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	बेयसियन एपिजेनोम-व्यापी साहचर्य अध्ययन (Bayesian EWAS)	बेयसियन GWAS
क्षेत्र	जैव सूचना विज्ञान	जैव सूचना विज्ञान
परिवार	Process / pipeline	Process / pipeline
उद्भव वर्ष	2010s (framework developed ~2013–2016)	2007–2009 (formal statistical framework)
प्रवर्तक	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)
प्रकार	Statistical association analysis	Statistical genetic association analysis
मौलिक स्रोत	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.
उपनाम	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
संबंधित	4	5
सारांश	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.