Compara mètodes

	Anàlisi Filogenètica Bayesiana	GWAS bayesià
Camp	Bioinformàtica	Bioinformàtica
Família	Process / pipeline	Process / pipeline
Any d'origen	1996–2001	2007–2009 (formal statistical framework)
Autor original	Rannala & Yang (1996); operationalized by Huelsenbeck et al. (MrBayes, 2001)	Matthew Stephens, David J. Balding, Jon Wakefield (key formalizers ca. 2007–2009)
Tipus	Probabilistic inference method	Statistical genetic association analysis
Font seminal	Ronquist, F., & Huelsenbeck, J. P. (2003). MrBayes 3: Bayesian phylogenetic inference under mixed models. Bioinformatics, 19(12), 1572–1574.	Stephens, M., & Balding, D. J. (2009). Bayesian statistical methods for genetic association studies. Nature Reviews Genetics, 10(10), 681–690.
Àlies	Bayesian phylogenetics, Bayesian inference of phylogeny, MCMC phylogenetics, Bayesian molecular phylogenetics	Bayesian GWAS, Bayesian genome-wide association analysis, Bayesian GWA study, BF-GWAS
Relacionats	3	5
Resum	Bayesian phylogenetic analysis uses Bayes' theorem and Markov chain Monte Carlo (MCMC) sampling to estimate the posterior probability distribution over phylogenetic trees and model parameters given observed sequence data. Unlike bootstrapped maximum-likelihood methods that return a single best tree, Bayesian inference yields a credible set of trees with associated posterior probabilities, providing a principled measure of phylogenetic uncertainty. It is the dominant framework for estimating divergence times and ancestral relationships in molecular evolution.	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.