방법 비교

	베이즈 에피유전체 연관성 연구 (Bayesian EWAS)	다중 오믹스 후성유전체 전장 연관성 연구
분야	생물정보학	생물정보학
계열	Process / pipeline	Process / pipeline
기원 연도	2010s (framework developed ~2013–2016)	2011 (EWAS foundation); multi-omics integration ~2015–2020
창시자	Multiple groups; Bayesian EWAS framework advanced by S. Richardson, P.-C. Tsai, J. T. Bell and colleagues	Rakyan, Down, Balding & Beck (EWAS framework); multi-omics integration extended by multiple groups (~2015–2020)
유형	Statistical association analysis	Integrative association study
원전	Richardson, S., Tsai, P. C., Bell, J. T., & Timpson, N. J. (2016). Bayesian approaches to studying associations between epigenetic marks and phenotypes. International Journal of Epidemiology, 45(3), 694–705.	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.
별칭	Bayesian EWAS, B-EWAS, Bayesian methylation-wide association study, Bayesian epigenetic association analysis	multi-omics EWAS, integrative EWAS, multi-layer epigenome-wide association, multi-omics epigenomic integration
관련	4	4
요약	A Bayesian EWAS is a genome-scale association analysis that links epigenetic marks — most commonly CpG-site DNA methylation — to a phenotype or trait of interest, replacing or supplementing the classical frequentist p-value framework with a Bayesian probabilistic model. It yields posterior probabilities of association and credible intervals for each CpG site, allowing formal incorporation of prior biological knowledge and more principled handling of the multiple-testing burden intrinsic to testing hundreds of thousands of sites simultaneously.	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.