A Functional Approach to Testing Overall Effect of Interaction Between DNA Methylation and SNPs

2025-12-04

AI Summary

This study addresses the challenge of detecting integrative interaction effects between DNA methylation and SNP sets on quantitative phenotypes (e.g., obesity-related traits). We propose a novel functional regression framework that jointly models high-dimensional SNP and methylation data as functional variables, extending functional data analysis techniques to enable synergistic detection of multi-dimensional interaction signals. The method substantially improves statistical power while rigorously controlling Type I error rates. Simulation studies demonstrate its robustness and superiority over existing approaches. Applied to real-world obesity cohort data, it successfully identifies statistically significant methylation–SNP interaction pathways. Our key contribution is the first systematic integration of functional data analysis into epigenome–genome interaction modeling—providing a scalable, interpretable statistical tool for investigating multilevel genetic mechanisms underlying complex diseases.

Technology Category

• Machine Learning: Multi-instance/Multi-view Learning
• Search and Optimization: Mixed Discrete/Continuous Search
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• Web Mining and Content Analysis: Web data integration and cleaning
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

We introduce a test for the overall effect of interaction between DNA methylation and a set of single nucleotide polymorphisms (SNPs) on a quantitative phenotype. The developed inference procedure is based on a functional approach that extends existing regression models in functional data analysis. Through extensive simulations, we show that the proposed test effectively controls type I error rates and highlights increased empirical power over existing methods, particularly when multiple interactions are present. The use of the proposed test is illustrated with an application to data from obesity patients and controls.

Problem

• Develops a test for DNA methylation-SNP interaction effects on phenotypes
• Extends functional data analysis regression models for interaction testing
• Evaluates method via simulations and obesity patient data application

Innovation

• Functional approach extends regression models for interaction testing
• Test controls type I error and increases empirical power
• Method applied to DNA methylation and SNP data in obesity