Semiparametric regression of multidimensional genetic pathway data: Least-squares kernel machines and linear mixed models
Semiparametric regression of multidimensional genetic pathway data: Least-squares kernel machines and linear mixed models
复制标题
DOI：
10.1111/j.1541-0420.2007.00799.x
复制
发表时间：
2007-12-01
期刊：
影响因子：
1.9
通讯作者：
Ghosh, Debashis
中科院分区：
文献类型：
--
作者：
Liu, Dawei;Lin, Xihong;Ghosh, Debashis
我们考虑了一个半参数回归模型，该模型将正常结果与协变量和遗传途径联系起来，其中协变量效应是参数化的，而多个基因表达的途径效应是使用最小二乘核机（LSKMs）参数化或非参数化的。这种统一的框架通过指定一个核函数，允许一个通路内多个基因的联合作用具有灵活的功能，并允许每个基因的表达效应可能是非线性的，同一通路内的基因可能以复杂的方式相互作用。这种半参数模型也使测试整体遗传通路效应成为可能。我们证明了LSKM半参数回归可以用线性混合模型来表示。因此，可以使用标准混合模型软件在线性混合模型框架内进行估计和推理。在相应的线性混合模型公式中，利用最佳线性无偏预测器可以得到协变量效应的回归系数和遗传通路效应的LSKM估计量。平滑参数和核参数可以用限制极大似然估计为方差分量。一种分数测试被开发来测试遗传通路效应。讨论了LSKM框架内的模型/变量选择。这些方法使用前列腺癌数据集进行了说明，并使用模拟进行了评估。
We consider a semiparametric regression model that relates a normal outcome to covariates and a genetic pathway, where the covariate effects are modeled parametrically and the pathway effect of multiple gene expressions is modeled parametrically or nonparametrically using least-squares kernel machines (LSKMs). This unified framework allows a flexible function for the joint effect of multiple genes within a pathway by specifying a kernel function and allows for the possibility that each gene expression effect might be nonlinear and the genes within the same pathway are likely to interact with each other in a complicated way. This semiparametric model also makes it possible to test for the overall genetic pathway effect. We show that the LSKM semiparametric regression can be formulated using a linear mixed model. Estimation and inference hence can proceed within the linear mixed model framework using standard mixed model software. Both the regression coefficients of the covariate effects and the LSKM estimator of the genetic pathway effect can be obtained using the best linear unbiased predictor in the corresponding linear mixed model formulation. The smoothing parameter and the kernel parameter can be estimated as variance components using restricted maximum likelihood. A score test is developed to test for the genetic pathway effect. Model/variable selection within the LSKM framework is discussed. The methods are illustrated using a prostate cancer data set and evaluated using simulations.