Heritability maps of human face morphology through large-scale automated three-dimensional phenotyping

Facial modeling and measurement based upon homologous topographical features

Measurement of human faces is fundamental to many applications from recognition to genetic phenotyping. While anthropometric landmarks provide a conventional set of homologous measurement points, digital scans are increasingly used for facial measurement, despite the difficulties in establishing their homology. We introduce an alternative basis for facial measurement, which 1) provides a richer information density than discrete point measurements, 2) derives its homology from shared facial topography (ridges, folds, etc.), and 3) quantifies local morphological variation following the conventions and practices of anatomical description. A parametric model that permits matching a broad range of facial variation by the adjustment of 71 parameters is demonstrated by modeling a sample of 80 adult human faces. The surface of the parametric model can be adjusted to match each photogrammetric surface mesh generally to within 1 mm, demonstrating a novel and efficient means for facial shape encoding. We examine how well this scheme quantifies facial shape and variation with respect to geographic ancestry and sex. We compare this analysis with a more conventional, landmark-based geometric morphometric (GMM) study with 43 landmarks placed on the same set of scans. Our multivariate statistical analysis using the 71 attribute values separates geographic ancestry groups and sexes with a high degree of reliability, and these results are broadly similar to those from GMM, but with some key differences that we discuss. This approach is compared with conventional, non-parametric methods for the quantification of facial shape, including generality, information density, and the separation of size and shape. Potential uses for phenotypic and dysmorphology studies are also discussed.

The level of protein in the maternal murine diet modulates the facial appearance of the offspring via mTORC1 signaling

The development of craniofacial skeletal structures is fascinatingly complex and elucidation of the underlying mechanisms will not only provide novel scientific insights, but also help develop more effective clinical approaches to the treatment and/or prevention of the numerous congenital craniofacial malformations. To this end, we performed a genome-wide analysis of RNA transcription from non-coding regulatory elements by CAGE-sequencing of the facial mesenchyme of human embryos and cross-checked the active enhancers thus identified against genes, identified by GWAS for the normal range human facial appearance. Among the identified active cis-enhancers, several belonged to the components of the PI3/AKT/mTORC1/autophagy pathway. To assess the functional role of this pathway, we manipulated it both genetically and pharmacologically in mice and zebrafish. These experiments revealed that mTORC1 signaling modulates craniofacial shaping at the stage of skeletal mesenchymal condensations, with subsequent fine-tuning during clonal intercalation. This ability of mTORC1 pathway to modulate facial shaping, along with its evolutionary conservation and ability to sense external stimuli, in particular dietary amino acids, indicate that the mTORC1 pathway may play a role in facial phenotypic plasticity. Indeed, the level of protein in the diet of pregnant female mice influenced the activity of mTORC1 in fetal craniofacial structures and altered the size of skeletogenic clones, thus exerting an impact on the local geometry and craniofacial shaping. Overall, our findings indicate that the mTORC1 signaling pathway is involved in the effect of environmental conditions on the shaping of craniofacial structures.

De Novo Dissecting the Three-Dimensional Facial Morphology of 2379 Han Chinese Individuals

Phenotypic diversity, especially that of facial morphology, has not been fully investigated in the Han Chinese, which is the largest ethnic group in the world. In this study, we systematically analyzed a total of 14,838 facial traits representing 15 categories with both a large-scale three-dimensional (3D) manual landmarking database and computer-aided facial segmented phenotyping in 2379 Han Chinese individuals. Our results illustrate that homogeneous and heterogeneous facial morphological traits exist among Han Chinese populations across the three geographical regions: Zhengzhou, Taizhou, and Nanning. We identified 1560 shared features from extracted phenotypes, which characterized well the basic facial morphology of the Han Chinese. In particular, heterogeneous phenotypes showing population structures corresponded to geographical subpopulations. The greatest facial variation among these geographical populations was the angle of glabella, left subalare, and right cheilion (p = 3.4 × 10-161). Interestingly, we found that Han Chinese populations could be classified into northern Han, central Han, and southern Han at the phenotypic level, and the facial morphological variation pattern of central Han Chinese was between the typical differentiation of northern and southern Han Chinese. This result was highly consistent with the results revealed by the genetic data. These findings provide new insights into the analysis of multidimensional phenotypes as well as a valuable resource for further facial phenotype-genotype association studies in Han Chinese and East Asian populations.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43657-023-00109-x.

Cis-regulatory landscapes in the evolution and development of the mammalian skull

Extensive morphological variation found in mammals reflects the wide spectrum of their ecological adaptations. The highest morphological diversity is present in the craniofacial region, where geometry is mainly dictated by the bony skull. Mammalian craniofacial development represents complex multistep processes governed by numerous conserved genes that require precise spatio-temporal control. A central question in contemporary evolutionary biology is how a defined set of conserved genes can orchestrate formation of fundamentally different structures, and therefore how morphological variability arises. In principle, differential gene expression patterns during development are the source of morphological variation. With the emergence of multicellular organisms, precise regulation of gene expression in time and space is attributed to cis-regulatory elements. These elements contribute to higher-order chromatin structure and together with trans-acting factors control transcriptional landscapes that underlie intricate morphogenetic processes. Consequently, divergence in cis-regulation is believed to rewire existing gene regulatory networks and form the core of morphological evolution. This review outlines the fundamental principles of the genetic code and genomic regulation interplay during development. Recent work that deepened our comprehension of cis-regulatory element origin, divergence and function is presented here to illustrate the state-of-the-art research that uncovered the principles of morphological novelty. This article is part of the theme issue 'The mammalian skull: development, structure and function'.

