方法对比
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| 贝叶斯全基因组表观遗传关联研究 (Bayesian EWAS)× | 多组学全表观基因组关联研究× | |
|---|---|---|
| 领域 | 生物信息学 | 生物信息学 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 2010s (framework developed ~2013–2016) | 2011 (EWAS foundation); multi-omics integration ~2015–2020 |
| 提出者≠ | Multiple groups; Bayesian EWAS framework advanced by S. Richardson, P.-C. Tsai, J. T. Bell and colleagues | Rakyan, Down, Balding & Beck (EWAS framework); multi-omics integration extended by multiple groups (~2015–2020) |
| 类型≠ | Statistical association analysis | Integrative association study |
| 开创性文献≠ | 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 ↗ | Rakyan, V. K., Down, T. A., Balding, D. J., & Beck, S. (2011). Epigenome-wide association studies for common human diseases. Nature Reviews Genetics, 12(8), 529–541. DOI ↗ |
| 别名 | Bayesian EWAS, B-EWAS, Bayesian methylation-wide association study, Bayesian epigenetic association analysis | multi-omics EWAS, integrative EWAS, multi-layer epigenome-wide association, multi-omics epigenomic integration |
| 相关 | 4 | 4 |
| 摘要≠ | 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 multi-omics epigenome-wide association study (multi-omics EWAS) systematically scans the entire epigenome — typically DNA methylation at CpG sites — for associations with a phenotype of interest, then integrates findings across additional omics layers such as transcriptomics, genomics, proteomics, or metabolomics. By linking epigenetic variation to molecular changes at multiple biological levels simultaneously, this approach identifies regulatory mechanisms and biomarkers that single-omics EWAS cannot resolve. |
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