Analysis of protein-coding genetic variation in 60,706 humans
Large-scale reference data sets of human genetic variation are critical for the medical and functional interpretation of DNA sequence changes. Here we describe the aggregation and analysis of high-quality exome (protein-coding region) DNA sequence data for 60,706 individuals of diverse ancestries ge...
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| Published in | Nature (London) Vol. 536; no. 7616; pp. 285 - 291 |
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| Main Authors | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
Nature Publishing Group UK
18.08.2016
Nature Publishing Group |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0028-0836 1476-4687 |
| DOI | 10.1038/nature19057 |
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| Abstract | Large-scale reference data sets of human genetic variation are critical for the medical and functional interpretation of DNA sequence changes. Here we describe the aggregation and analysis of high-quality exome (protein-coding region) DNA sequence data for 60,706 individuals of diverse ancestries 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 predicted protein-truncating variants, with 72% of these genes 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.
Exome sequencing data from 60,706 people of diverse geographic ancestry is presented, providing insight into genetic variation across populations, and illuminating the relationship between DNA variants and human disease.
An in-depth insight into human genetic variation
As part of the Exome Aggregation Consortium (ExAC) project, Daniel MacArthur and colleagues report on the generation and analysis of high-quality exome sequencing data from 60,706 individuals of diverse ancestry. This provides the most comprehensive catalogue of human protein-coding genetic variation to date, yielding unprecedented resolution for the analysis of very rare variants across multiple human populations. The catalogue is freely accessible and provides a critical reference panel for the clinical interpretation of genetic variants and the discovery of disease-related genes. |
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| AbstractList | Large-scale reference data sets of human genetic variation are critical for the medical and functional interpretation of DNA sequence changes. Here we describe the aggregation and analysis of high-quality exome (protein-coding region) DNA sequence data for 60,706 individuals of diverse ancestries 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 predicted protein-truncating variants, with 72% of these genes 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. Large-scale reference data sets of human genetic variation are critical for the medical and functional interpretation of DNA sequence changes. Here we describe the aggregation and analysis of high-quality exome (protein-coding region) DNA sequence data for 60,706 individuals of diverse ancestries 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 predicted protein-truncating variants, with 72% of these genes 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. Exome sequencing data from 60,706 people of diverse geographic ancestry is presented, providing insight into genetic variation across populations, and illuminating the relationship between DNA variants and human disease. An in-depth insight into human genetic variation As part of the Exome Aggregation Consortium (ExAC) project, Daniel MacArthur and colleagues report on the generation and analysis of high-quality exome sequencing data from 60,706 individuals of diverse ancestry. This provides the most comprehensive catalogue of human protein-coding genetic variation to date, yielding unprecedented resolution for the analysis of very rare variants across multiple human populations. The catalogue is freely accessible and provides a critical reference panel for the clinical interpretation of genetic variants and the discovery of disease-related genes. 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. |
| Audience | Academic |
