Accéder directement au contenu Accéder directement à la navigation
Nouvelle interface
Article dans une revue

Novel Methods for Epistasis Detection in Genome-Wide Association Studies

Abstract : 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.
Liste complète des métadonnées
Contributeur : Chloé-Agathe Azencott Connectez-vous pour contacter le contributeur
Soumis le : lundi 18 janvier 2021 - 18:13:57
Dernière modification le : samedi 22 octobre 2022 - 05:10:57


Fichiers éditeurs autorisés sur une archive ouverte



Lotfi Slim, Clement Chatelain, Jean-Philippe Vert, Chloé-Agathe Azencott. Novel Methods for Epistasis Detection in Genome-Wide Association Studies. PLoS ONE, 2020, 15 (11), pp.e0242927. ⟨10.1371/journal.pone.0242927⟩. ⟨hal-01984919v2⟩



Consultations de la notice


Téléchargements de fichiers