Electronic health record phenotypes associated with genetically regulated expression of CFTR and application to cystic fibrosis

Xue Zhong, Zhijun Yin, Gengjie Jia, Dan Zhou, Qiang Wei, Annika Faucon, Patrick Evans, Eric R. Gamazon, Bingshan Li, Ran Tao, Andrey Rzhetsky, Lisa Bastarache, Nancy J. Cox

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

The 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.
Original languageEnglish (US)
JournalGenetics in Medicine
DOIs
StatePublished - Apr 15 2020
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This 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.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

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