plinkQC:一种用于祖先推断,样本选择和人口遗传学质量控制的综合工具
Maha Syed1, Caroline Walter1, Hannah V Meyer1
1Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA.
bioRxiv : the preprint server for biology
|December 15, 2025
概括
一个新的R包,plinkQC,通过整合样本质量控制,祖先确定和相关性修剪来简化人口遗传分析. 该工具提高了数据质量,并最大限度地提高了样本集大小,以便进行强大的遗传研究.
科学领域:
- 人口遗传学 人口遗传学
- 生物信息学是一种生物信息学.
- 计算生物学是一种计算生物学.
背景情况:
- 高质量的数据集对于准确的人口遗传分析至关重要.
- 现有的软件包往往缺乏用于关键预处理步骤的集成解决方案,例如祖先识别和样本相关性评估.
研究的目的:
- 开发一个全面的R包,plinkQC,集成基本的质量控制和人口遗传分析的预处理步骤.
- 提供一个用户友好的工具,自动化祖先确定,样本相关性修剪和质量控制检查.
主要方法:
- 开发了plinkQC,一个R/CRAN包,结合了多种功能.
- 实施了预先训练的随机森林分类器,以准确确定祖先 (98%的准确性与5%的标记者重叠).
- 创建了基于图形的修剪方法,以选择最大数量的非相关样本集,考虑关系估计和样本质量.
主要成果:
- plinkQC成功地将祖先确定,样本相关性修剪和质量控制集成到一个单一的包中.
- 随机森林分类器在祖先预测中实现了高准确度.
- 基于图形的修剪方法与现有方法相比,识别了额外的样本,正如1000个基因组项目所证明的那样.
- 该包提供了详细的质量控制报告,并输出了清理的数据集.
结论:
- plinkQC为关键人群遗传数据预处理提供了统一和高效的解决方案.
- 该方案提高了样本数据集的质量和规模,促进了更强大的遗传研究.
- plinkQC 在CRAN上随时可用,在GitHub上有全面的文档和代码.
相关概念视频
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