Prenatal alcohol exposure (PAE) is a major preventable cause of health and developmental problems in children. It may cause irreversible damage to the brain, resulting in fetal alcohol spectrum disorder (FASD), which is characterized by learning difficulties, executive dysfunction, impaired speech, motor problems, and behavior problems.¹ Fetal alcohol spectrum disorder may affect 3% to 5% of mainstream school-aged children, with many remaining undiagnosed.² Fetal alcohol syndrome (FAS) is diagnosed when cognitive impairment occurs together with abnormalities of growth and a characteristic facial phenotype. Diagnostic criteria of FAS include short palpebral fissures, flat philtrum, and thin upper lip,³ but the condition has also been associated with midfacial and auricular anomalies.⁴ Detailed morphologic studies of the face have found a reduction in ear length and midfacial hypoplasia, evidenced by reduced midfacial depth and flattening of the nasal bridge and malar regions and a reduction in the size of the neurocranium and face (reduced upper facial width, head circumference, and total facial height), as well as retrognathia and micrognathia (reduced bigonial breadth), in individuals who received a diagnosis of partial FAS and in individuals with heavy PAE who do not meet the diagnostic criteria for FASD.^5-7 Furthermore, comparisons of the physical characteristics of children with FASD and the physical characteristics of children without FASD have shown that maternal drinking measures significantly correlated with facial dysmorphology, with higher levels of drinking predicting higher dysmorphology scores.^8-10 Together these findings suggest a possible dose-related association between PAE and craniofacial shape.

Many disorders present with characteristic abnormalities of the craniofacial complex. Precise descriptions of how and when these abnormalities emerge and change during childhood and adolescence can inform our understanding of their underlying pathology and facilitate diagnosis from craniofacial shape. In this paper we develop a framework for analysing how anatomical differences between populations emerge and change over time, and for binary group classification that adapts to the age of each participant. As a proxy for a disease-control comparison we use a database of 3D photographs of normally developing boys and girls to examine emerging sex-differences. Essentially we define 3D craniofacial ‘growth curves’ for each sex. Differences in the forehead, upper lip, chin and nose emerge primarily from different growth rates between the groups, whereas differences in the buccal region involve different growth directions. Differences in the forehead, buccal region and chin are evident before puberty, challenging the view that sex differences result from pubertal hormone levels. Classification accuracy was best for older children. This paper represents a significant methodological advance for the study of facial differences between growing populations and comprehensively describes developing craniofacial sex differences.

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.

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.