Multivariate association test using haplotype trend regression

Abstract

Genetic association analyses with haplotypes may be more powerful than analyses with single markers, under certain conditions. Furthermore, simultaneously considering multiple correlated traits may make use of additional information that would not be considered when analyzing individual traits. In this study, we propose a haplotype based test of association for multivariate quantitative traits in unrelated samples. Specifically, we extend a population based haplotype trend regression (HTR) approach to multivariate scenarios. We mainly focused on bivariate HTR, and the simulation results showed that the proposed method had correct pre-specified type-I error rates. The power of the proposed method was largely influenced by the size and source of correlation between variables, being greatest when correlation of a specific gene was opposite in sign to the residual correlation.

Figure 1

Sample −log (p-values) against the marker number using p-values from the likelihood ratio test (LRT). The 36^th to the 40^th SNPs were causal SNPs. A is for window-size of 1; B is for window-size of 3 and C is for window-size of 5.