A more rapid approach to systematically assessing published associations of genetic polymorphisms and disease risk: type 2 diabetes as a test case

Alex H Cho1, Xiaolei Jiang2, Devin M Mann3, Kensaku Kawamoto4, Timothy J Robinson5, Nancy Wang6, Jeanette J McCarthy2, Mark Woodward7, Geoffrey S Ginsburg1,21Center for Personalized Medicine and Department of Medicine, Duke University, Durham, NC, 2Institute for Genome Sciences and Policy, Duke University, Durham, NC, 3Section of Preventive Medicine and Epidemiology, Department of Medicine, Boston University School of Medicine, Boston, MA, 4Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, 5Medical College of Virginia, Richmond, VA, 6School of Medicine, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA; 7George Institute for Global Health and University of Sydney, AustraliaBackground: Comparative effectiveness research and research in genomic medicine are not orthogonal pursuits. Both require a robust evidence base, and each stands to benefit from applying the methods of the other. There is an exponentially growing literature reporting associations between single nucleotide polymorphisms (SNPs) and increased risk for diseases such as type 2 diabetes. Literature-based meta-analysis is an important method of assessing the validity of published gene-disease associations, but a traditional emphasis on exhaustiveness makes it difficult to study multiple polymorphisms efficiently. Here we describe a novel two-step search method for broadly yet systematically reviewing the literature to identify the "most-studied" gene-disease associations, thereby selecting those with a high possibility of replication on which to conduct abbreviated, simultaneous meta-analyses. This method was then applied to identify and evaluate the validity of SNPs reported to be associated with increased type 2 diabetes risk, to demonstrate proof of principle.Methods: A two-step MEDLINE search (1950 to present) was conducted in September 2007 for published genetic association data related to SNPs associated with risk of type 2 diabetes. The to