1932

Abstract

Our ability to make accurate and specific genetic diagnoses in individuals with severe developmental disorders has been transformed by data derived from genomic sequencing technologies. These data reveal both the patterns and rates of different mutational mechanisms and identify regions of the human genome with fewer mutations than would be expected. In outbred populations, the most common identifiable cause of severe developmental disorders is de novo mutation affecting the coding region in one of approximately 500 different genes, almost universally showing constraint. Simply combining the location of a de novo genomic event with its predicted consequence on the gene product gives significant diagnostic power. Our knowledge of the diversity of phenotypic consequences associated with comparable diagnostic genotypes at each locus is improving. Computationally useful phenotype data will improve diagnostic interpretation of ultrarare genetic variants and, in the long run, indicate which specific embryonic processes have been perturbed.

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2020-08-31
2024-06-22
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Literature Cited

  1. 1. 
    Abouelhoda M, Sobahy T, El-Kalioby M, Patel N, Shamseldin H et al. 2016. Clinical genomics can facilitate countrywide estimation of autosomal recessive disease burden. Genet. Med. 18:1244–49
    [Google Scholar]
  2. 2. 
    Aitken S, Firth HV, McRae J, Halachev M, Kini U et al. 2019. Finding diagnostically useful patterns in quantitative phenotypic data. Am. J. Hum. Genet. 105:933–46
    [Google Scholar]
  3. 3. 
    Allen AS, Berkovic SF, Cossette P, Delanty N, Dlugos D et al. 2013. De novo mutations in epileptic encephalopathies. Nature 501:217–21
    [Google Scholar]
  4. 4. 
    Amarasinghe KC, Li J, Halgamuge SK 2013. CoNVEX: copy number variation estimation in exome sequencing data using HMM. BMC Bioinform 14:Suppl. 2S2
    [Google Scholar]
  5. 5. 
    Amiri A, Peres NA. 2014. Diversity in the erg27 gene of Botrytis cinerea field isolates from strawberry defines different levels of resistance to the hydroxyanilide fenhexamid. Plant Dis 98:1131–37
    [Google Scholar]
  6. 6. 
    Ansari M, Poke G, Ferry Q, Williamson K, Aldridge R et al. 2014. Genetic heterogeneity in Cornelia de Lange syndrome (CdLS) and CdLS-like phenotypes with observed and predicted levels of mosaicism. J. Med. Genet. 51:659–68
    [Google Scholar]
  7. 7. 
    Bellairs R, Osmond M. 2014. Atlas of Chick Development Oxford, UK: Academic
    [Google Scholar]
  8. 8. 
    Bone WP, Washington NL, Buske OJ, Adams DR, Davis J et al. 2016. Computational evaluation of exome sequence data using human and model organism phenotypes improves diagnostic efficiency. Genet. Med. 18:608–17
    [Google Scholar]
  9. 9. 
    Bryson-Richardson R, Berger S, Currie P 2012. Atlas of Zebrafish Development London: Academic
    [Google Scholar]
  10. 10. 
    Clarren SK, Smith DW. 1978. The fetal alcohol syndrome. N. Engl. J. Med. 298:1063–67
    [Google Scholar]
  11. 11. 
    Davydov EV, Goode DL, Sirota M, Cooper GM, Sidow A, Batzoglou S 2010. Identifying a high fraction of the human genome to be under selective constraint using GERP++. PLOS Comput. Biol. 6:e1001025
    [Google Scholar]
  12. 12. 
    Deciphering Dev. Disord. Study 2017. Prevalence and architecture of de novo mutations in developmental disorders. Nature 542:433–38
    [Google Scholar]
  13. 13. 
    Derry JMJ, Ochs HD, Francke U 1994. Isolation of a novel gene mutated in Wiskott-Aldrich syndrome. Cell 78:635–44
    [Google Scholar]
  14. 14. 
    Dilella AG, Kwok SCM, Ledley FD, Marvit J, Woo SLC 1986. Molecular structure and polymorphic map of the human phenylalanine hydroxylase gene. Biochemistry 25:743–49
    [Google Scholar]
  15. 15. 
    Elledge SJ. 1996. Cell cycle checkpoints: preventing an identity crisis. Science 274:1664–72
    [Google Scholar]
  16. 16. 
    Fantes JA, Boland E, Ramsay J, Donnai D, Splitt M et al. 2008. FISH mapping of de novo apparently balanced chromosome rearrangements identifies characteristics associated with phenotypic abnormality. Am. J. Hum. Genet. 82:916–26
    [Google Scholar]
  17. 17. 
