Genetics in Medicine Journal Features Baylor Genetics for the Analysis of Metabolomic Profiling and Exome Data Supplements

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

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

Published: May 22, 2020



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.


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.


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.


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.

Genetics In Medicine (2020)


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