拖拉机工作流程管道:一个可扩展的下一个流程框架,用于本地祖先意识的全基因组协会研究
Nirav N Shah1, Taotao Tan1,2, Jessica Honorato-Mauer1
1Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.
bioRxiv : the preprint server for biology
|September 15, 2025
概括
这项研究引入了Nextflow工作流程,用于自动化使用Tractor GWAS对混合种群的遗传分析. 该管道有效地识别了新的祖先特异性遗传关联,改善了对不同个体的发现.
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.
- 基因组关联研究 基因组关联研究
背景情况:
- 传统的全基因组协会研究 (GWAS) 往往不包括混合个体,限制了多样化的种群中的遗传发现.
- 像拖拉机GWAS这样的现有方法需要复杂的生物信息学管道来进行本地祖先推断和分阶段.
- 需要简化,自动化工作流程,以促进混合祖先的遗传分析.
研究的目的:
- 开发和验证可扩展的Nextflow工作流程,用于自动化拖拉机GWAS在混合种群中的分析.
- 通过整合本地祖先信息,使祖先特定的遗传关联能够有效地识别.
- 减少分析具有多个祖先的个体所需的生物信息专业知识.
主要方法:
- 开发了一个模块化和可定制的Nextflow工作流程,集成基因组分相,本地祖先推断和Tractor GWAS.
- 应用管道分析32个血液生物标志物在6,245个混合个体 (AFR-欧元) 来自英国生物银行.
- 支持多个常用的生物信息学工具,以实现灵活性和协调.
主要成果:
- 工作流程展示了高效的可扩展性和已知的遗传关联的成功复制.
- 确定了新的祖先特定位置,主要是由非洲祖先区域的变异驱动的.
- 突出了当地祖先意识方法在发现以前错过的遗传信号在混合群体中的价值.
结论:
- 开发的Nextflow工作流程有效地自动化和简化了使用Tractor GWAS的混杂个体的遗传分析.
- 这种方法通过有效分析复杂的祖先来促进更广泛的遗传发现.
- 这些发现强调了将当地祖先信息纳入其中的重要性,以最大限度地从多样化的人口中获得见解.
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