Efficient multi-phenotype genome-wide analysis identifies genetic associations for unsupervised deep-learning-derived high-dimensional brain imaging phenotypes.

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作者： Bohong,Guo ^[1] ; Ziqian,Xie ^[2] ; Wei,He ^[2] ; Sheikh Muhammad Saiful,Islam ^[2] ; Assaf,Gottlieb ^[2] ; Han,Chen ^[3] ; Degui,Zhi ^[2]

作者单位： Department of Biostatistics & Data Science, School of Public Health, University of Texas Health Science Center, Houston, Texas 77030, USA. ^[1] Department of Bioinformatics and Systems Medicine, McWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, Texas 77030, USA. ^[2] Human Genetics Center, Department of Epidemiology, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas 77030, USA. ^[3]

关键词 Endophenotype GWAS Image-derived phenotypes Imaging genetics Multivariate Statistical genetics

DOI 10.1101/2024.12.06.24318618

PMID 39677479

发布时间 2025-01-04

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Efficient multi-phenotype genome-wide analysis identifies genetic associations for unsupervised deep-learning-derived high-dimensional brain imaging phenotypes.

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Efficient multi-phenotype genome-wide analysis identifies genetic associations for unsupervised deep-learning-derived high-dimensional brain imaging phenotypes.

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