The findings support the hypothesis that for people with epilepsy, those with pathogenic structural variants have an objectively more atypical face shape compared with those without. This was true when analysing the whole face or just two feature-rich parts of the face, the periorbital region and perinasal region. Our technique had a sensitivity of 60–80%, specificity of 69–78%, positive predictive value of 14–26% and negative predictive value of 95–98% in an independent validation sample of people with epilepsy, although only five individuals had pathogenic structural variants in this sample. These findings were not explained by age, ethnicity, facial injury, facial expression, anti-epileptic drug history or the technique used to detect pathogenic structural variants, including structural variant threshold size. Our method comprised computer-based facial shape analysis based on dense surface modelling. To our knowledge, this is the first time that dense surface modelling has been shown to discriminate, to a degree, between people with epilepsy with (different) pathogenic structural variants and those without pathogenic structural variants.
Of the three models, the whole face model is best at discriminating atypical facial shape at individual level, in the receiver operating characteristic curve analysis, and at group level, with a higher median FSD. This is in keeping with previous findings comparing the whole face with periorbital and perinasal regions in four known clinical syndromes (Hammond et al., 2005
). With an appropriate threshold, the expected and actual sensitivity and specificity of whole face FSD were 66–80% in detecting pathogenic structural variants. These findings were in spite of the heterogeneous nature of the group, comprising children and adults from three different European centres with different types of epilepsy and different pathogenic structural variants. Indeed, 31 of 43 patients have pathogenic structural variants that do not overlap with any others. Greater discrimination could be expected in more homogeneous groupings (Hammond et al., 2005
) and may emerge as more people with particular pathogenic structural variants are identified. We had only five patients with a recurrent pathogenic structural variant (16p13.11 deletion); no similarity was found in their face shape.
Evaluation for dysmorphism should be part of the clinical examination in epilepsy. Dysmorphism may be missed by untrained clinicians, and even clinical geneticists may take years to learn to recognize some patterns of facial dysmorphism (Reardon and Donnai, 2007
). FSD is a quantitative analytical construct that can identify novel patterns of abnormality of facial anatomy. FSD changes identified in this study do not directly translate to clinically observable dysmorphism. Indeed, some of the study participants with high FSD values were not thought to be dysmorphic when evaluated by their regular physician. We also found no difference for individual principal components in patients with and without pathogenic structural variants. This implies there are no shared facial features in people with diverse underlying genomic abnormalities, which is supported by visual inspection of the average face (Supplementary Fig. 3
). FSD may become more widely used as numbers of people with a given pattern of quantitative abnormality (FSD or other construct) increase, and may become of direct clinical use.
Compounding the lack of dysmorphology training in most clinicians caring for people with epilepsy, adult medicine is divided into different specialties, and physicians often do not consider a potential unifying genomic or genetic cause in patients (Maves et al., 2007
; Williams, 2007
). Being able to recognize and classify facial dysmorphism can lead physicians to consider alternative diagnoses or to request further relevant investigations, and this could be aided by the increasing use of 3D stereophotogrammetry in clinical settings (Heike et al., 2010
). MRI abnormalities and intellectual disability are known to correlate with pathogenic structural variants (Hochstenbach et al., 2009
; Sagoo et al., 2009
; Xiang et al., 2010
), as seen in our study population. Any of these observations should prompt detailed genetic studies (Galizia et al., 2012
Array comparative genomic hybridization is now part of clinical genetics practice, whereas whole exome and genome sequencing are just beginning to make their mark as clinical tests (Johnson et al., 2012
; Need et al., 2012
). Although in some cases, a clear genetic diagnosis will emerge, in others the mass of data from such tests will need additional interpretation (Hennekam and Biesecker, 2012
). Dense surface models could be used for this purpose. We note that whole face FSD was not correlated with the number of genes in the pathogenic structural variant interval when looking at all types of structural variants collectively. People with deletions are, in general, thought to have a more severe phenotype than those with duplications (Hanemaaijer et al., 2012
). We found that our patients with deletions had a similar FSD, but a significantly smaller structural variant interval size, by a factor of three to four, than those with duplications. This suggests that for a given interval size, deletions may indeed affect face shape more than duplications. Our findings may seem to be in contrast to a recent study that suggested no difference in duplication length and deletion length in a group of people with epilepsy and pathogenic structural variants (Striano et al., 2012
), but this may simply reflect differences in methodology and case numbers. We have only analysed structural variants that were considered pathogenic; facial development is likely to be complex, and in due course, the wider complement of individual genetic variation might be studied using dense surface modelling. Determination of the pathogenicity of structural variants is still an evolving area, and so the contribution of structural variants to phenotypes may be overestimated or underestimated (Craddock et al., 2010
; Vermeesch et al., 2011
), including those structural variants found in individuals with epilepsy (Striano et al., 2012
). Face shape analysis has already been successfully used to help determine the pathogenicity of a novel microdeletion (Hannes et al., 2012)
, and quantified face shape may help to identify new syndromes in the new generation of multicentre studies (Firth et al., 2011
Genes expressed in the forebrain during early development are known to affect human face formation (Marcucio et al., 2011)
