Cornelia de Lange syndrome in diverse populations
Dowsett, Leah; Porras, Antonio R.; Kruszka, Paul; Davis, Brandon; Hu, Tommy; Honey, Engela M.; Badoe, Eben; Thong, Meow-Keong; Leon, Eyby; Girisha, Katta M.; Shukla, Anju; Nayak, Shalini S.; Shotelersuk, Vorasuk; Megarbane, Andre; Phadke, Shubha; Sirisena, Nirmala D.; Dissanayake, Vajira H.W.; Ferreira, Carlos R.; Kisling, Monisha S.; Tanpaiboon, Pranoot; Uwineza, Annette; Mutesa, Leon; Tekendo-Ngongang, Cedrik; Wonkam, Ambroise; Fieggen, Karen; Batista, Leticia Cassimiro; Moretti-Ferreira, Danilo; Stevenson, Roger E.; Prijoles, Eloise J.; Everman, David; Clarkson, Kate; Worthington, Jessica; Kimonis, Virginia; Hisama, Fuki; Crowe, Carol; Wong, Paul; Johnson, Kisha; Clark, Robin D.; Bird, Lynne; Masser-Frye, Diane; McDonald, Marie; Willems, Patrick; Roeder, Elizabeth; Saitta, Sulgana; Anyane-Yeoba, Kwame; Demmer, Laurie; Hamajima, Naoki; Stark, Zornitza; Gillies, Greta; Hudgins, Louanne; Dave, Usha; Shalev, Stavit; Siu, Victoria; Ades, Ann; Dubbs, Holly; Raible, Sarah; Kaur, Maninder; Salzano, Emanuela; Jackson, Laird; Deardorff, Matthew; Kline, Antonie; Summar, Marshall; Muenke, Maximilian; Linguraru, Marius George; Krantz, Ian D.
Date:
2019-02
Abstract:
Cornelia 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.
Description:
Supplementary 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 selected