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

Abstract

Purpose

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.

Methods

We estimated genetically predicted expression (GReX) of cystic fibrosis transmembrane conductance regulator (CFTR) and tested for association with clinical diagnoses in the Vanderbilt University biobank (N = 9142 persons of European descent with 71 cases of CF). The top associated EHR phenotypes were assessed in combination as a phenotype risk score (PheRS) for discriminating CF case status in an additional 2.8 million patients from Vanderbilt University Medical Center (VUMC) and 125,305 adult patients including 25,314 CF cases from MarketScan, an independent external cohort.

Results

GReX of CFTR was associated with EHR phenotypes consistent with CF. PheRS constructed using the EHR phenotypes and weights discovered by the genetic associations improved discriminative power for CF over the initially proposed PheRS in both VUMC and MarketScan.

Conclusion

Our study demonstrates the power of EHRs for clinical description of CF and the benefits of using a genetics-informed weighing scheme in construction of a phenotype risk score. This research may find broad applications for phenomic studies of Mendelian disease genes.

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Fig. 1: Workflow of the study.
Fig. 2: Genetically regulated expression (GReX) of CFTR in brain hypothalamus correlates with dosage of DF508proxy.
Fig. 3: Haplotype-level genetically regulated expression (hGReX) of CFTR stratified by the presence of cystic fibrosis (CF) alleles.
Fig. 4: Phenotype risk score (PheRS) construction for cystic fibrosis (CF) and performance evaluation.

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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.

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Correspondence to Xue Zhong PhD or Nancy J. Cox PhD.

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E.R.G. receives an honorarium from the journal Circulation Research of the American Heart Association, as a member of the Editorial Board. He performed consulting on pharmacogenetic analysis with the City of Hope/Beckman Research Institute. The other authors declare no conflicts of interest.

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Zhong, X., Yin, Z., Jia, G. et al. Electronic health record phenotypes associated with genetically regulated expression of CFTR and application to cystic fibrosis. Genet Med 22, 1191–1200 (2020). https://doi.org/10.1038/s41436-020-0786-5

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Keywords

  • Mendelian
  • cystic fibrosis
  • CFTR
  • cis-regulated expression
  • phenotype risk score