The facial dysmorphology analysis technology in intellectual disability syndromes related to defects in the histones modifiers

Abstract

Genetic syndromes are frequently associated with Intellectual Disability (ID), as well as craniofacial dysmorphisms. A group of ID syndromes with typical abnormal face related to chromatin remodeling defects, have been recognized, coining the term chromatinopathies. This is a molecular heterogeneous subset of congenital disorders caused by mutations of the various components of the Chromatin-Marking System (CMS), including modifiers of DNA and chromatin remodelers. We performed a phenotypic study on a sample of 120 individuals harboring variants in genes codifying for the histones enzymes, using the DeepGestalt technology. Three experiments (two multiclass comparison experiments and a frontal face-crop analysis) were conducted, analyzing respectively a total of 181 pediatric images in the first comparison experiment and 180 in the second, all individuals belonging predominantly to Caucasian population. The classification results were expressed in terms of the area under the curve (AUC) of the receiver-operating-characteristic curve (ROC). Significant values of AUC and low p-values were registered for all syndromes in the three experiments, in comparison with each other, with other ID syndromes characterized by recognizable craniofacial dysmorphisms and with unaffected controls. Final findings indicated that this group of diseases is characterized by distinctive dysmorphisms, which result pathognomonic. A correct interrogation and use of adequate informatics aids, could become a valid support for clinicians.

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References

  1. 1.

    Field M, Tarpey P, Boyle J, et al. Mutations in the RSK2 (RPS6KA3) gene cause Coffin-Lowry syndrome and non syndromic X-linked mental retardation. Clin Genet. 2006;70:509–15.

    CAS  Article  Google Scholar 

  2. 2.

    Ng SB, Bigham AW, Buckingham KJ, et al. Exome sequencing identifies MLL2 mutations as a cause of Kabuki syndrome. Nature Genet. 2010;42:790–3.

    CAS  Article  Google Scholar 

  3. 3.

    Koolen DA, Kramer JM, Neveling K, et al. Mutations in the chromatin modifier gene KANSL1 cause the 17q21.31 microdeletion syndrome. Nature Genet. 2012;44:639–41.

    CAS  Article  Google Scholar 

  4. 4.

    Kleefstra T, Brunner HG, Amiel J, et al. Loss-of-function mutations in Euchromatin histone methyl transferase 1 (EHMT1) cause the 9q34 subtelomeric deletion syndrome. Am J Hum Genet. 2006;79:370–7.

    CAS  Article  Google Scholar 

  5. 5.

    Petrij F, Giles RH, Dauwerse HG, et al. Rubinstein-Taybi syndrome caused by mutations in the transcriptional co-activator CBP. Nature. 1995;376:348–51.

    CAS  Article  Google Scholar 

  6. 6.

    Clayton-Smith J, O’Sullivan J, Daly S, et al. Whole-exome-sequencing identifies mutations in histone acetyltransferase gene KAT6B in individuals with the Say-Barber-Biesecker variant of Ohdo syndrome. Am J Hum Genet. 2011;89:675–81.

    CAS  Article  Google Scholar 

  7. 7.

    Gurovich Y, Hanani Y, Bar O, et al. Identifying facial phenotypes of genetic disorders using deep learning. Nat Med. 2019;25:60–64.

    CAS  Article  Google Scholar 

  8. 8.

    Pantel JT, Zhao M, Mensah MA, et al. Advances in computer-assisted syndrome recognition by the example of inborn errors of metabolism. J Inherit Metab Dis. 2018;41:533–9.

    Article  Google Scholar 

  9. 9.

    Jiang Y, Wangler MF, McGuire AL, et al. The phenotypic spectrum of Xia-Gibbs syndrome. Am J Med Genet. 2018;176:1315–26.

    CAS  Article  Google Scholar 

  10. 10.

    Martinez-Monseny A, Cuadras D, Bolasell M et al. From gestalt to gene: early predictive dysmorphic features of PMM2-CDG. J Med Genet 2018; https://doi.org/10.1136/jmedgenet-2018-105588.

    Article  Google Scholar 

  11. 11.

    Knaus A, Pantel JT, Pendziwiat M, et al. Characterization of glycosylphosphatidylinositol biosynthesis defects by clinical features, flow cytometry, and automated image analysis. Genome Med. 2018;10:3.

