Abstract

BackgroundGenome wide association studies (GWAS) are applied to identify genetic loci, which are associated with complex traits and human diseases. Analogous to the evolution of gene expression analyses, pathway analyses have emerged as important tools to uncover functional networks of genome-wide association data. Usually, pathway analyses combine statistical methods with a priori available biological knowledge. To determine significance thresholds for associated pathways, correction for multiple testing and over-representation permutation testing is applied.ResultsWe systematically investigated the impact of three different permutation test approaches for over-representation analysis to detect false positive pathway candidates and evaluate them on genome-wide association data of Dilated Cardiomyopathy (DCM) and Ulcerative Colitis (UC). Our results provide evidence that the gold standard - permuting the case–control status – effectively improves specificity of GWAS pathway analysis. Although permutation of SNPs does not maintain linkage disequilibrium (LD), these permutations represent an alternative for GWAS data when case–control permutations are not possible. Gene permutations, however, did not add significantly to the specificity. Finally, we provide estimates on the required number of permutations for the investigated approaches.ConclusionsTo discover potential false positive functional pathway candidates and to support the results from standard statistical tests such as the Hypergeometric test, permutation tests of case control data should be carried out. The most reasonable alternative was case–control permutation, if this is not possible, SNP permutations may be carried out. Our study also demonstrates that significance values converge rapidly with an increasing number of permutations. By applying the described statistical framework we were able to discover axon guidance, focal adhesion and calcium signaling as important DCM-related pathways and Intestinal immune network for IgA production as most significant UC pathway.

Highlights

  • Genome wide association studies (GWAS) are applied to identify genetic loci, which are associated with complex traits and human diseases

  • Our study addresses the questions, which permutation strategy should be applied to GWAS data and how many permutations are required in order to reach reliable results

  • As lead application we employed our method to a GWAS dataset of 909 patients suffering from Dilated Cardiomyopathies (DCM) and 2,120 population-based controls

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Summary

Introduction

Genome wide association studies (GWAS) are applied to identify genetic loci, which are associated with complex traits and human diseases. The “Catalog of Published Genome-Wide Association Studies” [3] covers only those GWAS attempting to assay at least 100,000 SNPs in the initial stage and considers only SNP-trait associations with pvalues < 1.0 × 10−5. This catalogue lists currently (May, 13th, 2014) 1,920 different papers in PubMed for 1,079 different traits/diseases with 13,380 associations between variants and the respective traits (for each publication at most 50 SNPs are considered). Among the most comprehensive GWAS considering the screened sample size, Teslovich and co-workers [4] investigated the genome for common variants associated with plasma lipids in more than 100,000 individuals of European ancestry and reported over 95 significantly associated loci

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