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

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 a...

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Published in:Nature communications Vol. 15; no. 1; pp. 354 - 16
Main Authors: Yang, Zhijian, Wen, Junhao, Abdulkadir, Ahmed, Cui, Yuhan, Erus, Guray, Mamourian, Elizabeth, Melhem, Randa, Srinivasan, Dhivya, Govindarajan, Sindhuja T., Chen, Jiong, Habes, Mohamad, Masters, Colin L., Maruff, Paul, Fripp, Jurgen, Ferrucci, Luigi, Albert, Marilyn S., Johnson, Sterling C., Morris, John C., LaMontagne, Pamela, Marcus, Daniel S., Benzinger, Tammie L. S., Wolk, David A., Shen, Li, Bao, Jingxuan, Resnick, Susan M., Shou, Haochang, Nasrallah, Ilya M., Davatzikos, Christos
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 08.01.2024
Nature Publishing Group
Nature Portfolio
Subjects:
38/43
59/57
631/114/1305
639/705/117
692/308/2056
692/699/375/365
692/700/1421/1628
Alzheimer Disease - diagnostic imaging
Alzheimer Disease - genetics
Alzheimer's disease
Anatomy
Biomarkers
Brain
Brain - diagnostic imaging
Brain architecture
Cluster Analysis
Clustering
Deep learning
Disease
Endophenotypes
Gene polymorphism
Genetic factors
Health services
Heterogeneity
Humanities and Social Sciences
Humans
Hypertension
Machine learning
Magnetic resonance imaging
Medical imaging
Mental disorders
multidisciplinary
Neurodegenerative diseases
Neuroimaging
Nucleotides
Phenotypes
Polymorphism
Science
Science (multidisciplinary)
Single-nucleotide polymorphism
Susceptibility
ISSN:2041-1723, 2041-1723
Online Access:Get full text
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Description
Summary: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. Many diseases can display distinct brain imaging phenotypes across individuals, potentially reflecting disease subtypes. However, biological interpretability is limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Here, the authors describe a deep-learning method that links imaging phenotypes with genetic factors, thereby conferring genetic correlations to the disease subtypes.
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ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-023-44271-2