Expression QTL Modules as Functional Components Underlying Higher-Order Phenotypes

Lei Bao,Yan Cui,Xuefeng Xia,Cathal Seoighe

doi:10.1371/journal.pone.0014313

Lei Bao, Yan Cui + Show 2 more

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https://doi.org/10.1371/journal.pone.0014313

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Abstract

Systems genetics studies often involve the mapping of numerous regulatory relations between genetic loci and expression traits. These regulatory relations form a bipartite network consisting of genetic loci and expression phenotypes. Modular network organizations may arise from the pleiotropic and polygenic regulation of gene expression. Here we analyzed the expression QTL (eQTL) networks derived from expression genetic data of yeast and mouse liver and found 65 and 98 modules respectively. Computer simulation result showed that such modules rarely occurred in randomized networks with the same number of nodes and edges and same degree distribution. We also found significant within-module functional coherence. The analysis of genetic overlaps and the evidences from biomedical literature have linked some eQTL modules to physiological phenotypes. Functional coherence within the eQTL modules and genetic overlaps between the modules and physiological phenotypes suggests that eQTL modules may act as functional units underlying the higher-order phenotypes.

Highlights

Recent advances in the integration of quantitative genetics and expression genomics have provided a global view of gene expression traits and their implications in high-order phenotype variations [1,2,3,4,5,6,7,8]
The Genetical Genomics [9] approach systematically associates gene expression traits with regulatory genomic regions called expression quantitative trait loci [10]. This high-throughput approach identifies a large set of regulatory relations between genetic markers and expression traits, which compose bipartite networks that consist of two types of nodes, representing expression traits and expression QTL (eQTL) respectively
We identified an eQTL module sharing genetic components with a mouse obesity phenotype — the gonadal fat mass (GFM), and evidences from previous studies strongly support the functional relevance between the module genes and obesity

Summary

Introduction

Recent advances in the integration of quantitative genetics and expression genomics have provided a global view of gene expression traits and their implications in high-order phenotype variations [1,2,3,4,5,6,7,8]. The Genetical Genomics [9] approach systematically associates gene expression traits with regulatory genomic regions called expression quantitative trait loci (eQTLs) [10]. This high-throughput approach identifies a large set of regulatory relations between genetic markers and expression traits, which compose bipartite networks that consist of two types of nodes, representing expression traits and eQTLs respectively. A Bayesian method for eQTL network partition was developed by Zhang et al [15] The application of their method to a yeast eQTL network identified 20 modules with one eQTL and 9 modules with two eQTLs [15]

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