Characterizing the genetic architecture of drug response using gene-context interaction methods

Identifying factors that affect treatment response is a central objective of clinical research, yet the role of common genetic variation remains largely unknown. Here, we develop a framework to study the genetic architecture of response to commonly prescribed drugs in large biobanks. We quantify treatment response heritability for statins, metformin, warfarin, and methotrexate in the UK Biobank. We find that genetic variation modifies the primary effect of statins on LDL cholesterol (9% heritable) as well as their side effects on hemoglobin A1c and blood glucose (10% and 11% heritable, respectively). We identify dozens of genes that modify drug response, which we replicate in a retrospective pharmacogenomic study. Finally, we find that polygenic score (PGS) accuracy varies up to 2-fold depending on treatment status, showing that standard PGSs are likely to underperform in clinical contexts.

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Publication Details

Journal
Cell Genomics
Published
2024-12-01
DOI
https://doi.org/10.1016/j.xgen.2024.100722
Citations
10
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
3.61

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article

Characterizing the genetic architecture of drug response using gene-context interaction methods

Michał Sadowski, Akinyemi Oni‐Orisan, Michael Thompson, Tanushree Haldar et al.
10 citations
Cell Genomics
Computational Drug Discovery Methods
3.61
article

Characterizing the genetic architecture of drug response using gene-context interaction methods

Michał Sadowski, Akinyemi Oni‐Orisan, Michael Thompson, Tanushree Haldar, Ali Pazokitoroudi, Na Cai, Joel Mefford, Sriram S Sankararaman, Noah Zaitlen, Richard Border, Julien F. Ayroles, Andy Dahl
article en
10 citations

Abstract

Identifying factors that affect treatment response is a central objective of clinical research, yet the role of common genetic variation remains largely unknown. Here, we develop a framework to study the genetic architecture of response to commonly prescribed drugs in large biobanks. We quantify treatment response heritability for statins, metformin, warfarin, and methotrexate in the UK Biobank. We find that genetic variation modifies the primary effect of statins on LDL cholesterol (9% heritable) as well as their side effects on hemoglobin A1c and blood glucose (10% and 11% heritable, respectively). We identify dozens of genes that modify drug response, which we replicate in a retrospective pharmacogenomic study. Finally, we find that polygenic score (PGS) accuracy varies up to 2-fold depending on treatment status, showing that standard PGSs are likely to underperform in clinical contexts.

Cell GenomicsVol. 4(12)
University of California, Los Angeles (US), University of California, San Francisco (US), Princeton University (US), Helmholtz Zentrum München (DE), University of Chicago (US)
Chan Zuckerberg Initiative, National Institutes of Health, National Heart, Lung, and Blood Institute, National Institute of Mental Health, National Institute of General Medical Sciences, National Institute of Environmental Health Sciences
Openalex Percentile: Top 6%
Computational Drug Discovery Methods
3.61
DOIOpen Access PDFOpenAlex
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