Abstract
The power of genome-wide association studies can be improved by incorporating information from previous study findings, for example, results of genome-wide linkage analyses. Weighted false-discovery rate (FDR) control can incorporate genome-wide linkage scan results into the analysis of genome-wide association data by assigning single-nucleotide polymorphism (SNP) specific weights. Stratified FDR control can also be applied by stratifying the SNPs into high and low linkage strata. We applied these two FDR control methods to the data of North American Rheumatoid Arthritis Consortium (NARAC) study and the Framingham Heart Study (FHS), combining both association and linkage analysis results. For the NARAC study, we used linkage results from a previous genome scan of rheumatoid arthritis (RA) phenotype. For the FHS study, we obtained genome-wide linkage scores from the same 550 k SNP data used for the association analyses of three lipids phenotypes (HDL, LDL, TG). We confirmed some genes previously reported for association with RA and lipid phenotypes. Stratified and weighted FDR methods appear to give improved ranks to some of the replicated SNPs for the RA data, suggesting linkage scan results could provide useful information to improve genome-wide association studies.
Highlights
Use of prior or additional information may improve the power of single-nucleotide polymorphism (SNP)-disease association analysis
We applied these two falsediscovery rate (FDR) methods along with the original FDR method to the North American Rheumatoid Arthritis Consortium (NARAC) study data provided for Genetic Analysis Workshop 16 (GAW 16) using previously reported linkage study results for rheumatoid arthritis (RA) [4]
(page number not for citation purposes) http://www.biomedcentral.com/1753-6561/3/S7/S103. These analyses suggest several new thresholds did produce some differences in ranking associations with RA (e.g., CNTNAP2 on chromosome 7). values, but the effect was minimal
Summary
Use of prior or additional information may improve the power of single-nucleotide polymorphism (SNP)-disease association analysis. SFDR is designed to use prior information to assign SNPs into strata that are more or less likely to include true-positive associations, which can improve the power of GWAS, but is more robust than WFDR to uninformative or even misleading prior information [3]. We applied these two FDR methods along with the original FDR method to the North American Rheumatoid Arthritis Consortium (NARAC) study data provided for Genetic Analysis Workshop 16 (GAW 16) using previously reported linkage study results for rheumatoid arthritis (RA) [4]. We compared the regions of association identified by the different methods
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