Paediatric genomics: diagnosing rare disease in children

An Erratum to this article was published on 19 February 2018

This article has been updated

Key Points

  • Genomic technologies have had a greater impact on paediatrics than on many other fields of medicine, and this discipline is leading the way in the implementation of clinical genomics.

  • Appropriate use of whole-exome sequencing (WES) or whole-genome sequencing (WGS) has the potential to curtail the diagnostic process for patients with rare and ultra-rare paediatric disorders, often avoiding the need for invasive and expensive investigations.

  • Although WES and WGS have enormous diagnostic power, data interpretation remains very challenging because of a high incidence of novel and ultra-rare benign variants, incomplete knowledge, false assignment of variant pathogenicity and even false association of genes with disease in the literature.

  • An accurate genetic diagnosis benefits a child by enabling a better understanding of their prognosis, more personalized treatment and tailored management and surveillance. A precise genetic diagnosis also enables accurate genetic advice for individuals and their relatives with increased reproductive choice and improved access to information and support from patient support groups, education, health and social care.

  • WES and WGS require considerable computational resources to process, analyse and store the vast data sets generated by these technologies. For clinicians, the issue of data storage and access is linked to the question of whether their duty of care is limited to finding a diagnosis for the child's immediate problems or whether it extends beyond the scope of the initial investigation.

  • In the future, stratified therapy for rare paediatric disease based on the specific genetic diagnosis is likely to be possible, and effective and affordable therapies are likely to become available through drug repurposing and innovation.

Abstract

The majority of rare diseases affect children, most of whom have an underlying genetic cause for their condition. However, making a molecular diagnosis with current technologies and knowledge is often still a challenge. Paediatric genomics is an immature but rapidly evolving field that tackles this issue by incorporating next-generation sequencing technologies, especially whole-exome sequencing and whole-genome sequencing, into research and clinical workflows. This complex multidisciplinary approach, coupled with the increasing availability of population genetic variation data, has already resulted in an increased discovery rate of causative genes and in improved diagnosis of rare paediatric disease. Importantly, for affected families, a better understanding of the genetic basis of rare disease translates to more accurate prognosis, management, surveillance and genetic advice; stimulates research into new therapies; and enables provision of better support.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Figure 1: Available diagnostic rates based on whole-exome sequencing in classes of paediatric genomic diseases.
Figure 2: Genomic testing strategies and clinical heterogeneity.
Figure 3: Integration of clinical and laboratory workflows to optimize rare disease diagnosis using next-generation sequencing.
Figure 4: Multidisciplinary team review with phenotype-based variant ranking and interpretation.

Change history

  • 19 February 2018

    In Figure 2a of the above article, the resolution of microarrays was originally stated to be 50–100 Mb. The actual resolution is 50–100 kb. In addition, the key for Figure 2b referred to 30,000 coding variants instead of 20,000. These errors have been corrected online. Nature Reviews Genetics apologizes for these errors.

References

  1. 1

    European Organisation for Rare Diseases. Rare Diseases: Understanding this Public Health Priority. (Eurodis, 2005).

  2. 2

    Boycott, K. M. et al. International cooperation to enable the diagnosis of all rare genetic diseases. Am. J. Hum. Genet. 100, 695–705 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. 3

    Quintana-Murci, L. Understanding rare and common diseases in the context of human evolution. Genome Biol. 17, 225 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  4. 4

    Amberger, J., Bocchini, C. A., Scott, A. F. & Hamosh, A. Mckusick's online mendelian inheritance in man (OMIM). Nucleic Acids Res. 37, D793–D796 (2009).

    Article  CAS  PubMed  Google Scholar 

  5. 5

    Amberger, J. S., Bocchini, C. A., Schiettecatte, F., Scott, A. F. & Hamosh, A. OMIM.org: Online Mendelian Inheritance in Man (OMIM®), an online catalog of human genes and genetic disorders. Nucleic Acids Res. 43, D789–D798 (2015).

    Article  CAS  PubMed  Google Scholar 

  6. 6

    Yoon, P. W. et al. Contribution of birth defects and genetic diseases to pediatric hospitalizations. A population-based study. Arch. Pediatr. Adolesc. Med. 151, 1096–1103 (1997).

    Article  CAS  PubMed  Google Scholar 

  7. 7

    Dodge, J. A. et al. The importance of rare diseases: from the gene to society. Arch. Dis. Child 96, 791–792 (2011).

    Article  PubMed  Google Scholar 

  8. 8

    Wright, C. F. et al. Genetic diagnosis of developmental disorders in the DDD study: a scalable analysis of genome-wide research data. Lancet 385, 1305–1314 (2015). This is an important paper outlining a prototype clinical bioinformatics pipeline for the diagnosis of developmental disorders.

    Article  PubMed  PubMed Central  Google Scholar 

  9. 9

    Deciphering Developmental Disorders Study. Prevalence and architecture of de novo mutations in developmental disorders. Nature 542, 433–438 (2017). This is a landmark paper highlighting the major contribution of de novo mutation to developmental disorders.

  10. 10

    Austin, C. P. et al. Future of rare diseases research 2017-2027: an IRDiRC perspective. Clin. Transl Sci. https://doi.org/10.1111/cts.12500 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  11. 11

    Grozeva, D. et al. Targeted next-generation sequencing analysis of 1,000 individuals with intellectual disability. Hum. Mutat. 36, 1197–1204 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. 12

    Kochinke, K. et al. Systematic phenomics analysis deconvolutes genes mutated in intellectual disability into biologically coherent modules. Am. J. Hum. Genet. 98, 149–164 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. 13

    Torgerson, P. R. & Mastroiacovo, P. The global burden of congenital toxoplasmosis: a systematic review. Bull. World Health Organ. 91, 501–508 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  14. 14

    Del Campo, M. & Jones, K. L. A review of the physical features of the fetal alcohol spectrum disorders. Eur. J. Med. Genet. 60, 55–64 (2017).

