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Craniofacial dysmorphism is associated with thousands of genetic and environmental disorders. Delineation of salient facial characteristics can guide clinicians towards a correct clinical diagnosis and understanding the pathogenesis of the disorder. Abnormal facial shape might require craniofacial surgical intervention, with the restoration of normal shape an important surgical outcome. Facial anthropometric growth curves or standards of single inter-landmark measurements have traditionally supported assessments of normal and abnormal facial shape, for both clinical and research applications. However, these fail to capture the full complexity of facial shape. With the increasing availability of 3D photographs, methods of assessment that take advantage of the rich information contained in such images are needed. In this article we derive and present open-source three-dimensional (3D) growth curves of the human face. These are sequences of age and sex-specific expected 3D facial shapes and statistical models of the variation around the expected shape, derived from 5443 3D images. We demonstrate the use of these growth curves for assessing patients and show that they identify normal and abnormal facial morphology independent from age-specific facial features. 3D growth curves can facilitate use of state-of-the-art 3D facial shape assessment by the broader clinical and biomedical research community. This advance in phenotype description will support clinical diagnosis and the understanding of disease pathogenesis including genotype–phenotype relations.

Placental abnormalities have been sporadically implicated as a source of developmental heart defects. Yet it remains unknown how often the placenta is at the root of congenital heart defects (CHDs), and what the cellular mechanisms are that underpin this connection. Here, we selected three mouse mutant lines, Atp11a , Smg9 and Ssr2 , that presented with placental and heart defects in a recent phenotyping screen, resulting in embryonic lethality. To dissect phenotype causality, we generated embryo- and trophoblast-specific conditional knockouts for each of these lines. This was facilitated by the establishment of a new transgenic mouse, Sox2 -Flp, that enables the efficient generation of trophoblast-specific conditional knockouts. We demonstrate a strictly trophoblast-driven cause of the CHD and embryonic lethality in one of the three lines ( Atp11a ) and a significant contribution of the placenta to the embryonic phenotypes in another line ( Smg9 ). Importantly, our data reveal defects in the maternal blood-facing syncytiotrophoblast layer as a shared pathology in placentally induced CHD models. This study highlights the placenta as a significant source of developmental heart disorders, insights that will transform our understanding of the vast number of unexplained congenital heart defects. Placental dysfunction can affect heart development, but the prevalence of this causality has not been well established. Here, the authors use mouse genetic tools to show that the placenta may constitute a significant source of congenital heart defects.

Deep phenotyping is an emerging trend in precision medicine for genetic disease. The shape of the face is affected in 30–40% of known genetic syndromes. Here, we determine whether syndromes can be diagnosed from 3D images of human faces. We analyzed variation in three-dimensional (3D) facial images of 7057 subjects: 3327 with 396 different syndromes, 727 of their relatives, and 3003 unrelated, unaffected subjects. We developed and tested machine learning and parametric approaches to automated syndrome diagnosis using 3D facial images. Unrelated, unaffected subjects were correctly classified with 96% accuracy. Considering both syndromic and unrelated, unaffected subjects together, balanced accuracy was 73% and mean sensitivity 49%. Excluding unrelated, unaffected subjects substantially improved both balanced accuracy (78.1%) and sensitivity (56.9%) of syndrome diagnosis. The best predictors of classification accuracy were phenotypic severity and facial distinctiveness of syndromes. Surprisingly, unaffected relatives of syndromic subjects were frequently classified as syndromic, often to the syndrome of their affected relative. Deep phenotyping by quantitative 3D facial imaging has considerable potential to facilitate syndrome diagnosis. Furthermore, 3D facial imaging of “unaffected” relatives may identify unrecognized cases or may reveal novel examples of semidominant inheritance.

Gene disruptions can cause severe dysmorphologies like cleft palate, but what causes the subtle shifts in facial morphology that make each face unique? Studying mice, Attanasio et al. ( 1241006 ) identified over 4000 candidate genetic enhancers around genes driving craniofacial development. To avoid the challenge of recognizing individual mouse faces, optical projection tomography was used to link changes in facial morphology with alterations in the function of specific enhancers. Targeted deletion of individual craniofacial enhancers from the mouse genome sculpts facial shapes. The shape of the human face and skull is largely genetically determined. However, the genomic basis of craniofacial morphology is incompletely understood and hypothesized to involve protein-coding genes, as well as gene regulatory sequences. We used a combination of epigenomic profiling, in vivo characterization of candidate enhancer sequences in transgenic mice, and targeted deletion experiments to examine the role of distant-acting enhancers in craniofacial development. We identified complex regulatory landscapes consisting of enhancers that drive spatially complex developmental expression patterns. Analysis of mouse lines in which individual craniofacial enhancers had been deleted revealed significant alterations of craniofacial shape, demonstrating the functional importance of enhancers in defining face and skull morphology. These results demonstrate that enhancers are involved in craniofacial development and suggest that enhancer sequence variation contributes to the diversity of human facial morphology.

