Estimation and testing for the effect of a genetic pathway on a disease outcome using logistic kernel machine regression via logistic mixed models.
Estimation and testing for the effect of a genetic pathway on a disease outcome using logistic kernel machine regression via logistic mixed models.
复制标题
DOI：
10.1186/1471-2105-9-292
复制
发表时间：
2008-06-24
影响因子：
3
通讯作者：
Lin X
中科院分区：
文献类型：
--
作者：
Liu D;Ghosh D;Lin X
人们对生物途径的兴趣日益浓厚，因此需要新的统计方法来模拟和测试遗传途径对健康结果的影响。事实上，在一个通路内的基因倾向于相互作用，并以一种复杂的方式与结果相关，这使得非参数方法更可取。核机方法为路径效应的多维参数化和非参数化建模提供了一种方便、强大和统一的方法。本文提出了一个二元结果的逻辑核机回归模型。该模型使用核机将疾病风险与协变量参数化联系起来，并将遗传途径中的基因参数化或非参数化联系起来。非参数遗传通路效应允许同一通路内基因之间可能的相互作用以及遗传通路与结果的复杂关系。我们证明了模型组件的核机估计可以用逻辑混合模型来表示。因此，估算可以在使用标准统计软件的混合模型框架中进行。提出了一种基于高斯过程近似的分数检验方法来检验遗传通路效应。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。讨论了用广义核机模型对连续和离散结果的推广及其与广义线性混合模型的联系。逻辑核机回归及其扩展广义核机回归为离散和连续结果的路径效应建模提供了一种新颖而灵活的统计工具。它们与混合模型的密切联系和良好的性能使其在生物信息学和其他生物医学领域具有广阔的应用前景。
Growing interest on biological pathways has called for new statistical methods for modeling and testing a genetic pathway effect on a health outcome. The fact that genes within a pathway tend to interact with each other and relate to the outcome in a complicated way makes nonparametric methods more desirable. The kernel machine method provides a convenient, powerful and unified method for multi-dimensional parametric and nonparametric modeling of the pathway effect. In this paper we propose a logistic kernel machine regression model for binary outcomes. This model relates the disease risk to covariates parametrically, and to genes within a genetic pathway parametrically or nonparametrically using kernel machines. The nonparametric genetic pathway effect allows for possible interactions among the genes within the same pathway and a complicated relationship of the genetic pathway and the outcome. We show that kernel machine estimation of the model components can be formulated using a logistic mixed model. Estimation hence can proceed within a mixed model framework using standard statistical software. A score test based on a Gaussian process approximation is developed to test for the genetic pathway effect. The methods are illustrated using a prostate cancer data set and evaluated using simulations. An extension to continuous and discrete outcomes using generalized kernel machine models and its connection with generalized linear mixed models is discussed. Logistic kernel machine regression and its extension generalized kernel machine regression provide a novel and flexible statistical tool for modeling pathway effects on discrete and continuous outcomes. Their close connection to mixed models and attractive performance make them have promising wide applications in bioinformatics and other biomedical areas.
登录
查看更多内容
影响因子：
7
作者：
Grosu, P;Townsend, JP;Cavalieri, D
通讯作者：
Cavalieri, D
影响因子：
2.7
作者：
DAVIES, RB
通讯作者：
DAVIES, RB
影响因子：
1.9
作者：
LECESSIE, S;VANHOUWELINGEN, HC
通讯作者：
VANHOUWELINGEN, HC
影响因子：
1.9
作者：
Liu, Dawei;Lin, Xihong;Ghosh, Debashis
通讯作者：
Ghosh, Debashis
影响因子：
2.7
作者：
DAVIES, RB
通讯作者：
DAVIES, RB