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多重归算和其他分析归算基因型的方法的比较

Paul L Auer¹, Gao Wang², Guangyou Li²

¹Division of Biostatistics, Institute for Health & Equity, and Cancer Center, Medical College of Wisconsin, Milwaukee, WI, 53226, USA. pauer@mcw.edu.

|June 5, 2023

概括

我们建议使用剂量用于小等位基因频率 (MAF) ≥0.001和归算质量 (Rsq) ≥0.3.3的归算基因型. 与其他方法相比,这种方法为全基因组关联研究提供了更好的功率和速度.

科学领域:

遗传学和生物信息学遗传学和生物信息学
统计基因组学统计基因组学

背景情况:

基因型归算对于全基因组关联研究 (GWAS) 至关重要,使得低频变异的测试成为可能.
假定基因型具有不确定性,因此需要将这种不确定性整合到关联测试中的方法.
现有的方法包括用剂量回归和回归模型 (MRM) 混合.

研究的目的:

通过实体模型兼容完全条件规范 (SMCFCS) 引入一种新方法,将归算不确定性集成到使用多重归算 (MI) 的统计关联测试中.
为了比较MI-SMCFCS与无条件MI,剂量回归和MRM的性能.

主要方法:

开发了一种全新的全条件多重归算 (MI) 方法 (SMCFCS),以解释基因型归算不确定性.
模拟使用英国生物库数据进行了各种类基因频率和归算品质的模拟.
性能与无条件MI,回归与剂量以及回归模型 (MRM) 混合进行了评估.

主要成果:

无条件MI被发现是计算上昂贵和过于保守的.
剂量,MRM和MI-SMCFCS在检测关联方面表现出更强的能力,包括低频变异,同时控制了I型错误率.
在计算上,MRM和MI-SMCFCS比剂量方法更密集.

结论:

无条件MI方法不建议用于与假定基因型的关联测试,因为它具有保守的性质.
由于其性能,速度和易于实施,对具有小等位基因频率 (MAF) ≥0.001和归算质量 (Rsq) ≥0.3的归算基因型建议使用剂量方法.
剂量回归为处理GWAS中归算不确定性的实用和有效解决方案.

关键词:

关联测试是一种关联测试.在GWAS中,GWAS就是GWAS.多重的归咎是多重的归咎.

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