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SR-TWAS: leveraging multiple reference panels to improve transcriptome-wide association study power by ensemble machine learning.

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Title: SR-TWAS: leveraging multiple reference panels to improve transcriptome-wide association study power by ensemble machine learning.
Authors: Parrish RL; Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, 30322, USA.; Department of Biostatistics, Emory University School of Public Health, Atlanta, GA, 30322, USA., Buchman AS; Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA., Tasaki S; Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA., Wang Y; Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA., Avey D; Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA., Xu J; Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA., De Jager PL; Center for Translational and Computational Neuroimmunology, Department of Neurology and Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY, 10032, USA., Bennett DA; Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA., Epstein MP; Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, 30322, USA., Yang J; Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, 30322, USA. jingjing.yang@emory.edu.
Source: Nature communications [Nat Commun] 2024 Aug 05; Vol. 15 (1), pp. 6646. Date of Electronic Publication: 2024 Aug 05.
Publication Type: Journal Article; Research Support, N.I.H., Extramural
Journal Info: Publisher: Nature Pub. Group Country of Publication: England NLM ID: 101528555 Publication Model: Electronic Cited Medium: Internet ISSN: 2041-1723 (Electronic) Linking ISSN: 20411723 NLM ISO Abbreviation: Nat Commun Subsets: MEDLINE
Database: MEDLINE Ultimate
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