Utility of Gene Panels for the Diagnosis of Inborn Errors of Metabolism in a Metabolic Reference Center

Abstract

Next-generation sequencing (NGS) technologies have been proposed as a first-line test for the diagnosis of inborn errors of metabolism (IEM), a group of genetically heterogeneous disorders with overlapping or nonspecific phenotypes. Over a 3-year period, we prospectively analyzed 311 pediatric patients with a suspected IEM using four targeted gene panels. The rate of positive diagnosis was 61.86% for intermediary metabolism defects, 32.84% for complex molecular defects, 19% for hypoglycemic/hyperglycemic events, and 17% for mitochondrial diseases, and a conclusive molecular diagnosis was established in 2-4 weeks. Forty-one patients for whom negative results were obtained with the mitochondrial diseases panel underwent subsequent analyses using the NeuroSeq panel, which groups all genes from the individual panels together with genes associated with neurological disorders (1870 genes in total). This achieved a diagnostic rate of 32%. We next evaluated the utility of a tool, Phenomizer, for differential diagnosis, and established a correlation between phenotype and molecular findings in 39.3% of patients. Finally, we evaluated the mutational architecture of the genes analyzed by determining z-scores, loss-of-function observed/expected upper bound fraction (LOEUF), and haploinsufficiency (HI) scores. In summary, targeted gene panels for specific groups of IEMs enabled rapid and effective diagnosis, which is critical for the therapeutic management of IEM patients.

Keywords: differential diagnosis; genetic diagnosis; inborn errors of metabolism.

Figure 1

Changes in the rate of positive diagnosis (grey line) with the addition of new genes (dark bars) to each of the multi-gene panels over the 3-year study period. Global values represent the mean rate of positive diagnosis.

Figure 2

Representation of the overall rate of diagnosis achieved for each panel. For each panel the total number of patients analyzed and the corresponding diagnostic outcome are shown. * p < 0.05; ** p < 0.01 (correlational analysis with Chi-squared and Fisher’s test).

Figure 3

Inheritance pattern for single-gene diseases that were included in each individual panel and identified in successfully and inconclusively diagnosed patients. Abbreviations: ID, inconclusive diagnosis; D, diagnosis; AR, autosomal recessive; AD, autosomal dominant; ?, no inheritance pattern identified in the genes included in the genetic analysis.

Figure 4

(a) Representation of z-scores for autosomal dominant (▲) and X-linked (★) genes that are predicted to be more intolerant to functional variation. (b) LOEUF scores from gnomAD, and haploinsufficiency (HI) scores from ClinGen data. Low LOEUF scores (<0.35) indicate strong selection against predicted loss-of-function (pLoF) variation in a given gene. Genes indicated with a white circle have the highest score (3), meaning there is sufficient evidence for haploinsufficiency according to ClinGen.