Common polygenic variation enhances risk prediction for Alzheimer's disease

Escott-Price, Valentina ORCID: https://orcid.org/0000-0003-1784-5483, Sims, Rebecca ORCID: https://orcid.org/0000-0002-3885-1199, Bannister, Christian ORCID: https://orcid.org/0000-0001-8558-9480, Harold, Denise, Vronskaya, Maria, Majounie, Elisa ORCID: https://orcid.org/0000-0003-2800-1091, Badarinarayan, Nandini ORCID: https://orcid.org/0000-0002-6944-748X, 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 Alan ORCID: https://orcid.org/0000-0003-0870-9412, Jones, Lesley ORCID: https://orcid.org/0000-0002-3007-4612, Hardy, John, Seshadri, Sudha, Schellenberg, Gerard D., Amouyel, Philippe, Williams, Julie ORCID: https://orcid.org/0000-0002-4069-0259, GERAD/PERADES and IGAP, Consortia 2015. Common polygenic variation enhances risk prediction for Alzheimer's disease. Brain 138 (12) , pp. 3673-3684. 10.1093/brain/awv268

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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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Advanced Research Computing @ Cardiff (ARCCA) Medicine Neuroscience and Mental Health Research Institute (NMHRI) MRC Centre for Neuropsychiatric Genetics and Genomics (CNGG)
Subjects:	Q Science > QH Natural history > QH426 Genetics R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Publisher:	Oxford University Press
ISSN:	0006-8950
Date of Acceptance:	7 July 2015
Last Modified:	04 Mar 2023 03:02
URI:	https://orca.cardiff.ac.uk/id/eprint/82627

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