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

Mapping genes for human face shape: Exploration of univariate phenotyping strategies

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.

Mapping genes for human face shape: exploration of univariate phenotyping strategies

Human facial shape, while strongly heritable, involves both genetic and structural complexity, necessitating precise phenotyping for accurate assessment. Common phenotyping strategies include simplifying 3D facial features into univariate traits such as anthropometric measurements (e.g., inter-landmark distances), unsupervised dimensionality reductions (e.g., principal component analysis (PCA) and auto-encoder (AE) approaches), and assessing resemblance to particular facial gestalts (e.g., syndromic facial archetypes). This study provides a comparative assessment of these strategies in genome-wide association studies (GWASs) of 3D facial shape. Specifically, we investigated inter-landmark distances, PCA and AE-derived latent dimensions, and facial resemblance to random, extreme, and syndromic gestalts within a GWAS of 8,426 individuals of recent European ancestry. Inter-landmark distances exhibit the highest SNP-based heritability as estimated via LD score regression, followed by AE dimensions. Conversely, resemblance scores to extreme and syndromic facial gestalts display the lowest heritability, in line with expectations. Notably, the aggregation of multiple GWASs on facial resemblance to random gestalts reveals the highest number of independent genetic loci. This novel, easy-to-implement phenotyping approach holds significant promise for capturing genetically relevant morphological traits derived from complex biomedical imaging datasets, and its applications extend beyond faces. Nevertheless, these different phenotyping strategies capture different genetic influences on craniofacial shape. Thus, it remains valuable to explore these strategies individually and in combination to gain a more comprehensive understanding of the genetic factors underlying craniofacial shape and related traits.

Author Summary

Advancements linking variation in the human genome to phenotypes have rapidly evolved in recent decades and have revealed that most human traits are influenced by genetic variants to at least some degree. While many traits, such as stature, are straightforward to acquire and investigate, the multivariate and multipartite nature of facial shape makes quantification more challenging. In this study, we compared the impact of different facial phenotyping approaches on gene mapping outcomes. Our findings suggest that the choice of facial phenotyping method has an impact on apparent trait heritability and the ability to detect genetic association signals. These results offer valuable insights into the importance of phenotyping in genetic investigations, especially when dealing with highly complex morphological traits.

Connecting the dots towards precision orthodontics

Precision orthodontics entails the use of personalized clinical, biological, social and environmental knowledge of each patient for deep individualized clinical phenotyping and diagnosis combined with the delivery of care using advanced customized devices, technologies and biologics. From its historical origins as a mechanotherapy and materials driven profession, the most recent advances in orthodontics in the past three decades have been propelled by technological innovations including volumetric and surface 3D imaging and printing, advances in software that facilitate the derivation of diagnostic details, enhanced personalization of treatment plans and fabrication of custom appliances. Still, the use of these diagnostic and therapeutic technologies is largely phenotype driven, focusing mainly on facial/skeletal morphology and tooth positions. Future advances in orthodontics will involve comprehensive understanding of an individual's biology through omics, a field of biology that involves large-scale rapid analyses of DNA, mRNA, proteins and other biological regulators from a cell, tissue or organism. Such understanding will define individual biological attributes that will impact diagnosis, treatment decisions, risk assessment and prognostics of therapy. Equally important are the advances in artificial intelligence (AI) and machine learning, and its applications in orthodontics. AI is already being used to perform validation of approaches for diagnostic purposes such as landmark identification, cephalometric tracings, diagnosis of pathologies and facial phenotyping from radiographs and/or photographs. Other areas for future discoveries and utilization of AI will include clinical decision support, precision orthodontics, payer decisions and risk prediction. The synergies between deep 3D phenotyping and advances in materials, omics and AI will propel the technological and omics era towards achieving the goal of delivering optimized and predictable precision orthodontics.

