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

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. Masters, Paul Maruff, Jurgen Fripp, Luigi Ferrucci, Marilyn S. Albert, Sterling C. Johnson, John C. Morris, Pamela LaMontagne, Daniel S. Marcus, Tammie L. S. Benzinger, David A. Wolk, Li Shen, Jingxuan Bao, Susan M. Resnick, Haochang Shou, Ilya M. Nasrallah and Christos Davatzikos ()
Additional contact information
Zhijian Yang: University of Pennsylvania
Junhao Wen: University of Pennsylvania
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
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
Christos Davatzikos: University of Pennsylvania

Nature Communications, 2024, vol. 15, issue 1, 1-16

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-023-44271-2

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