手法を比較

	ベイズ的ゲノムワイド関連解析	系統解析
分野	バイオインフォマティクス	バイオインフォマティクス
系統	Process / pipeline	Process / pipeline
提唱年	2007–2009 (formal statistical framework)	1960s-1981 (distance trees ~1967; ML framework formalised 1981)
提唱者	Matthew Stephens, David J. Balding, Jon Wakefield (key formalizers ca. 2007–2009)	Joseph Felsenstein (maximum likelihood framework); Walter Fitch and Emanuel Margoliash (distance methods)
種類	Statistical genetic association analysis	Computational inference method
原典	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
別名	Bayesian GWAS, Bayesian genome-wide association analysis, Bayesian GWA study, BF-GWAS	molecular phylogenetics, phylogenetic inference, evolutionary tree reconstruction, phylogenomics
関連	5	5
概要	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.