genotypes and age-age 2 -sex-adjusted residual trait values.We kept 70,987 SNPs in the analyses that were on autosomes, had genotypic call rates  80%, HWE p  0.001 and MAF  10%.",
      "In this manuscript, we summarize the strategies that we pursued to conduct the 100K genome-wide study, providing an overview for a series of 17 companion manuscripts (Table 1 of the Overview) describing associations with specific collections of traits [26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42].The primary purpose of this project was to generate hypotheses regarding genetic factors that may contribute to the wide spectrum of phenotypic variables collected in the FHS through a genome-wide approach.More specifically, we primarily hypothesized that common genetic variants contributing to phenotypic variation can be detected through a genome-wide association study (GWAS) and that genetic loci contributing to phenotypic variation can be detected through linkage.Each manuscript also examines whether the 100K analyses replicated previously reported associations with consistent evidence from the literature for some specific traits.The main purpose of this series of publications is to describe the association results made available for investigators and to direct readers to their free availability in the database of Genotype and Phenotype (dbGaP) public repository http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007 at the National Center for Biotechnology Information (NCBI), where these comprehensive results are posted and may be browsed in the context of multiple genomic tracks includ-ing Entrez Gene, RefSeq, dbSNP, genetic markers, and OMIM.The deposition of these data in a public repository is consistent with the long tradition of publishing preliminary results from the FHS to benefit the wider scientific community.",
      "NIH-PA Author ManuscriptNIH-PA Author ManuscriptNat Rev Genet. Author manuscript; available in PMC 2013 November 01. Flint and EskinPage 21NIH-PA Author ManuscriptNIH-PA Author ManuscriptFigure 2. Overview of mouse GWASsNIH-PA Author ManuscriptMouse genome-wide association studies (GWASs) follow a common general approach. a |Mice in the study population are phenotyped for the traits of interest. Deciding which miceand their corresponding genetic structure to include in the study population is a key designconsideration in a mouse GWAS.",
      "Prioritizing GWAS results: a review of statisticalmethods and recommendations for their application. Am J Hum Genet 2010;86:6e22. [76] Leiserson MDM, Eldridge JV, Ramachandran S, Raphael BJ. Network analysis of GWASdata. Curr Opin Genet Dev 2013;23:602e10. [77] Jia P, Zhao Z. Network.assisted analysis to prioritize GWAS results: principles,methods and perspectives. Hum Genet 2014;133:125e38. [78] Jensen MK, Pers TH, Dworzynski P, Girman CJ, Brunak S, Rimm EB. Proteininteraction-based genome-wide analysis of incident coronary heart disease. Circ Cardiovasc Genet 2011;4:549e56. [79] Schadt EE, Sachs A, Friend S. Embracing complexity, inching closer to reality. Sci STKE2005;2005:pe40.",
      ", 2015) or GWAS summary statistics (Gusev et al. , 2016)could be used to facilitate the applications of our tools, especially ePheWAS, in such human cohorts. Altogether, this integrated systems genetics toolkit, which isfreely accessible on systems-genetics.org, can expedite in silicohypothesis generation and testing, facilitating the identificationand validation of new gene functions and gene networks inpopulations, which generally are robust and translate well acrossCell Systems 6, 113, January 24, 2018 11Please cite this article in press as: Li et al.",
      "It is worth mentioning that other reports shared the concern about deciding the SNPs in GWAs that merit follow-up and further replication analysis.Chen et al. 19 recently proposed an approach for selecting SNPs based on a hierarchical model.This approach, which is not strictly based on biological plausibility of candidate's genes, allows the users to incorporate existing information about the SNPs into the analysis.For instance, the algorithm ranks P values assuming a weighting function that incorporates prior information about linkage or association evidence.",
      "We performed a Bayesian gene-based GWAS analysis which is composed of 2 steps: We first conducted SNP-level association tests for the trio data using the likelihood ratio test (LRT) and obtained SNP level summary statistics and then conducted a gene-level GWAS on the summary statistics using a hierarchically structured prior that incorporates the SNPgene hierarchical structure.",
      "Even on an individual GWA study scale, there are numerous benefits toinstituting the approaches discussed herein. Active capture of the complete processwill not only aid in the accurate interpretation of the individual study results but willalso permit the interpretation of results in a more comprehensive fashion through theintegration multiple data sets and results. 7 Constructing Gene Networks to Enhance GWASand GOGE ResultsAs discussed, generating a GOGE data set and performing a first-pass analysis onthis scale of data is a major undertaking.6 Further Recommendations for Efficiency Gainsin GOGE StudiesRecent large-scale GWAS initiatives have made gains by employing economies ofscale in instituting centralized SNP genotyping, data coordination and control centers (http://www.hapmap.org, http://www.wtccc.org.uk/) [48], providing data setsthat have undergone common quality control checks and standardized annotation tomultiple researchers for individual analysis [31, 49]. Additionally, one of the mostrecent operational advances is the use of a single large common control populationfor multiple casecontrol GWAS studies [31].",
