Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Studiu de Asociație Epigenom-Wide Bayesian (EWAS Bayesian)× | Studiu de asociere la nivel de genom (GWAS)× | |
|---|---|---|
| Domeniu | Bioinformatică | Bioinformatică |
| Familie | Process / pipeline | Process / pipeline |
| Anul apariției≠ | 2010s (framework developed ~2013–2016) | 2005–2007 |
| Autorul original≠ | Multiple groups; Bayesian EWAS framework advanced by S. Richardson, P.-C. Tsai, J. T. Bell and colleagues | Klein et al. (age-related macular degeneration GWAS, 2005); landmark scale: Wellcome Trust Case Control Consortium (2007) |
| Tip≠ | Statistical association analysis | Observational genomic association study |
| 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 ↗ | Wellcome Trust Case Control Consortium. (2007). Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature, 447(7145), 661–678. link ↗ |
| Denumiri alternative | Bayesian EWAS, B-EWAS, Bayesian methylation-wide association study, Bayesian epigenetic association analysis | GWAS, genome-wide association analysis, whole-genome association study, WGAS |
| Înrudite≠ | 4 | 6 |
| 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. | A genome-wide association study (GWAS) systematically tests hundreds of thousands to millions of single-nucleotide polymorphisms (SNPs) across the human genome for statistical association with a trait or disease. By comparing allele frequencies between cases and controls — or by regressing SNP genotypes on a quantitative phenotype — GWAS identifies genomic loci that harbor common genetic variants contributing to complex traits. Since its large-scale debut in 2007, GWAS has catalogued thousands of robust disease–variant associations across virtually every common human condition. |
| ScholarGateSet de date ↗ |
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