Quantifying the relationship between gene expressions and trait values in general pedigrees.

Treating mRNA transcript abundances as quantitative traits and examining their relationships with clinical traits have been pursued by using an analytical approach of quantitative genetics. Recently, Kraft et al. presented a family expression association test (FEXAT) for correlation between gene expressions and trait values with a family-based (sibships) design. This statistic did not account for biological relationships of related subjects, which may inflate type I error rate and/or decrease power of statistical tests. In this article, we propose two new test statistics based on a variance-components approach for analyses of microarray data obtained from general pedigrees. Our methods accommodate covariance between relatives for unmeasured genetic effects and directly model covariates of clinical importance. The efficacy and validity of our methods are investigated by using simulated data under different sample sizes, family sizes, and family structures. The proposed LR method has correct type I error rate with moderate to large sample sizes regardless of family structure and family sizes. It has higher power with complex pedigrees and similar power to the FEXAT with sibships. The other proposed FEXAT(R) method is favorable with large family sizes, regardless of sample sizes and family structure. Our methods, robust to population stratification, are complementary to the FEXAT in expression-trait association studies.