1932

Abstract

Accurate diagnosis is the cornerstone of medicine; it is essential for informed care and promoting patient and family well-being. However, families with a rare genetic disease (RGD) often spend more than five years on a diagnostic odyssey of specialist visits and invasive testing that is lengthy, costly, and often futile, as 50% of patients do not receive a molecular diagnosis. The current diagnostic paradigm is not well designed for RGDs, especially for patients who remain undiagnosed after the initial set of investigations, and thus requires an expansion of approaches in the clinic. Leveraging opportunities to participate in research programs that utilize new technologies to understand RGDs is an important path forward for patients seeking a diagnosis. Given recent advancements in such technologies and international initiatives, the prospect of identifying a molecular diagnosis for all patients with RGDs has never been so attainable, but achieving this goal will require global cooperation at an unprecedented scale.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-genom-083118-015345
2020-08-31
2024-04-19
Loading full text...

Full text loading...

/deliver/fulltext/genom/21/1/annurev-genom-083118-015345.html?itemId=/content/journals/10.1146/annurev-genom-083118-015345&mimeType=html&fmt=ahah

Literature Cited

  1. 1. 
    ACMG Board Dir 2017. 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. 19:721–22
    [Google Scholar]
  2. 2. 
    Adachi T, Kawamura K, Furusawa Y, Nishizaki Y, Imanishi N et al. 2017. Japan's initiative on rare and undiagnosed diseases (IRUD): towards an end to the diagnostic odyssey. Eur. J. Hum. Genet. 25:1025–28
    [Google Scholar]
  3. 3. 
    Aref-Eshghi E, Bend EG, Colaiacovo S, Caudle M, Chakrabarti R et al. 2019. Diagnostic utility of genome-wide DNA methylation testing in genetically unsolved individuals with suspected hereditary conditions. Am. J. Hum. Genet. 104:685–700
    [Google Scholar]
  4. 4. 
    Aref-Eshghi E, Schenkel LC, Lin H, Skinner C, Ainsworth P et al. 2017. Clinical validation of a genome-wide DNA methylation assay for molecular diagnosis of imprinting disorders. J. Mol. Diagn. 19:848–56
    [Google Scholar]
  5. 5. 
    Austin CP, Cutillo CM, Lau LPL, Jonker AH, Rath A et al. 2017. Future of rare diseases research 2017–2027: an IRDiRC perspective. Clin. Transl. Sci. 11:21–27
    [Google Scholar]
  6. 6. 
    Balci TB, Hartley T, Xi Y, Dyment DA, Beaulieu CL et al. 2017. Debunking Occam's razor: diagnosing multiple genetic diseases in families by whole-exome sequencing. Clin. Genet. 92:281–89
    [Google Scholar]
  7. 7. 
    Bamshad MJ, Nickerson DA, Chong JX 2019. Mendelian gene discovery: fast and furious with no end in sight. Am. J. Hum. Genet. 105:448–55
    [Google Scholar]
  8. 8. 
    Barbosa M, Joshi RS, Garg P, Martin-Trujillo A, Patel N et al. 2018. Identification of rare de novo epigenetic variations in congenital disorders. Nat. Commun. 9:2064
    [Google Scholar]
  9. 9. 
    Bauman JG, Wiegant J, Borst P, van Duijn P 1980. A new method for fluorescence microscopical localization of specific DNA sequences by in situ hybridization of fluorochrome-labelled RNA. Exp. Cell Res. 128:485–90
    [Google Scholar]
  10. 10. 
    Beger RD, Dunn W, Schmidt MA, Gross SS, Kirwan JA et al. 2016. Metabolomics enables precision medicine: “a white paper, community perspective. .” Metabolomics 12:149
    [Google Scholar]
  11. 11. 
    Beynon JH. 1954. Qualitative analysis of organic compounds by mass spectrometry. Nature 174:735–37
    [Google Scholar]
  12. 12. 
