Statistical epistasis and progressive brain change in schizophrenia: an approach for examining the relationships between multiple genes

Abstract

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 MR 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 fourteen 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 SNPs that had a significant statistical interaction: 5 for PDE4B, 4 for RELN, 4 for ERBB4, 3 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 relationships among the genes that influence brain abnormalities in schizophrenia.