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.