Abstract
Recent work on genetic association studies suggests that much of the heritable variation in complex traits is unexplained, which indicates a need for using more biologically meaningful modeling approaches and appropriate statistical methods. In this study, we propose a biological framework and a corresponding statistical model incorporating multilevel biological measures, and illustrate it in the analysis of the real data provided by the Genetic Analysis Workshop (GAW) 19, which contains whole genome sequence (WGS), gene expression (GE), and blood pressure (BP) data. We investigate the direct effect of single-nucleotide variants (SNVs) on BP and GE, while considering the non-directional dependence between BP and GE, by using copula functions to jointly model BP and GE conditional on SNVs. We implement the method for analysis on a genome-wide scale, and illustrate it within an association analysis of 68,727 SNVs on chromosome 19 that lie in or around genes with available GE measures. Although there is no indication for inflated type I errors under the proposed method, our results show that the association tests have smaller p values than tests under univariate models for common and rare variants using single-variant tests and gene-based multimarker tests. Hence, considering multilevel biological measures and modeling the dependence structure between these measures by using a plausible graphical approach may lead to more informative findings than standard univariate tests of common variants and well-recognized gene-based rare variant tests.