1932

Abstract

DECIPHER (atabas of Genomi Varation and henotype in umans Using nsembl esources) shares candidate diagnostic variants and phenotypic data from patients with genetic disorders to facilitate research and improve the diagnosis, management, and therapy of rare diseases. The platform sits at the boundary between genomic research and the clinical community. DECIPHER aims to ensure that the most up-to-date data are made rapidly available within its interpretation interfaces to improve clinical care. Newly integrated cardiac case–control data that provide evidence of gene–disease associations and inform variant interpretation exemplify this mission. New research resources are presented in a format optimized for use by a broad range of professionals supporting the delivery of genomic medicine. The interfaces within DECIPHER integrate and contextualize variant and phenotypic data, helping to determine a robust clinico-molecular diagnosis for rare-disease patients, which combines both variant classification and clinical fit. DECIPHER supports discovery research, connecting individuals within the rare-disease community to pursue hypothesis-driven research.

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2023-08-25
2024-06-25
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Literature Cited

  1. 1.
    Ackerman JP, Bartos DC, Kapplinger JD, Tester DJ, Delisle BP, Ackerman MJ. 2016. The promise and peril of precision medicine: phenotyping still matters most. Mayo Clin. Proc. 91:1606–16
    [Google Scholar]
  2. 2.
    Adams D, Altucci L, Antonarakis SE, Ballesteros J, Beck S et al. 2012. BLUEPRINT to decode the epigenetic signature written in blood. Nat. Biotechnol. 30:224–26
    [Google Scholar]
  3. 3.
    Armstrong DR, Berrisford JM, Conroy MJ, Gutmanas A, Anyango S et al. 2020. PDBe: improved findability of macromolecular structure data in the PDB. Nucleic Acids Res. 48:D335–43
    [Google Scholar]
  4. 4.
    Ars E, Serra E, Garcia J, Kruyer H, Gaona A et al. 2000. Mutations affecting mRNA splicing are the most common molecular defects in patients with neurofibromatosis type 1. Hum. Mol. Genet. 9:237–47
    [Google Scholar]
  5. 5.
    Austin-Tse CA, Jobanputra V, Perry DL, Bick D, Taft RJ et al. 2022. Best practices for the interpretation and reporting of clinical whole genome sequencing. npj Genom. Med. 7:27
    [Google Scholar]
  6. 6.
    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]
  7. 7.
    Bennett JT, Tan TY, Alcantara D, Tetrault M, Timms AE et al. 2016. Mosaic activating mutations in FGFR1 cause encephalocraniocutaneous lipomatosis. Am. J. Hum. Genet. 98:579–87
    [Google Scholar]
  8. 8.
    Bennette CS, Gallego CJ, Burke W, Jarvik GP, Veenstra DL. 2015. The cost-effectiveness of returning incidental findings from next-generation genomic sequencing. Genet. Med. 17:587–95
    [Google Scholar]
  9. 9.
    Biasini M. 2015. pv: v1.8.1. Zenodo https://doi.org/10.5281/zenodo.20980
    [Google Scholar]
  10. 10.
    Blakes AJM, Gaul E, Lam W, Shannon N, Knapp KM et al. 2021. Pathogenic variants causing ABL1 malformation syndrome cluster in a myristoyl-binding pocket and increase tyrosine kinase activity. Eur. J. Hum. Genet. 29:593–603
    [Google Scholar]
  11. 11.
    Boussion S, Escande F, Jourdain AS, Smol T, Brunelle P et al. 2020. TAR syndrome: clinical and molecular characterization of a cohort of 26 patients and description of novel noncoding variants of RBM8A. Hum. Mutat. 41:1220–25
    [Google Scholar]
  12. 12.
    Boycott KM, Azzariti DR, Hamosh A, Rehm HL. 2022. Seven years since the launch of the Matchmaker Exchange: the evolution of genomic matchmaking. Hum. Mutat. 43:659–67
    [Google Scholar]
  13. 13.
    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]
  14. 14.
    Brnich SE, Abou Tayoun AN, Couch FJ, Cutting GR, Greenblatt MS et al. 2019. Recommendations for application of the functional evidence PS3/BS3 criterion using the ACMG/AMP sequence variant interpretation framework. Genome Med. 12:3
    [Google Scholar]
  15. 15.
