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| Epigenomweite Assoziationsstudie (EWAS)× | Analyse von Kopienzahlvariationen (CNV) – Erkennung und Interpretation von CNVs× | |
|---|---|---|
| Fachgebiet | Bioinformatik | Bioinformatik |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 2008–2011 (term and framework established c. 2011) | 1998–2006 |
| Urheber≠ | Rakyan, Down, Balding & Beck (conceptual framework); Illumina arrays enabled large-scale application | Pinkel et al. (array CGH); Redon et al. (genome-wide CNV map) |
| Typ≠ | Population-scale epigenomic association study | Genomic structural variant detection pipeline |
| Wegweisende Quelle≠ | 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 ↗ | Redon, R., Ishikawa, S., Fitch, K. R., et al. (2006). Global variation in copy number in the human genome. Nature, 444(7118), 444–454. DOI ↗ |
| Aliasnamen | EWAS, methylome-wide association study, epigenetic association study, DNA methylation association study | CNV analysis, copy number variant detection, CNV calling, somatic copy number alteration analysis |
| Verwandt≠ | 5 | 6 |
| Zusammenfassung≠ | 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. | Copy number variation (CNV) analysis is a genomic pipeline for detecting regions where individuals carry fewer or more copies of a DNA segment than the reference genome. CNVs span kilobases to megabases and are a major class of structural variation implicated in cancer, neurodevelopmental disorders, and population diversity. The pipeline typically processes SNP array intensities or read-depth signals from whole-genome sequencing, applies segmentation algorithms, calls gain and loss events, and annotates them against gene and clinical databases. |
| ScholarGateDatensatz ↗ |
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