Human Genetics Group, School of Life Sciences, University of Nottingham, Nottingham, UK.
Human Genetics Group, School of Life Sciences, University of Nottingham, Nottingham, UK; Biosciences School of Science & Technology, Nottingham Trent University, Nottingham, UK.
Neurobiol Aging. 2021 May;101:299.e13-299.e21. doi: 10.1016/j.neurobiolaging.2020.11.009. Epub 2020 Nov 12.
Synapse loss is an early event in late-onset Alzheimer's disease (LOAD). In this study, we have assessed the capacity of a polygenic risk score (PRS) restricted to synapse-encoding loci to predict LOAD. We used summary statistics from the International Genetics of Alzheimer's Project genome-wide association meta-analysis of 74,046 patients for model construction and tested the "synaptic PRS" in 2 independent data sets of controls and pathologically confirmed LOAD. The mean synaptic PRS was 2.3-fold higher in LOAD than that in controls (p < 0.0001) with a predictive accuracy of 72% in the target data set (n = 439) and 73% in the validation data set (n = 136), a 5%-6% improvement compared with the APOE locus (p < 0.00001). The model comprises 8 variants from 4 previously identified (BIN1, PTK2B, PICALM, APOE) and 2 novel (DLG2, MINK1) LOAD loci involved in glutamate signaling (p = 0.01) or APP catabolism or tau binding (p = 0.005). As the simplest PRS model with good predictive accuracy to predict LOAD, we conclude that synapse-encoding genes are enriched for LOAD risk-modifying loci. The synaptic PRS could be used to identify individuals at risk of LOAD before symptom onset.
突触丧失是晚期阿尔茨海默病(LOAD)的早期事件。在这项研究中,我们评估了仅限于突触编码基因座的多基因风险评分(PRS)预测 LOAD 的能力。我们使用了国际阿尔茨海默病遗传学项目全基因组关联荟萃分析的汇总统计数据来构建模型,并在 2 个独立的对照和经病理证实的 LOAD 数据集中测试了“突触 PRS”。LOAD 患者的平均突触 PRS 比对照组高 2.3 倍(p<0.0001),在目标数据集(n=439)中的预测准确率为 72%,在验证数据集(n=136)中的预测准确率为 73%,与 APOE 基因座相比提高了 5%-6%(p<0.00001)。该模型由 4 个先前确定的(BIN1、PTK2B、PICALM、APOE)和 2 个新的(DLG2、MINK1)与谷氨酸信号转导(p=0.01)或 APP 分解代谢或 tau 结合相关的 LOAD 基因座中的 8 个变体组成(p=0.005)。作为预测 LOAD 具有良好预测准确性的最简单 PRS 模型,我们得出结论,编码突触的基因富含 LOAD 风险修饰基因座。突触 PRS 可用于在症状出现前识别 LOAD 风险个体。