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Analysis of protein-coding genetic variation in 60,706 humans

Exome Aggregation Consortium, 1 , 2 , 3 , 4 , 1 , 2 , 1 , 2 , 5 , 1 , 2 , 6 , 5 , 2 , 2 , 1 , 2 , 7 , 2 , 8 , 9 , 10 , 11 , 1 , 2 , 12 , 1 , 2 , 5 , 1 , 2 , 2 , 1 , 2 , 6 , 13 , 1 , 2 , 6 , 1 , 2 , 1 , 2 , 2 , 1 , 2 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 2 , 23 , 1 , 6 , 24 , 19 , 20 , 18 , 1 , 2 , 6 , 2 , 1 , 2 , 6 , 18 , 2 , 25 , 18 , 2 , 26 , 27 , 28 , 29 , 2 , 27 , 28 , 18 , 2 , 18 , 6 , 24 , 19 , 20 , 18 , 16 , 2 , 1 , 2 , 18 , 30 , 2 , 31 , 6 , 27 , 25 , 32 , 2 , 33 , 34 , 35 , 36 , 2 , 37 , 2 , 26 , 27 , 2 , 18 , 26 , 38 , 39 , 40 , 41 , 42 , 2 , 26 , 27 , 28 , 43 , 6 , 8 , 44 , 45 , 46 , 47 , 48 , 1 , 2 , 6 , 1 , 2 , 5 , 49 , 24 , 19 , 20 , 50 , 51 , 52 , 2 , 6 , 27 , 25 , 32 , 24 , 19 , 20 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 44 , 60 , 1 , 2 , 6 , , 1 , 2

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      Summary

      Large-scale reference data sets of human genetic variation are critical for the medical and functional interpretation of DNA sequence changes. We describe the aggregation and analysis of high-quality exome (protein-coding region) sequence data for 60,706 individuals of diverse ethnicities generated as part of the Exome Aggregation Consortium (ExAC). This catalogue of human genetic diversity contains an average of one variant every eight bases of the exome, and provides direct evidence for the presence of widespread mutational recurrence. We have used this catalogue to calculate objective metrics of pathogenicity for sequence variants, and to identify genes subject to strong selection against various classes of mutation; identifying 3,230 genes with near-complete depletion of truncating variants with 72% having no currently established human disease phenotype. Finally, we demonstrate that these data can be used for the efficient filtering of candidate disease-causing variants, and for the discovery of human “knockout” variants in protein-coding genes.

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      Most cited references 39

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      A framework for variation discovery and genotyping using next-generation DNA sequencing data

      Recent advances in sequencing technology make it possible to comprehensively catalogue genetic variation in population samples, creating a foundation for understanding human disease, ancestry and evolution. The amounts of raw data produced are prodigious and many computational steps are required to translate this output into high-quality variant calls. We present a unified analytic framework to discover and genotype variation among multiple samples simultaneously that achieves sensitive and specific results across five sequencing technologies and three distinct, canonical experimental designs. Our process includes (1) initial read mapping; (2) local realignment around indels; (3) base quality score recalibration; (4) SNP discovery and genotyping to find all potential variants; and (5) machine learning to separate true segregating variation from machine artifacts common to next-generation sequencing technologies. We discuss the application of these tools, instantiated in the Genome Analysis Toolkit (GATK), to deep whole-genome, whole-exome capture, and multi-sample low-pass (~4×) 1000 Genomes Project datasets.
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        A global reference for human genetic variation

         Lachlan Coin (2016)
        The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.
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          Standards and Guidelines for the Interpretation of Sequence Variants: A Joint Consensus Recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology

          The American College of Medical Genetics and Genomics (ACMG) previously developed guidance for the interpretation of sequence variants. 1 In the past decade, sequencing technology has evolved rapidly with the advent of high-throughput next generation sequencing. By adopting and leveraging next generation sequencing, clinical laboratories are now performing an ever increasing catalogue of genetic testing spanning genotyping, single genes, gene panels, exomes, genomes, transcriptomes and epigenetic assays for genetic disorders. By virtue of increased complexity, this paradigm shift in genetic testing has been accompanied by new challenges in sequence interpretation. In this context, the ACMG convened a workgroup in 2013 comprised of representatives from the ACMG, the Association for Molecular Pathology (AMP) and the College of American Pathologists (CAP) to revisit and revise the standards and guidelines for the interpretation of sequence variants. The group consisted of clinical laboratory directors and clinicians. This report represents expert opinion of the workgroup with input from ACMG, AMP and CAP stakeholders. These recommendations primarily apply to the breadth of genetic tests used in clinical laboratories including genotyping, single genes, panels, exomes and genomes. This report recommends the use of specific standard terminology: ‘pathogenic’, ‘likely pathogenic’, ‘uncertain significance’, ‘likely benign’, and ‘benign’ to describe variants identified in Mendelian disorders. Moreover, this recommendation describes a process for classification of variants into these five categories based on criteria using typical types of variant evidence (e.g. population data, computational data, functional data, segregation data, etc.). Because of the increased complexity of analysis and interpretation of clinical genetic testing described in this report, the ACMG strongly recommends that clinical molecular genetic testing should be performed in a CLIA-approved laboratory with results interpreted by a board-certified clinical molecular geneticist or molecular genetic pathologist or equivalent.
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            Author and article information

