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High-throughput framework for genetic analyses of adverse drug reactions using electronic health records
Understanding the contribution of genetic variation to drug response can improve the delivery of precision medicine. However, genome-wide association studies (GWAS) for drug response are uncommon and are often hindered by small sample sizes. We present a high-throughput framework to efficiently iden...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8195357/ https://www.ncbi.nlm.nih.gov/pubmed/34061827 http://dx.doi.org/10.1371/journal.pgen.1009593 |
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author | Zheng, Neil S. Stone, Cosby A. Jiang, Lan Shaffer, Christian M. Kerchberger, V. Eric Chung, Cecilia P. Feng, QiPing Cox, Nancy J. Stein, C. Michael Roden, Dan M. Denny, Joshua C. Phillips, Elizabeth J. Wei, Wei-Qi |
author_facet | Zheng, Neil S. Stone, Cosby A. Jiang, Lan Shaffer, Christian M. Kerchberger, V. Eric Chung, Cecilia P. Feng, QiPing Cox, Nancy J. Stein, C. Michael Roden, Dan M. Denny, Joshua C. Phillips, Elizabeth J. Wei, Wei-Qi |
author_sort | Zheng, Neil S. |
collection | PubMed |
description | Understanding the contribution of genetic variation to drug response can improve the delivery of precision medicine. However, genome-wide association studies (GWAS) for drug response are uncommon and are often hindered by small sample sizes. We present a high-throughput framework to efficiently identify eligible patients for genetic studies of adverse drug reactions (ADRs) using “drug allergy” labels from electronic health records (EHRs). As a proof-of-concept, we conducted GWAS for ADRs to 14 common drug/drug groups with 81,739 individuals from Vanderbilt University Medical Center’s BioVU DNA Biobank. We identified 7 genetic loci associated with ADRs at P < 5 × 10(−8), including known genetic associations such as CYP2D6 and OPRM1 for CYP2D6-metabolized opioid ADR. Additional expression quantitative trait loci and phenome-wide association analyses added evidence to the observed associations. Our high-throughput framework is both scalable and portable, enabling impactful pharmacogenomic research to improve precision medicine. |
format | Online Article Text |
id | pubmed-8195357 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-81953572021-06-21 High-throughput framework for genetic analyses of adverse drug reactions using electronic health records Zheng, Neil S. Stone, Cosby A. Jiang, Lan Shaffer, Christian M. Kerchberger, V. Eric Chung, Cecilia P. Feng, QiPing Cox, Nancy J. Stein, C. Michael Roden, Dan M. Denny, Joshua C. Phillips, Elizabeth J. Wei, Wei-Qi PLoS Genet Research Article Understanding the contribution of genetic variation to drug response can improve the delivery of precision medicine. However, genome-wide association studies (GWAS) for drug response are uncommon and are often hindered by small sample sizes. We present a high-throughput framework to efficiently identify eligible patients for genetic studies of adverse drug reactions (ADRs) using “drug allergy” labels from electronic health records (EHRs). As a proof-of-concept, we conducted GWAS for ADRs to 14 common drug/drug groups with 81,739 individuals from Vanderbilt University Medical Center’s BioVU DNA Biobank. We identified 7 genetic loci associated with ADRs at P < 5 × 10(−8), including known genetic associations such as CYP2D6 and OPRM1 for CYP2D6-metabolized opioid ADR. Additional expression quantitative trait loci and phenome-wide association analyses added evidence to the observed associations. Our high-throughput framework is both scalable and portable, enabling impactful pharmacogenomic research to improve precision medicine. Public Library of Science 2021-06-01 /pmc/articles/PMC8195357/ /pubmed/34061827 http://dx.doi.org/10.1371/journal.pgen.1009593 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 Zheng, Neil S. Stone, Cosby A. Jiang, Lan Shaffer, Christian M. Kerchberger, V. Eric Chung, Cecilia P. Feng, QiPing Cox, Nancy J. Stein, C. Michael Roden, Dan M. Denny, Joshua C. Phillips, Elizabeth J. Wei, Wei-Qi High-throughput framework for genetic analyses of adverse drug reactions using electronic health records |
title | High-throughput framework for genetic analyses of adverse drug reactions using electronic health records |
title_full | High-throughput framework for genetic analyses of adverse drug reactions using electronic health records |
title_fullStr | High-throughput framework for genetic analyses of adverse drug reactions using electronic health records |
title_full_unstemmed | High-throughput framework for genetic analyses of adverse drug reactions using electronic health records |
title_short | High-throughput framework for genetic analyses of adverse drug reactions using electronic health records |
title_sort | high-throughput framework for genetic analyses of adverse drug reactions using electronic health records |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8195357/ https://www.ncbi.nlm.nih.gov/pubmed/34061827 http://dx.doi.org/10.1371/journal.pgen.1009593 |
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