下一代测序(NGS)的使用极大地改善了罕见疾病的诊断。然而,随着外显子组和基因组测序对变异体的检测越来越多,对基因组数据的分析变得越来越复杂。美国医学遗传学和基因组学学院(ACMG)和分子病理学协会(AMP)于2015年开发了5层分类方案,用于变异解释。此后被广泛采用。尽管努力将这些标准的应用差异降至最低,不一致仍然存在。临床基因组资源(ClinGen)联盟的变体固化专家组(VCEP)开发了单个基因的进一步规范,这也考虑到基因或疾病的特定特征。例如,在具有高度特征的面部完形的疾病中,“表型匹配”(PP4)比非综合征形式的智力障碍具有更高的致病证据。通过用于量化异形特征的相似性的计算方法,现在可以在ACMG/AMP标准的精细贝叶斯框架中使用这种分析的结果。
The use of next-generation sequencing (NGS) has dramatically improved the diagnosis of rare diseases. However, the analysis of genomic data has become complex with the increasing detection of variants by exome and genome sequencing. The American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) developed a 5-tier classification scheme in 2015 for variant interpretation, that has since been widely adopted. Despite efforts to minimise discrepancies in the application of these criteria, inconsistencies still occur. Further specifications for individual genes were developed by Variant Curation Expert Panels (VCEPs) of the Clinical Genome Resource (ClinGen) consortium, that also take into consideration gene or disease specific features. For instance, in disorders with a highly characerstic facial gestalt a \"phenotypic match\" (PP4) has higher pathogenic evidence than e.g. in a non-syndromic form of intellectual disability. With computational approaches for quantifying the similarity of dysmorphic features results of such analysis can now be used in a refined Bayesian framework for the ACMG/AMP criteria.