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.

Background: Non-invasive brain stimulation is being increasingly used to interrogate neurophysiology and modulate brain function. Despite the high scientific and therapeutic potential of non-invasive brain stimulation, experience in the developing brain has been limited. Objective: To determine the safety and tolerability of non-invasive neurostimulation in children across diverse modalities of stimulation and pediatric populations. Methods: A non-invasive brain stimulation program was established in 2008 at our pediatric, academic institution. Multi-disciplinary neurophysiological studies included single-and paired-pulse Transcranial Magnetic Stimulation (TMS) methods. Motor mapping employed robotic TMS. Interventional trials included repetitive TMS (rTMS) and transcranial direct current stimulation (tDCS). Standardized safety and tolerability measures were completed prospectively by all participants. Results: Over 10 years, 384 children underwent brain stimulation (median 13 years, range 0.8e18.0). Populations included typical development (n ¼ 118), perinatal stroke/cerebral palsy (n ¼ 101), mild traumatic brain injury (n ¼ 121) neuropsychiatric disorders (n ¼ 37), and other (n ¼ 7). No serious adverse events occurred. Drop-outs were rare (<1%). No seizures were reported despite >100 participants having brain injuries and/or epilepsy. Tolerability between single and paired-pulse TMS (542340 stimulations) and rTMS (3.0 million stimulations) was comparable and favourable. TMS-related headache was more common in perinatal stroke (40%) than healthy participants (13%) but was mild and self-limiting. Tolerability improved over time with side-effect frequency decreasing by >50%. Robotic TMS motor mapping was well-tolerated though neck pain was more common than with manual TMS (33% vs 3%). Across 612 tDCS sessions including 92 children, tolerability was favourable with mild itching/tingling reported in 37%. Conclusions: Standard non-invasive brain stimulation paradigms are safe and well-tolerated in children and should be considered minimal risk. Advancement of applications in the developing brain are warranted. A new and improved pediatric NIBS safety and tolerability form is included.

Purpose 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. Methods 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. Results 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. Conclusion 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.

The human face is an array of variable physical features that together make each of us unique and distinguishable. Striking familial facial similarities underscore a genetic component, but little is known of the genes that underlie facial shape differences. Numerous studies have estimated facial shape heritability using various methods. Here, we used advanced three-dimensional imaging technology and quantitative human genetics analysis to estimate narrow-sense heritability, heritability explained by common genetic variation, and pairwise genetic correlations of 38 measures of facial shape and size in normal African Bantu children from Tanzania. Specifically, we fit a linear mixed model of genetic relatedness between close and distant relatives to jointly estimate variance components that correspond to heritability explained by genome-wide common genetic variation and variance explained by uncaptured genetic variation, the sum representing total narrow-sense heritability. Our significant estimates for narrow-sense heritability of specific facial traits range from 28 to 67%, with horizontal measures being slightly more heritable than vertical or depth measures. Furthermore, for over half of facial traits, .90% of narrow-sense heritability can be explained by common genetic variation. We also find high absolute genetic correlation between most traits, indicating large overlap in underlying genetic loci. Not surprisingly, traits measured in the same physical orientation (i.e., both horizontal or both vertical) have high positive genetic correlations, whereas traits in opposite orientations have high negative correlations. The complex genetic architecture of facial shape informs our understanding of the intricate relationships among different facial features as well as overall facial development.

Allometry refers to the ways in which organismal shape is associated with size. It is a special case of integration, or the tendency for traits to covary, in that variation in size is ubiquitous and evolutionarily important. Allometric variation is so commonly observed that it is routinely removed from morphometric analyses or invoked as an explanation for evolutionary change. In this case, familiarity is mistaken for understanding because rarely do we know the mechanisms by which shape correlates with size or understand their significance. As with other forms of integration, allometric variation is generated by variation in developmental processes that affect multiple traits, resulting in patterns of covariation. Given this perspective, we can dissect the genetic and developmental determinants of allometric variation. Our work on the developmental and genetic basis for allometric variation in craniofacial shape in mice and humans has revealed that allometric variation is highly polygenic. Different measures of size are associated with distinct but overlapping patterns of allometric variation. These patterns converge in part on a common genetic basis. Finally, environmental modulation of size often generates variation along allometric trajectories, but the timing of genetic and environmental perturbations can produce deviations from allometric patterns when traits are differentially sensitive over developmental time. These results question the validity of viewing allometry as a singular phenomenon distinct from morphological integration more generally.

Automated phenotyping is essential for the creation of large, highly standardized datasets from anatomical imaging data. Such datasets can support large-scale studies of complex traits or clinical studies related to precision medicine or clinical trials. We have developed a method that generates 3D landmark data that meet the requirements of standard geometric morphometric analyses. The method is robust and can be implemented without high performance computing resources. We validated the method using both direct comparison to manual landmarking on the same individuals and also analyses of the variation patterns and outlier patterns in a large dataset of automated and manual landmark data. Direct comparison of manual and automated landmarks reveals that automated landmark data are less variable and but more highly integrated and reproducible. Automated data produce covariation structure that closely resembles that of manual landmarks. We further find that while our method does produce some landmarking errors, they tend to be readily detectable and can be fixed by adjusting parameters used in the registration and control point steps. Data generated using the method described here have been successfully used to study the genomic architecture of facial shape in two different genome-wide association studies of facial shape.

Allometric variation in the human face is complex and should not be regarded as a singular effect. This finding has important implications for how size is treated in studies of human facial shape and for the developmental basis for allometric variation more generally.

Hypohidrotic ectodermal dysplasia (HED) is the most prevalent type of ectodermal dysplasia (ED). ED is an umbrella term for a group of syndromes characterized by missing or malformed ectodermal structures, including skin, hair, sweat glands, and teeth. The X-linked recessive (XL), autosomal recessive (AR), and autosomal dominant (AD) types of HED are caused by mutations in the genes encoding ectodysplasin (EDA1), EDA receptor (EDAR), or EDAR-associated death domain (EDARADD). Patients with HED have a distinctive facial appearance, yet a quantitative analysis of the HED craniofacial phenotype using advanced three-dimensional (3D) technologies has not been reported. In this study, we characterized craniofacial morphology in subjects with X-linked hypohidrotic ectodermal dysplasia (XLHED) by use of 3D imaging and geometric morphometrics (GM), a technique that uses defined landmarks to quantify size and shape in complex craniofacial morphologies. We found that the XLHED craniofacial phenotype differed significantly from controls. Patients had a smaller and shorter face with a proportionally longer chin and midface, prominent midfacial hypoplasia, a more protrusive chin and mandible, a narrower and more pointed nose, shorter philtrum, a narrower mouth, and a fuller and more rounded lower lip. Our findings refine the phenotype of XLHED and may be useful both for clinical diagnosis of XLHED and to extend understanding of the role of EDA in craniofacial development.