Importance of genetic ancestry in pharmacogenomics for precision medicine

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.

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Publication Details

Journal
Pharmacogenomics
Published
2025-12-12
DOI
https://doi.org/10.1080/14622416.2026.2620360
Primary Topic
Pharmacogenetics and Drug Metabolism
Type
article
Field-Weighted Citation Impact
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Importance of genetic ancestry in pharmacogenomics for precision medicine

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Importance of genetic ancestry in pharmacogenomics for precision medicine

Rasika Venkatesh, Zinhle Cindi, Molly A Hall, Maxwell Salvatore, Karl Keat, Marylyn D Ritchie
article en

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.

PharmacogenomicsVol. 26(17-18)
Medical University of South Carolina (US), California University of Pennsylvania (US)
Good health and well-being
Openalex Percentile: Top 26%
Pharmacogenetics and Drug Metabolism
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