使用低深度高通量测序数据在自极多种群中构建相关性矩阵
Timothy P Bilton1,2, Sanjeev Kumar Sharma3, Matthew R Schofield4
1AgResearch, Invermay Agricultural Centre, Mosgiel, New Zealand. timothy.bilton@agresearch.co.nz.
概括
一种新方法通过使用低深度测序数据,估计了自极多体中的基因组相关性. 该GUSrelate包提供了准确的估计,与SNP数组数据的相关性很好,即使有序列错误.
科学领域:
- 基因组学就是基因组学.
- 人口遗传学 人口遗传学
- 生物信息学是一种生物信息学.
背景情况:
- 高通量测序 (HTS) 为多倍体提供了具有成本效益的基因组分析.
- 估计遗传关系对于人口研究至关重要.
- 现有的方法在低深度HTS数据中遇到错误.
研究的目的:
- 使用低深度的HTS数据,开发一种改进的基因组相关性估计器.
- 为了考虑HTS数据中固有的错误,例如测序错误和缺失的等位基因.
- 为构建基因组关系矩阵 (GRMs) 提供一个用户友好的工具.
主要方法:
- 从HTS数据构建GRM的新型估计器的开发.
- 在R包中的估计器的实施 GUSrelate.
- 通过模拟验证并应用于土豆基因型组.
主要成果:
- 在高测序深度下,GUSrelate的性能与现有方法相提并论.
- 在低测序深度下,GUSrelate显著降低了自我相关性估计中的偏差.
- 来自使用基因型测序 (GBS) 数据的GUSrelate的GRMs与基于SNP数组的GRMs有很强的相关性.
结论:
- GUSrelate提供了一种可靠的方法,可以从低深度的HTS数据中构建GRMs.
- 该套件有助于在非模型多类物种中进行基因组相关性估计.
- 该工具提高了使用HTS数据进行人口遗传分析的准确性.
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