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Williams-Beuren syndrome in diverse populations.

Paul S KruszkaAntonio R PorrasDeise Helena de SouzaAngélica MorescoVictoria HuckstadtAshleigh D GillAlec P BoyleTommy HuYonit A AddissieGary T K MokCedrik Tekendo-NgongangKaren FieggenEloise J PrijolesPranoot TanpaiboonEngela HoneyHo-Ming LukIvan F M LoMeow-Keong ThongPremala MuthukumarasamyKelly L JonesKhadija BelhassanKarim OuldimIhssane El BouchikhiLaila BouguenouchAnju ShuklaKatta Mohan GirishaNirmala D SirisenaVajira H W DissanayakeC Sampath PaththinigeRupesh MishraMonisha S KislingCarlos R FerreiraMaría Beatriz de HerrerosNi-Chung LeeSaumya S JamuarAngeline LaiEe Shien TanJiin Ying LimCham Breana Wen-MinNeerja GuptaStephanie Lotz-EsquivelRamsés Badilla-PorrasDalia Farouk HussenMona O El RubyEngy A AshaatSiddaramappa Jagdish PatilLeah DowsettAlison EatonA Micheil InnesVorasuk ShotelersukËben BadoeAmbroise WonkamMaría Gabriela ObregonBrian H Y ChungMilana TrubnykovaJorge La SernaBertha Elena Gallardo JugoMiguel Chávez PastorHugo Hernán Abarca BarrigaAndre MegarbaneBeth A KozelMieke M van HaelstRoger E StevensonMarshall SummarA Adebowale AdeyemoColleen A MorrisDanilo Moretti-FerreiraMarius George LinguraruMaximilian Muenke
Published in: American journal of medical genetics. Part A (2019)
Williams-Beuren syndrome (WBS) is a common microdeletion syndrome characterized by a 1.5Mb deletion in 7q11.23. The phenotype of WBS has been well described in populations of European descent with not as much attention given to other ethnicities. In this study, individuals with WBS from diverse populations were assessed clinically and by facial analysis technology. Clinical data and images from 137 individuals with WBS were found in 19 countries with an average age of 11 years and female gender of 45%. The most common clinical phenotype elements were periorbital fullness and intellectual disability which were present in greater than 90% of our cohort. Additionally, 75% or greater of all individuals with WBS had malar flattening, long philtrum, wide mouth, and small jaw. Using facial analysis technology, we compared 286 Asian, African, Caucasian, and Latin American individuals with WBS with 286 gender and age matched controls and found that the accuracy to discriminate between WBS and controls was 0.90 when the entire cohort was evaluated concurrently. The test accuracy of the facial recognition technology increased significantly when the cohort was analyzed by specific ethnic population (P-value < 0.001 for all comparisons), with accuracies for Caucasian, African, Asian, and Latin American groups of 0.92, 0.96, 0.92, and 0.93, respectively. In summary, we present consistent clinical findings from global populations with WBS and demonstrate how facial analysis technology can support clinicians in making accurate WBS diagnoses.
Keyphrases
  • intellectual disability
  • autism spectrum disorder
  • case report
  • machine learning
  • mass spectrometry
  • optical coherence tomography
  • data analysis