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Testing for Genetic Association in the Presence of Linkage and Gene–Covariate Interactions
Author(s) -
Callegaro Andrea,
Lebrec Jeremie J. P.,
HouwingDuistermaat Jeanine J.
Publication year - 2010
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.200900057
Subject(s) - covariate , test statistic , genetic association , statistic , logistic regression , heredity , linkage (software) , statistics , genetic linkage , score test , mathematics , genetics , statistical hypothesis testing , biology , genotype , gene , single nucleotide polymorphism
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.