Cornelia de Lange syndrome in diverse populations

dc.contributor.authorDowsett, Leah
dc.contributor.authorPorras, Antonio R.
dc.contributor.authorKruszka, Paul
dc.contributor.authorDavis, Brandon
dc.contributor.authorHu, Tommy
dc.contributor.authorHoney, Engela M.
dc.contributor.authorBadoe, Eben
dc.contributor.authorThong, Meow-Keong
dc.contributor.authorLeon, Eyby
dc.contributor.authorGirisha, Katta M.
dc.contributor.authorShukla, Anju
dc.contributor.authorNayak, Shalini S.
dc.contributor.authorShotelersuk, Vorasuk
dc.contributor.authorMegarbane, Andre
dc.contributor.authorPhadke, Shubha
dc.contributor.authorSirisena, Nirmala D.
dc.contributor.authorDissanayake, Vajira H.W.
dc.contributor.authorFerreira, Carlos R.
dc.contributor.authorKisling, Monisha S.
dc.contributor.authorTanpaiboon, Pranoot
dc.contributor.authorUwineza, Annette
dc.contributor.authorMutesa, Leon
dc.contributor.authorTekendo-Ngongang, Cedrik
dc.contributor.authorWonkam, Ambroise
dc.contributor.authorFieggen, Karen
dc.contributor.authorBatista, Leticia Cassimiro
dc.contributor.authorMoretti-Ferreira, Danilo
dc.contributor.authorStevenson, Roger E.
dc.contributor.authorPrijoles, Eloise J.
dc.contributor.authorEverman, David
dc.contributor.authorClarkson, Kate
dc.contributor.authorWorthington, Jessica
dc.contributor.authorKimonis, Virginia
dc.contributor.authorHisama, Fuki
dc.contributor.authorCrowe, Carol
dc.contributor.authorWong, Paul
dc.contributor.authorJohnson, Kisha
dc.contributor.authorClark, Robin D.
dc.contributor.authorBird, Lynne
dc.contributor.authorMasser-Frye, Diane
dc.contributor.authorMcDonald, Marie
dc.contributor.authorWillems, Patrick
dc.contributor.authorRoeder, Elizabeth
dc.contributor.authorSaitta, Sulgana
dc.contributor.authorAnyane-Yeoba, Kwame
dc.contributor.authorDemmer, Laurie
dc.contributor.authorHamajima, Naoki
dc.contributor.authorStark, Zornitza
dc.contributor.authorGillies, Greta
dc.contributor.authorHudgins, Louanne
dc.contributor.authorDave, Usha
dc.contributor.authorShalev, Stavit
dc.contributor.authorSiu, Victoria
dc.contributor.authorAdes, Ann
dc.contributor.authorDubbs, Holly
dc.contributor.authorRaible, Sarah
dc.contributor.authorKaur, Maninder
dc.contributor.authorSalzano, Emanuela
dc.contributor.authorJackson, Laird
dc.contributor.authorDeardorff, Matthew
dc.contributor.authorKline, Antonie
dc.contributor.authorSummar, Marshall
dc.contributor.authorMuenke, Maximilian
dc.contributor.authorLinguraru, Marius George
dc.contributor.authorKrantz, Ian D.
dc.date.accessioned2019-05-02T08:01:12Z
dc.date.issued2019-02
dc.descriptionSupplementary Table 1 Participants with photographs in Figures 2-5 from 10 countries. Supplementary Table 2. Geometric and texture feature comparison of Global (combined African descent, Asian, Latin American, Caucasian) CdLS individuals with normal controls using digital facial analysis technology. The ranges of the geometric linear features were normalized by the ear‐to‐ear distance. Geometric angle features are presented in degrees. Texture features were computed at three scales (r1, r2, and r3). Features are presented in order of their relevance for the diagnosis of CdLS. Supplementary Table 3. Geometric and texture feature comparison of African descent CdLS individuals with normal controls using digital facial analysis technology. The ranges of the geometric linear features were normalized by the ear‐to‐ear distance. Geometric angle features are presented in degrees. Texture features were computed at three scales (r1, r2, and r3). Features are presented in order of their relevance for the diagnosis of CdLS. Supplementary Table 4. Geometric and texture feature comparison of Asian CdLS individuals with normal controls using digital facial analysis technology. The ranges of the geometric linear features were normalized by the ear‐to‐ear distance. Geometric angle features are presented in degrees. Texture features were computed at three scales (r1, r2, and r3). Features are presented in order of their relevance for the diagnosis of CdLS. Supplementary Table 5. Geometric and texture feature comparison of Latin American CdLS individuals with normal controls using digital facial analysis technology. The ranges of the geometric linear features were normalized by the ear‐to‐ear distance. Geometric angle features are presented in degrees. Texture features were computed at three scales (r1, r2, and r3). Features are presented in order of their relevance for the diagnosis of CdLS. Supplementary Table 6. Geometric and texture feature comparison of Caucasian CdLS individuals with normal controls using digital facial analysis technology. The ranges of the geometric linear features were normalized by the ear‐to‐ear distance. Geometric angle features are presented in