Display options
Cite
Molina-Ramírez LP, Kyle C, Ellingford JM, et al. Personalised virtual gene panels reduce interpretation workload and maintain diagnostic rates of proband-only clinical exome sequencing for rare disorders. J Med Genet. 2021;doi: 10.1136/jmedgenet-2020-107303.
Molina-Ramírez, L. P., Kyle, C., Ellingford, J. M., Wright, R., Taylor, A., Bhaskar, S. S., Campbell, C., Jackson, H., Fairclough, A., Rousseau, A., Burghel, G. J., Dutton, L., Banka, S., Briggs, T. A., Clayton-Smith, J., Douzgou, S., Jones, E. A., Kingston, H. M., Kerr, B., Ealing, J., Somarathi, S., Chandler, K. E., Stuart, H. M., Burkitt-Wright, E. M., Newman, W. G., Bruce, I. A., Black, G. C., & Gokhale, D. (2021). Personalised virtual gene panels reduce interpretation workload and maintain diagnostic rates of proband-only clinical exome sequencing for rare disorders. Journal of medical genetics, . https://doi.org/10.1136/jmedgenet-2020-107303
Molina-Ramírez, Leslie Patricia, et al. "Personalised virtual gene panels reduce interpretation workload and maintain diagnostic rates of proband-only clinical exome sequencing for rare disorders." Journal of medical genetics vol. (2021). doi: https://doi.org/10.1136/jmedgenet-2020-107303
Molina-Ramírez LP, Kyle C, Ellingford JM, Wright R, Taylor A, Bhaskar SS, Campbell C, Jackson H, Fairclough A, Rousseau A, Burghel GJ, Dutton L, Banka S, Briggs TA, Clayton-Smith J, Douzgou S, Jones EA, Kingston HM, Kerr B, Ealing J, Somarathi S, Chandler KE, Stuart HM, Burkitt-Wright EM, Newman WG, Bruce IA, Black GC, Gokhale D. Personalised virtual gene panels reduce interpretation workload and maintain diagnostic rates of proband-only clinical exome sequencing for rare disorders. J Med Genet. 2021 Apr 20; doi: 10.1136/jmedgenet-2020-107303. Epub 2021 Apr 20. PMID: 33879512.
Copy Download .nbib Format: NLM
Share it on

J Med Genet. 2021 Apr 20; doi: 10.1136/jmedgenet-2020-107303. Epub 2021 Apr 20.

Personalised virtual gene panels reduce interpretation workload and maintain diagnostic rates of proband-only clinical exome sequencing for rare disorders.

Journal of medical genetics

Leslie Patricia Molina-Ramírez, Claire Kyle, Jamie M Ellingford, Ronnie Wright, Algy Taylor, Sanjeev S Bhaskar, Christopher Campbell, Harriet Jackson, Adele Fairclough, Abigail Rousseau, George J Burghel, Laura Dutton, Siddharth Banka, Tracy A Briggs, Jill Clayton-Smith, Sofia Douzgou, Elizabeth A Jones, Helen M Kingston, Bronwyn Kerr, John Ealing, Suresh Somarathi, Kate E Chandler, Helen M Stuart, Emma Mm Burkitt-Wright, William G Newman, Iain A Bruce, Graeme C Black, David Gokhale

Affiliations

  1. Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK.
  2. North West Genomic Laboratory Hub, Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust, Manchester, Greater Manchester, UK.
  3. Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust, Manchester, UK.
  4. Department of Neurology, Salford Royal NHS Foundation Trust, Salford, Salford, UK.
  5. Division of Infection, Immunity and Respiratory Medicine, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK.
  6. Paediatric ENT Department, Royal Manchester Children's Hospital, Manchester University NHS Foundation Trust, Manchester, UK.
  7. Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK [email protected].

PMID: 33879512 DOI: 10.1136/jmedgenet-2020-107303

Abstract

PURPOSE: The increased adoption of genomic strategies in the clinic makes it imperative for diagnostic laboratories to improve the efficiency of variant interpretation. Clinical exome sequencing (CES) is becoming a valuable diagnostic tool, capable of meeting the diagnostic demand imposed by the vast array of different rare monogenic disorders. We have assessed a clinician-led and phenotype-based approach for virtual gene panel generation for analysis of targeted CES in patients with rare disease in a single institution.

METHODS: Retrospective survey of 400 consecutive cases presumed by clinicians to have rare monogenic disorders, referred on singleton basis for targeted CES. We evaluated diagnostic yield and variant workload to characterise the usefulness of a clinician-led approach for generation of virtual gene panels that can incorporate up to three different phenotype-driven gene selection methods.

RESULTS: Abnormalities of the nervous system (54.5%), including intellectual disability, head and neck (19%), skeletal system (16%), ear (15%) and eye (15%) were the most common clinical features reported in referrals. Combined phenotype-driven strategies for virtual gene panel generation were used in 57% of cases. On average, 7.3 variants (median=5) per case were retained for clinical interpretation. The overall diagnostic rate of proband-only CES using personalised phenotype-driven virtual gene panels was 24%.

CONCLUSIONS: Our results show that personalised virtual gene panels are a cost-effective approach for variant analysis of CES, maintaining diagnostic yield and optimising the use of resources for clinical genomic sequencing in the clinic.

© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY. Published by BMJ.

Keywords: early diagnosis; genetics; genomics; medical

Conflict of interest statement

Competing interests: None declared.

Publication Types