Syndrome-informed phenotyping identifies a polygenic background for achondroplasia-like facial variation in the general population

Abstract

Human craniofacial shape is highly variable yet highly heritable with genetic variants interacting through multiple layers of development. Here, we hypothesize that Mendelian phenotypes represent the extremes of a phenotypic spectrum and, using achondroplasia as an example, we introduce a syndrome-informed phenotyping approach to identify genomic loci associated with achondroplasia-like facial variation in the normal population. We compared three-dimensional facial scans from 43 individuals with achondroplasia and 8246 controls to calculate achondroplasia-like facial scores. Multivariate GWAS of the control scores revealed a polygenic basis for normal facial variation along an achondroplasia-specific shape axis, identifying genes primarily involved in skeletal development. Jointly modeling these genes in two independent control samples showed craniofacial effects approximating the characteristic achondroplasia phenotype. These findings suggest that both complex and Mendelian genetic variation act on the same developmentally determined axes of facial variation, providing new insights into the genetic intersection of complex traits and Mendelian disorders.

Keywords: Achondroplasia; Complex Traits; Craniofacial Variation; Developmental Constraints; Genome-Wide Association Study; Mendelian Disease.

Figure 1.. Sample characteristics of the achondroplasia and control dataset.

(a) Age and sex distribution, (b) Average facial shapes.

Figure 2.. Achondroplasia-informed phenotyping.

(a) ACH trait axis spanning the ACH and control mean shapes. Morphs on the left and right side of the axis represent the extremes of the phenotypic spectrum. Controls (ID_1–3) can be scored along the axis by measuring the angle between their individual vectors and the ACH trait vector. Facial variation of the three control individuals is visualized as a heatmap. Red areas on the facial shape correspond to a local outward deviation from the control mean shape, blue indicates inward deviation. (b) Distribution of the facial trait scores for the full face (segment 1) for both the ACH (in green) and control (in beige) datasets. Values smaller than 1 indicate more ACH-like; values greater than 1 indicate less ACH-like. The mean facial shape of the 5 lowest and highest scoring individuals is shown for both ACH and control samples. (c) Manhattan plot of genomewide associations. For each SNP, the lowest p-value (CCA, right-tailed chi square) across all 58 significant facial segments is plotted. The full horizontal line represents the genome-wide significance threshold (p = 5e-8). Candidate genes are annotated to each genome-wide significant locus (n = 35).

Figure 3.. GO enrichment analysis.

Fold enrichment of Gene Ontology (GO) biological processes enriched in the ACH GWAS compared to different background sets. (a) ACH-informed GWAS versus uninformed GWAS of normal facial variation by White et al. (b) ACH-informed GWAS versus all genes previously identified through GWAS of facial shape. Only processes enriched in both studies are displayed. Node size corresponds to the number of genes mapped to each process.

Figure 4.. Multivariate genotype-phenotype mapping of mouse and human craniofacial shape.

Genetic marker loadings for the multivariate genotype-phenotype mapping of the GWAS candidate genes onto (a) mouse craniofacial shape and (c) human cranial vault shape. Genes are ordered by their relative contribution to the associated shape effects shown in (b) and (d), respectively. The top row shows the mean craniofacial shape colored according to the difference between the upper and lower extremes of the MGP shape axis. Red areas indicate a local inward deviation, blue indicates an outward deviation. The middle row shows the upper extreme of the MGP shape axis. The bottom row shows the lower extreme of the same shape axis.