A Simple and Computationally Efficient Sampling Approach to Covariate Adjustment for Multifactor Dimensionality Reduction Analysis of Epistasis
A Simple and Computationally Efficient Sampling Approach to Covariate Adjustment for Multifactor Dimensionality Reduction Analysis of Epistasis
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DOI：
10.1159/000319175
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发表时间：
2010-01-01
期刊：
影响因子：
1.8
通讯作者：
Moore, Jason H.
中科院分区：
文献类型：
--
作者：
Gui, Jiang;Andrew, Angeline S.;Moore, Jason H.
上位性或基因-基因相互作用是复杂性状遗传结构的基本组成部分，如疾病易感性。多因素降维（MDR）作为一种非参数和无模型的方法，在没有显著的边际遗传效应时检测上位性。然而，在许多复杂疾病的研究中，其他协变量，如发病年龄和吸烟状况，可能会产生强烈的主要影响，并可能干扰MDR实现其目标的能力。在本文中，我们提出了一种简单且计算效率高的采样方法来调整MDR中的协变量效应。我们通过模拟表明，调整后的MDR具有足够的能力来检测真实的基因-基因相互作用。我们还将我们的方法与协变量调整的最新技术进行了比较。结果表明，我们提出的方法具有相似的性能，但计算效率更高。然后，我们将这种新方法应用于新罕布什尔一项基于人群的膀胱癌研究的分析。版权所有：S. Karger AG，巴塞尔
Epistasis or gene-gene interaction is a fundamental component of the genetic architecture of complex traits such as disease susceptibility. Multifactor dimensionality reduction (MDR) was developed as a nonparametric and model-free method to detect epistasis when there are no significant marginal genetic effects. However, in many studies of complex disease, other covariates like age of onset and smoking status could have a strong main effect and may potentially interfere with MDR's ability to achieve its goal. In this paper, we present a simple and computationally efficient sampling method to adjust for covariate effects in MDR. We use simulation to show that after adjustment, MDR has sufficient power to detect true gene-gene interactions. We also compare our method with the state-of-art technique in covariate adjustment. The results suggest that our proposed method performs similarly, but is more computationally efficient. We then apply this new method to an analysis of a population-based bladder cancer study in New Hampshire. Copyright (C) 2010 S. Karger AG, Basel