    Article  PubMed  Google Scholar 

  15. 15

    Chavali, P. L. et al. Neurodevelopmental protein Musashi-1 interacts with the Zika genome and promotes viral replication. Science 357, 83–88 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. 16

    Firth, H. V. & Wright, C. F. & DDD Study. The Deciphering Developmental Disorders (DDD) study. Dev. Med. Child Neurol. 53, 702–703 (2011).

    Article  PubMed  Google Scholar 

  17. 17

    Baynam, G. et al. The rare and undiagnosed diseases diagnostic service — application of massively parallel sequencing in a state-wide clinical service. Orphanet J. Rare Dis. 11, 77 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  18. 18

    Doherty, E. S. et al. Muenke syndrome (FGFR3-related craniosynostosis): expansion of the phenotype and review of the literature. Am. J. Med. Genet. A 143A, 3204–3215 (2007).

    Article  CAS  PubMed  Google Scholar 

  19. 19

    Posey, J. E. et al. Resolution of disease phenotypes resulting from multilocus genomic variation. N. Engl. J. Med. 376, 21–31 (2017). This interesting paper focuses on diagnosing individuals with several independent rare genetic conditions.

    Article  CAS  PubMed  Google Scholar 

  20. 20

    Henn, B. M., Botigué, L. R., Bustamante, C. D., Clark, A. G. & Gravel, S. Estimating the mutation load in human genomes. Nat. Rev. Genet. 16, 333–343 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. 21

    Boycott, K. M., Vanstone, M. R., Bulman, D. E. & MacKenzie, A. E. Rare-disease genetics in the era of next-generation sequencing: discovery to translation. Nat. Rev. Genet. 14, 681–691 (2013).

    Article  CAS  PubMed  Google Scholar 

  22. 22

    Samocha, K. E. et al. A framework for the interpretation of de novo mutation in human disease. Nat. Genet. 46, 944–950 (2014). This is a useful paper outlining a model for predicting the number of de novo mutations expected by chance across the genome, which is essential for robust discovery of genes that cause novel dominant de novo disorders.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. 23

    Weiner, D. J. et al. Polygenic transmission disequilibrium confirms that common and rare variation act additively to create risk of autism spectrum disorders. Nat. Genet. 49, 978–985 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. 24

    The 1000 Genomes Project Consortium. A map of human genome variation from population-scale sequencing. Nature 467, 1061–1073 (2010).

  25. 25

    The 1000 Genomes Project Consortium. An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56–65 (2012).

  26. 26

    The 1000 Genomes Project Consortium. A global reference for human genetic variation. Nature 526, 68–74 (2015). This study presents a comprehensive account of the wealth of variation in the genomes of normal individuals.

  27. 27

    Katsanis, S. H. & Katsanis, N. Molecular genetic testing and the future of clinical genomics. Nat. Rev. Genet. 14, 415–426 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. 28

    Brewington, J. & Clancy, J. P. Diagnostic testing in cystic fibrosis. Clin. Chest Med. 37, 31–46 (2016).

    Article  PubMed  Google Scholar 

  29. 29

    Aartsma-Rus, A., Ginjaar, I. B. & Bushby, K. The importance of genetic diagnosis for Duchenne muscular dystrophy. J. Med. Genet. 53, 145–151 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. 30

    Speicher, M. R. & Carter, N. P. The new cytogenetics: blurring the boundaries with molecular biology. Nat. Rev. Genet. 6, 782–792 (2005). This is a useful Review of microarray technologies and their use for the diagnosis of rare paediatric syndromes.

    Article  CAS  PubMed  Google Scholar 

  31. 31

    Shaw-Smith, C. et al. Microarray based comparative genomic hybridisation (array-CGH) detects submicroscopic chromosomal deletions and duplications in patients with learning disability/mental retardation and dysmorphic features. J. Med. Genet. 41, 241–248 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. 32

    Crespi, B. J. & Procyshyn, T. L. Williams syndrome deletions and duplications: genetic windows to understanding anxiety, sociality, autism, and schizophrenia. Neurosci. Biobehav. Rev. 79, 14–26 (2017).

    Article  CAS  PubMed  Google Scholar 

  33. 33

    Sagoo, G. S. et al. Array CGH in patients with learning disability (mental retardation) and congenital anomalies: updated systematic review and meta-analysis of 19 studies and 13,926 subjects. Genet. Med. 11, 139–146 (2009).

    Article  CAS  PubMed  Google Scholar 

  34. 34

    Mardis, E. R. The impact of next-generation sequencing technology on genetics. Trends Genet. 24, 133–141 (2008).

    Article  CAS  PubMed  Google Scholar 

  35. 35

    Ansorge, W. J. Next-generation DNA sequencing techniques. N. Biotechnol. 25, 195–203 (2009).

    Article  CAS  PubMed  Google Scholar 

  36. 36

    Moorthie, S., Mattocks, C. J. & Wright, C. F. Review of massively parallel DNA sequencing technologies. Hugo J. 5, 1–12 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  37. 37

    Vissers, L. E. L. M. et al. A clinical utility study of exome sequencing versus conventional genetic testing in pediatric neurology. Genet. Med. 19, 1055–1063 (2017). This is an excellent study of the impact of NGS in clinical practice.

