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

doi:10.1038/s41436-020-0786-5

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 journal › Article › peer-review

8 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 language	English (US)
Journal	Genetics in Medicine
DOIs	https://doi.org/10.1038/s41436-020-0786-5
State	Published - Apr 15 2020
Externally published	Yes

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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@article{8189a5fb40714a588ec302aff8b45196,

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

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.",

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

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.",

year = "2020",

month = apr,

day = "15",

doi = "10.1038/s41436-020-0786-5",

language = "English (US)",

journal = "Genetics in Medicine",

issn = "1098-3600",

publisher = "Nature Publishing Group",

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TY - JOUR

T1 - Electronic health record phenotypes associated with genetically regulated expression of CFTR and application to cystic fibrosis

AU - Zhong, Xue

AU - Yin, Zhijun

AU - Jia, Gengjie

AU - Zhou, Dan

AU - Wei, Qiang

AU - Faucon, Annika

AU - Evans, Patrick

AU - Gamazon, Eric R.

AU - Li, Bingshan

AU - Tao, Ran

AU - Rzhetsky, Andrey

AU - Bastarache, Lisa

AU - Cox, Nancy J.

N1 - 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.

PY - 2020/4/15

Y1 - 2020/4/15

N2 - 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.

AB - 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.

UR - http://hdl.handle.net/10754/662598

UR - http://www.nature.com/articles/s41436-020-0786-5

U2 - 10.1038/s41436-020-0786-5

DO - 10.1038/s41436-020-0786-5

M3 - Article

C2 - 32296164

SN - 1098-3600

JO - Genetics in Medicine

JF - Genetics in Medicine

ER -

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

Abstract

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