Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Epigenome-Wide Association Study katika Utafiti wa Kielimu× | Genome-wide association study× | |
|---|---|---|
| Nyanja | Bioinformatiki | Bioinformatiki |
| Familia | Process / pipeline | Process / pipeline |
| Mwaka wa asili≠ | EWAS framework ~2011; educational applications ~2017–present | 2005–2007 |
| Mwanzilishi≠ | Rakyan, Down, Balding, and Beck (framework); applied to educational outcomes by Marioni, McCartney, and collaborators | Klein et al. (age-related macular degeneration GWAS, 2005); landmark scale: Wellcome Trust Case Control Consortium (2007) |
| Aina≠ | Observational epigenomic association design | Observational genomic association study |
| Chanzo asilia≠ | 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 ↗ | 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 ↗ |
| Majina mbadala | EWAS of educational attainment, educational EWAS, epigenetic association study, EWAS | GWAS, genome-wide association analysis, whole-genome association study, WGAS |
| Zinazohusiana≠ | 3 | 6 |
| Muhtasari≠ | An epigenome-wide association study (EWAS) applied to educational research scans DNA methylation levels at hundreds of thousands of CpG sites across the genome to identify loci whose methylation is statistically associated with educational attainment, cognitive ability, or related learning outcomes. By linking blood- or saliva-derived methylation profiles with school records or psychometric scores, EWAS offers a molecular window into how biological and environmental exposures may shape educationally relevant traits across the lifespan. | 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. |
| ScholarGateSeti ya data ↗ |
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