ArticleNucleic acids research2026
TWAS atlas 2.0: an updated data resource for transcriptome-wide association studies.
Article in Nucleic acids research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
2 citing papers in PubMed.
- AI semantics for biomedical data integration.bioRxiv : the preprint server for biology · 2026Article
- Decoding causal m6A: a bioinformatics roadmap for psychiatric disorders.Briefings in bioinformatics · 2026Review
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Authors and funding
10 authors.
Funding
Abstract
Transcriptome-wide association studies (TWAS), which combine large-scale GWAS and expression quantitative trait loci (eQTL) datasets to predict gene expression levels and identify trait-related genes, have recently been applied across many traits. TWAS Atlas is a comprehensive public resource that carefully curates TWAS results from a standard set of literature, helping researchers integrate and analyze numerous gene-trait associations. In TWAS Atlas 2.0 (https://ngdc.cncb.ac.cn/twas/), we not only include the latest high-quality TWAS findings but also incorporate GWAS datasets for performing TWAS analysis to expand the range of covered phenotypes, increasing gene-trait associations from 401 226 to 676 198, involving 590 traits and 24 870 genes. The knowledge graph has been significantly enhanced and enriched through information extraction from literature, now featuring new capabilities that greatly improve its usability and analytical power. We have developed multidimensional interactive analysis modules, including functional enrichment, Mendelian randomization, colocalization, and fine-mapping, providing researchers with comprehensive tools to explore causal relationships between genes and traits. These updates significantly enhance the utility of TWAS Atlas 2.0, facilitating a deeper understanding of gene expression and its regulation of complex traits.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.