Facial shape manifestations of growth faltering in Tanzanian children

Abstract

Variation in the shape of the human face and in stature is determined by complex interactions between genetic and environmental influences. One such environmental influence is malnourishment, which can result in growth faltering, usually diagnosed by means of comparing an individual's stature with a set of age-appropriate standards. These standards for stature, however, are typically ascertained in groups where people are at low risk for growth faltering. Moreover, genetic differences among populations with respect to stature are well established, further complicating the generalizability of stature-based diagnostic tools. In a large sample of children aged 5-19 years, we obtained high-resolution genomic data, anthropometric measures and 3D facial images from individuals within and around the city of Mwanza, Tanzania. With genome-wide complex trait analysis, we partitioned genetic and environmental variance for growth outcomes and facial shape. We found that children with growth faltering have faces that look like those of older and taller children, in a direction opposite to the expected allometric trajectory, and in ways predicted by the environmental portion of covariance at the community and individual levels. The environmental variance for facial shape varied subtly but significantly among communities, whereas genetic differences were minimal. These results reveal that facial shape preserves information about exposure to undernourishment, with important implications for refining assessments of nutritional status in children and the developmental-genetics of craniofacial variation alike.

Keywords: childhood growth; complex traits; craniofacial; facial imaging; growth faltering.

Figure 1

Map of Mwanza. The red circles indicate school locations, and the size of the circle indicates the percentage of the overall sample collected from each school.

Figure 2

Age and sex distribution of the study sample.

Figure 3

Boxplots for each growth outcome by school. Variation in all growth outcomes varies significantly across schools.

Figure 4

Scatterplot for school means for principal component (PC)1 of school environmental variables against PC1 of growth outcomes. Points are colour‐coded by local economy type, and the size of each point depicts the sample size at each school. The relationship is significant (r = −0.76, P < 1 × 10⁻⁶).

Figure 5

Analysis of genetic distances among school. (a) Histogram of F ^ST values for pair‐wise distances between schools. (b) Matrix correlations for pair‐wise genetic distances, differences in growth outcomes, geographic distances and mean differences in facial shape. Only the matrices of growth outcome and facial shape distances are significantly associated (P < 0.001, Mantel's permutation test, 999 iterations).

Figure 6

The relationship between growth faltering and facial shape. (a) 3D morphs corresponding to the partial regression scores for age, facial size, height for age, and growth faltering. Note that the slope for facial shape on age is negative and so the morphs are reversed so as to correspond to others in the columns. (b) Partial regression scores plotted for each growth variable. All growth variables are significantly related to facial shape as determined by multiple multivariate regression and permutation test as described by Collyer et al. (2015) (P < 0.001 with 1000 iterations). (c) The proportions of the total variance for facial shape explained by the model and by each growth variable. (d) Directions and magnitudes of correlations among the partial regression scores. The direction of facial shape variation for height‐for‐age is opposite to that of the other variables.

Figure 7

Illustration of how facial shape relates to variation in stature and age. This schematic is based on the relationships shown in Fig. 5.

Figure 8

Environmental variance for facial shape and growth faltering. (a) 3D morphs for environmental principal components (PCs) 1 and 3. (b) Regression of growth faltering (growth PC1) on the mean‐centred environmental residual landmark data. (c) Means for environmental PCs3 and 4 by growth faltering group. (d) School means for PC3 and PC4 against school means for growth PC1.

Figure 9

Visualization of correlations between environmental and genetic principal components (PCs).