方法对比

	贝叶斯系统发育分析	贝叶斯全基因组关联研究 (Bayesian GWAS)	系统发育分析
领域	生物信息学	生物信息学	生物信息学
方法族	Process / pipeline	Process / pipeline	Process / pipeline
起源年份	1996–2001	2007–2009 (formal statistical framework)	1960s-1981 (distance trees ~1967; ML framework formalised 1981)
提出者	Rannala & Yang (1996); operationalized by Huelsenbeck et al. (MrBayes, 2001)	Matthew Stephens, David J. Balding, Jon Wakefield (key formalizers ca. 2007–2009)	Joseph Felsenstein (maximum likelihood framework); Walter Fitch and Emanuel Margoliash (distance methods)
类型	Probabilistic inference method	Statistical genetic association analysis	Computational inference method
开创性文献	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.	Felsenstein, J. (2004). Inferring Phylogenies. Sinauer Associates. ISBN: 978-0878931774
别名	Bayesian phylogenetics, Bayesian inference of phylogeny, MCMC phylogenetics, Bayesian molecular phylogenetics	Bayesian GWAS, Bayesian genome-wide association analysis, Bayesian GWA study, BF-GWAS	molecular phylogenetics, phylogenetic inference, evolutionary tree reconstruction, phylogenomics
相关	3	5	5
摘要	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.	Phylogenetic analysis reconstructs the evolutionary history of organisms, genes, or proteins by comparing molecular sequence data and estimating the branching tree that best explains observed similarities and differences. Rooted in the work of Felsenstein and colleagues from the 1960s onward, it is a cornerstone technique in evolutionary biology, microbiology, epidemiology, and comparative genomics, supporting tasks from tracing viral outbreak origins to classifying novel species.