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.