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, PA.
medRxiv : the preprint server for health sciences
|April 22, 2024
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. 根据共变量进行调整,并使用像机器学习这样的先进模型,可以提高不同人群的预测准确性.
科学领域:
- 遗传学和基因组学 遗传学和基因组学
- 生物统计学 生物统计学
- 个性化医疗是个性化的医疗.
背景情况:
- 多基因分数 (PGS) 预测复杂的特征,但它们的表现在种群和个体之间有所不同.
- 除了基因之外的因素,例如个人和环境共变量,可以影响PGS准确性和效果大小.
结论:
- 共变效应显著影响PGSBMI的表现和解释在不同的祖先.
- 机器学习模型平均提高了PGSBMIR2的23%,而GxAge GWAS效应则提高了R2的7.8%.
- 考虑非线性协变效应和相互作用对于改善PGS模型及其临床实用性至关重要.
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