Efficient multilocus association testing for whole genome association studies using localized haplotype clustering

Abstract

Whole genome association studies are generating data sets with hundreds of thousands of markers genotyped on thousands of cases and controls. We show that whole genome haplotypic association testing with permutation to account for multiple testing is statistically powerful and computationally feasible on such data, using an efficient software implementation of a recently proposed method. We use realistic simulations to explore the statistical properties of the method, and show that for ungenotyped disease-susceptibility variants with population frequencies of 5% or less the haplotypic tests have markedly better power than single-marker tests. We propose a combined single-marker and haplotypic strategy, in which both single-marker and haplotypic tests are applied, with the minimum P-value adjusted for multiple testing by permutation which results in a test that is powerful for detecting both low-and high-frequency disease-susceptibility variants.