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A Phenome-Wide Association Study of genes associated with COVID-19 severity reveals shared genetics with complex diseases in the Million Veteran Program
The study aims to determine the shared genetic architecture between COVID-19 severity with existing medical conditions using electronic health record (EHR) data. We conducted a Phenome-Wide Association Study (PheWAS) of genetic variants associated with critical illness (n = 35) or hospitalization (n...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9049369/ https://www.ncbi.nlm.nih.gov/pubmed/35482673 http://dx.doi.org/10.1371/journal.pgen.1010113 |
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author | Verma, Anurag Tsao, Noah L. Thomann, Lauren O. Ho, Yuk-Lam Iyengar, Sudha K. Luoh, Shiuh-Wen Carr, Rotonya Crawford, Dana C. Efird, Jimmy T. Huffman, Jennifer E. Hung, Adriana Ivey, Kerry L. Levin, Michael G. Lynch, Julie Natarajan, Pradeep Pyarajan, Saiju Bick, Alexander G. Costa, Lauren Genovese, Giulio Hauger, Richard Madduri, Ravi Pathak, Gita A. Polimanti, Renato Voight, Benjamin Vujkovic, Marijana Zekavat, Seyedeh Maryam Zhao, Hongyu Ritchie, Marylyn D. Chang, Kyong-Mi Cho, Kelly Casas, Juan P. Tsao, Philip S. Gaziano, J. Michael O’Donnell, Christopher Damrauer, Scott M. Liao, Katherine P. |
author_facet | Verma, Anurag Tsao, Noah L. Thomann, Lauren O. Ho, Yuk-Lam Iyengar, Sudha K. Luoh, Shiuh-Wen Carr, Rotonya Crawford, Dana C. Efird, Jimmy T. Huffman, Jennifer E. Hung, Adriana Ivey, Kerry L. Levin, Michael G. Lynch, Julie Natarajan, Pradeep Pyarajan, Saiju Bick, Alexander G. Costa, Lauren Genovese, Giulio Hauger, Richard Madduri, Ravi Pathak, Gita A. Polimanti, Renato Voight, Benjamin Vujkovic, Marijana Zekavat, Seyedeh Maryam Zhao, Hongyu Ritchie, Marylyn D. Chang, Kyong-Mi Cho, Kelly Casas, Juan P. Tsao, Philip S. Gaziano, J. Michael O’Donnell, Christopher Damrauer, Scott M. Liao, Katherine P. |
author_sort | Verma, Anurag |
collection | PubMed |
description | The study aims to determine the shared genetic architecture between COVID-19 severity with existing medical conditions using electronic health record (EHR) data. We conducted a Phenome-Wide Association Study (PheWAS) of genetic variants associated with critical illness (n = 35) or hospitalization (n = 42) due to severe COVID-19 using genome-wide association summary data from the Host Genetics Initiative. PheWAS analysis was performed using genotype-phenotype data from the Veterans Affairs Million Veteran Program (MVP). Phenotypes were defined by International Classification of Diseases (ICD) codes mapped to clinically relevant groups using published PheWAS methods. Among 658,582 Veterans, variants associated with severe COVID-19 were tested for association across 1,559 phenotypes. Variants at the ABO locus (rs495828, rs505922) associated with the largest number of phenotypes (n(rs495828) = 53 and n(rs505922) = 59); strongest association with venous embolism, odds ratio (OR(rs495828) 1.33 (p = 1.32 x 10(−199)), and thrombosis OR(rs505922) 1.33, p = 2.2 x10(-265). Among 67 respiratory conditions tested, 11 had significant associations including MUC5B locus (rs35705950) with increased risk of idiopathic fibrosing alveolitis OR 2.83, p = 4.12 × 10(−191); CRHR1 (rs61667602) associated with reduced risk of pulmonary fibrosis, OR 0.84, p = 2.26× 10(−12). The TYK2 locus (rs11085727) associated with reduced risk for autoimmune conditions, e.g., psoriasis OR 0.88, p = 6.48 x10(-23), lupus OR 0.84, p = 3.97 x 10(−06). PheWAS stratified by ancestry demonstrated differences in genotype-phenotype associations. LMNA (rs581342) associated with neutropenia OR 1.29 p = 4.1 x 10(−13) among Veterans of African and Hispanic ancestry but not European. Overall, we observed a shared genetic architecture between COVID-19 severity and conditions related to underlying risk factors for severe and poor COVID-19 outcomes. Differing associations between genotype-phenotype across ancestries may inform heterogenous outcomes observed with COVID-19. Divergent associations between risk for severe COVID-19 with autoimmune inflammatory conditions both respiratory and non-respiratory highlights the shared pathways and fine balance of immune host response and autoimmunity and caution required when considering treatment targets. |
format | Online Article Text |