    Ferry Q, Steinberg J, Webber C, FitzPatrick DR, Ponting CP et al. 2014. Diagnostically relevant facial gestalt information from ordinary photos. eLife 3:e02020
    [Google Scholar]
  18. 18. 
    Firth HV, Wright CF 2011. The Deciphering Developmental Disorders (DDD) study. Dev. Med. Child Neurol. 53:702–3
    [Google Scholar]
  19. 19. 
    Gao Z, Moorjani P, Sasani TA, Pedersen BS, Quinlan AR et al. 2019. Overlooked roles of DNA damage and maternal age in generating human germline mutations. PNAS 116:9491–500
    [Google Scholar]
  20. 20. 
    Haldane JBS. 1957. The cost of natural selection. J. Genet. 55:511–24
    [Google Scholar]
  21. 21. 
    Hodgkinson A, Eyre-Walker A. 2011. Variation in the mutation rate across mammalian genomes. Nat. Rev. Genet. 12:756–66
    [Google Scholar]
  22. 22. 
    Homsy J, Zaidi S, Shen Y, Ware JS, Samocha KE et al. 2015. De novo mutations in congenital heart disease with neurodevelopmental and other congenital anomalies. Science 350:1262–66
    [Google Scholar]
  23. 23. 
    Ito T, Ando H, Suzuki T, Ogura T, Hotta K et al. 2010. Identification of a primary target of thalidomide teratogenicity. Science 327:1345–50
    [Google Scholar]
  24. 24. 
    Kagey MH, Newman JJ, Bilodeau S, Zhan Y, Orlando DA et al. 2011. Mediator and cohesin connect gene expression and chromatin architecture. Nature 467:430–35 Erratum. 2011 Nature 467:430
    [Google Scholar]
  25. 25. 
    Kaufman MH. 1992. The Atlas of Mouse Development London: Academic
    [Google Scholar]
  26. 26. 
    Keppler-Noreuil KM, Rios JJ, Parker VER, Semple RK, Lindhurst MJ et al. 2015. PIK3CA-related overgrowth spectrum (PROS): diagnostic and testing eligibility criteria, differential diagnosis, and evaluation. Am. J. Med. Genet. A 167:287–95
    [Google Scholar]
  27. 27. 
    Kilcullen M, Kandasamy Y, Watson D, Cadet-James Y 2019. Decisions to consent for autopsy after stillbirth: Aboriginal and Torres Strait Islander women's experiences. Aust. N. Z. J. Obstet. Gynaecol. https://doi.org/10.1111/ajo.13052
    [Crossref] [Google Scholar]
  28. 28. 
    Kimura M. 1968. Evolutionary rate at the molecular level. Nature 217:624–26
    [Google Scholar]
  29. 29. 
    Kohler S, Doelken SC, Mungall CJ, Bauer S, Firth HV et al. 2014. The Human Phenotype Ontology project: linking molecular biology and disease through phenotype data. Nucleic Acids Res 42:D966–74
    [Google Scholar]
  30. 30. 
    Kong A, Frigge ML, Masson G, Besenbacher S, Sulem P et al. 2012. Rate of de novo mutations and the importance of father's age to disease risk. Nature 488:471–75
    [Google Scholar]
  31. 31. 
    Kumar P, Henikoff S, Ng PC 2009. Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat. Protoc. 4:1073–81
    [Google Scholar]
  32. 32. 
    Lande R. 1994. Risk of population extinction from fixation of new deleterious mutations. Evolution 48:1460–69
    [Google Scholar]
  33. 33. 
    Lek M, Karczewski KJ, Minikel EV, Samocha KE, Banks E et al. 2016. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536:285–91
    [Google Scholar]
  34. 34. 
    Linderman MD, Chia D, Wallace F, Nothaft FA 2019. DECA: scalable XHMM exome copy-number variant calling with ADAM and Apache Spark. BMC Bioinform 20:493
    [Google Scholar]
  35. 35. 
    Lupski JR, Belmont JW, Boerwinkle E, Gibbs RA 2011. Clan genomics and the complex architecture of human disease. Cell 147:32–43
    [Google Scholar]
  36. 36. 
    Maher GJ, Ralph HK, Ding Z, Koelling N, Mlcochova H et al. 2018. Selfish mutations dysregulating RAS-MAPK signaling are pervasive in aged human testes. Genome Res 28:1779–90
    [Google Scholar]
  37. 37. 
    Marchuk DS, Crooks K, Strande N, Kaiser-Rogers K, Milko LV et al. 2018. Increasing the diagnostic yield of exome sequencing by copy number variant analysis. PLOS ONE 13:e0209185
    [Google Scholar]
  38. 38. 
