Abstract
The human face is a complex trait under strong genetic control, as evidenced by the striking visual similarity between twins. Nevertheless, heritability estimates of facial traits have often been surprisingly low or difficult to replicate. Furthermore, the construction of facial phenotypes that correspond to naturally perceived facial features remains largely a mystery. We present here a large-scale heritability study of face geometry that aims to address these issues. High-resolution, three-dimensional facial models have been acquired on a cohort of 952 twins recruited from the TwinsUK registry, and processed through a novel landmarking workflow, GESSA (Geodesic Ensemble Surface Sampling Algorithm). The algorithm places thousands of landmarks throughout the facial surface and automatically establishes point-wise correspondence across faces. These landmarks enabled us to intuitively characterize facial geometry at a fine level of detail through curvature measurements, yielding accurate heritability maps of the human face (www.heritabilitymaps.info).
Highlights
We demonstrate that local curvature traits, computed at each three-dimensional position across the facial surface, provide highly informative quantitative measurements of facial geometry, and explore for the first time their heritability
A multivariate analysis of these landmark-wise measurements, based on sparse PCA, indicated the presence of spatially coherent traits extending over larger areas of the face, whose heritability was estimated
We examined whether the use of facial traits derived from Geodesic distances (GDTs), only possible on 3D data, yielded any advantages compared to Euclidean traits
Summary
A system for high-resolution 3D photographic scanning, the 3dMD face imaging system, was used to generate anatomically precise three-dimensional polyhedral surfaces of the faces To capitalize on these representations, we developed a novel automated landmarking procedure, GESSA (Geodesic Ensemble Surface Sampling Algorithm). The availability of densely sampled landmark positions on each face enabled a wider range of facial traits to be defined, each capturing a specific aspect of face-shape variability. A number of facial lengths, including bizygomatic and nose width, had heritability estimates close to or greater than 0.7, values that are significantly higher than the ones previously encountered in the respective literature This is the first time that such a detailed and comprehensive evaluation of facial shape heritability has been investigated using a large cohort and 3D data capture technology. Our heritability findings are likely to support future genome-wide studies on facial geometry, while dense representations of facial surfaces through curvature indices may find further use in face recognition and reconstruction techniques
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