Genetic studies through the lens of gene networks

TL;DR

This paper reviews how integrating molecular data and machine learning to analyze gene networks enhances understanding of genetic contributions to complex traits and aids in identifying therapeutic targets.

Contribution

It introduces integrative approaches combining TWAS, gene co-expression, and drug profiles to better interpret genetic data through gene network analysis.

Findings

Effective gene modules identified via machine learning

Enhanced understanding of disease mechanisms

Potential therapeutic targets highlighted

Abstract

Understanding the genetic basis of complex traits is a longstanding challenge in the field of genomics. Genome-wide association studies (GWAS) have identified thousands of variant-trait associations, but most of these variants are located in non-coding regions, making the link to biological function elusive. While traditional approaches, such as transcriptome-wide association studies (TWAS), have advanced our understanding by linking genetic variants to gene expression, they often overlook gene-gene interactions. Here, we review current approaches to integrate different molecular data, leveraging machine learning methods to identify gene modules based on co-expression and functional relationships. These integrative approaches, like PhenoPLIER, combine TWAS and drug-induced transcriptional profiles to effectively capture biologically meaningful gene networks. This integration provides a context-specific understanding of disease processes while highlighting both core and peripheral genes. These insights pave the way for novel therapeutic targets and enhance the interpretability of genetic studies in personalized medicine.