    Biesecker LG, Spinner NB. 2013. A genomic view of mosaicism and human disease. Nat. Rev. Genet. 14:307–20
    [Google Scholar]
  13. 13. 
    Birney E, Vamathevan J, Goodhand P 2017. Genomics in healthcare: GA4GH looks to 2022. bioRxiv 203554. https://doi.org/10.1101/203554
    [Crossref]
  14. 14. 
    Bjornsson HT. 2015. The Mendelian disorders of the epigenetic machinery. Genome Res 25:1473–81
    [Google Scholar]
  15. 15. 
    Boycott KM, Hartley T, Adam S, Bernier F, Chong K et al. 2015. The clinical application of genome-wide sequencing for monogenic diseases in Canada: position statement of the Canadian College of Medical Geneticists. J Med. Genet. 52:431–37
    [Google Scholar]
  16. 16. 
    Boycott KM, Hartley T, Biesecker LG, Gibbs RA, Innes AM et al. 2019. A diagnosis for all rare genetic diseases: the horizon and the next frontiers. Cell 177:32–37
    [Google Scholar]
  17. 17. 
    Bragin E, Chatzimichali EA, Wright CF, Hurles ME, Firth HV et al. 2014. DECIPHER: database for the interpretation of phenotype-linked plausibly pathogenic sequence and copy-number variation. Nucleic Acids Res 42:D993–1000
    [Google Scholar]
  18. 18. 
    Bujak R, Struck-Lewicka W, Markuszewski MJ, Kaliszan R 2015. Metabolomics for laboratory diagnostics. J. Pharm. Biomed. Anal. 113:108–20
    [Google Scholar]
  19. 19. 
    Buske OJ, Girdea M, Dumitriu S, Gallinger B, Hartley T et al. 2015. PhenomeCentral: a portal for phenotypic and genotypic matchmaking of patients with rare genetic diseases. Hum. Mutat. 36:931–40
    [Google Scholar]
  20. 20. 
    Buske OJ, Schiettecatte F, Hutton B, Dumitriu S, Misyura A et al. 2015. The Matchmaker Exchange API: automating patient matching through the exchange of structured phenotypic and genotypic profiles. Hum. Mutat. 36:922–27
    [Google Scholar]
  21. 21. 
    Butcher DT, Cytrynbaum C, Turinsky AL, Siu MT, Inbar-Feigenberg M et al. 2017. CHARGE and Kabuki syndromes: gene-specific DNA methylation signatures identify epigenetic mechanisms linking these clinically overlapping conditions. Am. J. Hum. Genet. 100:773–88
    [Google Scholar]
  22. 22. 
    Cao Y, Tokita MJ, Chen ES, Ghosh R, Chen T et al. 2019. A clinical survey of mosaic single nucleotide variants in disease-causing genes detected by exome sequencing. Genome Med 11:48
    [Google Scholar]
  23. 23. 
    Cassini TA, Duncan L, Rives LC, Newman JH, Phillips JA et al. 2019. Whole genome sequencing reveals novel IGHMBP2 variant leading to unique cryptic splice-site and Charcot-Marie-Tooth phenotype with early onset symptoms. Mol. Genet. Genom. Med. 7:e00676
    [Google Scholar]
  24. 24. 
    Chong JX, Yu JH, Lorentzen P, Park KM, Jamal SM et al. 2016. Gene discovery for Mendelian conditions via social networking: De novo variants in KDM1A cause developmental delay and distinctive facial features. Genet. Med. 18:788–95
    [Google Scholar]
  25. 25. 
    Choufani S, Cytrynbaum C, Chung BHY, Turinsky AL, Grafodatskaya D et al. 2015. NSD1 mutations generate a genome-wide DNA methylation signature. Nat. Commun. 6:10207
    [Google Scholar]
  26. 26. 
    Clark MM, Stark Z, Farnaes L, Tan TY, White SM et al. 2018. Meta-analysis of the diagnostic and clinical utility of genome and exome sequencing and chromosomal microarray in children with suspected genetic diseases. npj Genom. Med. 3:16
    [Google Scholar]
  27. 27. 