    Article  PubMed  PubMed Central  Google Scholar 

  38. 38

    Shashi, V. et al. The utility of the traditional medical genetics diagnostic evaluation in the context of next-generation sequencing for undiagnosed genetic disorders. Genet. Med. 16, 176–182 (2014).

    Article  CAS  PubMed  Google Scholar 

  39. 39

    Weiss, M. M. et al. Best practice guidelines for the use of next-generation sequencing applications in genome diagnostics: a national collaborative study of Dutch genome diagnostic laboratories. Hum. Mutat. 34, 1313–1321 (2013).

    Article  PubMed  Google Scholar 

  40. 40

    Sun, Y. et al. Next-generation diagnostics: gene panel, exome, or whole genome? Hum. Mutat. 36, 648–655 (2015).

    Article  CAS  PubMed  Google Scholar 

  41. 41

    Ece Solmaz, A. et al. Targeted multi-gene panel testing for the diagnosis of Bardet Biedl syndrome: Identification of nine novel mutations across BBS1, BBS2, BBS4, BBS7, BBS9, BBS10 genes. Eur. J. Med. Genet. 58, 689–694 (2015).

    Article  PubMed  Google Scholar 

  42. 42

    Schrijver, I. Hereditary non-syndromic sensorineural hearing loss. J. Mol. Diagn. 6, 275–284 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. 43

    Myers, C. T. & Mefford, H. C. Advancing epilepsy genetics in the genomic era. Genome Med. 7, 91 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. 44

    Mastrangelo, M. Novel genes of early-onset epileptic encephalopathies: from genotype to phenotypes. Pediatr. Neurol. 53, 119–129 (2015).

    Article  PubMed  Google Scholar 

  45. 45

    Cheng, A. Y., Teo, Y.-Y. & Ong, R. T.-H. Assessing single nucleotide variant detection and genotype calling on whole-genome sequenced individuals. Bioinformatics 30, 1707–1713 (2014).

    Article  CAS  PubMed  Google Scholar 

  46. 46

    Beck, T. F. & Mullikin, J. C., NISC Comparative Sequencing Program & Biesecker, L. G. Systematic evaluation of Sanger validation of next-generation sequencing variants. Clin. Chem. 62, 647–654 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. 47

    Telenti, A. et al. Deep sequencing of 10,000 human genomes. Proc. Natl Acad. Sci. USA 113, 11901–11906 (2016).

    Article  CAS  PubMed  Google Scholar 

  48. 48

    Li, W. et al. Identifying human genome-wide CNV, LOH and UPD by targeted sequencing of selected regions. PLoS ONE 10, e0123081 (2014).

    PubMed  Google Scholar 

  49. 49

    de Ligt, J. et al. Detection of clinically relevant copy number variants with whole-exome sequencing. Hum. Mutat. 34, 1439–1448 (2013).

    Article  CAS  PubMed  Google Scholar 

  50. 50

    Noll, A. C. et al. Clinical detection of deletion structural variants in whole-genome sequences. npj Genomic Med. 1, 16026 (2016).

    Article  Google Scholar 

  51. 51

    Suzuki, T. et al. Precise detection of chromosomal translocation or inversion breakpoints by whole-genome sequencing. J. Hum. Genet. 59, 649–654 (2014).

    Article  CAS  PubMed  Google Scholar 

  52. 52

    Ellingford, J. M. et al. Validation of copy number variation analysis for next-generation sequencing diagnostics. Eur. J. Hum. Genet. 25, 719–724 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. 53

    Budworth, H. & McMurray, C. T. A brief history of triplet repeat diseases. Methods Mol. Biol. 1010, 3–17 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. 54

    Nowak, K. J. & Davies, K. E. Duchenne muscular dystrophy and dystrophin: pathogenesis and opportunities for treatment. EMBO Rep. 5, 872–876 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. 55

    Singh, N. N., Seo, J., Rahn, S. J. & Singh, R. N. A multi-exon-skipping detection assay reveals surprising diversity of splice isoforms of spinal muscular atrophy genes. PLoS ONE 7, e49595 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. 56

    Halvorsen, M. et al. Mosaic mutations in early-onset genetic diseases. Genet. Med. 18, 746–749 (2016).

    Article  CAS  PubMed  Google Scholar 

  57. 57

    Rios, J. J. & Delgado, M. R. Using whole-exome sequencing to identify variants inherited from mosaic parents. Eur. J. Hum. Genet. 23, 547–550 (2015).

    Article  CAS  PubMed  Google Scholar 

  58. 58

    Saudi Mendeliome Group. Comprehensive gene panels provide advantages over clinical exome sequencing for Mendelian diseases. Genome Biol. 16, 134 (2015).

  59. 59

    van El, C. G. et al. Whole-genome sequencing in health care: recommendations of the European Society of Human Genetics. Eur. J. Hum. Genet. 21, 580–584 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. 60

    Green, R. C. et al. ACMG recommendations for reporting of incidental findings in clinical exome and genome sequencing. Genet. Med. 15, 565–574 (2013). This is a controversial paper advocating routine opportunistic screening of genomic sequence data in adults and children.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. 61

    Matthijs, G. et al. Guidelines for diagnostic next-generation sequencing. Eur. J. Hum. Genet. 24, 2–5 (2016).

