Utilizing Large Electronic Medical Record Data Sets to Identify Novel Drug–Gene Interactions for Commonly Used Drugs

Real‐world prescribing of drugs differs from the experimental systems, physiological‐pharmacokinetic models, and clinical trials used in drug development and licensing, with drugs often used in patients with multiple comorbidities with resultant polypharmacy. The increasing availability of large biobanks linked to electronic healthcare records enables the potential to identify novel drug–gene interactions in large populations of patients. In this study we used three Scottish cohorts and UK Biobank to identify drug–gene interactions for the 50 most commonly used drugs and 162 variants in genes involved in drug pharmacokinetics. We defined two phenotypes based upon prescribing behavior—drug‐stop or dose‐decrease. Using this approach, we replicate 11 known drug–gene interactions including, for example, CYP2C9/CYP2C8 variants and sulfonylurea/thiazolidinedione prescribing and ABCB1/ABCG2 variants and statin prescribing. We identify eight novel associations after Bonferroni correction, three of which are replicated or validated in the UK Biobank or have other supporting results: The C‐allele at rs4918758 in CYP2C9 was associated with a 25% (15–44%) lower odds of dose reduction of quinine, P  = 1.6 × 10 −5 ; the A‐allele at rs9895420 in ABCC3 was associated with a 46% (24–62%) reduction in odds of dose reduction with doxazosin, P  = 1.2 × 10 −4 , and altered blood pressure response in the UK Biobank; the CYP2D6*2 variant was associated with a 30% (18–40%) reduction in odds of stopping ramipril treatment, P  = 1.01 × 10 −5 , with similar results seen for enalapril and lisinopril and with other CYP2D6 variants. This study highlights the scope of using large population bioresources linked to medical record data to explore drug–gene interactions at scale.