iRUNNER: A Baseline Mutation Burden Regression for Identifying Gene Interaction Between Rare Variants for Diseases

Area of Science:

• Genetics
• Genomics
• Computational Biology

Background:

• Complex multifactorial diseases arise from intricate genetic interactions.
• Identifying epistatic effects of rare variants is challenging for current genome-wide association studies.
• Existing methods lack efficiency in detecting rare variant interactions.

Purpose of the Study:

• To introduce iRUNNER, a novel mutation burden test for analyzing rare variant interaction effects on binary traits.
• To provide a powerful and efficient method for detecting epistatic impacts of rare variants.
• To improve the understanding of genetic contributions to complex diseases.

Main Methods:

• iRUNNER employs a recursive truncated negative-binomial regression model.
• It evaluates the relative enrichment of rare variant interaction burden in patients versus controls.
• The model incorporates multiple genomic features from public databases.

Main Results:

• iRUNNER demonstrated superior statistical power compared to existing epistasis tests in simulations.
• It maintained acceptable type I error rates, even with population stratification.
• Application to real datasets revealed significant gene-gene interaction detections, particularly in smaller samples, often missed by other methods.

Conclusions:

• iRUNNER is a powerful tool for detecting rare variant gene-gene interactions in complex diseases.
• The identified gene pairs form interconnected networks, offering insights into disease mechanisms.
• iRUNNER is integrated into the KGGSeq platform for rapid interaction analysis.