Data-driven trait heritability-based extraction of human facial phenotypes

A genome-wide association study (GWAS) of a complex, multi-dimensional morphological trait, such as the human face, typically relies on predefined and simplified phenotypic measurements, such as inter-landmark distances and angles. These measures are predominantly designed by human experts based on perceived biological or clinical knowledge. To avoid use handcrafted phenotypes (i.e., a priori expert-identified phenotypes), alternative automatically extracted phenotypic descriptors, such as features derived from dimension reduction techniques (e.g., principal component analysis), are employed. While the features generated by such computational algorithms capture the geometric variations of the biological shape, they are not necessarily genetically relevant. Therefore, genetically informed data-driven phenotyping is desirable. Here, we propose an approach where phenotyping is done through a data-driven optimization of trait heritability, defined as the degree of variation in a phenotypic trait in a population that is due to genetic variation. The resulting phenotyping process consists of two steps: 1) constructing a feature space that models shape variations using dimension reduction techniques, and 2) searching for directions in the feature space exhibiting high trait heritability using a genetic search algorithm (i.e., heuristic inspired by natural selection). We show that the phenotypes resulting from the proposed trait heritability-optimized training differ from those of principal components in the following aspects: 1) higher trait heritability, 2) higher SNP heritability, and 3) identification of the same number of independent genetic loci with a smaller number of effective traits. Our results demonstrate that data-driven trait heritability-based optimization enables the automatic extraction of genetically relevant phenotypes, as shown by their increased power in genome-wide association scans.

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.

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.

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.

Exploring the midline soft tissue surface changes from 12 to 15 years of age in three distinct country population cohorts

INTRODUCTION

Several studies have highlighted differences in the facial features in a White European population. Genetics appear to have a major influence on normal facial variation, and environmental factors are likely to have minor influences on face shape directly or through epigenetic mechanisms.

AIM

The aim of this longitudinal cohort study is to determine the rate of change in midline facial landmarks in three distinct homogenous population groups (Finnish, Latvian, and Welsh) from 12.8 to 15.3 years of age. This age range covers the pubertal growth period for the majority of boys and girls.

METHODS

A cohort of children aged 12 were monitored for facial growth in three countries [Finland (n = 60), Latvia (n = 107), and Wales (n = 96)]. Three-dimensional facial surface images were acquired (using either laser or photogrammetric methods) at regular intervals (6-12 months) for 4 years. Ethical approval was granted in each country. Nine midline landmarks were identified and the relative spatial positions of these surface landmarks were measured relative to the mid-endocanthion (men) over a 4-year period.

RESULTS

This study reports the children who attended 95 per cent of all scanning sessions (Finland 48 out of 60; Latvia 104 out of 107; Wales 50 out of 96). Considerable facial variation is seen for all countries and sexes. There are clear patterns of growth that show different magnitudes at different age groups for the different country groups, sexes, and facial parameters. The greatest single yearly growth rate (5.4 mm) was seen for Welsh males for men-pogonion distance at 13.6 years of age. Males exhibit greater rates of growth compared to females. These variations in magnitude and timings are likely to be influenced by genetic ancestry as a result of population migration.

CONCLUSION

The midline points are a simple and valid method to assess the relative spatial positions of facial surface landmarks. This study confirms previous reports on the subtle differences in facial shapes and sizes of male and female children in different populations and also highlights the magnitudes and timings of growth for various midline landmark distances to the men point.

Is the "Habsburg jaw" related to inbreeding?

Background: The "Habsburg jaw" has long been associated with inbreeding due to the high prevalence of consanguineous marriages in the Habsburg dynasty. However, it is thought that mandibular prognathism (MP) is under the influence of a dominant major gene.Aim: To investigate the relationship between the "Habsburg jaw" and the pedigree-based inbreeding coefficient (F) as a relative measure of genome homozygosity.Subjects and methods: The degree of MP and maxillary deficiency (MD) of 15 members of the Habsburg dynasty was quantified through the clinical analysis of 18 dysmorphic features diagnosed from 66 portraits.Results: A statistically significant correlation (r = 0.711, p = 0.003) between MP and MD was observed among individuals. Only MP showed a statistically significant positive regression on F as evidenced from univariate analysis (b = 6.36 ± 3.34, p = 0.040) and multivariate analysis (PCA) performed from single dysmorphic features (b = 14.10 ± 6.62, p = 0.027, for the first PC).Conclusion: Both MP and MD are generally involved in the "Habsburg jaw." The results showed a greater sensitivity to inbreeding for the lower third of the face and suggest a positive association between the "Habsburg jaw" and homozygosity and therefore a basically recessive inheritance pattern.

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.