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

• Published in: Cell genomics 2024-12, Vol.4 (12), p.100722, Article 100722
• Main Authors: 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
• Format: Article
• Language: English
• Subjects: Blood Glucose - drug effects; Blood Glucose - metabolism; Cholesterol, LDL - blood; Cholesterol, LDL - genetics; Female; gene-environment interactions; genetic heterogeneity; genetic testing; Genetic Variation - genetics; Glycated Hemoglobin - analysis; Glycated Hemoglobin - metabolism; heritability; heteroskedasticity; Humans; Hydroxymethylglutaryl-CoA Reductase Inhibitors - pharmacology; Hydroxymethylglutaryl-CoA Reductase Inhibitors - therapeutic use; Male; Metformin - pharmacology; Metformin - therapeutic use; Methotrexate - pharmacology; Methotrexate - therapeutic use; Multifactorial Inheritance - genetics; personalized medicine; Pharmacogenetics - methods; pharmacogenomics; Retrospective Studies; Warfarin - pharmacology; Warfarin - therapeutic use
• ISSN: 2666-979X; 2666-979X
• DOI: 10.1016/j.xgen.2024.100722

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. [Display omitted] •Large biobank data provides insights into the genetic architecture of drug response•Genome-wide genetic variation broadly modifies drug response•Hundreds of genes associated with drug response are identified•Drug use information should be accounted for in genetic risk prediction Sadowski et al. propose a framework to study the genetics of response to commonly prescribed drugs in large biobanks. They quantify the heritability of response to statins, metformin, warfarin, and methotrexate, and identify associated genes. Their analysis also shows the importance of accounting for drug use in genetic risk prediction.