    Costa T, Scriver CR, Childs B, Opitz JM, Reynolds JF 1985. The effect of Mendelian disease on human health: a measurement. Am. J. Med. Genet. 21:231–42
    [Google Scholar]
  28. 28. 
    Crowther LM, Poms M, Plecko B 2018. Multiomics tools for the diagnosis and treatment of rare neurological disease. J. Inherit. Metab. Dis. 41:425–34
    [Google Scholar]
  29. 29. 
    Cummings BB, Marshall JL, Tukiainen T, Lek M, Donkervoort S et al. 2017. Improving genetic diagnosis in Mendelian disease with transcriptome sequencing. Sci. Transl. Med. 9:eaal5209
    [Google Scholar]
  30. 30. 
    de Ligt J, Willemsen MH, van Bon BWM, Kleefstra T, Yntema HG et al. 2012. Diagnostic exome sequencing in persons with severe intellectual disability. N. Engl. J. Med. 367:1921–29
    [Google Scholar]
  31. 31. 
    Dyke SOM, Knoppers BM, Hamosh A, Firth HV, Hurles M et al. 2017. “Matching” consent to purpose: the example of the Matchmaker Exchange. Hum. Mutat. 38:1281–85
    [Google Scholar]
  32. 32. 
    Eisenberger T, Di Donato N, Baig SM, Neuhaus C, Beyer A et al. 2014. Targeted and genomewide NGS data disqualify mutations in MYO1A, the “DFNA48 gene”, as a cause of deafness. Hum. Mutat. 35:565–70
    [Google Scholar]
  33. 33. 
    Eldomery MK, Coban-Akdemir Z, Harel T, Rosenfeld JA, Gambin T et al. 2017. Lessons learned from additional research analyses of unsolved clinical exome cases. Genome Med 9:26
    [Google Scholar]
  34. 34. 
    Esquivel-Sada D, Nguyen MT. 2017. Diagnosis of rare diseases under focus: impacts for Canadian patients. J. Community Genet. 9:37–50
    [Google Scholar]
  35. 35. 
    Fahim AT, Daiger SP, Weleber RG 1993. Nonsyndromic retinitis pigmentosa overview. GeneReviews MP Adam, HH Ardinger, RA Pagon, SE Wallace, LJH Bean et al. Seattle: Univ. Wash https://www.ncbi.nlm.nih.gov/books/NBK1417
    [Google Scholar]
  36. 36. 
    Ferreira CR. 2019. The burden of rare diseases. Am. J. Med. Genet. A 179:885–92
    [Google Scholar]
  37. 37. 
    Ferreira CR, van Karnebeek CDM, Vockley J, Blau N 2019. A proposed nosology of inborn errors of metabolism. Genet. Med. 21:102–6
    [Google Scholar]
  38. 38. 
    Florian RT, Kraft F, Leitão E, Kaya S, Klebe S et al. 2019. Unstable TTTTA/TTTCA expansions in MARCH6 are associated with familial adult myoclonic epilepsy type 3. Nat. Commun. 10:4919
    [Google Scholar]
  39. 39. 
    Ford C, Hamerton J. 1956. The chromosomes of man. Nature 178:1020–23
    [Google Scholar]
  40. 40. 
    Frésard L, Smail C, Ferraro NM, Teran NA, Li X et al. 2019. Identification of rare-disease genes using blood transcriptome sequencing and large control cohorts. Nat. Med. 25:911–19
    [Google Scholar]
  41. 41. 
    Frommer M, McDonald LE, Millar DS, Collis CM, Watt F et al. 1992. A genomic sequencing protocol that yields a positive display of 5-methylcytosine residues in individual DNA strands. PNAS 89:1827–31
    [Google Scholar]
  42. 42. 
