Comparative study of multifactor dimensionality reduction and machine learning-based methods for analyzing gene-gene interaction effects on survival time

Genome-wide association studies have played a significant role in identifying genetic variants associated with diseases.However, single SNP analyses have shown limitations in explaining the heritability of complex traits.Increasing attention has been directed toward exploring gene-gene interactions to address these challenges.Studies on survival data with censoring remain relatively scarce compared to those focusing on binary or continuous traits.This study compares methodologies for detecting gene-gene interactions in survival data, focusing on multi-locus dimension reduction techniques and machine learning-based approaches.Representative methods were introduced, and their statistical power was evaluated through simulation studies under various realistic scenarios.Based on the results, this study proposes suitable methodologies for different scenarios, providing a practical guideline for effectively identifying gene-gene interaction effects in survival data.