      "Step 6: Report summarization and visualization. of GWAS in disease prediction.There are many steps during a gene-set analysis.They are shown below as Steps 1 through Step 6:",
      "ConclusionAuthor ManuscriptWe propose a shift from cataloging statistical genetic associations to using post-GWAS toolsto make biological sense of them. Incorporating the strategies outlined here should helpprioritize individual gene targets amenable to functional and mechanistic validation acrossspecies, which can create opportunities to better characterize polygenic risk for AUD, testthe prognostic utility of these loci and scores, and identify therapeutic starting points. AcknowledgmentsThis study was supported by National Institutes of Health grants R01AA020634 and P50AA022537 (MM);R01AA022994 (SH); K02DA032573 and U01MH109532 (AA); and K01AA024152 (JES).Author ManuscriptThe principal challenge that we are confronted with is the lack of a well- or even adequatelypowered GWAS of AUD. Even though the approaches outlined here more efficiently harnessall existing GWAS data, reliability of the results from these GWAS hinge on their samplesize. One of the largest efforts that is currently under way is being led by the PsychiatricGenomics Consortiums Substance Use Disorders group and includes 15,000 cases withDSM-IV alcohol dependence and >37, 000 controls that are largely alcohol exposed(Agrawal et al. 2016).",
      "The ultimate objectives  full descriptions ofthe susceptibility architecture of major biomedical traitsand translation of the findings into clinical practice remain distant. With completion of the initial wave of GWA scans, itis timely to consider the status of the field. This reviewconsiders each major step in the implementation of aGWA scan, highlighting areas where there is an emerging consensus over the ingredients for success, and thoseaspects for which considerable challenges remain.Joint (meta) analysis of data364 | May 2008 | volume 9from comparable GWA scans9,34,35,38,103 provides a lowcost approach to enhance power for both main andjoint (genegene and geneenvironment) effects, obtainin silico replication, inform SNP selection for subsequentreplication efforts and explore potential sources of heterogeneity.",
      "Methodological aspectsThe success of GWAS in detecting new associations and potential risk factors for any particular disease or condition depends greatly on the experimental design, on careful selection of the populations, on large number of cases and on collaborative analytical approaches.Meta-analysis is a method that combines the results of a number of surveys and of replication studies on the most promising variants.It investigates the underlying processes and has become standard practice for publications of GWAS that search for common genetic variants regulating complex traits and disease risk.",
      "To date, a growing body of comprehensive methods has been developed for downstream analyses of GWAS.Sharing of summary statistics can help enable these analyses, for example, by providing researchers with a more convenient way to look-up genetic association effect estimates to conduct causal inference analyses using methods such as two-sample Mendelian Randomization which assumes samples are non-overlapping 3,4 .In addition, sharing GWAS results can help researchers to further their understanding of the shared genetic basis of T2D with other traits of interest, to perform fine-mapping to pinpoint the causal genetic variants or identify genetic loci shared with other risk factors and disease outcomes.Therefore, the aim of this current work was to provide a reference dataset for researchers to utilize in order to conduct further genetic analyses, generate hypotheses and improve understanding of the aetiology, the biological pathways and mechanisms of T2D and related metabolic and cardiovascular diseases."
    ],
    [
      "2009;25:175460. 82. Elshire RJ, Glaubitz JC, Sun Q, Poland JA, Kawamoto K, Buckler ES, et al. Arobust, simple genotyping-by-sequencing (GBS) approach for high diversityspecies. PLoS One. 2011;6, e19379. 83. Ensemble Genomes. Available at: ftp://ftp.ensemblgenomes.org/. 84. Leinonen R, Sugawara H, Shumway M. The sequence read archive. NucleicAcids Res. 2011;39(Database issue):D1921. 85. Martin M. Cutadapt removes adapter sequences from high-throughputsequencing reads. EMBnet J. 2011;17:102. 86. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. Thesequence alignment/map format and SAMtools. Bioinformatics. 2009;25:20789. 87.",
      "Biesecker, L., Mullikin, J., Facio, F., Turner,C., Cherukuri, P., Blakesley, R., Bouffard, G.,Chines, P., Cruz, P., Hansen, N., Teer, J.,Maskeri, B., Young, A., Manolio, T., Wilson,A., Finkel, T., Hwang, P., Arai, A., Remaley,A., Sachdev, V., Shamburek, R., Cannon, R.,and Green, E. (2009) The ClinSeq Project:Piloting large-scale genome sequencing forresearch in genomic medicine. Genome Res. 19, 16651674. 32. Wang, Z., Gerstein, M., and Snyder, M. (2009)RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 10, 5763. 33. Mortazavi, A., Williams, B., McCue, K.,Schaeffer, L., and Wold, B.Ng, S., Turner, E., Robertson, P., Flygare, S.,Bigham, A., Lee, C., Shaffer, T., Wong, M.,Bhattacharjee, A., Eichler, E., Bamshad, M.,Nickerson, D., and Shendure, J. (2009)Targeted capture and massively parallelsequencing of 12 human exomes. Nature. 461, 272276. 31.",