    Glob. Comm. End Diagn. Odyssey Child. Rare Dis 2019. Global Commission year one report Rep., Glob. Comm. End Diagn. Odyssey Child. Rare Dis. https://globalrarediseasecommission.com/Report
  43. 43. 
    Gonorazky H, Liang M, Cummings B, Lek M, Micallef J et al. 2016. RNAseq analysis for the diagnosis of muscular dystrophy. Ann. Clin. Transl. Neurol. 3:55–60
    [Google Scholar]
  44. 44. 
    Graham E, Lee J, Price M, Tarailo-Graovac M, Matthews A et al. 2018. Integration of genomics and metabolomics for prioritization of rare disease variants: a 2018 literature review. J. Inherit. Metab. Dis. 41:435–45
    [Google Scholar]
  45. 45. 
    Guthrie R, Susi A. 1963. A simple phenylalanine method for detecting phenylketonuria in large populations of newborn infants. Pediatrics 32:338–43
    [Google Scholar]
  46. 46. 
    Harrison SM, Riggs ER, Maglott DR, Lee JM, Azzariti DR et al. 2016. Using ClinVar as a resource to support variant interpretation. Curr. Protoc. Hum. Genet. 89:8 16 1–23
    [Google Scholar]
  47. 47. 
    Hoischen A, van Bon BWM, Gilissen C, Arts P, van Lier B et al. 2010. De novo mutations of SETBP1 cause Schinzel-Giedion syndrome. Nat. Genet. 42:483–85
    [Google Scholar]
  48. 48. 
    Holt IJ, Harding AE, Morgan-Hughes JA 1988. Deletions of muscle mitochondrial DNA in patients with mitochondrial myopathies. Nature 331:717–19
    [Google Scholar]
  49. 49. 
    Huisman SA, Redeker EJW, Maas SM, Mannens MM, Hennekam RCM 2013. High rate of mosaicism in individuals with Cornelia de Lange syndrome. J. Med. Genet. 50:339–44
    [Google Scholar]
  50. 50. 
    Ingram VM. 1959. Abnormal human haemoglobins. III. The chemical difference between normal and sickle cell haemoglobins. Biochim. Biophys. Acta 36:402–11
    [Google Scholar]
  51. 51. 
    Innes AM, McInnes BL, Dyment DA 2018. Clinical and genetic heterogeneity in Dubowitz syndrome: implications for diagnosis, management and further research. Am. J. Med. Genet. C 178:387–97
    [Google Scholar]
  52. 52. 
    Ishiura H, Shibata S, Yoshimura J, Suzuki Y, Qu W et al. 2019. Noncoding CGG repeat expansions in neuronal intranuclear inclusion disease, oculopharyngodistal myopathy and an overlapping disease. Nat. Genet. 51:1222–32
    [Google Scholar]
  53. 53. 
    Ismail IT, Showalter MR, Fiehn O 2019. Inborn errors of metabolism in the era of untargeted metabolomics and lipidomics. Metabolites 9:242
    [Google Scholar]
  54. 54. 
    Jacobs PA, Court Brown WM, Baikie AG, Strong JA 1959. The somatic chromosomes in mongolism. Lancet 273:710
    [Google Scholar]
  55. 55. 
    Jeffreys AJ, Wilson V, Thein SL 1985. Hypervariable ‘minisatellite’ regions in human DNA. Nature 314:67–73
    [Google Scholar]
  56. 56. 
    Jimenez-Sanchez G, Childs B, Valle D 2014. The effect of Mendelian disease on human health. The Online Metabolic and Molecular Bases of Inherited Disease AL Beaudet, B Vogelstein, KW Kinzler, SE Antonarakis, A Ballabio et al. New York: McGraw-Hill https://ommbid.mhmedical.com/content.aspx?bookId=2709&sectionId=225070731
    [Google Scholar]
  57. 57. 
