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

초록

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.

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