Genetic ancestry in population pharmacogenomics : leveraging machine learning for computing polygenic risk scores and identifying geographic patterns associated with toxicity of commonly prescribed drugs
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Date
2024-07-11
Authors
Καραμπέρης, Καριοφύλλης
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Abstract
Genetic variability among individuals in drug treatment is still a global healthcare phenomenon and a significant contributing factor to the occurrence of adverse drug reactions, ranging from mild to severe forms. Exploring the relationship between the genome and geographical factors such as the ethnicity and genetic ancestry, numerous studies have uncovered compelling evidence of population-based heterogeneity and distinct patterns that can be further exploited. This suggests that prior to genome-based therapeutic interventions, understanding the genetic diversity within populations could offer valuable insights into the proximity of risk associated with commonly prescribed drugs. This concept, known as «Population Pharmacogenomics», holds immense promise in reshaping personalized medicine.
In the current research study, we have demonstrated a comprehensive pharmacogenomic analysis of 3.714 unrelated individuals from diverse populations and geographical regions to elucidate the worldwide distribution of 1.136 single nucleotide variants within 512 pharmacogenes which are known to be associated with drug-related adverse events. Subsequently, we have further extended our analysis by using multidisciplinary approaches such as machine learning, text-mining and computational predictive tools to assess the risk proximity at a worldwide scale, in commonly prescribed drugs.
Our findings support concealed patterns between Eastern and Western populations regarding the risk probability of experiencing adverse drug events linked to commonly prescribed medications. Individuals of East Asian ancestry, and to a lesser extent Oceanians, exhibit a lower risk of experiencing drug-related toxic events while genetic variability has also been observed in drug-gene interactions across populations. Emphasizing population pharmacogenomics predicated on genetic ancestry should be prioritized in personalised medicine.
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Keywords
Personalised medicine, Population pharmacogenomics, Genetic ancestry, Drug toxicity