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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Vydáno v:	Nature communications Ročník 15; číslo 1; s. 354 - 16
Hlavní autoři:	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
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Nature Publishing Group UK 08.01.2024 Nature Publishing Group Nature Portfolio
Témata:	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
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Popis
Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	2041-1723 2041-1723
DOI:	10.1038/s41467-023-44271-2