      "Extensive documentation canbe downloaded in either pdf or Hypertext formats. The Tutorial is especially helpful, but readers should be aware that its files are somewhat inconspicuously tuckedin with Sample Data files, rather than being included in the Map Manager QTXmanual. For the current example, genotype data were downloaded from the Mouse GenomeDatabase (2001) (http://www.informatics.jax.org/). Specifically, it consists of mousechromosome 1 genotypes from the CopelandJenkins backcross, and a selected subset of 10 markers spanning the entire 100 cM length of the chromosome.",
      "Resequencing of genomic regions of interest will also be necessary (see Electronic-DatabaseInformation for current examples).",
      ", 2012;Chesler et al. , 2003; Jha et al. , 2018b, 2018a; Li et al. , 2018; Williams et al. , 2016). We havetherefore assembled deep companion resources, including full sequence for both parents(Baker et al. , 2019; Keane et al. , 2011; McKnite et al. , 2012; Wang et al. , 2016b; Wu et al. ,2014). Access to data and statistical tools are available from open-source web services(GeneNetwork.org and Systems-Genetics.org) (Li et al. , 2018; Sloan et al. , 2016; Williamsand Williams, 2017).",
      "Best practice guidelines for the use of next-generation sequencing applications in genome diagnostics: A national collaborativestudy of dutch genome diagnostic laboratories. Human Mutation,34(10):13131321, Aug 2013. [363] D. Welter, J. MacArthur, J. Morales, T. Burdett, P. Hall,H. Junkins, A. Klemm, P. Flicek, T. Manolio, L. Hindor, and290BIBLIOGRAPHYet al. The nhgri gwas catalog, a curated resource of snp-trait associations. Nucleic Acids Research, 42(D1):D1001D1006, Dec2013. [364] Harm-Jan Westra, Marjolein J Peters, Tonu Esko, HaniehYaghootkar, Claudia Schurmann, Johannes Kettunen, Mark WChristiansen, Benjamin P Fairfax, Katharina Schramm, Joseph EPowell, and et al.",
      "Zweig, A. S., Karolchik, D., Kuhn, R. M., Haussler, D., and Kent,W. J. (2008). UCSC genome browser tutorial. Genomics 92, 75 84. Vol. 9, Summer 2010107Downloaded from http://www.lifescied.org/ by guest on May 12, 2015",
      "Useful Online Genomics Resources.",
      "Bioinformatics tools for pathogen whole-genome sequencing",
      "Genomic databases UCSC Human Genome Browser: visualize and browse genomes [4] https://genome.ucsc.edu/Ensembl: genomes and species tree [45] https://ensembl.org/GenBank: open access sequence database [44] https://www.ncbi.nlm.nih.gov/genbank/Multiple sequence alignment software MUSCLE: tool to align multiple sequences [52] http://www.ebi.ac.uk/Tools/msa/muscle/MAFFT: tool to align multiple genomic sequences [53] http://mafft.cbrc.jp/alignment/software/PRANK: tool to align multiple genomic sequences [54] http://www.ebi.ac.uk/goldman-srv/prank/Phylogenetic information TimeTree: database of divergence times [55] http://www.timetree.org/Mammalian supertrees: evolutionary trees [39] Fritz et al. [39] Analysis tools BLAST-basic local alignment search tool [46] https://blast.ncbi.nlm.nih.gov/Codeml-test for positive selection on phylogenies using multiple sequence alignments [56] http://abacus.gene.ucl.ac.uk/software/paml.html",
      "The '1000 genomes' project and related individual sequencing projects",
      "Sequencing a draft reference genomefor rainbow trout has been recently initiated; aconsortium involves collaborators from USDANational Cold and Cool Water Aquaculture(USDA-NCCCWA), Washington State University, University of Oregon, University ofCalifornia Davis, and West Virginia University (Palti et al. , 2011). The project will producea genome map and a draft reference genomesequence using the Illumina platform by sequencing the bacterial articial chromosomes(BACs) from the physical map minimal tilingpath (Palti, 2010; Miller et al. , 2011).",
      "Extensive documentation canbe downloaded in either pdf or Hypertext formats. The Tutorial is especially helpful, but readers should be aware that its files are somewhat inconspicuously tuckedin with Sample Data files, rather than being included in the Map Manager QTXmanual. For the current example, genotype data were downloaded from the Mouse GenomeDatabase (2001) (http://www.informatics.jax.org/). Specifically, it consists of mousechromosome 1 genotypes from the CopelandJenkins backcross, and a selected subset of 10 markers spanning the entire 100 cM length of the chromosome.",
      "The large number of bioinformatic tools that have beenmade available to scientists during the last few years has presented theproblem of which to use and how best to obtain scientifically valid answers(3). In this chapter, we will provide a guide for the most efficient way toanalyze a given sequence or to collect information regarding a gene, protein,structure, or interaction of interest by applying current publicly available software and databases that mainly use the World Wide Web.Hum Hered 63:678432122Gene/Protein Sequence AnalysisA Compilation of Bioinformatic ToolsBernd H. A. Rehm and Frank Reinecke1. IntroductionThe advent of automated high throughput DNA sequencing methods hasstrongly enabled genome sequencing strategies, culminating in determination ofthe entire human genome (1,2). An enormous amount of DNA sequence dataare available and databases still grow exponentially (see Fig. 22.1). Analysisof this overwhelming amount of data, including hundreds of genomes fromboth prokaryotes and eukaryotes, has given rise to the field of bioinformatics.",
