Detection of regulator genes and eQTLs in gene networks

TL;DR

This paper reviews computational methods for identifying eQTLs and reconstructing gene regulatory networks to understand how genetic variants influence gene expression and molecular pathways.

Contribution

It provides a comprehensive overview of analytical tools and software for eQTL detection, network reconstruction, and validation in systems biology.

Findings

Summarizes key methods for eQTL identification

Describes approaches for causal network reconstruction

Highlights tools for in silico network validation

Abstract

Genetic differences between individuals associated to quantitative phenotypic traits, including disease states, are usually found in non-coding genomic regions. These genetic variants are often also associated to differences in expression levels of nearby genes (they are "expression quantitative trait loci" or eQTLs for short) and presumably play a gene regulatory role, affecting the status of molecular networks of interacting genes, proteins and metabolites. Computational systems biology approaches to reconstruct causal gene networks from large-scale omics data have therefore become essential to understand the structure of networks controlled by eQTLs together with other regulatory genes, and to generate detailed hypotheses about the molecular mechanisms that lead from genotype to phenotype. Here we review the main analytical methods and softwares to identify eQTLs and their associated genes, to reconstruct co-expression networks and modules, to reconstruct causal Bayesian gene and module networks, and to validate predicted networks in silico.