Although schizophrenia is generally considered to occur as a consequence of multiple genes that interact with one another, very few methods have been developed to model epistasis. Phenotype definition has also been a major challenge for research on the genetics of schizophrenia. In this report, we use novel statistical techniques to address the high dimensionality of genomic data, and we apply a refinement in phenotype definition by basing it on the occurrence of brain changes during the early course of the illness, as measured by repeated magnetic resonance scans (i.e., an 'intermediate phenotype.') The method combines a machine-learning algorithm, the ensemble method using stochastic gradient boosting, with traditional general linear model statistics. We began with 14 genes that are relevant to schizophrenia, based on association studies or their role in neurodevelopment, and then used statistical techniques to reduce them to five genes and 17 single nucleotide polymorphisms (SNPs) that had a significant statistical interaction: five for PDE4B, four for RELN, four for ERBB4, three for DISC1 and one for NRG1. Five of the SNPs involved in these interactions replicate previous research in that, these five SNPs have previously been identified as schizophrenia vulnerability markers or implicate cognitive processes relevant to schizophrenia. This ability to replicate previous work suggests that our method has potential for detecting a meaningful epistatic relationship among the genes that influence brain abnormalities in schizophrenia.