    Karczewski KJ, Francioli LC, Tiao G, Cummings BB, Alföldi J et al. 2019. Variation across 141,456 human exomes and genomes reveals the spectrum of loss-of-function intolerance across human protein-coding genes. bioRxiv 531210. https://doi.org/10.1101/531210
    [Crossref]
  58. 58. 
    Kernohan KD, Frésard L, Zappala Z, Hartley T, Smith KS et al. 2017. Whole-transcriptome sequencing in blood provides a diagnosis of spinal muscular atrophy with progressive myoclonic epilepsy. Hum. Mutat. 38:611–14
    [Google Scholar]
  59. 59. 
    Knoll JHM, Nicholls RD, Magenis RE, Graham JM, Lalande M et al. 1989. Angelman and Prader-Willi syndromes share a common chromosome 15 deletion but differ in parental origin of the deletion. Am. J. Med. Genet. 32:285–90
    [Google Scholar]
  60. 60. 
    Köhler S, Carmody L, Vasilevsky N, Jacobsen JOB, Danis D et al. 2019. Expansion of the Human Phenotype Ontology (HPO) knowledge base and resources. Nucleic Acids Res 47:D1018–27
    [Google Scholar]
  61. 61. 
    Krawczak M, Reiss J, Cooper D 1992. The mutational spectrum of single base-pair substitutions in mRNA splice junctions of human genes: causes and consequences. Hum. Genet. 90:41–54
    [Google Scholar]
  62. 62. 
    Krawczynska N, Wierzba J, Wasag B 2019. Genetic mosaicism in a group of patients with Cornelia de Lange syndrome. Front. Pediatr. 7:203
    [Google Scholar]
  63. 63. 
    Kremer EJ, Pritchard M, Lynch M, Yu S, Holman K et al. 1991. Mapping of DNA instability at the fragile X to a trinucleotide repeat sequence p(CCG)n. Science 252:1711–14
    [Google Scholar]
  64. 64. 
    Kremer LS, Bader DM, Mertes C, Kopajtich R, Pichler G et al. 2017. Genetic diagnosis of Mendelian disorders via RNA sequencing. Nat. Commun. 8:15824
    [Google Scholar]
  65. 65. 
    Ledbetter DH, Riccardi VM, Airhart SD, Strobel RJ, Keenan BS, Crawford JD 1981. Deletions of chromosome 15 as a cause of the Prader-Willi syndrome. N. Engl. J. Med. 304:325–29
    [Google Scholar]
  66. 66. 
    Lee H, Huang AY, Wang L, Yoon AJ, Renteria G et al. 2020. Diagnostic utility of transcriptome sequencing for rare Mendelian diseases. Genet. Med. 22:490–99
    [Google Scholar]
  67. 67. 
    Lejeune J, Gautier M, Turpin R 1959. [Study of somatic chromosomes from 9 mongoloid children]. C. R. Hebd. Seances Acad. Sci. 248:1721–22 (in French)
    [Google Scholar]
  68. 68. 
    Lochmüller H, Le Cam Y, Jonker AH, Lau LP, Baynam G et al. 2017. “IRDiRC Recognized Resources”: a new mechanism to support scientists to conduct efficient, high-quality research for rare diseases. Eur. J. Hum. Genet. 25:162–65
    [Google Scholar]
  69. 69. 
    López-Bigas N, Audit B, Ouzounis C, Parra G, Guigó R 2005. Are splicing mutations the most frequent cause of hereditary disease. FEBS Lett 579:1900–3
    [Google Scholar]
  70. 70. 
    Lupski JR, Reid JG, Gonzaga-Jauregui C, Rio Deiros D, Chen DCY et al. 2010. Whole-genome sequencing in a patient with Charcot-Marie-Tooth neuropathy. N. Engl. J. Med. 362:1181–91
    [Google Scholar]
  71. 71. 
    Maiese DR, Keehn A, Lyon M, Flannery D, Watson M 2019. Current conditions in medical genetics practice. Genet. Med. 21:1874–77
    [Google Scholar]
  72. 72. 