  The idea that we can reconstruct a human face from a DNA sample has great appeal: DNA from a crime scene could be used to create a facial image of a suspect; the faces of prehistoric peoples could be reconstructed from their remains; the face of a child could be predicted in utero from amniocentesis. [...]it is also quite possible that many genes not known to play important roles in craniofacial development contribute to normal variation in the face.

The tetrapod forelimb and hindlimb are serially homologous structures that share a broad range of developmental pathways responsible for their patterning and outgrowth. Covariation between limbs, which can introduce constraints on the production of variation, is related to the duplication of these developmental factors. Despite this constraint, there is remarkable diversity in limb morphology, with a variety of functional relationships between and within forelimb and hindlimb elements. Here we assess a hierarchical model of limb covariation structure based on shared developmental factors. We also test whether selection for morphologically divergent forelimbs or hindlimbs is associated with reduced covariation between limbs. Our sample includes primates, murines, a carnivoran, and a chiropteran that exhibit varying degrees of forelimb and hindlimb specialization, limb size divergence, and/or phylogenetic relatedness. We analyze the pattern and significance of between-limb morphological covariation with linear distance data collected using standard morphometric techniques and analyzed by matrix correlations, eigenanalysis, and partial correlations. Results support a common limb covariation structure across these taxa and reduced covariation between limbs in nonquadruped species. This result indicates that diversity in limb morphology has evolved without signficant modifications to a common covariation structure but that the higher degree of functional limb divergence in bats and, to some extent, gibbons is associated with weaker integration between limbs. This result supports the hypothesis that limb divergence, particularly selection for increased functional specialization, involves the reduction of developmental factors common to both limbs, thereby reducing covariation.

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.

The major milestones in mouse placental development are well described, but our understanding is limited to how the placenta can adapt to damage or changes in the environment. By using stereology and expression of cell cycle markers, we found that the placenta grows under normal conditions not just by hyperplasia of trophoblast cells but also through extensive polyploidy and cell hypertrophy. In response to feeding a low protein diet to mothers prior to and during pregnancy, to mimic chronic malnutrition, we found that this normal program was altered and that it was influenced by the sex of the conceptus. Male fetuses showed intrauterine growth restriction (IUGR) by embryonic day (E) 18.5, just before term, whereas female fetuses showed IUGR as early as E16.5. This difference was correlated with differences in the size of the labyrinth layer of the placenta, the site of nutrient and gas exchange. Functional changes were implied based on up-regulation of nutrient transporter genes. The junctional zone was also affected, with a reduction in both glycogen trophoblast and spongiotrophoblast cells. These changes were associated with increased expression of Phlda2 and reduced expression of Egfr. Polyploidy, which results from endoreduplication, is a normal feature of trophoblast giant cells (TGC) but also spongiotrophoblast cells. Ploidy was increased in sinusoidal-TGCs and spongiotrophoblast cells, but not parietal-TGCs, in low protein placentas. These results indicate that the placenta undergoes a range of changes in development and function in response to poor maternal diet, many of which we interpret are aimed at mitigating the impacts on fetal and maternal health.

The human face is a complex assemblage of highly variable yet clearly heritable anatomic structures that together make each of us unique, distinguishable, and recognizable. Relatively little is known about the genetic underpinnings of normal human facial variation. To address this, we carried out a large genomewide association study and two independent replication studies of Bantu African children and adolescents from Mwanza, Tanzania, a region that is both genetically and environmentally relatively homogeneous. We tested for genetic association of facial shape and size phenotypes derived from 3D imaging and automated landmarking of standard facial morphometric points. SNPs within genes SCHIP1 and PDE8A were associated with measures of facial size in both the GWAS and replication cohorts and passed a stringent genomewide significance threshold adjusted for multiple testing of 34 correlated traits. For both SCHIP1 and PDE8A, we demonstrated clear expression in the developing mouse face by both whole-mount in situ hybridization and RNA-seq, supporting their involvement in facial morphogenesis. Ten additional loci demonstrated suggestive association with various measures of facial shape. Our findings, which differ from those in previous studies of European-derived whites, augment understanding of the genetic basis of normal facial development, and provide insights relevant to both human disease and forensics.

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.