Pharmacogenomic landscape in Thailand: Array-based profiling and EMR-linked medication exposure
TL;DR
An Asian-optimized SNP array supports scalable PGx phenotyping in Thai adults and highlights high-yield targets (CYP2C19–proton pump inhibitors; SLCO1B1–statins) for pre-emptive implementation.
Abstract
Pharmacogenomic (PGx) data in Thailand remain limited, and genetics-only surveys rarely quantify “realized actionability”—the overlap between actionable PGx phenotypes and real-world medication exposure. We profiled 4,662 Thai adults using SNP-array data and a pre-specified PGx panel (11 genes; 26 markers) with a hybrid required/optional calling policy for diplotype/phenotype assignment. CPIC level A/B gene–drug relationships were linked to hospital electronic medical record (EMR) prescription/dispensation data to quantify drug-specific realized actionability. Overall callability across gene-results was 98.62%, exceeding 99% for most genes and lower for CYP2C19 (95.99%) and NUDT15 (90.28%). Across nine phenotype-coded genes, 95.99% carried ≥1 CPIC-actionable result (median 2; IQR 2–3). Actionable prevalence among callable individuals was highest for CYP3A5 (58.54%) and CYP2C19 (56.67%), followed by ABCG2 (45.10%) and UGT1A1 (27.37%). EMR linkage identified 1,529 (32.58%) participants exposed to ≥1 study medication; omeprazole (n = 658) and statins were most common (atorvastatin n = 606; simvastatin n = 603). Among users, actionable phenotypes were frequent for CYP2C19–omeprazole (55.02%) and SLCO1B1–statins (21.95–23.05%). In conclusion, an Asian-optimized SNP array supports scalable PGx phenotyping in Thai adults. EMR linkage quantifies realized actionability and highlights high-yield targets (CYP2C19–proton pump inhibitors; SLCO1B1–statins) for pre-emptive implementation.