边际总结统计的层次联合分析-第二部分:对OMIC数据的高维仪器分析
Lai Jiang1, Jiayi Shen1, Burcu F Darst2,3
1Department of Population and Public Health Sciences, Division of Biostatistics, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.
Genetic epidemiology
|June 18, 2024
概括
我们开发了SHA-JAM,用于仪器变量分析的可扩展框架,以有效地分析高维的欧米数据. 这种方法改善了从遗传数据中推断因果关系,在速度和准确性方面超过了现有的方法.
科学领域:
- 遗传学和生物信息学
- 流行病学 流行病学
- 统计基因组学 统计基因组学
背景情况:
- 仪器变量 (IV) 分析对于观察性流行病学中的因果推断至关重要.
- 遗传变异在门德尔随机化和全转录组关联研究中充当有效的IV.
- 现有的多变量IV方法在高通量欧米数据下难以扩展.
研究的目的:
- 引入SHA-JAM,这是一个可扩展的层次框架,用于高维仪器变量分析.
- 为了使多种中间体和相关遗传变异在奥米学研究中的分析.
- 用SNP中间关联数据估计高维度风险因素对结果的条件影响.
主要方法:
- 利用一个层次模型 (hJAM) 来共同分析边际总结统计数据.
- 开发了SHA-JAM,这是一个可扩展的框架,包含SNP中间体或SNP基因表达关联估计作为预先信息.
- 应用框架来分析前列腺癌的代谢物和转录组关联,使用大规模的总结统计数据.
主要成果:
- 广泛的模拟表明SHA-JAM实现了比现有方法更高的AUC,更低的平均平方误差,以及比现有方法更快的计算速度.
- 在两项涉及代谢物和转录组数据的前列腺癌病例研究中证明了适用性.
- 利用来自超过14万名男子的GWAS总结统计数据和高维的欧米数据.
结论:
- 在欧米学研究中,SHA-JAM为高维的IV分析提供了可扩展和高效的解决方案.
- 该方法通过有效地整合遗传和OMIC总结统计数据来增强因果推理.
- 使用现代高通量数据,SHA-JAM是研究复杂疾病病因的一个有价值的工具.
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