Phenotype Risk Score but Not Genetic Risk Score Aids in Identifying Individuals With Systemic Lupus Erythematosus in the Electronic Health Record
Supported by the National Center for Research Resources, NIH (grant UL1-RR024975) and by the National Center for Advancing Translational Sciences, NIH (grant ULT-R000445). Dr. Barnado's work was supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases, NIH (grants 1K08-AR072757-01 and R01-AR080629) and by the Rheumatology Research Foundation (K Supplement Award). Dr. Wheless's work was supported by the US Department of Veterans Affairs Clinical Science Research and Development Service (project IK2CX002452). Dr. Sawalha's work was supported by the National Institute of Allergy and Infectious Diseases, NIH (grant R01-AI097134). Dr. Denny's involvement in this project was primarily as faculty at Vanderbilt University Medical Center prior to joining the NIH.
Author disclosures are available online at https://onlinelibrary.wiley.com/doi/10.1002/art.42544.
Abstract
Objective
Systemic lupus erythematosus (SLE) poses diagnostic challenges. We undertook this study to evaluate the utility of a phenotype risk score (PheRS) and a genetic risk score (GRS) to identify SLE individuals in a real-world setting.
Methods
Using a de-identified electronic health record (EHR) database with an associated DNA biobank, we identified 789 SLE cases and 2,261 controls with available MEGAEX genotyping. A PheRS for SLE was developed using billing codes that captured American College of Rheumatology SLE criteria. We developed a GRS with 58 SLE risk single-nucleotide polymorphisms (SNPs).
Results
SLE cases had a significantly higher PheRS (mean ± SD 7.7 ± 8.0 versus 0.8 ± 2.0 in controls; P < 0.001) and GRS (mean ± SD 12.2 ± 2.3 versus 11.0 ± 2.0 in controls; P < 0.001). Black individuals with SLE had a higher PheRS compared to White individuals (mean ± SD 10.0 ± 10.1 versus 7.1 ± 7.2, respectively; P = 0.002) but a lower GRS (mean ± SD 9.0 ± 1.4 versus 12.3 ± 1.7, respectively; P < 0.001). Models predicting SLE that used only the PheRS had an area under the curve (AUC) of 0.87. Adding the GRS to the PheRS resulted in a minimal difference with an AUC of 0.89. On chart review, controls with the highest PheRS and GRS had undiagnosed SLE.
Conclusion
We developed a SLE PheRS to identify established and undiagnosed SLE individuals. A SLE GRS using known risk SNPs did not add value beyond the PheRS and was of limited utility in Black individuals with SLE. More work is needed to understand the genetic risks of SLE in diverse populations.
