Cornelia de Lange syndrome in diverse populations

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.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 selectedPK 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.http://wileyonlinelibrary.com/journal/ajmga2020-02-01hj2019Genetic

Cornelia de Lange syndrome in diverse populations.

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

Cornelia de Lange Syndrome in Diverse Populations.

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.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 selectedPK 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.http://wileyonlinelibrary.com/journal/ajmga2020-02-01hj2019Genetic

Williams-Beuren syndrome in diverse populations

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