Genome-wide association studies (GWAS) refer to the method that uses correlation analysis to identify disease associated genes. Traditional research method did not consider the interaction between genes and had low accuracy and efficiency in the case of complex factors. Aimed at these aforementioned problems, this paper presents a key SNPs selecting algorithm based on mutual information. It constructs reversely the SNPs interaction network using simulation data based on the theory of mutual information and compares the difference of the statistics of SNPs interaction networks between case and control groups with the increase of the mutual information threshold. According to the selected threshold, we select the structural key SNPs. The results of experiments show that the method of parameter selection presented in this paper is useful to select the structural key SNPs.