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Alaimo JT, Glinton KE, Liu N, et al. Integrated analysis of metabolomic profiling and exome data supplements sequence variant interpretation, classification, and diagnosis. Genet Med. 2020;22(9):1560-1566doi: 10.1038/s41436-020-0827-0.
Alaimo, J. T., Glinton, K. E., Liu, N., Xiao, J., Yang, Y., Reid Sutton, V., & Elsea, S. H. (2020). Integrated analysis of metabolomic profiling and exome data supplements sequence variant interpretation, classification, and diagnosis. Genetics in medicine : official journal of the American College of Medical Genetics, 22(9), 1560-1566. https://doi.org/10.1038/s41436-020-0827-0
Alaimo, Joseph T, et al. "Integrated analysis of metabolomic profiling and exome data supplements sequence variant interpretation, classification, and diagnosis." Genetics in medicine : official journal of the American College of Medical Genetics vol. 22,9 (2020): 1560-1566. doi: https://doi.org/10.1038/s41436-020-0827-0
Alaimo JT, Glinton KE, Liu N, Xiao J, Yang Y, Reid Sutton V, Elsea SH. Integrated analysis of metabolomic profiling and exome data supplements sequence variant interpretation, classification, and diagnosis. Genet Med. 2020 Sep;22(9):1560-1566. doi: 10.1038/s41436-020-0827-0. Epub 2020 May 22. PMID: 32439973; PMCID: PMC7483344.
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Genet Med. 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.

Genetics in medicine : official journal of the American College of Medical Genetics

Joseph T Alaimo, Kevin E Glinton, Ning Liu, Jing Xiao, Yaping Yang, V Reid Sutton, Sarah H Elsea

Affiliations

  1. Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.
  2. Baylor Genetics, Houston, TX, USA.
  3. Department of Pediatrics, University of Missouri-Kansas City School of Medicine, Kansas City, MO, USA.
  4. Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA. [email protected].
  5. Baylor Genetics, Houston, TX, USA. [email protected].

PMID: 32439973 PMCID: PMC7483344 DOI: 10.1038/s41436-020-0827-0

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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