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Craniofacial phenotyping is critical for both syndrome delineation and diagnosis because craniofacial abnormalities occur in 30% of characterized genetic syndromes. Clinical reports, textbooks, and available software tools typically provide two-dimensional, static images and illustrations of the characteristic phenotypes of genetic syndromes. In this work, we provide an interactive web application that provides three-dimensional, dynamic visualizations for the characteristic craniofacial effects of 95 syndromes. Users can visualize syndrome facial appearance estimates quantified from data and easily compare craniofacial phenotypes of different syndromes. Our application also provides a map of morphological similarity between a target syndrome and other syndromes. Finally, users can upload 3D facial scans of individuals and compare them to our syndrome atlas estimates. In summary, we provide an interactive reference for the craniofacial phenotypes of syndromes that allows for precise, individual-specific comparisons of dysmorphology.
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Face , Software , Humanos , Fácies , Fenótipo , SíndromeRESUMO
Variations in the form of the human face, which plays a role in our individual identities and societal interactions, have fascinated scientists and artists alike. Here, we review our current understanding of the genetics underlying variation in craniofacial morphology and disease-associated dysmorphology, synthesizing decades of progress on Mendelian syndromes in addition to more recent results from genome-wide association studies of human facial shape and disease risk. We also discuss the various approaches used to phenotype and quantify facial shape, which are of particular importance due to the complex, multipartite nature of the craniofacial form. We close by discussing how experimental studies have contributed and will further contribute to our understanding of human genetic variation and then proposing future directions and applications for the field.
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Estudo de Associação Genômica Ampla , Humanos , FenótipoRESUMO
Cell fate determination is a necessary and tightly regulated process for producing different cell types and structures during development. Cranial neural crest cells (CNCCs) are unique to vertebrate embryos and emerge from the neural plate borders into multiple cell lineages that differentiate into bone, cartilage, neurons and glial cells. We have previously reported that Irf6 genetically interacts with Twist1 during CNCC-derived tissue formation. Here, we have investigated the mechanistic role of Twist1 and Irf6 at early stages of craniofacial development. Our data indicate that TWIST1 is expressed in endocytic vesicles at the apical surface and interacts with ß/δ-catenins during neural tube closure, and Irf6 is involved in defining neural fold borders by restricting AP2α expression. Twist1 suppresses Irf6 and other epithelial genes in CNCCs during the epithelial-to-mesenchymal transition (EMT) process and cell migration. Conversely, a loss of Twist1 leads to a sustained expression of epithelial and cell adhesion markers in migratory CNCCs. Disruption of TWIST1 phosphorylation in vivo leads to epidermal blebbing, edema, neural tube defects and CNCC-derived structural abnormalities. Altogether, this study describes a previously uncharacterized function of mammalian Twist1 and Irf6 in the neural tube and CNCCs, and provides new target genes for Twist1 that are involved in cytoskeletal remodeling.
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Crista Neural , Tubo Neural , Animais , Cateninas , Regulação da Expressão Gênica no Desenvolvimento , Mamíferos/genética , Crânio/metabolismo , delta CateninaRESUMO
Recognizing Mendelian causes is crucial in molecular diagnostics and counseling for patients with autism spectrum disorder (ASD). We explored facial dysmorphism and facial asymmetry in relation to genetic causes in ASD patients and studied the potential of objective facial phenotyping in discriminating between Mendelian and multifactorial ASD. In a cohort of 152 ASD patients, 3D facial images were used to calculate three metrics: a computational dysmorphism score, a computational asymmetry score, and an expert dysmorphism score. High scores for each of the three metrics were associated with Mendelian causes of ASD. The computational dysmorphism score showed a significant correlation with the average expert dysmorphism score. However, in some patients, different dysmorphism aspects were captured making the metrics potentially complementary. The computational dysmorphism and asymmetry scores both enhanced the individual expert dysmorphism scores in differentiating Mendelian from non-Mendelian cases. Furthermore, the computational asymmetry score enhanced the average expert opinion in predicting a Mendelian cause. By design, our study does not allow to draw conclusions on the actual point-of-care use of 3D facial analysis. Nevertheless, 3D morphometric analysis is promising for developing clinical dysmorphology applications in diagnostics and training.
