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


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-05

AUTHORS

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

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. More... »

PAGES

435-445

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10545-018-0139-6

DOI

http://dx.doi.org/10.1007/s10545-018-0139-6

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1103765796

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/29721916


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