Novel Methods for Epistasis Detection in Genome-Wide Association Studies

Lotfi Slim (1, 2) , Clement Chatelain (2) , Chloé-Agathe Azencott (1) , Jean-Philippe Vert (1)

1 CBIO - Centre de Bioinformatique
2 Sanofi-Aventis R&D

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.

Domaines: Machine Learning [stat.ML]; Bio-informatique [q-bio.QM]; Statistiques [math.ST]

Lotfi Slim, Clement Chatelain, Chloé-Agathe Azencott, Jean-Philippe Vert. Novel Methods for Epistasis Detection in Genome-Wide Association Studies. 2019. ⟨hal-01984919v1⟩