Towards solving the missing heritability in pharmacogenomics

Abstract

Pharmacogenomics (PGx) is widely recognized as an important aspect in personalized

Medicine. By analyzing and interpreting one’s genetic profile dose and drug adjustments

can be made. In this way, one can strive to improve the safety and efficacy of drug

treatments. Nonetheless, not all genetic variability in drug response can be explained with current PGx. In this thesis we explore the role of additional genetic factors which can explain this missing heritability. Firstly, rare and novel variants which are unaccounted for in routine PGx panels might play a role. Secondly, the complexity of pharmacogenes can result in an inability to unravel the genetic make-up of these genes. Thirdly, haplotype phasing is generally not taken into account in PGx. Fourthly, all genetic variants are currently summarized into one of four metabolic categories: poor metabolizers (PM), intermediate metabolizers (IM), normal metabolizers (NM) (previously EM) and ultra-rapid metabolizers (UM). However, enzyme activity is not a matter of ‘on’ or ‘off ’, but is more of a continuous scale. Finally, the effect of a genetic variant on drug metabolism shows substrate specific effects. This substrate specificity can result in erroneous extrapolation of variant effects to the entire range of substrates. The development of novel technologies to determine one’s genetic make-up is evolving rapidly, thereby providing opportunities for the field of PGx to address these issues. In this thesis we show that by using long-read sequencing or trio-based sequencing more information can be obtained which can lead to a better understanding of the (rare) variants and can help with haplotype phasing. Moreover, we have shown that by combining long-read sequencing with artificial intelligence a substantial increase in explained variability can be achieved.