Coffin–Lowry syndrome in Chinese

Fung, Jasmine Lee Fong; Rethanavelu, Kavitha; Luk, Ho‐Ming; Ho, Mandy; Lo, Ivan F.M.; Chung, Brian Hon Yin

doi:10.1002/ajmg.a.61323

Cited by 6 publications

(3 citation statements)

References 4 publications

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“…Furthermore, the DeepGestalt technology has been demonstrated to not be influenced by ethnicity, according to previously published works, which analyzed the facial features of individuals with different ancestries and affected by diverse genetic conditions [15][16][17] . These studies showed that the technology identifies facial gestalt independently from an individual' s ancestry.…”

Section: Discussionmentioning

confidence: 99%

DeepGestalt analysis of the SETD5-associated intellectual disability syndrome

Pascolini¹

2020

JTGG

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Aim: This is the first computer-assisted study focused on the craniofacial features of the intellectual disability (ID)/ developmental delay (DD) syndrome related to haploinsufficiency of the SETD5 gene (SET domain-containing protein 5, MIM#615743), which is a chromatin regulator. The purpose of this novel research is to better delineate the facial phenotype of this condition and identify the associated dysmorphic features to consider for clinical diagnosis. Methods: A total of 18 2D frontal images of previously published pediatric individuals (aged 1-14 years, Caucasian ethnicity) with SETD5 mutations (SETD5, cohort 1) were uploaded to the RESEARCH application of the Face2Gene online platform (V.19.1.3) (FDNA Inc., Boston, MA, USA). Images from this group of patients were compared with 36 photos of individuals with two other known chromatin disorders, specifically KBG (KBGS, cohort 2, 18 images) and Koolen-de Vries syndromes (KdVS, cohort 3, 18 images), which share with the SETD5-related ID syndrome a very similar facial gestalt and peculiar dysmorphisms. An additional cohort of 18 unaffected controls that were matched for age and ethnicity (Ctrl., controls, cohort 4) was also included in the comparison experiment. Results: Results obtained from the binary comparison analysis were expressed in terms of Area Under the Curve and its Receiver Operating Characteristic curve for aggregated splits. A high facial overlap between the SETD5-related phenotype and KBGS was demonstrated. Other conditions considered for the study were well recognized by the system and differentiated using the unaffected controls. Conclusion: This study confirms the presence of distinctive dysmorphic features that characterize the SETD5-related facial phenotype, providing observations about its possible role in facial morphogenesis.

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Section: Discussionmentioning

confidence: 99%

DeepGestalt analysis of the SETD5-associated intellectual disability syndrome

Pascolini¹

2020

JTGG

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“…To avoid overfitting, training and testing were performed using a leave-1-out classification scheme. Since ethnic background is a possible confounder of DeepGestalt [15,22,26,29,33], we designed classification experiments based on all images, images of White persons, and those of persons with other ethnicities, to benchmark the influence of ethnicity on SVM performance.…”

Section: Classificationmentioning

confidence: 99%

Efficiency of Computer-Aided Facial Phenotyping (DeepGestalt) in Individuals With and Without a Genetic Syndrome: Diagnostic Accuracy Study (Preprint)

