Genome-wide association study (GWAS) is an effective method to study genetic variants associated with complex diseases or traits. Marginal statistical test is the common method of GWAS, however there following weakness such as lack of consideration of correlation between the features and unstable threshold selection. In this paper, we discuss a new method of GWAS based on multi-step tests model for cardiocerebrovascular disease. The method can be divided into the following two steps: Gini index is used for first step feature selection to achieve a subset of single-nucleotide polymorphisms (SNPs), and then random forest recursive cluster elimination (RF-RCE) filters the associated SNPs subset from first-step candidate SNP set. Experiment results show that the multi-step feature selection is better than the single-step feature selection, and the selected SNPs are more suitable for cardio-cerebrovascular disease prediction.