    Margueron R, Reinberg D. 2011. The Polycomb complex PRC2 and its mark in life. Nature 469:343–49
    [Google Scholar]
  39. 39. 
    McCoy RC. 2017. Mosaicism in preimplantation human embryos: when chromosomal abnormalities are the norm. Trends Genet 33:448–63
    [Google Scholar]
  40. 40. 
    Mi HY, Poudel S, Muruganujan A, Casagrande JT, Thomas PD 2016. PANTHER version 10: expanded protein families and functions, and analysis tools. Nucleic Acids Res 44:D33642
    [Google Scholar]
  41. 41. 
    Mishima H, Suzuki H, Doi M, Miyazaki M, Watanabe S et al. 2019. Evaluation of Face2Gene using facial images of patients with congenital dysmorphic syndromes recruited in Japan. J. Hum. Genet. 64:789–94
    [Google Scholar]
  42. 42. 
    Mistry H, Heazell AE, Vincent O, Roberts T 2013. A structured review and exploration of the healthcare costs associated with stillbirth and a subsequent pregnancy in England and Wales. BMC Pregnancy Childbirth 13:236
    [Google Scholar]
  43. 43. 
    Mlakar J, Korva M, Tul N, Popović M, Poljšak-Prijatelj M et al. 2016. Zika virus associated with microcephaly. N. Engl. J. Med. 374:951–58
    [Google Scholar]
  44. 44. 
    Monies D, Abouelhoda M, Assoum M, Moghrabi N, Rafiullah R et al. 2019. Lessons learned from large-scale, first-tier clinical exome sequencing in a highly consanguineous population. Am. J. Hum. Genet 104:1182–201 Erratum. 2019 Am. J. Hum. Genet 105:879
    [Google Scholar]
  45. 45. 
    Nathans D, Smith HO. 1975. Restriction endonucleases in the analysis and restructuring of DNA molecules. Annu. Rev. Biochem. 44:273–93
    [Google Scholar]
  46. 46. 
    Neale BM, Kou Y, Liu L, Ma'ayan A, Samocha KE et al. 2012. Patterns and rates of exonic de novo mutations in autism spectrum disorders. Nature 485:242–45
    [Google Scholar]
  47. 47. 
    Ng SB, Buckingham KJ, Lee C, Bigham AW, Tabor HK et al. 2010. Exome sequencing identifies the cause of a mendelian disorder. Nat. Genet. 42:30–35
    [Google Scholar]
  48. 48. 
    Niemi MEK, Martin HC, Rice DL, Gallone G, Gordon S et al. 2018. Common genetic variants contribute to risk of rare severe neurodevelopmental disorders. Nature 562:268–71
    [Google Scholar]
  49. 49. 
    Niinivehmas SP, Salokas K, Latti S, Raunio H, Pentikainen OT 2015. Ultrafast protein structure-based virtual screening with Panther. J. Comput.-Aided Mol. Des. 29:989–1006
    [Google Scholar]
  50. 50. 
    Nik-Zainal S, Hall BA. 2019. Cellular survival over genomic perfection. Science 366:802–3
    [Google Scholar]
  51. 51. 
    Ogwulu CB, Jackson LJ, Heazell AE, Roberts TE 2015. Exploring the intangible economic costs of stillbirth. BMC Pregnancy Childbirth 15:188
    [Google Scholar]
  52. 52. 
    O'Rahilly R, Müller F, Streeter GL 1987. Developmental Stages in Human Embryos: Including a Revision of Streeter's “Horizons” and a Survey of the Carnegie Collection Washington, DC: Carnegie Inst. Wash.
    [Google Scholar]
  53. 53. 
    Pengelly RJ, Alom T, Zhang Z, Hunt D, Ennis S, Collins A 2017. Evaluating phenotype-driven approaches for genetic diagnoses from exomes in a clinical setting. Sci. Rep. 7:13509
    [Google Scholar]
  54. 54. 
    Rainger J, Bengani H, Campbell L, Anderson E, Sokhi K et al. 2012. Miller (Genee-Wiedemann) syndrome represents a clinically and biochemically distinct subgroup of postaxial acrofacial dysostosis associated with partial deficiency of DHODH. Hum. Mol. Genet. 21:3969–83
    [Google Scholar]
  55. 55. 
    Ramani R, Krumholz K, Huang YF, Siepel A 2019. PhastWeb: a web interface for evolutionary conservation scoring of multiple sequence alignments using phastCons and phyloP. Bioinformatics 35:2320–22
    [Google Scholar]
  56. 56. 
