方法对比
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| 基于网络的全基因组关联研究× | 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≠ | 2011–2013 (early tools); mature framework by 2015 | 2001 (term coined); widely adopted after 2005 |
| 提出者≠ | Jia et al. (dmGWAS, 2011); Baranzini et al.; multiple concurrent groups | Ritsert C. Jansen & Jan-Peter Nap |
| 类型≠ | Network-augmented association analysis | Association mapping method |
| 开创性文献≠ | Wang, Q., Yu, H., Zhao, Z., & Jia, P. (2015). EW_dmGWAS: edge-weighted dense module search for genome-wide association studies and gene expression profiles. Bioinformatics, 31(15), 2591–2594. link ↗ | Jansen, R. C., & Nap, J.-P. (2001). Genetical genomics: the added value from segregation. Trends in Genetics, 17(7), 388–391. DOI ↗ |
| 别名 | network GWAS, gene network GWAS, network-informed GWAS, NbGWAS | eQTL mapping, expression QTL analysis, transcriptomic QTL analysis, eQTL study |
| 相关 | 6 | 6 |
| 摘要≠ | Network-based GWAS integrates conventional genome-wide association study results with biological network data — such as protein-protein interaction (PPI) networks or gene co-expression graphs — to identify disease-relevant gene modules or subnetworks. Instead of reporting only the top individual SNPs, this approach propagates association signals through molecular interaction networks, surfacing gene clusters whose collective signal implicates them in complex-trait biology even when no single variant reaches genome-wide significance alone. | 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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