改善基因风险建模痴呆症从现实世界的数据在代表性不足的人口
Mingzhou Fu1,2, Leopoldo Valiente-Banuet1, Satpal S Wadhwa1
1Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, United States.
medRxiv : the preprint server for health sciences
|February 19, 2024
概括
使用功能基因组学的新痴呆风险模型在不同人群中优于现有方法. 这一进步改善了基因风险预测,并帮助精准医学诊断痴呆症.
科学领域:
- 遗传学 遗传学 是一个
- 计算生物学 计算生物学
- 神经科学是一个神经科学.
背景情况:
- 对痴呆症的遗传风险建模是有益的,但缺乏使用真实数据对不同人群的研究.
- 现有的模型往往不能充分代表代表性不足的群体.
研究的目的:
- 开发和验证一种新的痴呆症遗传风险模型.
- 将其性能与不同遗传祖先的既定模型 (APOE,PRS) 进行比较.
- 确定导致不同人群痴呆风险的遗传因素.
主要方法:
- 利用弹性网模型进行痴呆风险预测.
- 综合功能性基因组数据和来自多种神经退行性疾病GWAS的单核酸多态.
- 通过UCLA健康和我们所有人队伍的电子健康记录验证了该模型,在遗传祖先组之间进行了比较.
主要成果:
- 弹性网模型在祖先之间显著优于APOE和多基因风险评分模型.
- 在面积低于精度回忆曲线 (21-61%) 和面积低于接收器运行特征 (10-21%) 中取得了改进.
- 确定了共同的和祖先特定的痴呆风险基因和生物途径.
结论:
- 整合功能映射,多种神经退行性疾病数据和机器学习可以增强不同人群的痴呆遗传风险模型.
- 开发的模型显示了改善痴呆症诊断中的精准医学策略的潜力.
- 这些发现有助于更好地了解不同祖先的痴呆症遗传风险因素.
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