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The analysis of contemporary genomic data typically operates on one-dimensional phenotypic measurements (e.g. standing height). Here we report on a data-driven, family-informed strategy to facial phenotyping that searches for biologically relevant traits and reduces multivariate 3D facial shape variability into amendable univariate measurements, while preserving its structurally complex nature. We performed a biometric identification of siblings in a sample of 424 children, defining 1,048 sib-shared facial traits. Subsequent quantification and analyses in an independent European cohort (n = 8,246) demonstrated significant heritability for a subset of traits (0.17-0.53) and highlighted 218 genome-wide significant loci (38 also study-wide) associated with facial variation shared by siblings. These loci showed preferential enrichment for active chromatin marks in cranial neural crest cells and embryonic craniofacial tissues and several regions harbor putative craniofacial genes, thereby enhancing our knowledge on the genetic architecture of normal-range facial variation.
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Identificação Biométrica , Face/anatomia & histologia , Genômica , Imageamento Tridimensional , Herança Multifatorial/genética , Fenótipo , Irmãos , Adolescente , Criança , Pré-Escolar , Anormalidades Craniofaciais/genética , Conjuntos de Dados como Assunto , Europa (Continente)/etnologia , Face/anormalidades , Face/embriologia , Feminino , Estudos de Associação Genética , Humanos , Masculino , População Branca/genéticaRESUMO
Facial morphology is highly variable, both within and among human populations, and a sizable portion of this variation is attributable to genetics. Previous genome scans have revealed more than 100 genetic loci associated with different aspects of normal-range facial variation. Most of these loci have been detected in Europeans, with few studies focusing on other ancestral groups. Consequently, the degree to which facial traits share a common genetic basis across diverse sets of humans remains largely unknown. We therefore investigated the genetic basis of facial morphology in an East African cohort. We applied an open-ended data-driven phenotyping approach to a sample of 2,595 3D facial images collected on Tanzanian children. This approach segments the face into hierarchically arranged, multivariate features that capture the shape variation after adjusting for age, sex, height, weight, facial size and population stratification. Genome scans of these multivariate shape phenotypes revealed significant (p < 2.5 × 10-8) signals at 20 loci, which were enriched for active chromatin elements in human cranial neural crest cells and embryonic craniofacial tissue, consistent with an early developmental origin of the facial variation. Two of these associations were in highly conserved regions showing craniofacial-specific enhancer activity during embryological development (5q31.1 and 12q21.31). Six of the 20 loci surpassed a stricter threshold accounting for multiple phenotypes with study-wide significance (p < 6.25 × 10-10). Cross-population comparisons indicated 10 association signals were shared with Europeans (seven sharing the same associated SNP), and facilitated fine-mapping of causal variants at previously reported loci. Taken together, these results may point to both shared and population-specific components to the genetic architecture of facial variation.
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População Negra/genética , Face/anatomia & histologia , Estudo de Associação Genômica Ampla/métodos , Locos de Características Quantitativas , População Branca/genética , Adolescente , Criança , Pré-Escolar , Estudos de Coortes , Feminino , Humanos , Processamento de Imagem Assistida por Computador , Masculino , Polimorfismo de Nucleotídeo Único , Tanzânia , Adulto JovemRESUMO
OBJECTIVES: To develop and evaluate a geometric deep-learning network to automatically place seven palatal landmarks on digitized maxillary dental casts. SETTINGS AND SAMPLE POPULATION: The sample comprised individuals with permanent dentition of various ethnicities. The network was trained from manual landmark annotations on 732 dental casts and evaluated on 104 dental casts. MATERIALS AND METHODS: A geometric deep-learning network was developed to hierarchically learn features from point-clouds representing the 3D surface of each cast. These features predict the locations of seven palatal landmarks. RESULTS: Repeat-measurement reliability was <0.3 mm for all landmarks on all casts. Accuracy is promising. The proportion of test subjects with errors less than 2 mm was between 0.93 and 0.68, depending on the landmark. Unusually shaped and large palates generate the highest errors. There was no evidence for a difference in mean palatal shape estimated from manual compared to the automatic landmarking. The automatic landmarking reduces sample variation around the mean and reduces measurements of palatal size. CONCLUSIONS: The automatic landmarking method shows excellent repeatability and promising accuracy, which can streamline patient assessment and research studies. However, landmark indications should be subject to visual quality control.
