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dc.contributor.authorSun, Benjamin B
dc.contributor.authorKurki, Mitja I
dc.contributor.authorFoley, Christopher N
dc.contributor.authorMechakra, Asma
dc.contributor.authorChen, Chia-Yen
dc.contributor.authorMarshall, Eric
dc.contributor.authorWilk, Jemma B
dc.contributor.authorBiogen Biobank Team
dc.contributor.authorChahine, Mohamed
dc.contributor.authorChevalier, Philippe
dc.contributor.authorChristé, Georges
dc.contributor.authorFinnGen
dc.contributor.authorPalotie, Aarno
dc.contributor.authorDaly, Mark J
dc.contributor.authorRunz, Heiko
dc.date.accessioned2022-03-02T16:01:05Z
dc.date.available2022-03-02T16:01:05Z
dc.date.issued2022-03
dc.date.submitted2021-05-25
dc.identifier.issn0028-0836
dc.identifier.others41586-022-04394-w
dc.identifier.other4394
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/334593
dc.description.abstractGenome-wide association studies (GWAS) have identified thousands of genetic variants linked to the risk of human disease. However, GWAS have so far remained largely underpowered in relation to identifying associations in the rare and low-frequency allelic spectrum and have lacked the resolution to trace causal mechanisms to underlying genes1. Here we combined whole-exome sequencing in 392,814 UK Biobank participants with imputed genotypes from 260,405 FinnGen participants (653,219 total individuals) to conduct association meta-analyses for 744 disease endpoints across the protein-coding allelic frequency spectrum, bridging the gap between common and rare variant studies. We identified 975 associations, with more than one-third being previously unreported. We demonstrate population-level relevance for mutations previously ascribed to causing single-gene disorders, map GWAS associations to likely causal genes, explain disease mechanisms, and systematically relate disease associations to levels of 117 biomarkers and clinical-stage drug targets. Combining sequencing and genotyping in two population biobanks enabled us to benefit from increased power to detect and explain disease associations, validate findings through replication and propose medical actionability for rare genetic variants. Our study provides a compendium of protein-coding variant associations for future insights into disease biology and drug discovery.
dc.languageen
dc.publisherSpringer Science and Business Media LLC
dc.subjectArticle
dc.subject/631/208/205
dc.subject/631/208/457/649
dc.subject/692/699
dc.subject/631/208/727/2000
dc.subject/45/43
dc.subjectarticle
dc.titleGenetic associations of protein-coding variants in human disease.
dc.typeArticle
dc.date.updated2022-03-02T16:01:04Z
prism.endingPage102
prism.issueIdentifier7899
prism.publicationNameNature
prism.startingPage95
prism.volume603
dc.identifier.doi10.17863/CAM.82012
dcterms.dateAccepted2021-12-20
rioxxterms.versionofrecord10.1038/s41586-022-04394-w
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
dc.contributor.orcidSun, Benjamin B [0000-0001-6347-2281]
dc.contributor.orcidChahine, Mohamed [0000-0002-9500-2839]
dc.contributor.orcidChristé, Georges [0000-0003-2235-235X]
dc.contributor.orcidPalotie, Aarno [0000-0002-2527-5874]
dc.contributor.orcidRunz, Heiko [0000-0002-2133-7345]
dc.identifier.eissn1476-4687
cam.issuedOnline2022-02-23


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