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Systematic single-variant and gene-based association testing of thousands of phenotypes in 394,841 UK Biobank exomes
Genome-wide association studies have successfully discovered thousands of common variants associated with human diseases and traits, but the landscape of rare variations in human disease has not been explored at scale. Exome-sequencing studies of population biobanks provide an opportunity to systema...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9903662/ https://www.ncbi.nlm.nih.gov/pubmed/36778668 http://dx.doi.org/10.1016/j.xgen.2022.100168 |
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author | Karczewski, Konrad J. Solomonson, Matthew Chao, Katherine R. Goodrich, Julia K. Tiao, Grace Lu, Wenhan Riley-Gillis, Bridget M. Tsai, Ellen A. Kim, Hye In Zheng, Xiuwen Rahimov, Fedik Esmaeeli, Sahar Grundstad, A. Jason Reppell, Mark Waring, Jeff Jacob, Howard Sexton, David Bronson, Paola G. Chen, Xing Hu, Xinli Goldstein, Jacqueline I. King, Daniel Vittal, Christopher Poterba, Timothy Palmer, Duncan S. Churchhouse, Claire Howrigan, Daniel P. Zhou, Wei Watts, Nicholas A. Nguyen, Kevin Nguyen, Huy Mason, Cara Farnham, Christopher Tolonen, Charlotte Gauthier, Laura D. Gupta, Namrata MacArthur, Daniel G. Rehm, Heidi L. Seed, Cotton Philippakis, Anthony A. Daly, Mark J. Davis, J. Wade Runz, Heiko Miller, Melissa R. Neale, Benjamin M. |
author_facet | Karczewski, Konrad J. Solomonson, Matthew Chao, Katherine R. Goodrich, Julia K. Tiao, Grace Lu, Wenhan Riley-Gillis, Bridget M. Tsai, Ellen A. Kim, Hye In Zheng, Xiuwen Rahimov, Fedik Esmaeeli, Sahar Grundstad, A. Jason Reppell, Mark Waring, Jeff Jacob, Howard Sexton, David Bronson, Paola G. Chen, Xing Hu, Xinli Goldstein, Jacqueline I. King, Daniel Vittal, Christopher Poterba, Timothy Palmer, Duncan S. Churchhouse, Claire Howrigan, Daniel P. Zhou, Wei Watts, Nicholas A. Nguyen, Kevin Nguyen, Huy Mason, Cara Farnham, Christopher Tolonen, Charlotte Gauthier, Laura D. Gupta, Namrata MacArthur, Daniel G. Rehm, Heidi L. Seed, Cotton Philippakis, Anthony A. Daly, Mark J. Davis, J. Wade Runz, Heiko Miller, Melissa R. Neale, Benjamin M. |
author_sort | Karczewski, Konrad J. |
collection | PubMed |
description | Genome-wide association studies have successfully discovered thousands of common variants associated with human diseases and traits, but the landscape of rare variations in human disease has not been explored at scale. Exome-sequencing studies of population biobanks provide an opportunity to systematically evaluate the impact of rare coding variations across a wide range of phenotypes to discover genes and allelic series relevant to human health and disease. Here, we present results from systematic association analyses of 4,529 phenotypes using single-variant and gene tests of 394,841 individuals in the UK Biobank with exome-sequence data. We find that the discovery of genetic associations is tightly linked to frequency and is correlated with metrics of deleteriousness and natural selection. We highlight biological findings elucidated by these data and release the dataset as a public resource alongside the Genebass browser for rapidly exploring rare-variant association results. |
format | Online Article Text |
