Cystic fibrosis (CF), a disease caused by a spectrum of variants in the CFTR gene, is one of the most common Mendelian diseases. CF continues to pose diagnostic challenges because of the variability in its clinical manifestations.
Xue Zhong, PhD, Nancy Cox, PhD, and colleagues now describe a novel approach to identify clinical diagnoses (phenotypes) that are consistent with CF disease in electronic health records (EHRs).
They used genetically regulated expression of CFTR as a tool to find associated phenotypes in BioVU, Vanderbilt’s DNA biobank linked to de-identified EHRs. The top phenotypes were used to construct a phenotype risk score (PheRS), which improved the power to detect clinically diagnosed CF in 2.8 million VUMC EHRs and in an independent adult MarketScan cohort.
The study, reported in Genetics in Medicine, demonstrates the benefit of using genetics to construct a PheRS that could be used to detect and alert unrecognized CF patients and that will be valuable for studying other Mendelian diseases.
This research was supported by the National Institutes of Health (grants MH113362, HG009086, HG010718, HL122712, MH094267, HL108634).
