title: Detecting epistatic interactions contributing to human gene expression using the CEPH family data.
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).
authors.affiliation: Bioinformatics Center, Stowers Institute for Medical Research, 1000 East 50th Street, Kansas City, Missouri 64110, USA. hul@stowers-institute.org
authors.forename: Hua
authors.initials: H
authors.lastname: Li
authors.forename: Guimin
authors.initials: G
authors.lastname: Gao
authors.forename: Jian
authors.initials: J
authors.lastname: Li
authors.forename: Grier P
authors.initials: GP
authors.lastname: Page
authors.forename: Kui
authors.initials: K
authors.lastname: Zhang