期刊
JOURNAL OF THEORETICAL BIOLOGY
卷 241, 期 2, 页码 252-261出版社
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.jtbi.2005.11.036
关键词
gene-gene interactions; constructive induction; multifactor dimensionality reduction; entropy; machine learning; data mining
资金
- NCRR NIH HHS [RR018787] Funding Source: Medline
- NHLBI NIH HHS [HL65234] Funding Source: Medline
- NIAID NIH HHS [AI59694] Funding Source: Medline
- NICHD NIH HHS [HD047447] Funding Source: Medline
Detecting, characterizing, and interpreting gene-gene interactions or epistasis in studies of human disease susceptibility is both a mathematical and a computational challenge. To address this problem, we have previously developed a multifactor dimensionality reduction (MDR) method for collapsing high-dimensional genetic data into a single dimension (i.e. constructive induction) thus permitting interactions to be detected in relatively small sample sizes. In this paper, we describe a comprehensive and flexible framework for detecting and interpreting gene-gene interactions that utilizes advances in information theory for selecting interesting single-nucleotide polymorphisms (SNPs), MDR for constructive induction, machine learning methods for classification, and finally graphical models for interpretation. We illustrate the usefulness of this strategy using artificial datasets simulated from several different two-locus and three-locus epistasis models. We show that the accuracy, sensitivity, specificity, and precision of a naive Bayes classifier are significantly improved when SNPs are selected based on their information gain (i.e. class entropy removed) and reduced to a single attribute using MDR. We then apply this strategy to detecting, characterizing, and interpreting epistatic models in a genetic study (n = 500) of atrial fibrillation and show that both classification and model interpretation are significantly improved. (c) 2005 Elsevier Ltd. All rights reserved.
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