Abstract

Genome-wide association study has identified fruitful variants impacting heritable traits. Nevertheless, identifying critical genes underlying those significant variants has been a great task. Transcriptome-wide association study (TWAS) is an instrumental post-analysis to detect significant gene-trait associations focusing on modeling transcription-level regulations, which has made numerous progresses in recent years. Leveraging from expression quantitative loci (eQTL) regulation information, TWAS has advantages in detecting functioning genes regulated by disease-associated variants, thus providing insight into mechanisms of diseases and other phenotypes. Considering its vast potential, this review article comprehensively summarizes TWAS, including the methodology, applications and available resources.

This review provides a comprehensive summary of transcriptome-wide association study (TWAS) methods, applications and available resources.

Details

Title
Transcriptome-wide association studies: recent advances in methods, applications and available databases
Author
Mai, Jialin 1 ; Lu, Mingming 1 ; Gao, Qianwen 1 ; Zeng, Jingyao 2   VIAFID ORCID Logo  ; Xiao, Jingfa 1   VIAFID ORCID Logo 

1  Chinese Academy of Sciences and China National Center for Bioinformation, National Genomics Data Center, Beijing Institute of Genomics, Beijing, China (GRID:grid.464209.d) (ISNI:0000 0004 0644 6935); Chinese Academy of Sciences and China National Center for Bioinformation, CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Beijing, China (GRID:grid.464209.d) (ISNI:0000 0004 0644 6935); University of Chinese Academy of Sciences, Beijing, China (GRID:grid.410726.6) (ISNI:0000 0004 1797 8419) 
2  Chinese Academy of Sciences and China National Center for Bioinformation, National Genomics Data Center, Beijing Institute of Genomics, Beijing, China (GRID:grid.464209.d) (ISNI:0000 0004 0644 6935); Chinese Academy of Sciences and China National Center for Bioinformation, CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Beijing, China (GRID:grid.464209.d) (ISNI:0000 0004 0644 6935) 
Pages
899
Publication year
2023
Publication date
2023
Publisher
Nature Publishing Group
e-ISSN
23993642
Source type
Scholarly Journal
Language of publication
English
DOI
https://doi.org/10.1038/s42003-023-05279-y
ProQuest document ID
2859762260
Copyright
© The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.