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.