    Margulies M, Egholm M, Altman WE, Attiya S, Bader JS et al. 2005. Genome sequencing in microfabricated high-density picolitre reactors. Nature 437:376–80
    [Google Scholar]
  73. 73. 
    Marshall DA, Benchimol EI, MacKenzie A, Duque DR, MacDonald KV et al. 2018. Direct health-care costs for children diagnosed with genetic diseases are significantly higher than for children with other chronic diseases. Genet. Med. 21:1049–57
    [Google Scholar]
  74. 74. 
    McNulty SN, Evenson MJ, Corliss MM, Love-Gregory LD, Schroeder MC et al. 2019. Diagnostic utility of next-generation sequencing for disorders of somatic mosaicism: a five-year cumulative cohort. Am. J. Hum. Genet. 105:734–46
    [Google Scholar]
  75. 75. 
    Merker JD, Wenger AM, Sneddon T, Grove M, Zappala Z et al. 2018. Long-read genome sequencing identifies causal structural variation in a Mendelian disease. Genet. Med. 20:159–63
    [Google Scholar]
  76. 76. 
    Mirzaa G, Conway R, Graham JM, Dobyns WB 2013. PIK3CA-related segmental overgrowth. GeneReviews MP Adam, HH Ardinger, RA Pagon, SE Wallace, LJH Bean et al. Seattle: Univ. Wash https://www.ncbi.nlm.nih.gov/books/NBK153722
    [Google Scholar]
  77. 77. 
    Mizuguchi T, Suzuki T, Abe C, Umemura A, Tokunaga K et al. 2019. A 12-kb structural variation in progressive myoclonic epilepsy was newly identified by long-read whole-genome sequencing. J. Hum. Genet. 64:359–68
    [Google Scholar]
  78. 78. 
    Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B 2008. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat. Methods 5:621–28
    [Google Scholar]
  79. 79. 
    Mullis KB, Faloona FA. 1987. Specific synthesis of DNA in vitro via a polymerase-catalyzed chain reaction. Methods Enzymol 155:335–50
    [Google Scholar]
  80. 80. 
    Nagalakshmi U, Wang Z, Waern K, Shou C, Raha D et al. 2008. The transcriptional landscape of the yeast genome defined by RNA sequencing. Science 320:1344–49
    [Google Scholar]
  81. 81. 
    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]
  82. 82. 
    NGO Comm. Rare Dis 2019. Report from Rare Disease Day policy event at the United Nations. Second high level event of the NGO Committee for Rare Diseases. Rep., NGO Comm. Rare Dis. http://download2.eurordis.org.s3.amazonaws.com/ngocommittee/ngocommittee_report2019.pdf
  83. 83. 
    Ombrone D, Giocaliere E, Forni G, Malvagia S, la Marca G 2016. Expanded newborn screening by mass spectrometry: new tests, future perspectives. Mass Spectrom. Rev. 35:71–84
    [Google Scholar]
  84. 84. 
    Orphanet 2019. Prevalence and incidence of rare diseases: bibliographic data. Diseases listed by decreasing prevalence, incidence or number of published cases. Orphanet Rep. Ser. 2, Orphanet, Paris. http://www.orpha.net/orphacom/cahiers/docs/GB/Prevalence_of_rare_diseases_by_decreasing_prevalence_or_cases.pdf
  85. 85. 
    Ouellette AC, Mathew J, Manickaraj AK, Manase G, Zahavich L et al. 2018. Clinical genetic testing in pediatric cardiomyopathy: Is bigger better. Clin. Genet. 93:33–40
    [Google Scholar]
  86. 86. 
    Oz-Levi D, Olender T, Bar-Joseph I, Zhu Y, Marek-Yagel D et al. 2019. Noncoding deletions reveal a gene that is critical for intestinal function. Nature 571:107–11
    [Google Scholar]
  87. 87. 
