Parents' perspectives and performance evaluation of facial analysis technologies for the diagnosis of congenital disorders

Dissertation (MSc (Genetics))--University of Pretoria, 2022.Congenital disorders are a major health care burden. Most congenital disorders that are due to genetic causes do not have a cure, but an early and accurate diagnosis may alleviate associated symptoms and contribute to the correct management of the disorder. However, there is a lack of medical geneticists and doctors who can make these diagnoses in developing countries. Thus, facial analysis technologies can provide a quick and objective way to initially diagnose individuals with a congenital disorder where resources are limited because almost half of all inherited disorders have a typical facial gestalt. Chapter 1 is a literature review, focusing on facial analysis technologies and how it is used to make an initial diagnosis based on the typical facial features of an individual, with a special focus on Face2Gene. I briefly reviewed the four disorders under investigation in this study, their prevalence, cause, and particularly the typical facial features associated with each disorder. We first aimed to better understand parents’ views on the collection, storage, use, and publication of their children’s facial images for research and diagnosis. Large datasets of facial photographs are required to train facial analysis algorithms, and we wanted to better understand the public’s views on this topic. This was achieved by conducting an online survey, found in Chapter 2, aimed at parents of children with and without a congenital disorder. The second aim of this study was to determine and compare the diagnostic accuracies of two- dimensional facial analyses of congenital disorders. Face2Gene is a popular phenotyping web tool and is free to use for healthcare professionals. The technology does not, however, classify an individual as “non-syndromic” and will suggest likely syndromes to all submitted facial images. Differentiation between syndromic and non-syndromic individuals is important for clinicians to determine if the child requires further testing or investigation into a potential diagnosis. Chapter 3 aimed to establish how well Face2Gene can differentiate between syndromic and non-syndromic facial images, and we compared that to our in-house analyses of the facial features of individuals. Previous research showed that Face2Gene did not perform well in African ethnic groups before training. This is likely due to the algorithm’s training data mostly consisting of European individuals. It is also important to establish a diagnosis as early as possible, to ensure the correct management strategies are put in place. In Chapter 4, we thus aimed to establish how well the Face2Gene algorithm can differentiate between syndromic and non-syndromic facial images in different syndrome, ethnic, and age groups. We again compared that to the results from our in-house analyses.University of PretoriaBiochemistry, Genetics and Microbiology (BGM)MSc (Genetics)Unrestricte

An automatic approach for classification and categorisation of lip morphological traits

Classification of facial traits (e.g., lip shape) is an important area of medical research, for example, in determining associations between lip traits and genetic variants which may lead to a cleft lip. In clinical situations, classification of facial traits is usually performed subjectively directly on the individual or recorded later from a three-dimensional image, which is time consuming and prone to operator errors. The present study proposes, for the first time, an automatic approach for the classification and categorisation of lip area traits. Our approach uses novel three-dimensional geometric features based on surface curvatures measured along geodesic paths between anthropometric landmarks. Different combinations of geodesic features are analysed and compared. The effect of automatically identified categories on the face is visualised using a partial least squares method. The method was applied to the classification and categorisation of six lip shape traits (philtrum, Cupid’s bow, lip contours, lip-chin, and lower lip tone) in a large sample of 4747 faces of normal British Western European descents. The proposed method demonstrates correct automatic classification rate of up to 90%

Quantification of Facial Traits

Measuring facial traits by quantitative means is a prerequisite to investigate epidemiological, clinical, and forensic questions. This measurement process has received intense attention in recent years. We divided this process into the registration of the face, landmarking, morphometric quantification, and dimension reduction. Face registration is the process of standardizing pose and landmarking annotates positions in the face with anatomic description or mathematically defined properties (pseudolandmarks). Morphometric quantification computes pre-specified transformations such as distances. Landmarking: We review face registration methods which are required by some landmarking methods. Although similar, face registration and landmarking are distinct problems. The registration phase can be seen as a pre-processing step and can be combined independently with a landmarking solution. Existing approaches for landmarking differ in their data requirements, modeling approach, and training complexity. In this review, we focus on 3D surface data as captured by commercial surface scanners but also cover methods for 2D facial pictures, when methodology overlaps. We discuss the broad categories of active shape models, template based approaches, recent deep-learning algorithms, and variations thereof such as hybrid algorithms. The type of algorithm chosen depends on the availability of pre-trained models for the data at hand, availability of an appropriate landmark set, accuracy characteristics, and training complexity. Quantification: Landmarking of anatomical landmarks is usually augmented by pseudo-landmarks, i.e., indirectly defined landmarks that densely cover the scan surface. Such a rich data set is not amenable to direct analysis but is reduced in dimensionality for downstream analysis. We review classic dimension reduction techniques used for facial data and face specific measures, such as geometric measurements and manifold learning. Finally, we review symmetry registration and discuss reliability