      "2014) (https://github.com/jaxcs/Seqnature) developed in the Churchill group at the Jackson Laboratory is similar, andis tailored to RNA-seq in the DO. Author ManuscriptConstruction of an individualized pseudogenome for a sample requires prior knowledge ofvariant sites in that samples genome. In, for instance, an F1 cross between strains for whichwhole-genome sequencing data are available, imputing the pseudogenome is trivial. Genomes of recombinant individuals (e.g. , CC or DO) can be expressed as mosaics offounder haplotypes on the basis of genotyping (discussed previously), and a pseudogenomestitched together accordingly.",
      "In the following section, we provide an overview of the finished genome sequencing projects and report them in chronological order of their publication.",
      "Extensive documentation canbe downloaded in either pdf or Hypertext formats. The Tutorial is especially helpful, but readers should be aware that its files are somewhat inconspicuously tuckedin with Sample Data files, rather than being included in the Map Manager QTXmanual. For the current example, genotype data were downloaded from the Mouse GenomeDatabase (2001) (http://www.informatics.jax.org/). Specifically, it consists of mousechromosome 1 genotypes from the CopelandJenkins backcross, and a selected subset of 10 markers spanning the entire 100 cM length of the chromosome.",
      "Extensive documentation canbe downloaded in either pdf or Hypertext formats. The Tutorial is especially helpful, but readers should be aware that its files are somewhat inconspicuously tuckedin with Sample Data files, rather than being included in the Map Manager QTXmanual. For the current example, genotype data were downloaded from the Mouse GenomeDatabase (2001) (http://www.informatics.jax.org/). Specifically, it consists of mousechromosome 1 genotypes from the CopelandJenkins backcross, and a selected subset of 10 markers spanning the entire 100 cM length of the chromosome."
    ],
    [
      "We (Hein, Schierup and Wiuf) have published a300 page book on molecular population genetics titled Gene Genealogies, Sequence Variation and Evolution OxfordUniversity Press, and are presently developing a tutorial in association mapping that we hope to publish as a booklet in2006 and are also involved in a very large EU collaboration (Holland, Denmark, Iceland and UK) to find susceptibilitygenes for breast and prostate cancer. In comparative genomics, the most fundamental investigation is to find genes in a pair of aligned genomes.",
      "Key bioinformatic steps totake a genetic study from an initial linkage or association to laboratory genotyping are illustrated. The reader should note the role of genomic sequence as a common thread through every stageregions in man (see Chapter 5). Similar issues also exist in the establishment oftrue orthology between genes in different species, where one is identified to play arole in a disease model. If two genes are truly orthologous, their evolution closelyfollows patterns of speciation (Fitch, 2000).In general terms, the approaches we describe can be applied to sequence data from any collection of organisms, but our emphasis here is primarily onBioinformatics for Geneticists, Second Edition. Edited by Michael R. Barnes2007 John Wiley & Sons, Ltd ISBN 978-0-470-02619-9 (HB) ISBN 978-0-470-02620-5 (PB)\u0002C106CH 6 COMPARATIVE GENOMICSquestions of relevance to human genetics. We begin, in Section 6.2 by presenting anoverview of genome structure and content, providing a context for the subsequentdiscussions.4Assembling a View of theHuman GenomeColin A. M. SempleBioinformatics, MRC Human Genetics Unit, Edinburgh EH4 2XU, UK4.1 IntroductionThe miraculous birth of the draft human genome sequence took place againstthe odds. It was only made possible by parallel revolutions in the technologiesused to produce, store and analyse the sequence data, and by the development ofnew, large-scale consortia to organize and obtain funding for the work (Watson,1990). The initial flood of human sequence has subsided as the sequencing centres have sequenced genomes from other mammalian orders and beyond.2. von Heijne, G. Sequence analysis in molecular biology: Treasure trove or trivial pursuit(Academic Press, London, 1987). 3. Wolfsberg, T. G., Wetterstrand, K. A., Guyer, M. S., Collins, F. S. & Baxevanis, A. D. A usersguide to the human genome. Nature Genetics 32 (suppl) (2002). PrefaceI say locuslocus instead of genegene because if you work in human genetics longenough, you realize that you may never have a gene. But you learn not to let thatput you off. Peter A. HolmansMaking sense of the results of a genetic experiment is a challenge on any level.",
      "Methods for DNA sequencing are constantly being improved, with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000, an end that appears to be in sight (Hayden, 2014).In the very near future, whole-genome sequencing will be routinely available for clinical purposes, perhaps even beginning at birth.The major challenge ahead is the interpretation of this information.How do our genes interact with each other, and how does the environment contribute to the development of health and disease?What are the individual and societal implications of knowing our genome sequence?The answers to these and other important questions will unfold in the years ahead.Thus, we are truly in an era where precision medicine may soon become a reality.",