| Author | Pierce-Hoffman, Emma Howrigan, Daniel Kurki, Mitja I. Orozco, Lorena Kosmicki, Jack A. Glatt, Stephen J. Sullivan, Patrick F. Weisburd, Ben Fromer, Menachem Kiezun, Adam Elosua, Roberto Duncan, Laramie E. Rivas, Manuel A. Stevens, Christine McPherson, Ruth Berghout, Joanne Zou, James DePristo, Mark Ware, James S. Neale, Benjamin M. Saleheen, Danish Lek, Monkol Tusie-Luna, Maria T. Deflaux, Nicole Yu, Dongmei Kathiresan, Sekar Zhao, Fengmei Gabriel, Stacey B. Peloso, Gina M. Florez, Jose C. Won, Hong-Hee Sklar, Pamela Tsuang, Ming T. Stenson, Peter D. Hultman, Christina M. Shakir, Khalid Flannick, Jason Wilson, James G. Boehnke, Michael Tiao, Grace Estrada, Karol O’Donnell-Luria, Anne H. Banks, Eric Cummings, Beryl B. Gauthier, Laura Ruano-Rubio, Valentin Birnbaum, Daniel P. Natarajan, Pradeep Ardissino, Diego McGovern, Dermot McCarroll, Steven Danesh, John Ruderfer, Douglas M. Moonshine, Ami Levy Laakso, Markku Rose, Samuel A. Watkins, Hugh C. Scharf, Jeremiah M. Hill, Andrew J. Donnelly, Stacey Purcell, Shaun M. Karczewski, Konrad J. Goldstein, |
| AuthorAffiliation | 40 Department of Psychiatry and Behavioral Sciences, State University of New York,Upstate Medical University, Syracuse, NY, USA 30 Molecular Biology and Genomic Medicine Unit, Instituto Nacional de Ciencias M_dicas y Nutrici—n, Mexico City, Mexico 26 Harvard Medical School, Boston, MA, USA 3 School of Paediatrics and Child Health, University of Sydney, Sydney, NSW, Australia 50 Department of Biostatistics and Epidemiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA 10 NIHR Royal Brompton Cardiovascular Biomedical Research Unit, Royal Brompton Hospital, London, UK 39 Psychiatric Genetic Epidemiology & Neurobiology Laboratory, State University of New York,Upstate Medical University, Syracuse, NY, USA 47 Inflammatory Bowel Disease and Immunobiology Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA 43 Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland 20 Institute for Genomics and Multis |
| AuthorAffiliation_xml | – name: 22 The Center for Statistical Genetics, Icahn School of Medicine at Mount Sinai, New York, NY, USA – name: 35 Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA – name: 32 Department of Neurology, Massachusetts General Hospital, Boston, MA, USA – name: 45 Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK – name: 33 Vertex Pharmaceuticals, Boston, MA, USA – name: 30 Molecular Biology and Genomic Medicine Unit, Instituto Nacional de Ciencias M_dicas y Nutrici—n, Mexico City, Mexico – name: 56 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden – name: 2 Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA – name: 8 Department of Genetics, Harvard Medical School, Boston, MA, USA – name: 27 Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USA – name: 51 Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA – name: 46 Oxford NIHR Biomedical Research Centre, Oxford University Hospitals Foundation Trust, Oxford, UK – name: 54 Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA – name: 1 Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA – name: 19 Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA – name: 10 NIHR Royal Brompton Cardiovascular Biomedical Research Unit, Royal Brompton Hospital, London, UK – name: 44 Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK – name: 23 Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA – name: 57 Department of Public Health, University of Helsinki, Helsinki, Finland – name: 7 Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, USA – 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York,Upstate Medical University, Syracuse, NY, USA – name: 49 Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland – name: 60 Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS, USA – name: 36 Department of Public Health and Primary Care, Strangeways Research Laboratory, Cambridge, UK – name: 29 Immunogenomics and Metabolic Disease Laboratory, Instituto Nacional de Medicina Gen—mica, Mexico City, Mexico – name: 16 Institute of Medical Genetics, Cardiff University, Cardiff, UK – name: 37 Cardiovascular Epidemiology and Genetics, Hospital del Mar Medical Research Institute, Barcelona, Spain – name: 41 Department of Neuroscience and Physiology, State University of New York,Upstate Medical University, Syracuse, NY, USA – name: 42 Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden – name: 4 Institute for Neuroscience and Muscle Research, Childrens Hospital at 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Boston, MA, USA – name: 3 School of Paediatrics and Child Health, University of Sydney, Sydney, NSW, Australia – name: 31 Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University,Samsung Medical