Integrating whole-exome and genotyping array data for pharmacogenetic typing: a comparative evaluation with whole-genome sequencing in UK Biobank

Introduction Whole‐genome sequencing (WGS) provides comprehensive coverage of coding, non‐coding, and rare variants for pharmacogenetic star‐allele typing, but cost and computational demands limit its population-scale use. Whole-exome sequencing (WES) and imputed genotyping arrays are more widely available, yet their WGS-referenced performance remains incompletely defined. Methods We benchmarked star-allele calls from WES, imputed array data, and an integrated call set combining WES and array data against an Aldy-derived WGS analytical reference across 33 pharmacogenes in 1,450 multi-ancestry UK Biobank participants. We also developed PGxIntegrator, an open‐source workflow that harmonizes WES and array data and enables scalable Aldy‐based star‐allele calling. Results Using WES or imputed array data alone, 25 of 33 genes reached high diplotype concordance with the WGS analytical reference (≥95%); the integrated call set also achieved ≥95% concordance for 25 genes and improved or maintained concordance relative to the better‐performing single platform for 29 genes overall. The largest incremental gains among high‐concordance loci were observed for CYP1A1, CYP3A4, and DPYD, where integration improved concordance by 1.3–3.3 percentage points over the better‐performing single platform, consistent with complementary recovery of non-coding and rare haplotype‐defining variants. In contrast, UGT1A1 and CYP2D6 remained challenging despite integration. Discussion For CYP2D6, the WGS analytical reference was generated by a single computational caller and was not orthogonally validated; concordance therefore reflects agreement with this reference rather than clinical typing accuracy. These findings provide practical gene‐level guidance for population‐scale pharmacogenetic analysis when WGS is unavailable.