PT - JOURNAL ARTICLE AU - Lotfi Slim AU - Clément Chatelain AU - Chloé-Agathe Azencott AU - Jean-Philippe Vert TI - Novel Methods for Epistasis Detection in Genome-Wide Association Studies AID - 10.1101/442749 DP - 2018 Jan 01 TA - bioRxiv PG - 442749 4099 - http://biorxiv.org/content/early/2018/10/14/442749.short 4100 - http://biorxiv.org/content/early/2018/10/14/442749.full AB - As the size of genome-wide association studies (GWAS) increases, detecting interactions among single nucleotide polymorphisms (SNP) or genes associated to particular phenotypes is garnering more and more interest as a means to decipher the full genetic basis of complex diseases. Systematically testing interactions is however challenging both from a computational and from a statistical point of view, given the large number of possible interactions to consider. In this paper we propose a framework to identify pairwise interactions with a particular target variant, using a penalized regression approach. Narrowing the scope of interaction identification around a predetermined target provides increased statistical power and better interpretability, as well as computational scalability. We compare our new methods to state-of-the-art techniques for epistasis detection on simulated and real data, and demonstrate the benefits of our framework to identify pairwise interactions in several experimental settings.