Reanalysis of genomic data in rare disease: current practice and attitudes among Australian clinical and laboratory genetics services.
Journal
European journal of human genetics : EJHG
ISSN: 1476-5438
Titre abrégé: Eur J Hum Genet
Pays: England
ID NLM: 9302235
Informations de publication
Date de publication:
25 May 2024
25 May 2024
Historique:
received:
08
12
2023
accepted:
09
05
2024
revised:
19
03
2024
medline:
26
5
2024
pubmed:
26
5
2024
entrez:
25
5
2024
Statut:
aheadofprint
Résumé
Reanalyzing stored genomic data over time is highly effective in increasing diagnostic yield in rare disease. Automation holds the promise of delivering the benefits of reanalysis at scale. Our study aimed to understand current reanalysis practices among Australian clinical and laboratory genetics services and explore attitudes towards large-scale automated re-analysis. We collected audit data regarding testing and reanalysis volumes, policies and procedures from all Australian diagnostic laboratories providing rare disease genomic testing. A genetic health professionals' survey explored current practices, barriers to reanalysis, preferences and attitudes towards automation. Between 2018 and 2021, Australian diagnostic laboratories performed over 25,000 new genomic tests and 950 reanalyses, predominantly in response to clinician requests. Laboratory and clinical genetic health professionals (N = 134) identified workforce capacity as the principal barrier to reanalysis. No specific laboratory or clinical guidelines for genomic data reanalysis or policies were identified nationally. Perceptions of acceptability and feasibility of automating reanalysis were positive, with professionals emphasizing clinical and workflow benefits. In conclusion, there is a large and rapidly growing unmet need for reanalysis of existing genomic data. Beyond developing scalable automated reanalysis pipelines, leadership and policy are needed to successfully transform service delivery models and maximize clinical benefit.
Identifiants
pubmed: 38796577
doi: 10.1038/s41431-024-01633-8
pii: 10.1038/s41431-024-01633-8
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Department of Health | National Health and Medical Research Council (NHMRC)
ID : GNT2000001
Informations de copyright
© 2024. The Author(s).
Références
Dai P, Honda A, Ewans L, McGaughran J, Burnett L, Law M, et al. Recommendations for next generation sequencing data reanalysis of unsolved cases with suspected Mendelian disorders: a systematic review and meta-analysis. Genet Med. 2022;24:1618–29.
doi: 10.1016/j.gim.2022.04.021
pubmed: 35550369
Robertson AJ, Tan NB, Spurdle AB, Metke-Jimenez A, Sullivan C, Waddell N. Re-analysis of genomic data: an overview of the mechanisms and complexities of clinical adoption. Genet Med. 2022;24:798–810.
doi: 10.1016/j.gim.2021.12.011
pubmed: 35065883
Online Mendelian Inheritance in Man: https://omim.org/statistics/paceGraph , accessed 19 March 2024.
Lek M, Karczewski KJ, Minikel EV, Samocha KE, Banks E, Fennell T, et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature. 2016;536:285–91.
doi: 10.1038/nature19057
pubmed: 27535533
pmcid: 5018207
Karczewski KJ, Francioli LC, Tiao G, Cummings BB, Alfoldi J, Wang Q, et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature. 2020;581:434–43.
doi: 10.1038/s41586-020-2308-7
pubmed: 32461654
pmcid: 7334197
Hosseini SM, Kim R, Udupa S, Costain G, Jobling R, Liston E, et al. Reappraisal of reported genes for sudden arrhythmic death: evidence-based evaluation of gene validity for Brugada syndrome. Circulation. 2018;138:1195–205.
doi: 10.1161/CIRCULATIONAHA.118.035070
pubmed: 29959160
pmcid: 6147087
Deignan JL, Chung WK, Kearney HM, Monaghan KG, Rehder CW, Chao EC, et al. Points to consider in the reevaluation and reanalysis of genomic test results: a statement of the American College of Medical Genetics and Genomics (ACMG). Genet Med. 2019;21:1267–70.
doi: 10.1038/s41436-019-0478-1
pubmed: 31015575
pmcid: 6559819
Bombard Y, Brothers KB, Fitzgerald-Butt S, Garrison NA, Jamal L, James CA, et al. The responsibility to recontact research participants after reinterpretation of genetic and genomic research results. Am J Hum Genet. 2019;104:578–95.
doi: 10.1016/j.ajhg.2019.02.025
pubmed: 30951675
pmcid: 6451731
Stark Z, Schofield D, Martyn M, Rynehart L, Shrestha R, Alam K, et al. Does genomic sequencing early in the diagnostic trajectory make a difference? A follow-up study of clinical outcomes and cost-effectiveness. Genet Med. 2018;21:173–80.