    Cassa CA, Weghorn D, Balick DJ, Jordan DM, Nusinow D et al. 2017. Estimating the selective effects of heterozygous protein-truncating variants from human exome data. Nat. Genet. 49:806–10
    [Google Scholar]
  16. 16.
    Chatzimichali EA, Brent S, Hutton B, Perrett D, Wright CF et al. 2015. Facilitating collaboration in rare genetic disorders through effective matchmaking in DECIPHER. Hum. Mutat. 36:941–49
    [Google Scholar]
  17. 17.
    Chiang J, Chia TH, Yuen J, Shaw T, Li ST et al. 2021. Impact of variant reclassification in cancer predisposition genes on clinical care. JCO Precis. Oncol. 5:577–84
    [Google Scholar]
  18. 18.
    Chora JR, Iacocca MA, Tichy L, Wand H, Kurtz CL et al. 2022. The Clinical Genome Resource (ClinGen) Familial Hypercholesterolemia Variant Curation Expert Panel consensus guidelines for LDLR variant classification. Genet. Med. 24:293–306
    [Google Scholar]
  19. 19.
    Church DM, Lappalainen I, Sneddon TP, Hinton J, Maguire M et al. 2010. Public data archives for genomic structural variation. Nat. Genet. 42:813–14
    [Google Scholar]
  20. 20.
    Clin. Genome Resour. Seq. Var. Interpret. Work. Group 2021. ClinGen sequence variant interpretation recommendation for de novo criteria (PS2/PM6) Guidel. Doc. Version 1.1, Clin. Genome Resour. Natl. Inst. Health Bethesda, MD: https://clinicalgenome.org/site/assets/files/3461/svi_proposal_for_de_novo_criteria_v1_1.pdf
    [Google Scholar]
  21. 21.
    Collins RL, Glessner JT, Porcu E, Lepamets M, Brandon R et al. 2022. A cross-disorder dosage sensitivity map of the human genome. Cell 185:3041–55.e25
    [Google Scholar]
  22. 22.
    Cunningham F, Allen JE, Allen J, Alvarez-Jarreta J, Amode MR et al. 2022. Ensembl 2022. Nucleic Acids Res. 50:D988–95
    [Google Scholar]
  23. 23.
    Deciphering Dev. Disord. Study 2017. Prevalence and architecture of de novo mutations in developmental disorders. Nature 542:433–38
    [Google Scholar]
  24. 24.
    DiStefano MT, Goehringer S, Babb L, Alkuraya FS, Amberger J et al. 2022. The Gene Curation Coalition: a global effort to harmonize gene-disease evidence resources. Genet. Med. 24:1732–42
    [Google Scholar]
  25. 25.
    Dode C, Levilliers J, Dupont JM, De Paepe A, Le Du N et al. 2003. Loss-of-function mutations in FGFR1 cause autosomal dominant Kallmann syndrome. Nat. Genet. 33:463–65
    [Google Scholar]
  26. 26.
    Ellard S, Baple EL, Owens M, Eccles DM, Turnbull C et al. 2018. ACGS best practice guidelines for variant classification 2018 Guidel. Doc., Assoc. Clin. Genom. Sci. London: https://www.acgs.uk.com/media/10793/uk_practice_guidelines_for_variant_classification_2018_v10.pdf
    [Google Scholar]
  27. 27.
    ENCODE Proj. Consort 2012. An integrated encyclopedia of DNA elements in the human genome. Nature 489:57–74
    [Google Scholar]
  28. 28.
    Findlay GM, Daza RM, Martin B, Zhang MD, Leith AP et al. 2018. Accurate classification of BRCA1 variants with saturation genome editing. Nature 562:217–22
    [Google Scholar]
  29. 29.
    Firth HV, Richards SM, Bevan AP, Clayton S, Corpas M et al. 2009. DECIPHER: Database of Chromosomal Imbalance and Phenotype in Humans Using Ensembl Resources. Am. J. Hum. Genet. 84:524–33
    [Google Scholar]
  30. 30.
    Fokkema IF, Taschner PE, Schaafsma GC, Celli J, Laros JF, den Dunnen JT. 2011. LOVD v.2.0: the next generation in gene variant databases. Hum. Mutat. 32:557–63
    [Google Scholar]
  31. 31.
