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Yuk Yee Leung¹, Adam C Naj^2,3, Yi-Fan Chou²

¹Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. yyee@pennmedicine.upenn.edu.

Nature communications

|January 23, 2024

概括

本研究介绍了一种生物信息学策略,以统一来自阿尔茨海默氏病测序项目样本的多样性全外体测序 (WES) 数据. 该方法成功生成了高质量的联合遗传变异数据,有利于大规模的基因组学研究.

科学领域:

基因组学就是基因组学.
生物信息学是一种生物信息学.
计算生物学计算生物学

背景情况:

整体外体测序 (WES) 数据生成方法各不相同,使联合分析复杂化.
阿尔茨海默氏病测序项目 (ADSP) 涉及许多使用多种WES数据的研究.
在不同测序中心,捕获套件和研究中标准化分析是具有挑战性的.

研究的目的:

开发和提出一个生物信息学战略,共同调用来自多项研究的WES数据.
为了解决WES数据生成方法的异质性.
为阿尔茨海默病研究创建一个高质量,统一的变异数据集.

主要方法:

从九项研究中联合召集了20504个WES样本.
使用称为变体格式 (VCF) 的文件,涵盖捕获套件的结合.
处理VCF文件以减轻来自不同捕获套件和测序中心的批量效应.

主要成果:

识别了820万个自身体变异.
96.82%的鉴定变体是高质量的.
变种位于28,579个Ensembl转录中,其中41%是内在的,1.8%具有高预测致病性 (CADD>30).

结论:

提出的生物信息技术战略有效地从异质的WES样本中生成高质量的遗传数据.
这种方法使得在不同批次和平台上生成的WES数据能够进行可靠的联合分析.
改进的数据整合有利于更广泛的基因组学研究社区,特别是在像阿尔茨海默氏症这样的复杂疾病研究中.

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