MAAT: a new nonparametric Bayesian framework for incorporating multiple functional annotations in transcriptome-wide association studies
Transcriptome-wide association study (TWAS) has emerged as a powerful tool for translating the myriad variations identified by genome-wide association studies (GWAS) into regulated genes in the post-GWAS era. While integrating annotation information has been shown to enhance power, current annotation-assisted TWAS tools predominantly focus on epigenomic annotations. When including more annotations, the assumption of a positive correlation between annotation scores and SNPs' effect sizes, as adopted by current methods, often falls short. Here, we propose MAAT expanding the horizons of existing TWAS studies, generating a new model incorporating multiple annotations into TWAS and a new metric indicating the most important annotation.
Authors
- Pak C. Sham (ORCID: https://orcid.org/0000-0002-2533-7270)
- Xiang Li (ORCID: https://orcid.org/0000-0001-8842-1595)
- Zhe Li (ORCID: https://orcid.org/0009-0009-3725-2248)
- Teng Li (ORCID: https://orcid.org/0000-0002-6863-9308)
- Yan Zhang (ORCID: https://orcid.org/0000-0002-5302-3690)
- Han Wang (ORCID: https://orcid.org/0009-0008-1462-649X)
Institutions
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Hong Kong University of Science and Technology (HK)
- China Agricultural University (CN)
- University of Hong Kong (HK)
Publication Details
- Journal
- Genome biology
- Published
- 2025-02-04
- DOI
- https://doi.org/10.1186/s13059-025-03485-x
- Citations
- 2
- Primary Topic
- Genetic Associations and Epidemiology
- Type
- article
- Field-Weighted Citation Impact
- 3.63
Funders
- National Institutes of Health
- National Heart, Lung, and Blood Institute
- National Institute of Mental Health
- National Institute on Drug Abuse
- National Human Genome Research Institute
- NIH Office of the Director
- National Cancer Institute
- National Institute of Neurological Disorders and Stroke
- Common Fund