    Article  CAS  PubMed  Google Scholar 

  62. 62

    Boycott, K. et al. The clinical application of genome-wide sequencing for monogenic diseases in Canada: position statement of the Canadian College of Medical Geneticists. J. Med. Genet. 52, 431–437 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. 63

    Ormondroyd, E. et al. “Not pathogenic until proven otherwise”: perspectives of UK clinical genomics professionals toward secondary findings in context of a Genomic Medicine Multidisciplinary Team and the 100,000 Genomes Project. Genet. Med. https://doi.org/10.1038/gim.2017.157 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  64. 64

    Goldstein, D. B. et al. Sequencing studies in human genetics: design and interpretation. Nat. Rev. Genet. 14, 460–470 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. 65

    Wright, C. F. et al. Making new genetic diagnoses with old data: iterative reanalysis and reporting from genome-wide data in 1133 families withdevelopmental disorders. Genet. Med. https://doi.org/10.1038/gim.2017.246 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  66. 66

    Moorthie, S., Hall, A. & Wright, C. F. Informatics and clinical genome sequencing: opening the black box. Genet. Med. 15, 165–171 (2013).

    Article  PubMed  Google Scholar 

  67. 67

    Yen, J. L. et al. A variant by any name: quantifying annotation discordance across tools and clinical databases. Genome Med. 9, 7 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. 68

    Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. 69

    Endrullat, C., Glökler, J., Franke, P. & Frohme, M. Standardization and quality management in next-generation sequencing. Appl. Transl Genom. 10, 2–9 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  70. 70

    Salgado, D., Bellgard, M. I., Desvignes, J.-P. & Béroud, C. How to identify pathogenic mutations among all those variations: variant annotation and filtration in the genome sequencing era. Hum. Mutat. 37, 1272–1282 (2016).

    Article  CAS  PubMed  Google Scholar 

  71. 71

    McLaren, W. et al. The ensembl variant effect predictor. Genome Biol. 17, 122 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. 72

    Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly 6, 80–92 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. 73

    Desmet, F.-O. et al. Human Splicing Finder: an online bioinformatics tool to predict splicing signals. Nucleic Acids Res. 37, e67 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. 74

    Soemedi, R. et al. Pathogenic variants that alter protein code often disrupt splicing. Nat. Genet. 49, 848–855 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. 75

    Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016). This is a landmark paper describing the ExAC database.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. 76

    Popejoy, A. B. & Fullerton, S. M. Genomics is failing on diversity. Nature 538, 161–164 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. 77

    Retterer, K. et al. Clinical application of whole-exome sequencing across clinical indications. Genet. Med. 18, 696–704 (2016). This is a useful paper comparing the diagnostic yield of WES across different clinical indications.

    Article  CAS  PubMed  Google Scholar 

  78. 78

    Ansari, M. et al. Genetic heterogeneity in Cornelia de Lange syndrome (CdLS) and CdLS-like phenotypes with observed and predicted levels of mosaicism. J. Med. Genet. 51, 659–668 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. 79

    Chambers, C., Jansen, L. A. & Dhamija, R. Review of commercially available epilepsy genetic panels. J. Genet. Couns. 25, 213–217 (2016).

    Article  PubMed  Google Scholar 

  80. 80

    Strande, N. T. et al. Evaluating the clinical validity of gene-disease associations: an evidence-based framework developed by the Clinical Genome Resource. Am. J. Hum. Genet. 100, 895–906 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. 81

    Biesecker, L. G. Opportunities and challenges for the integration of massively parallel genomic sequencing into clinical practice: lessons from the ClinSeq project. Genet. Med. 14, 393–398 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  82. 82

    Ghouse, J. et al. Numerous Brugada syndrome-associated genetic variants have no effect on J-point elevation, syncope susceptibility, malignant cardiac arrhythmia, and all-cause mortality. Genet. Med. 19, 521–528 (2017).

    Article  PubMed  Google Scholar 

  83. 83

    Landrum, M. J. et al. ClinVar: public archive of interpretations of clinically relevant variants. Nucleic Acids Res. 44, D862–868 (2016).

    Article  CAS  PubMed  Google Scholar 

  84. 84

    Deciphering Developmental Disorders Study. Large-scale discovery of novel genetic causes of developmental disorders. Nature 519, 223–228 (2015).

  85. 85

    Köhler, S. et al. The Human Phenotype Ontology project: linking molecular biology and disease through phenotype data. Nucleic Acids Res. 42, D966–D974 (2014).

    Article  CAS  PubMed  Google Scholar 

  86. 86

    Richards, S. et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet. Med. 17, 405–424 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  87. 87

    Nykamp, K. et al. Sherloc: a comprehensive refinement of the ACMG-AMP variant classification criteria. Genet. Med. 19, 1105–1117 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  88. 88

    Bragin, E. et al. DECIPHER: database for the interpretation of phenotype-linked plausibly pathogenic sequence and copy-number variation. Nucleic Acids Res. 42, D993–D1000 (2014).

    Article  CAS  PubMed  Google Scholar 

  89. 89

    Chatzimichali, E. A. et al. Facilitating collaboration in rare genetic disorders through effective matchmaking in DECIPHER. Hum. Mutat. 36, 941–949 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  90. 90

    Kaye, J. The tension between data sharing and the protection of privacy in genomics research. Annu. Rev. Genom. Hum. Genet. 13, 415–431 (2012).

    Article  CAS  Google Scholar 

  91. 91

    Gymrek, M., McGuire, A. L., Golan, D., Halperin, E. & Erlich, Y. Identifying personal genomes by surname inference. Science 339, 321–324 (2013).

