Department of Medical Statistics and BioInformatics, Leiden University Medical Center, Postzone S5-P, PO Box 9600, 2300, RC, Leiden, the Netherlands.
Stat Med. 2012 May 20;31(11-12):1190-202. doi: 10.1002/sim.4370. Epub 2011 Oct 14.
In genetic association studies, a gene-set analysis can be more powerful than the separate analyses of multiple genetic variants and can offer unique insights into the genetic basis of many common human diseases. The goal of such an analysis is to study the joint effect of multiple single-nucleotide polymorphisms (SNPs) which belong to certain genes, and these genes are assumed to be involved in a common biological function. Currently, few approaches acknowledge the within-genes and between-genes correlations when testing for gene-set effects. Thus, here we propose a two-stage approach, which in the first stage uses a mixed-effects model with a general random-effects structure to capture the correlation between the SNPs and in the second stage tests for gene-set effects by using the empirical Bayes estimates of the random effects of the first stage as covariates in the model for the longitudinal phenotype. The advantage of this approach is its broad applicability because it can be used for any phenotypic outcome and any genetic model and can be implemented with standard statistical software.
在遗传关联研究中，基因集分析比多个遗传变异的单独分析更有效，可以为许多常见人类疾病的遗传基础提供独特的见解。这种分析的目的是研究属于某些基因的多个单核苷酸多态性 (SNP) 的联合效应，这些基因被认为涉及共同的生物学功能。目前，很少有方法在检测基因集效应时考虑基因内和基因间相关性。因此，我们在这里提出一种两阶段方法，在第一阶段使用具有通用随机效应结构的混合效应模型来捕获 SNP 之间的相关性，在第二阶段通过使用第一阶段的随机效应的经验贝叶斯估计作为协变量来测试基因集效应在模型中用于纵向表型。这种方法的优点是它的广泛适用性，因为它可以用于任何表型结果和任何遗传模型，并且可以使用标准统计软件来实现。
