The rising of transcriptome-wide association studies (TWAS) has provided an efficient analysis strategy to detect key genes of complex traits or diseases. In recent years, a large number of TWAS researches have been conducted and a huge volume of analysis data has been accumulated, which laid the foundation for further analysis such as data integration and visualization.

Recently, the research group from the Beijing Institute of Genomics of the Chinese Academy of Sciences/China National Center for Bioinformation developed a knowledgebase of transcriptome-wide association studies, TWAS Atlas, with the aim of exploring and mining gene-trait associations. The study was published in Nucleic Acids Research.

TWAS Atlas offers multiple ways for users to browse, search and download integrated data. Until now, TWAS Atlas 1.0 version has integrated high-quality TWAS data from 200 TWAS publications via manual curation, covering 401,266 gene-trait associations, 257 traits, 22,247 genes and 135 tissue types for human. TWAS Atlas also consists of the meta-data for each research, showing the source and annotations of TWAS data.

In addition, TWAS Atlas provides an online tool to construct a comprehensive and interactive knowledge graph for single nucleotide polymorphism (SNP)-gene-trait relationships through systematically integrating gene-trait association from TWAS and SNP-trait regulatory information from the GTEx database. The knowledge graph enables users to perform real-time interpretation and visualization of systematic network analysis incorporating multiple diseases, tissues and multi-omics data, providing personalized reference to explore multi-layer regulations on traits.