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Aprendizado Profundo , Humanos , Imageamento Tridimensional , Maxila , Palato , Reprodutibilidade dos TestesRESUMO
OBJECTIVES: Palatal shape contains a lot of information that is of clinical interest. Moreover, palatal shape analysis can be used to guide or evaluate orthodontic treatments. A statistical shape model (SSM) is a tool that, by means of dimensionality reduction, aims at compactly modeling the variance of complex shapes for efficient analysis. In this report, we evaluate several competing approaches to constructing SSMs for the human palate. SETTING AND SAMPLE POPULATION: This study used a sample comprising digitized 3D maxillary dental casts from 1,324 individuals. MATERIALS AND METHODS: Principal component analysis (PCA) and autoencoders (AE) are popular approaches to construct SSMs. PCA is a dimension reduction technique that provides a compact description of shapes by uncorrelated variables. AEs are situated in the field of deep learning and provide a non-linear framework for dimension reduction. This work introduces the singular autoencoder (SAE), a hybrid approach that combines the most important properties of PCA and AEs. We assess the performance of the SAE using standard evaluation tools for SSMs, including accuracy, generalization, and specificity. RESULTS: We found that the SAE obtains equivalent results to PCA and AEs for all evaluation metrics. SAE scores were found to be uncorrelated and provided an optimally compact representation of the shapes. CONCLUSION: We conclude that the SAE is a promising tool for 3D palatal shape analysis, which effectively combines the power of PCA with the flexibility of deep learning. This opens future AI driven applications of shape analysis in orthodontics and other related clinical disciplines.
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Aprendizado Profundo , Ortodontia , Humanos , Maxila , Modelos Estatísticos , PalatoRESUMO
INTRODUCTION: Although stereophotogrammetry is increasingly popular for 3-dimensional face scanning, commercial solutions remain quite expensive, limiting its accessibility. We propose a more affordable, custom-built photogrammetry setup (Stereo-Face 3D, SF3D) and evaluate its variability within and between systems. METHODS: Twenty-nine subjects and a mannequin head were imaged 3 times using SF3D and a commercially available system. An anthropometric mask was mapped viscoelastically onto the reconstructed meshes using MeshMonk (https://github.com/TheWebMonks/meshmonk). Within systems, shape variability was determined by calculating the root-mean-square error (RMSE) of the Procrustes distance between each of the subject's 3 scans and the subject's ground truth (calculated by averaging the mappings after a nonscaled generalized Procrustes superimposition). Intersystem variability was determined by similarly comparing the ground truth mappings of both systems. Two-factor Procrustes analysis of variance was used to partition the intersystem shape variability to understand the source of the discrepancies between the facial shapes acquired by both systems. RESULTS: The RMSEs of the within-system shape variability for 3dMDFace and SF3D were 0.52 ± 0.07 mm and 0.44 ± 0.16 mm, respectively. The corresponding values for the mannequin head were 0.42 ± 0.02 mm and 0.29 ± 0.03 mm, respectively. The between-systems RMSE was 1.6 ± 0.34 mm for the study group and 1.38 mm for the mannequin head. A 2-factor analysis indicated that variability attributable to the system was expressed mainly at the upper eyelids, nasal tip and alae, and chin areas. CONCLUSIONS: The variability values of the custom-built setup presented here were competitive to a state-of-the-art commercial system at a more affordable level of investment.
