TY - JOUR
T1 - Common polygenic variation enhances risk prediction for Alzheimer's disease
AU - Escott-Price, Valentina
AU - Sims, Rebecca
AU - Bannister, Christian
AU - Harold, Denise
AU - Vronskaya, Maria
AU - Majounie, Elisa
AU - Badarinarayan, Nandini
AU - GERAD/PERADES
AU - Proitsi, Petroula
AU - IGAP consortia
AU - Morgan, Kevin
AU - Passmore, Peter
AU - Holmes, Clive
AU - Powell, John
AU - Brayne, Carol
AU - Gill, Michael
AU - Mead, Simon
AU - Goate, Alison
AU - Cruchaga, Carlos
AU - Lambert, Jean-Charles
AU - van Duijn, Cornelia
AU - Maier, Wolfgang
AU - Ramirez, Alfredo
AU - Holmans, Peter
AU - Jones, Lesley
AU - Hardy, John
AU - Seshadri, Sudha
AU - Schellenberg, Gerard D.
AU - Amouyel, Philippe
AU - Williams, Julie
PY - 2015/12/1
Y1 - 2015/12/1
N2 - 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.
AB - 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.
KW - Alzheimer's disease
KW - polygenic score
KW - predictive model
UR - http://www.scopus.com/inward/record.url?scp=84951070268&partnerID=8YFLogxK
U2 - 10.1093/brain/awv268
DO - 10.1093/brain/awv268
M3 - Article
AN - SCOPUS:84951070268
SN - 0006-8950
VL - 138
SP - 3673
EP - 3684
JO - Brain
JF - Brain
IS - 12
ER -