Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Upigaji wa Vigezo× | Genome-wide association study× | |
|---|---|---|
| Nyanja | Bioinformatiki | Bioinformatiki |
| Familia | Process / pipeline | Process / pipeline |
| Mwaka wa asili≠ | 2009–2010 (modern high-throughput era) | 2005–2007 |
| Mwanzilishi≠ | Li et al. (SAMtools/bcftools, 2009); McKenna et al. (GATK, 2010) | Klein et al. (age-related macular degeneration GWAS, 2005); landmark scale: Wellcome Trust Case Control Consortium (2007) |
| Aina≠ | Computational genomics pipeline | Observational genomic association study |
| Chanzo asilia≠ | McKenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A., ... & DePristo, M. A. (2010). The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. Genome Research, 20(9), 1297–1303. 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 | SNP calling, genotyping from sequencing, mutation detection, variant detection | GWAS, genome-wide association analysis, whole-genome association study, WGAS |
| Zinazohusiana | 6 | 6 |
| Muhtasari≠ | Variant calling is the computational process of identifying positions in a sequenced genome that differ from a reference sequence — including single nucleotide polymorphisms (SNPs), small insertions and deletions (indels), and structural variants. It transforms aligned sequencing reads into an interpretable catalogue of genetic differences, forming the foundation for population genetics, disease-gene discovery, and clinical genomics applications. | 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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