interest. The expression component of this approach constructs a model that predicts mRNA expression for each gene using genetic variants. As a result, TWAS is capable of prioritising genes that may be involved in the disorder and assign them a direction of genetically predicted expression that is odds increasing (Wainberg et al., 2019). The direction of effect associated with the disorder derived from TWAS, that is, upregulation or downregulation, is particularly useful in the context of identifying compounds that could reverse the risk-increasing direction of expression. This method can also be extended to other quantitative functional data such as protein and alternatively spliced mRNA isoform abundance. Despite these useful features, TWAS alone is not a test of causality as genes identified may arise due to the confounding factors like co-regulation between genes or linkage disequilibrium between the variants associated with expression (Reay & Cairns, 2021). However, TWAS can be subjected to different statistical approaches to attempt to increase the fidelity to identify true risk genes for a trait (Hall et al., 2019; Mancuso et al., 2019).
