Approved research

Systematic identification of genetic associations with drug-induced adverse reactions

Lay summary

The person-to-person variability in drug response is a major problem in clinical practice, but systematic research on various phenotypes and populations has not been performed. Approaches, such as GWAS, to unravel the genetic underpinnings of drug metabolism and efficacy have been established, however, they had limited success in understanding the complex nature of drug-induced clinical variability, because single genetic variations within a limited genomic region are not enough to understand the true diversity of gene variation contributing to drug response. To assist in the selection of appropriate drug and dose for an individual patient based on genetic variability, we aim to focus explorations on the genetic variants which have a role in the person-to-person variability of drug responses in a systematic manner. Using whole-exome- or genome sequencing data, we will provide optimized algorithms that suggest top-ranked variants or genes for each defined drug-induced adverse reaction with possible genetic associations to unexpected drug toxicity. Specifically, we will (1) clearly define drug-induced phenotypes, (2) explore the possible genetic associations based not only on single variants, but on the integrative effect of variants or genes on interindividual variability of drug responses, and (3) suggest an optimized prediction algorithm for a specific drug based on the prioritized variant- and gene sets. Developing this systematic prediction framework that is optimized for pharmacogenetic assessments can help each individual achieve an optimal therapeutic response, avoid therapeutic failures, and minimize drug-induced toxicity. The estimated duration of our project is more than 3 years.