4.6 Article

Rare genetic variants in cellular transporters, metabolic enzymes, and nuclear receptors can be important determinants of interindividual differences in drug response

Journal

GENETICS IN MEDICINE
Volume 19, Issue 1, Pages 20-29

Publisher

ELSEVIER SCIENCE INC
DOI: 10.1038/gim.2016.33

Keywords

drug development; genetic variation; personalized; medicine; pharmacogenes; rare variants

Funding

  1. Marie Curie IEF fellowship for career development in the context of the European FP7 framework program
  2. Swedish Research Council [5949]

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Purpose: In this study we characterized the genetic variability of 146 clinically relevant genes influencing drug pharmacokinetics in African and European subpopulations, which are key determinants for interindividual variations in drug efficacy and adverse drug reactions. Methods: 13y integrating data from the 1000 Genomes Project (n = 1,092 individuals) and the Exome Sequencing Project (ESP; n = 6,503 individuals), single-nucleotide variants (SNVs) were identified and analyzed regarding frequency, functional consequences, and ethnic diversity. Results: In total, we found 12,152 SNVs in exons, 312 of which were novel. The majority of variants were rare (minor allele frequency (MAF) <1%; 92.9%) and nonsynonymous (56.2%). We calculated that individuals of European and African descent harbor, on average, 100.8 and 121.4 variants across the 146 pharmacogenes studied, respectively. Additionally, by analyzing variation patterns across these populations, we pinpointed potential priority genes for population-adjusted genetic profiling strategies. Furthermore, we estimated, based on our variant frequency analyses, that approximately 30-40% of functional variability in pharmacogenes can be attributed to rare variants. Conclusions: Our results indicate that these clinically important genes are genetically highly variable and differ considerably between populations. Furthermore, the large extent of rare variants emphasizes the need for sequencing-based approaches and effective functionality predictions to allow for true personalized medicine.

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