      "Key bioinformatic steps totake a genetic study from an initial linkage or association to laboratory genotyping are illustrated. The reader should note the role of genomic sequence as a common thread through every stageregions in man (see Chapter 5). Similar issues also exist in the establishment oftrue orthology between genes in different species, where one is identified to play arole in a disease model. If two genes are truly orthologous, their evolution closelyfollows patterns of speciation (Fitch, 2000).In general terms, the approaches we describe can be applied to sequence data from any collection of organisms, but our emphasis here is primarily onBioinformatics for Geneticists, Second Edition. Edited by Michael R. Barnes2007 John Wiley & Sons, Ltd ISBN 978-0-470-02619-9 (HB) ISBN 978-0-470-02620-5 (PB)\u0002C106CH 6 COMPARATIVE GENOMICSquestions of relevance to human genetics. We begin, in Section 6.2 by presenting anoverview of genome structure and content, providing a context for the subsequentdiscussions.4Assembling a View of theHuman GenomeColin A. M. SempleBioinformatics, MRC Human Genetics Unit, Edinburgh EH4 2XU, UK4.1 IntroductionThe miraculous birth of the draft human genome sequence took place againstthe odds. It was only made possible by parallel revolutions in the technologiesused to produce, store and analyse the sequence data, and by the development ofnew, large-scale consortia to organize and obtain funding for the work (Watson,1990). The initial flood of human sequence has subsided as the sequencing centres have sequenced genomes from other mammalian orders and beyond.2. von Heijne, G. Sequence analysis in molecular biology: Treasure trove or trivial pursuit(Academic Press, London, 1987). 3. Wolfsberg, T. G., Wetterstrand, K. A., Guyer, M. S., Collins, F. S. & Baxevanis, A. D. A usersguide to the human genome. Nature Genetics 32 (suppl) (2002). PrefaceI say locuslocus instead of genegene because if you work in human genetics longenough, you realize that you may never have a gene. But you learn not to let thatput you off. Peter A. HolmansMaking sense of the results of a genetic experiment is a challenge on any level.",
      "Key bioinformatic steps totake a genetic study from an initial linkage or association to laboratory genotyping are illustrated. The reader should note the role of genomic sequence as a common thread through every stageregions in man (see Chapter 5). Similar issues also exist in the establishment oftrue orthology between genes in different species, where one is identified to play arole in a disease model. If two genes are truly orthologous, their evolution closelyfollows patterns of speciation (Fitch, 2000).In general terms, the approaches we describe can be applied to sequence data from any collection of organisms, but our emphasis here is primarily onBioinformatics for Geneticists, Second Edition. Edited by Michael R. Barnes2007 John Wiley & Sons, Ltd ISBN 978-0-470-02619-9 (HB) ISBN 978-0-470-02620-5 (PB)\u0002C106CH 6 COMPARATIVE GENOMICSquestions of relevance to human genetics. We begin, in Section 6.2 by presenting anoverview of genome structure and content, providing a context for the subsequentdiscussions.4Assembling a View of theHuman GenomeColin A. M. SempleBioinformatics, MRC Human Genetics Unit, Edinburgh EH4 2XU, UK4.1 IntroductionThe miraculous birth of the draft human genome sequence took place againstthe odds. It was only made possible by parallel revolutions in the technologiesused to produce, store and analyse the sequence data, and by the development ofnew, large-scale consortia to organize and obtain funding for the work (Watson,1990). The initial flood of human sequence has subsided as the sequencing centres have sequenced genomes from other mammalian orders and beyond.2. von Heijne, G. Sequence analysis in molecular biology: Treasure trove or trivial pursuit(Academic Press, London, 1987). 3. Wolfsberg, T. G., Wetterstrand, K. A., Guyer, M. S., Collins, F. S. & Baxevanis, A. D. A usersguide to the human genome. Nature Genetics 32 (suppl) (2002). PrefaceI say locuslocus instead of genegene because if you work in human genetics longenough, you realize that you may never have a gene. But you learn not to let thatput you off. Peter A. HolmansMaking sense of the results of a genetic experiment is a challenge on any level.",
      "Ample time was allotted to answer questions and a copy of \"A Guide to Your Genome\" (National Human Genome Research Institute 2007) was provided to further assist participants' understanding and ability to communicate results with family members or others.",
      "Whether within 10 or 12 (or 8) years, such inexpensive sequencing will change both research and clinical care, and progress does not need to wait even that long.The National Human Genome Research Institute (NHGRI) plans to focus a significant portion of the sequencing capacity that it supports on medical sequencing.For instance, the NHGRI and the National Cancer Institute are actively considering a Human Cancer Genome Project, 22 which would use DNA sequencing and a host of other genome technologies to gather information about the mutations and functional abnormalities found in multiple samples from many major types of cancer.Medical sequencing should also provide important insight into many other diseases.For example, sequencing all exons in X-linked mental retardation syndromes may reveal much about their etiology.Sequencing candidate genes in the extremes of the distribution of quantitative traits should also reveal much of importance about common diseases, such as coronary atherosclerosis. 23With further technological advances, other previously unimaginable research approaches will become real.",
