创建,验证和整合高通量功能测试数据的可扩展方法,以改善临床变异分类
Samskruthi Reddy Padigepati1, David A Stafford1, Christopher A Tan1
1Invitae Corporation, San Francisco, CA, 94103, USA.
Human genetics
|July 31, 2024
概括
变异效应多重试验 (MAVEs) 为分类遗传变异提供了关键的功能数据. 该框架将MAVE数据整合到临床遗传测试中,改善变异解释和患者护理.
科学领域:
- 基因组学就是基因组学.
- 分子生物学分子生物学
- 临床遗传学 临床遗传学
背景情况:
- 在临床遗传测试中解释DNA序列变异是具有挑战性的,许多被归类为不确定的意义 (VUSs) 的变异.
- 传统上,从学术文献中获得的功能数据用于支持变体分类.
- 高通量功能测试,如变异效应的多重测试 (MAVEs),为变异解释提供可扩展的证据来源.
研究的目的:
- 描述生成,验证和将MAVE数据纳入临床遗传测试的框架.
- 使用MAVE数据开发一种半定量方法来进行变种分类.
- 提高基因检测实验室功能基因组学数据的临床实用性.
主要方法:
- 开发了使用单细胞基因表达的细胞证据模型,以评估44个临床相关基因的DNA变异效应.
- 将框架应用于现有的MAVE数据集中的22个基因.
- 从总共24个基因的综合建模数据,将其整合到一个半定量变异分类方法中.
主要成果:
- 将MAVE模型中的功能数据纳入变体分类方法.
- 通过使用MAVE获得的证据,对超过57,000个人的4043个观察到的变异进行了分类.
- 证明了使用高通量功能数据用于临床变异解释的可行性.
结论:
- 基因测试实验室可以生成,分析和验证高通量功能数据.
- 整合MAVE数据可以对患者进行更明确的临床变异分类.
- 这种方法解决了基因测试中大规模VUS解释的挑战.
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