ArticleAlzheimer's & dementia : the journal of the Alzheimer's Association2024
In vivo validation of late-onset Alzheimer's disease genetic risk factors.
Article in Alzheimer's & dementia : the journal of the Alzheimer's Association, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Considerations for the selection and phenotyping of mouse models for the study of Alzheimer's disease.STAR protocols · 2026Review
- WSB.APP/PS1 mice develop age-dependent cerebral amyloid angiopathy, cerebrovascular dysfunction, and white matter deficits.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- PSEN1 mutant marmoset fibroblasts mimic multi-omic signatures of Alzheimer's disease.bioRxiv : the preprint server for biology · 2026Article
- Trem2bioRxiv : the preprint server for biology · 2025Article
- WSB.bioRxiv : the preprint server for biology · 2025Article
- Modeling Alzheimer's Disease: A Review of Gene-Modified and Induced Animal Models, Complex Cell Culture Models, and Computational Modeling.Brain sciences · 2025Review
- In vivo validation of late-onset Alzheimer's disease genetic risk factors.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
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Abstract
introductionGenome-wide association studies have identified over 70 genetic loci associated with late-onset Alzheimer's disease (LOAD), but few candidate polymorphisms have been functionally assessed for disease relevance and mechanism of action.
methodsCandidate genetic risk variants were informatically prioritized and individually engineered into a LOAD-sensitized mouse model that carries the AD risk variants APOE ε4/ε4 and Trem2*R47H. The potential disease relevance of each model was assessed by comparing brain transcriptomes measured with the Nanostring Mouse AD Panel at 4 and 12 months of age with human study cohorts.
resultsWe created new models for 11 coding and loss-of-function risk variants. Transcriptomic effects from multiple genetic variants recapitulated a variety of human gene expression patterns observed in LOAD study cohorts. Specific models matched to emerging molecular LOAD subtypes. DISCUSSION: These results provide an initial functionalization of 11 candidate risk variants and identify potential preclinical models for testing targeted therapeutics. HIGHLIGHTS: A novel approach to validate genetic risk factors for late-onset AD (LOAD) is presented. LOAD risk variants were knocked in to conserved mouse loci. Variant effects were assayed by transcriptional analysis. Risk variants in Abca7, Mthfr, Plcg2, and Sorl1 loci modeled molecular signatures of clinical disease. This approach should generate more translationally relevant animal models.
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