Showing 1 - 20 results of 257 for search '"TWAS"', query time: 0.16s Refine Results
  1. 1
  2. 2
  3. 3
  4. 4
  5. 5

    FABIO: TWAS fine-mapping to prioritize causal genes for binary traits. by Haihan Zhang, Kevin He, Zheng Li, Lam C Tsoi, Xiang Zhou

    Published 2024-12-01
    “…While most existing TWAS approaches focus on marginal analyses through examining one gene at a time, recent developments in TWAS fine-mapping methods enable the joint modeling of multiple genes to refine the identification of potentially causal ones. …”
    Get full text
    Article
  6. 6

    OTTERS: a powerful TWAS framework leveraging summary-level reference data by Qile Dai, Geyu Zhou, Hongyu Zhao, Urmo Võsa, Lude Franke, Alexis Battle, Alexander Teumer, Terho Lehtimäki, Olli T. Raitakari, Tõnu Esko, eQTLGen Consortium, Michael P. Epstein, Jingjing Yang

    Published 2023-03-01
    “…Here, the authors present a TWAS framework OTTERS that adapts multiple polygenic risk score methods to estimate eQTL weights from summary-level eQTL data. …”
    Get full text
    Article
  7. 7

    TWAS revealed significant causal loci for milk production and its composition in Murrah buffaloes by Supriya Chhotaray, Vikas Vohra, Vishakha Uttam, Ameya Santhosh, Punjika Saxena, Rajesh Kumar Gahlyan, Gopal Gowane

    Published 2023-12-01
    “…This is a maiden attempt to identify milk production and its composition related genes using TWAS in Murrah buffaloes (Bubalus bubalis). TWAS was conducted on a test (N = 136) set of Murrah buffaloes genotyped through ddRAD sequencing. …”
    Get full text
    Article
  8. 8

    Novel Variance-Component TWAS method for studying complex human diseases with applications to Alzheimer's dementia. by Shizhen Tang, Aron S Buchman, Philip L De Jager, David A Bennett, Michael P Epstein, Jingjing Yang

    Published 2021-04-01
    “…Transcriptome-wide association studies (TWAS) have been widely used to integrate transcriptomic and genetic data to study complex human diseases. …”
    Get full text
    Article
  9. 9
  10. 10
  11. 11
  12. 12
  13. 13
  14. 14
  15. 15
  16. 16
  17. 17
  18. 18
  19. 19
  20. 20