Cited by 414Alzheimer's disease research and treatmentsGenetic Associations and EpidemiologyDementia and Cognitive Impairment Research

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Common polygenic variation enhances risk prediction for Alzheimer’s disease

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Escott-Price, Valentina, Sims, Rebecca, Bannister, Christian, Harold, Denise, Vronskaya, Maria, Majounie, Elisa, Badarinarayan, Nandini, GERAD/PERADES, IGAP consortia, Morgan, Kevin, Passmore, Peter, Holmes, Clive, Powell, John, Brayne, Carol, Gill, Michael, Mead, Simon, Goate, Alison, Cruchaga, Carlos, Lambert, Jean-Charles, van Duijn, Cornelia, Maier, Wolfgang, Ramirez, Alfredo, Holmans, Peter, Jones, Lesley, Hardy, John, Seshadri, Sudha, Schellenberg, Gerard D., Amouyel, Philippe, Williams, Julie·2015·Brain

DOI 10.1093/brain/awv268PMC PMC5006219

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Abstract

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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Additional file 1 of Assessment of a polygenic hazard score for the onset of pre-clinical Alzheimer’s disease
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Additional file 1 of Comparison of subjective cognitive decline and polygenic risk score in the prediction of all-cause dementia, Alzheimer’s disease and vascular dementia
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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
DOI: 10.1093/brain/awv268
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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Common polygenic variation enhances risk prediction for Alzheimer’s disease

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