Unified AI framework to uncover deep interrelationships between gene expression and Alzheimer’s disease neuropathologies

Nicasia Beebe-Wang, Safiye Celik, Ethan Weinberger, Pascal Sturmfels, Philip L. Jager, Sara Mostafavi () and Su-In Lee ()
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Nicasia Beebe-Wang: Paul G. Allen School of Computer Science and Engineering, University of Washington
Safiye Celik: Recursion Pharmaceuticals
Ethan Weinberger: Paul G. Allen School of Computer Science and Engineering, University of Washington
Pascal Sturmfels: Paul G. Allen School of Computer Science and Engineering, University of Washington
Philip L. Jager: Department of Neurology, Center for Translational and Computational Neuroimmunology, Columbia University Medical Center
Sara Mostafavi: Paul G. Allen School of Computer Science and Engineering, University of Washington
Su-In Lee: Paul G. Allen School of Computer Science and Engineering, University of Washington

Nature Communications, 2021, vol. 12, issue 1, 1-17

Abstract: Abstract Deep neural networks (DNNs) capture complex relationships among variables, however, because they require copious samples, their potential has yet to be fully tapped for understanding relationships between gene expression and human phenotypes. Here we introduce an analysis framework, namely MD-AD (Multi-task Deep learning for Alzheimer’s Disease neuropathology), which leverages an unexpected synergy between DNNs and multi-cohort settings. In these settings, true joint analysis can be stymied using conventional statistical methods, which require “harmonized” phenotypes and tend to capture cohort-level variations, obscuring subtler true disease signals. Instead, MD-AD incorporates related phenotypes sparsely measured across cohorts, and learns interactions between genes and phenotypes not discovered using linear models, identifying subtler signals than cohort-level variations which can be uniquely recapitulated in animal models and across tissues. We show that MD-AD exploits sex-specific relationships between microglial immune response and neuropathology, providing a nuanced context for the association between inflammatory genes and Alzheimer’s Disease.

Date: 2021
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DOI: 10.1038/s41467-021-25680-7

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