Assessing performance of pathogenicity predictors using clinically relevant variant datasets
dc.contributor.author | Gunning, AC | |
dc.contributor.author | Fryer, V | |
dc.contributor.author | Fasham, J | |
dc.contributor.author | Crosby, AH | |
dc.contributor.author | Ellard, S | |
dc.contributor.author | Baple, EL | |
dc.contributor.author | Wright, CF | |
dc.date.accessioned | 2020-09-02T14:22:05Z | |
dc.date.issued | 2020-08-25 | |
dc.description.abstract | Background Pathogenicity predictors are integral to genomic variant interpretation but, despite their widespread usage, an independent validation of performance using a clinically relevant dataset has not been undertaken. Methods We derive two validation datasets: an ‘open’ dataset containing variants extracted from publicly available databases, similar to those commonly applied in previous benchmarking exercises, and a ‘clinically representative’ dataset containing variants identified through research/diagnostic exome and panel sequencing. Using these datasets, we evaluate the performance of three recent meta-predictors, REVEL, GAVIN and ClinPred, and compare their performance against two commonly used in silico tools, SIFT and PolyPhen-2. Results Although the newer meta-predictors outperform the older tools, the performance of all pathogenicity predictors is substantially lower in the clinically representative dataset. Using our clinically relevant dataset, REVEL performed best with an area under the receiver operating characteristic curve of 0.82. Using a concordance-based approach based on a consensus of multiple tools reduces the performance due to both discordance between tools and false concordance where tools make common misclassification. Analysis of tool feature usage may give an insight into the tool performance and misclassification. Conclusion Our results support the adoption of meta-predictors over traditional in silico tools, but do not support a consensus-based approach as in current practice. | en_GB |
dc.description.sponsorship | Wellcome Trust | en_GB |
dc.description.sponsorship | Department of Health | en_GB |
dc.description.sponsorship | Health Innovation Challenge Fund | en_GB |
dc.identifier.citation | Published online 25 August 2020 | en_GB |
dc.identifier.doi | 10.1136/jmedgenet-2020-107003 | |
dc.identifier.grantnumber | WT200990/Z/16/Z | en_GB |
dc.identifier.grantnumber | WT200990/A/16/Z | en_GB |
dc.identifier.grantnumber | HICF-1009-003 | en_GB |
dc.identifier.grantnumber | WT098051 | en_GB |
dc.identifier.uri | http://hdl.handle.net/10871/122685 | |
dc.language.iso | en | en_GB |
dc.publisher | BMJ Publishing Group | en_GB |
dc.rights | © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY. Published by BMJ. This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/. | en_GB |
dc.title | Assessing performance of pathogenicity predictors using clinically relevant variant datasets | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2020-09-02T14:22:05Z | |
dc.identifier.issn | 0022-2593 | |
dc.description | This is the final version. Available on open access from BMJ Publishing Group via the DOI in this record | en_GB |
dc.identifier.journal | Journal of Medical Genetics | en_GB |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_GB |
dcterms.dateAccepted | 2020-06-20 | |
exeter.funder | ::Wellcome Trust | en_GB |
rioxxterms.version | VoR | en_GB |
rioxxterms.licenseref.startdate | 2020-06-20 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2020-09-02T14:17:35Z | |
refterms.versionFCD | VoR | |
refterms.dateFOA | 2020-09-02T14:22:10Z | |
refterms.panel | A | en_GB |
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Except where otherwise noted, this item's licence is described as © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY. Published by BMJ.
This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.