The Facial Characteristics of Individuals with Posterior Crossbite: A Cross-Sectional Study

Facial morphology is known to be influenced by genetic and environmental factors. Scientific evidence regarding facial parameters in patients with posterior crossbite is lacking. This study aimed to investigate the association between posterior crossbite and facial parameters. This cross-sectional study included 34 adolescents with and 34 adolescents without posterior crossbite in the age range from 13 to 15 years. Facial surface scans were acquired with a 3dMD imaging system, and landmark-based analysis was performed. Data were analyzed using the Mann-Whitney U test and Spearman's correlations. Individuals in the control group had lower face heights (females: p = 0.003, r = 0.45; males: p = 0.005, r = 0.57). The control group females presented with smaller intercanthal width (p = 0.04; r = 0.31) and anatomical nose width (p = 0.004; r = 0.43) compared with the crossbite group females. The males in the control group had wider nostrils. In the control group, significant correlations among different facial parameters were more common, including the correlations between eye width and other transversal face measurements. On the contrary, the facial width was correlated with nasal protrusion (r = 0.657; p < 0.01) and the morphological width of the nose (r = 0.505; p < 0.05) in the crossbite group alone. In both groups, the philtrum width was linked with the anatomical and morphological widths of the nose. Conclusions: Patients with posterior crossbites have increased face height and different patterns of facial proportions compared with individuals without crossbites.

Deep learning features in facial identification and the likelihood ratio bound

In recent years, the score-based likelihood ratio (SLR) method for facial comparison has attracted considerable research attention. This method relies on the match scores that are calculated from the features obtained from facial recognition systems, deep learning based in particular. However, this concept has not been completely understood. Therefore, this study is aimed at investigating deep learning facial features, and the SLR levels of their match scores. We propose a new interpretation that the deep learning feature is a class characteristic. Based on a large-scale data set experiment, we present evidence that the log SLR value of deep learning features can reach 8 in some data sets. The study results imply that the SLR of deep learning features is a useful method for facial identification, especially when the suspected image is obtained via a CCTV camera.

Advancement in Human Face Prediction Using DNA

The rapid improvements in identifying the genetic factors contributing to facial morphology have enabled the early identification of craniofacial syndromes. Similarly, this technology can be vital in forensic cases involving human identification from biological traces or human remains, especially when reference samples are not available in the deoxyribose nucleic acid (DNA) database. This review summarizes the currently used methods for predicting human phenotypes such as age, ancestry, pigmentation, and facial features based on genetic variations. To identify the facial features affected by DNA, various two-dimensional (2D)- and three-dimensional (3D)-scanning techniques and analysis tools are reviewed. A comparison between the scanning technologies is also presented in this review. Face-landmarking techniques and face-phenotyping algorithms are discussed in chronological order. Then, the latest approaches in genetic to 3D face shape analysis are emphasized. A systematic review of the current markers that passed the threshold of a genome-wide association (GWAS) of single nucleotide polymorphism (SNP)-face traits from the GWAS Catalog is also provided using the preferred reporting items for systematic reviews and meta-analyses (PRISMA), approach. Finally, the current challenges in forensic DNA phenotyping are analyzed and discussed.

The heritability of vocal tract structures estimated from structural MRI in a large cohort of Dutch twins

While language is expressed in multiple modalities, including sign, writing, or whistles, speech is arguably the most common. The human vocal tract is capable of producing the bewildering diversity of the 7000 or so currently spoken languages, but relatively little is known about its genetic bases, especially in what concerns normal variation. Here, we capitalize on five cohorts totaling 632 Dutch twins with structural magnetic resonance imaging (MRI) data. Two raters placed clearly defined (semi)landmarks on each MRI scan, from which we derived 146 measures capturing the dimensions and shape of various vocal tract structures, but also aspects of the head and face. We used Genetic Covariance Structure Modeling to estimate the additive genetic, common environmental or non-additive genetic, and unique environmental components, while controlling for various confounds and for any systematic differences between the two raters. We found high heritability, h2, for aspects of the skull and face, the mandible, the anteroposterior (horizontal) dimension of the vocal tract, and the position of the hyoid bone. These findings extend the existing literature, and open new perspectives for understanding the complex interplay between genetics, environment, and culture that shape our vocal tracts, and which may help explain cross-linguistic differences in phonetics and phonology.

Gattaca as a lens on contemporary genetics: marking 25 years into the film's "not-too-distant" future

The 1997 film Gattaca has emerged as a canonical pop culture reference used to discuss modern controversies in genetics and bioethics. It appeared in theaters a few years prior to the announcement of the "completion" of the human genome (2000), as the science of human genetics was developing a renewed sense of its social implications. The story is set in a near-future world in which parents can, with technological assistance, influence the genetic composition of their offspring on the basis of predicted life outcomes. The current moment-25 years after the film's release-offers an opportunity to reflect on where society currently stands with respect to the ideas explored in Gattaca. Here, we review and discuss several active areas of genetic research-genetic prediction, embryo selection, forensic genetics, and others-that interface directly with scenes and concepts in the film. On its silver anniversary, we argue that Gattaca remains an important reflection of society's expectations and fears with respect to the ways that genetic science has manifested in the real world. In accompanying supplemental material, we offer some thought questions to guide group discussions inside and outside of the classroom.

