Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Studiu de Asociație Epigenom-Wide Bayesian (EWAS Bayesian)× | GWAS bayesian× | |
|---|---|---|
| Domeniu | Bioinformatică | Bioinformatică |
| Familie | Process / pipeline | Process / pipeline |
| Anul apariției≠ | 2010s (framework developed ~2013–2016) | 2007–2009 (formal statistical framework) |
| Autorul original≠ | 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) |
| Tip≠ | Statistical association analysis | Statistical genetic association analysis |
| Sursa seminală≠ | 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. link ↗ | Stephens, M., & Balding, D. J. (2009). Bayesian statistical methods for genetic association studies. Nature Reviews Genetics, 10(10), 681–690. DOI ↗ |
| Denumiri alternative | 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 |
| Înrudite≠ | 4 | 5 |
| Rezumat≠ | 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. |
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