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
medRxiv : the preprint server for health sciences
|June 12, 2025
概括
这项研究开发了先进的模型,通过整合遗传,基因表达和蛋白质数据来预测阿尔茨海默病 (AD) 风险. 这些综合性风险模型显著改善了AD风险预测的传统多基因评分.
科学领域:
- 神经科学和遗传学 在神经科学和遗传学.
- 计算生物学和生物信息学
背景情况:
- 阿尔茨海默病 (AD) 是一种普遍的衰老疾病,具有复杂的遗传基础,没有有效的治疗方法.
- 目前使用多基因分数 (PGS) 的遗传风险预测通常无法捕捉疾病异质性,因为依赖常见变异.
研究的目的:
- 使用综合基因组,转录基因组和蛋白质基因组数据来描述阿尔茨海默病 (AD) 风险.
- 与现有方法相比,开发和评估综合性风险模型 (IRM) 以提高AD风险预测.
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 (GWAS,TWAS,PWAS).
- 开发了使用基因调节表达和临床共变量,使用弹性网逻辑回归和随机森林分类器的IRM.
- 使用AUROC,AUPRC,F1得分和平衡精度评估模型性能,与基线PGS和共变模型进行比较.
主要成果:
- 确定了104个基因组,319个转录组和17个蛋白质组与AD的关联.
- 发现了在信号传递,骨髓分化和免疫通路中丰富的新兴关联.
- 最好的IRM (具有转录和共变特征的随机森林) 实现了0.703的AUROC和0.622的AUPRC,超过了PGS和基线模型.
结论:
- 将单变量发现与多变量建模相结合,显著提高了AD风险预测.
- 这些发现为阿尔茨海默病背后的生物学途径提供了新的见解.
- 开发的IRM代表了个性化AD风险评估的一个有希望的进步.
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