  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 SNP 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-driven neuroimaging phenotypes.
