metPropagate: network-guided propagation of metabolomic information for prioritization of neurometabolic disease genes

MedRxiv : the Preprint Server for Health Sciences
E. J. GrahamSara Mostafavi

Abstract

Many inborn errors of metabolism (IEMs) are amenable to treatment, therefore early diagnosis before irreversible damage occurs is imperative. Despite recent advances, the genetic basis of many metabolic phenotypes remains unknown. For discovery purposes, Whole Exome Sequencing (WES) variant prioritization coupled with phenotype-guided clinical and bioinformatics expertise is currently the primary method used to identify novel disease-causing variants; however, it can be challenging to identify the causal candidate gene given the large number of plausible variants. Using untargeted metabolomics (UM) to prioritize metabolically relevant candidate genes is a promising approach to diagnosing known or novel IEMs in a single patient. Here, we present a network-based bioinformatics approach, metPropagate, that uses UM data from a single patient and a group of controls to prioritize candidate genes. We validate metProp on 107 patients with diagnosed IEMs and 11 patients with novel IEMs diagnosed through the TIDE gene discovery project at BC Children's Hospital. The metPropagate method ranks candidate genes by considering the network of interactions between them. This is done by using a graph smoothing algorithm called label propagation...Continue Reading

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