Whole genome sequencing reveals that genetic conditions are frequent in intensively ill children
Tóm tắt
With growing evidence that rare single gene disorders present in the neonatal period, there is a need for rapid, systematic, and comprehensive genomic diagnoses in ICUs to assist acute and long-term clinical decisions. This study aimed to identify genetic conditions in neonatal (NICU) and paediatric (PICU) intensive care populations. We performed trio whole genome sequence (WGS) analysis on a prospective cohort of families recruited in NICU and PICU at a single site in the UK. We developed a research pipeline in collaboration with the National Health Service to deliver validated pertinent pathogenic findings within 2–3 weeks of recruitment. A total of 195 families had whole genome analysis performed (567 samples) and 21% received a molecular diagnosis for the underlying genetic condition in the child. The phenotypic description of the child was a poor predictor of the gene identified in 90% of cases, arguing for gene agnostic testing in NICU/PICU. The diagnosis affected clinical management in more than 65% of cases (83% in neonates) including modification of treatments and care pathways and/or informing palliative care decisions. A 2–3 week turnaround was sufficient to impact most clinical decision-making. The use of WGS in intensively ill children is acceptable and trio analysis facilitates diagnoses. A gene agnostic approach was effective in identifying an underlying genetic condition, with phenotypes and symptomatology being primarily used for data interpretation rather than gene selection. WGS analysis has the potential to be a first-line diagnostic tool for a subset of intensively ill children.
Tài liệu tham khảo
NHS Maternity Statistics, England 2016–17. https://digital.nhs.uk/data-and-information/publications/statistical/nhs-maternity-statistics/2016-17. Accessed 1 Aug 2018
Neonatal Data Analysis Unit, Imperial College London. https://www.imperial.ac.uk/neonatal-data-analysis-unit. Accessed 1 Aug 2018
Meng L, Pammi M, Saronwala A et al (2017) Use of exome sequencing for infants in intensive care units. JAMA Pediatr 171:e173438. https://doi.org/10.1001/jamapediatrics.2017.3438
Thiffault I, Farrow E, Zellmer L et al (2018) Clinical genome sequencing in an unbiased pediatric cohort. Genet Med. https://doi.org/10.1038/s41436-018-0075-8
Tan TY, Dillon OJ, Stark Z et al (2017) Diagnostic impact and cost-effectiveness of whole-exome sequencing for ambulant children with suspected monogenic conditions. JAMA Pediatr 171:855. https://doi.org/10.1001/jamapediatrics.2017.1755
Powis Z, Farwell Hagman KD, Speare V et al (2018) Exome sequencing in neonates: diagnostic rates, characteristics, and time to diagnosis. Genet Med. https://doi.org/10.1038/gim.2018.11
Stark Z (2018) Meeting the challenges of implementing rapid genomic testing in acute pediatric care. Genet Med. https://doi.org/10.1038/gim.2018.37
Farnaes L, Hildreth A, Sweeney NM et al (2018) Rapid whole-genome sequencing decreases infant morbidity and cost of hospitalization. NPJ Genomic Med 3:10. https://doi.org/10.1038/s41525-018-0049-4
Mestek-Boukhibar L, Clement E, Jones WD et al (2018) Rapid paediatric sequencing (RaPS): comprehensive real-life workflow for rapid diagnosis of critically ill children. J Med Genet. https://doi.org/10.1136/jmedgenet-2018-105396
Howell KB, Eggers S, Dalziel K et al (2018) A population-based cost-effectiveness study of early genetic testing in severe epilepsies of infancy. Epilepsia 59:1177–1187. https://doi.org/10.1111/epi.14087
Smith HS, Swint JM, Lalani SR et al (2019) Clinical application of genome and exome sequencing as a diagnostic tool for pediatric patients: a scoping review of the literature. Genet Med 21:3–16. https://doi.org/10.1038/s41436-018-0024-6
