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

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webTWAS: a resource for disease candidate susceptibility genes identified by transcriptome-wide association study
Nucleic Acids Research ( IF 16.6 ) Pub Date : 2021-10-06 , DOI: 10.1093/nar/gkab957
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}

Affiliation

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.

中文翻译：

webTWAS：全转录组关联研究确定的疾病候选易感基因资源

全转录组关联研究 (TWAS) 的发展使研究人员能够更好地识别和解释许多疾病中的致病基因。然而，目前没有资源提供 TWAS 从已发布的 GWAS 汇总统计数据中发现的基因-疾病关联的全面列表。由于 TWAS 软件管道的复杂性，TWAS 分析也难以进行。为了解决这些问题，我们引入了一个名为 webTWAS 的新资源，它集成了一个包含当前可用的最全面的疾病 GWAS 数据集的数据库，以及由多个 TWAS 软件包识别的可靠的潜在因果基因集。具体而言，从 1298 个可下载的高质量欧洲 GWAS 摘要统计数据中优先考虑了针对各种人类疾病的 235 064 个基因疾病关联。基于三种流行且具有代表性的 TWAS 软件包，使用七种不同的统计模型计算关联。用户可以探索基因或疾病层面的关联，并使用 MeSH 疾病树轻松搜索相关研究或疾病。由于疾病的影响是高度组织特异性的，webTWAS 应用组织特异性富集分析来识别重要的组织。用户友好的网络服务器也可用于对用户提供的 GWAS 汇总统计数据运行自定义 TWAS 分析。webTWAS 可在 http://www.webtwas.net 免费获得。由于疾病的影响是高度组织特异性的，webTWAS 应用组织特异性富集分析来识别重要的组织。用户友好的网络服务器也可用于对用户提供的 GWAS 汇总统计数据运行自定义 TWAS 分析。webTWAS 可在 http://www.webtwas.net 免费获得。由于疾病的影响是高度组织特异性的，webTWAS 应用组织特异性富集分析来识别重要的组织。用户友好的网络服务器也可用于对用户提供的 GWAS 汇总统计数据运行自定义 TWAS 分析。webTWAS 可在 http://www.webtwas.net 免费获得。

更新日期：2021-10-06

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