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dc.contributor.authorBomba, Lorenzo
dc.contributor.authorWalter, Klaudia
dc.contributor.authorGuo, Qi
dc.contributor.authorSurendran, Praveen
dc.contributor.authorKundu, Kousik
dc.contributor.authorNongmaithem, Suraj
dc.contributor.authorKarim, Mohd Anisul
dc.contributor.authorStewart, Isobel D
dc.contributor.authorLangenberg, Claudia
dc.contributor.authorDanesh, John
dc.contributor.authorDi Angelantonio, Emanuele
dc.contributor.authorRoberts, David J
dc.contributor.authorOuwehand, Willem H
dc.contributor.authorINTERVAL study
dc.contributor.authorDunham, Ian
dc.contributor.authorButterworth, Adam S
dc.contributor.authorSoranzo, Nicole
dc.date.accessioned2022-05-31T23:30:28Z
dc.date.available2022-05-31T23:30:28Z
dc.date.issued2022-06-02
dc.identifier.issn0002-9297
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/337646
dc.description.abstractMetabolite levels measured in the human population are endophenotypes for biological processes. We combined sequencing data for 3,924 (whole-exome sequencing, WES, discovery) and 2,805 (whole-genome sequencing, WGS, replication) donors from a prospective cohort of blood donors in England. We used multiple approaches to select and aggregate rare genetic variants (minor allele frequency [MAF] < 0.1%) in protein-coding regions and tested their associations with 995 metabolites measured in plasma by using ultra-high-performance liquid chromatography-tandem mass spectrometry. We identified 40 novel associations implicating rare coding variants (27 genes and 38 metabolites), of which 28 (15 genes and 28 metabolites) were replicated. We developed algorithms to prioritize putative driver variants at each locus and used mediation and Mendelian randomization analyses to test directionality at associations of metabolite and protein levels at the ACY1 locus. Overall, 66% of reported associations implicate gene targets of approved drugs or bioactive drug-like compounds, contributing to drug targets' validating efforts.
dc.format.mediumPrint-Electronic
dc.publisherElsevier BV
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectWES
dc.subjectWGS
dc.subjectdrug targets
dc.subjectendophenotypes
dc.subjectloss-of-function
dc.subjectmetabolomics
dc.subjectmetabolon
dc.subjectproteomics
dc.subjectrare genetic variant
dc.subjectsequencing
dc.titleWhole-exome sequencing identifies rare genetic variants associated with human plasma metabolites.
dc.typeArticle
dc.publisher.departmentDepartment of Public Health And Primary Care, Cardiovascular Epidemiology Unit
dc.date.updated2022-05-31T09:17:13Z
prism.publicationDate2022
prism.publicationNameAm J Hum Genet
prism.startingPageS0002-9297(22)00157-4
dc.identifier.doi10.17863/CAM.85052
dcterms.dateAccepted2022-04-13
rioxxterms.versionofrecord10.1016/j.ajhg.2022.04.009
rioxxterms.versionVoR
dc.contributor.orcidLangenberg, Claudia [0000-0002-5017-7344]
dc.contributor.orcidDanesh, John [0000-0003-1158-6791]
dc.contributor.orcidDi Angelantonio, Emanuele [0000-0001-8776-6719]
dc.contributor.orcidButterworth, Adam [0000-0002-6915-9015]
dc.identifier.eissn1537-6605
rioxxterms.typeJournal Article/Review
cam.issuedOnline2022-05-13
cam.depositDate2022-05-31
pubs.licence-identifierapollo-deposit-licence-2-1
pubs.licence-display-nameApollo Repository Deposit Licence Agreement


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