Prioritizing disease-causing metabolic genes by integrating metabolomics with whole exome sequencing data

Abstract

The integration of metabolomics data with sequencing data is a key step towards improving the diagnostic process for finding the disease-causing gene(s) in patients suspected of having an inborn error of metabolism (IEM). The measured metabolite levels could provide additional phenotypical evidence to elucidate the degree of pathogenicity for variants found in metabolic genes. We present a computational approach, called Reafect, that calculates for each reaction in a metabolic pathway a score indicating whether that reaction is being deficient or not. When calculating this score, Reafect takes multiple factors into account: the magnitude and sign of alterations in the metabolite levels, the reaction distances between metabolites and reactions in the pathway, and the biochemical directionality of the reactions. We applied Reafect to untargeted metabolomics data of 72 patient samples with a known IEM and found that in 80% of the cases the correct deficient enzyme was ranked within the top 5% of all considered enzyme deficiencies. Next, we integrated Reafect with CADD scores (a measure for variant deleteriousness) and ranked the potential disease-causing genes of 27 IEM patients. We observed that this integrated approach significantly improved the prioritization of the disease-causing genes when compared with the two approaches individually. For 15/27 IEM patients the correct disease-causing gene was ranked within the top 0.2% of the set of potential disease-causing genes. Together, our findings suggest that metabolomics data improves the identification of disease-causing genetic variants in patients suffering from IEM.