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. 2020 Sep;22(9):1560-1566.
doi: 10.1038/s41436-020-0827-0. Epub 2020 May 22.

Integrated analysis of metabolomic profiling and exome data supplements sequence variant interpretation, classification, and diagnosis

Affiliations

Integrated analysis of metabolomic profiling and exome data supplements sequence variant interpretation, classification, and diagnosis

Joseph T Alaimo et al. Genet Med. 2020 Sep.

Abstract

Purpose: A primary barrier to improving exome sequencing diagnostic rates is the interpretation of variants of uncertain clinical significance. We aimed to determine the contribution of integrated untargeted metabolomics in the analysis of exome sequencing data by retrospective analysis of patients evaluated by both exome sequencing and untargeted metabolomics within the same clinical laboratory.

Methods: Exome sequencing and untargeted metabolomic data were collected and analyzed for 170 patients. Pathogenic variants, likely pathogenic variants, and variants of uncertain significance in genes associated with a biochemical phenotype were extracted. Metabolomic data were evaluated to determine if these variants resulted in biochemical abnormalities that could be used to support their interpretation using current American College of Genetics and Genomics (ACMG) guidelines.

Results: Metabolomic data contributed to the interpretation of variants in 74 individuals (43.5%) over 73 different genes. The data allowed for the reclassification of 9 variants as likely benign, 15 variants as likely pathogenic, and 3 variants as pathogenic. Metabolomic data confirmed a clinical diagnosis in 21 cases, for a diagnostic rate of 12.3% in this population.

Conclusion: Untargeted metabolomics can serve as a useful adjunct to exome sequencing by providing valuable functional data that may not otherwise be clinically available, resulting in improved variant classification.

Keywords: exome sequencing; functional analysis; genome; metabolomics; variant interpretation.

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Conflict of interest statement

DISCLOSURES

Dr. Alaimo and Dr. Xiao declare no other potential conflicts of interest.

Figures

Fig. 1.
Fig. 1.. Relative contribution of metabolomic data to variant interpretation.
(a) Metabolomic data contributed to the interpretation of a variant in 44% of clinical cases. (b) In these cases, 74 individuals carried 145 variants amenable to interpretation via untargeted metabolomics. Of these variants, metabolomics helped to rule out the contribution of 60%, confirmed the pathogenicity of 11.7%, upgraded 12.4%, and downgraded 6.2%, according the ACMG variant interpretation guidelines.

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