Influence of the nutritional status on facial morphology in young Japanese women

Evidence regarding the possible influence of nutritional status on the facial morphology has thus far been insufficient. We examined whether or not the physical body compositions and dietary behaviors were correlated with any morphological characteristics of the face. One hundred and fifteen young Japanese women participated. Variables representing the dietary behaviors were extracted from self-reported survey data, and corresponding three-dimensional (3D) facial images and body compositions were examined. Multivariate analyses identified significant relationships between the nutritional status and facial topography (p < 0.05). The clustering method revealed the existence of three dietary condition patterns ("balanced diet", "high-calorie-diet" with obesity tendency, and "imbalanced low-calorie-diet" with sarcopenic obesity tendency). Among these three patterns, a round face (increased facial width; analysis of variance [ANOVA], p < 0.05) was observed in the high-calorie-diet pattern, while the imbalanced low-calorie-diet pattern showed a more masculine face (increased face height, decreased eye height, increased non-allometric sexual shape differences; ANOVA, p < 0.05), thus suggesting the possibility of sex-hormonal influences. In summary, the body composition and dietary behaviors were found to influence the facial morphology, and potential biological influences were discussed.

Craniofacial superimposition: a review of focus distance estimation methods and an extension to profile view photographs

Craniofacial superimposition concerns the photographic overlay of skulls and faces, for skeletal identification. As a phased method that depends on photographic optics first and anatomical comparisons second, superimposition is strongly underpinned by the physics of light travel through glass lenses. So that the downstream (and dependent) anatomical evaluations are not thwarted or erroneous identification decisions risked, it is critical that the optical prerequisites for valid image comparisons are met. As focus distance sets the perspective, the focus distance used for skull photography must be matched to that used at face photography, so that anatomically comparable 1:1 images are obtained. In this paper, we review the pertinent camera optics that set these nonnegotiable fundamentals and review a recently proposed method for focus distance estimation. We go beyond the original method descriptions to explain the mathematical justification for the PerspectiveX algorithm and provide an extension to profile images. This enables the first scientifically grounded use of profile view (or partial profile view) photographs in craniofacial superimposition. Proof of concept is provided by multiple worked examples of the focus distance estimation for frontal and profile view images of three of the authors at known focus distances. This innovation (1) removes longstanding trial-and-error components of present-day superimposition methods, (2) provides the first systematic and complete optical basis for image comparison in craniofacial superimposition, and (3) will enable anatomical comparison standards to be established from a valid grassroots basis where complexities of camera vantage point are removed as interfering factors.

Genetic influences on dentognathic morphology in the Jirel population of Nepal

Patterns of genetic variation and covariation impact the evolution of the craniofacial complex and contribute to clinically significant malocclusions in modern human populations. Previous quantitative genetic studies have estimated the heritabilities and genetic correlations of skeletal and dental traits in humans and nonhuman primates, but none have estimated these quantitative genetic parameters across the dentognathic complex. A large and powerful pedigree from the Jirel population of Nepal was leveraged to estimate heritabilities and genetic correlations in 62 maxillary and mandibular arch dimensions, incisor and canine lengths, and post-canine tooth crown areas (N ≥ 739). Quantitative genetic parameter estimation was performed using maximum likelihood-based variance decomposition. Residual heritability estimates were significant for all traits, ranging from 0.269 to 0.898. Genetic correlations were positive for all trait pairs. Principal components analyses of the phenotypic and genetic correlation matrices indicate an overall size effect across all measurements on the first principal component. Additional principal components demonstrate positive relationships between post-canine tooth crown areas and arch lengths and negative relationships between post-canine tooth crown areas and arch widths, and between arch lengths and arch widths. Based on these findings, morphological variation in the human dentognathic complex may be constrained by genetic relationships between dental dimensions and arch lengths, with weaker genetic correlations between these traits and arch widths allowing for variation in arch shape. The patterns identified are expected to have impacted the evolution of the dentognathic complex and its genetic architecture as well as the prevalence of dental crowding in modern human populations.

Decoding the Human Face: Progress and Challenges in Understanding the Genetics of Craniofacial Morphology

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.

Predicting Physical Appearance from DNA Data-Towards Genomic Solutions

The idea of forensic DNA intelligence is to extract from genomic data any information that can help guide the investigation. The clues to the externally visible phenotype are of particular practical importance. The high heritability of the physical phenotype suggests that genetic data can be easily predicted, but this has only become possible with less polygenic traits. The forensic community has developed DNA-based predictive tools by employing a limited number of the most important markers analysed with targeted massive parallel sequencing. The complexity of the genetics of many other appearance phenotypes requires big data coupled with sophisticated machine learning methods to develop accurate genomic predictors. A significant challenge in developing universal genomic predictive methods will be the collection of sufficiently large data sets. These should be created using whole-genome sequencing technology to enable the identification of rare DNA variants implicated in phenotype determination. It is worth noting that the correctness of the forensic sketch generated from the DNA data depends on the inclusion of an age factor. This, however, can be predicted by analysing epigenetic data. An important limitation preventing whole-genome approaches from being commonly used in forensics is the slow progress in the development and implementation of high-throughput, low DNA input sequencing technologies. The example of palaeoanthropology suggests that such methods may possibly be developed in forensics.

