title: Network analysis of GWAS data.
abstract: Genome-wide association studies (GWAS) identify genetic variants that distinguish a control population from a population with a specific trait. Two challenges in GWAS are: (1) identification of the causal variant within a longer haplotype that is associated with the trait; (2) identification of causal variants for polygenic traits that are caused by variants in multiple genes within a pathway. We review recent methods that use information in protein-protein and protein-DNA interaction networks to address these two challenges.
authors.affiliation: Department of Computer Science, Brown University, Providence, RI 02912, United States; Center for Computational Molecular Biology, Brown University, Providence, RI 02912, United States.
authors.forename: Mark D M
authors.initials: MD
authors.lastname: Leiserson
authors.forename: Jonathan V
authors.initials: JV
authors.lastname: Eldridge
authors.forename: Sohini
authors.initials: S
authors.lastname: Ramachandran
authors.forename: Benjamin J
authors.initials: BJ
authors.lastname: Raphael
mesh_terms: D019295:Computational Biology / Q000379:methods; D020869:Gene Expression Profiling / Q000379:methods; D053263:Gene Regulatory Networks; D055106:Genome-Wide Association Study / Q000379:methods*; D006801:Humans; D025941:Protein Interaction Mapping / Q000379:methods*; D060066:Protein Interaction Maps / Q000235:genetics*