AI 中文总结
该研究针对生物医学中遗传与表观遗传因素对二元健康结果影响的挑战，提出逻辑函数回归模型，通过局部核使甲基化测量值与SNP基因型相互作用，能稳定推断区域水平相互作用，模拟显示其校准I型错误和检验效能优于经典方法。
AI 中文摘要
了解遗传和表观遗传因素如何共同影响二元健康结果仍然是生物医学研究中的一项重大挑战。我们提出了一种全局检验方法，用于检测DNA甲基化与一组单核苷酸多态性（SNP）之间的相互作用对二元表型的总体影响。我们提出了一种逻辑函数回归模型，其中将CpG位点的甲基化测量值转化为平滑的函数预测器，通过局部核与离散的SNP基因型相互作用。该框架能够在考虑SNP周围甲基化空间结构的同时，对区域水平的相互作用进行稳定推断。大量模拟表明，与经典的SNP-CpG成对分析相比，所提出的检验具有良好校准I型错误和更高的检验效能。使用来自肥胖病例对照研究的公开甲基化和基因分型数据说明了该方法的实际相关性。
英文摘要
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.
