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The etiology of schizophrenia likely involves genetic interactions. DISC1, a promising candidate susceptibility gene, encodes a protein which interacts with many other proteins, including CIT, NDEL1, NDE1, FEZ1 and PAFAH1B1, some of which also have been associated with psychosis. We tested for epistasis between these genes in a schizophrenia case-control study using machine learning algorithms (MLAs: random forest, generalized boosted regression andMonteCarlo logic regression). Convergence of MLAs revealed a subset of seven SNPs that were subjected to 2-SNP interaction modeling using likelihood ratio tests for nested unconditional logistic regression models. Of the 7C2 = 21 interactions, four were significant at the α = 0.05 level: DISC1 rs1411771-CIT rs10744743 OR = 3.07 (1.37, 6.98) p = 0.007; CIT rs3847960-CIT rs203332 OR = 2.90 (1.45, 5.79) p = 0.003; CIT rs3847960-CIT rs440299 OR = 2.16 (1.04, 4.46) p = 0.038; one survived Bonferroni correction (NDEL1 rs4791707-CIT rs10744743 OR = 4.44 (2.22, 8.88) p = 0.00013). Three of four interactions were validated via functional magnetic resonance imaging (fMRI) in an independent sample of healthy controls; risk associated alleles at both SNPs predicted prefrontal cortical inefficiency during the N-back task, a schizophrenia-linked intermediate biological phenotype: rs3847960-rs440299; rs1411771-rs10744743, rs4791707-rs10744743 (SPM5 p < 0.05, corrected), although we were unable to statistically replicate the interactions in other clinical samples. Interestingly, the CIT SNPs are proximal to exons that encode theDISC1 interaction domain. In addition, the 3' UTR DISC1 rs1411771 is predicted to be an exonic splicing enhancer and the NDEL1 SNP is ~3,000 bp from the exon encoding the region of NDEL1 that interacts with the DISC1 protein, giving a plausible biological basis for epistasis signals validated by fMRI.

Catechol-O-methyltransferase (COMT) regulates dopamine degradation and is located in a genomic region that is deleted in a syndrome associated with psychosis, making it a promising candidate gene for schizophrenia. COMT also has been shown to influence prefrontal cortex processing efficiency. Prefrontal processing dysfunction is a common finding in schizophrenia, and a background of inefficient processing may modulate the effect of other candidate genes. Using the NIMH sibling study (SS), a non-independent case-control set, and an independent German (G) case-control set, we performed conditional/unconditional logistic regression to test for epistasis between SNPs in COMT (rs2097603, Val158Met (rs4680), rs165599) and polymorphisms in other schizophrenia susceptibility genes. Evidence for interaction was evaluated using a likelihood ratio test (LRT) between nested models. SNPs in RGS4, G72, GRM3, and DISC1 showed evidence for significant statistical epistasis with COMT. A striking result was found in RGS4: three of five SNPs showed a significant increase in risk [LRT P-values: 90387 = 0.05 (SS); SNP4 = 0.02 (SS), 0.02 (G); SNP18 = 0.04 (SS), 0.008 (G)] in interaction with COMT; main effects for RGS4 SNPs were null. Significant results for SNP4 and SNP18 were also found in the German study. We were able to detect statistical interaction between COMT and polymorphisms in candidate genes for schizophrenia, many of which had no significant main effect. In addition, we were able to replicate other studies, including allelic directionality. The use of epistatic models may improve replication of psychiatric candidate gene studies.