Global testing of SNP-methylation interactions on binary phenotypes via a logistic functional regression model

2026-07-10

AI Summary

This study addresses the challenge of effectively evaluating the joint interaction between single nucleotide polymorphisms (SNPs) and DNA methylation in relation to binary health outcomes. To this end, the authors propose a novel logistic functional regression model that treats CpG site methylation levels as smooth functions and incorporates a local kernel approach to model region-level interactions with discrete SNP genotypes. This framework uniquely integrates functional data analysis with local kernel strategies to enable a global test for SNP–methylation interaction effects while preserving the spatial structure inherent in methylation data. Simulation studies demonstrate that the method maintains strict control of Type I error while achieving substantially higher statistical power compared to conventional pairwise interaction analyses. Application to a case–control dataset on obesity further confirms its practical utility and effectiveness.

Abstract

Understanding how genetic and epigenetic factors jointly influence binary health outcomes remains a major challenge in biomedical research. We propose a global test for the overall effect of interactions between DNA methylation and a set of single nucleotide polymorphisms (SNPs) on a binary phenotype. We propose a logistic functional regression model in which methylation measurements at CpG sites are transformed into smooth functional predictors interacting with discrete SNP genotypes through a localized kernel. This framework enables stable inference on region-level interactions while accounting for the spatial structure of methylation around SNPs. Extensive simulations show that the proposed test provides well-calibrated type I error and improved power over classical SNP-CpG pairwise analyses. The practical relevance of the method is illustrated using publicly available methylation and genotyping data from an obesity case-control study.

Problem

• SNP-methylation interaction
• binary phenotype
• global test
• DNA methylation
• genetic-epigenetic interaction

Innovation

• logistic functional regression
• SNP-methylation interaction
• global test
• functional predictor
• localized kernel