            Affiliations
            [1 ]Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
            [2 ]Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
            [3 ]School of Paediatrics and Child Health, University of Sydney, Sydney, NSW, Australia
            [4 ]Institute for Neuroscience and Muscle Research, Childrens Hospital at Westmead, Sydney, NSW, Australia
            [5 ]Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA, USA
            [6 ]Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
            [7 ]Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, USA
            [8 ]Department of Genetics, Harvard Medical School, Boston, MA, USA
            [9 ]National Heart and Lung Institute, Imperial College London, London, UK
            [10 ]NIHR Royal Brompton Cardiovascular Biomedical Research Unit, Royal Brompton Hospital, London, UK
            [11 ]MRC Clinical Sciences Centre, Imperial College London, London, UK
            [12 ]Genome Sciences, University of Washington, Seattle, WA, USA
            [13 ]Program in Bioinformatics and Integrative Genomics, Harvard Medical School, Boston, MA, USA
            [14 ]Mouse Genome Informatics, Jackson Laboratory, Bar Harbor, ME, USA
            [15 ]Center for Biomedical Informatics and Biostatistics, University of Arizona, Tucson, AZ, USA
            [16 ]Institute of Medical Genetics, Cardiff University, Cardiff, UK
            [17 ]Google Inc, Mountain View, CA, USA
            [18 ]Broad Institute of MIT and Harvard, Cambridge, MA, USA
            [19 ]Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
            [20 ]Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
            [21 ]The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
            [22 ]The Center for Statistical Genetics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
            [23 ]Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA
            [24 ]Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
            [25 ]Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
            [26 ]Harvard Medical School, Boston, MA, USA
            [27 ]Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USA
            [28 ]Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
            [29 ]Immunogenomics and Metabolic Disease Laboratory, Instituto Nacional de Medicina Gen—mica, Mexico City, Mexico
            [30 ]Molecular Biology and Genomic Medicine Unit, Instituto Nacional de Ciencias M_dicas y Nutrici—n, Mexico City, Mexico
            [31 ]Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University,Samsung Medical Center, Seoul, Republic of Korea
            [32 ]Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
            [33 ]Vertex Pharmaceuticals, Boston, MA, USA
            [34 ]Department of Cardiology, University Hospital, Parma, Italy
            [35 ]Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
            [36 ]Department of Public Health and Primary Care, Strangeways Research Laboratory, Cambridge, UK
            [37 ]Cardiovascular Epidemiology and Genetics, Hospital del Mar Medical Research Institute, Barcelona, Spain
            [38 ]Department of Pathology and Cancer Center, Massachusetts General Hospital, Boston, MA, USA
            [39 ]Psychiatric Genetic Epidemiology & Neurobiology Laboratory, State University of New York,Upstate Medical University, Syracuse, NY, USA
            [40 ]Department of Psychiatry and Behavioral Sciences, State University of New York,Upstate Medical University, Syracuse, NY, USA
            [41 ]Department of Neuroscience and Physiology, State University of New York,Upstate Medical University, Syracuse, NY, USA
            [42 ]Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
            [43 ]Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
            [44 ]Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
            [45 ]Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
            [46 ]Oxford NIHR Biomedical Research Centre, Oxford University Hospitals Foundation Trust, Oxford, UK
            [47 ]Inflammatory Bowel Disease and Immunobiology Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
            [48 ]Atherogenomics Laboratory, University of Ottawa Heart Institute, Ottawa, ON, Canada
            [49 ]Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
            [50 ]Department of Biostatistics and Epidemiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
            [51 ]Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
            [52 ]Center for Non-Communicable Diseases, Karachi, , Pakistan
            [53 ]Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
            [54 ]Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
            [55 ]Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
            [56 ]Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
            [57 ]Department of Public Health, University of Helsinki, Helsinki, Finland
            [58 ]Department of Psychiatry, University of California, San Diego, CA, USA
            [59 ]Radcliffe Department of Medicine, University of Oxford, Oxford, UK
            [60 ]Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS, USA
            Author notes
            [*]

            These authors contributed equally to this work and names appear in alphabetical order

            [#]

            List of collaborators to appear at the end of manuscript

            Journal
            0410462
            6011
            Nature
            Nature
            Nature
            0028-0836
            1476-4687
            19 August 2016
            18 August 2016
            17 February 2017
            : 536
            : 7616
            : 285-291
            27535533
            5018207
            10.1038/nature19057
            NIHMS798561

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