    Foreman J, Brent S, Perrett D, Bevan AP, Hunt SE et al. 2022. DECIPHER: supporting the interpretation and sharing of rare disease phenotype-linked variant data to advance diagnosis and research. Hum. Mutat. 43:682–97
    [Google Scholar]
  32. 32.
    Frankish A, Diekhans M, Jungreis I, Lagarde J, Loveland JE et al. 2021. GENCODE 2021. Nucleic Acids Res. 49:D916–23
    [Google Scholar]
  33. 33.
    Gaudet P, Michel PA, Zahn-Zabal M, Britan A, Cusin I et al. 2017. The neXtProt knowledgebase on human proteins: 2017 update. Nucleic Acids Res. 45:D177–82
    [Google Scholar]
  34. 34.
    Gelb BD, Cave H, Dillon MW, Gripp KW, Lee JA et al. 2018. ClinGen's RASopathy Expert Panel consensus methods for variant interpretation. Genet. Med. 20:1334–45
    [Google Scholar]
  35. 35.
    Goranitis I, Wu Y, Lunke S, White SM, Tan TY et al. 2022. Is faster better? An economic evaluation of rapid and ultra-rapid genomic testing in critically ill infants and children. Genet. Med. 24:1037–44
    [Google Scholar]
  36. 36.
    Green DJ, Sallah SR, Ellingford JM, Lovell SC, Sergouniotis PI. 2020. Variability in gene expression is associated with incomplete penetrance in inherited eye disorders. Genes 11:179
    [Google Scholar]
  37. 37.
    Ha C, Kim JW, Jang JH. 2021. Performance evaluation of SpliceAI for the prediction of splicing of NF1 variants. Genes 12:1308
    [Google Scholar]
  38. 38.
    Hamilton MJ, Suri M. 2019. CDK13-related disorder. Adv. Genet. 103:163–82
    [Google Scholar]
  39. 39.
    Hamosh A, Amberger JS, Bocchini C, Scott AF, Rasmussen SA. 2021. Online Mendelian Inheritance in Man (OMIM®): Victor McKusick's magnum opus. Am. J. Med. Genet. A 185:3259–65
    [Google Scholar]
  40. 40.
    Hamosh A, Wohler E, Martin R, Griffith S, Rodrigues EDS et al. 2022. The impact of GeneMatcher on international data sharing and collaboration. Hum. Mutat. 43:668–73
    [Google Scholar]
  41. 41.
    Hershberger RE, Givertz MM, Ho CY, Judge DP, Kantor PF et al. 2018. Genetic evaluation of cardiomyopathy: a clinical practice resource of the American College of Medical Genetics and Genomics (ACMG). Genet. Med. 20:899–909
    [Google Scholar]
  42. 42.
    Hershberger RE, Hedges DJ, Morales A. 2013. Dilated cardiomyopathy: the complexity of a diverse genetic architecture. Nat. Rev. Cardiol. 10:531–47
    [Google Scholar]
  43. 43.
    Hosseini SM, Kim R, Udupa S, Costain G, Jobling R et al. 2018. Reappraisal of reported genes for sudden arrhythmic death: evidence-based evaluation of gene validity for Brugada syndrome. Circulation 138:1195–205
    [Google Scholar]
  44. 44.
    Huang N, Lee I, Marcotte EM, Hurles ME. 2010. Characterising and predicting haploinsufficiency in the human genome. PLOS Genet. 6:e1001154
    [Google Scholar]
  45. 45.
    Imp. Coll. Lond. Cardiovasc. Genom. Precis. Med. Team 2022. Cardiac VariantFX. GitHub https://github.com/ImperialCardioGenetics/variantfx/blob/main/README.md
    [Google Scholar]
  46. 46.
    Ingles J, Goldstein J, Thaxton C, Caleshu C, Corty EW et al. 2019. Evaluating the clinical validity of hypertrophic cardiomyopathy genes. Circ. Genom. Precis. Med. 12:e002460
    [Google Scholar]
  47. 47.
    Ivanovski I, Djuric O, Broccoli S, Caraffi SG, Accorsi P et al. 2020. Mowat-Wilson syndrome: growth charts. Orphanet J. Rare Dis. 15:151
    [Google Scholar]
  48. 48.
