Discriminating Power of Localized Three-Dimensional Facial Morphology

Abstract

Many genetic syndromes involve a facial gestalt that suggests a preliminary diagnosis to an experienced clinical geneticist even before a clinical examination and genotyping are undertaken. Previously, using visualization and pattern recognition, we showed that dense surface models (DSMs) of full face shape characterize facial dysmorphology in Noonan and in 22q11 deletion syndromes. In this much larger study of 696 individuals, we extend the use of DSMs of the full face to establish accurate discrimination between controls and individuals with Williams, Smith-Magenis, 22q11 deletion, or Noonan syndromes and between individuals with different syndromes in these groups. However, the full power of the DSM approach is demonstrated by the comparable discriminating abilities of localized facial features, such as periorbital, perinasal, and perioral patches, and the correlation of DSM-based predictions and molecular findings. This study demonstrates the potential of face shape models to assist clinical training through visualization, to support clinical diagnosis of affected individuals through pattern recognition, and to enable the objective comparison of individuals sharing other phenotypic or genotypic properties.

Fine-grained facial phenotype–genotype analysis in Wolf–Hirschhorn syndrome

“…25,26 Previously, studies using 3D DSMs of face shape have delineated common facial features in a range of neurodevelopmental conditions, often, in addition, establishing accurate discriminating characteristics or assisting the determination of phenotype-genotype correlations. [26][27][28][29][30][31][32][33][34] We have shown that DSMbased analysis provides a very accurate instrument for classifying faces or facial regions along the control-WHS spectrum. In addition, for the first time, we have used DSMs to construct mean face surfaces matched for age and size for fine-grained analysis of six individuals, so that subtle face shape differences can be related to the underlying genotype.…”

“…30,40 DSM building and closest mean classification DSMs were constructed using techniques described elsewhere. 25,26 A DSM refers to the set of principal components (PCs) or principal component analysis (PCA) models accounting for the shape variation in the surfaces included. For a set of faces with wide age variation, the first mode of variation (PC1) follows the shape of a typical growth curve and is highly correlated with age.…”

Face shape differs in phylogenetically related populations

“…1,2 Using highly sensitive models of facial morphology, it has been possible to detect subtle differences in atypical patients and inform genotype-phenotype studies. 3,4 Advanced molecular genetic techniques have established increasingly detailed correlations between genotype and phenotype.…”

Three-dimensional face shape in Fabry disease

“…12,13 At these higher levels of accuracy, less experienced clinicians may be encouraged to undertake appropriate genetic testing where it is available and there is uncertainty about the diagnosis. The dynamic and colour-coded visualizations of face-shape differences may be of use in clinical training.…”