综合性多omics方法识别分子途径并改善阿尔茨海默病风险预测
Rasika Venkatesh1,2, Katie M Cardone2, Yuki Bradford2
1Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Alzheimer's & dementia : the journal of the Alzheimer's Association
|November 13, 2025
概括
这项研究整合了多omics数据,以改善阿尔茨海默病 (AD) 风险预测. 新的综合性风险模型显著优于传统的多基因评分,提供了新的生物学见解.
科学领域:
- 神经遗传学 神经遗传学
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 阿尔茨海默病 (AD) 是一种复杂的神经退行性疾病,遗传和分子基础不明.
- 目前的多基因分数 (PGS) 解释了有限的AD遗传性.
- 阿尔茨海默病发病的异质性需要先进的预测建模.
研究的目的:
- 开发和评估综合性风险模型 (IRM),以更好地预测阿尔茨海默病 (AD) 风险.
- 识别与AD相关的新型遗传和分子信号.
- 将IRM与传统PGS的性能进行比较.
主要方法:
- 对来自阿尔茨海默氏病测序项目 (ADSP) 的15480名个人进行了基因组,转录组和蛋白质组范围的关联研究 (G/T/PWAS).
- 进行路径丰富分析以确定生物机制.
- 使用基因调节表达和临床共变量开发的IRM,使用随机森林和弹性网模型进行评估.
主要成果:
- 通过G/T/PWAS识别了已知的和新的AD相关信号.
- 丰富分析突出显示了胆固醇和免疫信号通路.
- 性能最好的IRM (具有转录基因数据的随机森林) 实现了AUROC的0.703和AUPRC的0.622,超过了PGS.
结论:
- 将多omics数据与多变量建模集成,可以显著改善AD风险预测.
- 这种方法为AD背后的生物学途径提供了新的见解.
- 开发了一种可靠的方法来预测晚期发作的AD的遗传风险.
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