    Jaganathan K, Kyriazopoulou Panagiotopoulou S, McRae JF, Darbandi SF, Knowles D et al. 2019. Predicting splicing from primary sequence with deep learning. Cell 176:535–48.e24
    [Google Scholar]
  49. 49.
    Jordan E, Peterson L, Ai T, Asatryan B, Bronicki L et al. 2021. Evidence-based assessment of genes in dilated cardiomyopathy. Circulation 144:7–19
    [Google Scholar]
  50. 50.
    Jumper J, Evans R, Pritzel A, Green T, Figurnov M et al. 2021. Highly accurate protein structure prediction with AlphaFold. Nature 596:583–89
    [Google Scholar]
  51. 51.
    Kaplanis J, Samocha KE, Wiel L, Zhang Z, Arvai KJ et al. 2020. Evidence for 28 genetic disorders discovered by combining healthcare and research data. Nature 586:757–62
    [Google Scholar]
  52. 52.
    Karczewski KJ, Francioli LC, Tiao G, Cummings BB, Alfoldi J et al. 2020. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature 581:434–43
    [Google Scholar]
  53. 53.
    Kelly MA, Caleshu C, Morales A, Buchan J, Wolf Z et al. 2018. Adaptation and validation of the ACMG/AMP variant classification framework for MYH7-associated inherited cardiomyopathies: recommendations by ClinGen's Inherited Cardiomyopathy Expert Panel. Genet. Med. 20:351–59
    [Google Scholar]
  54. 54.
    Kohler S, Gargano M, Matentzoglu N, Carmody LC, Lewis-Smith D et al. 2021. The Human Phenotype Ontology in 2021. Nucleic Acids Res. 49:D1207–17
    [Google Scholar]
  55. 55.
    Landrum MJ, Lee JM, Benson M, Brown GR, Chao C et al. 2018. ClinVar: improving access to variant interpretations and supporting evidence. Nucleic Acids Res. 46:D1062–67
    [Google Scholar]
  56. 56.
    Lansdon LA, Cadieux-Dion M, Yoo B, Miller N, Cohen ASA et al. 2021. Factors affecting migration to GRCh38 in laboratories performing clinical next-generation sequencing. J. Mol. Diagn. 23:651–57
    [Google Scholar]
  57. 57.
    Lee K, Krempely K, Roberts ME, Anderson MJ, Carneiro F et al. 2018. Specifications of the ACMG/AMP variant curation guidelines for the analysis of germline CDH1 sequence variants. Hum. Mutat. 39:1553–68
    [Google Scholar]
  58. 58.
    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]
  59. 59.
    Lindeboom RG, Supek F, Lehner B. 2016. The rules and impact of nonsense-mediated mRNA decay in human cancers. Nat. Genet. 48:1112–18
    [Google Scholar]
  60. 60.
    Lord J, Gallone G, Short PJ, McRae JF, Ironfield H et al. 2019. Pathogenicity and selective constraint on variation near splice sites. Genome Res. 29:159–70
    [Google Scholar]
  61. 61.
    Ma L, Roman-Campos D, Austin ED, Eyries M, Sampson KS et al. 2013. A novel channelopathy in pulmonary arterial hypertension. N. Engl. J. Med. 369:351–61
    [Google Scholar]
  62. 62.
    McGurk KA, Zheng SL, Henry A, Josephs K, Edwards M et al. 2022. Correspondence on “ACMG SF v3.0 list for reporting of secondary findings in clinical exome and genome sequencing: a policy statement of the American College of Medical Genetics and Genomics (ACMG)” by Miller et al. Genet. Med. 24:744–46
    [Google Scholar]
  63. 63.
    McKenna WJ, Judge DP. 2021. Epidemiology of the inherited cardiomyopathies. Nat. Rev. Cardiol. 18:22–36
    [Google Scholar]
  64. 64.
    McLaren W, Gil L, Hunt SE, Riat HS, Ritchie GR et al. 2016. The Ensembl Variant Effect Predictor. Genome Biol. 17:122
    [Google Scholar]
  65. 65.
    Mester JL, Ghosh R, Pesaran T, Huether R, Karam R et al. 2018. Gene-specific criteria for PTEN variant curation: recommendations from the ClinGen PTEN Expert Panel. Hum. Mutat. 39:1581–92
    [Google Scholar]
  66. 66.
