Electronic health record (EHR) data linked to DNA biobanks are a valuable resource for understanding the phenotypic effects of human genetic variation. We previously developed the phenotype risk score (PheRS) as an approach to quantify the extent to which a patient’s clinical features resemble a given Mendelian disease. Using PheRS, we have uncovered novel associations between Mendelian disease-like phenotypes and rare genetic variants, and identified patients who may have undiagnosed Mendelian disease. Although the PheRS approach is conceptually simple, it involves multiple mapping steps and was previously only available as custom scripts, limiting the approach’s usability. Thus, we developed the phers R package, a complete and user-friendly set of functions and maps for performing a PheRS-based analysis on linked clinical and genetic data. The package includes up-to-date maps between EHR-based phenotypes (i.e., ICD codes and phecodes), human phenotype ontology (HPO) terms, and Mendelian diseases. Starting with occurrences of ICD codes, the package enables the user to calculate phenotype risk scores, validate the scores using case-control analyses, and perform genetic association analyses. By increasing PheRS’s transparency and usability, the phers R package will help improve our understanding of the relationships between rare genetic variants and clinically meaningful human phenotypes. Availability The phers R package is free and open-source, and available on CRAN and at https://phers.hugheylab.org. Supplementary information Supplementary data are available at Bioinformatics online.

Urine drug screening (UDS) assays can rapidly and sensitively detect drugs of abuse, but can also produce spurious results due to interfering substances. We previously developed an approach to identify interfering medications using electronic health record (EHR) data, but the approach was limited to UDS assays for which presumptive positives were confirmed using more specific methods. Here we adapted the approach to search for medications that cause false positives on UDS assays lacking confirmation data. From our institution’s EHR data, we used our previous dataset of 698,651 UDS and confirmation results. We also collected 211,108 UDS results for acetaminophen, ethanol, and salicylates. Both datasets included individuals’ prior medication exposures. We hypothesized that the odds of a presumptive positive would increase following exposure to an interfering medication independently of exposure to the assay’s target drug(s). For a given assay-medication pair, we quantified potential interference as an odds ratio from logistic regression. We evaluated interference of selected compounds in spiking experiments. Compared to the approach requiring confirmation data, our adapted approach showed only modestly diminished ability to detect interfering medications. Applying our approach to the new data, we discovered and validated multiple compounds that can cause presumptive positives on the UDS assay for acetaminophen. Our approach can reveal interfering medications using EHR data from institutions at which UDS results are not routinely confirmed.

Electronic health record (EHR) data linked to DNA biobanks are a valuable resource for understanding the phenotypic effects of human genetic variation. We previously developed the phenotype risk score (PheRS) as an approach to quantify the extent to which a patient’s clinical features resemble a given Mendelian disease. Using PheRS, we have uncovered novel associations between Mendelian diseaselike phenotypes and rare genetic variants, and identified patients who may have undiagnosed Mendelian disease. Although the PheRS approach is conceptually simple, it involves multiple mapping steps and was previously only available as custom scripts, limiting the approach’s usability. Thus, we developed the phers R package, a complete and user-friendly set of functions and maps for performing a PheRS-based analysis on linked clinical and genetic data. The package includes up-to-date maps between EHR-based phenotypes (i.e., ICD codes and phecodes), human phenotype ontology (HPO) terms, and Mendelian diseases. Starting with occurrences of ICD codes, the package enables the user to calculate phenotype risk scores, validate the scores using case-control analyses, and perform genetic association analyses. By increasing PheRS’s transparency and usability, the phers R package will help improve our understanding of the relationships between rare genetic variants and clinically meaningful human phenotypes.AvailabilityThe phers R package is free and open-source, and available on CRAN and at https://phers.hugheylab.org.Contactjakejhughey@gmail.comSupplementary informationSupplementary data are available at Bioinformatics online.

Background: Urine drug screening (UDS) assays can rapidly and sensitively detect drugs of abuse, but can also produce spurious results due to interfering substances. We previously developed an approach to identify interfering medications using electronic health record (EHR) data, but the approach was limited to UDS assays for which presumptive positives were confirmed using more specific methods. Here we adapted the approach to search for medications that cause false positives on UDS assays lacking confirmation data. Methods: From our institution's EHR data, we used our previous dataset of 698,651 UDS and confirmation results. We also collected 211,108 UDS results for acetaminophen, ethanol, and salicylates. Both datasets included individuals' prior medication exposures. We hypothesized that the odds of a presumptive positive would increase following exposure to an interfering ingredient independently of exposure to the assay's target drug(s). For a given assay-ingredient pair, we quantified potential interference as an odds ratio from logistic regression. We evaluated interference of selected compounds in spiking experiments. Results: Compared to the approach requiring confirmation data, our adapted approach showed only modestly diminished ability to detect interfering ingredients. Applying our approach to the new data, three ingredients had a higher odds ratio on the acetaminophen assay than acetaminophen itself did: levodopa, carbidopa, and entacapone. The first two, as well as related compounds methyldopa and alpha-methyldopamine, produced presumptive positives at < 40 mcg/mL. Conclusions: Our approach can reveal interfering medications using EHR data from institutions at which UDS results are not routinely confirmed.