title: A gene-based information gain method for detecting gene-gene interactions in case-control studies.
abstract: 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.
authors.affiliation: Nature Computation Lab, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.|School of Life Science and Technology, Harbin Institute of Technology, Harbin, China.|Department of Statistics Genetics, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
authors.forename: Jin
authors.initials: J
authors.lastname: Li
authors.affiliation: Nature Computation Lab, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
authors.forename: Dongli
authors.initials: D
authors.lastname: Huang
authors.affiliation: Nature Computation Lab, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
authors.forename: Maozu
authors.initials: M
authors.lastname: Guo
authors.affiliation: Nature Computation Lab, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
authors.forename: Xiaoyan
authors.initials: X
authors.lastname: Liu
authors.affiliation: Nature Computation Lab, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
authors.forename: Chunyu
authors.initials: C
authors.lastname: Wang
authors.affiliation: Nature Computation Lab, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
authors.forename: Zhixia
authors.initials: Z
authors.lastname: Teng
authors.affiliation: Department of Statistics Genetics, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
authors.forename: Ruijie
authors.initials: R
authors.lastname: Zhang
authors.affiliation: Department of Statistics Genetics, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
authors.forename: Yongshuai
authors.initials: Y
authors.lastname: Jiang
authors.affiliation: Department of Statistics Genetics, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
authors.forename: Hongchao
authors.initials: H
authors.lastname: Lv
authors.affiliation: Department of Statistics Genetics, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|Center of Computer Science, School of Basic Medical Sciences, Harbin Medical University, Harbin, China.
authors.forename: Limei
authors.initials: L
authors.lastname: Wang
mesh_terms: D001172:Arthritis, Rheumatoid / Q000235:genetics* / Q000473:pathology; D003198:Computer Simulation; D004843:Epistasis, Genetic; D055106:Genome-Wide Association Study / Q000379:methods*; D005838:Genotype; D006801:Humans; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D018401:Sample Size