A Monte Carlo test of linkage disequilibrium for single nucleotide polymorphisms

BACKGROUND: Genetic association studies, especially genome-wide studies, make use of linkage disequilibrium(LD) information between single nucleotide polymorphisms (SNPs). LD is also used for studying genome structure and has been valuable for evolutionary studies. The strength of LD is commonly measured by r(2), a statistic closely related to the Pearson's χ(2 )statistic. However, the computation and testing of linkage disequilibrium using r(2 )requires known haplotype counts of the SNP pair, which can be a problem for most population-based studies where the haplotype phase is unknown. Most statistical genetic packages use likelihood-based methods to infer haplotypes. However, the variability of haplotype estimation needs to be accounted for in the test for linkage disequilibrium. FINDINGS: We develop a Monte Carlo based test for LD based on the null distribution of the r(2 )statistic. Our test is based on r(2 )and can be reported together with r(2). Simulation studies show that it offers slightly better power than existing methods. CONCLUSIONS: Our approach provides an alternative test for LD and has been implemented as a R program for ease of use. It also provides a general framework to account for other haplotype inference methods in LD testing.