针对血压表型的机器学习模型结合了多个多基因风险得分
Yana Hrytsenko1,2,3, Benjamin Shea3, Michael Elgart1,2
1Department of Medicine, Brigham and Women's Hospital, Boston, MA.
medRxiv : the preprint server for health sciences
|January 3, 2024
概括
非线性机器学习模型可以提高血压预测的准确性. 结合多个多基因风险评分 (PRS) 进一步提高预测,特别是非白人个体,推进高血压的精准医学.
科学领域:
- 遗传学和基因组学 在
- 心血管疾病研究研究
- 机器学习应用 机器学习应用
背景情况:
- 准确预测系统和透气血压 (SBP,DBP) 对于预防心血管疾病至关重要.
- 传统的预测模型通常依赖于人口统计和临床因素,其捕捉遗传倾向的能力有限.
- 多基因风险评分 (PRS) 提供了一种方法来量化个人对高血压等复杂特征的遗传易感性.
结论:
- 非线性ML模型通过整合人口,临床和遗传数据来提高血压预测.
- 多个PRS集体提供比单个PRS更大的预测能力,在种族/种族群体之间有差异性好处.
- 这些发现支持高级ML和PRS在个性化高血压风险评估中的有用性.
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