    Pena LDM, Jiang YH, Schoch K, Spillmann RC, Walley N et al. 2018. Looking beyond the exome: a phenotype-first approach to molecular diagnostic resolution in rare and undiagnosed diseases. Genet. Med. 20:464–69
    [Google Scholar]
  88. 88. 
    Philippakis AA, Azzariti DR, Beltran S, Brookes AJ, Brownstein CA et al. 2015. The Matchmaker Exchange: a platform for rare disease gene discovery. Hum. Mutat. 36:915–21
    [Google Scholar]
  89. 89. 
    Piskol R, Ramaswami G, Li JB 2013. Reliable identification of genomic variants from RNA-seq data. Am. J. Hum. Genet. 93:641–51
    [Google Scholar]
  90. 90. 
    Posey JE, Harel T, Liu P, Rosenfeld JA, James RA et al. 2017. Resolution of disease phenotypes resulting from multilocus genomic variation. N. Engl. J. Med. 376:21–31
    [Google Scholar]
  91. 91. 
    Ramoni RB, Mulvihill JJ, Adams DR, Allard P, Ashley EA et al. 2017. The Undiagnosed Diseases Network: accelerating discovery about health and disease. Am. J. Hum. Genet. 100:185–92
    [Google Scholar]
  92. 92. 
    Rehm HL, Berg JS, Brooks LD, Bustamante CD, Evans JP et al. 2015. ClinGen—the clinical genome resource. N. Engl. J. Med. 372:2235–42
    [Google Scholar]
  93. 93. 
    Retterer K, Juusola J, Cho MT, Vitazka P, Millan F et al. 2016. Clinical application of whole-exome sequencing across clinical indications. Genet. Med. 18:696–704
    [Google Scholar]
  94. 94. 
    Richards S, Aziz N, Bale S, Bick D, Das S et al. 2015. 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. 17:405–23
    [Google Scholar]
  95. 95. 
    Riordan JR, Rommens J, Kerem B, Alon N, Rozmahel R et al. 1989. Identification of the cystic fibrosis gene: cloning and characterization of complementary DNA. Science 245:1066–73
    [Google Scholar]
  96. 96. 
    Salmon LB, Orenstein N, Markus-Bustani K, Ruhrman-Shahar N, Kilim Y et al. 2018. Improved diagnostics by exome sequencing following raw data reevaluation by clinical geneticists involved in the medical care of the individuals tested. Genet. Med. 21:1443–51
    [Google Scholar]
  97. 97. 
    Sanger F, Nicklen S, Coulson AR 1977. DNA sequencing with chain-terminating inhibitors. PNAS 74:5463–67
    [Google Scholar]
  98. 98. 
    Schena M, Shalon D, Davis RW, Brown PO 1995. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270:467–70
    [Google Scholar]
  99. 99. 
    Schenkel LC, Kernohan KD, McBride A, Reina D, Hodge A et al. 2017. Identification of epigenetic signature associated with alpha thalassemia/mental retardation X-linked syndrome. Epigenet. Chromatin 10:10
    [Google Scholar]
  100. 100. 
    Schmidt J, Wollnik B. 2018. Hallermann-Streiff syndrome: a missing molecular link for a highly recognizable syndrome. Am. J. Med. Genet. C 178:398–406
    [Google Scholar]
  101. 101. 
    Schouten JP, McElgunn CJ, Waaijer R, Zwijnenburg D, Diepvens F, Pals G 2002. Relative quantification of 40 nucleic acid sequences by multiplex ligation-dependent probe amplification. Nucleic Acids Res 30:e57
    [Google Scholar]
  102. 102. 
    Schrimpe-Rutledge AC, Codreanu SG, Sherrod SD, McLean JA 2016. Untargeted metabolomics strategies—challenges and emerging directions. J. Am. Soc. Mass Spectrom. 27:1897–1905
    [Google Scholar]
  103. 103. 
    Shabani M, Dyke SOM, Marelli L, Borry P 2019. Variant data sharing by clinical laboratories through public databases: consent, privacy and further contact for research policies. Genet. Med. 21:1031–37
    [Google Scholar]
  104. 104. 
