Show simple item record

dc.contributor.authorSchofield, Paul
dc.contributor.authorJacobson, Julius
dc.contributor.authorBaynam, Jaques
dc.contributor.authoret al
dc.date.accessioned2022-04-21T23:31:00Z
dc.date.available2022-04-21T23:31:00Z
dc.identifier.issn1087-0156
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/336355
dc.description.abstractDespite great strides in the development and wide acceptance of standards for exchanging structured information about genomic variants, the development of standards for computational phenotype analysis for translational genomics has lagged behind. Phenotypic features (signs, symptoms, laboratory and imaging findings, results of physiological tests, etc.) are of essential clinical importance, yet exchanging them in conjunction with genomic variation is often overlooked or even neglected. In the clinical domain, significant work has been dedicated to the development of computational phenotypes.1 Traditionally, these approaches have largely relied on rule-based methods and large sources of clinical data to identify cohorts of patients with or without a specific disease.2–5 However, they were not developed to enable deep phenotyping of phenotypic abnormalities, to facilitate computational analysis of interpatient phenotypic similarity, or to support computational decision support. To address this, the Global Alliance for Genomics and Health6 (GA4GH) has developed the Phenopacket schema, which supports exchange of computable longitudinal case-level phenotypic information for diagnosis of and research on all types of disease including Mendelian and complex genetic diseases, cancer, and infectious diseases
dc.description.sponsorshipThis work was supported by 7RM1HG010860-02 (NHGRI). Additional funding was as follows. PNR was supported by NLM contract #75N97019P00280, NIH NHGRI RM1HG010860, NIH OD R24OD011883, NIH NICHD 1R01HD103805-01. HH was supported by NIH OD R24OD011883. GIS was supported by ELIXIR, the research infrastructure for life-science data. CGC was supported by NIH NCATS U24TR002306. KCL was supported by NIH OD 5UM1OD023221. MB was supported by BioMedIT Network project of Swiss Institute of Bioinformatics (SIB) and Swiss Personalized Health Network (SPHN). AHW was supported by NIH NHGRI K99HG010157, NIH NHGRI R00HG010157. CJM, MAH, MCM-T, JAM, DD were supported by NIH NHGRI RM1HG010860, NIH OD R24OD011883. AM-J was supported by Australian Genomics. Australian Genomics is supported by the National Health and Medical Research Council (GNT1113531). DS, JOBJ were supported by NIH NHGRI RM1HG010860, NIH OD R24OD011883, NIH NICHD 1R01HD103805-01. MD was supported by NIH NHGRI U54HG004028, NIH NHGRI 5U01HG008473-03, NIH NCATS OT2TR003434-01S1U54HG008033-01. GSB was supported by Roy Hill Community Foundation, Angela Wright Bennett Foundation, McCusker Charitable Foundation, Borlaug Foundation, Stan Perron Charitable Foundation. LB was supported by NIH NHGRI U41HG006834 (Clinical Genome Resource). MC was supported by EMBL-EBI Core Funds and Wellcome Trust GA4GH award number 201535/Z/16/Z. AH was supported by NIH NHGRI 1U41HG006627, NIH NHGRI 1U54HG006542, NIH NHGRI 1RM1HG010860. PNS was supported by The Alan Turing Trust. NLH was supported by NIH NHGRI RM1HG010860, NIH OD R24OD011883, U.S. Department of Energy Contract DE-AC02-05CH11231. NP was supported by Moorfields Eye Charity. NQ-R was supported by EU Horizon 2020 research and innovation programme grant agreement 825575 (EJP-RD). OE was supported by NIH grants UL1TR002384, R01CA194547, P01CA214274 LLS SCOR grants 180078-01, 7021-20, Starr Cancer Consortium Grant I11-0027. HL was supported by CIHR Foundation Grant on Precision Health for Neuromuscular Diseases FDN-167281. RT was supported by CIHR postdoctoral fellowship award MFE-171275. LDS was supported by Genome Canada and NIH NHGRI U24HG011025. SO was supported by AMED. DP, LM, AP, SB, MR, RK were supported by EU Horizon 2020 research and innovation programme grant agreements 779257 (Solve-RD) and 825575 (EJP-RD). RRF was supported by NLM contract #75N97019P00280.
dc.publisherNature Research
dc.rightsAll Rights Reserved
dc.rights.urihttp://www.rioxx.net/licenses/all-rights-reserved
dc.titleThe GA4GH Phenopacket schema: A computable representation of clinical data for precision medicine
dc.typeArticle
dc.publisher.departmentDepartment of Physiology, Development And Neuroscience
dc.date.updated2022-04-21T12:31:58Z
prism.publicationNameNature Biotechnology
dc.identifier.doi10.17863/CAM.83775
dcterms.dateAccepted2022-05-12
rioxxterms.versionAM
dc.contributor.orcidSchofield, Paul [0000-0002-5111-7263]
rioxxterms.typeJournal Article/Review
pubs.funder-project-idAlan Turing Institute (Unknown)
cam.orpheus.counter5*
cam.depositDate2022-04-21
pubs.licence-identifierapollo-deposit-licence-2-1
pubs.licence-display-nameApollo Repository Deposit Licence Agreement
rioxxterms.freetoread.startdate2025-04-21


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record