Combined SNP feature selection based on relief and SVM-RFE

The genome-wide association study(GWAS)on SNPs faces two big issues: high dimensional SNP data with small sample characteristics and complex mechanisms of genetic diseases.This paper proposed a combined SNP feature selection method through bring feature selection methods into GWAS.The method included two stages: filter stage,it used Relief algorithm to eliminate irrelevant SNP features,wrapper stage,it used support vector machine based recursive feature reduction(SVM-RFE) algorithm to select the key SNPs set.Experiments show that the proposed method has an obviously better performance than SVM RFE algorithm,and also gains higher classification accuracy than Relief-SVM algorithm,which provides an effective way for SNP genome-wide association analysis.