方法对比
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| 贝叶斯eQTL分析× | eQTL分析× | |
|---|---|---|
| 领域 | 生物信息学 | 生物信息学 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 2000s–2010s | 2001 (term coined); widely adopted after 2005 |
| 提出者≠ | Matthew Stephens, David J. Balding (Bayesian framework for genetic association); extended by multiple groups for eQTL context | Ritsert C. Jansen & Jan-Peter Nap |
| 类型≠ | Probabilistic genomic association method | Association mapping method |
| 开创性文献≠ | Stephens, M., & Balding, D. J. (2009). Bayesian statistical methods for genetic association studies. Nature Reviews Genetics, 10(10), 681–690. DOI ↗ | Jansen, R. C., & Nap, J.-P. (2001). Genetical genomics: the added value from segregation. Trends in Genetics, 17(7), 388–391. DOI ↗ |
| 别名 | Bayesian eQTL mapping, probabilistic eQTL analysis, Bayesian QTL mapping for gene expression, eQTL fine-mapping | eQTL mapping, expression QTL analysis, transcriptomic QTL analysis, eQTL study |
| 相关 | 6 | 6 |
| 摘要≠ | 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. | eQTL analysis identifies genomic loci (variants, typically SNPs) whose genotype statistically associates with variation in the expression level of one or more genes. By jointly profiling DNA-level variation and RNA-level expression in the same individuals, eQTL studies decode the regulatory grammar of the genome — revealing which variants control how much a gene is transcribed, in which tissues, and under what conditions. |
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