MAAT: a new nonparametric Bayesian framework for incorporating multiple functional annotations in transcriptome-wide association studies.
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| Title: | MAAT: a new nonparametric Bayesian framework for incorporating multiple functional annotations in transcriptome-wide association studies. |
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| Authors: | Wang H; College of Science, China Agricultural University, Beijing, China., Li X; Department of Statistics and Actuarial Science, School of Computing and Data Science, The University of Hong Kong, Hong Kong SAR, China., Li T; Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China., Li Z; 4+4 Medical Doctor Program, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China., Sham PC; Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.; Centre for PanorOmic Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China., Zhang YD; Department of Statistics and Actuarial Science, School of Computing and Data Science, The University of Hong Kong, Hong Kong SAR, China. doraz@hku.hk. |
| Source: | Genome biology [Genome Biol] 2025 Feb 04; Vol. 26 (1), pp. 21. Date of Electronic Publication: 2025 Feb 04. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: BioMed Central Ltd Country of Publication: England NLM ID: 100960660 Publication Model: Electronic Cited Medium: Internet ISSN: 1474-760X (Electronic) Linking ISSN: 14747596 NLM ISO Abbreviation: Genome Biol Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 1474-760X |
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| DOI: | 10.1186/s13059-025-03485-x |
