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dc.contributor.authorZhong, Xue
dc.contributor.authorYin, Zhijun
dc.contributor.authorJia, Gengjie
dc.contributor.authorZhou, Dan
dc.contributor.authorWei, Qiang
dc.contributor.authorFaucon, Annika
dc.contributor.authorEvans, Patrick
dc.contributor.authorGamazon, Eric R.
dc.contributor.authorLi, Bingshan
dc.contributor.authorTao, Ran
dc.contributor.authorRzhetsky, Andrey
dc.contributor.authorBastarache, Lisa
dc.contributor.authorCox, Nancy J.
dc.date.accessioned2020-04-21T11:50:38Z
dc.date.available2020-04-21T11:50:38Z
dc.date.issued2020-04-16
dc.date.submitted2019-09-17
dc.identifier.citationZhong, X., Yin, Z., Jia, G., Zhou, D., Wei, Q., Faucon, A., … Cox, N. J. (2020). Electronic health record phenotypes associated with genetically regulated expression of CFTR and application to cystic fibrosis. Genetics in Medicine. doi:10.1038/s41436-020-0786-5
dc.identifier.issn1098-3600
dc.identifier.issn1530-0366
dc.identifier.doi10.1038/s41436-020-0786-5
dc.identifier.urihttp://hdl.handle.net/10754/662598
dc.description.abstractThe increasing use of electronic health records (EHRs) and biobanks offers unique opportunities to study Mendelian diseases. We described a novel approach to summarize clinical manifestations from patient EHRs into phenotypic evidence for cystic fibrosis (CF) with potential to alert unrecognized patients of the disease.
dc.description.sponsorshipThis work was funded by the National Institutes of Health (NIH) grants R01MH113362, U01HG009086, R35HG010718, R01HL122712, 1P50MH094267, and U01HL108634-01. A.R. also acknowledges support from the Defense Advanced Research Projects Agency (DARPA) Big Mechanism program under Army Research Office (ARO) contract W911NF1410333, the King Abdullah University of Science and Technology (KAUST), and a gift from Liz and Kent Dauten. BioVU and the Synthetic Derivative of Vanderbilt University Medical Center are supported by the National Center for Advancing Translational Science grant UL1TR000445 from NIH; the genotypes in BioVU used for the analyses described were funded by NIH grants RC2GM092618 and U01HG004603.
dc.publisherSpringer Nature
dc.relation.urlhttp://www.nature.com/articles/s41436-020-0786-5
dc.rightsArchived with thanks to Genetics in Medicine
dc.titleElectronic health record phenotypes associated with genetically regulated expression of CFTR and application to cystic fibrosis
dc.typeArticle
dc.identifier.journalGenetics in Medicine
dc.rights.embargodate2020-10-15
dc.eprint.versionPost-print
dc.contributor.institutionDivision of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
dc.contributor.institutionVanderbilt Genetics Institute, Nashville, TN, USA.
dc.contributor.institutionDepartment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.
dc.contributor.institutionDepartment of Medicine, Institute of Genomics and Systems Biology, University of Chicago, Chicago, IL, USA.
dc.contributor.institutionHuman Genetics Graduate Program, Vanderbilt University, Nashville, TN, USA.
dc.contributor.institutionDepartment of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, USA.
dc.contributor.institutionCommittee on Genomics, Genetics and Systems Biology, University of Chicago, Chicago, IL, USA.
dc.contributor.institutionDepartment of Human Genetics, University of Chicago, Chicago, IL, USA
dc.date.accepted2020-03-17
dc.date.published-online2020-04-16
dc.date.published-print2020-07


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