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

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

doi:10.1016/j.xgen.2024.100722

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

Cell Genom. 2024 Dec 11;4(12):100722. doi: 10.1016/j.xgen.2024.100722. Epub 2024 Dec 4.

Authors

Affiliations

¹ Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA 90095, USA. Electronic address: michalsadowski@ucla.edu.
² Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA 90095, USA.
³ Department of Neurology, University of California Los Angeles, Los Angeles, CA 90095, USA.
⁴ Institute for Human Genetics, University of California San Francisco, San Francisco, CA 94143, USA; Department of Clinical Pharmacy, University of California San Francisco, San Francisco, CA 94143, USA.
⁵ Department of Neurology, University of California Los Angeles, Los Angeles, CA 90095, USA; Department of Computer Science, University of California Los Angeles, Los Angeles, CA 90095, USA; Department of Computational Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA.
⁶ Department of Computer Science, University of California Los Angeles, Los Angeles, CA 90095, USA.
⁷ Helmholtz Pioneer Campus, Helmholtz Munich, 85764 Neuherberg, Germany; Computational Health Centre, Helmholtz Munich, 85764 Neuherberg, Germany; School of Medicine and Health, Technical University of Munich, 80333 Munich, Germany.
⁸ Department of Ecology and Evolution, Princeton University, Princeton, NJ 08544, USA; Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA.
⁹ Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA 90095, USA; Department of Computer Science, University of California Los Angeles, Los Angeles, CA 90095, USA; Department of Computational Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA; Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA.
¹⁰ Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL 60637, USA.
¹¹ Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA 90095, USA; Department of Neurology, University of California Los Angeles, Los Angeles, CA 90095, USA; Department of Computational Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA; Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA. Electronic address: nzaitlen@ucla.edu.

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.

Keywords: gene-environment interactions; genetic heterogeneity; genetic testing; heritability; heteroskedasticity; personalized medicine; pharmacogenomics.

MeSH terms

Blood Glucose / drug effects
Blood Glucose / metabolism
Cholesterol, LDL / blood
Cholesterol, LDL / genetics
Female
Genetic Variation / genetics
Glycated Hemoglobin / analysis
Glycated Hemoglobin / metabolism
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
Pharmacogenetics / methods
Retrospective Studies
Warfarin* / pharmacology
Warfarin* / therapeutic use

Substances

Warfarin
Hydroxymethylglutaryl-CoA Reductase Inhibitors
Metformin
Methotrexate
Glycated Hemoglobin
Cholesterol, LDL
Blood Glucose

Abstract

MeSH terms

Substances

Grants and funding