Relating multivariate shapes to genescapes using phenotype-biological process associations for craniofacial shape

Realistic mappings of genes to morphology are inherently multivariate on both sides of the equation. The importance of coordinated gene effects on morphological phenotypes is clear from the intertwining of gene actions in signaling pathways, gene regulatory networks, and developmental processes underlying the development of shape and size. Yet, current approaches tend to focus on identifying and localizing the effects of individual genes and rarely leverage the information content of high-dimensional phenotypes. Here, we explicitly model the joint effects of biologically coherent collections of genes on a multivariate trait – craniofacial shape – in a sample of n = 1145 mice from the Diversity Outbred (DO) experimental line. We use biological process Gene Ontology (GO) annotations to select skeletal and facial development gene sets and solve for the axis of shape variation that maximally covaries with gene set marker variation. We use our process-centered, multivariate genotype-phenotype (process MGP) approach to determine the overall contributions to craniofacial variation of genes involved in relevant processes and how variation in different processes corresponds to multivariate axes of shape variation. Further, we compare the directions of effect in phenotype space of mutations to the primary axis of shape variation associated with broader pathways within which they are thought to function. Finally, we leverage the relationship between mutational and pathway-level effects to predict phenotypic effects beyond craniofacial shape in specific mutants. We also introduce an online application that provides users the means to customize their own process-centered craniofacial shape analyses in the DO. The process-centered approach is generally applicable to any continuously varying phenotype and thus has wide-reaching implications for complex trait genetics.

Facial asymmetry in parents of children on the autism spectrum

Greater facial asymmetry has been consistently found in children with autism spectrum disorder (ASD) relative to children without ASD. There is substantial evidence that both facial structure and the recurrence of ASD diagnosis are highly heritable within a nuclear family. Furthermore, sub-clinical levels of autistic-like behavioural characteristics have also been reported in first-degree relatives of individuals with ASD, commonly known as the 'broad autism phenotype'. Therefore, the aim of the current study was to examine whether a broad autism phenotype expresses as facial asymmetry among 192 biological parents of autistic individuals (134 mothers) compared to those of 163 age-matched adults without a family history of ASD (113 females). Using dense surface-modelling techniques on three dimensional facial images, we found evidence for greater facial asymmetry in parents of autistic individuals compared to age-matched adults in the comparison group (p = 0.046, d = 0.21 [0.002, 0.42]). Considering previous findings and the current results, we conclude that facial asymmetry expressed in the facial morphology of autistic children may be related to heritability factors. LAY ABSTRACT: In a previous study, we showed that autistic children presented with greater facial asymmetry than non-autistic children. In the current study, we examined the amount of facial asymmetry shown on three-dimensional facial images of 192 parents of autistic children compared to a control group consisting of 163 similarly aged adults with no known history of autism. Although parents did show greater levels of facial asymmetry than those in the control group, this effect is statistically small. We concluded that the facial asymmetry previously found in autistic children may be related to genetic factors.

Genome scans of facial features in East Africans and cross-population comparisons reveal novel associations

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.

Genetic factors play an important role in shaping human facial features. Over the last decade, studies have identified numerous genes associated with various facial traits. The vast majority of these studies have focused on European or Asian populations, while African populations have been underrepresented. Increasing the diversity of these analyses can reveal novel associations and cross-population analyses can help deepen our understanding of known genetic associations. We therefore performed a genome scan of 3D facial features in African children from Tanzania and then compared our results to Europeans. We found 20 regions of the genome associated with facial shape in Tanzanian children, 10 of which were also present in Europeans, indicating evidence for a partly shared genetic basis for human facial shape across populations. In addition, about half of the genetic associations observed in Tanzanians were not present in Europeans, and some of the shared signals differed between populations in the specific genetic variants associated or specific facial traits affected. These results shed light on the shared and population-specific genetic contributors to normal-range facial variation.

Heritable functional architecture in human visual cortex

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We analyzed retinotopic maps from monozygotic and dizygotic twin pairs.

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Visual field maps in V1-V3 are more similar in monozygotic twins.

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Heritability is greater in V1 and V3 for polar angle and population receptive field sizes.

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Eccentricity maps show lesser degree of heritability.

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Further evidence for link between cortical morphology and topology of retinotopic maps.

How much of the functional organization of our visual system is inherited? Here we tested the heritability of retinotopic maps in human visual cortex using functional magnetic resonance imaging. We demonstrate that retinotopic organization shows a closer correspondence in monozygotic (MZ) compared to dizygotic (DZ) twin pairs, suggesting a partial genetic determination. Using population receptive field (pRF) analysis to examine the preferred spatial location and selectivity of these neuronal populations, we estimate a heritability around 10–20% for polar angle preferences and spatial selectivity, as quantified by pRF size, in extrastriate areas V2 and V3. Our findings are consistent with heritability in both the macroscopic arrangement of visual regions and stimulus tuning properties of visual cortex. This could constitute a neural substrate for variations in a range of perceptual effects, which themselves have been found to be at least partially genetically determined. These findings also add convergent evidence for the hypothesis that functional map topology is linked with cortical morphology.

3D facial phenotyping by biometric sibling matching used in contemporary genomic methodologies

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.

