Escott-Price Valentina, Sims Rebecca, Bannister Christian, Harold Denise, Vronskaya Maria, Majounie Elisa, Badarinarayan Nandini, Morgan Kevin, Passmore Peter, Holmes Clive, Powell John, Brayne Carol, Gill Michael, Mead Simon, Goate Alison, Cruchaga Carlos, Lambert Jean-Charles, van Duijn Cornelia, Maier Wolfgang, Ramirez Alfredo, Holmans Peter, Jones Lesley, Hardy John, Seshadri Sudha, Schellenberg Gerard D, Amouyel Philippe, Williams Julie
1 Institute of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, UK
1 Institute of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, UK.
Brain. 2015 Dec;138(Pt 12):3673-84. doi: 10.1093/brain/awv268. Epub 2015 Oct 21.
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.
识别阿尔茨海默病高危人群对于预后和早期干预至关重要。我们研究了阿尔茨海默病的多基因结构以及阿尔茨海默病预测模型的准确性，包括模型中纳入和排除多基因成分的情况。本研究使用了来自强大数据集的基因型数据，该数据集包含从国际阿尔茨海默病基因组计划（IGAP）获得的17008例病例和37154例对照。多基因评分分析测试了在一个样本集中确定与疾病相关的等位基因在独立样本中相对于对照在病例中是否显著富集。通过敏感性、特异性、受试者操作特征曲线下面积（AUC）以及阳性和阴性预测值，在IGAP数据的一个子集中（3049例病例和1554例对照的样本，这些样本可获得APOE基因型数据）研究了疾病预测准确性。我们观察到有显著证据表明阿尔茨海默病中存在多基因成分富集（P = 4.9×10⁻²⁶）。在排除APOE和其他全基因组关联区域后，这种富集仍然显著（P = 3.4×10⁻¹⁹）。通过以APOE、多基因评分、性别和年龄作为预测因子的逻辑回归模型，实现了最佳预测准确性，AUC = 78.2%（95%置信区间77 - 80%）。总之，阿尔茨海默病有显著的多基因成分，这对阿尔茨海默病风险具有预测效用，并且可能是一种有价值的研究工具，可补充实验设计，包括预防性临床试验、干细胞选择以及高/低风险临床研究。在模拟一系列样本疾病患病率时，我们发现多基因评分几乎使病例预测从随机水平提高了一倍，在多基因极端情况下预测增加。
