השוואת שיטות

	מחקר אסוציאציה בייסיאני על פני כל האפיגנום (Bayesian EWAS)	GWAS בייסיאני
תחום	ביואינפורמטיקה	ביואינפורמטיקה
משפחה	Process / pipeline	Process / pipeline
שנת המקור	2010s (framework developed ~2013–2016)	2007–2009 (formal statistical framework)
הוגה השיטה	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)
סוג	Statistical association analysis	Statistical genetic association analysis
מקור מכונן	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.
כינויים	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
קשורות	4	5
תקציר	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.