Center, Seoul, Republic of Korea – name: 24 Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA – name: 15 Center for Biomedical Informatics and Biostatistics, University of Arizona, Tucson, AZ, USA – name: 20 Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA – name: 58 Department of Psychiatry, University of California, San Diego, CA, USA – name: 38 Department of Pathology and Cancer Center, Massachusetts General Hospital, Boston, MA, USA – name: 47 Inflammatory Bowel Disease and Immunobiology Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA – name: 25 Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, MA, 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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/27535533$$D View this record in MEDLINE/PubMed http://kipublications.ki.se/Default.aspx?queryparsed=id:134199653$$DView record from Swedish Publication Index |
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| ContentType | Journal Article |
| Contributor | Kosmicki, Jack A Scharf, Jeremiah M Weisburd, Ben Fromer, Menachem Elosua, Roberto McCarthy, Mark I Birnbaum, Daniel P Watkins, Hugh C Berghout, Joanne DePristo, Mark Aukrust, Ingvild Barr, Cathy L Lek, Monkol Altshuler, David M Sullivan, Patrick F Tsuang, Ming T Peloso, Gina M Rose, Samuel A Abboud, Hanna E Deflaux, Nicole Yu, Dongmei Neale, Benjamin M Sklar, Pamela Bell, Graeme I Wilson, James G Flannick, Jason Purcell, Shaun M Estrada, Karol Minikel, Eric V Budman, Cathy L Thomas, Brett P Bowden, Donald W Coppola, Giovanni McCarroll, Steven Chambers, John C Danesh, John Tusie-Luna, Maria T Stenson, Peter D Donnelly, Stacey Cummings, Beryl B Goldstein, Jackie Atzmon, Gil Gabriel, Stacey B Palotie, Aarno Florez, Jose C Rivas, Manuel A Pierce-Hoffman, Emma Howrigan, Daniel Kurki, Mitja I Orozco, Lorena Kiezun, Adam Stevens, Christine McPherson, Ruth Cooper, David N Zou, James Ruderfer, Douglas M Saleheen, Danish Duncan, Laramie E Daly, Mark J Clarke, Robert Ware, James S Kathiresan, Sekar MacArthur, Daniel G Hultman, Christina M Zhao, Fengmei Bergen |
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– sequence: 79 givenname: Daniel G surname: MacArthur fullname: MacArthur, Daniel G – sequence: 80 givenname: Hanna E surname: Abboud fullname: Abboud, Hanna E – sequence: 81 givenname: Goncalo surname: Abecasis fullname: Abecasis, Goncalo – sequence: 82 givenname: Carlos A surname: Aguilar-Salinas fullname: Aguilar-Salinas, Carlos A – sequence: 83 givenname: Olimpia surname: Arellano-Campos fullname: Arellano-Campos, Olimpia – sequence: 84 givenname: Gil surname: Atzmon fullname: Atzmon, Gil – sequence: 85 givenname: Ingvild surname: Aukrust fullname: Aukrust, Ingvild – sequence: 86 givenname: Cathy L surname: Barr fullname: Barr, Cathy L – sequence: 87 givenname: Graeme I surname: Bell fullname: Bell, Graeme I – sequence: 88 givenname: Sarah surname: Bergen fullname: Bergen, Sarah – sequence: 89 givenname: Lise surname: Bjørkhaug fullname: Bjørkhaug, Lise – sequence: 90 givenname: John surname: Blangero fullname: Blangero, John – sequence: 91 givenname: Donald W surname: Bowden fullname: Bowden, Donald W – sequence: 92 givenname: Cathy L surname: Budman fullname: Budman, Cathy L – sequence: 93 givenname: Noël P surname: Burtt fullname: Burtt, Noël P – sequence: 94 givenname: Federico surname: Centeno-Cruz fullname: Centeno-Cruz, Federico – sequence: 95 givenname: John C surname: Chambers fullname: Chambers, John C – sequence: 96 givenname: Kimberly surname: Chambert fullname: Chambert, Kimberly – sequence: 97 givenname: Robert surname: Clarke fullname: Clarke, Robert – sequence: 98 givenname: Rory surname: Collins fullname: Collins, Rory – sequence: 99 givenname: Giovanni surname: Coppola fullname: Coppola, Giovanni |
| Copyright | The Author(s) 2016 COPYRIGHT 2016 Nature Publishing Group Copyright Nature Publishing Group Aug 18, 2016 |
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| Snippet | Large-scale reference data sets of human genetic variation are critical for the medical and functional interpretation of DNA sequence changes. Here we describe... Large-scale reference data sets of human genetic variation are critical for the medical and functional interpretation of DNA sequence changes. We describe the... |
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| Title | Analysis of protein-coding genetic variation in 60,706 humans |
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