Pantel¹,

Hajjir²,

Danyel³

et al. 2020

Preprint

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BACKGROUND Collectively, an estimated 5% of the population have a genetic disease. Many of them feature characteristics that can be detected by facial phenotyping. Face2Gene CLINIC is an online app for facial phenotyping of patients with genetic syndromes. DeepGestalt, the neural network driving Face2Gene, automatically prioritizes syndrome suggestions based on ordinary patient photographs, potentially improving the diagnostic process. Hitherto, studies on DeepGestalt’s quality highlighted its sensitivity in syndromic patients. However, determining the accuracy of a diagnostic methodology also requires testing of negative controls. OBJECTIVE The aim of this study was to evaluate DeepGestalt's accuracy with photos of individuals with and without a genetic syndrome. Moreover, we aimed to propose a machine learning–based framework for the automated differentiation of DeepGestalt’s output on such images. METHODS Frontal facial images of individuals with a diagnosis of a genetic syndrome (established clinically or molecularly) from a convenience sample were reanalyzed. Each photo was matched by age, sex, and ethnicity to a picture featuring an individual without a genetic syndrome. Absence of a facial gestalt suggestive of a genetic syndrome was determined by physicians working in medical genetics. Photos were selected from online reports or were taken by us for the purpose of this study. Facial phenotype was analyzed by DeepGestalt version 19.1.7, accessed via Face2Gene CLINIC. Furthermore, we designed linear support vector machines (SVMs) using Python 3.7 to automatically differentiate between the 2 classes of photographs based on DeepGestalt's result lists. RESULTS We included photos of 323 patients diagnosed with 17 different genetic syndromes and matched those with an equal number of facial images without a genetic syndrome, analyzing a total of 646 pictures. We confirm DeepGestalt’s high sensitivity (top 10 sensitivity: 295/323, 91%). DeepGestalt’s syndrome suggestions in individuals without a craniofacially dysmorphic syndrome followed a nonrandom distribution. A total of 17 syndromes appeared in the top 30 suggestions of more than 50% of nondysmorphic images. DeepGestalt’s top scores differed between the syndromic and control images (area under the receiver operating characteristic [AUROC] curve 0.72, 95% CI 0.68-0.76; <i>P</i><.001). A linear SVM running on DeepGestalt’s result vectors showed stronger differences (AUROC 0.89, 95% CI 0.87-0.92; <i>P</i><.001). CONCLUSIONS DeepGestalt fairly separates images of individuals with and without a genetic syndrome. This separation can be significantly improved by SVMs running on top of DeepGestalt, thus supporting the diagnostic process of patients with a genetic syndrome. Our findings facilitate the critical interpretation of DeepGestalt’s results and may help enhance it and similar computer-aided facial phenotyping tools.

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“…The CLS pathogenic gene, ribosomal protein S6 kinase polypeptide 3 ( RPS6KA3 ), is located at Xp22.2 [ 2 ]. More than 200 cases of CLS have been reported abroad, and 18 cases with confirmed pathogenic genes have been reported in China up to 2021 [ 3 , 4 , 5 , 6 , 7 , 8 , 9 ]. Here, we present the clinical data of two cases of CLS in twin brothers diagnosed and followed up at the Children’s Hospital affiliated with Zhejiang University School of Medicine (Hangzhou, China) in April 2022 and analyzed the phenotypic and genotypic characteristics of CLS cases in China.…”

Section: Introductionmentioning

confidence: 99%

Coffin-Lowry Syndrome Induced by RPS6KA3 Gene Variation in China: A Case Report in Twins

Jin

Shu

2022

Medicina

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Background and objectives: Coffin-Lowry Syndrome (CLS), a rare neurodegenerative disorder, is mainly diagnosed based on clinical manifestations and molecular analyses. In total, about 20 cases of CLS have been reported in China. Here, we report two cases of CLS in identical twin brothers and examine their potential causative mutations. Methods: The Trio mode was used in this analysis, i.e., DNA from the proband and his parents was sequenced. Furthermore, DNA from the proband’s twin brother was used for confirmation. Results: A hemizygous variation was detected in the 11th exon of the RPS6KA3 gene, c.898C>T (p.R300*) of the proband, and the same site variation was detected in his identical twin brother; however, the mutation was not detected in his parents. Conclusions: The RPS6KA3 gene mutation c.898C>T (p.R300*) is the causative factor of familial CLS. The variant detected was reported for the first time in the Chinese population. Additionally, by analyzing the previous literature, we were able to summarize the phenotypic and genetic characteristics of GLS in China.

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Coffin–Lowry syndrome in Chinese

Cited by 6 publications

References 4 publications

DeepGestalt analysis of the SETD5-associated intellectual disability syndrome

DeepGestalt analysis of the SETD5-associated intellectual disability syndrome

Efficiency of Computer-Aided Facial Phenotyping (DeepGestalt) in Individuals With and Without a Genetic Syndrome: Diagnostic Accuracy Study (Preprint)

Coffin-Lowry Syndrome Induced by RPS6KA3 Gene Variation in China: A Case Report in Twins

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