Sammenlign metoder
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| Genomdækkende associationsstudie (GWAS)× | Analyse af kopitalvarians× | |
|---|---|---|
| Fagområde | Bioinformatik | Bioinformatik |
| Familie | Process / pipeline | Process / pipeline |
| Oprindelsesår≠ | 2005–2007 | 1998–2006 |
| Ophavsperson≠ | Klein et al. (age-related macular degeneration GWAS, 2005); landmark scale: Wellcome Trust Case Control Consortium (2007) | Pinkel et al. (array CGH); Redon et al. (genome-wide CNV map) |
| Type≠ | Observational genomic association study | Genomic structural variant detection pipeline |
| Oprindelig kilde≠ | 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 ↗ | 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 ↗ |
| Aliasser | GWAS, genome-wide association analysis, whole-genome association study, WGAS | CNV analysis, copy number variant detection, CNV calling, somatic copy number alteration analysis |
| Relaterede | 6 | 6 |
| Resumé≠ | 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. | 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. |
| ScholarGateDatasæt ↗ |
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