Noonan syndrome on the African Continent

Abstract

BackgroundNoonan syndrome is a common genetic syndrome caused by pathogenic variants in genes in the Ras/MAPK signaling pathway. The medical literature has an abundance of studies on Noonan syndrome, but few are from the African continent.MethodsThe medical literature was searched for studies on Noonan syndrome from the African continent and these reports were added to our experience in Africa. Facial analysis was reviewed from two previous reports from our group using a support vector machine (SVM) algorithm and an analysis using the Face2Gene convolutional neural network technology.ResultsIndividuals with Noonan syndrome from reports in African populations have the classic phenotype characteristics including typical minor facial anomalies such as widely spaced eyes (31–100%), short stature (71–100%), and congenital heart disease with pulmonary stenosis found in 24–100% of patients. Similarly, the genotypes are similar with the majority of variants occurring in the gene PTPN11 (72%) and 36% of these variants occurred in the amino acid residue Asn308, which is most commonly found in other populations. The two separate facial analysis algorithms successfully discriminated Africans with NS from unaffected matched individuals with area under the curve (AUC) of the receiver operator characteristic of 0.94 (SVM) and 0.979 for the Face2Gene research methodology.ConclusionFew studies characterizing Noonan syndrome in Africans have been conducted, highlighting the need for more genetic and genomic research in African populations. Available clinical data, genotypes, and facial analysis technology data show that individuals of African descent with NS can be efficiently diagnosed using available standards.

Rubinstein–Taybi syndrome in diverse populations

“…Limitations of this study include the small size of our clinical cohort, particularly the African and Middle Eastern groups. This reflects the difficulty in data acquisition in non‐Caucasian populations and further supports the need for extra research efforts in individuals from diverse populations, especially in sub‐Saharan Africa (Tekendo‐Ngongang & Kruszka, 2020). The poor performance of the Face2Gene algorithm on the African group may be partially explained by the small sample size tested, although the minimum size requirement for the experiment ( n = 10) was met (Gurovich et al, 2019).…”

Phenotype–genotype analysis of 242 individuals with RASopathies: 18‐year experience of a tertiary center in Brazil

“…Conversely, for the other two variants identified, there are differences when we compare our data with other from Europe/the United States and Argentina: 12, 6.3, and 1.7% for p.Asn308Ser, and 11, 0.1, and 13.3% for p.Met504Val, respectively. In a review of NS reports from the African continent, the authors noticed that 1/3 of the variants in PTPN11 involved the residue Asn308, with an even distribution between Asp and Ser, suggesting that the variant p.Asn308Ser is overrepresented in that population (Tekendo‐Ngongang & Kruszka, 2020). We may hypothesize that the increased frequency of the variant p.Asn308Ser in our population could be explained by the African descent background in our admixtured population.…”

Automated Facial Recognition for Noonan Syndrome Using Novel Deep Convolutional Neural Network With Additive Angular Margin Loss

“…Although previous literature has shown that the DCNN-based facial recognition models can assist in diagnosing genetic syndromes ( Gurovich et al, 2019 ; Qin et al, 2020 ), only a few studies have used DCNNs to identify NS. In 2020, Tekendo-Ngongang and Kruszka (2020) applied DeepGestalt, a DCNN-based architecture, to develop a NS facial recognition model. Their model discriminated NS patients from matched healthy individuals with an AUC of 0.979.…”

“…Many genetic syndromes have craniofacial alterations ( Hart and Hart, 2009 ), and facial appearance can be a momentous clue in making an early diagnosis of syndromes ( Kuru et al, 2014 ). The utility of traditional machine learning methods and deep learning methods for diagnosing NS based on pattern recognition of face images has been explored previously by several researchers ( Boehringer et al, 2006 ; Kruszka et al, 2017 ; Tekendo-Ngongang and Kruszka, 2020 ; Porras et al, 2021 ). In 2019, Gurovich et al (2019) presented a deep DCNN framework, called DeepGestalt, trained on a database of over 17,000 pictures of faces representing more than 200 genetic syndromes.…”