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全基因组测序研究的生物统计学方面:预处理和质量控制
Raphael O Betschart1, Cristian Riccio1, Domingo Aguilera-Garcia2
1Cardio-CARE, Medizincampus Davos, Davos, Switzerland.
Biometrical journal. Biometrische Zeitschrift
|July 11, 2024
概括
高通量全基因组测序 (WGS) 需要强大的预处理和质量控制 (QC). 本研究概述了大型WGS数据的高效QC指标和管道,确保遗传关联研究的数据完整性.
科学领域:
- 基因组学和生物信息学
- 高通量测序数据分析 高通量测序数据分析
背景情况:
- 由于DNA测序技术的进步,大规模的全基因组测序 (WGS) 研究越来越普遍.
- 预处理和质量控制 (QC) 是表型和基因型之间的关联分析前的关键步骤.
- 许多生物统计学家对处理WGS数据是新手,需要明确的指导方针和方法.
研究的目的:
- 为大规模WGS研究提供预处理和QC的全面概述.
- 详细说明WGS数据分析的各个阶段适用的基本质量控制指标.
- 为了说明QC程序,使用来自大型人类WGS研究的真实世界数据.
主要方法:
- 描述Illumina的短读测序技术和一般WGS预处理管道.
- 关键质量控制指标的概述:原始数据,后映射/调整,后变量调用和多样本变量调用.
- 使用DRAGEN原始阅读档案 (ORA) 压缩原始数据的实证数据和使用GENEtic Sequencing Study Hamburg-Davos (GENESIS-HD) 数据进行QC验证.
主要成果:
- 确定的主要QC指标包括遗传相似性,样本交叉污染,Het/Hom比率偏差,相关性和覆盖范围.
- 对于原始WGS文件,DRAGEN ORA实现了5.6:1的压缩比,压缩时间与基因组覆盖线性相关.
- 在合理的时间范围内,已经证明了对9000多个人类基因组的预处理,联合调用和QC的可行性.
结论:
- 大规模WGS研究的预处理,联合调用和QC是可以实现和有效的.
- 已建立的质量控制程序易于获得,并且有效地确保WGS数据的质量.
- 这些发现支持WGS数据在大型遗传研究中的可靠应用.
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