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webTWAS 2.0: update platform for identifying complex disease susceptibility genes through transcriptome-wide association study

Affiliations
  • 1Key Laboratory for Bio-Electromagnetic Environment and Advanced Medical Theranostics, School of Biomedical Engineering and Informatics, Nanjing Medical University,101 Longmian Ave, Nanjing, Jiangsu 211166, China.
  • 2Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania-Perelman School of Medicine, 421 Curie Blvd, Philadelphia, PA 19104, USA.
  • 3Department of Pharmacology, School of Basic Medical Sciences, Tianjin Medical University, 22 Qixiangtai Road, Tianjin 300203, China.
  • 4Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, 1 Chengdian Road, Quzhou, Zhejiang 324003, China.

Published on:

November 11, 2024

Abstract

Transcriptome-wide association study (TWAS) has successfully identified numerous complex disease susceptibility genes in the post-genome-wide association study (GWAS) era. Over the past 3 years, the focus of TWAS algorithms has shifted from merely identifying associations to understanding how single nucleotide polymorphisms (SNPs) regulate gene expression, with a growing emphasis on incorporating fine-mapping techniques. Additionally, the rapid increase in GWAS summary statistics, driven largely by the UK Biobank and other consortia, has made it essential to update our webTWAS resource. To address these challenges and meet the growing needs of researchers, we developed webTWAS 2.0, an updated platform for identifying susceptibility genes for human complex diseases using TWAS. Additionally, webTWAS 2.0 provides an online TWAS analysis tool that simplifies conducting TWAS analyses. The updated resource includes 7247 GWAS summary statistics covering 1588 complex human diseases from 192 publications. It also incorporates multiple TWAS methods, such as sTF-TWAS, 3’aTWAS and GIFT, along with an updated interactive visualization tool that allows users to easily explore significant associations across different methods. Other upgrades include a personalized online analysis tool for user-submitted GWAS data and a refined search function that makes it easier to identify relevant associations and meet diverse user needs more efficiently. webTWAS 2.0 is freely accessible at http://www.webtwas.net.

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