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...

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Autores principales:	Neil S Zheng, Cosby A Stone, Lan Jiang, Christian M Shaffer, V Eric Kerchberger, Cecilia P Chung, QiPing Feng, Nancy J Cox, C Michael Stein, Dan M Roden, Joshua C Denny, Elizabeth J Phillips, Wei-Qi Wei
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Genetics QH426-470
Acceso en línea:	https://doaj.org/article/eb60b47e605444cfae9ea2d0eeb2459b
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spelling	oai:doaj.org-article:eb60b47e605444cfae9ea2d0eeb2459b2021-12-02T20:02:43ZHigh-throughput framework for genetic analyses of adverse drug reactions using electronic health records.1553-73901553-740410.1371/journal.pgen.1009593https://doaj.org/article/eb60b47e605444cfae9ea2d0eeb2459b2021-06-01T00:00:00Zhttps://doi.org/10.1371/journal.pgen.1009593https://doaj.org/toc/1553-7390https://doaj.org/toc/1553-7404Understanding 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.Neil S ZhengCosby A StoneLan JiangChristian M ShafferV Eric KerchbergerCecilia P ChungQiPing FengNancy J CoxC Michael SteinDan M RodenJoshua C DennyElizabeth J PhillipsWei-Qi WeiPublic Library of Science (PLoS)articleGeneticsQH426-470ENPLoS Genetics, Vol 17, Iss 6, p e1009593 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Genetics QH426-470
spellingShingle	Genetics QH426-470 Neil S Zheng Cosby A Stone Lan Jiang Christian M Shaffer V Eric Kerchberger Cecilia P Chung QiPing Feng Nancy J Cox C Michael Stein Dan M Roden Joshua C Denny Elizabeth J Phillips Wei-Qi Wei High-throughput framework for genetic analyses of adverse drug reactions using electronic health records.
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	article
author	Neil S Zheng Cosby A Stone Lan Jiang Christian M Shaffer V Eric Kerchberger Cecilia P Chung QiPing Feng Nancy J Cox C Michael Stein Dan M Roden Joshua C Denny Elizabeth J Phillips Wei-Qi Wei
author_facet	Neil S Zheng Cosby A Stone Lan Jiang Christian M Shaffer V Eric Kerchberger Cecilia P Chung QiPing Feng Nancy J Cox C Michael Stein Dan M Roden Joshua C Denny Elizabeth J Phillips Wei-Qi Wei
author_sort	Neil S Zheng
title	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_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_sort	high-throughput framework for genetic analyses of adverse drug reactions using electronic health records.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/eb60b47e605444cfae9ea2d0eeb2459b
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High-throughput framework for genetic analyses of adverse drug reactions using electronic health records.

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