Valproic acid-induced changes of 4D nuclear morphology in astrocyte cells

Histone deacetylase inhibitors, such as valproic acid (VPA), have important clinical therapeutic and cellular reprogramming applications. They induce chromatin reorganization that is associated with altered cellular morphology. However, there is a lack of comprehensive characterization of VPA-induced changes of nuclear size and shape. Here, we quantify 3D nuclear morphology of primary human astrocyte cells treated with VPA over time (hence, 4D). We compared volumetric and surface-based representations and identified seven features that jointly discriminate between normal and treated cells with 85% accuracy on day 7. From day 3, treated nuclei were more elongated and flattened and then continued to morphologically diverge from controls over time, becoming larger and more irregular. On day 7, most of the size and shape descriptors demonstrated significant differences between treated and untreated cells, including a 24% increase in volume and 6% reduction in extent (shape regularity) for treated nuclei. Overall, we show that 4D morphometry can capture how chromatin reorganization modulates the size and shape of the nucleus over time. These nuclear structural alterations may serve as a biomarker for histone (de-)acetylation events and provide insights into mechanisms of astrocytes-to-neurons reprogramming.

hReg-CNCC reconstructs a regulatory network in human cranial neural crest cells and annotates variants in a developmental context

Cranial Neural Crest Cells (CNCC) originate at the cephalic region from forebrain, midbrain and hindbrain, migrate into the developing craniofacial region, and subsequently differentiate into multiple cell types. The entire specification, delamination, migration, and differentiation process is highly regulated and abnormalities during this craniofacial development cause birth defects. To better understand the molecular networks underlying CNCC, we integrate paired gene expression & chromatin accessibility data and reconstruct the genome-wide human Regulatory network of CNCC (hReg-CNCC). Consensus optimization predicts high-quality regulations and reveals the architecture of upstream, core, and downstream transcription factors that are associated with functions of neural plate border, specification, and migration. hReg-CNCC allows us to annotate genetic variants of human facial GWAS and disease traits with associated cis-regulatory modules, transcription factors, and target genes. For example, we reveal the distal and combinatorial regulation of multiple SNPs to core TF ALX1 and associations to facial distances and cranial rare disease. In addition, hReg-CNCC connects the DNA sequence differences in evolution, such as ultra-conserved elements and human accelerated regions, with gene expression and phenotype. hReg-CNCC provides a valuable resource to interpret genetic variants as early as gastrulation during embryonic development. The network resources are available at https://github.com/AMSSwanglab/hReg-CNCC .

Impact of low-frequency coding variants on human facial shape

The contribution of low-frequency variants to the genetic architecture of normal-range facial traits is unknown. We studied the influence of low-frequency coding variants (MAF < 1%) in 8091 genes on multi-dimensional facial shape phenotypes in a European cohort of 2329 healthy individuals. Using three-dimensional images, we partitioned the full face into 31 hierarchically arranged segments to model facial morphology at multiple levels, and generated multi-dimensional phenotypes representing the shape variation within each segment. We used MultiSKAT, a multivariate kernel regression approach to scan the exome for face-associated low-frequency variants in a gene-based manner. After accounting for multiple tests, seven genes (AR, CARS2, FTSJ1, HFE, LTB4R, TELO2, NECTIN1) were significantly associated with shape variation of the cheek, chin, nose and mouth areas. These genes displayed a wide range of phenotypic effects, with some impacting the full face and others affecting localized regions. The missense variant rs142863092 in NECTIN1 had a significant effect on chin morphology and was predicted bioinformatically to have a deleterious effect on protein function. Notably, NECTIN1 is an established craniofacial gene that underlies a human syndrome that includes a mandibular phenotype. We further showed that nectin1a mutations can affect zebrafish craniofacial development, with the size and shape of the mandibular cartilage altered in mutant animals. Findings from this study expanded our understanding of the genetic basis of normal-range facial shape by highlighting the role of low-frequency coding variants in several novel genes.

Familiar occurrence of facial asymmetry: a pilot study

BACKGROUND

The point at which "normal" asymmetry becomes "abnormal" can be defined by an aesthetic limit and a functional limit. The underlying causes are still not fully discovered; the etiology includes congenital disorders, acquired diseases, and traumatic and developmental deformities. Our purpose was to investigate the possible genetic liability in the transmissibility of the asymmetric traits, through an analysis developed by twofold approach: 1) exploring and recording the family history through the use of a specific questionnaire; and 2) examining differences in laterality between the patients and their corresponding parent by a facial analysis.

METHODS

A total of 52 Italian subjects (57% females, 43% males; mean age: 11.7 years), showing a clinically detectable asymmetry, were selected. Individuals in the sample were selected according to the diagnosis of facial asymmetry, non-syndromic patients, participation by informed consent, and negative medical history of the maxillo-facial complex. A specifically designed questionnaire was used to investigate the presence of the asymmetric trait in the family. Differences in length between distance from the anthropometric points to the facial midline and to horizontal reference were measured on a frontal facial photograph. For all the subjects recruited the same analysis was performed on the frontal facial photographs of both the parents. A descriptive and interferential statistical analysis was performed on the data.

RESULTS

Concerning the linear measurement, in a high percentage of parent-child pairs there is a correspondence of laterality of asymmetry traits, with a more common relation with the maternal trait. Sixty-five percent of parents with correspondence of laterality reported a positive family history of asymmetry.

CONCLUSIONS

The analysis of the obtained data shows that the mother is the parent most involved in the correspondence of laterality. Further analysis would be appropriate to investigate this result.

