Personalised virtual gene panels reduce interpretation workload and maintain diagnostic rates of proband-only clinical exome sequencing for rare disorders. Issue 4 (20th April 2021)

• Record Type: Journal Article
• Authors: Molina-Ramírez, Leslie Patricia; Kyle, Claire; Ellingford, Jamie M; Wright, Ronnie; Taylor, Algy; Bhaskar, Sanjeev S; Campbell, Christopher; Jackson, Harriet; Fairclough, Adele; Rousseau, Abigail; Burghel, George J; Dutton, Laura; Banka, Siddharth; Briggs, Tracy A; Clayton-Smith, Jill; Douzgou, Sofia; Jones, Elizabeth A; Kingston, Helen M; Kerr, Bronwyn; Ealing, John; Somarathi, Suresh; Chandler, Kate E; Stuart, Helen M; Burkitt-Wright, Emma MM; Newman, William G; Bruce, Iain A; Black, Graeme C; Gokhale, David
• Is Part Of: Journal of medical genetics. Volume 59:Issue 4(2022)
• Page Start: 393
• Page End: 398
• Publication Date: 2021-04-20
• Subjects: genomics -- early diagnosis -- genetics -- medical; Medical genetics -- Periodicals; 616.042
• DOI: 10.1136/jmedgenet-2020-107303
• Languages: English
• ISSNs: 1468-6244

Abstract

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.