A Network based Covariance Test for Detecting Network Multivariate eQTL

in Saccharomyces cerevisiae

Huili Yuan¹, Zhenyei Li ¹, Nelson L.S. Tang² and Minghua Deng^{1,3,4, §}

1. LMAM, School of Mathematical Sciences, Peking University, Beijing 100871, PR China.
2. Department of Chemical Pathology, Prince of Wales Hospital, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong, China.
3. Center for Quantitative Biology, Peking University, Beijing 100871, PR China.
4. Center for Statistical Science, Peking University, Beijing 100871, PR China.

^§E-mail: dengmh@math.pku.edu.cn

Abstract

Expression 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 network structure has not been considered. So we propose a process and algorithm to identify the marker which affects the structure 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.

When 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.

We 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.

Source codes:

The algorithm is implemented in R language and the source code is available here.

Usage:

Please see the ReadMe file for details.

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Last Update: 05/31/2015

Questions, comments, suggestions, please contact hlyuan@pku.edu.cn , dengmh@math.pku.edu.cn