Inferring Gene Regulatory Networks from a Population of Yeast Segregants

Chen Chen,Tony R Hazbun,Min Zhang,Dabao Zhang

doi:10.1038/s41598-018-37667-4

Abstract

Constructing gene regulatory networks is crucial to unraveling the genetic architecture of complex traits and to understanding the mechanisms of diseases. On the basis of gene expression and single nucleotide polymorphism data in the yeast, Saccharomyces cerevisiae, we constructed gene regulatory networks using a two-stage penalized least squares method. A large system of structural equations via optimal prediction of a set of surrogate variables was established at the first stage, followed by consistent selection of regulatory effects at the second stage. Using this approach, we identified subnetworks that were enriched in gene ontology categories, revealing directional regulatory mechanisms controlling these biological pathways. Our mapping and analysis of expression-based quantitative trait loci uncovered a known alteration of gene expression within a biological pathway that results in regulatory effects on companion pathway genes in the phosphocholine network. In addition, we identify nodes in these gene ontology-enriched subnetworks that are coordinately controlled by transcription factors driven by trans-acting expression quantitative trait loci. Altogether, the integration of documented transcription factor regulatory associations with subnetworks defined by a system of structural equations using quantitative trait loci data is an effective means to delineate the transcriptional control of biological pathways.

Highlights

Gene expression is a fundamental step in the flow of information from an organism’s genotype to phenotype
It is computationally fast and allows for parallel implementation, outperforming the adaptive lasso based algorithm[23], and the sparsity-aware maximum likelihood algorithm[24], in terms of both accuracy and speed, for identifying regulatory effects in different network structures. This parallel implementation makes it feasible to evaluate the significance of regulatory effects via the bootstrap method. Using this approach we identified subnetworks that were enriched in gene ontology categories suggesting an extrinsic regulatory mechanism controlling these biological networks
Our expression quantitative trait loci (eQTL) predictions uncovered a known alteration of gene expression within a biological pathway that results in regulatory effects on companion pathway genes in the phosphocholine network

Summary

Introduction

Gene expression is a fundamental step in the flow of information from an organism’s genotype to phenotype. Genes rarely act in isolation; instead, they interact with each other and make up gene regulatory networks to function as a whole[2] The study of this mechanism is crucial for understanding the properties and functions of genes, which help reveal the genetic architecture of complex traits and diseases. Much effort has been devoted to using genetical genomics data for genome-wide association (GWA) analysis of gene expression, i.e., expression quantitative trait loci (eQTL) mapping[18]. While the cis effects of a gene represent direct regulations, indirect regulations of trans-eQTL are likely caused by interactions among genes These eQTL provide insight on the functional sequences of the gene expression, and an indirect interrogation of the functional landscape of gene regulations[19]

Methods

Results

Conclusion