      "Key bioinformatic steps totake a genetic study from an initial linkage or association to laboratory genotyping are illustrated. The reader should note the role of genomic sequence as a common thread through every stageregions in man (see Chapter 5). Similar issues also exist in the establishment oftrue orthology between genes in different species, where one is identified to play arole in a disease model. If two genes are truly orthologous, their evolution closelyfollows patterns of speciation (Fitch, 2000).In general terms, the approaches we describe can be applied to sequence data from any collection of organisms, but our emphasis here is primarily onBioinformatics for Geneticists, Second Edition. Edited by Michael R. Barnes2007 John Wiley & Sons, Ltd ISBN 978-0-470-02619-9 (HB) ISBN 978-0-470-02620-5 (PB)\u0002C106CH 6 COMPARATIVE GENOMICSquestions of relevance to human genetics. We begin, in Section 6.2 by presenting anoverview of genome structure and content, providing a context for the subsequentdiscussions.4Assembling a View of theHuman GenomeColin A. M. SempleBioinformatics, MRC Human Genetics Unit, Edinburgh EH4 2XU, UK4.1 IntroductionThe miraculous birth of the draft human genome sequence took place againstthe odds. It was only made possible by parallel revolutions in the technologiesused to produce, store and analyse the sequence data, and by the development ofnew, large-scale consortia to organize and obtain funding for the work (Watson,1990). The initial flood of human sequence has subsided as the sequencing centres have sequenced genomes from other mammalian orders and beyond.2. von Heijne, G. Sequence analysis in molecular biology: Treasure trove or trivial pursuit(Academic Press, London, 1987). 3. Wolfsberg, T. G., Wetterstrand, K. A., Guyer, M. S., Collins, F. S. & Baxevanis, A. D. A usersguide to the human genome. Nature Genetics 32 (suppl) (2002). PrefaceI say locuslocus instead of genegene because if you work in human genetics longenough, you realize that you may never have a gene. But you learn not to let thatput you off. Peter A. HolmansMaking sense of the results of a genetic experiment is a challenge on any level."
    ],
    [
      "In birds, where erythrocyte telomere length (ETL) is measured, the majority of species sampled have shown no sex difference (36).Nonetheless, bird telomere dynamics are complex and, as with humans, may be affected by environment and stress.For example, a longitudinal study of black-tailed gulls (Larus crassitostris) over 2-5 years found no correlation between ETL and age or sex.Rather, ETL attrition was correlated with reduced food availability and environmental stressors (55).In a captive zebra finch (Taeniopygia guttata) population, male and female mean telomere length decreased with increasing age of the animals, but did differ between sexes (56).As these examples illustrate, the relationship between telomere length, lifespan, and sex is likely to be complex in other vertebrates.Telomere attrition rates in humans are not constant, and when sex differences in telomere length first appear is unclear.Telomere attrition occurs rapidly from birth, slowing around 4 years of age, and the subsequent trajectory of telomere attrition continues to change in an age-and sex-specific fashion (43).In some studies, neonates show no sex differences, regardless of tissue used [eg, (44)].But in others, female newborns are reported to have longer telomeres than males [eg, (45)].Interestingly, a twin study comparing adults reported that women had longer mean LTL than men when samesex twin pairs (mono-and dizygotic) were compared.In contrast, men and women from opposite-sex twin pairs had similar telomere lengths, a difference that the authors attributed to antenatal influences of opposite-sex twins on one another (46).Results from studies of LTL in prepubescent children are mixed, reporting mean LTL either greater in females than in males (47) or not different (48).In two studies of adolescents (ages 13-18 years old), mean LTL was greater in females than in males (49,50), suggesting that sex differences in telomere length may arise during sexual maturation.A longitudinal study of Danish twins found that women had longer LTLs at baseline and displayed decelerated LTL attrition following menopause (51).Crucially, while LTL in women declined with age, the relationship between LTL attrition and age was no longer significant if menopausal status was included as a covariate.These examples illustrate that while many studies find greater telomere length in females, this trend is not universal.In some organisms, there is no clear relationship between telomere length and lifespan.Age-related telomere attrition could not be detected in Daphnia pulex (57) or sea urchin species (Strongylocentrotus franciscanus and Lytechinus variegatus) (58).Studies in C. elegans examining natural variation in telomere length and experimentally manipulated telomere length detect no correlation with lifespan (59,60), and in Drosophila, which uses a telomerase-independent mechanism for telomere maintenance, there is a similar lack of correlation between longevity and telomere length (61).Similarly, data on sex differences in age-related telomere shortening are mixed.For example, in the ant species Lasius niger, the rate of telomere shortening is more rapid in short-lived males compared to longer-lived females.But, mean telomere length does not differ between the two types of females, queens and workers, despite the fact that queens live much longer than workers (up to 28 years vs 2-3 months) (62).These findings suggest that the question of how telomere shortening affects aging across species and how sex affects telomere attrition rates are complex.In sum, as adults men have shorter telomeres than women in most populations sampled (39).Whether the sex difference in telomere length appears shortly after conception or later in life is unclear.Similarly, whether the sex difference in telomere lengths observed in adult humans results from slower attrition rates, differential telomere length at earlier ages, sex differences in the effects of telomere length on survival, sex differences in telomere maintenance, or other factors in not clear.Additional, carefully controlled longitudinal studies on the dynamics of telomere length and attrition rates in multiple tissues using standardized methods are needed to better evaluate the mechanisms creating sex differences in human telomere attrition during aging.",