Decoding of facial strains via conformable piezoelectric interfaces

Devices that facilitate nonverbal communication typically require high computational loads or have rigid and bulky form factors that are unsuitable for use on the face or on other curvilinear body surfaces. Here, we report the design and pilot testing of an integrated system for decoding facial strains and for predicting facial kinematics. The system consists of mass-manufacturable, conformable piezoelectric thin films for strain mapping; multiphysics modelling for analysing the nonlinear mechanical interactions between the conformable device and the epidermis; and three-dimensional digital image correlation for reconstructing soft-tissue surfaces under dynamic deformations as well as for informing device design and placement. In healthy individuals and in patients with amyotrophic lateral sclerosis, we show that the piezoelectric thin films, coupled with algorithms for the real-time detection and classification of distinct skin-deformation signatures, enable the reliable decoding of facial movements. The integrated system could be adapted for use in clinical settings as a nonverbal communication technology or for use in the monitoring of neuromuscular conditions.

Modelling the shape of the pig scapula

BACKGROUND

The shape of pig scapula is complex and is important for sow robustness and health. To better understand the relationship between 3D shape of the scapula and functional traits, it is necessary to build a model that explains most of the morphological variation between animals. This requires point correspondence, i.e. a map that explains which points represent the same piece of tissue among individuals. The objective of this study was to further develop an automated computational pipeline for the segmentation of computed tomography (CT) scans to incorporate 3D modelling of the scapula, and to develop a genetic prediction model for 3D morphology.

RESULTS

The surface voxels of the scapula were identified on 2143 CT-scanned pigs, and point correspondence was established by predicting the coordinates of 1234 semi-landmarks on each animal, using the coherent point drift algorithm. A subsequent principal component analysis showed that the first 10 principal components covered more than 80% of the total variation in 3D shape of the scapula. Using principal component scores as phenotypes in a genetic model, estimates of heritability ranged from 0.4 to 0.8 (with standard errors from 0.07 to 0.08). To validate the entire computational pipeline, a statistical model was trained to predict scapula shape based on marker genotype data. The mean prediction reliability averaged over the whole scapula was equal to 0.18 (standard deviation = 0.05) with a higher reliability in convex than in concave regions.

CONCLUSIONS

Estimates of heritability of the principal components were high and indicated that the computational pipeline that processes CT data to principal component phenotypes was associated with little error. Furthermore, we showed that it is possible to predict the 3D shape of scapula based on marker genotype data. Taken together, these results show that the proposed computational pipeline closes the gap between a point cloud representing the shape of an animal and its underlying genetic components.

Facial shape and allometry quantitative trait locus intervals in the Diversity Outbred mouse are enriched for known skeletal and facial development genes

The biology of how faces are built and come to differ from one another is complex. Discovering normal variants that contribute to differences in facial morphology is one key to untangling this complexity, with important implications for medicine and evolutionary biology. This study maps quantitative trait loci (QTL) for skeletal facial shape using Diversity Outbred (DO) mice. The DO is a randomly outcrossed population with high heterozygosity that captures the allelic diversity of eight inbred mouse lines from three subspecies. The study uses a sample of 1147 DO animals (the largest sample yet employed for a shape QTL study in mouse), each characterized by 22 three-dimensional landmarks, 56,885 autosomal and X-chromosome markers, and sex and age classifiers. We identified 37 facial shape QTL across 20 shape principal components (PCs) using a mixed effects regression that accounts for kinship among observations. The QTL include some previously identified intervals as well as new regions that expand the list of potential targets for future experimental study. Three QTL characterized shape associations with size (allometry). Median support interval size was 3.5 Mb. Narrowing additional analysis to QTL for the five largest magnitude shape PCs, we found significant overrepresentation of genes with known roles in growth, skeletal and facial development, and sensory organ development. For most intervals, one or more of these genes lies within 0.25 Mb of the QTL's peak. QTL effect sizes were small, with none explaining more than 0.5% of facial shape variation. Thus, our results are consistent with a model of facial diversity that is influenced by key genes in skeletal and facial development and, simultaneously, is highly polygenic.

Same father, same face: Deep learning reveals selection for signaling kinship in a wild primate

Many animals rely on facial traits to recognize their kin; however, whether these traits have been selected specifically for this function remains unknown. Using deep learning for face recognition, we present the first evidence that interindividual facial resemblance has been selected to signal paternal kinship. Mandrills (Mandrillus sphinx) live in matrilineal societies, in which females spend their entire lives not only with maternal half-sisters (MHS) but also with paternal half-sisters (PHS). We show that PHS have more differentiated social relationships compared to nonkin, suggesting the existence of kin recognition mechanisms. We further demonstrate that facial resemblance increases with genetic relatedness. However, PHS resemble each other visually more than MHS do, despite both kin categories sharing similar degrees of genetic relatedness. This paternally derived facial resemblance among PHS indicates selection to facilitate kin recognition. This study also highlights the potential of artificial intelligence to study phenotypic evolution.

Hunting for genes that shape human faces: Initial successes and challenges for the future

There is ample evidence from heritability studies, genetic syndromes and experimental animal models that facial morphology is strongly influenced by genes. In this brief review, we present an up-to-date overview of the efforts to identify genes associated with the size and shape of human facial features. We discuss recent methodological advances that have led to breakthroughs, but also the multitude of challenges facing the field. We offer perspective on possible applications of this line of research, particularly in the context of the precision genomics movement.

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.

Prenatal alcohol exposure and facial morphology in a UK cohort

BACKGROUND

High levels of prenatal alcohol exposure are known to cause an array of adverse outcomes including fetal alcohol syndrome (FAS); however, the effects of low to moderate exposure are less-well characterized. Previous findings suggest that differences in normal-range facial morphology may be a marker for alcohol exposure and related adverse effects.

