เปรียบเทียบวิธี

	Bayesian epigenome-wide association study	การศึกษาความสัมพันธ์ทั่วทั้งจีโนมแบบเบย์
สาขาวิชา	ชีวสารสนเทศศาสตร์	ชีวสารสนเทศศาสตร์
ตระกูล	Process / pipeline	Process / pipeline
ปีกำเนิด	2010s (framework developed ~2013–2016)	2007–2009 (formal statistical framework)
ผู้ริเริ่ม	Multiple groups; Bayesian EWAS framework advanced by S. Richardson, P.-C. Tsai, J. T. Bell and colleagues	Matthew Stephens, David J. Balding, Jon Wakefield (key formalizers ca. 2007–2009)
ประเภท	Statistical association analysis	Statistical genetic association analysis
แหล่งต้นตำรับ	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.	Stephens, M., & Balding, D. J. (2009). Bayesian statistical methods for genetic association studies. Nature Reviews Genetics, 10(10), 681–690.
ชื่อเรียกอื่น	Bayesian EWAS, B-EWAS, Bayesian methylation-wide association study, Bayesian epigenetic association analysis	Bayesian GWAS, Bayesian genome-wide association analysis, Bayesian GWA study, BF-GWAS
ที่เกี่ยวข้อง	4	5
สรุป	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.	Bayesian GWAS applies Bayesian statistical inference to genome-wide association studies, replacing classical p-value thresholds with Bayes factors and posterior probabilities. This framework naturally incorporates prior knowledge about effect sizes and variant frequencies, quantifies evidence for association on a continuous scale, and supports principled fine-mapping of causal variants within associated loci. It is widely used in complex trait genetics, population genomics, and translational research where uncertainty quantification and multi-variant modeling matter.