影响多基因分数表现的风险因素跨越多样化的队列
Daniel Hui1, Scott Dudek1, Krzysztof Kiryluk2
1Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States.
eLife
|January 24, 2025
概括
个人和环境因素对身体质量指数 (BMI) 的多基因评分 (PGS) 绩效产生重大影响. 考虑到这些共变量,并使用诸如机器学习之类的先进模型,可以在不同人群中提高BMI PGS准确性.
科学领域:
- 遗传学 是一个遗传学.
- 生物统计学 生物统计学
- 人口健康 人口健康
背景情况:
- 多基因分数 (PGS) 预测复杂的特征,但它们的表现可能有所不同.
- 除了祖先之外的因素,如个人和环境共变量,可能会影响PGS的准确性.
研究的目的:
- 分析共变量分层和相互作用对身体质量指数多基因分数 (PGSBMI) 性能的影响.
- 确定影响PGSBMI的共变量,并探索增强模型性能的方法.
主要方法:
- 在欧洲和非洲祖先队伍中分析PGSBMI表现 (N=512,723).
- 通过二进制和连续的共同变量进行分层,评估PGSBMI-共同变量相互作用效应.
- 量子回归,机器学习 (神经网络) 和GxAge GWAS用于模型改进的应用.
主要成果:
- 18/62共变量显示了PGSBMI表现的显著,可复制的R2差异.
- 年龄,性别和生活方式因素等共变量显示出相当大的性能差异.
- 机器学习模型的相对R2平均提高了23%,GxAge GWAS提高了7.8%.
结论:
- 共同变量显著影响PGSBMI的表现和对不同祖先的影响.
- 通过考虑共变相互作用和非线性效应,可以提高模型性能.
- 先进的建模技术为使用PGS的BMI预测提供了更高的准确性.
关键词:
在GWAS中,GWAS就是GWAS.身体质量指数 (BMI) 是指身体质量指数.计算生物学是计算生物学.遗传学 遗传学 遗传学 是一个基因环境 基因环境基因组学就是基因组学.人类 人类 人类 人类 人类 人类 人类多基因分数的多基因分数系统生物学 系统生物学更多相关视频
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