Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions
Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions
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DOI：
10.1093/bioinformatics/btf869
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发表时间：
2003-02-12
期刊：
影响因子：
5.8
通讯作者：
Moore, JH
中科院分区：
文献类型：
--
作者：
Hahn, LW;Ritchie, MD;Moore, JH
动机：人类基因的多态现象正在被大量描述。确定哪些基因多态性和哪些环境因素与常见的复杂疾病有关已成为一项艰巨的任务。这在一定程度上是因为任何单一遗传变异的影响可能取决于其他遗传变异(基因-基因相互作用或上位性)和环境因素(基因-环境相互作用)。检测和描述多种因素之间的相互作用既是一项统计挑战，也是一项计算挑战。为了解决这个问题，我们开发了一种多因素降维(MDR)方法，将高维遗传数据压缩到一维，从而允许在相对较小的样本量中检测到相互作用。本文介绍了MDR方法和一个MDR软件包。结果：我们开发了一个将MDR和交叉验证策略相结合的程序，用于估计多因素模型的分类和预测误差。该软件可用于分析2-15个遗传和/或环境因素之间的交互作用。该数据集可以包含多达500个总变量和最多4000个研究对象。
Motivation: Polymorphisms in human genes are being described in remarkable numbers. Determining which polymorphisms and which environmental factors are associated with common, complex diseases has become a daunting task. This is partly because the effect of any single genetic variation will likely be dependent on other genetic variations (gene-gene interaction or epistasis) and environmental factors (gene-environment interaction). Detecting and characterizing interactions among multiple factors is both a statistical and a computational challenge. To address this problem, we have developed a multifactor dimensionality reduction (MDR) method for collapsing high-dimensional genetic data into a single dimension thus permitting interactions to be detected in relatively small sample sizes. In this paper, we describe the MDR approach and an MDR software package.Results: We developed a program that integrates MDR with a cross-validation strategy for estimating the classification and prediction error of multifactor models. The software can be used to analyze interactions among 2-15 genetic and/or environmental factors. The dataset may contain up to 500 total variables and a maximum of 4000 study subjects.