Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Байєсівський аналіз копійного числа (CNV)× | Повногеномне асоціативне дослідження (GWAS)× | |
|---|---|---|
| Галузь | Біоінформатика | Біоінформатика |
| Родина | Process / pipeline | Process / pipeline |
| Рік появи≠ | 2004–2007 | 2005–2007 |
| Автор методу≠ | Colella et al. (QuantiSNP); Fridlyand et al. (HMM-based Bayesian CNV) | Klein et al. (age-related macular degeneration GWAS, 2005); landmark scale: Wellcome Trust Case Control Consortium (2007) |
| Тип≠ | Probabilistic genomic analysis pipeline | Observational genomic association study |
| Основоположне джерело≠ | Colella, S., Yau, C., Taylor, J. M., Mirza, G., Butler, H., Clouston, P., Bassett, A. S., Seller, A., Holmes, C. C., & Ragoussis, J. (2007). QuantiSNP: an Objective Bayes Hidden-Markov Model to detect and accurately map copy number variation using SNP genotyping data. Nucleic Acids Research, 35(6), 2013–2025. 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 ↗ |
| Інші назви | Bayesian CNV analysis, Bayesian CNV calling, probabilistic CNV detection, Bayesian HMM-CNV | GWAS, genome-wide association analysis, whole-genome association study, WGAS |
| Пов'язані | 6 | 6 |
| Підсумок≠ | Bayesian copy number variation (CNV) analysis is a probabilistic framework for detecting genomic segments where an individual's DNA copy count deviates from the diploid norm. By placing prior distributions over copy-number states and updating them with array CGH, SNP array, or sequencing read-depth evidence, the approach yields posterior probabilities for each copy-number state along the genome, providing statistically principled uncertainty quantification that frequentist segmentation methods lack. | 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. |
| ScholarGateНабір даних ↗ |
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