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dc.contributor.authorHopkins, JJ
dc.contributor.authorWakeling, MN
dc.contributor.authorJohnson, MB
dc.contributor.authorFlanagan, SE
dc.contributor.authorLaver, TW
dc.date.accessioned2023-12-11T09:51:22Z
dc.date.issued2023-12-04
dc.date.updated2023-12-11T09:35:42Z
dc.description.abstractIn silico predictive tools can help determine the pathogenicity of variants. The 2015 American College of Medical Genetics and Genomics (ACMG) guidelines recommended that scores from these tools can be used as supporting evidence of pathogenicity. A subsequent publication by the ClinGen Sequence Variant Interpretation Working Group suggested that high scores from some tools were sufficiently predictive to be used as moderate or strong evidence of pathogenicity. REVEL is a widely used metapredictor that uses the scores of 13 individual in silico tools to calculate the pathogenicity of missense variants. Its ability to predict missense pathogenicity has been assessed extensively; however, no study has previously tested whether its performance is affected by whether the missense variant acts via a loss-of-function (LoF) or gain-of-function (GoF) mechanism. We used a highly curated dataset of 66 confirmed LoF and 65 confirmed GoF variants to evaluate whether this affected the performance of REVEL. 98% of LoF and 100% of GoF variants met the author-recommended REVEL threshold of 0.5 for pathogenicity, while 89% of LoF and 88% of GoF variants exceeded the 0.75 threshold. However, while 55% of LoF variants met the threshold recommended for a REVEL score to count as strong evidence of pathogenicity from the ACMG guidelines (0.932), only 35% of GoF variants met this threshold (). GoF variants are therefore less likely to receive the highest REVEL scores which would enable the REVEL score to be used as strong evidence of pathogenicity. This has implications for classification with the ACMG guidelines as GoF variants are less likely to meet the criteria for pathogenicity. <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>P</mi> <mo>=</mo> <mn>0.0352</mn> </math> </jats:inline-formula>). GoF variants are therefore less likely to receive the highest REVEL scores which would enable the REVEL score to be used as strong evidence of pathogenicity. This has implications for classification with the ACMG guidelines as GoF variants are less likely to meet the criteria for pathogenicity.</jats:p>en_GB
dc.description.sponsorshipWellcome Trusten_GB
dc.description.sponsorshipResearch Englanden_GB
dc.description.sponsorshipNational Institute for Health and Care Research (NIHR)en_GB
dc.format.extent1-6
dc.identifier.citationVol. 2023, article 8857940en_GB
dc.identifier.doihttps://doi.org/10.1155/2023/8857940
dc.identifier.grantnumber223187/Z/21/Zen_GB
dc.identifier.urihttp://hdl.handle.net/10871/134767
dc.identifierORCID: 0000-0002-5094-9148 (Hopkins, Jasmin J)
dc.identifierORCID: 0000-0002-6542-9241 (Wakeling, Matthew N)
dc.identifierORCID: 0000-0002-6519-6687 (Johnson, Matthew B)
dc.identifierScopusID: 57191429364 (Johnson, Matthew B)
dc.identifierORCID: 0000-0002-8670-6340 (Flanagan, Sarah E)
dc.identifierScopusID: 13408960500 (Flanagan, Sarah E)
dc.identifierORCID: 0000-0001-6399-0089 (Laver, Thomas W)
dc.identifierScopusID: 6506037245 (Laver, Thomas W)
dc.language.isoenen_GB
dc.publisherHindawi / Wileyen_GB
dc.rights© 2023 Jasmin J. Hopkins et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en_GB
dc.titleREVEL Is Better at Predicting Pathogenicity of Loss-of-Function than Gain-of-Function Variantsen_GB
dc.typeArticleen_GB
dc.date.available2023-12-11T09:51:22Z
dc.contributor.editorChen, J-M
dc.identifier.issn1059-7794
dc.descriptionThis is the final version. Available on open access from Hindawi via the DOI in this recorden_GB
dc.descriptionData Availability: The list of variants used in this study are included in Supplementary Table 1.en_GB
dc.identifier.eissn1098-1004
dc.identifier.journalHuman Mutation: Variation, Informatics and Diseaseen_GB
dc.relation.ispartofHuman Mutation, 2023
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_GB
dcterms.dateAccepted2023-11-21
rioxxterms.versionVoRen_GB
rioxxterms.licenseref.startdate2023-12-04
rioxxterms.typeJournal Article/Reviewen_GB
refterms.dateFCD2023-12-11T09:48:16Z
refterms.versionFCDVoR
refterms.dateFOA2023-12-11T09:51:27Z
refterms.panelAen_GB


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© 2023 Jasmin J. Hopkins et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Except where otherwise noted, this item's licence is described as © 2023 Jasmin J. Hopkins et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.