    Article  CAS  PubMed  Google Scholar 

  92. 92

    Buske, O. J. et al. PhenomeCentral: a portal for phenotypic and genotypic matchmaking of patients with rare genetic diseases. Hum. Mutat. 36, 931–940 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  93. 93

    Philippakis, A. A. et al. The Matchmaker Exchange: a platform for rare disease gene discovery. Hum. Mutat. 36, 915–921 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  94. 94

    Sobreira, N., Schiettecatte, F., Valle, D. & Hamosh, A. GeneMatcher: a matching tool for connecting investigators with an interest in the same gene. Hum. Mutat. 36, 928–930 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  95. 95

    ACMG Board Of Directors. Laboratory and clinical genomic data sharing is crucial to improving genetic health care: a position statement of the American College of Medical Genetics and Genomics. Genet. Med. 19, 721–722 (2017).

  96. 96

    Ramoni, R. B. et al. The Undiagnosed Diseases Network: accelerating discovery about health and disease. Am. J. Hum. Genet. 100, 185–192 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. 97

    Bowdin, S. et al. Recommendations for the integration of genomics into clinical practice. Genet. Med. 18, 1075–1084 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. 98

    Thevenon, J. et al. Diagnostic odyssey in severe neurodevelopmental disorders: toward clinical whole-exome sequencing as a first-line diagnostic test. Clin. Genet. 89, 700–707 (2016).

    Article  CAS  PubMed  Google Scholar 

  99. 99

    Petrikin, J. E., Willig, L. K., Smith, L. D. & Kingsmore, S. F. Rapid whole genome sequencing and precision neonatology. Semin. Perinatol 39, 623–631 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  100. 100

    Saunders, C. J. et al. Rapid whole-genome sequencing for genetic disease diagnosis in neonatal intensive care units. Sci. Transl Med. 4, 154ra135 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. 101

    Miller, D. T. et al. Consensus statement: chromosomal microarray is a first-tier clinical diagnostic test for individuals with developmental disabilities or congenital anomalies. Am. J. Hum. Genet. 86, 749–764 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. 102

    Meng, L. et al. Use of exome sequencing for infants in intensive care units: ascertainment of severe single-gene disorders and effect on medical management. JAMA Pediatr. 171, e173438 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  103. 103

    Hartley, T. et al. Whole-exome sequencing is a valuable diagnostic tool for inherited peripheral neuropathies: outcomes from a cohort of 50 families. Clin. Genet. https://doi.org/10.1111/cge.13101 (2017).

    Article  CAS  PubMed  Google Scholar 

  104. 104

    Warr, A. et al. Exome sequencing: current and future perspectives. G3 5, 1543–1550 (2015).

    Article  PubMed  Google Scholar 

  105. 105

    Belkadi, A. et al. Whole-genome sequencing is more powerful than whole-exome sequencing for detecting exome variants. Proc. Natl Acad. Sci. USA 112, 5473–5478 (2015).

    Article  CAS  PubMed  Google Scholar 

  106. 106

    Boycott, K. M. & Innes, A. M. When one diagnosis is not enough. N. Engl. J. Med. 376, 83–85 (2017).

    Article  PubMed  Google Scholar 

  107. 107

    Robinson, E. B. et al. Genetic risk for autism spectrum disorders and neuropsychiatric variation in the general population. Nat. Genet. 48, 552–555 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. 108

    Patel, K. A. et al. Type 1 diabetes genetic risk score: a novel tool to discriminate monogenic and type 1 diabetes. Diabetes 65, 2094–2099 (2016). This study provides a demonstration of the application of a genetic risk score derived from genome-wide association studies to discriminate between common complex and rare monogenic disease.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. 109

    Oram, R. A. et al. A type 1 diabetes genetic risk score can aid discrimination between type 1 and type 2 diabetes in young adults. Diabetes Care 39, 337–344 (2016).

    Article  CAS  PubMed  Google Scholar 

  110. 110

    Adam, M. P. et al. GeneReviews® (University of Washington, Seattle, 2017).

    Google Scholar 

  111. 111

    Vissers, L. E. L. M., Gilissen, C. & Veltman, J. A. Genetic studies in intellectual disability and related disorders. Nat. Rev. Genet. 17, 9–18 (2016).

    Article  CAS  PubMed  Google Scholar 

  112. 112

    Tan, T. Y. et al. Diagnostic impact and cost-effectiveness of whole-exome sequencing for ambulant children with suspected monogenic conditions. JAMA Pediatr. 171, 855–862 (2017). This study presents evidence that cost-effectiveness is maximized by early application of WES in the diagnostic pathway of children with suspected monogenic conditions.

    Article  PubMed  PubMed Central  Google Scholar 

  113. 113

    Stark, Z. et al. Prospective comparison of the cost-effectiveness of clinical whole-exome sequencing with that of usual care overwhelmingly supports early use and reimbursement. Genet. Med. 19, 867–874 (2017).

    Article  PubMed  Google Scholar 

  114. 114

    Stark, Z. et al. A prospective evaluation of whole-exome sequencing as a first-tier molecular test in infants with suspected monogenic disorders. Genet. Med. 18, 1090–1096 (2016).

    Article  CAS  PubMed  Google Scholar 

  115. 115

    Soden, S. E. et al. Effectiveness of exome and genome sequencing guided by acuity of illness for diagnosis of neurodevelopmental disorders. Sci. Transl Med. 6, 265ra168 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. 116

    Willig, L. K. et al. Whole-genome sequencing for identification of Mendelian disorders in critically ill infants: a retrospective analysis of diagnostic and clinical findings. Lancet Respir. Med. 3, 377–387 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  117. 117

    Konstan, M. W. et al. Assessment of safety and efficacy of long-term treatment with combination lumacaftor and ivacaftor therapy in patients with cystic fibrosis homozygous for the F508del-CFTR mutation (PROGRESS): a phase 3, extension study. Lancet Respir. Med. 5, 107–118 (2017).

