A generalized combinatorial approach for detecting gene-by-gene and gene-by-environment interactions with application to nicotine dependence
A generalized combinatorial approach for detecting gene-by-gene and gene-by-environment interactions with application to nicotine dependence
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DOI：
10.1086/518312
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发表时间：
2007-06-01
影响因子：
9.8
通讯作者：
Li, Ming D.
中科院分区：
文献类型：
--
作者：
Lou, Xiang-Yang;Chen, Guo-Bo;Li, Ming D.
确定基因与环境之间的相互作用一直是遗传学面临的最大挑战之一。由于存在被称为“维度诅咒”的问题，传统的方法通常是不够的。最近的组合方法，如多因素降维(MDR)方法、组合划分方法和限制划分方法，与统一了生物学、统计遗传学和进化理论的表型景观的概念有直接的对应关系。然而，现有的方法有几个限制，例如不允许协变量，这限制了它们的实际使用。在这项研究中，我们报告了一种广义MDR(GMDR)方法，该方法允许对离散和定量协变量进行调整，并且适用于各种基于总体的研究设计中的二分和连续表型。计算机模拟表明，与文献中现有的方法相比，GMDR方法在识别上位基因座方面具有更好的性能。我们应用我们提出的方法对四个被报道与尼古丁依赖相关的基因进行了遗传学研究，发现CHRNB4和NTRK2之间存在显著的联合作用。此外，我们的实例表明，新提出的GMDR方法能够提高预测能力，表明其在实践中的使用是合理的。总而言之，与其他现有方法相比，GMDR更好地服务于确定人口变化的贡献者的目的。
The determination of gene-by-gene and gene-by-environment interactions has long been one of the greatest challenges in genetics. The traditional methods are typically inadequate because of the problem referred to as the "curse of dimensionality." Recent combinatorial approaches, such as the multifactor dimensionality reduction (MDR) method, the combinatorial partitioning method, and the restricted partition method, have a straightforward correspondence to the concept of the phenotypic landscape that unifies biological, statistical genetics, and evolutionary theories. However, the existing approaches have several limitations, such as not allowing for covariates, that restrict their practical use. In this study, we report a generalized MDR (GMDR) method that permits adjustment for discrete and quantitative covariates and is applicable to both dichotomous and continuous phenotypes in various population-based study designs. Computer simulations indicated that the GMDR method has superior performance in its ability to identify epistatic loci, compared with current methods in the literature. We applied our proposed method to a genetics study of four genes that were reported to be associated with nicotine dependence and found significant joint action between CHRNB4 and NTRK2. Moreover, our example illustrates that the newly proposed GMDR approach can increase prediction ability, suggesting that its use is justified in practice. In summary, GMDR serves the purpose of identifying contributors to population variation better than do the other existing methods.