| Title: | Characterizing the genetic architecture of drug response using gene-context interaction methods |
| Journal: | Cell Genomics |
| Published: | 4 Dec 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/39637863/ |
| DOI: | https://doi.org/10.1016/j.xgen.2024.100722 |
Publication 14054
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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.</p>
14 Keywords
- Blood Glucose
- Cholesterol, LDL
- Female
- Genetic Variation
- Glycated Hemoglobin
- Humans
- Hydroxymethylglutaryl-CoA Reductase Inhibitors
- Male
- Metformin
- Methotrexate
- Multifactorial Inheritance
- Pharmacogenetics
- Retrospective Studies
- Warfarin
12 Authors
- 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
1 Application
| Application ID | Title |
|---|---|
| 33127 | Methods for large-scale medical and population genetic data |
Enabling scientific discoveries that improve human health