Open AccessCurrent status of studies on genome-wide gene-gene interactions
Author(s) -
Jiawei Shen,
Xiaohan Hu,
Yongyong Shi
Publication year - 2011
Publication title -
yichuan
Language(s) - English
Resource type - Journals
ISSN - 0253-9772
DOI - 10.3724/sp.j.1005.2011.00820
Subject(s) - multifactor dimensionality reduction , genome wide association study , locus (genetics) , computer science , bayesian probability , genetic association , curse of dimensionality , genome , random forest , dimensionality reduction , computational biology , data mining , gene , machine learning , biology , artificial intelligence , genetics , single nucleotide polymorphism , genotype
Complex diseases have affected human's health throughout the world. Hundreds of studies show that complex diseases are caused by multiple loci. Currently, genome-wide association studies(GWAS) only focus on the single locus that contributes to the susceptibility of a certain disease. However, the interaction between genes could be one of the main factors that lead to complex traits. This fact has initiated scientists to propose some algorithms to detect these interactions, such as the penalized logistic regression model, multifactor dimensionality reduction method, set association analysis method, Bayesian networks analysis method and random forest. However, these algorithms are of high complexity, hypothesis-driven, causing over fitting of data, or not sensible of data at low dimensions. In this paper, we reviewed these algorithms, and then demonstrated a new algorithm based on GPU to provide a powerful strategy to analyze gene-gene interaction in genome-wide association datasets. This algorithm is of low computing complexity, free of hypothesis, not affected by single locus marginal effect, and also of high stability and speed.