下一代测序基于数据的关联测试,对一组遗传标记物进行复杂反应测试,使用通用线性模型框架
Zheng Xu1, Song Yan2,3,4, Cong Wu5
1Department of Mathematics and Statistics, Wright State University, Dayton, Ohio, 45324, USA.
Mathematics (Basel, Switzerland)
|May 9, 2024
概括
新的下一代测序 (NGS) 数据的通用线性模型方法改善了对二进制和计数表型的遗传关联测试,优于传统的基于基因型的方法,特别是在测序深度低的情况下.
科学领域:
- 遗传学 是一个遗传学.
- 统计遗传学 统计遗传学
- 生物信息学是一种生物信息学.
背景情况:
- 关联测试对于理解遗传变异-表型关系至关重要.
- 在关联研究中常见的基于基因型的方法,可以通过基因型调用准确度来限制.
- 下一代测序 (NGS) 数据为直接关联测试提供了机会,没有先前的基因型调用.
研究的目的:
- 将现有的基于线性模型的NGS关联测试框架扩展到通用线性模型 (GLM) 框架.
- 开发和评估基于NGS数据的组测试方法,用于各种响应类型,包括二进制和计数数据.
- 为了比较新的基于GLM的NGS方法与传统的基于基因型的方法的性能.
主要方法:
- 将线性模型 (LM) 框架扩展为用于关联测试的通用线性模型 (GLM) 框架.
- 开发基于NGS数据的组测试方法,能够处理连续,二进制 (逻辑回归) 和计数 (Poisson回归) 响应.
- 进行广泛的模拟研究,以评估I型错误控制和用基因型方法进行性能比较.
主要成果:
- 所有测试方法都显示有控制的I型错误.
- 与相应的基因型方法相比,基于NGS数据的测试方法显示出更高的性能.
- 对于二进制和计数响应,NGS方法的优势尤其明显,尤其是在低序列深度条件下.
结论:
- 开发的基于GLM的框架有效地扩展了NGS关联测试,以适应连续数据之外的复杂响应变量.
- 这些基于NGS数据的新方法比传统的基于基因型的方法具有显著的优势,特别是对于二进制和计数表型.
- 该研究通过为各种数据类型提供强大的NGS关联测试工具来解决文献中的差距.
关键词:
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