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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...

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Autores principales: 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.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
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.
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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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