PDF(Pubmed)
Abstract：
Genomic variant prioritization is crucial for identifying disease-associated genetic variations. Integrating facial and clinical feature analyses into this process enhances performance. This study demonstrates the integration of facial analysis (GestaltMatcher) and Human Phenotype Ontology analysis (CADA) within VarFish, an open-source variant analysis framework. Challenges related to non-open-source components were addressed by providing an open-source version of GestaltMatcher, facilitating on-premise facial analysis to address data privacy concerns. Performance evaluation on 163 patients recruited from a German multi-center study of rare diseases showed PEDIA\'s superior accuracy in variant prioritization compared to individual scores. This study highlights the importance of further benchmarking and future integration of advanced facial analysis approaches aligned with ACMG guidelines to enhance variant classification.
摘要：
基因组变异优先化对于识别疾病相关的遗传变异至关重要。将面部和临床特征分析集成到该过程中可增强性能。这项研究证明了VarFish中面部分析（GestaltMatcher）和人类表型本体分析（CADA）的整合，一个开源的变体分析框架。通过提供GestaltMatcher的开源版本，解决了与非开源组件相关的挑战。促进内部面部分析，以解决数据隐私问题。对德国罕见疾病多中心研究招募的163名患者的性能评估显示，与个体得分相比，PEDIA在变体优先排序方面具有更高的准确性。这项研究强调了进一步基准测试和未来整合与ACMG指南一致的高级面部分析方法以增强变体分类的重要性。
