Sparse modeling of interactions enables fast detection of genome-wide epistasis in biobank-scale studies.

The lack of computational methods capable of detecting epistasis in biobanks has led to uncertainty about the role of non-additive genetic effects on complex trait variation. The marginal epistasis framework is a powerful approach because it estimates the likelihood of a SNP being involved in any interaction, thereby reducing the multiple testing burden. Current implementations of this approach have failed to scale genome wide in large human studies. To address this, we present the sparse marginal epistasis (SME) test, which concentrates the scans for epistasis to regions of the genome that have known functional enrichment for a quantitative trait of interest. By leveraging the sparse nature of this modeling setup, we develop a statistical algorithm that allows SME to run 10-90 times faster than state-of-the-art epistatic mapping methods. In a study of complex traits measured in 349,411 individuals from the UK Biobank, we show that reducing searches of epistasis to variants in functionally enriched regions facilitates the identification of genetic interactions associated with regulatory genomic elements.