Genetic association studies offer an opportunity to find genetic variants underlying complex human diseases. The success of this approach depends on the linkage disequilibrium (LD) between markers and the disease variant(s) in a local region of the genome. Because, in the region with a disease mutation, the LD pattern among markers may differ between cases and controls, in some scenarios, it is useful to compare a measure of this LD, to map disease mutations. For example, using the composite correlation to characterize the LD among markers, Zaykin et al. recently suggested an "LD contrast" test and showed that it has high power under certain haplotype-driven disease models. Furthermore, it is likely that individual variants observed at different positions in a gene act jointly with each other to influence the phenotype, and the LD contrast test is also a useful method to detect such joint action. However, the LD among markers introduced by mutations and their joint action is usually confounded by background LD, which is measured at the population level, especially in a local region with disease mutations. Because the measures of LD that are usually used, such as the composite correlation, represent both effects, they may not be optimal for the purpose of detecting association when high background LD exists. Here, we describe a test that improves the LD contrast test by taking into account the background LD. Because the proposed test is developed in a regression framework, it is very flexible and can be extended to continuous traits and to incorporate covariates. Our simulation results demonstrate the validity and substantially higher power of the proposed method over current methods. Finally, we illustrate our new method by applying it to real data from the International Collaborative Study on Hypertension in Blacks.
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