In genome-wide association (GWA) studies, hundreds of thousands of single-nucleotide polymorphisms (SNP) are tested for association with a disease trait. Typically, GWA studies give equal consideration to all SNPs tested, regardless of existing knowledge of an SNP's functionality or biological plausibility of association. Because many tests are conducted, very low statistical significance thresholds (P < 5 × 10(-8)) are required to identify true associations with confidence. By restricting GWA analyses to SNPs with enhanced prior probabilities of association, we can reduce the number of tests conducted and relax the required significance threshold, increasing power to detect association. In this analysis of existing GWA data on pancreatic cancer cases (n = 1,736) and controls (n = 1,802) of European descent (the PanScan study), we conduct a GWA scan restricted to SNPs that have been reported to associate with human phenotypes in previous GWA studies (with P < 5 × 10(-8)). Using this method, we drastically reduce the number of tests conducted (from ~550,000 to 1,087) and test only SNPs that are known to be (or tag) variants that influence human biological processes. Of the 1,087 SNPs tested, the strongest association observed was for HNF1A SNP rs7310409 (P = 3 × 10(-5); P(Bonferroni) = 0.03), an SNP known to associate with circulating C-reactive protein. This association was replicated in an independent sample of 1,094 cases and 1,165 controls (P = 0.02), producing a highly significant association in the combined data sets (P = 2 × 10(-6); P(Bonferroni) = 0.002). The HNF1A region also harbors variants that influence several human traits, including maturity-onset diabetes of the young, type 2 diabetes, low-density lipoprotein cholesterol, and N-glycan levels. This novel "pleiotropy scan" method may be useful for identifying susceptibility loci for other cancer phenotypes.
©2011 AACR.