Abstract
Many inborn errors of metabolism (IEMs) are amenable to treatment, therefore early diagnosis is imperative. Whole-exome sequencing (WES) variant prioritization coupled with phenotype-guided clinical and bioinformatics expertise is typically used to identify disease-causing variants; however, it can be challenging to identify the causal candidate gene when a large number of rare and potentially pathogenic variants are detected. Here, we present a network-based approach, metPropagate, that uses untargeted metabolomics (UM) data from a single patient and a group of controls to prioritize candidate genes in patients with suspected IEMs. We validate metPropagate on 107 patients with IEMs diagnosed in Miller et al. (2015) and 11 patients with both CNS and metabolic abnormalities. The metPropagate method ranks candidate genes by label propagation, a graph-smoothing algorithm that considers each gene’s metabolic perturbation in addition to the network of interactions between neighbors. metPropagate was able to prioritize at least one causative gene in the top 20th percentile of candidate genes for 92% of patients with known IEMs. Applied to patients with suspected neurometabolic disease, metPropagate placed at least one causative gene in the top 20th percentile in 9/11 patients, and ranked the causative gene more highly than Exomiser’s phenotype-based ranking in 6/11 patients. Interestingly, ranking by a weighted combination of metPropagate and Exomiser scores resulted in improved prioritization. The results of this study indicate that network-based analysis of UM data can provide an additional mode of evidence to prioritize causal genes in patients with suspected IEMs.
Highlights
Inborn errors of metabolism (IEMs) are the largest group of genetic diseases amenable to therapy, and are defined as any condition that leads to a disruption of a metabolic pathway, irrespective of whether it is associated with an abnormal biochemical test[1]
We found that the combined metPropagate and Exomiser score prioritized the causative gene in the top 20th percentile in 10/11 patients (11/13 genes), the top 10th percentile in 8/11 patients (8/13 genes) and the top 5th percentile in 4/11 patients
We present metPropagate, an algorithm that uses a protein-protein functional interaction network and metabolomic information to prioritize candidate genes in patients with suspected IEMs
Summary
Inborn errors of metabolism (IEMs) are the largest group of genetic diseases amenable to therapy, and are defined as any condition that leads to a disruption of a metabolic pathway, irrespective of whether it is associated with an abnormal biochemical test[1]. It makes sense that genes that interact with metabolic genes have a higher likelihood of being prioritized by a metabolomics-driven prioritization algorithm This association may lead genes with a large HMDB neighborhood to be more vulnerable to false positive metabolic enrichment; across all 19,354 genes in 107 patients, we find a positive correlation between percentage of metabolic first-degree neighbors and a gene’s median percentile ranking. Given these findings, we decided to build a model that combined metPropagate and Exomiser-Phenotype rankings in order to reduce the likelihood of false-positive prioritization. This indicates that when applied to a single patient, using metPropagate and Exomiser in conjunction can increase the likelihood of prioritizing the causative gene
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