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Common polygenic variation enhances risk prediction for Alzheimer’s disease
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The identification of subjects at high risk for Alzheimer's disease is important for prognosis and early intervention. We investigated the polygenic architecture of Alzheimer's disease and the accuracy of Alzheimer's disease prediction models, including and excluding the polygenic component in the model. This study used genotype data from the powerful dataset comprising 17 008 cases and 37 154 controls obtained from the International Genomics of Alzheimer's Project (IGAP). Polygenic score analysis tested whether the alleles identified to associate with disease in one sample set were significantly enriched in the cases relative to the controls in an independent sample. The disease prediction accuracy was investigated in a subset of the IGAP data, a sample of 3049 cases and 1554 controls (for whom APOE genotype data were available) by means of sensitivity, specificity, area under the receiver operating characteristic curve (AUC) and positive and negative predictive values. We observed significant evidence for a polygenic component enriched in Alzheimer's disease (P = 4.9 × 10(-26)). This enrichment remained significant after APOE and other genome-wide associated regions were excluded (P = 3.4 × 10(-19)). The best prediction accuracy AUC = 78.2% (95% confidence interval 77-80%) was achieved by a logistic regression model with APOE, the polygenic score, sex and age as predictors. In conclusion, Alzheimer's disease has a significant polygenic component, which has predictive utility for Alzheimer's disease risk and could be a valuable research tool complementing experimental designs, including preventative clinical trials, stem cell selection and high/low risk clinical studies. In modelling a range of sample disease prevalences, we found that polygenic scores almost doubles case prediction from chance with increased prediction at polygenic extremes.
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“risk and seems to provide an improved method for risk prediction over more selective PRS or standard predictors such as APOEε4 alleles, age, and sex [118, 122, 123].”
From Genetic architecture of Alzheimer's disease (2020) · cites this paper as [122]
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Self-citation
Metadata
- Authors
- Escott-Price, V., Sims, R., Bannister, C., Harold, D., Vronskaya, M., Majounie, E., Badarinarayan, N., GERAD/PERADES, consortia, I., Morgan, K., Passmore, P., Holmes, C., Powell, J., Brayne, C., Gill, M., Mead, S., Goate, A., Cruchaga, C., Lambert, J.-C., van Duijn, C., Maier, W., Ramirez, A., Holmans, P., Jones, L., Hardy, J., Seshadri, S., Schellenberg, G. D., Amouyel, P., & Williams, J.
- Affiliations
- Cardiff University (GB)
- Trinity College Dublin (IE)
- University of Nottingham (GB)
- Queen's University Belfast (GB)
- University of Cambridge (GB)
- King's College London (GB)
- Washington University in St. Louis (US)
- Inserm (FR)
- University of Bonn (DE)
- UCL Queen Square Institute of Neurology (GB)
- Boston University (US)
- ORCID
- Sims, Rebecca
- Bannister, Christian
- Harold, Denise
- Badarinarayan, Nandini
- Morgan, Kevin
- Passmore, Peter
- Holmes, Clive
- Powell, John
- Brayne, Carol
- Gill, Michael
- Mead, Simon
- Goate, Alison
- Cruchaga, Carlos
- Lambert, Jean-Charles
- van Duijn, Cornelia
- Holmans, Peter
- Jones, Lesley
- Hardy, John
- Seshadri, Sudha
- Schellenberg, Gerard D.
- Amouyel, Philippe
- Williams, Julie
- Venue
- Brain
- Publisher
- Oxford University Press (OUP)
- Published
- 2015-10-21
- Volume
- 138
- Issue
- 12
- Pages
- 3673-3684
- ISSN
- 0006-8950 (ISSN-L)
- 1460-2156
- PMC
- PMC5006219
- OpenAlex
- W2193207784
- Cited by
- 414
- References
- 29
- Citations by year
- 2026: 20 · 2025: 34 · 2024: 29 · 2023: 52 · 2022: 45 · 2021: 51 · 2020: 51 · 2019: 52 · 2018: 31 · 2017: 31 · 2016: 16 · 2015: 2
- FWCI
- 19.64
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Brain (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (publishedVersion)
- Nottingham ePrints (University of Nottingham) (submittedVersion, cc-by)
- Repository@Nottingham (University of Nottingham) (submittedVersion, cc-by)
- EUR Research Repository (Erasmus University Rotterdam) (submittedVersion)
- ePrints Soton (University of Southampton) (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- ORCA Online Research @Cardiff (Cardiff University) (submittedVersion)
- Research Portal (King's College London) (submittedVersion, other-oa)
- Repository@Nottingham (University of Nottingham) (submittedVersion, other-oa)
- HighWire Press Open Archive (submittedVersion)
- DZNE Pub (submittedVersion)
- Research Portal (Queen's University Belfast) (submittedVersion)
- Funders
- National Institute for Social Care and Health Research
- Health and Care Research Wales
- EU Joint Programme – Neurodegenerative Disease Research
- Alzheimer's Society
- National Institute for Health and Care Research (NF-SI-0611-10084)
- Cardiff University
- Dementias Platform UK
- Medical Research Council (MR/L023784/2, MR/L023784/1, MR/L501517/1, MR/K013041/1, MR/L501529/1, MR/L010305/1, MR/M009076/1)
- National Institute on Aging (U01AG049505, P30AG010124, U01AG032984, R01AG033193, R01AG008122, U24AG021886)
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Keywords
- Alzheimer's disease, polygenic risk score, polygenic architecture, risk prediction, apolipoprotein E, genome-wide association study, prediction accuracy
- MeSH terms
- Alleles, Alzheimer Disease, Apolipoproteins E, Genetic Testing, Genotype, Humans, Risk, ROC Curve, Genetic Variation, Logistic Models, Case-Control Studies, Genetic Predisposition to Disease, Multifactorial Inheritance, Genome-Wide Association Study
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