Pharmacogenomics (PGx) is widely recognized as an important aspect in personalizedMedicine. By analyzing and interpreting one’s genetic profile dose and drug adjustmentscan be made. In this way,... Show morePharmacogenomics (PGx) is widely recognized as an important aspect in personalizedMedicine. By analyzing and interpreting one’s genetic profile dose and drug adjustmentscan be made. In this way, one can strive to improve the safety and efficacy of drugtreatments. Nonetheless, not all genetic variability in drug response can be explained withcurrent 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 tounravel the genetic make-up of these genes. Thirdly, haplotype phasing is generally nottaken into account in PGx. Fourthly, all genetic variants are currently summarized intoone 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 theentire range of substrates. The development of novel technologies to determine one’sgenetic make-up is evolving rapidly, thereby providing opportunities for the field of PGxto 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. Show less
