使用多重功能数据来减少代表性不足的人群中的变异分类不平等
Moez Dawood1,2,3, Shawn Fayer4,5, Sriram Pendyala5,6
1Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA. Moez.Dawood@bcm.edu.
Genome medicine
|December 4, 2024
概括
变异效应多重检测 (MAVEs) 可以减少不同祖先的不确定的意义 (VUS) 遗传变异的分类差异. 生成和式MAVE数据对于公平的变异分类和改进的计算预测器训练至关重要.
科学领域:
- 基因组学就是基因组学.
- 临床遗传学 临床遗传学
- 生物信息学是一种生物信息学.
背景情况:
- 多重变异效应测试 (MAVEs) 为基因中的所有单个变异生成和式的功能数据.
- MAVEs有潜力解决变体分类差异,特别是在不同人群中对不确定的意义的变体 (VUS) 进行分类.
研究的目的:
- 调查欧洲类和非欧洲类遗传祖先之间的遗传变异分类差异.
- 评估MAVE数据在重新分类具有不确定的意义的变种 (VUS) 和减轻基于祖先的差异方面的有用性.
主要方法:
- 从我们所有人和基因组聚合数据库中分析了超过40万个人的临床意义分类.
- 将临床校准的MAVE数据纳入BRCA1,TP53和PTEN基因的自动变异重新分类规则中.
- 利用直角统计方法,分析不同祖先群体的证据代码影响.
主要成果:
- 在多个医疗专业的非欧洲类遗传祖先群体中发现了VUS的更高的流行率.
- 在非欧洲血统中观察到良性/可能良性变异的增加率,以及在欧洲血统中观察到致病性/可能致病性变异.
- 证明MAVE数据在非欧洲类祖先个体中显著重新分类了VUS,有效地弥补了观察到的差异.
结论:
- 优先生成和式MAVE数据对于减少VUS差异至关重要.
- 来自MAVE研究的公平培训数据将改善未来变种分类计算预测器的性能.
关键词:
我们所有人,我们所有人良性 良性的股权资本 股权资本遗传祖先的基因谱不公平 不公平 不公平马维 (MAVE) 是一个很棒的平台.这是一个错误的意义.变体效应多重测定多重测定.致病性 致病性 致病性这就是VUS VUS VUS.不确定的意义的变体.这是一个GnomADAD.更多相关视频
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