Computational analysis of gene-gene interactions using multifactor dimensionality reduction

被引：215

作者：

Moore, JH ^{[1
]}

机构：

[1] Dartmouth Coll Sch Med, Frank Lane Res Scholar Computat Genet, Computat Genet Lab, Lebanon, NH 03756 USA

来源：

EXPERT REVIEW OF MOLECULAR DIAGNOSTICS | 2004年 / 4卷 / 06期

关键词：

data mining; epistasis; genetic architecture; machine learning; software;

D O I：

10.1586/14737159.4.6.795

中图分类号：

R36 [病理学];

学科分类号：

100104 [病理学与病理生理学];

摘要：

Understanding the relationship between DNA sequence variations and biologic traits Is expected to Improve the diagnosis, prevention and treatment of common human diseases. Success in characterizing genetic architecture will depend on our ability to address nonlinearttles In the genotype-to-phenotype mapping relationship as a result of gone-gene Interactions, or epistasis. This review addresses the challenges associated with the detection and characterization of epistasis. A novel strategy known as muttifactor dimensionality reduction that was specifically designed for the Identification of multilocus genetic effects Is presented. Several case studies that demonstrate the detection of gene-gene Interactions In common diseases such as atrial fibrillation, Type 11 diabetes and essential hypertension are also discussed.

引用

页码：795 / 803

页数：9

共 55 条

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