3D face morphology classification for medical applications

Classification of facial morphology traits is an important problem for many medical applications, especially with regard to determining associations between facial morphological traits or facial abnormalities and genetic variants. A modern approach to the classification of facial characteristics(traits) is to use three-dimensional facial images. In clinical practice, classification is usually performed manually, which makes the process very tedious, time-consuming and prone to operator error. Also using simple landmark-to-landmark facial measurements may not accurately represent the underlying complex three-dimensional facial shape. This thesis presents the first automatic approach for classification and categorisation of facial morphological traits with application to lips and nose traits. It also introduces new 3D geodesic curvature features obtained along the geodesic paths between 3D facial anthropometric landmarks. These geometric features were used for lips and nose traits classification and categorisation. Finally, the influence of the discovered categories on the facial physical appearance are analysed using a new visualisation method in order to gain insight into suitability of categories for description of the underlying facial traits. The proposed approach was tested on the ALSPAC (Avon Longitudinal Study of Parents and Children) dataset consisting of 4747 3D full face meshes. The classification accuracy obtained using expert manual categories was not very high, in the region of 72%-79%, indicating that the manual categories may be unreliable. In an attempt to improve these accuracies,an automatic categorisation method was applied. In general,the classification accuracies based on the automatic lip categories were higher than those obtained using the manual categories by at least 8% and the automatic categories were found to be statistically more significant in the lip area than the manual categories. The same approach was used to categorise the nose traits, the result indicating that the proposed categorisation approach was capable of categorising any face morphological trait without the ground truth about its traits categories. Additionally, to test the robustness of the proposed features, they were used in a popular problem of gender classification and analysis. The results demonstrated superior classification accuracy to that of comparable methods. Finally, a discovery phase of a genome wide association analysis(GWAS) was carried out for 11 automatic lip and nose traits categories. As a result, statistically significant associations were found between four traits and six single nucleotide polymorphisms (SNPs). This is a very good result considering that for the 27 manual lip traits categories provided by medical expert, the associations were found between two traits and two SNPs only. This result testifies that the method proposed in this thesis for automatic categorisation of 3D facial morphology has a considerable potential for application to GWAS

Variations in Craniofacial and Velopharyngeal Structures Among Individuals with 22q11.2 Deletion Syndrome

22q11.2 deletion syndrome is the most common genetic cause of velopharyngeal dysfunction. Studies examining 22q11.2 deletion syndrome have thus far primarily focused on variations in the bony framework. Limited information exists regarding the velopharyngeal muscle variations for this clinically challenging population. However, with advances in MRI, muscle and soft tissue imaging is possible. A series of experiments were thus designed to explore and validate the use of our research methodology on normal control participants and a single participant with 22q11.2 deletion syndrome, before initiating the study on a larger sample of children with 22q11.2 deletion syndrome. The overarching aims of this investigation were to examine craniofacial and velopharyngeal characteristics among children with 22q11.2 deletion syndrome and to determine whether craniofacial measures can predict velopharyngeal structure and muscle configurations in this population. This investigation represents the first large scale attempt to image children with 22q11.2 DS without sedation. The aim of Study I was to validate the use of a supine MRI scanner over an upright scanner to obtain data of interest. Study II was focused on the application of a child-friendly MRI protocol to ensure data collection on young pediatric participants without the use of sedation. The aim of Study III was to translate our child-friendly MRI scanning protocol to a clinical population and assess feasibility in a single participant with 22q11.2 deletion syndrome. Study IV assessed craniofacial and velopharyngeal characteristics among children with 22q11.2 deletion syndrome using the imaging protocol detailed in studies one, two, and three. Results from this study suggest that children with 22q11.2 deletion syndrome have several craniofacial and velopharyngeal characteristics that are significantly different compared to children with normal velopharyngeal anatomy. This investigation describes a safe and effective method to obtain MRI data in a clinically complex population without the use of sedation. Individuals with 22q11.2 deletion syndrome present with unique velopharyngeal muscle variations that may contribute to the high rate of velopharyngeal dysfunction associated with this syndrome

Generation and characterisation of human embryonic stem cells deficient in ZDHHC8, a gene deleted in 22q11.2 deletion syndrome

