Common polygenic variation enhances risk prediction for Alzheimer's disease

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.

Heritability estimates for Alzheimer’s disease in genome-wide association studies increase substantially when weak effect loci are also considered. Escott-Price et al. investigate the polygenic architecture of Alzheimer’s disease and the accuracy of prediction models, and show that including the polygenic component of risk significantly improves accuracy of case prediction.

Figure 1

ROC curves for predictive models with different predictors for risk of Alzheimer’s disease. GWAS = GWA study; PS = polygenic score.

Figure 2

Deciles of the polygenic score distribution with estimated range of predictive probabilities per decile (box-plots) and the proportion of cases (and controls) correctly predicted. PS = polygenic score.