Common polygenic variation enhances risk prediction for Alzheimer's disease

Valentina Escott-Price, Rebecca Sims, Christian Bannister, Denise Harold, Maria Vronskaya, Elisa Majounie, Nandini Badarinarayan, Kevin Morgan, Peter Passmore, Clive Holmes, John Powell, Carol Brayne, Michael Gill, Simon Mead, Alison Goate, Carlos Cruchaga, Jean Charles Lambert, Cornelia Van Duijn, Wolfgang Maier, Alfredo RamirezPeter Holmans, Lesley Jones, John Hardy, Sudha Seshadri, Gerard D. Schellenberg, Philippe Amouyel, Julie Williams

Research output: Contribution to journalArticlepeer-review

220 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)3673-3684
Number of pages12
Issue number12
StatePublished - Dec 1 2015
Externally publishedYes


  • Alzheimer's disease
  • polygenic score
  • predictive model

ASJC Scopus subject areas

  • Medicine(all)


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