Сравнение на методи

	Мрежово базиран анализ на eQTL	Байесов анализ на eQTL
Област	Биоинформатика	Биоинформатика
Семейство	Process / pipeline	Process / pipeline
Година на възникване	2008–2013 (network-integrated extensions of eQTL mapping)	2000s–2010s
Създател	Multiple groups; foundational eQTL work by Cheung et al. (2005) and Stranger et al. (2007); network integration extended by Zhu et al. (2008) and others	Matthew Stephens, David J. Balding (Bayesian framework for genetic association); extended by multiple groups for eQTL context
Тип	Statistical genomics / network analysis pipeline	Probabilistic genomic association method
Основополагащ източник	Skinner, M. E., Uzilov, A. V., Stein, L. D., Mungall, C. J., & Holmes, I. H. (2009). JBrowse: a next-generation genome browser. Genome Research, 19(9), 1630–1638.	Stephens, M., & Balding, D. J. (2009). Bayesian statistical methods for genetic association studies. Nature Reviews Genetics, 10(10), 681–690.
Други названия	network eQTL, network-integrated eQTL mapping, graph-based eQTL analysis, eQTL network analysis	Bayesian eQTL mapping, probabilistic eQTL analysis, Bayesian QTL mapping for gene expression, eQTL fine-mapping
Свързани	5	6
Резюме	Network-based eQTL analysis extends classical eQTL mapping by embedding genetic variant-to-expression associations within gene regulatory or protein interaction networks. Rather than treating each SNP-gene pair independently, this approach leverages network topology — such as co-expression modules or known pathway structures — to improve statistical power, reduce multiple testing burden, and reveal how genetic variants perturb entire regulatory programs rather than isolated transcripts.	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.