Encore: Genetic Association Interaction Network centrality pipeline and application to SLE exome data

Area of Science:

• Genomics and Bioinformatics
• Computational Biology
• Network Analysis

Background:

• Construction and analysis of gene-gene interaction networks from sequencing data require specialized open-source tools.
• Existing methods may not adequately address the complexity of genome-wide association studies (GWAS) and rare variant data.
• Visualization and interpretation of complex genetic networks remain a challenge.

Purpose of the Study:

• To introduce Encore, an open-source network analysis pipeline designed for GWAS and rare variant data.
• To provide robust methods for constructing and analyzing genetic association interaction networks (epistasis networks).
• To facilitate the identification of disease susceptibility hubs and novel candidate genes through network analysis.

Main Methods:

• Encore utilizes two approaches for network construction: information-theory and generalized linear models.
• Incorporates data filtering techniques like Random Forest/Random Jungle and Evaporative Cooling/Relief-F for effect enrichment.
• Employs SNPrank network centrality to identify critical nodes (susceptibility hubs) and integrates with online visualization tools.

Main Results:

• Analysis of Systemic Lupus Erythematosus (SLE) exome resequencing data using Encore.
• Verification of known SLE genes (HLA-DRB, NCF2) as high-interaction hubs.
• Identification of 14 previously associated SLE genes and three novel candidate genes (ST8SIA4, CMTM4, C2CD4B) within the epistasis network.

Conclusions:

• Encore provides a comprehensive open-source tool for epistasis network analysis.
• This study presents the first epistasis network analysis of SLE exome data.
• The pipeline successfully identifies known and novel candidate genes relevant to complex diseases.