    Mighton C, Charames GS, Wang M, Zakoor KR, Wong A et al. 2019. Variant classification changes over time in BRCA1 and BRCA2. Genet. Med. 21:2248–54
    [Google Scholar]
  67. 67.
    Milewicz DM, Braverman AC, De Backer J, Morris SA, Boileau C et al. 2021. Marfan syndrome. Nat. Rev. Dis. Primers 7:64
    [Google Scholar]
  68. 68.
    Miller DT, Lee K, Abul-Husn NS, Amendola LM, Brothers K et al. 2022. ACMG SF v3.1 list for reporting of secondary findings in clinical exome and genome sequencing: a policy statement of the American College of Medical Genetics and Genomics (ACMG). Genet. Med. 24:1407–14
    [Google Scholar]
  69. 69.
    Mirshahi UL, Colclough K, Wright CF, Wood AR, Beaumont RN et al. 2022. Reduced penetrance of MODY-associated HNF1A/HNF4A variants but not GCK variants in clinically unselected cohorts. Am. J. Hum. Genet. 109:2018–28
    [Google Scholar]
  70. 70.
    Mistry J, Chuguransky S, Williams L, Qureshi M, Salazar GA et al. 2021. Pfam: the protein families database in 2021. Nucleic Acids Res. 49:D412–19
    [Google Scholar]
  71. 71.
    Morales J, Pujar S, Loveland JE, Astashyn A, Bennett R et al. 2022. A joint NCBI and EMBL-EBI transcript set for clinical genomics and research. Nature 604:310–15
    [Google Scholar]
  72. 72.
    Muenke M, Gripp KW, McDonald-McGinn DM, Gaudenz K, Whitaker LA et al. 1997. A unique point mutation in the fibroblast growth factor receptor 3 gene (FGFR3) defines a new craniosynostosis syndrome. Am. J. Hum. Genet. 60:555–64
    [Google Scholar]
  73. 73.
    Muenke M, Schell U, Hehr A, Robin NH, Losken HW et al. 1994. A common mutation in the fibroblast growth factor receptor 1 gene in Pfeiffer syndrome. Nat. Genet. 8:269–74
    [Google Scholar]
  74. 74.
    Muschol NM, Pape D, Kossow K, Ullrich K, Arash-Kaps L et al. 2019. Growth charts for patients with Sanfilippo syndrome (mucopolysaccharidosis type III). Orphanet J. Rare Dis. 14:93
    [Google Scholar]
  75. 75.
    Nagy E, Maquat LE. 1998. A rule for termination-codon position within intron-containing genes: when nonsense affects RNA abundance. Trends Biochem. Sci. 23:198–99
    [Google Scholar]
  76. 76.
    Niehaus A, Azzariti DR, Harrison SM, DiStefano MT, Hemphill SE et al. 2019. A survey assessing adoption of the ACMG-AMP guidelines for interpreting sequence variants and identification of areas for continued improvement. Genet. Med. 21:1699–701
    [Google Scholar]
  77. 77.
    Norman CS, O'Gorman L, Gibson J, Pengelly RJ, Baralle D et al. 2017. Identification of a functionally significant tri-allelic genotype in the Tyrosinase gene (TYR) causing hypomorphic oculocutaneous albinism (OCA1B). Sci. Rep. 7:4415
    [Google Scholar]
  78. 78.
    O'Leary NA, Wright MW, Brister JR, Ciufo S, Haddad D et al. 2016. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res. 44:D733–45
    [Google Scholar]
  79. 79.
    Oetjens MT, Kelly MA, Sturm AC, Martin CL, Ledbetter DH. 2019. Quantifying the polygenic contribution to variable expressivity in eleven rare genetic disorders. Nat. Commun. 10:4897
    [Google Scholar]
  80. 80.
    Osmond M, Hartley T, Johnstone B, Andjic S, Girdea M et al. 2022. PhenomeCentral: 7 years of rare disease matchmaking. Hum. Mutat. 43:674–81
    [Google Scholar]
  81. 81.
    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]
  82. 82.
    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]
  83. 83.
    Rahit K, Tarailo-Graovac M. 2020. Genetic modifiers and rare Mendelian disease. Genes 11:239
    [Google Scholar]
  84. 84.
    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]
  85. 85.
