Polygenic modelling and machine learning approaches in pharmacogenomics: importance in downstream analysis of genome-wide association study data.

Genome-wide association studies (GWAS) have identified genetic variations associated with adverse drug effects in pharmacogenomics (PGx) research. Yet, interpreting the biological implications of these associations remains a challenge. This review highlights two promising post-GWAS methods for PGx. First, we discuss the polygenic architecture of the PGx traits, especially for drug-induced liver injury (DILI). Their experimental modelling using multiple donors' human primary hepatocytes and human liver organoids demonstrated the polygenic architecture of DILI susceptibility and found biological vulnerability in genetically high-risk tissue donors. Second, we discuss the challenges of interpreting the roles of variants in non-coding regions. Beyond methods involving expression quantitative trait locus analysis and massively parallel reporter assays, we suggest the use of in silico mutagenesis through machine learning methods to understand the roles of variants in transcriptional regulation. This review underscores the importance of these post-GWAS methods in providing critical insights into PGx, potentially facilitating drug development and personalized treatment. (149 words).