degrees. Texture features were computed at three scales (r1, r2, and r3). Features are presented in order of their relevance for the diagnosis of CdLS. Supplementary Figure 1. Global: Graph of area under the ROC curve (AUC), accuracy, sensitivity, and specificity versus the number of features selected. Supplementary Figure 2. African: Graph of area under the ROC curve (AUC), accuracy, sensitivity, and specificity versus the number of features selected. Supplementary Figure 3. Asian: Graph of area under the ROC curve (AUC), accuracy, sensitivity, and specificity versus the number of features selected. Supplementary Figure 4. Latin American: Graph of area under the ROC curve (AUC), accuracy, sensitivity, and specificity versus the number of features selected. Supplementary Figure 5. Caucasian: Graph of area under the ROC curve (AUC), accuracy, sensitivity, and specificity versus the number of features selecteden_ZA
dc.description.abstractCornelia de Lange syndrome (CdLS) is a dominant multisystemic malformation syndrome due to mutations in five genes—NIPBL, SMC1A, HDAC8, SMC3, and RAD21. The characteristic facial dysmorphisms include microcephaly, arched eyebrows, synophrys, short nose with depressed bridge and anteverted nares, long philtrum, thin lips, micrognathia, and hypertrichosis. Most affected individuals have intellectual disability, growth deficiency, and upper limb anomalies. This study looked at individuals from diverse populations with both clinical and molecularly confirmed diagnoses of CdLS by facial analysis technology. Clinical data and images from 246 individuals with CdLS were obtained from 15 countries. This cohort included 49% female patients and ages ranged from infancy to 37 years. Individuals were grouped into ancestry categories of African descent, Asian, Latin American, Middle Eastern, and Caucasian. Across these populations, 14 features showed a statistically significant difference. The most common facial features found in all ancestry groups included synophrys, short nose with anteverted nares, and a long philtrum with thin vermillion of the upper lip. Using facial analysis technology we compared 246 individuals with CdLS to 246 gender/age matched controls and found that sensitivity was equal or greater than 95% for all groups. Specificity was equal or greater than 91%. In conclusion, we present consistent clinical findings from global populations with CdLS while demonstrating how facial analysis technology can be a tool to support accurate diagnoses in the clinical setting. This work, along with prior studies in this arena, will assist in earlier detection, recognition, and treatment of CdLS worldwide.en_ZA
dc.description.departmentGeneticsen_ZA
dc.description.embargo2020-02-01
dc.description.librarianhj2019en_ZA
dc.description.sponsorshipPK and MM are supported by the Division of Intramural Research at the National Human Genome Research, NIH. Partial funding of this project was from a philanthropic gift from the Government of Abu Dhabi to the Children's National Health System. VS is supported by the Chulalongkorn Academic Advancement Into Its 2nd Century Project and the Thailand Research Fund. We would also like to acknowledge other clinicians who supported this work—MZ, JP, and GC. We would like to acknowledge that IDK, LD, MK, and SR are supported by the CdLS Center Endowed Funds at The Children's Hospital of Philadelphia and PO1 HD052860 from the NICHD. ES is supported by a fellowship from PKS Italia and PKSKids USA. LD was also supported by a postdoctoral training grant (T32 GM008638) from the NIGMS.en_ZA
dc.description.urihttp://wileyonlinelibrary.com/journal/ajmgaen_ZA
dc.identifier.citationDowsett L, Porras AR, Kruszka P, et al. Cornelia de Lange syndrome in diverse populations. American Journal of Medical Genetics, Part A. 2019;179A:150–158. https://doi.org/10. 1002/ajmg.a.61033.en_ZA
dc.identifier.issn1552-4825 (print)
dc.identifier.issn1552-4833 (online)
dc.identifier.other10.1002/ajmg.a.61033
dc.identifier.urihttp://hdl.handle.net/2263/69018
dc.language.isoenen_ZA
dc.publisherWileyen_ZA
dc.rights© 2019 Wiley Periodicals Inc. This is the pre-peer reviewed version of the following article : Cornelia de Lange syndrome in diverse populations. American Journal of Medical Genetics, Part A. 2019;179A:150–158. https://doi.org/10. 1002/ajmg.a.61033. The definite version is available at : http://wileyonlinelibrary.com/journal/ajmga.en_ZA
dc.subjectCornelia de Lange syndrome (CdLS)en_ZA
dc.subjectDiverse populationsen_ZA
dc.subjectFacial analysis technologyen_ZA
dc.subjectUnderrepresented minoritiesen_ZA
dc.subjectNIPBLen_ZA
dc.titleCornelia de Lange syndrome in diverse populationsen_ZA
dc.typePostprint Articleen_ZA

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