Callegaro Andrea, Lebrec Jeremie J P, Houwing-Duistermaat Jeanine J
Department of Medical Statistics and Bioinformatics, Leiden University MC, Leiden, The Netherlands.
Biom J. 2010 Feb;52(1):22-33. doi: 10.1002/bimj.200900057.
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.
为了在存在连锁的情况下研究基于家系的关联性，我们通过在均值中加入基因型效应，扩展了一个为基因连锁分析提出的广义线性混合模型（Lebrec和van Houwelingen，《人类遗传学》64卷，5 - 15页，2007年）。相应的计分检验是一个基于家系的加权关联性检验统计量，其中权重取决于连锁效应以及其他基因和共享环境效应。为了在存在基因 - 协变量相互作用的情况下检验基因关联性，我们提出一种线性回归方法，其中将家系特异性计分统计量对家系特异性协变量进行回归。这两种统计量都很容易计算。模拟结果表明，在存在环境效应的情况下，针对家系内方差结构调整权重可能是一种有效的方法。在存在基因 - 协变量相互作用的情况下用于基因关联性的检验统计量提高了检测关联性的效能。为作说明，我们分析了来自GAW15的类风湿性关节炎数据。对吸烟和抗环瓜氨酸肽进行调整后，增加了与DR位点关联性的显著性。