    Shamseldin HE, Maddirevula S, Faqeih E, Ibrahim N, Hashem M et al. 2017. Increasing the sensitivity of clinical exome sequencing through improved filtration strategy. Genet. Med. 19:593–98
    [Google Scholar]
  105. 105. 
    Shashi V, Conkie-Rosell A, Rosell B, Schoch K, Vellore K et al. 2014. The utility of the traditional medical genetics diagnostic evaluation in the context of next-generation sequencing for undiagnosed genetic disorders. Genet. Med. 16:176–82
    [Google Scholar]
  106. 106. 
    Shendure J, Porreca GJ, Reppas NB, Lin X, McCutcheon JP et al. 2005. Accurate multiplex polony sequencing of an evolved bacterial genome. Science 309:1728–32
    [Google Scholar]
  107. 107. 
    Shi H, Enriquez A, Rapadas M, Martin EMMA, Wang R et al. 2017. NAD deficiency, congenital malformations, and niacin supplementation. N. Engl. J. Med. 377:544–52
    [Google Scholar]
  108. 108. 
    Siegel DH. 2018. PHACE syndrome: infantile hemangiomas associated with multiple congenital anomalies: clues to the cause. Am. J. Med. Genet. C 178:407–13
    [Google Scholar]
  109. 109. 
    Silverman EK, Allard P, Loscalzo J, Mulvihill JJ, Korrick SA 2019. Reported environmental exposures are inversely associated with obtaining a genetic diagnosis in the Undiagnosed Diseases Network. Am. J. Med. Genet. A 179:958–65
    [Google Scholar]
  110. 110. 
    Southern EM. 1975. Detection of specific sequences among DNA fragments separated by gel electrophoresis. J. Mol. Biol. 98:503–17
    [Google Scholar]
  111. 111. 
    Strande NT, Riggs ER, Buchanan AH, Ceyhan-Birsoy O, DiStefano M et al. 2017. 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
    [Google Scholar]
  112. 112. 
    Stuurman KE, Joosten M, van der Burgt I, Elting M, Yntema HG et al. 2019. Prenatal ultrasound findings of rasopathies in a cohort of 424 fetuses: update on genetic testing in the NGS era. J. Med. Genet. 56:654–61
    [Google Scholar]
  113. 113. 
    Taruscio D, Groft SC, Cederroth H, Melegh B, Lasko P et al. 2015. Undiagnosed Diseases Network International (UDNI): white paper for global actions to meet patient needs. Mol. Genet. Metab. 116:223–25
    [Google Scholar]
  114. 114. 
    Turnbull C, Scott RH, Thomas E, Jones L, Murugaesu N et al. 2018. The 100000 Genomes Project: bringing whole genome sequencing to the NHS. BMJ 361:k1687
    [Google Scholar]
  115. 115. 
    Wallace D, Singh G, Lott M, Hodge J, Schurr T et al. 1988. Mitochondrial DNA mutation associated with Leber's hereditary optic neuropathy. Science 242:1427–30
    [Google Scholar]
  116. 116. 
    Warner JP, Barron LH, Goudie D, Kelly K, Dow D et al. 1996. A general method for the detection of large CAG repeat expansions by fluorescent PCR. J. Med. Genet. 33:1022–26
    [Google Scholar]
  117. 117. 
    World Econ. Forum 2019. Breaking Barriers to Health Data project. World Economic Forum https://www.weforum.org/projects/breaking-barriers-to-health-data-project
    [Google Scholar]
  118. 118. 
    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]
  119. 119. 
    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]
  120. 120. 
    Yu S, Pritchard M, Kremer E, Lynch M, Nancarrow J et al. 1991. Fragile X genotype characterized by an unstable region of DNA. Science 252:1179–81
    [Google Scholar]
/content/journals/10.1146/annurev-genom-083118-015345
Loading
/content/journals/10.1146/annurev-genom-083118-015345
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error