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Gene-SGAN: discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering

Author

Listed:
  • Zhijian Yang

    (University of Pennsylvania
    University of Pennsylvania)

  • Junhao Wen

    (University of Pennsylvania
    University of Southern California)

  • Ahmed Abdulkadir

    (Lausanne University Hospital (CHUV) and University of Lausanne)

  • Yuhan Cui

    (University of Pennsylvania)

  • Guray Erus

    (University of Pennsylvania)

  • Elizabeth Mamourian

    (University of Pennsylvania)

  • Randa Melhem

    (University of Pennsylvania)

  • Dhivya Srinivasan

    (University of Pennsylvania)

  • Sindhuja T. Govindarajan

    (University of Pennsylvania)

  • Jiong Chen

    (University of Pennsylvania)

  • Mohamad Habes

    (University of Texas San Antonio Health Science Center)

  • Colin L. Masters

    (The University of Melbourne)

  • Paul Maruff

    (The University of Melbourne)

  • Jurgen Fripp

    (Australian e-Health Research Centre CSIRO)

  • Luigi Ferrucci

    (MedStar Harbor Hospital)

  • Marilyn S. Albert

    (Johns Hopkins University School of Medicine)

  • Sterling C. Johnson

    (University of Wisconsin School of Medicine and Public Health)

  • John C. Morris

    (Washington University in St. Louis)

  • Pamela LaMontagne

    (Washington University School of Medicine)

  • Daniel S. Marcus

    (Washington University School of Medicine)

  • Tammie L. S. Benzinger

    (Washington University in St. Louis
    Washington University School of Medicine)

  • David A. Wolk

    (University of Pennsylvania)

  • Li Shen

    (University of Pennsylvania)

  • Jingxuan Bao

    (University of Pennsylvania)

  • Susan M. Resnick

    (National Institute on Aging)

  • Haochang Shou

    (University of Pennsylvania)

  • Ilya M. Nasrallah

    (University of Pennsylvania
    University of Pennsylvania)

  • Christos Davatzikos

    (University of Pennsylvania)

Abstract

Disease 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.

Suggested Citation

  • Zhijian Yang & Junhao Wen & Ahmed Abdulkadir & Yuhan Cui & Guray Erus & Elizabeth Mamourian & Randa Melhem & Dhivya Srinivasan & Sindhuja T. Govindarajan & Jiong Chen & Mohamad Habes & Colin L. Master, 2024. "Gene-SGAN: discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-023-44271-2
    DOI: 10.1038/s41467-023-44271-2
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    References listed on IDEAS

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    4. Zhijian Yang & Ilya M. Nasrallah & Haochang Shou & Junhao Wen & Jimit Doshi & Mohamad Habes & Guray Erus & Ahmed Abdulkadir & Susan M. Resnick & Marilyn S. Albert & Paul Maruff & Jurgen Fripp & John C, 2021. "A deep learning framework identifies dimensional representations of Alzheimer’s Disease from brain structure," Nature Communications, Nature, vol. 12(1), pages 1-15, December.
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