Abstract
It is believed that epistatic interactions among loci contribute to variations in quantitative traits. Several methods are available to detect epistasis using population-based data. However, methods to characterize epistasis for quantitative traits in family-based association analysis are not well developed, especially for studying thousands of gene expression traits. Here, we proposed a linear mixed-model approach to detect epistasis for quantitative traits using family data. The proposed method was implemented in a widely used software program SOLAR. We evaluated the power of the method by simulation studies and applied this method to the analysis of the Centre d'Etude du Polymorphisme Humain family gene expression data provided by Genetics Analysis Workshop 15 (GAW15).
Highlights
With the ability to measure simultaneously thousands of gene expression traits, understanding the causes of transcriptional variation has been of great interest
We have extended the association-based linear regression model [2,3] by adding a random polygenic effect into the model to allow for familial data for epistasis detection of quantitative traits
Statistical methods Based on the linear regression model of Cokerham and Zeng [2], we propose a linear mixed model for detecting epistatic interactions for quantitative traits using family-based data: y = μ + a1x1 + d1z1 + a2x2 + d2z2 + iaax1x2 + iadx1z2 + idaz1x2 + iddz1z2 + Wβ + v + ε
Summary
With the ability to measure simultaneously thousands of gene expression traits, understanding the causes of transcriptional variation has been of great interest. It is essential to analyze epistatic interactions between loci that contribute to variations in gene expression traits. Several statistical methods for studying epistatic interactions between loci for quantitative traits using populations of unrelated individuals or from experimental designs have been developed [2,3,4,5,6]. For quantitative traits using family-based samples (related individuals), epistatic testing has been incorporated into the variancecomponent linkage analysis and implemented in the software SOLAR [7]. Epistatic detection on the basis of the linkage analysis can only locate the two interacting loci in wide confidence intervals and will have small power for data sets with small sample sizes, such as in the GAW15 (Genetic Analysis Workshop 15) CEPH (Centre d'Etude du Polymorphisme Humain) data set, which only (page number not for citation purposes)
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