Exhaustive search of the SNP-SNP interactome identifies replicated epistatic effects on brain volume

The SNP-SNP interactome has rarely been explored in the context of neuroimaging genetics (or quantitative genetics, in general) mainly due to the complexity of conducting ~10 pairwise statistical tests. However, recent advances in machine learning, specifically the iterative sure independence screening (SIS) method, have enabled the analysis of datasets where the number of predictors is much larger than the number of observations. The SIS method ranks the predictors in a set based on their cumulative marginal effect on some dependent variable. In this way, SIS can identify a subset of predictors that explain the maximum amount of variance in a given dependent variable. Using an implementation of the SIS algorithm (called EPISIS), we used exhaustive search of the genome-wide, SNP-SNP interactome to identify and prioritize SNPs for interaction analysis. We identified a significant SNP pair, rs1345203 and rs1213205, associated with temporal lobe volume. We further examined the full-brain, voxelwise effects of the SNP-SNP interaction in the ADNI dataset and separately in an independent dataset of young healthy twins (QTIM). We found that each additional loading in the epistatic effect was associated with ~5% greater brain regional brain volume (a protective effect) in both the ADNI and QTIM samples.