ANALYSIS OF GENE EXPRESSION VARIANCE AND GENETIC REGULATION OF GENE EXPRESSION BASED ON VARIANCE ASSOCIATION MAPPING

Background The objective of this work is the application of advanced computational and statistical methods to identify interactions between genetic variants of brain-expressed genes. The focus is particularly on the SNPs that significantly influence the gene expression in a non-additive manner. The phenomenon, called epistasis, is one of the possible explanations of the missing heritability of complex disorders resulting from classical SNP-by-SNP analysis of Genome Wide Association Studies. The discovery of epistatic interaction at genome-wide level has always been challenging due to the extremely high number of tests required in the absence of biological hypotheses. However, epistasis on gene expression as an intermediate phenotype may be more tractable due the larger effect size expected with respect to disease traits, and its quantitative nature. Methods In this project, we apply a step-wise approach to the identification and replication of SNP-SNP interactions in brain transcriptome data based on variance association mapping, which allows for the prioritization of genes for subsequent epistasis discovery based on the identification of genes with genotype-dependent variance. Results We have implemented a method for variance association mapping on brain (DLPFC) RNASeq data (from CommonMind Consortium) and identified a set of candidate expression variance QTL. Epistasis-discovery was then performed, resulting with the identification of statistically significant cis and trans effects, for which replication in additional brain datasets is ongoing. To evaluate the biological medical relevance of the interactions, bioinformatics analyses are in progress. Discussion Our results, which putatively identify complex gene-gene interactions contributing to the regulation of brain gene expression, could highlight functional mechanisms that may underlie common psychiatric disorders.