在高风险的非洲祖先队列中使用多基因风险得分和临床特征进行初级开放角度视眼的多模式预测
Yan Zhu1, Aude Benigne Ikuzwe Sindikubwabo2, Yuki Bradford3
1Center for Genetics of Complex Disease, Department of Ophthalmology, University of Pennsylvania, Philadelphia, PA 19104, USA.
medRxiv : the preprint server for health sciences
|November 19, 2025
概括
这项研究开发了使用多基因风险评分 (PRS) 的机器学习模型,以改善在非洲祖先的个体中早期检测初级开角玻璃眼 (POAG). 策划的PRS显示出公平的玻璃眼风险分层的巨大潜力.
科学领域:
- 眼科医生 眼科 眼科
- 遗传学 是一个遗传学.
- 计算生物学 计算生物学
背景情况:
- 主要开角青光眼 (POAG) 不成比例地影响非洲血统的人.
- 目前对POAG的早期检测工具是有限的,特别是在风险人群中.
研究的目的:
- 开发和验证机器学习模型,用于在非洲祖先人群中早期POAG风险分层.
- 评估与临床特征相结合的祖先匹配多基因风险得分 (PRS) 的实用性.
主要方法:
- 利用最大的非洲祖先POAG队列 (N=7,352) 进行模型开发.
- 综合的临床特征与四个祖先匹配的PRS (两个全基因组,两个策划).
- 在一个干净的队列上训练模型 (N=196) 并对1013名嫌疑人进行了测试.
主要成果:
- 结合PRS的机器学习模型显示POAG的预测准确度有所提高.
- 精选的PRS (MEGA PRS,MTAG PRS) 显示出特别有效的效果.
- MEGA PRS预测与关键临床指标相关联:眼内压力 (IOP),杯与盘的比率 (CDR) 和视网膜神经纤维层 (RNFL) 厚度.
- 整合PRS与眼间不对称的特征进一步增强了预测性能.
结论:
- 精选的多基因风险评分,如MEGA PRS和MTAG PRS,是早期青光眼风险分层的有价值,可解释和公平的工具.
- 这些发现支持使用PRS来增强在代表性不足的人群中早期POAG检测.
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