Dissecting the functional landscape of rare diseases through genomic variation in a heterogeneous cohort of 11,000 patients

Abstract

Rare diseases (RDs) pose a major diagnostic challenge. Genetic and phenotypic heterogeneity, incomplete knowledge of disease mechanisms, and limitations in variant clinical interpretation leave many patients without a molecular diagnosis. Meanwhile, the growing volume of genomic data generated in clinical practice offers an opportunity to develop data-driven methodologies for exploring disease mechanisms and supporting the reanalysis of unsolved cases. In this study, we aggregated real-world genomic data from 11,084 unrelated patients clinically classified into 122 diseases. We built a multi-disease genomic variant frequency database (FJD-DB), which enabled the development of variant and gene-disease association scores by means of case-control subcohort comparisons across 32 disease groups. Functional enrichment analyses were then used to highlight disease-associated protein domains, pathways, biological processes, and phenotypes. Finally, the resulting knowledge was integrated into a data-driven framework for the guided reanalysis of unsolved RD patients applied to Inherited Retinal Dystrophies (IRD) patients as first use case. The FJD-DB contained over 45 million unique variants, including [~]184,000 potentially pathogenic variants. We identified disease-associated pathogenic variants and highlighted both established and candidate disease genes. We identified 179 protein domains, 124 Human Phenotype Ontology terms, 79 Reactome pathways, and 72 Gene Ontology biological processes significantly enriched across multiple diseases, revealing disease-specific functional signatures. Integration of disease-associated variant, gene, and functional signals enabled the development of a data-driven framework for guided reanalysis of unsolved RD cases. Applied to over 1,100 unsolved IRD cases, this data-driven reanalysis yielded clinically relevant findings in 32 patients, including three molecular diagnoses, 25 possibly solved cases, and four cases in which prioritized variants highlighted four candidate genes. We illustrate how aggregated real-world genomic data can be leveraged to identify disease-associated molecular signals generating novel biological hypotheses. A unified analytical framework provides a scalable strategy for knowledge discovery and guided reanalysis, facilitating the identification of overlooked and potentially novel genetic causes of RDs.