Coexpression network based on natural variation in human gene expression reveals gene interactions and functions

Area of Science:

• Genomics
• Systems Biology
• Human Genetics

Background:

• Gene interactions form complex networks essential for cellular functions.
• Analyzing these networks offers insights into gene functions and biological processes.
• Normal variation in human gene expression provides a resource for inferring gene networks.

Purpose of the Study:

• To construct and analyze human gene coexpression networks using natural variation in gene expression.
• To identify biological processes, predict functions of uncharacterized genes, and explore gene-disease associations.

Main Methods:

• Inferred gene networks by analyzing correlations in expression levels of over 8.5 million gene pairs.
• Utilized data from three independent samples of immortalized B cells.
• Applied network analysis to identify subnetworks, predict gene functions, and assess gene clustering related to disease susceptibility.

Main Results:

• Constructed comprehensive gene coexpression networks revealing co-occurring biological processes like translation and glycolysis.
• Predicted and experimentally validated functions for genes CHCHD2 and TMEM111, placing TMEM111 in the endoplasmic reticulum-associated secretory pathway.
• Identified interactions between disease-associated genes, such as IFIH1 (type 1 diabetes susceptibility) and YES1 (glucose transport).
• Observed nonrandom clustering of disease-predisposing genes within the network, suggesting network-based identification of disease susceptibility candidates.

Conclusions:

• Gene coexpression network analysis is a powerful tool for understanding gene function in normal cellular processes.
• These networks provide insights into the genetic architecture of complex diseases and can help identify candidate genes influencing disease susceptibility.
• The study highlights the utility of leveraging natural variation in gene expression for systems-level biological insights.