Computational Methods for the Pharmacogenetic Interpretation of Next Generation Sequencing Data

Frontiers in Pharmacology

Kohei Fujikura¹  Volker M. Lauschke²  Souren Mkrtchian²  Yitian Zhou² 

[1] Department of Diagnostic Pathology, Kobe University Graduate School of Medicine, Kobe, Japan;Section of Pharmacogenetics, Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden;

关键词: precision medicine;    personalized medicine;    variant effect prediction;    ADME;    NGS;    rare variant analysis;   

DOI  :  10.3389/fphar.2018.01437

来源: DOAJ

【 摘 要 】

Up to half of all patients do not respond to pharmacological treatment as intended. A substantial fraction of these inter-individual differences is due to heritable factors and a growing number of associations between genetic variations and drug response phenotypes have been identified. Importantly, the rapid progress in Next Generation Sequencing technologies in recent years unveiled the true complexity of the genetic landscape in pharmacogenes with tens of thousands of rare genetic variants. As each individual was found to harbor numerous such rare variants they are anticipated to be important contributors to the genetically encoded inter-individual variability in drug effects. The fundamental challenge however is their functional interpretation due to the sheer scale of the problem that renders systematic experimental characterization of these variants currently unfeasible. Here, we review concepts and important progress in the development of computational prediction methods that allow to evaluate the effect of amino acid sequence alterations in drug metabolizing enzymes and transporters. In addition, we discuss recent advances in the interpretation of functional effects of non-coding variants, such as variations in splice sites, regulatory regions and miRNA binding sites. We anticipate that these methodologies will provide a useful toolkit to facilitate the integration of the vast extent of rare genetic variability into drug response predictions in a precision medicine framework.

【 授权许可】

Unknown