    Richards CS, Bale S, Bellissimo DB, Das S, Grody WW et al. 2008. ACMG recommendations for standards for interpretation and reporting of sequence variations: revisions 2007. Genet. Med. 10:294–300
    [Google Scholar]
  86. 86.
    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. 17:405–24
    [Google Scholar]
  87. 87.
    Riggs ER, Andersen EF, Cherry AM, Kantarci S, Kearney H et al. 2020. Technical standards for the interpretation and reporting of constitutional copy-number variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics (ACMG) and the Clinical Genome Resource (ClinGen). Genet. Med. 22:245–57
    [Google Scholar]
  88. 88.
    Rivera-Munoz EA, Milko LV, Harrison SM, Azzariti DR, Kurtz CL et al. 2018. ClinGen Variant Curation Expert Panel experiences and standardized processes for disease and gene-level specification of the ACMG/AMP guidelines for sequence variant interpretation. Hum. Mutat. 39:1614–22
    [Google Scholar]
  89. 89.
    Rosina E, Pezzani L, Pezzoli L, Marchetti D, Bellini M et al. 2022. Atypical, composite, or blended phenotypes: how different molecular mechanisms could associate in double-diagnosed patients. Genes 13:1275
    [Google Scholar]
  90. 90.
    Satterlee JS, Chadwick LH, Tyson FL, McAllister K, Beaver J et al. 2019. The NIH Common Fund/Roadmap Epigenomics Program: successes of a comprehensive consortium. Sci. Adv. 5:eaaw6507
    [Google Scholar]
  91. 91.
    Simonis N, Migeotte I, Lambert N, Perazzolo C, de Silva DC et al. 2013. FGFR1 mutations cause Hartsfield syndrome, the unique association of holoprosencephaly and ectrodactyly. J. Med. Genet. 50:585–92
    [Google Scholar]
  92. 92.
    Sormann J, Schewe M, Proks P, Jouen-Tachoire T, Rao S et al. 2022. Gain-of-function mutations in KCNK3 cause a developmental disorder with sleep apnea. Nat. Genet. 54:1534–43
    [Google Scholar]
  93. 93.
    Stenson PD, Mort M, Ball EV, Chapman M, Evans K et al. 2020. The Human Gene Mutation Database (HGMD®): optimizing its use in a clinical diagnostic or research setting. Hum. Genet. 139:1197–207
    [Google Scholar]
  94. 94.
    Stevens CA 2019. Rubinstein-Taybi syndrome. GeneReviews MP Adam, HH Ardinger, RA Pagon, SE Wallace, LJH Bean, et al Seattle: Univ. Wash. https://www.ncbi.nlm.nih.gov/sites/books/NBK1526
    [Google Scholar]
  95. 95.
    Swaminathan GJ, Bragin E, Chatzimichali EA, Corpas M, Bevan AP et al. 2012. DECIPHER: web-based, community resource for clinical interpretation of rare variants in developmental disorders. Hum. Mol. Genet. 21:R37–44
    [Google Scholar]
  96. 96.
    Tavtigian SV, Greenblatt MS, Harrison SM, Nussbaum RL, Prabhu SA et al. 2018. Modeling the ACMG/AMP variant classification guidelines as a Bayesian classification framework. Genet. Med. 20:1054–60
    [Google Scholar]
  97. 97.
    Tayoun ANA, Pesaran T, DiStefano MT, Oza A, Rehm HL et al. 2018. Recommendations for interpreting the loss of function PVS1 ACMG/AMP variant criterion. Hum. Mutat. 39:1517–24
    [Google Scholar]
  98. 98.
    Thormann A, Halachev M, McLaren W, Moore DJ, Svinti V et al. 2019. Flexible and scalable diagnostic filtering of genomic variants using G2P with Ensembl VEP. Nat. Commun. 10:2373
    [Google Scholar]
  99. 99.
    Van Gils J, Magdinier F, Fergelot P, Lacombe D. 2021. Rubinstein-Taybi syndrome: a model of epigenetic disorder. Genes 12:968
    [Google Scholar]
  100. 100.
    Walsh R, Mazzarotto F, Whiffin N, Buchan R, Midwinter W et al. 2019. Quantitative approaches to variant classification increase the yield and precision of genetic testing in Mendelian diseases: the case of hypertrophic cardiomyopathy. Genome Med. 11:5
    [Google Scholar]
  101. 101.
