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dc.contributor.authorLawless, Dylanen
dc.contributor.authorLango Allen, Hanaen
dc.contributor.authorThaventhiran, Jamesen
dc.contributor.authorNIHR BioResource–Rare Diseases Consortium,en
dc.contributor.authorHodel, Flaviaen
dc.contributor.authorAnwar, Rashidaen
dc.contributor.authorFellay, Jacquesen
dc.contributor.authorWalter, Jolan Een
dc.contributor.authorSavic, Sinisaen
dc.date.accessioned2020-01-04T00:31:05Z
dc.date.available2020-01-04T00:31:05Z
dc.identifier.issn0271-9142
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/300403
dc.description.abstractWhile widespread genome sequencing ushers in a new era of preventive medicine, the tools for predictive genomics are still lacking. Time and resource limitations mean that human diseases remain uncharacterized because of an inability to predict clinically relevant genetic variants. A strategy of targeting highly conserved protein regions is used commonly in functional studies. However, this benefit is lost for rare diseases where the attributable genes are mostly conserved. An immunological disorder exemplifying this challenge occurs through damaging mutations in RAG1 and RAG2 which presents at an early age with a distinct phenotype of life-threatening immunodeficiency or autoimmunity. Many tools exist for variant pathogenicity prediction, but these cannot account for the probability of variant occurrence. Here, we present a method that predicts the likelihood of mutation for every amino acid residue in the RAG1 and RAG2 proteins. Population genetics data from approximately 146,000 individuals was used for rare variant analysis. Forty-four known pathogenic variants reported in patients and recombination activity measurements from 110 RAG1/2 mutants were used to validate calculated scores. Probabilities were compared with 98 currently known human cases of disease. A genome sequence dataset of 558 patients who have primary immunodeficiency but that are negative for RAG deficiency were also used as validation controls. We compared the difference between mutation likelihood and pathogenicity prediction. Our method builds a map of most probable mutations allowing pre-emptive functional analysis. This method may be applied to other diseases with hopes of improving preparedness for clinical diagnosis.
dc.languageengen
dc.publisherSpringer Nature
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectRecombination activating genes 1 and 2 (RAG1, RAG2)en
dc.subjectgenomicsen
dc.subjectpathogenic varianten
dc.subjectpredictiveen
dc.titlePredicting the Occurrence of Variants in RAG1 and RAG2.en
dc.typeArticle
prism.endingPage701
prism.issueIdentifier7en
prism.publicationNameJournal of Clinical Immunologyen
prism.startingPage688
prism.volume39en
dc.identifier.doi10.17863/CAM.47477
dcterms.dateAccepted2019-07-15en
rioxxterms.versionofrecord10.1007/s10875-019-00670-zen
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2019-07-15en
dc.contributor.orcidLango Allen, Hana [0000-0002-7803-8688]
dc.contributor.orcidThaventhiran, James [0000-0001-8616-074X]
dc.identifier.eissn1573-2592
rioxxterms.typeJournal Article/Reviewen
cam.issuedOnline2019-08-06en


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International