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Smaller and larger deletions of the Williams Beuren syndrome region implicate genes involved in mild facial phenotype, epilepsy and autistic traits.

ABSTRACT: Williams Beuren syndrome (WBS) is a multisystemic disorder caused by a hemizygous deletion of 1.5 Mb on chromosome 7q11.23 spanning 28 genes. A few patients with larger and smaller WBS deletion have been reported. They show clinical features that vary between isolated SVAS to the full spectrum of WBS phenotype, associated with epilepsy or autism spectrum behavior. Here we describe four patients with atypical WBS 7q11.23 deletions. Two carry ~3.5 Mb larger deletion towards the telomere that includes Huntingtin-interacting protein 1 (HIP1) and tyrosine 3-monooxygenase/tryptophan 5-monooxigenase activation protein gamma (YWHAG) genes. Other two carry a shorter deletion of ~1.2 Mb at centromeric side that excludes the distal WBS genes BAZ1B and FZD9. Along with previously reported cases, genotype-phenotype correlation in the patients described here further suggests that haploinsufficiency of HIP1 and YWHAG might cause the severe neurological and neuropsychological deficits including epilepsy and autistic traits, and that the preservation of BAZ1B and FZD9 genes may be related to mild facial features and moderate neuropsychological deficits. This report highlights the importance to characterize additional patients with 7q11.23 atypical deletions comparing neuropsychological and clinical features between these individuals to shed light on the pathogenic role of genes within and flanking the WBS region.

SUBMITTER: Fusco C

PROVIDER: S-EPMC3865388 | biostudies-literature | 2014 Jan

REPOSITORIES: biostudies-literature

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Smaller and larger deletions of the Williams Beuren syndrome region implicate genes involved in mild facial phenotype, epilepsy and autistic traits.

Fusco Carmela C Micale Lucia L Augello Bartolomeo B Teresa Pellico Maria M Menghini Deny D Alfieri Paolo P Cristina Digilio Maria M Mandriani Barbara B Carella Massimo M Palumbo Orazio O Vicari Stefano S Merla Giuseppe G

European journal of human genetics : EJHG 20130612 1

Williams Beuren syndrome (WBS) is a multisystemic disorder caused by a hemizygous deletion of 1.5 Mb on chromosome 7q11.23 spanning 28 genes. A few patients with larger and smaller WBS deletion have been reported. They show clinical features that vary between isolated SVAS to the full spectrum of WBS phenotype, associated with epilepsy or autism spectrum behavior. Here we describe four patients with atypical WBS 7q11.23 deletions. Two carry ~3.5 Mb larger deletion towards the telomere that inclu ...[more]

PMID: 23756441

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Project description:Background: Williams-Beuren syndrome (WBS) is a rare genetic syndrome with a characteristic "elfin" facial gestalt. The "elfin" facial characteristics include a broad forehead, periorbital puffiness, flat nasal bridge, short upturned nose, wide mouth, thick lips, and pointed chin. Recently, deep convolutional neural networks (CNNs) have been successfully applied to facial recognition for diagnosing genetic syndromes. However, there is little research on WBS facial recognition using deep CNNs. Objective: The purpose of this study was to construct an automatic facial recognition model for WBS diagnosis based on deep CNNs. Methods: The study enrolled 104 WBS children, 91 cases with other genetic syndromes, and 145 healthy children. The photo dataset used only one frontal facial photo from each participant. Five face recognition frameworks for WBS were constructed by adopting the VGG-16, VGG-19, ResNet-18, ResNet-34, and MobileNet-V2 architectures, respectively. ImageNet transfer learning was used to avoid over-fitting. The classification performance of the facial recognition models was assessed by five-fold cross validation, and comparison with human experts was performed. Results: The five face recognition frameworks for WBS were constructed. The VGG-19 model achieved the best performance. The accuracy, precision, recall, F1 score, and area under curve (AUC) of the VGG-19 model were 92.7 ± 1.3%, 94.0 ± 5.6%, 81.7 ± 3.6%, 87.2 ± 2.0%, and 89.6 ± 1.3%, respectively. The highest accuracy, precision, recall, F1 score, and AUC of human experts were 82.1, 65.9, 85.6, 74.5, and 83.0%, respectively. The AUCs of each human expert were inferior to the AUCs of the VGG-16 (88.6 ± 3.5%), VGG-19 (89.6 ± 1.3%), ResNet-18 (83.6 ± 8.2%), and ResNet-34 (86.3 ± 4.9%) models. Conclusions: This study highlighted the possibility of using deep CNNs for diagnosing WBS in clinical practice. The facial recognition framework based on VGG-19 could play a prominent role in WBS diagnosis. Transfer learning technology can help to construct facial recognition models of genetic syndromes with small-scale datasets.

Dataset Information

Smaller and larger deletions of the Williams Beuren syndrome region implicate genes involved in mild facial phenotype, epilepsy and autistic traits.

Publications

Smaller and larger deletions of the Williams Beuren syndrome region implicate genes involved in mild facial phenotype, epilepsy and autistic traits.

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