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Fotogrametria , Implantes Dentários , Face , Imageamento TridimensionalRESUMO
Human facial shape, while strongly heritable, involves both genetic and structural complexity, necessitating precise phenotyping for accurate assessment. Common phenotyping strategies include simplifying 3D facial features into univariate traits such as anthropometric measurements (e.g., inter-landmark distances), unsupervised dimensionality reductions (e.g., principal component analysis (PCA) and auto-encoder (AE) approaches), and assessing resemblance to particular facial gestalts (e.g., syndromic facial archetypes). This study provides a comparative assessment of these strategies in genome-wide association studies (GWASs) of 3D facial shape. Specifically, we investigated inter-landmark distances, PCA and AE-derived latent dimensions, and facial resemblance to random, extreme, and syndromic gestalts within a GWAS of 8,426 individuals of recent European ancestry. Inter-landmark distances exhibit the highest SNP-based heritability as estimated via LD score regression, followed by AE dimensions. Conversely, resemblance scores to extreme and syndromic facial gestalts display the lowest heritability, in line with expectations. Notably, the aggregation of multiple GWASs on facial resemblance to random gestalts reveals the highest number of independent genetic loci. This novel, easy-to-implement phenotyping approach holds significant promise for capturing genetically relevant morphological traits derived from complex biomedical imaging datasets, and its applications extend beyond faces. Nevertheless, these different phenotyping strategies capture different genetic influences on craniofacial shape. Thus, it remains valuable to explore these strategies individually and in combination to gain a more comprehensive understanding of the genetic factors underlying craniofacial shape and related traits. Author Summary: Advancements linking variation in the human genome to phenotypes have rapidly evolved in recent decades and have revealed that most human traits are influenced by genetic variants to at least some degree. While many traits, such as stature, are straightforward to acquire and investigate, the multivariate and multipartite nature of facial shape makes quantification more challenging. In this study, we compared the impact of different facial phenotyping approaches on gene mapping outcomes. Our findings suggest that the choice of facial phenotyping method has an impact on apparent trait heritability and the ability to detect genetic association signals. These results offer valuable insights into the importance of phenotyping in genetic investigations, especially when dealing with highly complex morphological traits.
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Analysis of population structure and genomic ancestry remains an important topic in human genetics and bioinformatics. Commonly used methods require high-quality genotype data to ensure accurate inference. However, in practice, laboratory artifacts and outliers are often present in the data. Moreover, existing methods are typically affected by the presence of related individuals in the dataset. In this work, we propose a novel hybrid method, called SAE-IBS, which combines the strengths of traditional matrix decomposition-based (e.g., principal component analysis) and more recent neural network-based (e.g., autoencoders) solutions. Namely, it yields an orthogonal latent space enhancing dimensionality selection while learning non-linear transformations. The proposed approach achieves higher accuracy than existing methods for projecting poor quality target samples (genotyping errors and missing data) onto a reference ancestry space and generates a robust ancestry space in the presence of relatedness. We introduce a new approach and an accompanying open-source program for robust ancestry inference in the presence of missing data, genotyping errors, and relatedness. The obtained ancestry space allows for non-linear projections and exhibits orthogonality with clearly separable population groups.
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Genética Populacional , Redes Neurais de Computação , Humanos , Genótipo , Análise de Componente PrincipalRESUMO
Facial ancestry can be described as variation that exists in facial features that are shared amongst members of a population due to environmental and genetic effects. Even within Europe, faces vary among subregions and may lead to confounding in genetic association studies if unaccounted for. Genetic studies use genetic principal components (PCs) to describe facial ancestry to circumvent this issue. Yet the phenotypic effect of these genetic PCs on the face has yet to be described, and phenotype-based alternatives compared. In anthropological studies, consensus faces are utilized as they depict a phenotypic, not genetic, ancestry effect. In this study, we explored the effects of regional differences on facial ancestry in 744 Europeans using genetic and anthropological approaches. Both showed similar ancestry effects between subgroups, localized mainly to the forehead, nose, and chin. Consensus faces explained the variation seen in only the first three genetic PCs, differing more in magnitude than shape change. Here we show only minor differences between the two methods and discuss a combined approach as a possible alternative for facial scan correction that is less cohort dependent, more replicable, non-linear, and can be made open access for use across research groups, enhancing future studies in this field.
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Antropologia , Testa , Queixo , Consenso , Europa (Continente)RESUMO
Multi-view data offer advantages over single-view data for characterizing individuals, which is crucial in precision medicine toward personalized prevention, diagnosis, or treatment follow-up. Here, we develop a network-guided multi-view clustering framework named netMUG to identify actionable subgroups of individuals. This pipeline first adopts sparse multiple canonical correlation analysis to select multi-view features possibly informed by extraneous data, which are then used to construct individual-specific networks (ISNs). Finally, the individual subtypes are automatically derived by hierarchical clustering on these network representations. We applied netMUG to a dataset containing genomic data and facial images to obtain BMI-informed multi-view strata and showed how it could be used for a refined obesity characterization. Benchmark analysis of netMUG on synthetic data with known strata of individuals indicated its superior performance compared with both baseline and benchmark methods for multi-view clustering. In addition, the real-data analysis revealed subgroups strongly linked to BMI and genetic and facial determinants of these classes. NetMUG provides a powerful strategy, exploiting individual-specific networks to identify meaningful and actionable strata. Moreover, the implementation is easy to generalize to accommodate heterogeneous data sources or highlight data structures.
