Two-stage designs to identify the effects of SNP combinations on complex diseases.

The genetic basis of complex diseases is expected to be highly heterogeneous, with many disease genes, where each gene by itself has only a small effect. Based on the nonlinear contributions of disease genes across the genome to complex diseases, we introduce the concept of single nucleotide polymorphism (SNP) synergistic blocks. A two-stage approach is applied to detect the genetic association of synergistic blocks with a disease. In the first stage, synergistic blocks associated with a complex disease are identified by clustering SNP patterns and choosing blocks within a cluster that minimize a diversity criterion. In the second stage, a logistic regression model is given for a synergistic block. Using simulated case-control data, we demonstrate that our method has reasonable power to identify gene-gene interactions. To further evaluate the performance of our method, we apply our method to 17 loci of four candidate genes for paranoid schizophrenia in a Chinese population. Five synergistic blocks are found to be associated with schizophrenia, three of which are negatively associated (odds ratio, OR < 0.3, P < 0.05), while the others are positively associated (OR > 2.0, P < 0.05). The mathematical models of these five synergistic blocks are presented. The results suggest that there may be interactive effects for schizophrenia among variants of the genes neuregulin 1 (NRG1, 8p22-p11), G72 (13q34), the regulator of G-protein signaling-4 (RGS4, 1q21-q22) and frizzled 3 (FZD3, 8p21). Using synergistic blocks, we can reduce the dimensionality in a multi-locus association analysis, and evaluate the sizes of interactive effects among multiple disease genes on complex phenotypes.