Further delineation of Aymé‐Gripp syndrome and use of automated facial analysis tool

Abstract

Aymé-Gripp syndrome (AGS) is an autosomal dominant multisystem disorder caused by specific heterozygous variants in MAF. The resulting aberrant protein shows impaired GSK-mediated MAF phosphorylation. AGS is characterized by congenital cataracts, sensorineural hearing loss, short stature, intellectual disability, and distinctive facial features with brachycephaly. Cardiac and joint phenotypes are present in nearly half of patients. We review information on 10 published individuals with MAF mutations and clinical AGS and describe five additional patients, including three with novel mutations. Joint problems, typically including radioulnar synostosis and joint limitations, were present in 9/15 patients. Hip replacement in young adulthood was needed in four patients. Pericarditis occurred in 6/15 individuals. An automated facial analysis of 2D photos was used to compare the facial phenotype of 13 individuals from the literature or reported here, with facial photos of a control cohort of unaffected individuals and a cohort of Down syndrome patients. A multiclass approach yielded an accuracy of 86.86% and 89.05%, respectively, in two independent experiments compared to a random chance of 37.74%. In binary comparisons of AGS and Down syndrome, the area under the curve (AUC) was 0.994 (P < .001) and 1.0 (P < .001), respectively. Binary comparisons of AGS and unaffected controls yielded AUC of 0.994 (P < .001) and 0.989 (P = .003), respectively, suggesting that the facial phenotype of AGS could clearly be distinguished from unaffected individuals and from Down syndrome patients. Automated facial analysis may be helpful in the identification and evaluation of individuals suspected to have AGS.

Maternally inherited MAF variant associated with variable expression of Aymé‐Gripp syndrome

“…Dominant variants in MAF have been reported with two allelic conditions, Aymé‐Gripp syndrome and multiple types of presenile or juvenile hereditary cataract. The proband in this report presented with the typical findings seen in Aymé‐Gripp syndrome including congenital cataract, sensorineural hearing loss, severe ID, autism spectrum disorder, seizures, and dysmorphic facial features that are consistent with those previously described (Amudhavalli, Hanson, Angle, Bontempo, & Gripp, ). Surprisingly, the identified variant was inherited from his mother who has normal intellectual ability but was found with late‐onset unilateral posterior subcapsular cataract, proteinuria, and a couple of physical findings of Aymé‐Gripp syndrome.…”

Skeletal abnormalities are common features in Aymé‐Gripp syndrome

“…Detailed clinical features are described in Figure 1 and Table 1 Due to rarity of AYGRPS, only 15 affected individuals have been reported to date. [6][7][8] In line with the previous studies, the present findings confirm that all disease-causing MAF mutations specifically affect residues located within the GSK3 recognition motifs, and that among those, Ser54 and Pro59 seem to be the most frequent affected ones ( Figure S1). Reviewing the clinical features of all individuals, the Skeletal and joint abnormalities had previously been observed in individuals with AYGRPS.…”

“…Reviewing the clinical features of all individuals, the Skeletal and joint abnormalities had previously been observed in individuals with AYGRPS. 6,7 In our original description of the condition, we reported short stature, malar hypoplasia, and joint limitations, occurring predominantly in older patients. 6 Recently, Javadiyan and colleagues reported a maternally inherited MAF change (p.(Pro59Arg)) in a 20-year-old individual with AYGRPS.…”

The Connective Tissue Disorder Associated with Recessive Variants in the SLC39A13 Zinc Transporter Gene (Spondylo-Dysplastic Ehlers–Danlos Syndrome Type 3): Insights from Four Novel Patients and Follow-Up on Two Original Cases

“…De-identified data of images from 8 patients (the six patients reported here, plus two of the patients described by Giunta et al (2008) whose photographs had been submitted to us for clinical consultation) were uploaded to the Face2Gene Research app [9] and matched to controls by age, sex, and ethnicity to produce artificial composite images. Because of the small number of patients, the comparison was run twice [10] with two different sets of matched controls. The comparison and separation quality between the three groups was evaluated by measuring the area under the curve (AUC) of the receiver operating characteristic (ROC) curve.…”

“…For these binary comparisons, the data was split randomly multiple times into training sets and test sets. Each such set contained half of the samples from the group, and this random process was repeated 10 times [8,10]. The results of the binary comparisons were reported both numerically and graphically.…”