方法对比

	贝叶斯全基因组关联研究 (Bayesian GWAS)	多基因风险评分
领域	生物信息学	遗传学
方法族	Process / pipeline	Process / pipeline
起源年份	2007–2009 (formal statistical framework)	2007
提出者	Matthew Stephens, David J. Balding, Jon Wakefield (key formalizers ca. 2007–2009)	Shaun Purcell & Nicholas Wray
类型	Statistical genetic association analysis	Predictive genomic method
开创性文献	Stephens, M., & Balding, D. J. (2009). Bayesian statistical methods for genetic association studies. Nature Reviews Genetics, 10(10), 681–690.	Purcell, S. M., Wray, N. R., Stone, J. L., Visscher, P. M., O'Donovan, M. C., Sullivan, P. F., & Sklar, P. (2007). Common polygenic variation contributes to risk of schizophrenia. Nature, 460(7256), 748–752.
别名	Bayesian GWAS, Bayesian genome-wide association analysis, Bayesian GWA study, BF-GWAS	PRS, Polygenic score, Genomic risk score
相关	5	4
摘要	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.	A polygenic risk score (PRS) is a summary measure that aggregates the effects of many genetic variants across the genome to predict an individual's genetic predisposition to disease or other complex traits. Developed initially by Purcell and colleagues in 2007, PRS methods combine genome-wide association study (GWAS) results with an individual's genotype to generate a personalized risk estimate. PRS approaches have transformed precision medicine by enabling risk stratification and early intervention in populations at high genetic risk.