Prioritizing GWAS Results: A Review of Statistical Methods and Recommendations for Their Application

• Published in: American journal of human genetics Vol. 86; no. 1; pp. 6 - 22
• Main Authors: Cantor, Rita M., Lange, Kenneth, Sinsheimer, Janet S.
• Format: Journal Article
• Language: English
• Published: Cambridge, MA Elsevier Inc 2010; Cell Press; Elsevier
• Subjects: Algorithms; Animals; Bayes Theorem; Biological and medical sciences; Databases, Genetic; Epistasis, Genetic; Fundamental and applied biological sciences. Psychology; General aspects. Genetic counseling; Genes; Genetic disorders; Genetics; Genetics of eukaryotes. Biological and molecular evolution; Genome-Wide Association Study - instrumentation; Genome-Wide Association Study - methods; Genomics; Genotype; Humans; Medical genetics; Medical sciences; Meta-analysis; Meta-Analysis as Topic; Models, Statistical; Molecular and cellular biology; Mutation; Polymorphism, Single Nucleotide; Regression Analysis; Review; Studies; Human; Statistical method; Genetics; Review; Application; Recommendation; Bibliographic review
• ISSN: 0002-9297; 1537-6605
• DOI: 10.1016/j.ajhg.2009.11.017

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.