Evaluating the use of paralogous protein domains to increase data availability for missense variant classification

Abstract

Background 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”). Methods 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. Results 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. Conclusions 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.

Comparison of the ABC and ACMG systems for variant classification

“…The functional grading in step A of the ABC system can also easily be aided by bioinformatic tools (like REVEL, missenseAI, spliceAI or paralog comparisons) [ 13 ], and there is no need to default the likelihood of variant pathogenicity (to 10%) or the relationship between criteria strengths (as the square root of the value above: 350–18,7–4,3–2,08), as in the Bayesian-based ACMG tool [ 2 ]. Similarly, the clinical grading in step B can be aided by tools using HPO terms to prioritize variants, see e.g., the rare disease diagnostic tools (Exomiser) of the Genomics England 100 K study [ 14 ].…”

Genetic constraint at single amino acid resolution in protein domains improves missense variant prioritisation and gene discovery

“…Given its high positive predictive value, we propose that HMC could be used as a constraint metric applied through PP2 following ACMG guidelines in clinical variant interpretation (PP2: “Missense variant in a gene that has a low rate of benign missense variation and where missense variants are a common mechanism of disease” [ 41 ]). Within the existing mutational constraint scores, missense constraint at the gene or regional level (gnomAD MOEUF and CCR) has been shown to provide supporting evidence of pathogenicity through the PP2 criterion within the ACMG clinical interpretation framework [ 42 ]. Since HMC has higher precision over these gene-level or regional-level constraints, we recommend evaluating PP2 by using HMC first where possible (activating PP2 if HMC < 0.8) before applying gene/region-level constraint as illustrated in Additional File 1 : Fig.…”