Permutation-based approaches do not adequately allow for linkage disequilibrium in gene-wide multi-locus association analysis.

ABSTRACT: Additional information about risk genes or risk pathways for diseases can be extracted from genome-wide association studies through analyses of groups of markers. The most commonly employed approaches involve combining individual marker data by adding the test statistics, or summing the logarithms of their P-values, and then using permutation testing to derive empirical P-values that allow for the statistical dependence of single-marker tests arising from linkage disequilibrium (LD). In the present study, we use simulated data to show that these approaches fail to reflect the structure of the sampling error, and the effect of this is to give undue weight to correlated markers. We show that the results obtained are internally inconsistent in the presence of strong LD, and are externally inc

SUBMITTER: Moskvina V 

PROVIDER: S-EPMC3400741 | biostudies-literature | 2012 Aug

REPOSITORIES: biostudies-literature