格斯塔尔特匹配器数据库 - 在罕见的人类疾病中面部表型变异性的全球参考
Hellen Lesmann1,2, Alexander Hustinx2, Shahida Moosa3
1Institute of Human Genetics, University of Bonn, Bonn, NRW, Germany.
medRxiv : the preprint server for health sciences
|July 28, 2023
概括
面部形状变异在遗传祖先之间有很大差异,影响下一代表型化 (NGP) 工具. 通过使用各种全球数据来增强NGP,例如GestaltMatcher数据库,可以提高罕见遗传疾病的诊断准确度.
科学领域:
- 遗传学 遗传学 是一个
- 医疗成像医学成像
- 生物信息学是一种生物信息学.
背景情况:
- 面部异形症在诊断罕见的遗传疾病方面存在重大挑战,原因是表型变异性.
- 下一代表型 (NGP) 工具与多样化的患者群体作斗争,限制了它们的临床实用性.
- 遗传祖先影响面部特征,使医疗成像中的自动模式识别复杂化.
研究的目的:
- 系统地分析遗传祖先对面部形状障碍症的影响.
- 建立并利用GestaltMatcher数据库 (GMDB) 来获取各种罕见疾病的面部成像数据.
- 评估数据多样性对NGP工具性能的影响.
主要方法:
- 从全球8346名患有581种罕见疾病的8346名患者收集了10980张正面面部图像.
- 根据 FAIR 原则建立了 GestaltMatcher 数据库 (GMDB).
- 通过基因祖先分层的多种培训和测试数据集分析了NGP的性能.
主要成果:
- 纳入非欧洲患者数据显著提高了GestaltMatcher的性能,提高了前五名的准确性+11.29%.
- 在不影响欧洲患者数据准确性的情况下,实现了性能改进.
- 现在,GestaltMatcher数据库 (GMDB) 包括了大量来自亚洲 (19%) 和非洲 (7%) 的人口的数据,超过40%的非洲数据以前没有发表过.
结论:
- 面部形的跨祖先表型变异性混了NGP工具.
- 增加数据多样性的国际合作对于在临床实践中推进NGP至关重要.
- GestaltMatcher 数据库 (GMDB) 作为临床医生和NGP开发人员的重要参考和培训资源.
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