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

Publisher: BioMed Central Country of Publication: England NLM ID: 101475844 Publication Model: Electronic Cited Medium: Internet ISSN: 1756-994X (Electronic) Linking ISSN: 1756994X NLM ISO Abbreviation: Genome Med Subsets: MEDLINE

Original Publication: [London] : BioMed Central

Computational Biology* ; Proteins*; Humans ; Protein Domains ; Bayes Theorem ; Mutation, Missense

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.
(© 2023. The Author(s).)

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United Kingdom WT_ Wellcome Trust; WT200990/A/16/Z United Kingdom WT_ Wellcome Trust

Keywords: Bayesian; Genomic medicine; Missense variant; Protein domain; Variant classification

0 (Proteins)

Date Created: 20231213 Date Completed: 20231216 Latest Revision: 20241015

20241015

PMC10714540

10.1186/s13073-023-01264-6

38087376