Gene-SGAN: discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering
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Published:2024-01-08
Issue:1
Volume:15
Page:
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ISSN:2041-1723
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Container-title:Nature Communications
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language:en
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Short-container-title:Nat Commun
Author:
Yang ZhijianORCID, Wen JunhaoORCID, Abdulkadir AhmedORCID, Cui Yuhan, Erus GurayORCID, Mamourian ElizabethORCID, Melhem Randa, Srinivasan Dhivya, Govindarajan Sindhuja T., Chen Jiong, Habes Mohamad, Masters Colin L., Maruff Paul, Fripp Jurgen, Ferrucci LuigiORCID, Albert Marilyn S., Johnson Sterling C.ORCID, Morris John C., LaMontagne PamelaORCID, Marcus Daniel S., Benzinger Tammie L. S.ORCID, Wolk David A., Shen LiORCID, Bao JingxuanORCID, Resnick Susan M., Shou HaochangORCID, Nasrallah Ilya M.ORCID, Davatzikos ChristosORCID
Abstract
AbstractDisease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases. Many diseases can display multiple distinct brain phenotypes across individuals, potentially reflecting disease subtypes that can be captured using MRI and machine learning methods. However, biological interpretability and treatment relevance are limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Herein, we describe Gene-SGAN – a multi-view, weakly-supervised deep clustering method – which dissects disease heterogeneity by jointly considering phenotypic and genetic data, thereby conferring genetic correlations to the disease subtypes and associated endophenotypic signatures. We first validate the generalizability, interpretability, and robustness of Gene-SGAN in semi-synthetic experiments. We then demonstrate its application to real multi-site datasets from 28,858 individuals, deriving subtypes of Alzheimer’s disease and brain endophenotypes associated with hypertension, from MRI and single nucleotide polymorphism data. Derived brain phenotypes displayed significant differences in neuroanatomical patterns, genetic determinants, biological and clinical biomarkers, indicating potentially distinct underlying neuropathologic processes, genetic drivers, and susceptibility factors. Overall, Gene-SGAN is broadly applicable to disease subtyping and endophenotype discovery, and is herein tested on disease-related, genetically-associated neuroimaging phenotypes.
Funder
U.S. Department of Health & Human Services | NIH | National Institute on Aging U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke
Publisher
Springer Science and Business Media LLC
Subject
General Physics and Astronomy,General Biochemistry, Genetics and Molecular Biology,General Chemistry,Multidisciplinary
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