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UID:5507@i2m.univ-amu.fr
DTSTART;TZID=Europe/Paris:20130606T140000
DTEND;TZID=Europe/Paris:20130606T150000
DTSTAMP:20241029T171315Z
URL:https://www.i2m.univ-amu.fr/evenements/c-a-azencott-max-planck-institu
 te-large-scale-network-guided-feature-selection-in-genome-wide-association
 -mapping/
SUMMARY: (...): C.-A. Azencott (Max Planck Institute) : Large scale network
 -guided feature selection in genome-wide association mapping
DESCRIPTION:: Large scale network-guided feature selection in genome-wide a
 ssociation mapping. By Chloé-Agathe Azencott\, Max Planck Institute.\n\nG
 enome-wide association studies (GWAS)\\\, in which hundreds of thousands\n
 or millions of single nucleotide polymorphisms (or SNPs) are genotyped\nfo
 r up to tens of thousands of individuals\\\, are a powerful tool to\ndetec
 t genetic loci likely to be associated with a complex trait. In\nrecent ye
 ars\\\, they have made it possible to identify hundreds of new\nsusceptibi
 lity loci for common diseases. However\\\, they still often fail\nto expla
 in much of the phenotypic variability that is known to be due to\ngenetic 
 causes. It is therefore imperative to establish methods that can\nbetter e
 xtract biological signal from large GWAS data sets.\n\nModeling the joint 
 effects of multiple genomic loci is a major avenue to\nfollow to achieve t
 his goal. While several methods for multi-locus\nmapping have been propose
 d\\\, it is often unclear how to relate the\ndetected loci to current biol
 ogical knowledge. Integrating information\nabout biological pathways and n
 etworks to GWAS can help detecting\nmeaningful and interpretable associati
 ons. However\\\, the few multi-locus\nmethods that attempt it are either r
 estricted to investigating a limited number of predetermined sets of loci\
 \\, or do not scale to genome-wide settings.\n\nWe present a new efficient
  method to discover sets of features that are maximally\nassociated with a
 n output variable\\\, while being connected in an underlying\nnetwork. Thi
 s method\\\, based on a min-cut reformulation\\\, outperforms all its\ncom
 parison partners in terms of runtime and easily scales to millions of\nvar
 iables. In simulation studies\\\, it exhibits higher precision than other\
 nmethods in detecting true causal features. On real GWAS data from Arabido
 psis\nthaliana\\\, it detects loci that enable accurate phenotype predicti
 on\nand are supported by the literature.
CATEGORIES:Séminaire,Signal et Apprentissage
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DTSTART:20130331T030000
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