Testing and estimation in marker-set association study using semiparametric quantile regression kernel machine.
Testing and estimation in marker-set association study using semiparametric quantile regression kernel machine.
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DOI：
10.1111/biom.12438
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发表时间：
2016-06
期刊：
影响因子：
1.9
通讯作者：
Tzeng JY
中科院分区：
文献类型：
--
作者：
Kong D;Maity A;Hsu FC;Tzeng JY
我们考虑部分线性模型的分位数回归，其中感兴趣的结果与协变量和标记集（例如，基因或途径）相关。对协变量效应进行了参数化建模，并利用核机对多位点的标记集效应进行了建模。我们提出了一种有效的算法来解决相应的优化问题，以估计协变量的影响，并引入了一个强大的测试来检测标记集的整体效果。我们的测试受到传统分数测试的启发，并借鉴了排列测试的思想。我们的估计和测试程序进行了数值评估，并应用维生素干预预防中风试验数据来评估空腹同型半胱氨酸水平变化的遗传关联。
We consider quantile regression for partially linear models where an outcome of interest is related to covariates and a marker set (e.g., gene or pathway). The covariate effects are modeled parametrically and the marker set effect of multiple loci is modeled using kernel machine. We propose an efficient algorithm to solve the corresponding optimization problem for estimating the effects of covariates and also introduce a powerful test for detecting the overall effect of the marker set. Our test is motivated by traditional score test, and borrows the idea of permutation test. Our estimation and testing procedures are evaluated numerically and applied to assess genetic association of change in fasting homocysteine level using the Vitamin Intervention for Stroke Prevention Trial data.