    Redin C, Brand H, Collins RL, Kammin T, Mitchell E et al. 2017. The genomic landscape of balanced cytogenetic abnormalities associated with human congenital anomalies. Nat. Genet. 49:36–45
    [Google Scholar]
  57. 57. 
    Retterer K, Scuffins J, Schmidt D, Lewis R, Pineda-Alvarez D et al. 2015. Assessing copy number from exome sequencing and exome array CGH based on CNV spectrum in a large clinical cohort. Genet. Med. 17:623–29
    [Google Scholar]
  58. 58. 
    Rogers MF, Shihab HA, Mort M, Cooper DN, Gaunt TR, Campbell C 2018. FATHMM-XF: accurate prediction of pathogenic point mutations via extended features. Bioinformatics 34:511–13
    [Google Scholar]
  59. 59. 
    Ruderfer DM, Hamamsy T, Lek M, Karczewski KJ, Kavanagh D et al. 2016. Patterns of genic intolerance of rare copy number variation in 59,898 human exomes. Nat. Genet. 48:1107–11
    [Google Scholar]
  60. 60. 
    Saiki RK, Bugawan TL, Horn GT, Mullis KB, Erlich HA 1986. Analysis of enzymatically amplified β-globin and HLA-DQα DNA with allele-specific oligonucleotide probes. Nature 324:163–66
    [Google Scholar]
  61. 61. 
    Saleheen D, Natarajan P, Armean IM, Zhao W, Rasheed A et al. 2017. Human knockouts and phenotypic analysis in a cohort with a high rate of consanguinity. Nature 544:235–39
    [Google Scholar]
  62. 62. 
    Samocha KE, Robinson EB, Sanders SJ, Stevens C, Sabo A et al. 2014. A framework for the interpretation of de novo mutation in human disease. Nat. Genet. 46:944–50
    [Google Scholar]
  63. 63. 
    Sanders SJ, Murtha MT, Gupta AR, Murdoch JD, Raubeson MJ et al. 2012. De novo mutations revealed by whole-exome sequencing are strongly associated with autism. Nature 485:237–41
    [Google Scholar]
  64. 64. 
    Seabright M. 1971. A rapid banding technique for human chromosomes. Lancet 298:971–72
    [Google Scholar]
  65. 65. 
    Sharkey AM, Macklon NS. 2013. The science of implantation emerges blinking into the light. Reprod. Biomed. Online 27:453–60
    [Google Scholar]
  66. 66. 
    Shihab HA, Gough J, Cooper DN, Stenson PD, Barker GL et al. 2013. Predicting the functional, molecular, and phenotypic consequences of amino acid substitutions using hidden Markov models. Hum. Mutat. 34:57–65
    [Google Scholar]
  67. 67. 
    Short PJ, McRae JF, Gallone G, Sifrim A, Won H et al. 2018. De novo mutations in regulatory elements in neurodevelopmental disorders. Nature 555:611–16
    [Google Scholar]
  68. 68. 
    Sirmaci A, Spiliopoulos M, Brancati F, Powell E, Duman D et al. 2011. Mutations in ANKRD11 cause KBG syndrome, characterized by intellectual disability, skeletal malformations, and macrodontia. Am. J. Hum. Genet. 89:289–94
    [Google Scholar]
  69. 69. 
    Smedley D, Jacobsen JO, Jäger M, Köhler S, Holtgrewe M et al. 2015. Next-generation diagnostics and disease-gene discovery with the Exomiser. Nat. Protoc. 10:2004–15
    [Google Scholar]
  70. 70. 
    Smedley D, Robinson PN. 2015. Phenotype-driven strategies for exome prioritization of human Mendelian disease genes. Genome Med 7:81
    [Google Scholar]
  71. 71. 
    Speicher MR, Carter NP. 2005. The new cytogenetics: blurring the boundaries with molecular biology. Nat. Rev. Genet. 6:782–92
    [Google Scholar]
  72. 72. 
    Srivastava S, Love-Nichols JA, Dies KA, Ledbetter DH, Martin CL et al. 2019. Meta-analysis and multidisciplinary consensus statement: exome sequencing is a first-tier clinical diagnostic test for individuals with neurodevelopmental disorders. Genet. Med. 21:2413–21
    [Google Scholar]
  73. 73. 
    Sudarsanam P, Winston F. 2000. The Swi/Snf family: nucleosome-remodeling complexes and transcriptional control. Trends Genet 16:345–51
    [Google Scholar]
  74. 74. 
    Takata A, Nakashima M, Saitsu H, Mizuguchi T, Mitsuhashi S et al. 2019. Comprehensive analysis of coding variants highlights genetic complexity in developmental and epileptic encephalopathy. Nat. Commun. 10:2506
    [Google Scholar]
  75. 75. 
