– C.-A. Azencott (Max Planck Institute) : Large scale network-guided feature selection in genome-wide association mapping 6 juin 201322 mai 2013 adminFrumam Carte non disponible Date/heure Date(s) - 06/06/201314 h 00 min - 15 h 00 min Catégories Pas de Catégories Large scale network-guided feature selection in genome-wide association mapping. By Chloé-Agathe Azencott, Max Planck Institute. Genome-wide association studies (GWAS), in which hundreds of thousands or millions of single nucleotide polymorphisms (or SNPs) are genotyped for up to tens of thousands of individuals, are a powerful tool to detect genetic loci likely to be associated with a complex trait. In recent years, they have made it possible to identify hundreds of new susceptibility loci for common diseases. However, they still often fail to explain much of the phenotypic variability that is known to be due to genetic causes. It is therefore imperative to establish methods that can better extract biological signal from large GWAS data sets. Modeling the joint effects of multiple genomic loci is a major avenue to follow to achieve this goal. While several methods for multi-locus mapping have been proposed, it is often unclear how to relate the detected loci to current biological knowledge. Integrating information about biological pathways and networks to GWAS can help detecting meaningful and interpretable associations. However, the few multi-locus methods that attempt it are either restricted to investigating a limited number of predetermined sets of loci, or do not scale to genome-wide settings. We present a new efficient method to discover sets of features that are maximally associated with an output variable, while being connected in an underlying network. This method, based on a min-cut reformulation, outperforms all its comparison partners in terms of runtime and easily scales to millions of variables. In simulation studies, it exhibits higher precision than other methods in detecting true causal features. On real GWAS data from Arabidopsis thaliana, it detects loci that enable accurate phenotype prediction and are supported by the literature.