title: Pathway-based approaches for analysis of genomewide association studies.
abstract: Published genomewide association (GWA) studies typically analyze and report single-nucleotide polymorphisms (SNPs) and their neighboring genes with the strongest evidence of association (the "most-significant SNPs/genes" approach), while paying little attention to the rest. Borrowing ideas from microarray data analysis, we demonstrate that pathway-based approaches, which jointly consider multiple contributing factors in the same pathway, might complement the most-significant SNPs/genes approach and provide additional insights into interpretation of GWA data on complex diseases.
authors.affiliation: Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA. kai@mail.med.upenn.edu
authors.forename: Kai
authors.initials: K
authors.lastname: Wang
authors.forename: Mingyao
authors.initials: M
authors.lastname: Li
authors.forename: Maja
authors.initials: M
authors.lastname: Bucan
mesh_terms: D000465:Algorithms; D005796:Genes / Q000235:genetics*; D020022:Genetic Predisposition to Disease; D055106:Genome-Wide Association Study; D006801:Humans; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D012107:Research Design; D015398:Signal Transduction