A gene-based information gain method for detecting gene-gene interactions in case-control studies

被引：18

作者：

Li, Jin ^{[1
,2
,3
]}

Huang, Dongli ^{[1
]}

Guo, Maozu ^{[1
]}

Liu, Xiaoyan ^{[1
]}

Wang, Chunyu ^{[1
]}

Teng, Zhixia ^{[1
]}

Zhang, Ruijie ^{[3
]}

Jiang, Yongshuai ^{[3
]}

Lv, Hongchao ^{[3
]}

Wang, Limei ^{[3
,4
]}

机构：

[1] Harbin Inst Technol, Sch Comp Sci & Technol, Nat Computat Lab, Harbin 150001, Peoples R China

[2] Harbin Inst Technol, Sch Life Sci & Technol, Harbin 150001, Peoples R China

[3] Harbin Med Univ, Dept Stat Genet, Coll Bioinformat Sci & Technol, Harbin 150086, Peoples R China

[4] Harbin Med Univ, Sch Basic Med Sci, Ctr Comp Sci, Harbin 150086, Peoples R China

来源：

EUROPEAN JOURNAL OF HUMAN GENETICS | 2015年 / 23卷 / 11期

基金：

中国国家自然科学基金; 高等学校博士学科点专项科研基金;

关键词：

MENDELIAN-INHERITANCE; STATISTICAL-METHODS; CO-ASSOCIATION; EPISTASIS;

D O I：

10.1038/ejhg.2015.16

中图分类号：

Q5 [生物化学]; Q7 [分子生物学];

学科分类号：

071010 ; 081704 ;

摘要：

Currently, most methods for detecting gene-gene interactions (GGIs) in genome-wide association studies are divided into SNP-based methods and gene-based methods. Generally, the gene-based methods can be more powerful than SNP-based methods. Some gene-based entropy methods can only capture the linear relationship between genes. We therefore proposed a nonparametric gene-based information gain method (GBIGM) that can capture both linear relationship and nonlinear correlation between genes. Through simulation with different odds ratio, sample size and prevalence rate, GBIGM was shown to be valid and more powerful than classic KCCU method and SNP-based entropy method. In the analysis of data from 17 genes on rheumatoid arthritis, GBIGM was more effective than the other two methods as it obtains fewer significant results, which was important for biological verification. Therefore, GBIGM is a suitable and powerful tool for detecting GGIs in case-control studies.

引用

页码：1566 / 1572

页数：7

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