Comparar métodos

	Análise Bayesiana de Variação do Número de Cópias	GWAS Bayesiano
Área	Bioinformática	Bioinformática
Família	Process / pipeline	Process / pipeline
Ano de origem	2004–2007	2007–2009 (formal statistical framework)
Autor original	Colella et al. (QuantiSNP); Fridlyand et al. (HMM-based Bayesian CNV)	Matthew Stephens, David J. Balding, Jon Wakefield (key formalizers ca. 2007–2009)
Tipo	Probabilistic genomic analysis pipeline	Statistical genetic association analysis
Fonte seminal	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.	Stephens, M., & Balding, D. J. (2009). Bayesian statistical methods for genetic association studies. Nature Reviews Genetics, 10(10), 681–690.
Outros nomes	Bayesian CNV analysis, Bayesian CNV calling, probabilistic CNV detection, Bayesian HMM-CNV	Bayesian GWAS, Bayesian genome-wide association analysis, Bayesian GWA study, BF-GWAS
Relacionados	6	5
Resumo	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.	Bayesian GWAS applies Bayesian statistical inference to genome-wide association studies, replacing classical p-value thresholds with Bayes factors and posterior probabilities. This framework naturally incorporates prior knowledge about effect sizes and variant frequencies, quantifies evidence for association on a continuous scale, and supports principled fine-mapping of causal variants within associated loci. It is widely used in complex trait genetics, population genomics, and translational research where uncertainty quantification and multi-variant modeling matter.