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      Whole-Genome Sequencing of the World’s Oldest People

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          Abstract

          Supercentenarians (110 years or older) are the world’s oldest people. Seventy four are alive worldwide, with twenty two in the United States. We performed whole-genome sequencing on 17 supercentenarians to explore the genetic basis underlying extreme human longevity. We found no significant evidence of enrichment for a single rare protein-altering variant or for a gene harboring different rare protein altering variants in supercentenarian compared to control genomes. We followed up on the gene most enriched for rare protein-altering variants in our cohort of supercentenarians, TSHZ3, by sequencing it in a second cohort of 99 long-lived individuals but did not find a significant enrichment. The genome of one supercentenarian had a pathogenic mutation in DSC2, known to predispose to arrhythmogenic right ventricular cardiomyopathy, which is recommended to be reported to this individual as an incidental finding according to a recent position statement by the American College of Medical Genetics and Genomics. Even with this pathogenic mutation, the proband lived to over 110 years. The entire list of rare protein-altering variants and DNA sequence of all 17 supercentenarian genomes is available as a resource to assist the discovery of the genetic basis of extreme longevity in future studies.

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          Most cited references26

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          Gene Ontology Annotations and Resources

          The Gene Ontology (GO) Consortium (GOC, http://www.geneontology.org) is a community-based bioinformatics resource that classifies gene product function through the use of structured, controlled vocabularies. Over the past year, the GOC has implemented several processes to increase the quantity, quality and specificity of GO annotations. First, the number of manual, literature-based annotations has grown at an increasing rate. Second, as a result of a new ‘phylogenetic annotation’ process, manually reviewed, homology-based annotations are becoming available for a broad range of species. Third, the quality of GO annotations has been improved through a streamlined process for, and automated quality checks of, GO annotations deposited by different annotation groups. Fourth, the consistency and correctness of the ontology itself has increased by using automated reasoning tools. Finally, the GO has been expanded not only to cover new areas of biology through focused interaction with experts, but also to capture greater specificity in all areas of the ontology using tools for adding new combinatorial terms. The GOC works closely with other ontology developers to support integrated use of terminologies. The GOC supports its user community through the use of e-mail lists, social media and web-based resources.
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            Genetic Signatures of Exceptional Longevity in Humans

            Like most complex phenotypes, exceptional longevity is thought to reflect a combined influence of environmental (e.g., lifestyle choices, where we live) and genetic factors. To explore the genetic contribution, we undertook a genome-wide association study of exceptional longevity in 801 centenarians (median age at death 104 years) and 914 genetically matched healthy controls. Using these data, we built a genetic model that includes 281 single nucleotide polymorphisms (SNPs) and discriminated between cases and controls of the discovery set with 89% sensitivity and specificity, and with 58% specificity and 60% sensitivity in an independent cohort of 341 controls and 253 genetically matched nonagenarians and centenarians (median age 100 years). Consistent with the hypothesis that the genetic contribution is largest with the oldest ages, the sensitivity of the model increased in the independent cohort with older and older ages (71% to classify subjects with an age at death>102 and 85% to classify subjects with an age at death>105). For further validation, we applied the model to an additional, unmatched 60 centenarians (median age 107 years) resulting in 78% sensitivity, and 2863 unmatched controls with 61% specificity. The 281 SNPs include the SNP rs2075650 in TOMM40/APOE that reached irrefutable genome wide significance (posterior probability of association = 1) and replicated in the independent cohort. Removal of this SNP from the model reduced the accuracy by only 1%. Further in-silico analysis suggests that 90% of centenarians can be grouped into clusters characterized by different “genetic signatures” of varying predictive values for exceptional longevity. The correlation between 3 signatures and 3 different life spans was replicated in the combined replication sets. The different signatures may help dissect this complex phenotype into sub-phenotypes of exceptional longevity.
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              Genetic influence on human lifespan and longevity.

              There is an intense search for longevity genes in both animal models and humans. Human family studies have indicated that a modest amount of the overall variation in adult lifespan (approximately 20-30%) is accounted for by genetic factors. But it is not known if genetic factors become increasingly important for survival at the oldest ages. We study the genetic influence on human lifespan and how it varies with age using the almost extinct cohorts of Danish, Finnish and Swedish twins born between 1870 and 1910 comprising 20,502 individuals followed until 2003-2004. We first estimate mean lifespan of twins by lifespan of co-twin and then turn to the relative recurrence risk of surviving to a given age. Mean lifespan for male monozygotic (MZ) twins increases 0.39 [95% CI (0.28, 0.50)] years for every year his co-twin survives past age 60 years. This rate is significantly greater than the rate of 0.21 (0.11, 0.30) for dizygotic (DZ) males. Females and males have similar rates and these are negligible before age 60 for both MZ and DZ pairs. We moreover find that having a co-twin surviving to old ages substantially and significantly increases the chance of reaching the same old age and this chance is higher for MZ than for DZ twins. The relative recurrence risk of reaching age 92 is 4.8 (2.2, 7.5) for MZ males, which is significantly greater than the 1.8 (0.10, 3.4) for DZ males. The patterns for females and males are very similar, but with a shift of the female pattern with age that corresponds to the better female survival. Similar results arise when considering only those Nordic twins that survived past 75 years of age. The present large population based study shows genetic influence on human lifespan. While the estimated overall strength of genetic influence is compatible with previous studies, we find that genetic influences on lifespan are minimal prior to age 60 but increase thereafter. These findings provide a support for the search for genes affecting longevity in humans, especially at advanced ages.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2014
                12 November 2014
                : 9
                : 11
                : e112430
                Affiliations
                [1 ]Depts. of Developmental Biology and Genetics, Stanford University, Stanford, CA, United States of America
                [2 ]Institute for Systems Biology, Seattle, WA, United States of America
                [3 ]Gerontology Research Group, Los Angeles, CA, United States of America
                [4 ]David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States of America
                UCL Institute of Neurology, United Kingdom
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: SKK LSC LH HJG KF. Performed the experiments: HJG KF JCR HL GG GJM JDS. Analyzed the data: HJG KF JCR HL GG GJM JDS. Contributed reagents/materials/analysis tools: NSC LSC. Contributed to the writing of the manuscript: HJG KF JCR NSC HL GG GJM JDS LH LSC SKK.

                Article
                PONE-D-14-32860
                10.1371/journal.pone.0112430
                4229186
                25390934
                9eab29a4-4f0d-4d50-963c-bd118422befb
                Copyright @ 2014

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 22 July 2014
                : 29 September 2014
                Page count
                Pages: 10
                Funding
                This work was supported by the Ellison Medical Foundation/American Federation for Aging Research Fellowship, Stanford Dean’s Fellowship, The Paul Glenn Foundation Biology of Aging Seed Grant, National Institute of General Medical Sciences Center for Systems Biology (P50 GM076547) and the University of Luxembourg – Institute for Systems Biology Program. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Computational Biology
                Genome Analysis
                Developmental Biology
                Organism Development
                Aging
                Genetics
                Human Genetics
                Molecular Biology
                Molecular Biology Techniques
                Sequencing Techniques
                Genome Sequencing
                Physiology
                Physiological Processes
                Medicine and Health Sciences
                Custom metadata
                The authors confirm that all data underlying the findings are fully available without restriction. The data are available from supercentenarians.stanford.edu and from Google Genomics dataset 18254571932956699773. Apply for data access at http://goo.gl/MGcYJ5.

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