混合基因组和外基因组测序方法以公正,高质量和成本有效的方式捕获遗传变异
Toni A Boltz1,2, Benjamin B Chu3, Calwing Liao1,2,4
1Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
bioRxiv : the preprint server for biology
|September 16, 2024
概括
混合基因组外基因组 (BGE) 方法提供了具有成本效益的,低通的全基因组和深度全外基因组测序. 这种可扩展的技术准确地捕捉了包括SNP和CNV在内的遗传变异,特别是在代表性不足的人群中.
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.
- 人口遗传学 人口遗传学
背景情况:
- 基因组测序技术经常面临成本和可访问性障碍.
- 捕捉代表性不足的人群中的遗传多样性对于全面的基因组研究至关重要.
- 现有的方法可能无法有效地平衡整个基因组和整个外基因组测序深度.
研究的目的:
- 为大规模的基因组研究引入和评估混合基因组外体 (BGE) 技术.
- 评估BGE在不同人群中的准确性和成本效益.
- 证明BGE在发现SNP和CNV等遗传变异方面的能力.
主要方法:
- 采用混合基因组外组 (BGE) DNA库混合方法.
- 来自精神疾病协会研究 (PUMAS) 项目中代表性不足的人口群体的超过53,000个样本的测序.
- 对Illumina全球查阵列数据和对拷贝数变异 (CNV) 发现的基因组准确性进行评估.
主要成果:
- 在各种PUMAS队列中,BGE实现了各种PUMAS队列中归算的基因型的高一致率 (MAF≥1%的≥95%).
- 即使在低小等位基频率 (MAF) 的SNP和混合种群中,一致率也保持强.
- BGE在检测至少跨越3个异构的蛋白编码拷贝数变异 (CNV) 方面显示出~90%的正预测值.
结论:
- BGE是一种可扩展和有效的方法,以显著降低成本 (28%的深度全基因组测序) 捕获SNP,indel和CNV.
- 该技术在大型基因组研究中显示出高准确性和实用性,特别是在代表性不足的人群中.
- BGE有可能扩大对基因组测试的访问,并加速全球的基因组发现.
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