Gui Jiang, Andrew Angeline S, Andrews Peter, Nelson Heather M, Kelsey Karl T, Karagas Margaret R, Moore Jason H
Dartmouth Medical School, Lebanon, NH 03756, USA.
Ann Hum Genet. 2011 Jan;75(1):20-8. doi: 10.1111/j.1469-1809.2010.00624.x. Epub 2010 Nov 22.
A central goal of human genetics is to identify susceptibility genes for common human diseases. An important challenge is modelling gene-gene interaction or epistasis that can result in nonadditivity of genetic effects. The multifactor dimensionality reduction (MDR) method was developed as a machine learning alternative to parametric logistic regression for detecting interactions in the absence of significant marginal effects. The goal of MDR is to reduce the dimensionality inherent in modelling combinations of polymorphisms using a computational approach called constructive induction. Here, we propose a Robust Multifactor Dimensionality Reduction (RMDR) method that performs constructive induction using a Fisher's Exact Test rather than a predetermined threshold. The advantage of this approach is that only statistically significant genotype combinations are considered in the MDR analysis. We use simulation studies to demonstrate that this approach will increase the success rate of MDR when there are only a few genotype combinations that are significantly associated with case-control status. We show that there is no loss of success rate when this is not the case. We then apply the RMDR method to the detection of gene-gene interactions in genotype data from a population-based study of bladder cancer in New Hampshire.
人类遗传学的一个核心目标是识别常见人类疾病的易感基因。一个重要的挑战是对基因-基因相互作用或上位性进行建模，这可能导致遗传效应的非加性。多因素降维（MDR）方法是作为一种机器学习方法而开发的，用于在不存在显著边际效应的情况下检测相互作用，以替代参数逻辑回归。MDR的目标是使用一种称为构造性归纳的计算方法来降低对多态性组合进行建模时固有的维度。在此，我们提出一种稳健多因素降维（RMDR）方法，该方法使用Fisher精确检验而非预定阈值来执行构造性归纳。这种方法的优点是在MDR分析中仅考虑具有统计学意义的基因型组合。我们通过模拟研究表明，当只有少数基因型组合与病例对照状态显著相关时，这种方法将提高MDR的成功率。我们还表明，在并非如此的情况下，成功率不会降低。然后，我们将RMDR方法应用于新罕布什尔州一项基于人群的膀胱癌研究的基因型数据中基因-基因相互作用的检测。
