手法を比較

	機械学習支援エピゲノムワイド関連解析（ML-EWAS）	ゲノムワイド関連解析 (GWAS)
分野	バイオインフォマティクス	バイオインフォマティクス
系統	Process / pipeline	Process / pipeline
提唱年	2010s (methodological consolidation ~2015–2020)	2005–2007
提唱者	Teschendorff, Relton, and others in the epigenomics field	Klein et al. (age-related macular degeneration GWAS, 2005); landmark scale: Wellcome Trust Case Control Consortium (2007)
種類	Integrative omics analysis pipeline	Observational genomic association study
原典	Teschendorff, A. E., & Relton, C. L. (2018). Statistical and integrative system-level analysis of DNA methylation data. Nature Reviews Genetics, 19(3), 129–147.	Wellcome Trust Case Control Consortium. (2007). Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature, 447(7145), 661–678.
別名	ML-EWAS, machine learning EWAS, ML-assisted EWAS, epigenome-wide association study with machine learning	GWAS, genome-wide association analysis, whole-genome association study, WGAS
関連	3	6
概要	Machine learning-assisted EWAS integrates conventional epigenome-wide association testing with machine learning models to identify DNA methylation sites associated with a phenotype of interest. By combining the statistical rigour of EWAS with the pattern-recognition power of algorithms such as elastic net, random forest, or gradient boosting, this approach handles the extreme dimensionality of methylation arrays (450,000–850,000 CpG sites) more effectively than univariate testing alone, and can capture non-linear and interaction effects that standard linear models miss.	A genome-wide association study (GWAS) systematically tests hundreds of thousands to millions of single-nucleotide polymorphisms (SNPs) across the human genome for statistical association with a trait or disease. By comparing allele frequencies between cases and controls — or by regressing SNP genotypes on a quantitative phenotype — GWAS identifies genomic loci that harbor common genetic variants contributing to complex traits. Since its large-scale debut in 2007, GWAS has catalogued thousands of robust disease–variant associations across virtually every common human condition.