id | pubmed-9903662 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-99036622023-02-10 Systematic single-variant and gene-based association testing of thousands of phenotypes in 394,841 UK Biobank exomes Karczewski, Konrad J. Solomonson, Matthew Chao, Katherine R. Goodrich, Julia K. Tiao, Grace Lu, Wenhan Riley-Gillis, Bridget M. Tsai, Ellen A. Kim, Hye In Zheng, Xiuwen Rahimov, Fedik Esmaeeli, Sahar Grundstad, A. Jason Reppell, Mark Waring, Jeff Jacob, Howard Sexton, David Bronson, Paola G. Chen, Xing Hu, Xinli Goldstein, Jacqueline I. King, Daniel Vittal, Christopher Poterba, Timothy Palmer, Duncan S. Churchhouse, Claire Howrigan, Daniel P. Zhou, Wei Watts, Nicholas A. Nguyen, Kevin Nguyen, Huy Mason, Cara Farnham, Christopher Tolonen, Charlotte Gauthier, Laura D. Gupta, Namrata MacArthur, Daniel G. Rehm, Heidi L. Seed, Cotton Philippakis, Anthony A. Daly, Mark J. Davis, J. Wade Runz, Heiko Miller, Melissa R. Neale, Benjamin M. Cell Genom Resource Genome-wide association studies have successfully discovered thousands of common variants associated with human diseases and traits, but the landscape of rare variations in human disease has not been explored at scale. Exome-sequencing studies of population biobanks provide an opportunity to systematically evaluate the impact of rare coding variations across a wide range of phenotypes to discover genes and allelic series relevant to human health and disease. Here, we present results from systematic association analyses of 4,529 phenotypes using single-variant and gene tests of 394,841 individuals in the UK Biobank with exome-sequence data. We find that the discovery of genetic associations is tightly linked to frequency and is correlated with metrics of deleteriousness and natural selection. We highlight biological findings elucidated by these data and release the dataset as a public resource alongside the Genebass browser for rapidly exploring rare-variant association results. Elsevier 2022-08-15 /pmc/articles/PMC9903662/ /pubmed/36778668 http://dx.doi.org/10.1016/j.xgen.2022.100168 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Resource Karczewski, Konrad J. Solomonson, Matthew Chao, Katherine R. Goodrich, Julia K. Tiao, Grace Lu, Wenhan Riley-Gillis, Bridget M. Tsai, Ellen A. Kim, Hye In Zheng, Xiuwen Rahimov, Fedik Esmaeeli, Sahar Grundstad, A. Jason Reppell, Mark Waring, Jeff Jacob, Howard Sexton, David Bronson, Paola G. Chen, Xing Hu, Xinli Goldstein, Jacqueline I. King, Daniel Vittal, Christopher Poterba, Timothy Palmer, Duncan S. Churchhouse, Claire Howrigan, Daniel P. Zhou, Wei Watts, Nicholas A. Nguyen, Kevin Nguyen, Huy Mason, Cara Farnham, Christopher Tolonen, Charlotte Gauthier, Laura D. Gupta, Namrata MacArthur, Daniel G. Rehm, Heidi L. Seed, Cotton Philippakis, Anthony A. Daly, Mark J. Davis, J. Wade Runz, Heiko Miller, Melissa R. Neale, Benjamin M. Systematic single-variant and gene-based association testing of thousands of phenotypes in 394,841 UK Biobank exomes |
title | Systematic single-variant and gene-based association testing of thousands of phenotypes in 394,841 UK Biobank exomes |
title_full | Systematic single-variant and gene-based association testing of thousands of phenotypes in 394,841 UK Biobank exomes |
title_fullStr | Systematic single-variant and gene-based association testing of thousands of phenotypes in 394,841 UK Biobank exomes |
title_full_unstemmed | Systematic single-variant and gene-based association testing of thousands of phenotypes in 394,841 UK Biobank exomes |
title_short | Systematic single-variant and gene-based association testing of thousands of phenotypes in 394,841 UK Biobank exomes |
title_sort | systematic single-variant and gene-based association testing of thousands of phenotypes in 394,841 uk biobank exomes |
topic | Resource |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9903662/ https://www.ncbi.nlm.nih.gov/pubmed/36778668 http://dx.doi.org/10.1016/j.xgen.2022.100168 |
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