Escott-Price, Valentina and Sims, Rebecca and Bannister, Christian and Harold, Denise and Vronskaya, Maria and Majounie, Elisa and Badarinarayan, Nandini and Morgan, Kevin and Passmore, Peter and Holmes, Clive and Powell, John and Lovestone, Simon and Brayne, Carol and Gill, Michael and Mead, Simon and Goate, Alison and Cruchaga, Carlos and Lambert, Jean-Charles and van Duijn, Cornelia and Maier, Wolfgang and Ramirez, Alfredo and Holmans, Peter and Jones, Lesley and Hardy, John and Seshadri, Sudha and Schellenberg, Gerard D. and Amouyel, Philippe and Williams, Julie
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 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.
||Alzheimer’s disease, polygenic score, predictive model
||University of Nottingham UK Campus > Faculty of Medicine and Health Sciences > School of Life Sciences > School of Molecular Medical Sciences > Human Genetics Research Group
||10 Mar 2016 13:43
||22 Oct 2016 13:20
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