1. Electronic health record phenotypes associated with genetically regulated expression of CFTR and application to cystic fibrosis
- Author
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Eric R. Gamazon, Andrey Rzhetsky, Patrick Evans, Lisa Bastarache, Nancy J. Cox, Gengjie Jia, Ran Tao, Dan Zhou, Qiang Wei, Bingshan Li, Zhijun Yin, Annika Faucon, and Xue Zhong
- Subjects
Adult ,Cystic Fibrosis ,Cystic Fibrosis Transmembrane Conductance Regulator ,Disease ,Bioinformatics ,Cystic fibrosis ,Article ,symbols.namesake ,Electronic Health Records ,Humans ,Medicine ,health care economics and organizations ,Genetics (clinical) ,Framingham Risk Score ,biology ,business.industry ,medicine.disease ,Phenotype ,Biobank ,Cystic fibrosis transmembrane conductance regulator ,Mutation ,Cohort ,biology.protein ,Mendelian inheritance ,symbols ,business - 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. 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. 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. 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.
- Published
- 2020