preSCRIPT: Large-scale prescription search and annotation engine for pharmacogenomic studies

BACKGROUND: Pharmacogenetics (PGx) has traditionally focused on a small number of high-impact variants affecting drug response due to the fact that PGx studies are labor-intensive and therefore low-throughput. Population biobanks linked to electronic health records (EHRs), including the UK Biobank (UKB) with prescription data for ~ 230,000 individuals offer opportunities to scale PGx research. This, however, comes with a challenge as EHRs do not provide direct treatment response outcomes. One way to overcome this is to draw indirect drug response phenotypes from prescription records. METHODS: Here, we propose preSCRIPT, a framework to filter and annotate raw prescriptions from the UKB to derive phenotypes for analyses which includes an algorithm to distinguish short prescription gaps from true dose changes. As a proof of concept, we applied preSCRIPT to warfarin, paracetamol, codeine, amitriptyline, simvastatin, aspirin, and amlodipine and derived therapy length and median daily doses. We tested associations for those seven drugs and two phenotypes across single-nucleotide polymorphisms (SNPs), cytochrome P450 (CYP) genes, and human leukocyte antigen (HLA) alleles. RESULTS: We recovered known associations such as CYP2D6 variants with amitriptyline therapy length and dose, CYP2C9/CYP4F2/CYP2C19 with warfarin dose, and CYP2D6 with codeine dose. For drugs without formal PGx guidelines, we identified an association between CYP2D6 enzyme activity and aspirin therapy length and several SNPs, including rs62471929 (CYP3A5), a variant for amlodipine dose, which reached nominal significance in an independent hold-out set. CONCLUSIONS: Overall, preSCRIPT provides a scalable framework for prescription-based discovery in pharmacogenomics. As a proof of concept it recovers established PGx associations and nominates novel, hypothesis-generating candidate loci.