Genetic and multi‐omic risk assessment of Alzheimer's disease implicates core associated biological domains
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Abstract
INTRODUCTION: Alzheimer's disease (AD) is the predominant dementia globally, with heterogeneous presentation and penetrance of clinical symptoms, variable presence of mixed pathologies, potential disease subtypes, and numerous associated endophenotypes. Beyond the difficulty of designing treatments that address the core pathological characteristics of the disease, therapeutic development is challenged by the uncertainty of which endophenotypic areas and specific targets implicated by those endophenotypes to prioritize for further translational research. However, publicly funded consortia driving large-scale open science efforts have produced multiple omic analyses that address both disease risk relevance and biological process involvement of genes across the genome. METHODS: Here we report the development of an informatic pipeline that draws from genetic association studies, predicted variant impact, and linkage with dementia associated phenotypes to create a genetic risk score. This is paired with a multi-omic risk score utilizing extensive sets of both transcriptomic and proteomic studies to identify system-level changes in expression associated with AD. These two elements combined constitute our target risk score that ranks AD risk genome-wide. The ranked genes are organized into endophenotypic space through the development of 19 biological domains associated with AD in the described genetics and genomics studies and accompanying literature. The biological domains are constructed from exhaustive Gene Ontology (GO) term compilations, allowing automated assignment of genes into objectively defined disease-associated biology. This rank-and-organize approach, performed genome-wide, allows the characterization of aggregations of AD risk across biological domains. RESULTS: The top AD-risk-associated biological domains are Synapse, Immune Response, Lipid Metabolism, Mitochondrial Metabolism, Structural Stabilization, and Proteostasis, with slightly lower levels of risk enrichment present within the other 13 biological domains. DISCUSSION: This provides an objective methodology to localize risk within specific biological endophenotypes and drill down into the most significantly associated sets of GO terms and annotated genes for potential therapeutic targets.
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Proteomic clustering has identified subgroups particularly responsive to drugs that modulate synaptic function and restore blood-brain barrier integrity.
“Proteomic clustering has identified subgroups particularly responsive to drugs that modulate synaptic function and restore BBB integrity342,345,347,350,352,399,400.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [342]
The Results section (‘Assessment of clinical trial targets’; page unavailable) reports that targets of Alzheimer’s drugs under clinical investigation are enriched for Synapse and Vasculature terms. It does not report proteomic clustering that identified patient subgroups, demonstrate that any subgroup is particularly responsive to those drugs, or show restoration of blood–brain barrier integrity. (LLM, conf=0.83)
Experimental — accuracy not yet measuredAnalyses using similarity network fusion have identified Alzheimer's disease subtypes characterized by immune activation, mitochondrial dysfunction, lipid dysregulation, and blood-brain barrier disruption.
“These analyses have identified subtypes characterized by immune activation, mitochondrial dysfunction, lipid dysregulation, and BBB disruption342,352,399,400.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [342]
The Discussion (page not available in our copy) identifies immune response and lipid metabolism as upregulated domains and mitochondrial metabolism as a downregulated domain in Alzheimer’s disease. But it says the study did not explore subtypes; it does not report similarity network fusion or identify a subtype characterized by blood-brain barrier disruption. The cited work supports several biological themes in the sentence, not the claimed subtype findings. (LLM, conf=0.88)
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- Authors
- Cary, G. A., Wiley, J. C., Gockley, J., Keegan, S., Amirtha Ganesh, S. S., Heath, L., Butler, R. R., Mangravite, L. M., Logsdon, B. A., Longo, F. M., Levey, A., Greenwood, A. K., & Carter, G. W.
- Affiliations
- Jackson Laboratory (US)
- Sage Bionetworks (US)
- Sage Bionetworks Seattle Washington USA
- Stanford Medicine (US)
- Emory University (US)
- Corresponding author
- Carter, Gregory W.
- ORCID
- Venue
- Alzheimer's & Dementia: Translational Research & Clinical Interventions
- Publisher
- Wiley
- Published
- 2024-04
- Volume
- 10
- Issue
- 2
- Pages
- e12461-e12461
- ISSN
- 2352-8737 (ISSN-L)
- PMC
- PMC11033838
- OpenAlex
- W4395003777
- Cited by
- 49
- References
- 24
- Citations by year
- 2026: 20 · 2025: 20 · 2024: 7 · 2023: 2
- FWCI
- 13.38
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Alzheimer s & Dementia Translational Research & Clinical Interventions (publishedVersion, cc-by-nc)
- medRxiv (acceptedVersion, cc-by-nc-nd)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- The Mouseion at the JAXlibrary (Jackson Laboratory) (submittedVersion, cc-by-nc)
- Funders
- Mayo Foundation for Medical Education and Research
- Arizona Department of Health Services (211002)
- Topics
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- Alzheimer's disease, polygenic risk score, endophenotypes, Gene Ontology, transcriptomics, proteomics, synapses, immune response, lipid metabolism, mitochondrial metabolism, proteostasis, therapeutic target, genetic association studies, variant effect prediction
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