    Article  CAS  PubMed  Google Scholar 

  118. 118

    Worthey, E. A. et al. Making a definitive diagnosis: successful clinical application of whole exome sequencing in a child with intractable inflammatory bowel disease. Genet. Med. 13, 255–262 (2011). This study provides the first published example where WES was successfully used in the clinic to diagnose and treat a child suffering from a severe rare disease.

    Article  PubMed  Google Scholar 

  119. 119

    Tarailo-Graovac, M. et al. Exome sequencing and the management of neurometabolic disorders. N. Engl. J. Med. 374, 2246–2255 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  120. 120

    Stalke, A. et al. Diagnosis of monogenic liver diseases in childhood by next-generation sequencing. Clin. Genet. https://doi.org/10.1111/cge.13120 (2017).

    Article  CAS  PubMed  Google Scholar 

  121. 121

    Ormondroyd, E. et al. Insights from early experience of a Rare Disease Genomic Medicine Multidisciplinary Team: a qualitative study. Eur. J. Hum. Genet. 25, 680–686 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  122. 122

    Moynihan, R., Doust, J. & Henry, D. Preventing overdiagnosis: how to stop harming the healthy. BMJ 344, e3502 (2012).

    Article  PubMed  Google Scholar 

  123. 123

    Newman-Toker, D. E. A unified conceptual model for diagnostic errors: underdiagnosis, overdiagnosis, and misdiagnosis. Diagnosis 1, 43–48 (2014).

    Article  PubMed  Google Scholar 

  124. 124

    Cummings, B. B. et al. Improving genetic diagnosis in Mendelian disease with transcriptome sequencing. Sci. Transl Med. 9, eaal5209 (2017). This is an excellent paper demonstrating the improved diagnostic power of combining transcriptome analysis with NGS for the diagnosis of rare neuromuscular disease.

    Article  PubMed  PubMed Central  Google Scholar 

  125. 125

    Riordan, J. R. et al. Identification of the cystic fibrosis gene: cloning and characterization of complementary DNA. Science 245, 1066–1073 (1989).

    Article  CAS  Google Scholar 

  126. 126

    Castellani, C. & CFTR2 Team. CFTR2: How will it help care? Paediatr. Respir. Rev. 14 (Suppl. 1), 2–5 (2013).

    Article  PubMed  Google Scholar 

  127. 127

    Claustres, M. et al. CFTR-France, a national relational patient database for sharing genetic and phenotypic data associated with rare CFTR variants. Hum. Mutat. 38, 1297–1315 (2017).

    Article  CAS  PubMed  Google Scholar 

  128. 128

    Bainbridge, M. N. et al. Whole-genome sequencing for optimized patient management. Sci. Transl Med. 3, 87re3 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  129. 129

    Finkel, R. S. et al. Treatment of infantile-onset spinal muscular atrophy with nusinersen: a phase 2, open-label, dose-escalation study. Lancet 388, 3017–3026 (2016).

    Article  CAS  PubMed  Google Scholar 

  130. 130

    Finkel, R. S. et al. Nusinersen versus sham control in infantile-onset spinal muscular atrophy. N. Engl. J. Med. 377, 1723–1732 (2017).

    Article  CAS  PubMed  Google Scholar 

  131. 131

    Dever, D. P. et al. CRISPR/Cas9 β-globin gene targeting in human haematopoietic stem cells. Nature 539, 384–389 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  132. 132

    Gaudelli, N. M. et al. Programmable base editing of A·T to G·C in genomic DNA without DNA cleavage. Nature 551, 464–471 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  133. 133

    Cox, D. B. T. et al. RNA editing with CRISPR-Cas13. Science 358, 1019–1027 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  134. 134

    Lionel, A. C. et al. Improved diagnostic yield compared with targeted gene sequencing panels suggests a role for whole-genome sequencing as a first-tier genetic test. Genet. Med. https://doi.org/10.1038/gim.2017.119 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  135. 135

    Taylor-Cousar, J. L. et al. Tezacaftor-ivacaftor in patients with cystic fibrosis homozygous for Phe508del. N. Engl. J. Med. 377, 2013–2023 (2017).

    Article  CAS  PubMed  Google Scholar 

  136. 136

    Friedman, J. M. et al. Genomic newborn screening: public health policy considerations and recommendations. BMC Med. Genom. 10, 9 (2017).

    Article  Google Scholar 

  137. 137

    Berg, J. S. et al. Newborn sequencing in genomic medicine and public health. Pediatrics 139, e20162252 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  138. 138

    Dale, A. P. & Read, R. C. Genetic susceptibility to meningococcal infection. Expert Rev. Anti Infect. Ther. 11, 187–199 (2013).

    Article  CAS  PubMed  Google Scholar 

  139. 139

    Bønnelykke, K. & Ober, C. Leveraging gene-environment interactions and endotypes for asthma gene discovery. J. Allergy Clin. Immunol. 137, 667–679 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  140. 140

    Michels, A. et al. Prediction and prevention of type 1 diabetes: update on success of prediction and struggles at prevention. Pediatr. Diabetes 16, 465–484 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  141. 141

    Burke, W. et al. The translational potential of research on the ethical, legal, and social implications of genomics. Genet. Med. 17, 12–20 (2015).

