Modeling 3D facial shape from DNA.

Human facial diversity is substantial, complex, and largely scientifically unexplained. We used spatially dense quasi-landmarks to measure face shape in population samples with mixed West African and European ancestry from three locations (United States, Brazil, and Cape Verde). Using bootstrapped response-based imputation modeling (BRIM), we uncover the relationships between facial variation and the effects of sex, genomic ancestry, and a subset of craniofacial candidate genes. The facial effects of these variables are summarized as response-based imputed predictor (RIP) variables, which are validated using self-reported sex, genomic ancestry, and observer-based facial ratings (femininity and proportional ancestry) and judgments (sex and population group). By jointly modeling sex, genomic ancestry, and genotype, the independent effects of particular alleles on facial features can be uncovered. Results on a set of 20 genes showing significant effects on facial features provide support for this approach as a novel means to identify genes affecting normal-range facial features and for approximating the appearance of a face from genetic markers.

A spatially-dense regression study of facial form and tissue depth: towards an interactive tool for craniofacial reconstruction.

Forensic Craniofacial Reconstruction (CFR) is an investigative technique used to illicit recognition of a deceased person by reconstructing the most likely face starting from the skull. A key component in most CFR methods are estimates of facial soft tissue depths (TD) at particular points (landmarks) on the skull based on averages from databases of TD recordings. These databases vary in their method of extraction, number and position of landmarks (usually sparse <100), condition of the body, population studied, and sub-categorization of the data. In this work a new dataset is presented in a novel manner based on 156 CT scans using a spatially-dense set (∼7500) of TD recordings to allow for a complete understanding of TD variation interpolating between typical landmarks. Furthermore, to unravel the interplay between soft-tissue layers, skull and facial morphology, TD and Facial Form (FF) are investigated both separately and combined. Using a partial least squares regression (PLSR) analysis, which allows for working with multivariate and spatially-dense data, on metadata of Sex, Age and BMI, different significant patterns on TD and FF variation were found. A similar, but with TD and FF combined, PLSR generated a model useful to report on both, in function of Sex, Age and BMI. In contrast to other datasets and due to the continuous nature of the regression there is no need for data sub-categorization. In further contrast, previous datasets have been presented in tabulated form, which is impractical for spatially-dense data. Instead an interactive tool was built to visualize the regression model in an accessible way for CFR practitioners as well as anatomists. The tool is free to the community and forms a base for data contributions to augment the model and its future use in practice.

Dysmorphometrics: the modelling of morphological abnormalities.

BACKGROUND: The study of typical morphological variations using quantitative, morphometric descriptors has always interested biologists in general. However, unusual examples of form, such as abnormalities are often encountered in biomedical sciences. Despite the long history of morphometrics, the means to identify and quantify such unusual form differences remains limited. METHODS: A theoretical concept, called dysmorphometrics, is introduced augmenting current geometric morphometrics with a focus on identifying and modelling form abnormalities. Dysmorphometrics applies the paradigm of detecting form differences as outliers compared to an appropriate norm. To achieve this, the likelihood formulation of landmark superimpositions is extended with outlier processes explicitly introducing a latent variable coding for abnormalities. A tractable solution to this augmented superimposition problem is obtained using Expectation-Maximization. The topography of detected abnormalities is encoded in a dysmorphogram. RESULTS: We demonstrate the use of dysmorphometrics to measure abrupt changes in time, asymmetry and discordancy in a set of human faces presenting with facial abnormalities. CONCLUSION: The results clearly illustrate the unique power to reveal unusual form differences given only normative data with clear applications in both biomedical practice & research.