Abstract
Genetic ancestry refers to an individual's biogeographical origins inferred from correlated allele frequencies shared with individuals from similar ancestral regions. Understanding the complexities of genetic ancestry has proven beneficial in the field of pharmacogenomics (PGx), where personalized medication regimens are optimizing therapeutic outcomes while minimizing the risk of side effects. With the rise in the availability of electronic health records (EHR), population-specific genetic data can be integrated with clinical data using machine learning approaches to improve personalized treatment plans. Furthermore, multiomics data such as the transcriptome, methylome, proteome, and metabolome, paired with advances in machine learning methods, provide a more comprehensive approach to understanding genetic variation. The expansion of PGx studies in diverse populations can broaden the impact of precision medicine, particularly among underrepresented groups.