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

Sadowski, Michal; Thompson, Mike; Mefford, Joel; Haldar, Tanushree; Oni-Orisan, Akinyemi; Border, Richard; Pazokitoroudi, Ali; Cai, Na; Ayroles, Julien F.; Sankararaman, Sriram; Dahl, Andy W.; Zaitlen, Noah

doi:10.6082/c27h5-eja59

Published December 4, 2024 | Version v1

Journal article Open

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

1. University of California, Los Angeles
2. University of California, San Francisco
3. Helmholtz Munich
4. Princeton University
5. University of Chicago

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.

Data availability

This paper analyzes existing, publicly available data. These accession numbers for the datasets are listed in the key resources table.

All original code has been deposited at https://github.com/michalsad/txewas_scripts and is publicly available as of the date of publication. DOIs are listed in the key resources table.

Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

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