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| Analisis eQTL Deret Waktu× | Studi Asosiasi Seluruh Genom (GWAS)× | |
|---|---|---|
| Bidang | Bioinformatika | Bioinformatika |
| Keluarga | Process / pipeline | Process / pipeline |
| Tahun asal≠ | 2010s–2019 (concept established earlier; dynamic framework formalized ~2019) | 2005–2007 |
| Pencetus≠ | Multiple groups; formalized by Strober et al. and others in the context of cellular differentiation (2019) | Klein et al. (age-related macular degeneration GWAS, 2005); landmark scale: Wellcome Trust Case Control Consortium (2007) |
| Tipe≠ | Genetic mapping method | Observational genomic association study |
| Sumber perintis≠ | Fair, B. J., et al. (2020). Gene expression variability in human and chimpanzee populations share common determinants. eLife, 9, e59929. link ↗ | 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 ↗ |
| Alias | dynamic eQTL analysis, longitudinal eQTL mapping, ts-eQTL, temporal eQTL | GWAS, genome-wide association analysis, whole-genome association study, WGAS |
| Terkait≠ | 2 | 6 |
| Ringkasan≠ | Time-series eQTL analysis identifies genetic variants (eQTLs) whose effect on gene expression changes over time or across developmental stages. By combining longitudinal RNA-seq data with individual genotypes, the method captures how the same SNP can activate, silence, or reshape gene regulation at different time points — revealing the temporal architecture of the genome's regulatory program in processes such as differentiation, disease progression, and environmental response. | 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. |
| ScholarGateSet data ↗ |
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