    Tan R, Wang Y, Kleinstein SE, Liu Y, Zhu X et al. 2014. An evaluation of copy number variation detection tools from whole-exome sequencing data. Hum. Mutat. 35:899–907
    [Google Scholar]
  76. 76. 
    Tinschert S, Naumann I, Stegmann E, Buske A, Kaufmann D et al. 2000. Segmental neurofibromatosis is caused by somatic mutation of the neurofibromatosis type 1 (NF1) gene. Eur. J. Hum. Genet. 8:455–59
    [Google Scholar]
  77. 77. 
    Toriello HV 2008. Statement on guidance for genetic counseling in advanced paternal age. Genet. Med. 10:457–60
    [Google Scholar]
  78. 78. 
    Tran TD, Kwon YK. 2013. The relationship between modularity and robustness in signalling networks. J. R. Soc. Interface 10:20130771
    [Google Scholar]
  79. 79. 
    Tsurusaki Y, Okamoto N, Ohashi H, Kosho T, Imai Y et al. 2012. Mutations affecting components of the SWI/SNF complex cause Coffin-Siris syndrome. Nat. Genet. 44:376–78
    [Google Scholar]
  80. 80. 
    van Bakel M, Einarsson I, Arnaud C, Craig S, Michelsen SI et al. 2014. Monitoring the prevalence of severe intellectual disability in children across Europe: feasibility of a common database. Dev. Med. Child Neurol. 56:361–69
    [Google Scholar]
  81. 81. 
    van Weerd JH, Badi I, van den Boogaard M, Stefanovic S, van de Werken HJG et al. 2014. A large permissive regulatory domain exclusively controls Tbx3 expression in the cardiac conduction system. Circ. Res. 115:432–41
    [Google Scholar]
  82. 82. 
    Veeramah KR, Johnstone L, Karafet TM, Wolf D, Sprissler R et al. 2013. Exome sequencing reveals new causal mutations in children with epileptic encephalopathies. Epilepsia 54:1270–81
    [Google Scholar]
  83. 83. 
    Veltman JA, Brunner HG. 2012. De novo mutations in human genetic disease. Nat. Rev. Genet. 13:565–75
    [Google Scholar]
  84. 84. 
    Viljoen JW, de Villiers JP, van Zyl AJ, Mezzavilla M, Pepper M 2019. Establishment and equilibrium levels of deleterious mutations in large populations. Sci. Rep. 9:10
    [Google Scholar]
  85. 85. 
    Vissers LE, de Ligt J, Gilissen C, Janssen I, Steehouwer M et al. 2010. A de novo paradigm for mental retardation. Nat. Genet. 42:1109–12
    [Google Scholar]
  86. 86. 
    Weatherall DJ. 2001. Phenotype-genotype relationships in monogenic disease: lessons from the thalassaemias. Nat. Rev. Genet. 2:245–55
    [Google Scholar]
  87. 87. 
    Widdows K, Reid HE, Roberts SA, Camacho EM, Heazell AEP 2018. Saving babies’ lives project impact and results evaluation (SPiRE): a mixed methodology study. BMC Pregnancy Childbirth 18:43
    [Google Scholar]
  88. 88. 
    Wieczorek D, Bögershausen N, Beleggia F, Steiner-Haldenstätt S, Pohl E et al. 2013. A comprehensive molecular study on Coffin-Siris and Nicolaides-Baraitser syndromes identifies a broad molecular and clinical spectrum converging on altered chromatin remodeling. Hum. Mol. Genet. 22:5121–35
    [Google Scholar]
  89. 89. 
    Wright CF, Fitzgerald TW, Jones WD, Clayton S, McRae JF et al. 2015. Genetic diagnosis of developmental disorders in the DDD study: a scalable analysis of genome-wide research data. Lancet 385:1305–14
    [Google Scholar]
  90. 90. 
    Wright CF, McRae JF, Clayton S, Gallone G, Aitken 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. 20:1216–23
    [Google Scholar]
  91. 91. 
    Wright CF, Prigmore E, Rajan D, Handsaker J, McRae J et al. 2019. Clinically-relevant postzygotic mosaicism in parents and children with developmental disorders in trio exome sequencing data. Nat. Commun. 10:2985
    [Google Scholar]
  92. 92. 
    Xiao B, Wang LL, Liu HL, Fan YJ, Xu Y et al. 2019. Uniparental isodisomy caused autosomal recessive diseases: NGS-based analysis allows the concurrent detection of homogenous variants and copy-neutral loss of heterozygosity. Mol. Genet. Genom. Med. 7:e00945
    [Google Scholar]
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