Originally published as Genetics Published Articles Ahead of Print on September 15, 2004.

Genetics, Vol. 168, 2395-2405, December 2004, Copyright © 2004
doi:10.1534/genetics.104.031666

Quantifying the Relationship Between Gene Expressions and Trait Values in General Pedigrees

Yan Lu*, Peng-Yuan Liu*, Yong-Jun Liu*, Fu-Hua Xu* and Hong-Wen Deng*,{dagger},{ddagger},1

* Osteoporosis Research Center, Creighton University, Omaha, Nebraska 68131
{dagger} Key Laboratory of Biomedical Information Engineering of Ministry of Education and Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, People's Republic of China
{ddagger} Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha, Hunan 410081, People's Republic of China

1 Corresponding author: Osteoporosis Research Center, Creighton University, 601 N. 30th St., Suite 6787, Omaha, NE 68131.
E-mail: deng{at}creighton.edu

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.