title: Testing for genetic association in the presence of linkage and gene-covariate interactions.
abstract: In order to study family-based association in the presence of linkage, we extend a generalized linear mixed model proposed for genetic linkage analysis (Lebrec and van Houwelingen (2007), Human Heredity 64, 5-15) by adding a genotypic effect to the mean. The corresponding score test is a weighted family-based association tests statistic, where the weight depends on the linkage effect and on other genetic and shared environmental effects. For testing of genetic association in the presence of gene-covariate interaction, we propose a linear regression method where the family-specific score statistic is regressed on family-specific covariates. Both statistics are straightforward to compute. Simulation results show that adjusting the weight for the within-family variance structure may be a powerful approach in the presence of environmental effects. The test statistic for genetic association in the presence of gene-covariate interaction improved the power for detecting association. For illustration, we analyze the rheumatoid arthritis data from GAW15. Adjusting for smoking and anti-cyclic citrullinated peptide increased the significance of the association with the DR locus.
authors.affiliation: Department of Medical Statistics and Bioinformatics, Leiden University MC, Leiden, The Netherlands. a.callegaro@lumc.nl
authors.forename: Andrea
authors.initials: A
authors.lastname: Callegaro
authors.forename: Jeremie J P
authors.initials: JJ
authors.lastname: Lebrec
authors.forename: Jeanine J
authors.initials: JJ
authors.lastname: Houwing-Duistermaat
mesh_terms: D001172:Arthritis, Rheumatoid / Q000235:genetics; D003198:Computer Simulation; D008040:Genetic Linkage; D020022:Genetic Predisposition to Disease; D055106:Genome-Wide Association Study / Q000379:methods*; D005838:Genotype; D006801:Humans; D016014:Linear Models; D008957:Models, Genetic