Optimizing gene prioritization for clinical diagnosis of metabolic genetic disorders

The expansion of next-generation sequencing has generated vast genomic datasets, but translating this information into clinically actionable tools for inherited metabolic disorders (IMDs) remains challenging. In this study, we systematically mapped gene-phenotype associations in IMDs using curated data from OMIM, ClinVar, Orphanet, and the Genetic Testing Registry (GTR). From 372 OMIM entries, we identified 228 genes definitively associated with metabolic diseases (GAMD). These genes displayed uneven chromosomal distribution, wide variability in pathogenic variant load, and strong clustering of phenotypes, particularly among amino acid metabolism disorders. Autosomal recessive inheritance was predominant. Integrating variant pathogenicity, phenotype prevalence, and diagnostic test availability, we designed two evidence-based diagnostic panels. The Subnotification Panel highlights under-tested but clinically relevant genes linked to more prevalent IMDs, aiming to address diagnostic underrepresentation. The Initial Screening Panel prioritizes genes with a high proportion of pathogenic variants, broad test accessibility, and strong clinical relevance, offering an efficient tool for first-line diagnostics. By bridging the gap between large-scale genomic information and precision clinical application, these panels provide a scalable and strategic framework to enhance diagnostic accuracy, support early intervention, and improve equity in the management of metabolic diseases.