Abstract
Genetic ancestry plays a major role in pharmacogenomics, and a deeper understanding of the genetic diversity among individuals holds immerse promise for reshaping personalized medicine. In this pivotal study, we have conducted a large-scale genomic analysis of 1,136 pharmacogenomic variants employing machine learning algorithms on 3,714 individuals from publicly available datasets to assess the risk proximity of experiencing drug-related adverse events. Our findings indicate that Admixed Americans and Europeans have demonstrated a higher risk of experiencing drug toxicity, whereas individuals with East Asian ancestry and, to a lesser extent, Oceanians displayed a lower risk proximity. Polygenic risk scores for drug-gene interactions did not necessarily follow similar assumptions, reflecting distinct genetic patterns and population-specific differences that vary depending on the drug class. Overall, our results provide evidence that genetic ancestry is a pivotal factor in population pharmacogenomics and should be further exploited to strengthen even more personalized drug therapy.