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

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...

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Veröffentlicht in:Nucleic acids research Jg. 50; H. D1; S. D1123 - D1130
Hauptverfasser: Cao, Chen, Wang, Jianhua, Kwok, Devin, Cui, Feifei, Zhang, Zilong, Zhao, Da, Li, Mulin Jun, Zou, Quan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: England Oxford University Press 07.01.2022
Schlagworte:
Database Issue
Databases, Genetic
Gene Expression Profiling
Genetic Association Studies
Genetic Diseases, Inborn - classification
Genetic Diseases, Inborn - genetics
Genetic Predisposition to Disease
Genome-Wide Association Study
Humans
Polymorphism, Single Nucleotide - genetics
Quantitative Trait Loci - genetics
Software
Transcriptome - genetics
ISSN:0305-1048, 1362-4962, 1362-4962
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Zusammenfassung: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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The authors wish it to be known that, in their opinion, the first two authors should be regarded as Joint First Authors.
ISSN:0305-1048
1362-4962
1362-4962
DOI:10.1093/nar/gkab957