GA4GH phenopacket-driven characterization of genotype-phenotype correlations in Mendelian disorders

• Authors: REKERLE Lauren; DANIS Daniel; REHBURG Filip; GRAEFE Adam S L; BÍLY Viktor; CABALLERO-OTEYZA Andres; CACHEIRO Pilar; CHIMIRRI Leonardo; CHONG Jessica X; CONNELLY Evan; DE VRIES BERT A B; DINGEMANS Alexander J M; DUYZEND Michael H; FREIBERGER Tomáš; GEHLE Petra; GROZA Tudor; HANSEN Peter; JACOBSEN Julius O B; KLOCPERK Adam; LADEWIG Markus S; LOVE Michael I; MARCELLO Allison J; MORDHORST Alexander; MUNOZ-TORRES Monica C; REESE Justin; SCHUETZ Catharina; SMEDLEY Damian; STRAUSS Timmy; VLADYKA Ondrej; ZOCCHE David; THUN Sylvia; MUNGALL Christopher J; HAENDEL Melissa A; ROBINSON Peter N
• Year of publication: 2026
• Type: Peer-reviewed scientific article
• Magazine / Source: AMERICAN JOURNAL OF HUMAN GENETICS
• MU Faculty or unit: Faculty of Medicine
• web: https://www.sciencedirect.com/science/article/pii/S0002929725004720?via%3Dihub
• Doi: https://doi.org/10.1016/j.ajhg.2025.12.001
• Keywords: GA4GH Phenopacket; genotype-phenotype correlations; Mendelian disorders; standardized data representation; rare disease genomics
• Description: Comprehensively characterizing genotype-phenotype correlations (GPCs) in Mendelian disease would create new opportunities for improving clinical management and understanding disease biology. However, heterogeneous approaches to data sharing, reuse, and analysis have hindered progress in the field. We developed Genotype-Phenotype Statistical Evaluation of Associations (GPSEA), a software package that leverages the Global Alliance for Genomics and Health (GA4GH) Phenopacket Schema to represent case-level clinical and genetic data about individuals. GPSEA applies an independent filtering strategy to boost statistical power to detect categorical GPCs represented by Human Phenotype Ontology terms. GPSEA additionally enables visualization and analysis of continuous phenotypes, clinical severity scores, and survival data such as age of onset of disease or clinical manifestations. We applied GPSEA to 85 cohorts with 6,179 previously published individuals with variants in one of 81 genes associated with 122 Mendelian diseases and identified 253 significant GPCs, with 48 cohorts having at least one statistically significant GPC. These results highlight the power of standardized representations of clinical data for scalable discovery of GPCs in Mendelian disease.