    Article  Google Scholar 

  12. 12.

    Ferreira CR, Altassan R, Marques-Da-Silva D, Francisco R, Jaeken J, Morava E. Recognizable phenotypes in CDG. J Inherit Metab Dis. 2018;41:541–53.

    CAS  Article  Google Scholar 

  13. 13.

    Zarate YA, Smith-Hicks CL, Greene C, et al. Natural history and genotype-phenotype correlations in 72 individuals with SATB2-associated syndrome. Am J Med Genet A. 2018;176:925–35.

    Article  Google Scholar 

  14. 14.

    Hadj-Rabia S, Schneider H, Navarro E, et al. Automatic recognition of the XLHED phenotype from facial images. Am J Med Genet A. 2017;173:2408–14.

    CAS  Article  Google Scholar 

  15. 15.

    Liehr T, Acquarola N, Pyle K, et al. Next generation phenotyping in Emanuel and Pallister-Killian syndrome using computer-aided facial dysmorphology analysis of 2D photos. Clin Genet. 2018;93:378–81.

    CAS  Article  Google Scholar 

  16. 16.

    Vorravanpreecha N, Lertboonnum T, Rodjanadit R, Sriplienchan P, Rojnueangnit K Studying Down syndrome recognition probabilities in Thai children with de-identified computer-aided facial analysis. Am J Med Genet A. 2018. https://doi.org/10.1002/ajmg.a.40483.

    Article  Google Scholar 

  17. 17.

    Amudhavalli SM, Hanson R, Angle B, Bontempo K, Gripp KW. Further delineation of Aymè-Gripp syndrome and use of automated facial analysis tool. Am J Med Gen A. 2018;176:1648–56.

    CAS  Article  Google Scholar 

  18. 18.

    Lumaka A, Cosemans N, Lulebo Mampasi A, et al. Facial dysmorphism is influenced by ethnic background of the patient and of the evaluator. Clin Genet. 2017;92:166–71.

    CAS  Article  Google Scholar 

  19. 19.

    Valentine M, Bihm DCJ, Wolf L, et al. Computer-aided recognition of facial attributes for fetal alcohol spectrum disorders. Pediatrics. 2017;140:6.

    Article  Google Scholar 

  20. 20.

    Gardner OK, Haynes K, Schweitzer D, et al. Familial Recurrence of 3MC syndrome in consanguineous families: a clinical and molecular diagnostic approach with review of the literature. Cleft Palate Craniofac J. 2017;54:739–48.

    Article  Google Scholar 

  21. 21.

    Basel-Vanagaite L, Wolf L, Orin M, et al. Recognition of the Cornelia de Lange syndrome phenotype with facial dysmorphology novel analysis. Clin Genet. 2016;89:557–63.

    CAS  Article  Google Scholar 

  22. 22.

    Gripp KW, Baker L, Telegrafi A, Monaghan KG. The role of objective facial analysis using FDNA in making diagnoses following whole exome analysis. Report of two patients with mutations in the BAF complex genes. Am J Med Genet. 2016;170:1754–62.

    CAS  Article  Google Scholar 

  23. 23.

    Kayembe KT, Kasole LT, Mbuyi-Musanzayi S, et al. Microtia in Cornelia de Lange syndrome: a case from Democratic Republic of the Congo. Clin Dysm. 2016;25:178–8.

    Article  Google Scholar 

  24. 24.

    Lumaka A, Lukoo R, Mubungu G, et al. Williams-Beuren syndrome: pitfalls for diagnosis in limited resources setting. Clin Case Rep. 2016;4:294–7.

    Article  Google Scholar 

  25. 25.

    Larizza L, Finelli P Developmental disorders with intellectual disability driven by chromatin dysregulation: clinical overlaps and molecular mechanisms. Clin Genet. 2018. https://doi.org/10.1111/cge.13365.

    Article  Google Scholar 

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Correspondence to Giulia Pascolini.

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Conflict of interest:

Nicole Fleischer is an employee of FDNA Inc., the company providing Face2Gene.

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Pascolini, G., Fleischer, N., Ferraris, A. et al. The facial dysmorphology analysis technology in intellectual disability syndromes related to defects in the histones modifiers. J Hum Genet 64, 721–728 (2019). https://doi.org/10.1038/s10038-019-0598-0

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