Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Utafiti wa Uhusiano wa Epigenome-kote kwa Msingi wa Mfululizo wa Wakati× | Utafiti wa Chama cha Epigenome-kote (EWAS)× | |
|---|---|---|
| Nyanja | Bioinformatiki | Bioinformatiki |
| Familia | Process / pipeline | Process / pipeline |
| Mwaka wa asili≠ | 2010s | 2008–2011 (term and framework established c. 2011) |
| Mwanzilishi≠ | Extended from EWAS (Rakyan et al., 2011); longitudinal designs formalised by multiple groups ~2010s | Rakyan, Down, Balding & Beck (conceptual framework); Illumina arrays enabled large-scale application |
| Aina≠ | Longitudinal epigenomic association pipeline | Population-scale epigenomic association study |
| Chanzo asilia≠ | Pidsley, R., Zotenko, E., Peters, T. J., Lawrence, M. G., Risbridger, G. P., Molloy, P., ... & Clark, S. J. (2016). Critical evaluation of the Illumina MethylationEPIC BeadChip microarray for whole-genome DNA methylation profiling. Genome Biology, 17(1), 208. 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 ↗ |
| Majina mbadala | time-series EWAS, longitudinal EWAS, repeated-measures EWAS, dynamic methylation association study | EWAS, methylome-wide association study, epigenetic association study, DNA methylation association study |
| Zinazohusiana≠ | 3 | 5 |
| Muhtasari≠ | A time-series epigenome-wide association study (time-series EWAS) extends the classic cross-sectional EWAS design to longitudinal settings, measuring DNA methylation across the entire epigenome at multiple time points within the same subjects. The goal is to identify CpG sites whose methylation levels change systematically over time, or to characterise how epigenetic associations with an exposure or phenotype evolve across developmental stages, treatment periods, or disease trajectories. | An epigenome-wide association study (EWAS) is a hypothesis-free, genome-scale method that systematically tests whether epigenetic marks — predominantly CpG-site DNA methylation — differ between individuals with and without a trait, disease, or exposure. By scanning hundreds of thousands of genomic positions simultaneously, EWAS identifies loci where the epigenome is reproducibly associated with a phenotype, offering a layer of biological regulation that classical GWAS does not capture. |
| ScholarGateSeti ya data ↗ |
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