Dataset Information

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.

SUBMITTER: Gunning AC

PROVIDER: S-EPMC10714540 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature

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Publications

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

Gunning Adam Colin AC Wright Caroline Fiona CF

Genome medicine 20231212 1

<h4>Background</h4>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").<h4>Methods</h4>We constructed a database of all possible coding ...[more]

PMID: 38087376

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Dataset Information

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

Background

Methods

Results

Conclusions

Publications

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

Similar Datasets

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