通过通过表型嵌入整合电子健康记录来提高多基因风险预测性能
Leqi Xu1, Wangjie Zheng1, Jiaqi Hu2
1Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA.
American journal of human genetics
|December 5, 2025
概括
电子健康记录嵌入增强的多基因风险评分 (EEPRS) 通过使用电子健康记录 (EHR) 数据嵌入来改善疾病预测. 这种新的方法增强了传统的多基因风险评分 (PRS),以更好地预测心血管疾病.
科学领域:
- 遗传学 遗传学是一种遗传学.
- 生物医学信息学 生物医学信息学
- 计算生物学 计算生物学
背景情况:
- 电子健康记录 (EHR) 为疾病预测提供了丰富的临床数据.
- 传统的多基因风险评分 (PRS) 在捕捉复杂的EHR表型方面存在局限性.
- 整合EHR数据与基因组信息对于推进预测模型至关重要.
研究的目的:
- 引入EHR嵌入增强的PRS (EEPRS) 以使用EHR表型嵌入改进疾病预测.
- 通过GWAS总结统计利用先进的嵌入技术 (Word2Vec,GPT).
- 通过结合复杂的EHR结构来提高PRS的准确性和可解释性.
主要方法:
- 通过从EHR中获得表型嵌入来开发EEPRS.
- 使用了Word2Vec和GPT来生成EHR嵌入.
- 进行了基于EHR嵌入的GWAS和分层分类,以确定特征集群.
- 与单一特征PRS和MTAG_PRS在英国生物银行和All of Us队列中的EEPRS性能进行了比较.
- 开发了EEPRS_optimal和MTAG_EEPRS,用于数据适应性和多特征PRS优化.
主要成果:
- 在英国生物库的41个特征中,EEPRS的表现始终优于单一特征PRS,特别是在心血管集群中.
- 基于EHR嵌入的PRS显示了与循环系统疾病的强有力的关联.
- EEPRS_optimal和MTAG_EEPRS在预测准确度方面取得了进一步的改进.
- 在我们所有人队列中的验证证实了EEPRS对特定疾病的好处.
结论:
- EEPRS提供了一个强大的和可解释的框架来增强PRS.
- 整合EHR嵌入可以显著提高单特征和多特征PRS的预测能力.
- EEPRS在利用大规模生物库数据进行个性化疾病风险预测方面取得了重大进展.
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