Linganisha mbinu

	Bayesian GWAS	Alama ya Hatari ya Poligeniki
Nyanja	Bioinformatiki	Jenetiki
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	2007–2009 (formal statistical framework)	2007
Mwanzilishi	Matthew Stephens, David J. Balding, Jon Wakefield (key formalizers ca. 2007–2009)	Shaun Purcell & Nicholas Wray
Aina	Statistical genetic association analysis	Predictive genomic method
Chanzo asilia	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.
Majina mbadala	Bayesian GWAS, Bayesian genome-wide association analysis, Bayesian GWA study, BF-GWAS	PRS, Polygenic score, Genomic risk score
Zinazohusiana	5	4
Muhtasari	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.