Exome Sequencing and the Management of Neurometabolic Disorders

Abstract

Background: Whole-exome sequencing has transformed gene discovery and diagnosis in rare diseases. Translation into disease-modifying treatments is challenging, particularly for intellectual developmental disorder. However, the exception is inborn errors of metabolism, since many of these disorders are responsive to therapy that targets pathophysiological features at the molecular or cellular level.

Methods: To uncover the genetic basis of potentially treatable inborn errors of metabolism, we combined deep clinical phenotyping (the comprehensive characterization of the discrete components of a patient's clinical and biochemical phenotype) with whole-exome sequencing analysis through a semiautomated bioinformatics pipeline in consecutively enrolled patients with intellectual developmental disorder and unexplained metabolic phenotypes.

Results: We performed whole-exome sequencing on samples obtained from 47 probands. Of these patients, 6 were excluded, including 1 who withdrew from the study. The remaining 41 probands had been born to predominantly nonconsanguineous parents of European descent. In 37 probands, we identified variants in 2 genes newly implicated in disease, 9 candidate genes, 22 known genes with newly identified phenotypes, and 9 genes with expected phenotypes; in most of the genes, the variants were classified as either pathogenic or probably pathogenic. Complex phenotypes of patients in five families were explained by coexisting monogenic conditions. We obtained a diagnosis in 28 of 41 probands (68%) who were evaluated. A test of a targeted intervention was performed in 18 patients (44%).

Conclusions: Deep phenotyping and whole-exome sequencing in 41 probands with intellectual developmental disorder and unexplained metabolic abnormalities led to a diagnosis in 68%, the identification of 11 candidate genes newly implicated in neurometabolic disease, and a change in treatment beyond genetic counseling in 44%. (Funded by BC Children's Hospital Foundation and others.).

Figure 1. Flow Chart Showing Gene-Discovery Approach with the Use of Collaborative Phenomics and Semiautomated Genomics

This research process of uncovering the genetic basis of potentially treatable neurometabolic conditions in patients with an intellectual developmental disorder (IDD) involves a combination of deep clinical phenotyping (the comprehensive characterization of the discrete components of a patient’s clinical and biochemical phenotype), whole-exome sequencing analysis, team interpretation, validation, and translation to the clinical setting. Whole-exome sequencing analysis is performed on DNA samples obtained from the proband and on available samples from parents and any affected siblings. The semiautomated gene-discovery pipeline involves manual inspection of data quality and collaborative interactions among clinicians, laboratory scientists, and bioinformaticians for interpretation, candidate-gene selection, and subsequent experimental and clinical validations. The benefits of a diagnosis for patients and their families are shown; in some cases, the diagnosis enables a targeted treatment strategy. Details are provided in the Supplementary Appendix.