关于线性混合模型中的家族关系的几个测试统计数据的非对称分布
Nicholas Devogel1, Paul L Auer1, Regina Manansala2
1Division of Biostatistics, Medical College of Wisconsin, Milwaukee, Wisconsin, USA.
Statistics in medicine
|June 22, 2023
概括
本研究研究了使用自值方法在线性混合模型中对家族关系效应的统计测试. 它推导出非对称的零分布,并提供一个样本大小公式,帮助遗传关联研究.
科学领域:
- 统计遗传学 统计遗传学
- 量化遗传学 量化遗传学
- 生物信息学是一种生物信息学.
背景情况:
- 线性混合模型 (LMM) 对于分析复杂的遗传数据至关重要,特别是在考虑家族关系时.
- 对于LMMs中的家族关系效应,现有的统计测试需要强大的理论依据来准确地应用.
- 序列内核关联测试 (SKAT) 被广泛用于遗传罕见变异关联研究.
研究的目的:
- 检查概率比率测试 (LRT),限制概率比率测试 (RLRT),F-测试和SKAT统计数据对LMMs预期的家族关系 (FR) 影响的非对称分布.
- 在一般复制家族 (GRFS) 设置下推导这些测试的非对称零分布.
- 提供样本大小计算公式,并比较不同测试统计数据的功率.
主要方法:
- 使用自值方法分析LMM中FR效应的协差结构.
- 在复制家族设置下,为日志概率和受限日志概率建立了固有值的倍数.
- 在GRFS下为LRT,RLRT,F和SKAT统计得出的非对称零分布,包括在罕见变异测试中为SKAT的特定导出.
主要成果:
- 在一般复制家族设置下,为四个关键统计测试 (LRT,RLRT,F,SKAT) 导出了非对称零分布.
- 基于对FR效应大小的限制性最大概率估计,开发了一个简单的样本大小计算公式.
- 进行模拟研究以进行功率比较,并将测试应用于英国生物库数据.
结论:
- 该研究提供了一个全面的理论框架,用于测试LMMs的家族关系效应.
- 衍生分布和样本大小公式为研究人员在遗传关联研究中提供了实用工具.
- 这些发现有助于选择适当的统计测试和设计研究以检测家族关系效应.
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