Computational Tools to Assess the Functional Consequences of Rare and Noncoding Pharmacogenetic Variability

Interindividual differences in drug response are a common concern in both drug development and across layers of care. While genetics clearly influences drug response and toxicity of many drugs, a substantial fraction of the heritable pharmacological and toxicological variability remains unexplained by known genetic polymorphisms. In recent years, population-scale sequencing projects have unveiled tens of thousands of coding and noncoding pharmacogenetic variants with unclear functional effects that might explain at least part of this missing heritability. However, translating these personalized variant signatures into drug response predictions and actionable advice remains challenging and constitutes one of the most important frontiers of contemporary pharmacogenomics. Conventional prediction methods are primarily based on evolutionary conservation, which drastically reduces their predictive accuracy when applied to poorly conserved pharmacogenes. Here, we review the current state-of-the-art of computational variant effect predictors across variant classes and critically discuss their utility for pharmacogenomics. Besides missense variants, we discuss recent progress in the evaluation of synonymous, splice, and noncoding variations. Furthermore, we discuss emerging possibilities to assess haplotypes and structural variations. We advocate for the development of algorithms trained on pharmacogenomic instead of pathogenic data sets to improve the predictive accuracy in order to facilitate the utilization of next-generation sequencing data for personalized clinical decision support and precision pharmacogenomics.

Automated summary

Interindividual differences in drug response are common, influenced by genetics, but much variability remains unexplained by known genetic polymorphisms. Population-scale sequencing projects have identified many pharmacogenetic variants with unclear functional effects, presenting a challenge in translating them into actionable advice for personalized medicine. Developing algorithms trained on pharmacogenomic data sets is essential to improve predictive accuracy and utilize next-generation sequencing data for precision pharmacogenomics.