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Turner syndrome in diverse populations.

Paul S KruszkaYonit A AddissieCedrik Tekendo-NgongangKelly L JonesSarah K SavageNeerja GuptaNirmala D SirisenaVajira H W DissanayakeC Sampath PaththinigeTeresa AravenaSheela NampoothiriDhanya YesodharanKatta Mohan GirishaSiddaramappa Jagdish PatilSaumya Shekhar JamuarJasmine Chew-Yin GohAgustini UtariNydia SihombingRupesh MishraNeer Shoba ChitrakarBrenda C IrieleEzana LulsegedAndre MegarbaneAnnette UwinezaElizabeth Eberechi OyenusiOluwarotimi Bolaji OlopadeOlufemi Adetola FasanmadeMilagros M Duenas-RoqueMeow-Keong ThongJoanna Y L TungGary T K MokNicole FleischerGodfrey M RwegereraMaría Beatriz de HerrerosJohnathan WattsKaren FieggenVictoria HuckstadtAngélica MorescoMaría Gabriela ObregonDalia Farouk HussenNeveen A AshaatEngy A AshaatBrian H Y ChungEben BadoeSultana M H FaradzMona O El RubyVorasuk ShotelersukAmbroise WonkamEkanem Nsikak EkureShubha Rao PhadkeAntonio Richieri-CostaMaximilian Muenke
Published in: American journal of medical genetics. Part A (2019)
Turner syndrome (TS) is a common multiple congenital anomaly syndrome resulting from complete or partial absence of the second X chromosome. In this study, we explore the phenotype of TS in diverse populations using clinical examination and facial analysis technology. Clinical data from 78 individuals and images from 108 individuals with TS from 19 different countries were analyzed. Individuals were grouped into categories of African descent (African), Asian, Latin American, Caucasian (European descent), and Middle Eastern. The most common phenotype features across all population groups were short stature (86%), cubitus valgus (76%), and low posterior hairline 70%. Two facial analysis technology experiments were conducted: TS versus general population and TS versus Noonan syndrome. Across all ethnicities, facial analysis was accurate in diagnosing TS from frontal facial images as measured by the area under the curve (AUC). An AUC of 0.903 (p < .001) was found for TS versus general population controls and 0.925 (p < .001) for TS versus individuals with Noonan syndrome. In summary, we present consistent clinical findings from global populations with TS and additionally demonstrate that facial analysis technology can accurately distinguish TS from the general population and Noonan syndrome.
Keyphrases
  • deep learning
  • gene expression
  • dna methylation
  • electronic health record
  • working memory
  • genetic diversity
  • african american