Tests of association between quantitative traits and haplotypes in a reduced-dimensional space

Candidate gene association tests are currently performed using several intragenic SNPs simultaneously, by testing SNP haplotype or genotype effects in multifactorial diseases or traits. The number of haplotypes drastically increases with an increase in the number of typed SNPs. As a result, large numbers of haplotypes will introduce large degrees of freedom in haplotype-based tests, and thus limit the power of the tests. In this study we propose using the principal component method to reduce the dimension, and then construct association tests on the lower-dimensional space to test the association between haplotypes and a quantitative trait using population-based samples. The proposed method allows ambiguous haplotypes. We use simulation studies to evaluate the type I error rate of the tests, and compare the power of the proposed tests with that of the tests without dimension reduction, and the tests with dimension reduction by merging rare haplotypes. The simulation results show that the proposed tests have correct type I error rates and are more powerful than other tests in most cases considered in our simulation studies.