手法を比較

	多 Omic's エピゲノムワイド関連解析	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	2011 (EWAS foundation); multi-omics integration ~2015–2020	2005–2007
提唱者	Rakyan, Down, Balding & Beck (EWAS framework); multi-omics integration extended by multiple groups (~2015–2020)	Klein et al. (age-related macular degeneration GWAS, 2005); landmark scale: Wellcome Trust Case Control Consortium (2007)
種類	Integrative association study	Observational genomic association study
原典	Rakyan, V. K., Down, T. A., Balding, D. J., & Beck, S. (2011). Epigenome-wide association studies for common human diseases. Nature Reviews Genetics, 12(8), 529–541.	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	multi-omics EWAS, integrative EWAS, multi-layer epigenome-wide association, multi-omics epigenomic integration	GWAS, genome-wide association analysis, whole-genome association study, WGAS
関連	4	6
概要	A multi-omics epigenome-wide association study (multi-omics EWAS) systematically scans the entire epigenome — typically DNA methylation at CpG sites — for associations with a phenotype of interest, then integrates findings across additional omics layers such as transcriptomics, genomics, proteomics, or metabolomics. By linking epigenetic variation to molecular changes at multiple biological levels simultaneously, this approach identifies regulatory mechanisms and biomarkers that single-omics EWAS cannot resolve.	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.