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Brain. 2015 Dec;138(Pt 12):3673-84. doi: 10.1093/brain/awv268. Epub 2015 Oct 21.

Common polygenic variation enhances risk prediction for Alzheimer's disease.

Author information

1
1 Institute of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, UK EscottPriceV@cardiff.ac.uk WilliamsJ@cardiff.ac.uk.
2
1 Institute of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, UK.
3
2 School of Medicine, Trinity College Dublin, College Green, Dublin 2, Ireland.
4
3 Institute of Genetics, Queens Medical Centre, University of Nottingham, UK.
5
4 Ageing Group, Centre for Public Health, School of Medicine, Dentistry and Biomedical Sciences, Queens University Belfast, UK.
6
5 Division of Clinical Neurosciences, School of Medicine, University of Southampton, Southampton, UK.
7
6 Kings College London, Institute of Psychiatry, Department of Neuroscience, De Crespigny Park, Denmark Hill, London.
8
7 Institute of Public Health, University of Cambridge, Cambridge, UK.
9
8 Mercers Institute for Research on Aging, St. James Hospital and Trinity College, Dublin, Ireland.
10
9 MRC Prion Unit, Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK.
11
10 Neuroscience Department, Icahn School of Medicine at Mount Sinai, New York, USA.
12
11 Departments of Psychiatry, Neurology and Genetics, Washington University School of Medicine, St Louis, MO 63110, USA.
13
12 Inserm U744, Lille, 59000, France 13 Université Lille 2, Lille, 59000, France 14 Institut Pasteur de Lille, Lille, 59000, France.
14
15 Department of Epidemiology, Erasmus Medical Centre, Rotterdam, The Netherlands.
15
16 Department of Psychiatry and Psychotherapy, University of Bonn, 53127 Bonn, Germany 17 German Centre for Neurodegenerative Diseases (DZNE), Bonn, 53175, Germany.
16
16 Department of Psychiatry and Psychotherapy, University of Bonn, 53127 Bonn, Germany 18 Institute of Human Genetics, University of Bonn, 53127, Bonn, Germany.
17
19 Department of Molecular Neuroscience and Reta Lilla Weston Laboratories, Institute of Neurology, London, UK.
18
20 Department of Neurology, Boston University School of Medicine, Boston, MA 02118, USA.
19
21 Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA.
20
12 Inserm U744, Lille, 59000, France 13 Université Lille 2, Lille, 59000, France 14 Institut Pasteur de Lille, Lille, 59000, France 22 Centre Hospitalier Régional Universitaire de Lille, Lille, 59000, France.

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.

KEYWORDS:

Alzheimer’s disease; polygenic score; predictive model

PMID:
26490334
PMCID:
PMC5006219
DOI:
10.1093/brain/awv268
[Indexed for MEDLINE]
Free PMC Article

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