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The diagnosis of inborn errors of metabolism by an integrative "multi-omics" approach: A perspective encompassing genomics, transcriptomics, and proteomics.
J. Inherit. Metab. Dis. 43, 25-35 (2020)
Given the rapidly decreasing cost and increasing speed and accessibility of massively parallel technologies, the integration of comprehensive genomic, transcriptomic, and proteomic data into a "multi-omics" diagnostic pipeline is within reach. Even though genomic analysis has the capability to reveal all possible perturbations in our genetic code, analysis typically reaches a diagnosis in just 35% of cases, with a diagnostic gap arising due to limitations in prioritization and interpretation of detected variants. Here we review the utility of complementing genetic data with transcriptomic data and give a perspective for the introduction of proteomics into the diagnostic pipeline. Together these methodologies enable comprehensive capture of the functional consequence of variants, unobtainable by the analysis of each methodology in isolation. This facilitates functional annotation and reprioritization of candidate genes and variants-a promising approach to shed light on the underlying molecular cause of a patient's disease, increasing diagnostic rate, and allowing actionability in clinical practice.
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4.036
1.498
14
17
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Publication type
Article: Journal article
Document type
Review
Keywords
Multi-omics ; Genomics ; Transcriptomics ; Proteomics ; Diagnostics; Mitochondrial Complex; Sequence Variants; Expression; Mutations; Genes; Identification; Association; Inheritance; Guidelines; Landscape
Language
english
Publication Year
2020
Prepublished in Year
2019
HGF-reported in Year
2019
ISSN (print) / ISBN
0141-8955
e-ISSN
1573-2665
Quellenangaben
Volume: 43,
Issue: 1,
Pages: 25-35
Publisher
Springer
Publishing Place
111 River St, Hoboken 07030-5774, Nj Usa
Reviewing status
Peer reviewed
Institute(s)
Institute of Human Genetics (IHG)
POF-Topic(s)
30501 - Systemic Analysis of Genetic and Environmental Factors that Impact Health
Research field(s)
Genetics and Epidemiology
PSP Element(s)
G-500700-001
WOS ID
WOS:000508201000004
PubMed ID
31119744
Erfassungsdatum
2019-05-27