背景:罕见错义变异的分类仍然是基因组医学中持续存在的挑战。致病性的证据通常很少,关于如何权衡不同证据类别的决定可能是主观的。我们使用贝叶斯变体分类框架来研究变体共定位的性能,错义约束,并跨旁系蛋白质结构域(“元结构域”)汇总数据。
方法:我们构建了人类基因组中所有可能的编码单核苷酸变体的数据库,并使用PFam预测来注释跨蛋白质结构域的结构等效位置。我们计算了ClinVar数据库中这些等效位置的致病性和良性错义变异的数量,计算每个元域的区域约束分数,并评估了这种方法与现有的错义约束度量标准,用于对变异致病性和良性进行分类。
结果:在同一蛋白质中相同氨基酸位置的替代致病错义变体提供了致病性的有力证据(正似然比,LR+=85)。此外,在不同蛋白质的等效位置上,临床注释的致病性或良性错义变体可以提供致病性(LR+=7)或良性(LR+=5)的中等证据,分别。依次应用这些方法(通过PM5)将致病性错义变体分类的灵敏度从27%提高到41%。错义约束还可以为某些变体提供致病性的有力证据,但是它的缺失并没有提供仁慈的证据。
结论:我们建议在对新的错义变体进行分类时,使用来自不同基因的相关蛋白质结构域的结构等效位置来增加变体共定位的证据。此外,我们主张采用基于数字证据的方法,在变体解释中整合不同的数据。
Classification of rare missense variants remains an ongoing challenge in genomic medicine. Evidence of pathogenicity is often sparse, and decisions about how to weigh different evidence classes may be subjective. We used a Bayesian variant classification framework to investigate the performance of variant co-localisation, missense constraint, and aggregating data across paralogous protein domains (\"meta-domains\").
We constructed a database of all possible coding single nucleotide variants in the human genome and used PFam predictions to annotate structurally-equivalent positions across protein domains. We counted the number of pathogenic and benign missense variants at these equivalent positions in the ClinVar database, calculated a regional constraint score for each meta-domain, and assessed this approach versus existing missense constraint metrics for classifying variant pathogenicity and benignity.
Alternative pathogenic missense variants at the same amino acid position in the same protein provide strong evidence of pathogenicity (positive likelihood ratio, LR+ = 85). Additionally, clinically annotated pathogenic or benign missense variants at equivalent positions in different proteins can provide moderate evidence of pathogenicity (LR+ = 7) or benignity (LR+ = 5), respectively. Applying these approaches sequentially (through PM5) increases sensitivity for classifying pathogenic missense variants from 27 to 41%. Missense constraint can also provide strong evidence of pathogenicity for some variants, but its absence provides no evidence of benignity.
We propose using structurally equivalent positions across related protein domains from different genes to augment evidence for variant co-localisation when classifying novel missense variants. Additionally, we advocate adopting a numerical evidence-based approach to integrating diverse data in variant interpretation.