    Walsh R, Thomson KL, Ware JS, Funke BH, Woodley J et al. 2017. Reassessment of Mendelian gene pathogenicity using 7,855 cardiomyopathy cases and 60,706 reference samples. Genet. Med. 19:192–203
    [Google Scholar]
  102. 102.
    Weghorn D, Balick DJ, Cassa C, Kosmicki JA, Daly MJ et al. 2019. Applicability of the mutation-selection balance model to population genetics of heterozygous protein-truncating variants in humans. Mol. Biol. Evol. 36:1701–10
    [Google Scholar]
  103. 103.
    Weiss KH 2016. Wilson disease. GeneReviews MP Adam, HH Ardinger, RA Pagon, SE Wallace, LJH Bean, et al Seattle: Univ. Wash. https://www.ncbi.nlm.nih.gov/books/NBK1512
    [Google Scholar]
  104. 104.
    Whiffin N, Minikel E, Walsh R, O'Donnell-Luria AH, Karczewski K et al. 2017. Using high-resolution variant frequencies to empower clinical genome interpretation. Genet. Med. 19:1151–58
    [Google Scholar]
  105. 105.
    White J, Mazzeu JF, Hoischen A, Jhangiani SN, Gambin T et al. 2015. DVL1 frameshift mutations clustering in the penultimate exon cause autosomal-dominant Robinow syndrome. Am. J. Hum. Genet. 96:612–22
    [Google Scholar]
  106. 106.
    White KE, Cabral JM, Davis SI, Fishburn T, Evans WE et al. 2005. Mutations that cause osteoglophonic dysplasia define novel roles for FGFR1 in bone elongation. Am. J. Hum. Genet. 76:361–67
    [Google Scholar]
  107. 107.
    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]
  108. 108.
    Wright CF, FitzPatrick DR, Firth HV. 2018. Paediatric genomics: diagnosing rare disease in children. Nat. Rev. Genet. 19:253–68
    [Google Scholar]
  109. 109.
    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]
  110. 110.
    Wright CF, Quaife NM, Ramos-Hernandez L, Danecek P, Ferla MP et al. 2021. Non-coding region variants upstream of MEF2C cause severe developmental disorder through three distinct loss-of-function mechanisms. Am. J. Hum. Genet. 108:1083–94
    [Google Scholar]
  111. 111.
    Wright CF, West B, Tuke M, Jones SE, Patel K et al. 2019. Assessing the pathogenicity, penetrance, and expressivity of putative disease-causing variants in a population setting. Am. J. Hum. Genet. 104:275–86
    [Google Scholar]
  112. 112.
    Zahn-Zabal M, Michel PA, Gateau A, Nikitin F, Schaeffer M et al. 2020. The neXtProt knowledgebase in 2020: data, tools and usability improvements. Nucleic Acids Res. 48:D328–34
    [Google Scholar]
  113. 113.
    Zastrow DB, Baudet H, Shen W, Thomas A, Si Y et al. 2018. Unique aspects of sequence variant interpretation for inborn errors of metabolism (IEM): the ClinGen IEM Working Group and the phenylalanine hydroxylase gene. Hum. Mutat. 39:1569–80
    [Google Scholar]
  114. 114.
    Zemel BS, Pipan M, Stallings VA, Hall W, Schadt K et al. 2015. Growth charts for children with Down syndrome in the United States. Pediatrics 136:e1204–11
    [Google Scholar]
  115. 115.
    Zerbino DR, Wilder SP, Johnson N, Juettemann T, Flicek PR. 2015. The Ensembl Regulatory Build. Genome Biol. 16:56
    [Google Scholar]
  116. 116.
    Zhang K, Lin G, Han D, Han Y, Peng R, Li J. 2022. Adaptation of ACMG-ClinGen technical standards for copy number variant interpretation concordance. Front. Genet. 13:829728
    [Google Scholar]
  117. 117.
    Zupan A, Fakin A, Battelino S, Jarc-Vidmar M, Hawlina M et al. 2019. Clinical and haplotypic variability of Slovenian USH2A patients homozygous for the c.11864G>A nonsense mutation. Genes 10:1015
    [Google Scholar]
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