A systematic analysis of gene-gene interaction in multiple sclerosis

Area of Science:

• Genetics
• Bioinformatics
• Computational Biology

Background:

• Genome-wide association studies (GWAS) partially explain complex disease heritability due to limitations in assessing variant interactions.
• Epistasis, the interaction between distant genetic loci, is crucial for understanding complex diseases but challenging to model.
• Existing tools for epistasis analysis, like EpiGWAS, require refined application for comprehensive disease map analysis.

Purpose of the Study:

• To develop and apply a computational pipeline for investigating gene-gene interactions (epistasis) across multiple disease maps using EpiGWAS.
• To identify biologically relevant epistatic interactions contributing to complex diseases, using multiple sclerosis as a case study.
• To explore the potential of identified interactions for developing novel combination therapies.

Main Methods:

• Developed a novel pipeline to apply EpiGWAS across 19 disease maps from the MetaCore pathway database.
• Utilized a multiple sclerosis GWAS dataset from the Wellcome Trust Case Control Consortium 2.
• Constructed epistatic networks by linking interacting genes within each disease map.

Main Results:

• Generated connected, hub-containing epistatic networks complementary to known disease maps.
• Identified 4 epistatic gene pairs with missense variants and 25 pairs with deleterious epistatic effects mediated by eQTLs.
• Highlighted significant interactions, including GLI-1/SUFU and IP10/NF-κB, with the latter showing therapeutic potential.

Conclusions:

• The developed pipeline effectively utilizes EpiGWAS to uncover biologically interpretable epistatic interactions.
• Identified epistatic interactions offer potential targets for combination therapy development in complex diseases.
• The approach provides a framework for dissecting the genetic architecture of complex diseases beyond additive effects.