Monday, March 24, 2014

Modeling 3D Facial Shape from DNA

While still at its infancy, this technology is quite fascinating

Abstract

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.

Link (Open Access) 


......Since both categorical and continuous variables can be modeled using BRIM, this approach might be used to test for relationships between facial features and other factors, e.g., age, adiposity, and temperament. The methods illustrated here also provide for the development of diagnostic tools by modeling validated cases of overt craniofacial dysmorphology. Most directly, our methods provide the means of identifying the genes that affect facial shape and for modeling the effects of these genes to generate a predicted face. Although much more work is needed before we can know how many genes will be required to estimate the shape of a face in some useful way and many more populations need to be studied before we can know how generalizable the results are, these results provide both the impetus and analytical framework for these studies.....

Some interesting figures:

Figure 1: Workflow for 3D face scan processing.
A) original surface, B) trimmed to exclude non-face parts, C) reflected to make mirror image, D) anthropometric mask of quasi-landmarks, E) remapped, F) reflected remapped, G) symmetrized, H) reconstructed.
 
Figure 3: Transformations and heat maps showing how face shape is affected by (A) RIP-A and (B) RIP-S.
The top row of each panel shows the shape transformations three standard deviations below and above the mean of the RIPs in this sample. The second row shows the R2 (proportion of the total variation in each quasi-landmark) and the three primary facial shape change parameters: area ratio, curvature difference, and normal displacement. The bottom row shows in yellow the regions of the face that are statistically significantly different (p<0.001) between the two transformations. The max R2 values for RIP-A and RIP-S are 40.83% and 38.21%, respectively. 
Figure 4: Relationships between the ancestry and sex RIP variables and their initial predictor variables.
(A) RIP-A with genomic ancestry; genomic ancestry is calculated using the core panel of 68 AIMs and RIP-A is calculated using this ancestry estimate on the set of three populations combined (N = 592). Populations are indicated as shown in the legend with United States participants shown with black circles, Brazilians with red circles, and Cape Verdeans with blue circles. (B) Histograms of RIP-S by self-reported sex.
Figure 6: Transformations and heat maps showing how face shape is affected by three particular RIP-G variables.
The initial predictor variables are SNPs in the genes (A) SLC35D1 (B) FGFR1, and (C) LRP6. The top row of each panel shows the shape transformations near the extreme values of the particular RIP-G shown. The second row shows the R2 (proportion of the facial total variation), the three primary facial shape change parameters: area ratio, curvature difference, and normal displacement. The max R2 values for A, B, and C are 11.68%, 15.16% and 10.10%, respectively.


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