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.

The genetic component of human longevity: New insights from the analysis of pathway‐based SNP‐SNP interactions

“…Single‐SNP analyses may miss such a complexity, primarily because if a genetic factor operates through a mechanism involving multiple genes, and is also affected by environmental factors, the single investigation may not examine statistical interactions between loci that are informative about the biological and biochemical pathways underpinning the phenotype. On the contrary, SNP interactions may carry more information about the phenotype than those observed from individual SNPs alone (Gerke, Lorenz & Cohen, 2009; Su et al., 2015). Assessing SNP‐SNP interactions at the gene level co‐occurring within a specific phenotype and not due to linkage disequilibrium (LD) can overcome this problem, possibly finding specific subprocesses more strongly associated with the phenotype than single‐SNP analysis (Cordell, 2009).…”

SNP-SNP Interaction Analysis on Soybean Oil Content under Multi-Environments

“…Guia et al [ 71 ] reveals interaction of important gene variants involved in allergy by MDR. Su et al [ 72 ] applied the multifactor dimension reduction method to analyze gene-gene and gene-environmental interactions of childhood asthma. Gui et al [ 73 ] detected gene-gene interactions with application to the genetic analysis of bladder cancer susceptibility with multifactor dimensionality reduction method.…”

Transferring entropy to the realm of GxG interactions

“…The IG introduced by Su et al [ 19 ] is inspired by Moore et al [ 10 ], and it is defined as the CMI of a pair of variants given the phenotype minus the mutual information of this pair, i.e. …”