Bellou Eftychia, Kim Woori, Leonenko Ganna, Tao Feifei, Simmonds Emily, Wu Ying, Mattsson-Carlgren Niklas, Hansson Oskar, Nagle Michael W, Escott-Price Valentina
UK Dementia Research Institute at Cardiff, Cardiff University, Hadyn Ellis Building, Maindy Road, Cardiff, CF24 4HQ, UK.
Eisai Inc, Cambridge, MA, 02140, USA.
Alzheimers Res Ther. 2025 Jan 6;17(1):6. doi: 10.1186/s13195-024-01664-9.
The success of selecting high risk or early-stage Alzheimer's disease individuals for the delivery of clinical trials depends on the design and the appropriate recruitment of participants. Polygenic risk scores (PRS) show potential for identifying individuals at risk for Alzheimer's disease (AD). Our study comprehensively examines AD PRS utility using various methods and models.
We compared the PRS prediction accuracy in ADNI (N = 568) and BioFINDER (N = 766) cohorts using five disease risk modelling approaches, three PRS derivation methods, two AD genome-wide association study (GWAS) statistics and two sets of SNPs: the whole genome and microglia-selective regions only.
The best prediction accuracy was achieved when modelling genetic risk by using two predictors: APOE and remaining PRS (AUC = 0.72-0.76). Microglial PRS showed comparable accuracy to the whole genome (AUC = 0.71-0.74). The individuals' risk scores differed substantially, with the largest discrepancies (up to 70%) attributable to the GWAS statistics used.
Our work benchmarks the best PRS derivation and modelling strategies for AD genetic prediction.
为临床试验挑选高风险或早期阿尔茨海默病个体的成功与否取决于设计和参与者的适当招募。多基因风险评分（PRS）显示出识别阿尔茨海默病（AD）风险个体的潜力。我们的研究使用各种方法和模型全面检验了AD PRS的效用。
我们使用五种疾病风险建模方法、三种PRS推导方法、两种AD全基因组关联研究（GWAS）统计数据以及两组单核苷酸多态性（SNP）：仅全基因组和小胶质细胞选择性区域，比较了ADNI（N = 568）和BioFINDER（N = 766）队列中PRS的预测准确性。
当使用两个预测因子对遗传风险进行建模时，即APOE和其余的PRS，实现了最佳预测准确性（曲线下面积[AUC]=0.72 - 0.76）。小胶质细胞PRS显示出与全基因组相当的准确性（AUC = 0.71 - 0.74）。个体的风险评分差异很大，最大差异（高达70%）归因于所使用的GWAS统计数据。
我们的工作为AD遗传预测的最佳PRS推导和建模策略设定了基准。
