Web-based tool improves phenotype capture for Common Variable Immunodeficiency in UK cohort

Background: Patients with Common Variable Immunodeficiency (CVID) exhibit diverse clinical manifestations, indicating heterogeneity in pathogenic mechanisms. Systematic application of standardised phenotyping in large cohorts is essential to dissect this heterogeneity. The Human Phenotype Ontology (HPO) provides a structured framework for capturing and comparing disease phenotypes. Objective: To evaluate the implementation and outcomes of HPO-based phenotyping in CVID patients enrolled for whole-genome sequencing in a large national adult primary immunodeficiency cohort. Methods: We developed a web-based Phenotype Capture Tool and delivered structured clinician training to standardise HPO annotation. Numerical laboratory parameters were mapped to corresponding HPO terms to enrich patient records. Results: We coded the phenotypes of 526 CVID patients across 11 UK centres. Clinician training increased phenotype granularity and improved phenotyping consistency between clinicians. We assigned 883 unique HPO terms across the cohort and applied logical rules to the terms to classify patients into an infection-only group and a complex phenotype group (42% vs 58%, respectively). Patients in the complex phenotype group were significantly more likely to have reduced switched memory and expanded CD21low B cells, as well as pathogenic variants in IUIS-listed genes overall and pathogenic NFKB1 variants specifically. Having a pathogenic variant in an IUIS-listed gene was associated with Autoimmune hemolytic anemia and having a pathogenic NFKB1 variant specifically was associated with Autoimmune neutropenia. Conclusion: This is the first study to systematically collect granular HPO-coded phenotypes in a large real-world CVID cohort, refining the CVID landscape and providing a comprehensive CVID HPO term set relevant for international research. Clinical Implication: HPO allows systematic capture of CVID phenotypes with low inter-clinician variability and improves comparison of cohorts, enhancing identification of disease heterogeneity essential to support genotype-phenotype studies and targeted therapeutic strategies.