TY - JOUR
T1 - Integration of genomics and metabolomics for prioritization of rare disease variants: a 2018 literature review
AU - Graham, Emma
AU - Lee, Jessica
AU - Price, Magda
AU - Tarailo-Graovac, Maja
AU - Matthews, Allison
AU - Engelke, Udo
AU - Tang, Jeffrey
AU - Kluijtmans, Leo A. J.
AU - Wevers, Ron A.
AU - Wasserman, Wyeth W.
AU - van Karnebeek, Clara D. M.
AU - Mostafavi, Sara
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85047248689&origin=inward
UR - https://www.ncbi.nlm.nih.gov/pubmed/29721916
U2 - https://doi.org/10.1007/s10545-018-0139-6
DO - https://doi.org/10.1007/s10545-018-0139-6
M3 - Review article
C2 - 29721916
SN - 0141-8955
VL - 41
SP - 435
EP - 445
JO - Journal of Inherited Metabolic Disease
JF - Journal of Inherited Metabolic Disease
IS - 3
ER -