Evaluating the use of paralogous protein domains to increase data availability for missense variant classification
dc.contributor.author | Gunning, AC | |
dc.contributor.author | Wright, CF | |
dc.date.accessioned | 2023-12-06T09:43:51Z | |
dc.date.issued | 2023-12-12 | |
dc.date.updated | 2023-12-06T07:13:32Z | |
dc.description.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. | en_GB |
dc.description.sponsorship | Wellcome Trust | en_GB |
dc.description.sponsorship | National Institute for Health and Care Research (NIHR | en_GB |
dc.identifier.citation | Vol. 15, article 110 | en_GB |
dc.identifier.doi | 10.1186/s13073-023-01264-6 | |
dc.identifier.grantnumber | WT200990/Z/16/Z | en_GB |
dc.identifier.grantnumber | WT200990/A/16/Z | en_GB |
dc.identifier.uri | http://hdl.handle.net/10871/134736 | |
dc.identifier | ORCID: 0000-0003-2958-5076 (Wright, Caroline) | |
dc.language.iso | en | en_GB |
dc.publisher | BMC | en_GB |
dc.relation.url | https://zenodo.org/doi/10.5281/zenodo.10159779 | en_GB |
dc.rights | © The Author(s) 2023. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecom mons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. | |
dc.subject | Variant classification | en_GB |
dc.subject | missense variant | en_GB |
dc.subject | protein domain | en_GB |
dc.subject | Bayesian | en_GB |
dc.subject | genomic medicine | en_GB |
dc.title | Evaluating the use of paralogous protein domains to increase data availability for missense variant classification | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2023-12-06T09:43:51Z | |
dc.identifier.issn | 1756-994X | |
dc.description | This is the final version. Available on open access from BMC via the DOI in this record | en_GB |
dc.description | Availability of data and materials: The full database is available on Zenodo: https://zenodo.org/doi/10.5281/zenodo.10159779 | en_GB |
dc.identifier.journal | Genome Medicine | en_GB |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_GB |
dcterms.dateAccepted | 2023-11-22 | |
dcterms.dateSubmitted | 2023-07-25 | |
rioxxterms.version | VoR | en_GB |
rioxxterms.licenseref.startdate | 2023-11-22 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2023-12-06T07:13:35Z | |
refterms.versionFCD | AM | |
refterms.dateFOA | 2023-12-15T13:47:40Z | |
refterms.panel | A | en_GB |
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regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this
licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecom mons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.