IDENTIFICATION OF GENETIC MUTATIONS ASSOCIATED WITH RARE INHERITED METABOLIC DISORDERS

Abstract

Inherited metabolic disorders are an eclectic group of rare genetic diseases in which a significant number of patients go undiagnosed despite the application of state-of-the-art genomic testing. In this work, a combined multi-omics diagnostics approach was used, comprising deep long-read whole genome sequencing, untargeted metabolomics, personalized genome-scale metabolic modeling using constraint-based reconstruction and analysis (COBRA), and functional validation in a prospective cohort of 342 individuals with suspected metabolic diseases who have not been diagnosed after standard clinical genetic testing. The final or most probable molecular diagnosis in 187 out of 342 cases was the integrated methodology, which is a diagnostic yield of 54.7 percent, an increase of 23.5 percent over the previous whole exome sequencing alone with a diagnostic yield of 31.2 percent. Of the cases that were diagnosed, 112 cases were known metabolic genes, and 75 cases were novel gene-disease interactions, or previously unknown non-coding regulatory variants. The COBRA modeling, which was personalized, was found to have a strong correlation with the experimental measurements of the metabolite concentrations. The largest integrated gain index was found with mitochondrial disorders with the largest effect sizes observed in splice-altering variants. Compared to the entire exome sequencing alone, median time-to-diagnosis was decreased to 96 days, and there was a desirable incremental cost-effectiveness ratio of 14,600 US dollars/additional diagnosis. These findings indicate that a significant improvement in performance is observed when using multi-omics integration as compared to using genomic-only methodology in the diagnosis of inherited metabolic disorders and make it possible to reclassify variants of uncertain value, discover new associations with the disease, and achieve better clinical outcomes by providing earlier precision therapeutic intervention.