, and we considered the foetal expression of genes contained in the pathogenic structural variants of our patients using public resources. The level of gene expression is a crude measure and does not account for gene interactions or effects of a pathogenic structural variant on genes outside its interval. We found that although whole face FSD was not correlated with the number of genes in a pathogenic structural variant interval, it showed significant positive correlation with the number of contained genes expressed highly in the foetal forebrain (50–200 days). Facial structures develop in late embryonic and early foetal life, driven by complex molecular interactions between surface ectoderm and underlying forebrain and neural crest cells. It is conceivable, therefore, that these forebrain-expressed genes may be candidates for facial development and dysmorphism, and possibly also epilepsy. Also, we noted a trend between a greater number of deleted forebrain-expressed genes and a higher FSD, which needs confirmation in a larger group. It may also be possible to identify individual genes contributing to face shape using dense surface models. A recent genome-wide association study suggests a developmental gene, PAX3
, may influence the height of the nasal root (Paternoster et al., 2012
is known to be necessary for neural crest cell development and migration, and mutations in PAX3
are associated with spina bifida and sensorineural hearing loss as well as facial dysmorphism (Pingault et al., 2010
). The authors used landmark-based anthropometry, which is less able to detect differences in some facial regions than dense surface modelling (Hammond and Suttie, 2012
Other related uses for stereophotogrammetry and dense surface models include the further investigation of consequences of structural variants that are already known to be pathogenic. We have used a model previously to show reduced facial fat in a subject with a deletion encompassing a gene involved in fatty acid metabolism (Kasperavičiūt ė et al., 2011
). The technique may be useful to characterize facial differences in people with novel syndromes, or when pleiotropy is found, such as for 16p13.11 microdeletion.
Our aim was to explore the utility of objective face shape analysis in relation to presence or absence of a known pathogenic structural variant, not in relation to presence or absence of epilepsy itself as a phenotype. We point out that we were not seeking to identify a ‘face’ associated with epilepsy per se. Given the heterogeneity of epilepsy in every aspect, this concept is dangerous nonsense, which we raise specifically to dismiss explicitly. Even in patients with pathogenic structural variants, there were no shared facial features, suggesting actual facial shapes are as varied as the underlying pathogenic structural variants.
There are limitations that need consideration. Landmarking of facial features is the one subjective step in stereophotogrammetry and dense surface modelling. With more experience and optimization of the landmarks used, the intra-operator and inter-operator reproducibility might be further improved as noted in studies using radiographic landmarks (Houston, 1983
). In our study, the operator who landmarked control images was different to the one who landmarked patient images, and a small non-significant reproducibility error was identified. A further potential confounding factor is facial injury. Individuals with epilepsy have a 1.6 times greater risk of accident than the general population (van den Broek and Beghi, 2004)
, and this is related to the type and frequency of seizures (Tiamkao et al., 2009
). Such injuries include fractures, contusions and burns, which often affect the face. Previous facial morphometric studies have ignored facial injuries or excluded such cases on the basis of patients’ recall of injuries (Hammond et al., 2005
; Evison et al., 2010
; Kau et al., 2010
). Our findings held after blinded exclusion of cases with suspected acquired facial deformity. The effect of facial expression is less easy to discern. Children and people with intellectual disability may be less likely to maintain a neutral expression during image capture. We used a surrogate marker, lip closure, to determine if expression was neutral in an objective manner; lip closure was associated with differences in FSD and may account for part of the increase in FSD in those with intellectual disability. Point mutations, chromosomal translocations and inversions, and small pathogenic structural variants, with sizes below the threshold for detection by our methods, could also contribute to atypical face shape, and would have been missed. With more comprehensive methods of detecting pathological genetic changes, such as next-generation sequencing techniques, re-evaluation of dense surface models in future datasets will allow further exploration of abnormalities of face shape.
Our findings are in Europeans referred to neurology clinics with a diagnosis of epilepsy. Ethnicity influences facial appearance, and at least in certain genomic disorders, either makes dysmorphic features less obvious or less easily detected by physicians (McDonald-McGinn et al., 2005
). We were unable to investigate other ethnicities because of lack of ethnicity matched control subjects for comparison, but we found no difference in the three different groups used here. We found that age had no effect on FSD. This was important to exclude, as it is known that some genetic conditions show greater dysmorphism in childhood, such as Noonan or Beckwith–Wiedemann syndromes (Choufani et al., 2010
; Romano et al., 2010
Anti-epileptic drugs may also be a source of bias in this population. Some drugs, especially ‘older’ ones (those licensed before ~1990), may have adverse effects on the face, such as gingival hyperplasia, acne, facial coarsening or weight gain (Collaborative Group for Epidemiology of Epilepsy, 1988
). The number of drugs and the proportion that took each drug were not significantly different between patients with or without pathogenic structural variants (Supplementary Table 5
). Finally, the role of other potential confounding factors, such as body weight, has not been elucidated.
In conclusion, we have shown that 3D stereophotogrammetry and dense surface modelling offer a promising avenue for further evaluation of the full phenotype of epilepsy related to clinically relevant genomic structural variants. We show the technique is robust and reproducible for analysing facial shape. As technical and bioinformatics advances make genomic analysis more comprehensive and available, equal sophistication in phenotyping methods is likely to prove necessary. Face shape analysis may contribute to deepening phenotypic evaluation.