    Article  PubMed  Google Scholar 

  142. 142

    Hercher, L. & Jamal, L. An old problem in a new age: revisiting the clinical dilemma of misattributed paternity. Appl. Transl Genom. 8, 36–39 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  143. 143

    Jackson, L., Goldsmith, L., O'Connor, A. & Skirton, H. Incidental findings in genetic research and clinical diagnostic tests: a systematic review. Am. J. Med. Genet. A 158A, 3159–3167 (2012).

    Article  PubMed  Google Scholar 

  144. 144

    Botkin, J. R. et al. Points to consider: ethical, legal, and psychosocial implications of genetic testing in children and adolescents. Am. J. Hum. Genet. 97, 6–21 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  145. 145

    Clarke, A. J. Managing the ethical challenges of next-generation sequencing in genomic medicine. Br. Med. Bull. 111, 17–30 (2014).

    Article  PubMed  Google Scholar 

  146. 146

    Wright, C. F., Middleton, A. & Parker, M. in Genomic Medicine Principles and Practice (eds Kumar, D. & Eng, C.) 250–258 (Oxford Univ. Press, 2014).

    Google Scholar 

  147. 147

    Anderson, J. A. et al. Parents perspectives on whole genome sequencing for their children: qualified enthusiasm? J. Med. Eth. 43, 535–539 (2016).

    Article  Google Scholar 

  148. 148

    Horn, R. & Parker, M. Opening Pandora's box?: ethical issues in prenatal whole genome and exome sequencing. Prenat. Diagn. 23, 34–39 (2017).

    Google Scholar 

  149. 149

    Newson, A. J. Whole genome sequencing in children: ethics, choice and deliberation. J. Med. Eth. 43, 540–542 (2017).

    Article  Google Scholar 

  150. 150

    Committee on Bioethics et al. Ethical and policy issues in genetic testing and screening of children. Pediatrics 131, 620–622 (2013).

  151. 151

    Burstein, M. D., Robinson, J. O., Hilsenbeck, S. G., McGuire, A. L. & Lau, C. C. Pediatric data sharing in genomic research: attitudes and preferences of parents. Pediatrics 133, 690–697 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  152. 152

    Wright, C. F., Hurles, M. E. & Firth, H. V. Principle of proportionality in genomic data sharing. Nat. Rev. Genet. 17, 1–2 (2016).

    Article  CAS  PubMed  Google Scholar 

  153. 153

    Muddyman, D., Smee, C., Griffin, H. & Kaye, J. Implementing a successful data-management framework: the UK10K managed access model. Genome Med. 5, 100 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  154. 154

    Wilfond, B. S. & Carpenter, K. J. Incidental findings in pediatric research. J. Law Med. Eth. 36, 332–340 (2008).

    Article  Google Scholar 

  155. 155

    Eldomery, M. K. et al. Lessons learned from additional research analyses of unsolved clinical exome cases. Genome Med. 9, 26 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  156. 156

    Carrieri, D. et al. Recontacting in clinical genetics and genomic medicine? We need to talk about it. Eur. J. Hum. Genet. 25, 520–521 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  157. 157

    Gliwa, C. & Berkman, B. E. Do researchers have an obligation to actively look for genetic incidental findings? Am. J. Bioeth 13, 32–42 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  158. 158

    Crawford, G., Foulds, N., Fenwick, A., Hallowell, N. & Lucassen, A. Genetic medicine and incidental findings: it is more complicated than deciding whether to disclose or not. Genet. Med. 15, 896–899 (2013).

    Article  PubMed  Google Scholar 

  159. 159

    Clayton, E. W. Incidental findings in genetics research using archived DNA. J. Law Med. Eth. 36, 286–291 (2008).

    Article  Google Scholar 

  160. 160

    Wolf, S. M. et al. Managing incidental findings and research results in genomic research involving biobanks and archived data sets. Genet. Med. 14, 361–384 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  161. 161

    Kalia, S. S. et al. Recommendations for reporting of secondary findings in clinical exome and genome sequencing, 2016 update (ACMG SF v2.0): a policy statement of the American College of Medical Genetics and Genomics. Genet. Med. 19, 249–255 (2017).

    Article  PubMed  Google Scholar 

  162. 162

    Mand, C., Gillam, L., Delatycki, M. B. & Duncan, R. E. Predictive genetic testing in minors for late-onset conditions: a chronological and analytical review of the ethical arguments. J. Med. Eth. 38, 519–524 (2012).

    Article  Google Scholar 

  163. 163

    Shkedi-Rafid, S., Fenwick, A., Dheensa, S. & Lucassen, A. M. Genetic testing of children for adult-onset conditions: opinions of the British adult population and implications for clinical practice. Eur. J. Hum. Genet. 23, 1281–1285 (2015).

    Article  PubMed  Google Scholar 

  164. 164

    Caga-anan, E. C. F., Smith, L., Sharp, R. R. & Lantos, J. D. Testing children for adult-onset genetic diseases. Pediatrics 129, 163–167 (2012).

    Article  PubMed  Google Scholar 

  165. 165

    Claustres, M. et al. Recommendations for reporting results of diagnostic genetic testing (biochemical, cytogenetic and molecular genetic). Eur. J. Hum. Genet. 22, 160–170 (2014).

    Article  PubMed  Google Scholar 

  166. 166

    Wright, C. F. et al. Policy challenges of clinical genome sequencing. BMJ 347, f6845 (2013).

