表型和基因组数据的整合性协调改善了多项研究骨质疏松症研究中的骨矿物质密度预测
Anqi Liu1, Jianing Liu1, Lang Wu2,3
1Department of Biomedical Informatics, College of Medicine, The Ohio State University.
medRxiv : the preprint server for health sciences
|June 4, 2025
概括
协调骨质疏松症数据可以改善骨矿物质密度 (BMD) 的预测. 该框架在数据集中标准化了表型和基因组变量,提高了骨质疏松症研究和精密医学的准确性.
科学领域:
- 遗传学和骨健康 遗传学和骨健康
- 在医疗保健中的数据科学.
- 骨质疏松症的流行病学
背景情况:
- 骨质疏松症研究需要在各种数据集中提供一致的数据,以准确评估骨矿物质密度 (BMD).
- 现有的多数据集研究由于表型和基因组数据的变化而面临挑战.
- 标准化对于提高骨质疏松症研究发现的概括性至关重要.
研究的目的:
- 开发和评估一个协调框架,用于在骨质疏松症研究中标准化表型和基因组数据.
- 为了提高骨矿物质密度 (BMD) 评估在多个美国数据集的准确性和通用性.
- 为了促进大规模骨质疏松症数据集的集成,以便进行先进的研究.
主要方法:
- 使用数据字典标准化关键的表型变量 (BMD,BMI,年龄,性别,种族/种族).
- 采用链式方程 (MICE) 的多重归算来处理缺失的数据.
- 应用主要成分分析 (PCA) 基于基因组数据批量效应的纠正和BMD标准化的残余回归.
主要成果:
- 在调整后,身体质量指数 (BMI) 的研究间变化显著减少 (Ω2 = 0.0028).
- 获得了改善的BMD预测性能,增加了解释变量 (R2 = 0.14).
- PCA证实了基因数据的有效对齐,减少了批量效应,提高了跨研究的兼容性.
结论:
- 证明了骨质疏松症研究数据协调框架的可行性和有效性.
- 该框架提高了BMD预测的准确性,并支持更具包容性的骨质疏松风险评估.
- 强调数据协调在推进骨质疏松症预防和管理的精准医学方面的潜力.
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