Krahasoni metodat

	Analiza Bajeziane e Variacionit të Numrit të Kopjeve	GWAS Bayesiane
Fusha	Bioinformatikë	Bioinformatikë
Familja	Process / pipeline	Process / pipeline
Viti i origjinës	2004–2007	2007–2009 (formal statistical framework)
Krijuesi	Colella et al. (QuantiSNP); Fridlyand et al. (HMM-based Bayesian CNV)	Matthew Stephens, David J. Balding, Jon Wakefield (key formalizers ca. 2007–2009)
Lloji	Probabilistic genomic analysis pipeline	Statistical genetic association analysis
Burimi themelues	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.
Emërtime të tjera	Bayesian CNV analysis, Bayesian CNV calling, probabilistic CNV detection, Bayesian HMM-CNV	Bayesian GWAS, Bayesian genome-wide association analysis, Bayesian GWA study, BF-GWAS
Të lidhura	6	5
Përmbledhja	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.