ReviewNature reviews. Neurology2016
Genetic variants in Alzheimer disease - molecular and brain network approaches.
Review in Nature reviews. Neurology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
63 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Mediterranean diet and dementia: MRI marker evidence from meta-analysis.European journal of medical research · 2025Pooled it
- Association between LRP1 C766T polymorphism and Alzheimer's disease susceptibility: a meta-analysis.Scientific reports · 2017Pooled it
- Alzheimer's Disease as a Multi-Layer Network Disorder: A Systems Biology Framework Integrating Multi-Omics Mechanisms.Biomedicines · 2026Review
- Complex Networks in Contemporary Science and Technology.Entropy (Basel, Switzerland) · 2026Article
- Nanomaterial strategies for mitigating protein misfolding and neuroinflammation in neurodegenerative diseases.Zoological research · 2026Review
- Analysis of the potential regulatory mechanism of PIEZO1 in Alzheimer's disease based on RNA sequencing.IBRO neuroscience reports · 2025Article
- Honey as a Neuroprotective Agent: Molecular Perspectives on Its Role in Alzheimer's Disease.Nutrients · 2025Review
- Early Onset Memory Deficit of WMI Rats Compared to Their Nearly Isogenic WLIs Is Reversed by Enriched Environment in Females.Genes, brain, and behavior · 2025Article
- Modeling gene interactions in polygenic prediction via geometric deep learning.Genome research · 2025Article
- Genetic-based patient stratification in Alzheimer's disease.Scientific reports · 2024Article
- Spatial transcriptomic patterns underlying amyloid-β and tau pathology are associated with cognitive dysfunction in Alzheimer's disease.Cell reports · 2024Article
- Deep learning combining FDG-PET and neurocognitive data accurately predicts MCI conversion to Alzheimer's dementia 3-year post MCI diagnosis.Neurobiology of disease · 2023Article
- Assessment of Alzheimer-related pathologies of dementia using machine learning feature selection.Alzheimer's research & therapy · 2023Article
- Omics-based biomarkers discovery for Alzheimer's disease.Cellular and molecular life sciences : CMLS · 2022Review
- Early impairment of cortical circuit plasticity and connectivity in the 5XFAD Alzheimer's disease mouse model.Translational psychiatry · 2022Article
- Network Comparison with Interpretable Contrastive Network Representation Learning.Journal of data science, statistics, and visualisation · 2022Article
- Deep learning-based brain transcriptomic signatures associated with the neuropathological and clinical severity of Alzheimer's disease.Brain communications · 2022Article
- Identification of the molecular subgroups in Alzheimer's disease by transcriptomic data.Frontiers in neurology · 2022Article
- Two Opposing Functions of Angiotensin-Converting Enzyme (ACE) That Links Hypertension, Dementia, and Aging.International journal of molecular sciences · 2021Review
- Integration of functional genomics data to uncover cell type-specific pathways affected in Parkinson's disease.Biochemical Society transactions · 2021Review
3 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Genetic studies in late-onset Alzheimer disease (LOAD) are aimed at identifying core disease mechanisms and providing potential biomarkers and drug candidates to improve clinical care of AD. However, owing to the complexity of LOAD, including pathological heterogeneity and disease polygenicity, extraction of actionable guidance from LOAD genetics has been challenging. Past attempts to summarize the effects of LOAD-associated genetic variants have used pathway analysis and collections of small-scale experiments to hypothesize functional convergence across several variants. In this Review, we discuss how the study of molecular, cellular and brain networks provides additional information on the effects of LOAD-associated genetic variants. We then discuss emerging combinations of these omic data sets into multiscale models, which provide a more comprehensive representation of the effects of LOAD-associated genetic variants at multiple biophysical scales. Furthermore, we highlight the clinical potential of mechanistically coupling genetic variants and disease phenotypes with multiscale brain models.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.