# Turning heterogeneity of statistical epistasis networks to an advantage

## TL;DR

This paper introduces a workflow to manage inconsistencies in genetic interaction studies, turning diverse results into useful insights for biomedical research.

## Contribution

A novel workflow is introduced to characterize and leverage heterogeneity in GWAIS results using Statistical Epistasis Networks.

## Key findings

- Comparing SNP-pair rankings and SENs helps identify clusters of protocols with similar outcomes.
- SENs enable visualization of epistasis detection variation and prioritize recurrent interactions.
- Aggregating SENs can enhance the utility of GWAIS for understanding disease genetics.

## Abstract

Epistasis detection is hindered by multiple challenges, including the proliferation of analytic tools and the diverse methodological choices made in Genome-Wide Association Interaction Studies (GWAIS). These factors often produce inconsistent and only partially overlapping results, with individual methods emphasizing distinct aspects of epistasis. Although comparative evaluations of GWAIS approaches exist, they generally do not identify the factors responsible for methodological discrepancies or assess their implications for biomedical research. Consequently, it remains unclear which features of GWAIS strategies contribute most to these differences and which methods are most appropriate for revealing specific genetic architectures. Here, we present a workflow designed to characterize heterogeneity in GWAIS results and derive practical recommendations systematically. First, we assess non-replicability by comparing single nucleotide polymorphisms-pair rankings and Statistical Epistasis Networks (SENs)—graphs in which nodes represent genetic loci and edges denote epistatic interactions—to identify clusters of protocols with similar outcomes. SENs provide a structured framework for visualizing and comparing variation in epistasis detection, enabling prioritization of interactions recurrently identified across methods. Second, we propose strategies to reduce heterogeneity and enhance robustness, with particular emphasis on interpretability. Notably, we demonstrate that differences among SENs can be informative rather than disadvantageous, as they yield complementary perspectives on disease genetics. Finally, we highlight the benefits of informed SEN aggregation, showing how this approach can strengthen the utility of GWAIS for elucidating biological mechanisms relevant to disease prevention, diagnosis, and management.