id | pubmed-9049369 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-90493692022-04-29 A Phenome-Wide Association Study of genes associated with COVID-19 severity reveals shared genetics with complex diseases in the Million Veteran Program Verma, Anurag Tsao, Noah L. Thomann, Lauren O. Ho, Yuk-Lam Iyengar, Sudha K. Luoh, Shiuh-Wen Carr, Rotonya Crawford, Dana C. Efird, Jimmy T. Huffman, Jennifer E. Hung, Adriana Ivey, Kerry L. Levin, Michael G. Lynch, Julie Natarajan, Pradeep Pyarajan, Saiju Bick, Alexander G. Costa, Lauren Genovese, Giulio Hauger, Richard Madduri, Ravi Pathak, Gita A. Polimanti, Renato Voight, Benjamin Vujkovic, Marijana Zekavat, Seyedeh Maryam Zhao, Hongyu Ritchie, Marylyn D. Chang, Kyong-Mi Cho, Kelly Casas, Juan P. Tsao, Philip S. Gaziano, J. Michael O’Donnell, Christopher Damrauer, Scott M. Liao, Katherine P. PLoS Genet Research Article The study aims to determine the shared genetic architecture between COVID-19 severity with existing medical conditions using electronic health record (EHR) data. We conducted a Phenome-Wide Association Study (PheWAS) of genetic variants associated with critical illness (n = 35) or hospitalization (n = 42) due to severe COVID-19 using genome-wide association summary data from the Host Genetics Initiative. PheWAS analysis was performed using genotype-phenotype data from the Veterans Affairs Million Veteran Program (MVP). Phenotypes were defined by International Classification of Diseases (ICD) codes mapped to clinically relevant groups using published PheWAS methods. Among 658,582 Veterans, variants associated with severe COVID-19 were tested for association across 1,559 phenotypes. Variants at the ABO locus (rs495828, rs505922) associated with the largest number of phenotypes (n(rs495828) = 53 and n(rs505922) = 59); strongest association with venous embolism, odds ratio (OR(rs495828) 1.33 (p = 1.32 x 10(−199)), and thrombosis OR(rs505922) 1.33, p = 2.2 x10(-265). Among 67 respiratory conditions tested, 11 had significant associations including MUC5B locus (rs35705950) with increased risk of idiopathic fibrosing alveolitis OR 2.83, p = 4.12 × 10(−191); CRHR1 (rs61667602) associated with reduced risk of pulmonary fibrosis, OR 0.84, p = 2.26× 10(−12). The TYK2 locus (rs11085727) associated with reduced risk for autoimmune conditions, e.g., psoriasis OR 0.88, p = 6.48 x10(-23), lupus OR 0.84, p = 3.97 x 10(−06). PheWAS stratified by ancestry demonstrated differences in genotype-phenotype associations. LMNA (rs581342) associated with neutropenia OR 1.29 p = 4.1 x 10(−13) among Veterans of African and Hispanic ancestry but not European. Overall, we observed a shared genetic architecture between COVID-19 severity and conditions related to underlying risk factors for severe and poor COVID-19 outcomes. Differing associations between genotype-phenotype across ancestries may inform heterogenous outcomes observed with COVID-19. Divergent associations between risk for severe COVID-19 with autoimmune inflammatory conditions both respiratory and non-respiratory highlights the shared pathways and fine balance of immune host response and autoimmunity and caution required when considering treatment targets. Public Library of Science 2022-04-28 /pmc/articles/PMC9049369/ /pubmed/35482673 http://dx.doi.org/10.1371/journal.pgen.1010113 Text en https://creativecommons.org/publicdomain/zero/1.0/This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 (https://creativecommons.org/publicdomain/zero/1.0/) public domain dedication. |
spellingShingle | Research Article Verma, Anurag Tsao, Noah L. Thomann, Lauren O. Ho, Yuk-Lam Iyengar, Sudha K. Luoh, Shiuh-Wen Carr, Rotonya Crawford, Dana C. Efird, Jimmy T. Huffman, Jennifer E. Hung, Adriana Ivey, Kerry L. Levin, Michael G. Lynch, Julie Natarajan, Pradeep Pyarajan, Saiju Bick, Alexander G. Costa, Lauren Genovese, Giulio Hauger, Richard Madduri, Ravi Pathak, Gita A. Polimanti, Renato Voight, Benjamin Vujkovic, Marijana Zekavat, Seyedeh Maryam Zhao, Hongyu Ritchie, Marylyn D. Chang, Kyong-Mi Cho, Kelly Casas, Juan P. Tsao, Philip S. Gaziano, J. Michael O’Donnell, Christopher Damrauer, Scott M. Liao, Katherine P. A Phenome-Wide Association Study of genes associated with COVID-19 severity reveals shared genetics with complex diseases in the Million Veteran Program |
title | A Phenome-Wide Association Study of genes associated with COVID-19 severity reveals shared genetics with complex diseases in the Million Veteran Program |
title_full | A Phenome-Wide Association Study of genes associated with COVID-19 severity reveals shared genetics with complex diseases in the Million Veteran Program |
title_fullStr | A Phenome-Wide Association Study of genes associated with COVID-19 severity reveals shared genetics with complex diseases in the Million Veteran Program |
title_full_unstemmed | A Phenome-Wide Association Study of genes associated with COVID-19 severity reveals shared genetics with complex diseases in the Million Veteran Program |
title_short | A Phenome-Wide Association Study of genes associated with COVID-19 severity reveals shared genetics with complex diseases in the Million Veteran Program |
title_sort | phenome-wide association study of genes associated with covid-19 severity reveals shared genetics with complex diseases in the million veteran program |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9049369/ https://www.ncbi.nlm.nih.gov/pubmed/35482673 http://dx.doi.org/10.1371/journal.pgen.1010113 |
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