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| Bayesiansk eQTL-analyse× | Pathway Enrichment Analysis× | |
|---|---|---|
| Fagområde | Bioinformatik | Bioinformatik |
| Familie | Process / pipeline | Process / pipeline |
| Oprindelsesår≠ | 2000s–2010s | 2003–2005 |
| Ophavsperson≠ | Matthew Stephens, David J. Balding (Bayesian framework for genetic association); extended by multiple groups for eQTL context | Mootha et al. (2003); systematised by Subramanian et al. (2005) |
| Type≠ | Probabilistic genomic association method | Statistical functional annotation method |
| Oprindelig kilde≠ | Stephens, M., & Balding, D. J. (2009). Bayesian statistical methods for genetic association studies. Nature Reviews Genetics, 10(10), 681–690. DOI ↗ | Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., & Mesirov, J. P. (2005). Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences, 102(43), 15545–15550. DOI ↗ |
| Aliasser | Bayesian eQTL mapping, probabilistic eQTL analysis, Bayesian QTL mapping for gene expression, eQTL fine-mapping | PEA, overrepresentation analysis, ORA, functional enrichment analysis |
| Relaterede | 6 | 6 |
| Resumé≠ | 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. | Pathway enrichment analysis (PEA) is a statistical approach that takes a list of genes or proteins of interest — typically derived from a differential expression or proteomics experiment — and identifies which pre-defined biological pathways or functional gene sets are represented more often than expected by chance. By mapping individual molecular changes onto curated pathway knowledge bases such as KEGG, Gene Ontology, or Reactome, PEA translates long gene lists into interpretable biological processes, making it a central tool in the post-analysis of high-throughput omics experiments. |
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