We designed a sparse representation clustering (SRC) model to select the significant single nucleotide polymorphisms (SNPs) and proposed a novel SRC with a sliding window model for functional magnetic resonance imaging (fMRI) voxels selection. Then we combined two types of data to classify schizophrenia patients from healthy controls by linear support vector machine (SVM) to achieve a better diagnosis of schizophrenia. The effectiveness of the selected variables (SNPs or voxels) was validated by the leave one out (LOO) cross-validation method. The experimental results show that our proposed SRC method can effectively select the most discriminative variables in both SNPs and fMRI data. In particular, the combination of complementary fMRI and SNP data can significantly improve the classification of schizophrenia patients, which provides new insights in the study of schizophrenia.