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Diagnosing childhood-onset inborn errors of metabolism by next-generation sequencing

Abstract

Background Inborn errors of metabolism (IEMs) underlie a substantial proportion of paediatric disease burden but their genetic diagnosis can be challenging using the traditional approaches.

Methods We designed and validated a next-generation sequencing (NGS) panel of 226 IEM genes, created six overlapping phenotype-based subpanels and tested 102 individuals, who presented clinically with suspected childhood-onset IEMs.

Results In 51/102 individuals, NGS fully or partially established the molecular cause or identified other actionable diagnoses. Causal mutations were identified significantly more frequently when the biochemical phenotype suggested a specific IEM or a group of IEMs (p<0.0001), demonstrating the pivotal role of prior biochemical testing in guiding NGS analysis. The NGS panel helped to avoid further invasive, hazardous, lengthy or expensive investigations in 69% individuals (p<0.0001). Additional functional testing due to novel or unexpected findings had to be undertaken in only 3% of subjects, demonstrating that the use of NGS does not significantly increase the burden of subsequent follow-up testing. Even where a molecular diagnosis could not be achieved, NGS-based approach assisted in the management and counselling by reducing the likelihood of a high-penetrant genetic cause.

Conclusion NGS has significant clinical utility for the diagnosis of IEMs. Biochemical testing and NGS analysis play complementary roles in the diagnosis of IEMs. Incorporating NGS into the diagnostic algorithm of IEMs can improve the accuracy of diagnosis.

  • Inborn Errors of Metabolism
  • Metabolic disorders
  • Next Generation Sequencing

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