Transcriptome-wide association studies: recent advances in methods, applications and available databases

被引:0
|
作者
Jialin Mai
Mingming Lu
Qianwen Gao
Jingyao Zeng
Jingfa Xiao
机构
[1] Chinese Academy of Sciences and China National Center for Bioinformation,National Genomics Data Center, Beijing Institute of Genomics
[2] Chinese Academy of Sciences and China National Center for Bioinformation,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics
[3] University of Chinese Academy of Sciences,undefined
来源
关键词
D O I
暂无
中图分类号
学科分类号
摘要
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.
引用
收藏
相关论文
共 50 条
  • [1] Transcriptome-wide association studies: recent advances in methods, applications and available databases
    Mai, Jialin
    Lu, Mingming
    Gao, Qianwen
    Zeng, Jingyao
    Xiao, Jingfa
    COMMUNICATIONS BIOLOGY, 2023, 6 (01)
  • [2] Editorial: Statistical methods for genome-wide association studies (GWAS) and transcriptome-wide association studies (TWAS) and their applications
    Shao, Mengting
    Zhang, Zilong
    Sun, Huiyan
    He, Jingni
    Wang, Juexin
    Zhang, Qingrun
    Cao, Chen
    FRONTIERS IN GENETICS, 2023, 14
  • [3] Transcriptome-Wide Association Studies (TWAS): Methodologies, Applications, and Challenges
    Evans, Patrick
    Nagai, Taylor
    Konkashbaev, Anuar
    Zhou, Dan
    Knapik, Ela W.
    Gamazon, Eric R.
    CURRENT PROTOCOLS, 2024, 4 (02):
  • [4] Opportunities and challenges for transcriptome-wide association studies
    Wainberg, Michael
    Sinnott-Armstrong, Nasa
    Mancuso, Nicholas
    Barbeira, Alvaro N.
    Knowles, David A.
    Golan, David
    Ermel, Raili
    Ruusalepp, Arno
    Quertermous, Thomas
    Hao, Ke
    Bjorkegren, Johan L. M.
    Im, Hae Kyung
    Pasaniuc, Bogdan
    Rivas, Manuel A.
    Kundaje, Anshul
    NATURE GENETICS, 2019, 51 (04) : 592 - 599
  • [5] RECONSIDERING THE VALIDITY OF TRANSCRIPTOME-WIDE ASSOCIATION STUDIES
    de Leeuw, Christiaan
    Werme, Josefin
    Savage, Jeanne
    Peyrot, Wouter
    Posthuma, Danielle
    EUROPEAN NEUROPSYCHOPHARMACOLOGY, 2021, 51 : E82 - E82
  • [6] Opportunities and challenges for transcriptome-wide association studies
    Michael Wainberg
    Nasa Sinnott-Armstrong
    Nicholas Mancuso
    Alvaro N. Barbeira
    David A. Knowles
    David Golan
    Raili Ermel
    Arno Ruusalepp
    Thomas Quertermous
    Ke Hao
    Johan L. M. Björkegren
    Hae Kyung Im
    Bogdan Pasaniuc
    Manuel A. Rivas
    Anshul Kundaje
    Nature Genetics, 2019, 51 : 592 - 599
  • [7] Statistical power of transcriptome-wide association studies
    He, Ruoyu
    Xue, Haoran
    Pan, Wei
    GENETIC EPIDEMIOLOGY, 2022, 46 (08) : 572 - 588
  • [8] Network regression analysis in transcriptome-wide association studies
    Xiuyuan Jin
    Liye Zhang
    Jiadong Ji
    Tao Ju
    Jinghua Zhao
    Zhongshang Yuan
    BMC Genomics, 23
  • [9] Some statistical consideration in transcriptome-wide association studies
    Xue, Haoran
    Pan, Wei
    GENETIC EPIDEMIOLOGY, 2020, 44 (03) : 221 - 232
  • [10] Multi-tissue transcriptome-wide association studies
    Grinberg, Nastasiya F.
    Wallace, Chris
    GENETIC EPIDEMIOLOGY, 2021, 45 (03) : 324 - 337