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TransferTWAS: A transfer learning framework for cross-tissue transcriptome-wide association study.

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Title: TransferTWAS: A transfer learning framework for cross-tissue transcriptome-wide association study.
Authors: Lai D; Department of Statistics and Actuarial Science, School of Computing and Data Science, The University of Hong Kong, Hong Kong SAR, China., Wang H; College of Science, China Agricultural University, Beijing, China., Gu T; Department of Biostatistics, Columbia University Mailman School of Public Health, New York, NY, USA., Wu S; Department of Statistics and Actuarial Science, School of Computing and Data Science, The University of Hong Kong, Hong Kong SAR, China., Liu DJ; Institute for Personalized Medicine, College of Medicine, Pennsylvania State University, University Park, PA, USA., 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. Electronic address: doraz@hku.hk.
Source: American journal of human genetics [Am J Hum Genet] 2025 Aug 07; Vol. 112 (8), pp. 1936-1947. Date of Electronic Publication: 2025 Jun 30.
Publication Type: Journal Article; Research Support, N.I.H., Extramural
Journal Info: Publisher: Cell Press Country of Publication: United States NLM ID: 0370475 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1537-6605 (Electronic) Linking ISSN: 00029297 NLM ISO Abbreviation: Am J Hum Genet Subsets: MEDLINE
Database: MEDLINE Ultimate
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  Data: TransferTWAS: A transfer learning framework for cross-tissue transcriptome-wide association study.
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  Data: <searchLink fieldCode="AU" term="%22Lai+D%22">Lai D</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="%22Wang+H%22">Wang H</searchLink>; College of Science, China Agricultural University, Beijing, China.<br /><searchLink fieldCode="AU" term="%22Gu+T%22">Gu T</searchLink>; Department of Biostatistics, Columbia University Mailman School of Public Health, New York, NY, USA.<br /><searchLink fieldCode="AU" term="%22Wu+S%22">Wu S</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="%22Liu+DJ%22">Liu DJ</searchLink>; Institute for Personalized Medicine, College of Medicine, Pennsylvania State University, University Park, PA, USA.<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. Electronic address: doraz@hku.hk.
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  Data: <searchLink fieldCode="JN" term="%220370475%22">American journal of human genetics</searchLink> [Am J Hum Genet] 2025 Aug 07; Vol. 112 (8), pp. 1936-1947. <i>Date of Electronic Publication: </i>2025 Jun 30.
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        Value: 10.1016/j.ajhg.2025.06.006
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