      "With new methodologies to assess relative telomere length by Q-PCR, studies were designed to address the impact of telomere length on aging, aging associated pathologies, and mortality.One such study has correlated shorter leukocyte telomere lengths at age 60 with a three times higher risk of heart disease and an eightfold increase in risk of infection-related death (36), thereby associating measured relative cellular aging with disease and life expectancy.In a similar way, chronic stress was shown to correlate with short leukocyte telomere length, a phenomenon attributed to higher levels of oxidative stress at the cellular level (70).More recent studies have linked telomere length in smooth muscle cells with senescence and disease severity in patients with atherosclerosis (141,150).Leukocyte telomere length was also short in a cohort of similar patients and associated with a higher risk of developing occult cardiovascular disease (71).More data are needed to understand and validate the use of leukocyte telomere length as a biomarker for cardiovascular and other diseases.",
      "Shortening of the telomeres at the ends of chromosomes has been associated with age-related disease and mortality [16][17][18].A recent study identified a common haplotype of four SNPs in the human telomerase reverse transcriptase gene (hTERT) that is enriched in centenarians and associated with longer telomere length [19].It was also shown that centenarians and their offspring maintain longer telomeres compared with controls and that longer telomeres are associated with protection from age-related diseases, better cognitive function and lipid profiles of healthy ageing [19].",
      "Adult studies have also found a negative correlation with baseline telomere length, suggesting a negative feedback regulation of leukocyte telomere length (Farzaneh-Far et al. 2010;Aviv et al. 2009;Epel et al. 2008;Nordfjall et al. 2009).It is possible that while our follow-up period was shorter than Shalev et al. 2013 and adult studies, which had a minimum of 5 year intervals with the exception of Puterman et al. (2015) who followed for a one-year time period, there may be biological regulation of telomere length at 4 and 5 years of age such that shorter telomeres are more robustly maintained, whereas longer telomeres have greater rates of decline, over a short period of one year.It is unlikely that this relationship is due to assay error or regression to the mean given the consistency of our findings across studies.We have had similar findings of longer telomeres having greater rates of decline and shorter telomeres being maintained in our different studies (Farzaneh-Far et al. 2010;Epel et al. 2008;Puterman et al. 2015).The single, consistent predictor of the rate of telomere attrition shown in multiple adult and the few child longitudinal studies is the baseline measurement of telomere length at the start of each study.This suggests the importance of understanding predictors of telomere length prior to adulthood, as it determines in part the rate of change (Revesz et al. 2014a, b;Nordfjall et al. 2009).Moreover, longitudinal studies in adults have had found that telomere attrition rate is dependent on baseline telomere length independent of any phenotypic predictors of shortening, such as disease or demographic variables (Nordfjall et al. 2009), attesting to the importance of studies to evaluate risk factors for shortening prior to adulthood.Rates of decline in childhood may be particularly relevant for later chronic disease risk as shorter telomere length has been implicated in disease progression through exposure to cellular senescence, inflammatory cytokines and adipocyte hypertrophy (Raschenberger et al. 2015;Willeit et al. 2014;Monickaraj et al. 2012;Fyhrquist et al. 2013).Telomere attrition in adultsAdditional early childhood telomere studies are also needed so as to better interpret disease across the lifespan.Specifically, retrospective adult studies have pointed to the importance of early life exposures, finding associations of shorter telomere length in adulthood with perinatal complications, and stressful and deprived early childhood environments, including factors associated with lower socioeconomic status and those of physical or social neglect (Drury et al. 2012;Shalev et al. 2014;Tyrka et al. 2010, Kananen et al. 2010).Some adult studies have found that predictors of shorter telomere length involving inflammation and oxidative stress exposures such as depression are only observed in younger adults compared with middle aged and older ones (Philips et al. 2013), suggesting it may be harder to tease out risk factors for accelerated attrition as, firstly, these processes happen early in life, and, secondly, repeat exposures to inflammation and oxidative stress may statistically plateau out across older age groups.",
