A network based covariance test for detecting multivariate eQTL in saccharomyces cerevisiae

Abstract

BackgroundExpression quantitative trait locus (eQTL) analysis has been widely used to understand how genetic variations affect gene expressions in the biological systems. Traditional eQTL is investigated in a pair-wise manner in which one SNP affects the expression of one gene. In this way, some associated markers found in GWAS have been related to disease mechanism by eQTL study. However, in real life, biological process is usually performed by a group of genes. Although some methods have been proposed to identify a group of SNPs that affect the mean of gene expressions in the network, the change of co-expression pattern has not been considered. So we propose a process and algorithm to identify the marker which affects the co-expression pattern of a pathway. Considering two genes may have different correlations under different isoforms which is hard to detect by the linear test, we also consider the nonlinear test.ResultsWhen we applied our method to yeast eQTL dataset profiled under both the glucose and ethanol conditions, we identified a total of 166 modules, with each module consisting of a group of genes and one eQTL where the eQTL regulate the co-expression patterns of the group of genes. We found that many of these modules have biological significance.ConclusionsWe propose a network based covariance test to identify the SNP which affects the structure of a pathway. We also consider the nonlinear test as considering two genes may have different correlations under different isoforms which is hard to detect by linear test.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-015-0245-0) contains supplementary material, which is available to authorized users.

Set‐based differential covariance testing for genomics

“…The liquid association method [11] was developed to decribe ternary relationships among genes, but the underlying motivation is based on the concept of differential covariance. For tests of biological systems, ensemble tests of covariance matrices for multiple genes in a proposed network or pathway can provide insight that might not be apparent in tests of individual gene pairs [20]. Classical likelihood approaches require the sample size n to be large compared to the number of features p [8,1].…”