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Common polygenic variation can predict risk of Alzheimer’s disease

Escott-Price, Valentina; Sims, Rebecca; Bannister, Christian; Harold, Denise; Vronskaya, Maria; Majounie, Elisa; Badarinarayan, Nandini; Morgan, Kevin; Passmore, Peter; Holmes, Clive; Powell, John; Lovestone, Simon; 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

Authors

Valentina Escott-Price

Rebecca Sims

Christian Bannister

Denise Harold

Maria Vronskaya

Elisa Majounie

Nandini Badarinarayan

KEVIN MORGAN kevin.morgan@nottingham.ac.uk
Professor of Human Genomics and Molecular Genetics

Peter Passmore

Clive Holmes

John Powell

Simon Lovestone

Carol Brayne

Michael Gill

Simon Mead

Alison Goate

Carlos Cruchaga

Jean-Charles Lambert

Cornelia van Duijn

Wolfgang Maier

Alfredo Ramirez

Peter Holmans

Lesley Jones

John Hardy

Sudha Seshadri

Gerard D. Schellenberg

Philippe Amouyel

Julie Williams



Abstract

Background: 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 (AD) and the accuracy of AD prediction models, including and excluding the polygenic component in the model.
Methods: 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 by means of sensitivity, specificity, Area Under the receiver operating characteristic Curve (AUC) and positive predictive value (PPV).
Results: We observed significant evidence for a polygenic component enriched in Alzheimer’s disease (p=4.9x10-26). This enrichment remained significant after APOE and other genome-wide associated regions were excluded (p=3.4x10 19). The best prediction accuracy AUC=78% was achieved by a logistic regression model with APOE, the polygenic score as predictors and age. When looking at the genetic component only, the PPV was 81%, increasing to 82% when age was added as a predictor. Setting the total normalised polygenic score of greater than 0.91, the positive predictive value has reached 90%.
Conclusion: Polygenic score has strong predictive utility of Alzheimer’s disease risk and is a valuable research tool in experimental designs, e.g. for selecting Alzheimer’s disease patients into clinical trials.

Journal Article Type Article
Publication Date Oct 21, 2015
Journal Brain
Print ISSN 0006-8950
Electronic ISSN 1460-2156
Publisher Oxford University Press (OUP)
Peer Reviewed Peer Reviewed
APA6 Citation Escott-Price, V., Sims, R., Bannister, C., Harold, D., Vronskaya, M., Majounie, E., …Williams, J. (2015). Common polygenic variation can predict risk of Alzheimer’s disease. Brain, doi:10.1093/brain/awv268
DOI https://doi.org/10.1093/brain/awv268
Keywords Alzheimer’s disease, polygenic score, predictive model
Publisher URL http://brain.oxfordjournals.org/content/138/12/3673
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf



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