METHODS

In the Avon Longitudinal Study of Parents and Children, we tested for an association between maternal alcohol consumption and six FAS-related facial phenotypes in their offspring, using both self-report questionnaires and the maternal genotype at rs1229984 in ADH1B as measures of maternal alcohol consumption.

RESULTS

In both self-reported alcohol consumption (N = 4233) and rs1229984 genotype (N = 3139) analyses, we found no strong statistical evidence for an association between maternal alcohol consumption and facial phenotypes tested. The directions of effect estimates were compatible with the known effects of heavy alcohol exposure, but confidence intervals were largely centered around zero.

CONCLUSIONS

There is no strong evidence, in a sample representative of the general population, for an effect of prenatal alcohol exposure on normal-range variation in facial morphology.

Influence of heritability on craniofacial soft tissue characteristics of monozygotic twins, dizygotic twins, and their siblings using Falconer's method and principal components analysis

Objective

The purpose of this study was to investigate the influence of heritability on the craniofacial soft tissue cephalometric characteristics of monozygotic (MZ) twins, dizygotic (DZ) twins, and their siblings (SIB).

Methods

The samples comprised Korean adult twins and their siblings (mean age, 39.8 years; MZ group, n = 36 pairs; DZ group, n = 13 pairs of the same gender; and SIB group, n = 26 pairs of the same gender). Thirty cephalometric variables were measured to characterize facial profile, facial height, soft-tissue thickness, and projection of nose and lip. Falconer's method was used to calculate heritability (low heritability, h² < 0.2; high heritability, h² > 0.9). After principal components analysis (PCA) was performed to extract the models, we calculated the intraclass correlation coefficient (ICC) value and heritability of each component.

Results

The MZ group exhibited higher ICC values for all cephalometric variables than DZ and SIB groups. Among cephalometric variables, the highest h²_(MZ-DZ) and h²_(MZ-SIB) values were observed for the nasolabial angle (NLA, 1.544 and 2.036), chin angle (1.342 and 1.112), soft tissue chin thickness (2.872 and 1.226), and upper lip thickness ratio (1.592 and 1.026). PCA derived eight components with 84.5% of a cumulative explanation. The components that exhibited higher values of h²_(MZ-DZ) and h²_(MZ-SIB) were PCA2, which includes facial convexity, NLA, and nose projection (1.026 and 0.972), and PCA7, which includes chin angle and soft tissue chin thickness (2.107 and 1.169).

Conclusions

The nose and soft tissue chin were more influenced by genetic factors than other soft tissues.

Spatially Dense 3D Facial Heritability and Modules of Co-heritability in a Father-Offspring Design

Introduction: The human face is a complex trait displaying a strong genetic component as illustrated by various studies on facial heritability. Most of these start from sparse descriptions of facial shape using a limited set of landmarks. Subsequently, facial features are preselected as univariate measurements or principal components and the heritability is estimated for each of these features separately. However, none of these studies investigated multivariate facial features, nor the co-heritability between different facial features. Here we report a spatially dense multivariate analysis of facial heritability and co-heritability starting from data from fathers and their children available within ALSPAC. Additionally, we provide an elaborate overview of related craniofacial heritability studies.

Methods: In total, 3D facial images of 762 father-offspring pairs were retained after quality control. An anthropometric mask was applied to these images to establish spatially dense quasi-landmark configurations. Partial least squares regression was performed and the (co-)heritability for all quasi-landmarks (∼7160) was computed as twice the regression coefficient. Subsequently, these were used as input to a hierarchical facial segmentation, resulting in the definition of facial modules that are internally integrated through the biological mechanisms of inheritance. Finally, multivariate heritability estimates were obtained for each of the resulting modules.

Results: Nearly all modular estimates reached statistical significance under 1,000,000 permutations and after multiple testing correction (p ≤ 1.3889 × 10^-3), displaying low to high heritability scores. Particular facial areas showing the greatest heritability were similar for both sons and daughters. However, higher estimates were obtained in the former. These areas included the global face, upper facial part (encompassing the nasion, zygomas and forehead) and nose, with values reaching 82% in boys and 72% in girls. The lower parts of the face only showed low to moderate levels of heritability.

Conclusion: In this work, we refrain from reducing facial variation to a series of individual measurements and analyze the heritability and co-heritability from spatially dense landmark configurations at multiple levels of organization. Finally, a multivariate estimation of heritability for global-to-local facial segments is reported. Knowledge of the genetic determination of facial shape is useful in the identification of genetic variants that underlie normal-range facial variation.

Facial Genetics: A Brief Overview

Historically, craniofacial genetic research has understandably focused on identifying the causes of craniofacial anomalies and it has only been within the last 10 years, that there has been a drive to detail the biological basis of normal-range facial variation. This initiative has been facilitated by the availability of low-cost hi-resolution three-dimensional systems which have the ability to capture the facial details of thousands of individuals quickly and accurately. Simultaneous advances in genotyping technology have enabled the exploration of genetic influences on facial phenotypes, both in the present day and across human history. There are several important reasons for exploring the genetics of normal-range variation in facial morphology.     - Disentangling the environmental factors and relative parental biological contributions to heritable traits can help to answer the age-old question "why we look the way that we do?"     - Understanding the etiology of craniofacial anomalies; e.g., unaffected family members of individuals with non-syndromic cleft lip/palate (nsCL/P) have been shown to differ in terms of normal-range facial variation to the general population suggesting an etiological link between facial morphology and nsCL/P.     - Many factors such as ancestry, sex, eye/hair color as well as distinctive facial features (such as, shape of the chin, cheeks, eyes, forehead, lips, and nose) can be identified or estimated using an individual's genetic data, with potential applications in healthcare and forensics.     - Improved understanding of historical selection and adaptation relating to facial phenotypes, for example, skin pigmentation and geographical latitude.     - Highlighting what is known about shared facial traits, medical conditions and genes.

