Similarity-based multimarker association tests for continuous traits

Abstract

Testing multiple markers simultaneously not only can capture the linkage disequilibrium patterns but also can decrease the number of tests and thus alleviate the multiple-testing penalty. If a gene is associated with a phenotype, subjects with similar genotypes in this gene should also have similar phenotypes. Based on this concept, we have developed a general framework that is applicable to continuous traits. Two similarity-based tests (namely, SIMc and SIMp tests) were derived as special cases of the general framework. In our simulation study, we compared the power of the two tests with that of the single-marker analysis, a standard haplotype regression, and a popular and powerful kernel machine regression. Our SIMc test outperforms other tests when the average R(2) (a measure of linkage disequilibrium) between the causal variant and the surrounding markers is larger than 0.3 or when the causal allele is common (say, frequency = 0.3). Our SIMp test outperforms other tests when the causal variant was introduced at common haplotypes (the maximum frequency of risk haplotypes >0.4). We also applied our two tests to an adiposity data set to show their utility.