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Exome sequencing has higher diagnostic yield compared to simulated disease-specific panels in children with suspected monogenic disorders

European Journal of Human Geneticsvolume 26pages644651 (2018) | Download Citation



As test costs decline, whole-exome sequencing (WES) has become increasingly used for clinical diagnosis, and now represents the primary alternative to gene panel testing for patients with a suspected genetic disorder. We sought to compare the diagnostic yield of singleton-WES with simulated application of commercial gene panels in children suspected of having a genetically heterogeneous condition. Recruitment, singleton-WES and phenotype-driven variant analysis was completed for 145 paediatric patients. At recruitment, clinicians were required to propose commercial gene panel tests as an alternative to WES and nominate a phenotype-driven candidate gene list. In WES-diagnosed children, three commercial options for each proposed panel were identified and evaluated for hypothetical diagnostic yield assuming 100% analytical sensitivity and specificity. We compared the price of WES with the least costly panel in WES-diagnosed children. In WES-undiagnosed children, we evaluated the exonic coverage of their phenotype-driven gene list using aggregate data. WES diagnoses were made in genes not included in at least one-of-three commercial panels in 42% of cases. Had a panel been selected instead, 23% of WES-diagnosed children would not have been diagnosed. In 26% of cases, the least costly panel option would have been more expensive than WES. Evaluation of WES coverage found that at the most stringent level of 20× coverage, the likelihood of missing a clinically relevant variant in a candidate gene list was maximally 8%. The broader coverage of WES makes it a superior alternative to gene panel testing at similar financial cost for children with suspected complex monogenic phenotypes.

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The authors are grateful to all the children and families involved in this study. Statistical support was provided by Susan Donath from the Clinical Epidemiology and Biostatistics Unit of the Murdoch Children’s Research Institute. Genetic counselling support was provided by the Melbourne Genomics funded genetic counsellors and data-management support from Nessie Mupfeki. Systems and standards support was provided by the Clinical Genomics and Bioinformatics Advisory Group and the Clinical Genomics Advisory Group. The study was funded by the founding organisations of the Melbourne Genomics Health Alliance (Royal Melbourne Hospital, Royal Children’s Hospital, University of Melbourne, Walter and Eliza Hall Institute, Murdoch Children’s Research Institute, Australian Genome Research Facility and CSIRO) and the State Government of Victoria (Department of Health and Human Services). The involvement of AGRF was supported by sponsorship from Bioplatforms Australia and the NCRIS program. Further support and backing for this study was provided by the Royal Children’s Hospital, Melbourne and Victorian Clinical Genetics Services staff for referring patients.

Author information


  1. Victorian Clinical Genetics Services, Murdoch Children’s Research Institute, Melbourne, Australia

    • Oliver James Dillon
    • , Sebastian Lunke
    • , Zornitza Stark
    • , Alison Yeung
    • , Susan M. White
    •  & Tiong Yang Tan
  2. Department of Paediatrics, University of Melbourne, Melbourne, Australia

    • Oliver James Dillon
    • , Clara Gaff
    • , Susan M. White
    •  & Tiong Yang Tan
  3. Melbourne Genomics Health Alliance, Melbourne, Australia

    • Zornitza Stark
    • , Natalie Thorne
    •  & Clara Gaff


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  1. Melbourne Genomics Health Alliance

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    The authors declare that they have no conflict of interest.

    Corresponding authors

    Correspondence to Susan M. White or Tiong Yang Tan.

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