Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Мультиоміксне епігеном-широке асоціативне дослідження× | Аналіз eQTL× | |
|---|---|---|
| Галузь | Біоінформатика | Біоінформатика |
| Родина | Process / pipeline | Process / pipeline |
| Рік появи≠ | 2011 (EWAS foundation); multi-omics integration ~2015–2020 | 2001 (term coined); widely adopted after 2005 |
| Автор методу≠ | Rakyan, Down, Balding & Beck (EWAS framework); multi-omics integration extended by multiple groups (~2015–2020) | Ritsert C. Jansen & Jan-Peter Nap |
| Тип≠ | Integrative association study | Association mapping method |
| Основоположне джерело≠ | 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 ↗ | Jansen, R. C., & Nap, J.-P. (2001). Genetical genomics: the added value from segregation. Trends in Genetics, 17(7), 388–391. DOI ↗ |
| Інші назви | multi-omics EWAS, integrative EWAS, multi-layer epigenome-wide association, multi-omics epigenomic integration | eQTL mapping, expression QTL analysis, transcriptomic QTL analysis, eQTL study |
| Пов'язані≠ | 4 | 6 |
| Підсумок≠ | 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. | eQTL analysis identifies genomic loci (variants, typically SNPs) whose genotype statistically associates with variation in the expression level of one or more genes. By jointly profiling DNA-level variation and RNA-level expression in the same individuals, eQTL studies decode the regulatory grammar of the genome — revealing which variants control how much a gene is transcribed, in which tissues, and under what conditions. |
| ScholarGateНабір даних ↗ |
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