Machine learning optimized polygenic scores for blood cell traits identify sex-specific trajectories and genetic correlations with disease

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dc.contributor.authorXu, Yu
dc.contributor.authorVuckovic, Dragana
dc.contributor.authorRitchie, Scott C
dc.contributor.authorAkbari, Parsa
dc.contributor.authorJiang, Tao
dc.contributor.authorGrealey, Jason
dc.contributor.authorButterworth, Adam S
dc.contributor.authorOuwehand, Willem H
dc.contributor.authorRoberts, David J
dc.contributor.authorDi Angelantonio, Emanuele
dc.contributor.authorDanesh, John
dc.contributor.authorSoranzo, Nicole
dc.contributor.authorInouye, Michael
dc.date.accessioned2022-01-21T00:30:45Z
dc.date.available2022-01-21T00:30:45Z
dc.date.issued2022-01-12
dc.identifier.issn2666-979X
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/332819
dc.description.abstractIn this issue of Cell Genomics, Xu et al. report a comprehensive analysis of the genetics of 26 blood cell traits, leveraging data from two large biobanks to construct and make available machine-learning optimized polygenic scores (PGSs). In addition to delivering insights into the biology and clinical associations of these traits, the authors evaluate and provide recommendations on methods for PGS construction.
dc.publisherElsevier BV
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectBlood cell trait
dc.subjectDisease assocations
dc.subjectMachine learning
dc.subjectMethod
dc.subjectPolygenic score
dc.subjectPopulation stratification
dc.titleMachine learning optimized polygenic scores for blood cell traits identify sex-specific trajectories and genetic correlations with disease
dc.typeArticle
dc.publisher.departmentDepartment of Public Health And Primary Care, Cardiovascular Epidemiology Unit
dc.date.updated2022-01-19T15:04:45Z
prism.endingPage100086
prism.issueIdentifier1
prism.number100086
prism.publicationDate2022
prism.publicationNameCell Genomics
prism.startingPage100086
prism.volume2
dc.identifier.doi10.17863/CAM.80253
dcterms.dateAccepted2021-12-13
rioxxterms.versionofrecord10.1016/j.xgen.2021.100086
rioxxterms.versionVoR
dc.contributor.orcidRitchie, Scott [0000-0002-8454-9548]
dc.contributor.orcidButterworth, Adam [0000-0002-6915-9015]
dc.contributor.orcidDi Angelantonio, Emanuele [0000-0001-8776-6719]
dc.contributor.orcidDanesh, John [0000-0003-1158-6791]
dc.contributor.orcidInouye, Michael [0000-0001-9413-6520]
dc.identifier.eissn2666-979X
rioxxterms.typeJournal Article/Review
pubs.funder-project-idMedical Research Council (MR/L003120/1)
pubs.funder-project-idBritish Heart Foundation (None)
pubs.funder-project-idBritish Heart Foundation (RG/18/13/33946)
pubs.funder-project-idESRC (ES/T013192/1)
pubs.funder-project-idEuropean Commission Horizon 2020 (H2020) Societal Challenges (101016775)
pubs.funder-project-idNational Institute for Health Research (IS-BRC-1215-20014)
cam.issuedOnline2022-01-12
cam.depositDate2022-01-19
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