Integration of genomics and metabolomics for prioritization of rare disease variants: a 2018 literature review

Emma Graham,Sara Mostafavi,Udo Engelke,Jeffrey Tang,Maja Tarailo‐Graovac,Allison Matthews,Wyeth W Wasserman,Leo A J Kluijtmans,Jessica Lee,Magda Price,Ron A Wevers,Clara D M Van Karnebeek

doi:10.1007/s10545-018-0139-6

Abstract

Many inborn errors of metabolism (IEMs) are amenable to treatment; therefore, early diagnosis and treatment 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 clinical and bioinformatics expertise is the primary method used to identify novel disease-causing variants; however, causation is often difficult to establish due to the number of plausible variants. Integrated analysis of untargeted metabolomics (UM) and WES or whole genome sequencing (WGS) data is a promising systematic approach for identifying disease-causing variants. In this review, we provide a literature-based overview of UM methods utilizing liquid chromatography mass spectrometry (LC-MS), and assess approaches to integrating WES/WGS and LC-MS UM data for the discovery and prioritization of variants causing IEMs. To embed this integrated -omics approach in the clinic, expansion of gene-metabolite annotations and metabolomic feature-to-metabolite mapping methods are needed.

Highlights

Inborn errors of metabolism (IEMs) are the largest group of genetic diseases amenable to causal therapy, and are caused by genetic variants that disrupt the function of enzymes or other proteins involved in cellular metabolism, leading to energy deficit and/or accumulation of toxins (Van Bokhoven 2011; del Rosario et al 2012; Rauch et al 2012; Ellison et al 2013)
Whole exome sequencing (WES) is the primary tool for discovery of the genetic basis of IEMs, and establishment of a genetic-based diagnosis that, in some cases, can lead to improved outcomes through targeted interventions. The promise of this approach was illustrated by a recent neurometabolic gene discovery study (Tarailo-Graovac et al 2016), in which deep phenotyping and WES achieved a diagnostic yield of 68% in patients with unexplained phenotypes, identified novel human disease genes, and most importantly enabled targeted intervention for improved outcomes in 44% of the patients
We provide an overview of WES-enabled variant prioritization, untargeted metabolomics methods utilizing liquid chromatography

Summary

Introduction

Inborn errors of metabolism (IEMs) are the largest group of genetic diseases amenable to causal therapy, and are caused by genetic variants that disrupt the function of enzymes or other proteins involved in cellular metabolism, leading to energy deficit and/or accumulation of toxins (Van Bokhoven 2011; del Rosario et al 2012; Rauch et al 2012; Ellison et al 2013). We first provide an overview of existing approaches for processing and analyzing untargeted LC-MS metabolomics data for IEM diagnosis and discovery.

Results

Conclusion