Порівняння методів

	Байєсівський аналіз eQTL	Байєсівське GWAS
Галузь	Біоінформатика	Біоінформатика
Родина	Process / pipeline	Process / pipeline
Рік появи	2000s–2010s	2007–2009 (formal statistical framework)
Автор методу	Matthew Stephens, David J. Balding (Bayesian framework for genetic association); extended by multiple groups for eQTL context	Matthew Stephens, David J. Balding, Jon Wakefield (key formalizers ca. 2007–2009)
Тип	Probabilistic genomic association method	Statistical genetic association analysis
Основоположне джерело	Stephens, M., & Balding, D. J. (2009). Bayesian statistical methods for genetic association studies. Nature Reviews Genetics, 10(10), 681–690.	Stephens, M., & Balding, D. J. (2009). Bayesian statistical methods for genetic association studies. Nature Reviews Genetics, 10(10), 681–690.
Інші назви	Bayesian eQTL mapping, probabilistic eQTL analysis, Bayesian QTL mapping for gene expression, eQTL fine-mapping	Bayesian GWAS, Bayesian genome-wide association analysis, Bayesian GWA study, BF-GWAS
Пов'язані	6	5
Підсумок	Bayesian eQTL analysis identifies genetic variants (eQTLs) that regulate gene expression by combining genotype and RNA-seq data within a probabilistic framework. Unlike frequentist approaches that rely on p-value thresholds, the Bayesian formulation produces posterior probabilities of association, enabling principled fine-mapping of causal variants and coherent uncertainty quantification across thousands of gene-SNP pairs simultaneously.	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.