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| Байесов анализ на eQTL× | Байесов ГВАС× | |
|---|---|---|
| Област | Биоинформатика | Биоинформатика |
| Семейство | Process / pipeline | Process / pipeline |
| Година на възникване≠ | 2000s–2010s | 2007–2009 (formal statistical framework) |
| Създател≠ | 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 |
| Основополагащ източник | Stephens, M., & Balding, D. J. (2009). Bayesian statistical methods for genetic association studies. Nature Reviews Genetics, 10(10), 681–690. DOI ↗ | Stephens, M., & Balding, D. J. (2009). Bayesian statistical methods for genetic association studies. Nature Reviews Genetics, 10(10), 681–690. DOI ↗ |
| Други названия | 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 |
| Свързани≠ | 6 | 5 |
| Резюме≠ | 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. |
| ScholarGateНабор от данни ↗ |
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