Identification of five novel genetic loci related to facial morphology by genome-wide association studies

BACKGROUND

Face morphology is strongly determined by genetic factors. However, only a small number of genes related to face morphology have been identified to date. Here, we performed a two-stage genome-wide association study (GWAS) of 85 face morphological traits in 7569 Koreans (5643 in the discovery set and 1926 in the replication set).

RESULTS

In this study, we analyzed 85 facial traits, including facial angles. After discovery GWAS, 128 single nucleotide polymorphisms (SNPs) showing an association of P < 5 × 10^- 6 were selected to determine the replication of the associations, and meta-analysis of discovery GWAS and the replication analysis resulted in five genome-wide significant loci. The OSR1-WDR35 [rs7567283, G allele, beta (se) = -0.536 (0.096), P = 2.75 × 10^- 8] locus was associated with the facial frontal contour; the HOXD1-MTX2 [rs970797, A allele, beta (se) = 0.015 (0.003), P = 3.97 × 10^- 9] and WDR27 [rs3736712, C allele, beta (se) = 0.293 (0.048), P = 8.44 × 10^- 10] loci were associated with eye shape; and the SOX9 [rs2193054, C allele, beta (se) (ln-transformed) = -0.007 (0.001), P = 6.17 × 10^- 17] and DHX35 [rs2206437, A allele, beta (se) = -0.283 (0.047), P = 1.61 × 10^- 9] loci were associated with nose shape. WDR35 and SOX9 were related to known craniofacial malformations, i.e., cranioectodermal dysplasia 2 and campomelic dysplasia, respectively. In addition, we found three independent association signals in the SOX9 locus, and six known loci for nose size and shape were replicated in this study population. Interestingly, four SNPs within these five face morphology-related loci showed discrepancies in allele frequencies among ethnic groups.

CONCLUSIONS

We identified five novel face morphology loci that were associated with facial frontal contour, nose shape, and eye shape. Our findings provide useful genetic information for the determination of face morphology.

The genetic correlation between scapula shape and shoulder lesions in sows

Shoulder lesions and body condition of sows at weaning have both environmental and genetic causes. The traits can be scored at farm level, and following recording, the traits can be included in the breeding goal and directional selection can be applied. However, to further increase the genetic progress of these traits, it is advantageous to develop indicator traits on the selection candidates (test boars or gilts, not yet exhibiting the phenotype themselves). It has previously been suggested that the scapula morphology and the spine of scapula might be a key factor for the sow to develop shoulder lesions. In this study, we developed 11 novel traits describing the morphology of the shoulder blade based on computed tomography images from scanned test boars. These traits include the area, length, width, height, and volume of the shoulder blade as well as 6 traits obtained from principal component analysis, describing 80% of the variation observed for the scapula spine profile. The analyzed traits have moderate to high heritability (h2 from 0.29 to 0.78, SE = 0.06), low to medium genetic correlations with shoulder lesions (up to 0.4, SE = 0.1), and body condition scoring at weaning (up to 0.25, SE = 0.1). These novel phenotypes can now be recorded automatically and accurately prior to selection of the AI boars. If such recordings are included in multivariate genomic selection models, it is expected to improve the genetic progress of shoulder lesions and body condition score by weaning.

Genetics of the human face: Identification of large-effect single gene variants

To discover specific variants with relatively large effects on the human face, we have devised an approach to identifying facial features with high heritability. This is based on using twin data to estimate the additive genetic value of each point on a face, as provided by a 3D camera system. In addition, we have used the ethnic difference between East Asian and European faces as a further source of face genetic variation. We use principal components (PCs) analysis to provide a fine definition of the surface features of human faces around the eyes and of the profile, and chose upper and lower 10% extremes of the most heritable PCs for looking for genetic associations. Using this strategy for the analysis of 3D images of 1,832 unique volunteers from the well-characterized People of the British Isles study and 1,567 unique twin images from the TwinsUK cohort, together with genetic data for 500,000 SNPs, we have identified three specific genetic variants with notable effects on facial profiles and eyes.

Ensemble landmarking of 3D facial surface scans

Landmarking of 3D facial surface scans is an important analysis step in medical and biological applications, such as genome-wide association studies (GWAS). Manual landmarking is often employed with considerable cost and rater dependent variability. Landmarking automatically with minimal training is therefore desirable. We apply statistical ensemble methods to improve automated landmarking of 3D facial surface scans. Base landmarking algorithms using features derived from 3D surface scans are combined using either bagging or stacking. A focus is on low training complexity of maximal 40 training samples with template based landmarking algorithms that have proved successful in such applications. Additionally, we use correlations between landmark coordinates by introducing a search strategy guided by principal components (PCs) of training landmarks. We found that bagging has no useful impact, while stacking strongly improves accuracy to an average error of 1.7 mm across all 21 landmarks in this study, a 22% improvement as compared to a previous, comparable algorithm. Heritability estimates in twin pairs also show improvements when using facial distances from landmarks. Ensemble methods allow improvement of automatic, accurate landmarking of 3D facial images with minimal training which is advantageous in large cohort studies for GWAS and when landmarking needs change or data quality varies.