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. 2022 Jan 7;50(D1):D1123-D1130.
doi: 10.1093/nar/gkab957.

webTWAS: a resource for disease candidate susceptibility genes identified by transcriptome-wide association study

Chen Cao  1   2   3 , Jianhua Wang  4 , Devin Kwok  5 , Feifei Cui  1   2 , Zilong Zhang  1   2 , Da Zhao  1   2 , Mulin Jun Li  4 , Quan Zou  1   2
Affiliations

webTWAS: a resource for disease candidate susceptibility genes identified by transcriptome-wide association study

Chen Cao et al. Nucleic Acids Res. .

Abstract

The development of transcriptome-wide association studies (TWAS) has enabled researchers to better identify and interpret causal genes in many diseases. However, there are currently no resources providing a comprehensive listing of gene-disease associations discovered by TWAS from published GWAS summary statistics. TWAS analyses are also difficult to conduct due to the complexity of TWAS software pipelines. To address these issues, we introduce a new resource called webTWAS, which integrates a database of the most comprehensive disease GWAS datasets currently available with credible sets of potential causal genes identified by multiple TWAS software packages. Specifically, a total of 235 064 gene-diseases associations for a wide range of human diseases are prioritized from 1298 high-quality downloadable European GWAS summary statistics. Associations are calculated with seven different statistical models based on three popular and representative TWAS software packages. Users can explore associations at the gene or disease level, and easily search for related studies or diseases using the MeSH disease tree. Since the effects of diseases are highly tissue-specific, webTWAS applies tissue-specific enrichment analysis to identify significant tissues. A user-friendly web server is also available to run custom TWAS analyses on user-provided GWAS summary statistics data. webTWAS is freely available at http://www.webtwas.net.

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Figures

Figure 1.
Figure 1.
The overall architecture of the webTWAS platform. (A) Data processing workflow and query results. (B) Framework of online web server for running custom TWAS analyses.
Figure 2.
Figure 2.
Interface of webTWAS resource. (A) Interface for searching GWAS datasets/publications by disease and by browsing the MeSH ontology tree. (B) Example of disease webpage, showing potential causal genes for type 1 diabetes. (C) Interface for searching TWAS associations by gene. (D) Example of gene webpage, showing associated diseases for gene MAP3K6.
Figure 3.
Figure 3.
Interface for online TWAS analysis. (A) Job submission interface. Users must specify parameters (red), upload GWAS summary statistics (light green) and select a reference tissue/statistical model (purple). (B) Example of job results webpage showing table of significant genes (blue), link to download CSV file (yellow) and Manhattan plot (dark green).
See this image and copyright information in PMC

References

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