The 22q11.2 Deletion Syndrome is caused by a deletion on the chromosome 22q11.2. Individuals carrying 22q11.2 deletion have an increased risk to develop schizophrenia and Parkinson disease. However, how this deletion leads to the development of these diseases and the specific role of the individual 22q11.2 genes remains largely unknown. In order to understand the neuronal cell types and developmental stages in which the 22q11.2 genes may function, we investigated the temporal and spatial expression profile of the genes located in the 22q11.2DS by RT-PCR. Human embryonic stem cells (hESCs) was used to generate excitatory projection neurons, cortical interneurons, GABAergic medium spiny neurons (MSN) and dopaminergic neurons. This study revealed that several genes appear to exhibit a specific temporal expression profile. Moreover, another group of genes were found to be preferentially expressed in dopaminergic neural lineage. Within the 22q11.2 deletion region, ZDHHC8 is an interesting candidate due to its implication in the physiology and morphology of the neurons. I generated a hESCs cellular model carrying a heterozygous deletion of ZDHHC8. These cells can be induced toward cortical projection neuron fate in a comparable temporal kinetics to that of the parental control cells. However, phenotypic characterisation revealed that ZDHHC8 mutation altered the motility and the spontaneous calcium activity in ZDHHC8+/- neurons. Interestingly, transcriptomic analysis of excitatory progenitors identified altered expression of genes regulating calcium activity and axonal growth (motility). Furthermore, this study suggests that ZDHHC8 may also be involved in neuronal development, patterning and synaptic signalling. In conclusion, this thesis provides further knowledges regarding the expression of the 22q11.2DS genes which would be valuable to guide future studies either in cellular or animal models. Furthermore, this study indicates that ZDHHC8 function is involved in several aspects of neuron development that potentially plays a role in the aetiology of 22q11.2DS

Presented Abstracts from the Thirty Third Annual Education Conference of the National Society of Genetic Counselors (New Orleans, LA, September 2014)

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146864/1/jgc41067.pd

Clinical and molecular investigation of rare congenital defects of the palate

Cleft palate (CP) affects around 1/1500 live births and, along with cleft lip, is one of the most common forms of birth defect. The studies presented here focus on unusual defects of the palate, especially to understand better the rarely reported but surprisingly common condition called submucous cleft palate (SMCP). The frequency and consequences of SMCP from a surgical perspective were first investigated based on the caseload of the North Thames Cleft Service at Great Ormond Street Hospital and St Andrew's Centre, Broomfield Hospital, Mid Essex Hospitals Trust. It was previously reported that up to 80% of individuals with unrepaired SMCP experience speech difficulties as a consequence of velopharyngeal insufficiency (VPI). Attempted repair of the palatal defect can sometimes give poor results, so controversies still exist about the correct choice of surgical technique to use. Over 23 years, 222 patients at The North Thames Cleft Service underwent operations to manage SMCP. Nearly half of them (42.8%) were diagnosed with 22q11.2 deletion syndrome (22q11.2 DS). The first operation was palate repair, with an exception of one case, followed by a second surgical intervention required in approximately half of the patients. A third procedure to manage VPI was carried out in 6% of patients. To better understand the histological anatomy of the palatal muscles in cleft patients, biopsies were taken from levator veli palatini (LVP) and/or palatopharyngeus (PP) muscles during surgical correction of CP. Muscles were compared from patients with SMCP to those with overt CP and also to controls. The controls consisted of descending PP muscle fibres from healthy children who underwent a tonsillectomy operation for obstructive sleep apnoea or recurrent chronic tonsillitis. Fifty-seven biopsy samples were available from children between 10 months to 9 years of age. Individual biopsy samples were also available from patients with achondroplasia, Apert, Cornelia de Lange and Kabuki syndromes. The study showed a prevalence of fast fibres in both muscles in all CP types. However, in both SMCP LVP and SMCP 22q11.2 DS LVP, this trend was reversed in favour of slow fibres. Single cases with syndromes did not reveal any obvious differences compared to more common cleft types. Mutations in TBX22 are a frequent genetic cause of cleft palate and SMCP. The functional role of the encoded TBX22 transcription factor was investigated in a mouse model with SMCP. Cell lineage-specific fluorescence activated cell sorting of a conditional allele of Tbx22, was used to look at the RNA-Seq transcriptome in developing palatal shelves, with a view to identify downstream target genes. Eleven up regulated genes reached statistical significance after multiple testing correction in cranial mesoderm (CM) derived cells when comparing Tbx22null/Y and WT samples (Cspg4, Foxp2, Reln, Bmpr1b, Adgrb3, Sox6, Zim1, Scarna13, Fat1, Notch3, Peg3). Eleven genes were down regulated in the same comparison (Nr2f2, Lars2, Ahr, Aplnr, Emcn, Npnt, Apln, Ccr2, Tll1, Snord34, Snord99). Comparing Tbx22null/Y and WT in cranial neural crest (CNC) derived cells, only Cxcl14 was up regulated, while Tbx22 was down regulated. Osteoclast differentiation, calcium signalling, focal adhesion, Wnt signalling and cell adhesion molecule pathways were the most enriched pathways in functional annotation of significantly differentially expressed genes analysis. Finally, a family with an unusual velopharyngeal anatomy was investigated in order to determine the likely genetic cause. This involved the implementation of genetic technologies in an autosomal dominant multigeneration Egyptian family with 8 affected individuals who presented with absent uvula, short posterior border of the soft palate and abnormal pillars of the fauces. Using a combination of cytogenetic, linkage analysis and exome sequencing, followed by more detailed segregation and functional analysis, a dominantly acting missense mutation in the activation domain of FOXF2 was revealed. This variant was found to co-segregate with a copy number variant of unknown significance that could not at this stage be causally distinguished from the point mutation