Investigating the genetic interactions of schizophrenia using gene-based statistical epistasis

The interplay between genotype and phenotype is governed by a multitude of genetic interactions (GIs), and the mapping of GI networks holds significant importance for two main reasons: (1) GIs offer a valuable means to uncover compensatory biological mechanisms by modelling biological robustness, thereby identifying functional relationships between genes. This aspect is particularly relevant for biological exploration and translational research, as biological systems have evolved to compensate for genetic (i.e. variations, mutations) and environmental (i.e. drug efficacy) perturbations by leveraging compensatory relationships between genes, pathways, and biological processes; (2) GI facilitates the identification of the direction (positive/alleviating or negative/aggravating interactions) and magnitude of epistatic interactions that influence the resulting phenotype. While comprehensive GI databases exist for organisms like yeast, generating GIs for human diseases through experimental biology methods such as systematic deletion analysis is infeasible. Furthermore, generating disease-specific GIs in humans has not been previously attempted. We will use a large schizophrenia case-control genetic dataset to generate and validate GIs.