UK Dementia Research Institute, Cardiff University, Cardiff, UK.
VIB Center for Brain & Disease Research, Leuven, Belgium.
Nat Commun. 2021 Jul 23;12(1):4506. doi: 10.1038/s41467-021-24082-z.
Polygenic Risk Scores (PRS) for AD offer unique possibilities for reliable identification of individuals at high and low risk of AD. However, there is little agreement in the field as to what approach should be used for genetic risk score calculations, how to model the effect of APOE, what the optimal p-value threshold (pT) for SNP selection is and how to compare scores between studies and methods. We show that the best prediction accuracy is achieved with a model with two predictors (APOE and PRS excluding APOE region) with pT<0.1 for SNP selection. Prediction accuracy in a sample across different PRS approaches is similar, but individuals' scores and their associated ranking differ. We show that standardising PRS against the population mean, as opposed to the sample mean, makes the individuals' scores comparable between studies. Our work highlights the best strategies for polygenic profiling when assessing individuals for AD risk.
多基因风险评分 (PRS) 为 AD 提供了可靠识别高风险和低风险个体的独特可能性。然而，在遗传风险评分计算应采用哪种方法、如何模拟 APOE 的影响、选择 SNP 的最佳 p 值阈值 (pT) 以及如何比较研究和方法之间的评分等方面，该领域尚未达成共识。我们表明，使用具有两个预测因子 (APOE 和排除 APOE 区域的 PRS) 的模型，SNP 选择的 pT<0.1 可实现最佳预测准确性。不同 PRS 方法的样本中的预测准确性相似，但个体的分数及其相关排名不同。我们表明，与针对样本平均值相比，将 PRS 标准化为人群平均值可使个体之间的分数在研究之间具有可比性。我们的工作强调了在评估 AD 风险个体时进行多基因分析的最佳策略。