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A genome-wide association study (GWAS) of a complex, multi-dimensional morphological trait, such as the human face, typically relies on predefined and simplified phenotypic measurements, such as inter-landmark distances and angles. These measures are predominantly designed by human experts based on perceived biological or clinical knowledge. To avoid use handcrafted phenotypes (i.e., a priori expert-identified phenotypes), alternative automatically extracted phenotypic descriptors, such as features derived from dimension reduction techniques (e.g., principal component analysis), are employed. While the features generated by such computational algorithms capture the geometric variations of the biological shape, they are not necessarily genetically relevant. Therefore, genetically informed data-driven phenotyping is desirable. Here, we propose an approach where phenotyping is done through a data-driven optimization of trait heritability, defined as the degree of variation in a phenotypic trait in a population that is due to genetic variation. The resulting phenotyping process consists of two steps: 1) constructing a feature space that models shape variations using dimension reduction techniques, and 2) searching for directions in the feature space exhibiting high trait heritability using a genetic search algorithm (i.e., heuristic inspired by natural selection). We show that the phenotypes resulting from the proposed trait heritability-optimized training differ from those of principal components in the following aspects: 1) higher trait heritability, 2) higher SNP heritability, and 3) identification of the same number of independent genetic loci with a smaller number of effective traits. Our results demonstrate that data-driven trait heritability-based optimization enables the automatic extraction of genetically relevant phenotypes, as shown by their increased power in genome-wide association scans.
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Transcriptional regulation exhibits extensive robustness, but human genetics indicates sensitivity to transcription factor (TF) dosage. Reconciling such observations requires quantitative studies of TF dosage effects at trait-relevant ranges, largely lacking so far. TFs play central roles in both normal-range and disease-associated variation in craniofacial morphology; we therefore developed an approach to precisely modulate TF levels in human facial progenitor cells and applied it to SOX9, a TF associated with craniofacial variation and disease (Pierre Robin sequence (PRS)). Most SOX9-dependent regulatory elements (REs) are buffered against small decreases in SOX9 dosage, but REs directly and primarily regulated by SOX9 show heightened sensitivity to SOX9 dosage; these RE responses partially predict gene expression responses. Sensitive REs and genes preferentially affect functional chondrogenesis and PRS-like craniofacial shape variation. We propose that such REs and genes underlie the sensitivity of specific phenotypes to TF dosage, while buffering of other genes leads to robust, nonlinear dosage-to-phenotype relationships.
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Síndrome de Pierre Robin , Fatores de Transcrição SOX9 , Humanos , Fatores de Transcrição SOX9/genética , Síndrome de Pierre Robin/genética , Regulação da Expressão Gênica , Sequências Reguladoras de Ácido Nucleico , FenótipoRESUMO
Human craniofacial shape is highly variable yet highly heritable with genetic variants interacting through multiple layers of development. Here, we hypothesize that Mendelian phenotypes represent the extremes of a phenotypic spectrum and, using achondroplasia as an example, we introduce a syndrome-informed phenotyping approach to identify genomic loci associated with achondroplasia-like facial variation in the normal population. We compared three-dimensional facial scans from 43 individuals with achondroplasia and 8246 controls to calculate achondroplasia-like facial scores. Multivariate GWAS of the control scores revealed a polygenic basis for normal facial variation along an achondroplasia-specific shape axis, identifying genes primarily involved in skeletal development. Jointly modeling these genes in two independent control samples showed craniofacial effects approximating the characteristic achondroplasia phenotype. These findings suggest that both complex and Mendelian genetic variation act on the same developmentally determined axes of facial variation, providing new insights into the genetic intersection of complex traits and Mendelian disorders.
