Common polygenic variation can predict risk of Alzheimer’s disease
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 m...
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| Format: | Article |
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Oxford Univerity Press
2015
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| Online Access: | https://eprints.nottingham.ac.uk/32217/ |
| _version_ | 1848794361280593920 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Escott-Price, Valentina |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T19:14:58Z |
| format | Article |
| id | nottingham-32217 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:14:58Z |
| publishDate | 2015 |
| publisher | Oxford Univerity Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-322172020-05-04T17:18:58Z https://eprints.nottingham.ac.uk/32217/ 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 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. Oxford Univerity Press 2015-10-21 Article PeerReviewed 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 and Williams, Julie (2015) Common polygenic variation can predict risk of Alzheimer’s disease. Brain . ISSN 1460-2156 Alzheimer’s disease polygenic score predictive model http://brain.oxfordjournals.org/content/138/12/3673 doi:10.1093/brain/awv268 doi:10.1093/brain/awv268 |
| spellingShingle | Alzheimer’s disease polygenic score predictive model 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 Common polygenic variation can predict risk of Alzheimer’s disease |
| title | Common polygenic variation can predict risk of Alzheimer’s disease |
| title_full | Common polygenic variation can predict risk of Alzheimer’s disease |
| title_fullStr | Common polygenic variation can predict risk of Alzheimer’s disease |
| title_full_unstemmed | Common polygenic variation can predict risk of Alzheimer’s disease |
| title_short | Common polygenic variation can predict risk of Alzheimer’s disease |
| title_sort | common polygenic variation can predict risk of alzheimer’s disease |
| topic | Alzheimer’s disease polygenic score predictive model |
| url | https://eprints.nottingham.ac.uk/32217/ https://eprints.nottingham.ac.uk/32217/ https://eprints.nottingham.ac.uk/32217/ |