Vissers LELM, Van Nimwegen KJM, Schieving JH et al (2017) A clinical utility study of exome sequencing versus conventional genetic testing in pediatric neurology. Genet Med 19:1055–1063. https://doi.org/10.1038/gim.2017.1
Berg JS, Agrawal PB, Bailey DB et al (2017) Newborn sequencing in genomic medicine and public health. Pediatrics 139:e20162252. https://doi.org/10.1542/peds.2016-2252
Char DS, Lee SS-J, Magnus D, Cho M (2018) Anticipating uncertainty and irrevocable decisions: provider perspectives on implementing whole-genome sequencing in critically ill children with heart disease. Genet Med. https://doi.org/10.1038/gim.2018.25
Braverman G, Shapiro ZE, Bernstein JA (2018) Ethical issues in contemporary clinical genetics. Mayo Clin Proc Innov Qual Outcomes 2:81–90. https://doi.org/10.1016/J.MAYOCPIQO.2018.03.005
Tan N, Amendola LM, O’Daniel JM et al (2017) Is “incidental finding” the best term?: a study of patients’ preferences. Genet Med 19:176–181. https://doi.org/10.1038/gim.2016.96
Köhler S, Vasilevsky NA, Engelstad M et al (2017) The human phenotype ontology in 2017. Nucleic Acids Res 45:D865–D876. https://doi.org/10.1093/nar/gkw1039
Carss KJ, Arno G, Erwood M et al (2017) Comprehensive rare variant analysis via whole-genome sequencing to determine the molecular pathology of inherited retinal disease. Am J Hum Genet 100:75–90. https://doi.org/10.1016/j.ajhg.2016.12.003
McLaren W, Gil L, Hunt SE et al (2016) The Ensembl Variant Effect Predictor. Genome Biol 17:122. https://doi.org/10.1186/s13059-016-0974-4
Landrum MJ, Lee JM, Benson M et al (2018) ClinVar: improving access to variant interpretations and supporting evidence. Nucleic Acids Res 46:D1062–D1067. https://doi.org/10.1093/nar/gkx1153
Lek M, Karczewski KJ, Minikel EV et al (2016) Analysis of protein-coding genetic variation in 60,706 humans. Nature 536:285–291. https://doi.org/10.1038/nature19057
Calabrese C, Simone D, Diroma MA et al (2014) MToolBox: a highly automated pipeline for heteroplasmy annotation and prioritization analysis of human mitochondrial variants in high-throughput sequencing. Bioinformatics 30:3115–3117. https://doi.org/10.1093/bioinformatics/btu483
Greene D, Richardson S, Turro E (2017) OntologyX: a suite of R packages for working with ontological data. Bioinformatics 33:1104–1106. https://doi.org/10.1093/bioinformatics/btw763
Alston CL, Heidler J, Dibley MG et al (2018) Biallelic mutations in NDUFA6 establish its role in early-onset isolated mitochondrial complex I deficiency. Am J Hum Genet. https://doi.org/10.1016/J.AJHG.2018.08.013
Oates EC, Jones KJ, Donkervoort S et al (2018) Congenital titinopathy: comprehensive characterisation and pathogenic insights. Ann Neurol 83:1105–1124. https://doi.org/10.1002/ana.25241
Ostrander BEP, Butterfield RJ, Pedersen BS et al (2018) Whole-genome analysis for effective clinical diagnosis and gene discovery in early infantile epileptic encephalopathy. NPJ Genomic Med 3:22. https://doi.org/10.1038/s41525-018-0061-8
Turnbull C, Scott RH, Thomas E et al (2018) The 100 000 Genomes Project: bringing whole genome sequencing to the NHS. BMJ 361:k1687. https://doi.org/10.1136/BMJ.K1687
Whole genome sequencing of babies, Nuffield Council on Bioethics. http://nuffieldbioethics.org/wp-content/uploads/Nuffield-Council-on-Bioethics-briefing-note-whole-genome-sequencing-of-babies.pdf. Accessed 30 Jul 2018
Short PJ, McRae JF, Gallone G et al (2018) De novo mutations in regulatory elements in neurodevelopmental disorders. Nature 555:611–616. https://doi.org/10.1038/nature25983
Wright CF, McRae JF, Clayton S et al (2018) Making new genetic diagnoses with old data: iterative reanalysis and reporting from genome-wide data in 1,133 families with developmental disorders. Genet Med. https://doi.org/10.1038/gim.2017.246
Dolzhenko E, van Vugt JJFA, Shaw RJ et al (2017) Detection of long repeat expansions from PCR-free whole-genome sequence data. Genome Res 27:1895–1903. https://doi.org/10.1101/gr.225672.117