Prioritizing GWAS results: A review of statistical methods and recommendations for their application.
Genome-wide association studies (GWAS) have rapidly become a standard method for disease gene discovery. A substantial number of recent GWAS indicate that for most disorders, only a few common variants are implicated and the associated SNPs explain only a small fraction of the genetic risk. This review is written from the viewpoint that findings from the GWAS provide preliminary genetic information that is available for additional analysis by statistical procedures that accumulate evidence, and that these secondary analyses are very likely to provide valuable information that will help prioritize the strongest constellations of results. We review and discuss three analytic methods to combine preliminary GWAS statistics to identify genes, alleles, and pathways for deeper investigations. Meta-analysis seeks to pool information from multiple GWAS to increase the chances of finding true positives among the false positives and provides a way to combine associations across GWAS, even when the original data are unavailable. Testing for epistasis within a single GWAS study can identify the stronger results that are revealed when genes interact. Pathway analysis of GWAS results is used to prioritize genes and pathways within a biological context. Following a GWAS, association results can be assigned to pathways and tested in aggregate with computational tools and pathway databases. Reviews of published methods with recommendations for their application are provided within the framework for each approach.
Cantor
Rita M
RM
Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA. rcantor@mednet.ucla.edu
Lange
Kenneth
K
Sinsheimer
Janet S
JS
D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D030541:Databases, Genetic; D004843:Epistasis, Genetic; D005823:Genetics; D055106:Genome-Wide Association Study / Q000295:instrumentation / Q000379:methods*; D005838:Genotype; D006801:Humans; D015201:Meta-Analysis as Topic; D015233:Models, Statistical; D009154:Mutation; D020641:Polymorphism, Single Nucleotide; D012044:Regression Analysis