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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.
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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  Data: <searchLink fieldCode="AU" term="%22Wang+H%22">Wang H</searchLink>; College of Science, China Agricultural University, Beijing, China.<br /><searchLink fieldCode="AU" term="%22Li+X%22">Li X</searchLink>; Department of Statistics and Actuarial Science, School of Computing and Data Science, The University of Hong Kong, Hong Kong SAR, China.<br /><searchLink fieldCode="AU" term="%22Li+T%22">Li T</searchLink>; 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.<br /><searchLink fieldCode="AU" term="%22Li+Z%22">Li Z</searchLink>; 4+4 Medical Doctor Program, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.<br /><searchLink fieldCode="AU" term="%22Sham+PC%22">Sham PC</searchLink>; 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.<br /><searchLink fieldCode="AU" term="%22Zhang+YD%22">Zhang YD</searchLink>; Department of Statistics and Actuarial Science, School of Computing and Data Science, The University of Hong Kong, Hong Kong SAR, China. doraz@hku.hk.
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  Data: <searchLink fieldCode="JN" term="%22100960660%22">Genome biology</searchLink> [Genome Biol] 2025 Feb 04; Vol. 26 (1), pp. 21. <i>Date of Electronic Publication: </i>2025 Feb 04.
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22BioMed+Central+Ltd%22">BioMed Central Ltd </searchLink><i>Country of Publication: </i>England <i>NLM ID: </i>100960660 <i>Publication Model: </i>Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>1474-760X (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2214747596%22">14747596 </searchLink><i>NLM ISO Abbreviation: </i>Genome Biol <i>Subsets: </i>MEDLINE
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