title: Research on single nucleotide polymorphisms interaction detection from network perspective.
abstract: Single Nucleotide Polymorphisms (SNPs) found in Genome-Wide Association Study (GWAS) mainly influence the susceptibility of complex diseases, but they still could not comprehensively explain the relationships between mutations and diseases. Interactions between SNPs are considered so important for deeply understanding of those relationships that several strategies have been proposed to explore such interactions. However, part of those methods perform poorly when marginal effects of disease loci are weak or absent, others may lack of considering high-order SNPs interactions, few methods have achieved the requirements in both performance and accuracy. Considering the above reasons, not only low-order, but also high-order SNP interactions as well as main-effect SNPs, should be taken into account in detection methods under an acceptable computational complexity. In this paper, a new pairwise (or low-order) interaction detection method IG (Interaction Gain) is introduced, in which disease models are not required and parallel computing is utilized. Furthermore, high-order SNP interactions were proposed to be detected by finding closely connected function modules of the network constructed from IG detection results. Tested by a wide range of simulated datasets and four WTCCC real datasets, the proposed methods accurately detected both low-order and high-order SNP interactions as well as disease-associated main-effect SNPS and it surpasses all competitors in performances. The research will advance complex diseases research by providing more reliable SNP interactions.
authors.affiliation: College of Computer Science and Technology, Jilin University, Changchun, People's Republic of China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, People's Republic of China.
authors.forename: Lingtao
authors.initials: L
authors.lastname: Su
authors.affiliation: College of Computer Science and Technology, Jilin University, Changchun, People's Republic of China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, People's Republic of China.
authors.forename: Guixia
authors.initials: G
authors.lastname: Liu
authors.affiliation: College of Computer Science and Information Technology, Northeast Normal University, Changchun, People's Republic of China.
authors.forename: Han
authors.initials: H
authors.lastname: Wang
authors.affiliation: College of Computer Science and Technology, Jilin University, Changchun, People's Republic of China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, People's Republic of China.
authors.forename: Yuan
authors.initials: Y
authors.lastname: Tian
authors.affiliation: College of Computer Science and Technology, Jilin University, Changchun, People's Republic of China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, People's Republic of China.
authors.forename: Zhihui
authors.initials: Z
authors.lastname: Zhou
authors.affiliation: College of Computer Science and Technology, Jilin University, Changchun, People's Republic of China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, People's Republic of China.
authors.forename: Liang
authors.initials: L
authors.lastname: Han
authors.affiliation: College of Computer Science and Technology, Jilin University, Changchun, People's Republic of China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, People's Republic of China.
authors.forename: Lun
authors.initials: L
authors.lastname: Yan
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D004843:Epistasis, Genetic; D020022:Genetic Predisposition to Disease; D055106:Genome-Wide Association Study; D006801:Humans; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide