期刊
ANNALS OF HUMAN GENETICS
卷 75, 期 -, 页码 20-28出版社
WILEY
DOI: 10.1111/j.1469-1809.2010.00624.x
关键词
Epistasis; machine learning; data mining
资金
- American Cancer Society [IRG-82-003-22]
- NIH [LM009012, LM010098, AI59694, CA57494, ES007373]
- NATIONAL CANCER INSTITUTE [P30CA023108, R01CA057494] Funding Source: NIH RePORTER
- NATIONAL CENTER FOR RESEARCH RESOURCES [P20RR024475] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF ALLERGY AND INFECTIOUS DISEASES [R01AI059694] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF ENVIRONMENTAL HEALTH SCIENCES [P42ES007373] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES [P20GM103534] Funding Source: NIH RePORTER
- NATIONAL LIBRARY OF MEDICINE [R01LM010098, R01LM009012] Funding Source: NIH RePORTER
P>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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据