পদ্ধতির তুলনা করুন

	বেয়েশীয় GWAS	বেয়েশীয় eQTL বিশ্লেষণ
ক্ষেত্র	জৈব তথ্যবিজ্ঞান	জৈব তথ্যবিজ্ঞান
পরিবার	Process / pipeline	Process / pipeline
উদ্ভবের বছর	2007–2009 (formal statistical framework)	2000s–2010s
প্রবর্তক	Matthew Stephens, David J. Balding, Jon Wakefield (key formalizers ca. 2007–2009)	Matthew Stephens, David J. Balding (Bayesian framework for genetic association); extended by multiple groups for eQTL context
ধরন	Statistical genetic association analysis	Probabilistic genomic association method
মৌলিক উৎস	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.
অপর নাম	Bayesian GWAS, Bayesian genome-wide association analysis, Bayesian GWA study, BF-GWAS	Bayesian eQTL mapping, probabilistic eQTL analysis, Bayesian QTL mapping for gene expression, eQTL fine-mapping
সম্পর্কিত	5	6
সারসংক্ষেপ	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.	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.