Ewans LJ, Schofield D, Shrestha R, Zhu Y, Gayevskiy V, Ying K, et al. Whole-exome sequencing reanalysis at 12 months boosts diagnosis and is cost-effective when applied early in Mendelian disorders. Genet Med. 2018;20:1564–74.
doi: 10.1038/gim.2018.39
pubmed: 29595814
Medical Services Advisory Committee, Australia: https://www9.health.gov.au/mbs/fullDisplay.cfm?type=item&q=73360&qt=ItemID . Accessed 19 March 2024
O’Brien TD, Campbell NE, Potter AB, Letaw JH, Kulkarni A, Richards CS. Artificial intelligence (AI)-assisted exome reanalysis greatly aids in the identification of new positive cases and reduces analysis time in a clinical diagnostic laboratory. Genet Med. 2022;24:192–200.
doi: 10.1016/j.gim.2021.09.007
pubmed: 34906498
Baker SW, Murrell JR, Nesbitt AI, Rechter KB, Balciuniene J, Zhao X, et al. Automated clinical exome reanalysis reveals novel diagnoses. J Mol Diagn. 2019;21:38–48.
doi: 10.1016/j.jmoldx.2018.07.008
pubmed: 30577886
James KN, Clark MM, Camp B, Kint C, Schols P, Batalov S, et al. Partially automated whole-genome sequencing reanalysis of previously undiagnosed pediatric patients can efficiently yield new diagnoses. NPJ Genom Med. 2020;5:33.
doi: 10.1038/s41525-020-00140-1
pubmed: 32821428
pmcid: 7419288
Matalonga L, Hernandez-Ferrer C, Piscia D, Solve-RD SNV-indel working group, Schule R, Synofzik M, et al. Solving patients with rare diseases through programmatic reanalysis of genome-phenome data. Eur J Hum Genet. 2021;29:1337–47.
doi: 10.1038/s41431-021-00852-7
pubmed: 34075210
pmcid: 8440686
Mensah NE, Sabir AH, Bond A, Roworth W, Irving M, Davies AC, et al. Automated reanalysis application to assist in detecting novel gene-disease associations after genome sequencing. Genet Med. 2022;24:811–20.
doi: 10.1016/j.gim.2021.11.021
pubmed: 34949530
Proctor E, Silmere H, Raghavan R, Hovmand P, Aarons G, Bunger A, et al. Outcomes for implementation research: conceptual distinctions, measurement challenges, and research agenda. Adm Policy Ment Health. 2011;38:65–76.
doi: 10.1007/s10488-010-0319-7
pubmed: 20957426
Weiner BJ, Lewis CC, Stanick C, Powell PJ, Dorsey CN, Clary AS, et al. Psychometric assessment of three newly developed implementation outcome measures. Implement Sci. 2017;12:108.
doi: 10.1186/s13012-017-0635-3
pubmed: 28851459
pmcid: 5576104
Sekhon M, Cartwright M, Francis JJ. Acceptability of healthcare interventions: an overview of reviews and development of a theoretical framework. BMC Health Serv Res. 2017;17:88.
doi: 10.1186/s12913-017-2031-8
pubmed: 28126032
pmcid: 5267473
Sekhon M, Cartwright M, Francis JJ. Development of a theory-informed questionnaire to assess the acceptability of healthcare interventions. BMC Health Serv Res. 2022;22:279.
doi: 10.1186/s12913-022-07577-3
pubmed: 35232455
pmcid: 8887649
Stemler S. An overview of content analysis. Practical assessment, research, and evaluation. 2000;7:17.
Hsieh HF, Shannon SE. Three approaches to qualitative content analysis. Qual Health Res. 2005;15:1277–88.
doi: 10.1177/1049732305276687
pubmed: 16204405
Mordaunt DA, Dalziel K, Goranitis I, Stark Z. Uptake of funded genomic testing for syndromic and non-syndromic intellectual disability in Australia. Eur J Hum Genet. 2023;31:977–9.
doi: 10.1038/s41431-023-01417-6
pubmed: 37400487
pmcid: 10474079
Bombard Y, Mighton C. Recontacting clinical genetics patients with reclassified results: equity and policy challenges. Eur J Hum Genet. 2019;27:505–6.
doi: 10.1038/s41431-018-0313-1
pubmed: 30568242
Outram SM, Rego S, Norstad M, Ackerman S. The need to standardize the reanalysis of genomic sequencing results: findings from interviews with underserved families in genomic research. J. Bioeth. Inq. 2023;21:95–104.
Wenger AM, Guturu H, Bernstein JA, Bejerano G. Systematic reanalysis of clinical exome data yields additional diagnoses: implications for providers. Genet Med. 2017;19:209–14.
doi: 10.1038/gim.2016.88
pubmed: 27441994
Klaic M, Kapp S, Hudson P, Chapman W, Denehy L, Story D, et al. Implementability of healthcare interventions: an overview of reviews and development of a conceptual framework. Implement Sci. 2022;17:10.
doi: 10.1186/s13012-021-01171-7
pubmed: 35086538
pmcid: 8793098
Fehlberg Z, Stark Z, Best S. Reanalysis of genomic data, how do we do it now and what if we automate it? A qualitative study. Eur J Hum Genet. 2024;32:521–8.
doi: 10.1038/s41431-023-01532-4
pubmed: 38212661
pmcid: 11061153
Brett GR, Wilkins EJ, Creed ET, West K, Jarmolowicz A, Valente G, et al. Genetic counseling in the era of genomics: what’s all the fuss about? J Genet Couns. 2018;27:1010–21.
doi: 10.1007/s10897-018-0216-x
pubmed: 29368275