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Introduction: Multi-view data offer advantages over single-view data for characterizing individuals, which is crucial in precision medicine toward personalized prevention, diagnosis, or treatment follow-up. Methods: Here, we develop a network-guided multi-view clustering framework named netMUG to identify actionable subgroups of individuals. This pipeline first adopts sparse multiple canonical correlation analysis to select multi-view features possibly informed by extraneous data, which are then used to construct individual-specific networks (ISNs). Finally, the individual subtypes are automatically derived by hierarchical clustering on these network representations. Results: We applied netMUG to a dataset containing genomic data and facial images to obtain BMI-informed multi-view strata and showed how it could be used for a refined obesity characterization. Benchmark analysis of netMUG on synthetic data with known strata of individuals indicated its superior performance compared with both baseline and benchmark methods for multi-view clustering. The clustering derived from netMUG achieved an adjusted Rand index of 1 with respect to the synthesized true labels. In addition, the real-data analysis revealed subgroups strongly linked to BMI and genetic and facial determinants of these subgroups. Discussion: netMUG provides a powerful strategy, exploiting individual-specific networks to identify meaningful and actionable strata. Moreover, the implementation is easy to generalize to accommodate heterogeneous data sources or highlight data structures.
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The cranial vault in humans is highly variable, clinically relevant, and heritable, yet its genetic architecture remains poorly understood. Here, we conduct a joint multi-ancestry and admixed multivariate genome-wide association study on 3D cranial vault shape extracted from magnetic resonance images of 6772 children from the ABCD study cohort yielding 30 genome-wide significant loci. Follow-up analyses indicate that these loci overlap with genomic risk loci for sagittal craniosynostosis, show elevated activity cranial neural crest cells, are enriched for processes related to skeletal development, and are shared with the face and brain. We present supporting evidence of regional localization for several of the identified genes based on expression patterns in the cranial vault bones of E15.5 mice. Overall, our study provides a comprehensive overview of the genetics underlying normal-range cranial vault shape and its relevance for understanding modern human craniofacial diversity and the etiology of congenital malformations.
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Craniossinostoses , Estudo de Associação Genômica Ampla , Criança , Humanos , Animais , Camundongos , Crânio/diagnóstico por imagem , Craniossinostoses/genética , Ossos Faciais , Encéfalo/diagnóstico por imagemRESUMO
Background and Objective; Genetic risk factors for childhood cancer may also influence facial morphology. 3D photography can be used in the recognition of differences in face shape among individuals. In previous research, 3D facial photography was used to identify increased facial asymmetry and greater deviation from normal facial morphology in a group of individuals with distinct morphological features who had childhood cancer compared to healthy controls. In this study, we aim to determine whether there is a difference in facial morphology between children with cancer without previously selected morphological features and healthy controls, detected with 3D facial photography. METHODS: Facial 3D photographic images were obtained of children with a newly diagnosed malignancy. The resulting sample comprised 13 different cancer types. Patients were excluded if they had a known genetic cause of the cancer. Patients were compared to healthy controls, matched for sex, age and ethnic background. The degree of asymmetry and overall deviation of an individual's face from an age and sex typical control face were measured. RESULTS: A total of 163 patients of European descent were included. No significant difference in asymmetry between patients and controls could be identified. On average, patients deviated more from an age and sex typical face than the controls. CONCLUSION: This study shows that children with cancer deviate more than controls, possibly suggesting a higher prevalence of genetic anomalies within this group. The results suggest that this is not sufficient to discriminate patients from controls. Further research is necessary to explore the patterns of individual variation among the overall deviation of patients and controls.
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Imageamento Tridimensional , Neoplasias , Criança , Face , Assimetria Facial/diagnóstico por imagem , Humanos , FotografaçãoRESUMO
Evidence from model organisms and clinical genetics suggests coordination between the developing brain and face, but the role of this link in common genetic variation remains unknown. We performed a multivariate genome-wide association study of cortical surface morphology in 19,644 individuals of European ancestry, identifying 472 genomic loci influencing brain shape, of which 76 are also linked to face shape. Shared loci include transcription factors involved in craniofacial development, as well as members of signaling pathways implicated in brain-face cross-talk. Brain shape heritability is equivalently enriched near regulatory regions active in either forebrain organoids or facial progenitors. However, we do not detect significant overlap between shared brain-face genome-wide association study signals and variants affecting behavioral-cognitive traits. These results suggest that early in embryogenesis, the face and brain mutually shape each other through both structural effects and paracrine signaling, but this interplay may not impact later brain development associated with cognitive function.