      "Blackburn and Epel, a health psychologist who did original research on how specific lifestyle and psychological habits can protect telomeres, published The Telomere Effect (Blackburn & Epel, 2017), in which they suggested that individuals with shorter telomeres developed diseases earlier in life (a shorter \"disease span\").What follows is the evidence from these authors, their colleagues, and other researchers describing how length of telomeres contributes to mind-body connection and healthy longevity.",
      "As early as at the time of birth, each of the 92 telomeres of the human genome has its own characteristic length.Additionally, each telomere shortens by its individual attrition rate.In general, longer telomeres at birth are associated with higher age-dependent attrition rates and vice versa.Overall, telomere shortening appears more dynamic in males.In recent literature, there are hints that the average telomere length may be higher in women and that their annual shortening rate may be somewhat lower (Vaziri et al., 1993;Rufer et al., 1998;Jeanclos et al., 2000), but these reported differences failed to reach statistical signifi cance except for one study (Jeanclos et al., 2000).Here, we provide compelling evidence that this is indeed the case.It is generally accepted that telomeres shorten during DNA replication both in vitro and in vivo.In individuals, short telomeres are considered to be a sign of advanced age.Cawthon and coworkers (2003) showed that telomere shortening in humans likely contributes to mortality, supporting the hypothesis that they might act as a mitotic clock (Allsopp et al., 1992).Telomere length dynamics, however, does not seem to Abstract.During aging, telomeres are gradually shortened, eventually leading to cellular senescence.By T/C-FISH (telomere/centromere-FISH), we investigated human telomere length differences on single chromosome arms of 205 individuals in different age groups and sexes.For all chromosome arms, we found a linear correlation between telomere length and donor age.Generally, males had shorter telomeres and higher attrition rates.Every chromosome arm had its individual age-specifi c telomere length and erosion pattern, resulting in an unexpected heterogeneity in chromosomespecifi c regression lines.This differential erosion pattern, however, does not seem to be accidental, since we found a correlation between average telomere length of single chromosome arms in newborns and their annual attrition rate.Apart from the above-mentioned sex-specifi c discrepancies, chromosome arm-specifi c telomere lengths were strikingly similar in men and women.This implies a mechanism that arm specifi cally regulates the telomere length independent of gender, thus leading to interchromosomal telomere variations.In conclusion, these data suggest that with increasing physical and genetic length of whole chromosomes, the corresponding telomeres also tend to be longer and that recombination rate and telomere length are inversely proportional.In conclusion, a combination of overall and chromosomespecifi c shorter telomeres and more pronounced age-dependent telomere erosion could be observed in males.There is a prospective clinical study strongly suggesting that longer telomeres decrease the risk of dying (Cawthon et al., 2003).With this in mind, the telomere length discrepancies between the sexes may indeed be a factor infl uencing the differences in their life expectancy.In every chromosome a linear decline of telomere length with age was observed, being more pronounced in men independent of the examined chromosome arm.This might suggest that telomere length on single chromosome arms may be infl uenced by the same factors which determine overall telomere length.S. Mayer a S. Brderlein a S. Perner a I. Waibel a A. Holdenried a N. Ciloglu a C. Hasel a T. Mattfeldt a K.V. Nielsen b P. Mller a a Institute of Pathology, University of Ulm, Ulm (Germany); b DakoCytomation A/S, Glostrup (Denmark) follow uniformity.In previous studies, sex-specifi c differences in telomere length and attrition rate of men and women were found (Benetos et al., 2001;Cawthon et al., 2003;Nawrot et al., 2004), suggesting gender differences in behavior of telomeres.In individual chromosome arms, telomere length was also shown not to be homogeneous (Lansdorp et al., 1996;Benn, 1997;Martens et al., 1998;Surralles et al., 1999;Hao and Tan, 2001;Londono-Vallejo et al., 2001;Graakjaer et al., 2003), some telomeres being signifi cantly shorter, others longer than the average length.To date, these characteristics in telomere lengths could not be set in a biological context, as only a few groups have provided detailed information about chromosome-specifi c patterns of telomere distribution (Lansdorp et al., 1996;Graakjaer et al., 2003).Whether accumulation of short telomeres (Martens et al., 2000;Londono-Vallejo et al., 2001) or rather the shortest telomere of one specifi c chromosome arm (Hemann et al., 2001) elicits senescence, remains an open question so far.",
      "Shortening of the telomeres at the ends of chromosomes has been associated with age-related disease and mortality [16][17][18].A recent study identified a common haplotype of four SNPs in the human telomerase reverse transcriptase gene (hTERT) that is enriched in centenarians and associated with longer telomere length [19].It was also shown that centenarians and their offspring maintain longer telomeres compared with controls and that longer telomeres are associated with protection from age-related diseases, better cognitive function and lipid profiles of healthy ageing [19]."
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