    Article  PubMed  Google Scholar 

  167. 167

    FitzPatrick, D. R. Resequencing at scale in neurodevelopmental disorders. Nat. Genet. 49, 488–489 (2017).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The authors are all members of the management team of the Deciphering Developmental Disorders (DDD) Study, which undertakes independent research commissioned by the Health Innovation Challenge Fund [grant number HICF-1009-003], a parallel funding partnership between the Wellcome Trust and the Department of Health, and the Wellcome Trust Sanger Institute [grant number WT098051]. The views expressed in this publication are those of the authors and not necessarily those of the Wellcome Trust or the Department of Health. The study has UK Research Ethics Committee approval (10/H0305/83, granted by the Cambridge South REC, and GEN/284/12 granted by the Republic of Ireland REC). The research team acknowledges the support of the National Institute for Health Research, through the Comprehensive Clinical Research Network. This study makes use of DECIPHER, which is funded by the Wellcome Trust. H.V.F. is supported by The Wellcome Trust award 200990/Z/16/Z 'Designing, developing and delivering integrated foundations for genomic medicine'.

Author information

Affiliations

Authors

Contributions

C.F.W. and H.V.F. drafted and revised the manuscript. D.R.F. provided editorial input, designed some of the figures and contributed to the ideas and discussions on which the article is based.

Corresponding author

Correspondence to Helen V. Firth.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Related links

PowerPoint slides

Glossary

Developmental disorders

Diseases with their genesis in embryonic life or early fetal brain development.

Structural variants

Blocks of DNA >1 kb that differ relative to the reference genome or general population, including inversions, balanced translocations and copy number variants (for example, deletions and duplications).

Mosaicism

The presence of two or more populations of cells with different genotypes in an individual who has developed from a single fertilized egg.

Penetrance

The proportion of individuals with a particular genotype who show features of the condition (however mildly). If some individuals with the genotype never show any features, the condition is said to have incomplete (or reduced) penetrance. If features develop with age, the condition is said to have age-dependent penetrance.

Expressivity

The phenotypic variability and severity that a given genotype shows in individuals penetrant for the condition.

Locus heterogeneity

When variants in a number of different genes independently cause the same phenotype.

Allelic heterogeneity

When different mutations at the same locus cause the same phenotype.

Monoallelic

Describes a mutation that affects only one copy of a gene. Autosomal dominant, de novo dominant or X-linked disorders are caused by a monoallelic pathogenic variant.

Biallelic variants

Mutations that affect both copies of a gene. Autosomal recessive disorders are caused by pathogenic biallelic variants.

Whole-exome sequencing

(WES). Next-generation sequencing of the entire protein-coding portion of the genome. In humans, the total length of coding and splicing regions is estimated to be ~35 Mb and comprises ~20,000 genes (1–2% of the genome).

Whole-genome sequencing

(WGS). Next-generation sequencing of the entire genome, which, in humans, is typically ~3,000 Mb.

Single-gene tests

Approaches that enable detailed analysis of a single gene. In addition to sequence analysis, they usually also include an assessment of dosage in order to detect exon-level deletions and duplications, which are often difficult to detect with current approaches to whole-exome sequencing and whole-genome sequencing.

Cytogenetic tests

Genome-wide tests that analyse the number and structure of chromosomes, including copy number variants, but do not provide information about the DNA sequence.

Karyotype

The chromosomal complement of a cell. Large-scale chromosomal imbalances can be detected using karyotyping approaches, such as imaging Giemsa-banded chromosomes with light microscopy.

Copy number variants

(CNVs). Structural variants that involve either a deletion or a duplication of a section of DNA relative to the reference genome.

Single nucleotide variants

(SNVs). Differences within a population, or between an individual and a reference genome, that affect a single base pair of DNA.

Gene panels

Subsets of genes (usually linked to a particular phenotype) that are incorporated into a laboratory-based gene capture kit or that form the basis of computer-based virtual gene panels, which are applied to a subset of variant data from a clinical exome or a whole-exome sequencing and/or whole-genome sequencing assay.

Minor allele frequencies

(MAFs). Measurements of how often the less common allele occurs at a given polymorphic locus.

Human Phenotype Ontology

(HPO). A standardized vocabulary of phenotypic abnormalities encountered in human disease. Each term in the HPO describes a phenotypic abnormality, such as atrial septal defect. The HPO currently contains ~11,000 terms.

Causative genotypes

Genotypes in which a single locus is perturbed and that have a high positive predictive value for a restricted pattern of morphological, biochemical or physiological features of clinical significance.

Blended phenotype

A mixed phenotype that results from causal variants in two or more genes. The phenotypes may either be distinct, with discrete (composite) manifestations, or overlapping, with similar phenotypic manifestations that are impossible to disentangle.

Genetic risk scores

Quantitative measures of genetic predisposition to a trait that are calculated from data for multiple (usually low-risk) genetic variants, which are usually obtained from genome-wide association studies.

Pleiotropy

The phenomenon whereby variants in a single gene may cause multiple phenotypic expressions or disorders.

Transcriptomics

A global approach for looking at gene expression patterns. This can involve measurements of thousands of genes simultaneously with microarrays or measurements of small numbers of genes that are facilitated by global sequence information from expressed sequence tag or genome-sequencing projects.

Epigenomics

A global approach for looking at the complete collection of epigenetic marks, such as DNA methylation and histone modifications, and other molecules that can transmit epigenetic information, such as non-coding RNAs, that exist in a cell at any given point in time.

Metabolomics

A global approach using quantitative analytical methods to look at the entire metabolic content of a cell or organism at a given time.

Proteomics

A global approach for looking at the complete collection of proteins in a cell or tissue at a given time.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Wright, C., FitzPatrick, D. & Firth, H. Paediatric genomics: diagnosing rare disease in children. Nat Rev Genet 19, 253–268 (2018). https://doi.org/10.1038/nrg.2017.116

Download citation

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing