IBAS: Interaction-bridged association studies discovering novel genes underlying complex traits

Abstract

The contribution of genetic variants to a complex phenotype may be mediated by various forms of complicated interactions. Currently, the discovery of genetic variants underlying interaction is limited, partly due to that the real interaction patterns are diverse and unknown, whereas exhaustively examining all potential combinations confers the risk of overfitting and instability. We propose IBAS, Interaction-Bridged Association Study, a new model using statistical learning techniques to extract representations of interaction patterns in transcriptome data, which act as a mediator for the next genotype-phenotype association test. Using simulated perturbation experiments, it is demonstrated that IBAS is more robust to noise than similar mediation-based protocols replying on single-genes, i.e., transcriptome-wide association studies (TWAS). By applying IBAS to real genotype-phenotype and expression data, we reported additional genes underlying complex traits as well as their biological annotations. IBAS unlocks the power of integrating gene-gene interactions in association mapping without concerning overfitting and instability.