Abstract
The contribution of low-frequency variants to the genetic architecture of normal-range facial traits is unknown. We studied the influence of low-frequency coding variants (MAF < 1%) in 8091 genes on multi-dimensional facial shape phenotypes in a European cohort of 2329 healthy individuals. Using three-dimensional images, we partitioned the full face into 31 hierarchically arranged segments to model facial morphology at multiple levels, and generated multi-dimensional phenotypes representing the shape variation within each segment. We used MultiSKAT, a multivariate kernel regression approach to scan the exome for face-associated low-frequency variants in a gene-based manner. After accounting for multiple tests, seven genes (AR, CARS2, FTSJ1, HFE, LTB4R, TELO2, NECTIN1) were significantly associated with shape variation of the cheek, chin, nose and mouth areas. These genes displayed a wide range of phenotypic effects, with some impacting the full face and others affecting localized regions. The missense variant rs142863092 in NECTIN1 had a significant effect on chin morphology and was predicted bioinformatically to have a deleterious effect on protein function. Notably, NECTIN1 is an established craniofacial gene that underlies a human syndrome that includes a mandibular phenotype. We further showed that nectin1a mutations can affect zebrafish craniofacial development, with the size and shape of the mandibular cartilage altered in mutant animals. Findings from this study expanded our understanding of the genetic basis of normal-range facial shape by highlighting the role of low-frequency coding variants in several novel genes.
Highlights
The contribution of low-frequency variants to the genetic architecture of normal-range facial traits is unknown
While we expect that common variants, with a minor allele frequency (MAF) greater than 1%, account for most of the heritable variation in facial morphology, low frequency (MAF < 1%) genetic variants may play an important role
The success of this Genome-wide association studies (GWASs) was attributed in part to an innovative data-driven phenotyping approach, in which the 3D facial surfaces were partitioned into hierarchically organized regions, each defined by multiple axes of shape variation
Summary
The contribution of low-frequency variants to the genetic architecture of normal-range facial traits is unknown. After accounting for multiple tests, seven genes (AR, CARS2, FTSJ1, HFE, LTB4R, TELO2, NECTIN1) were significantly associated with shape variation of the cheek, chin, nose and mouth areas These genes displayed a wide range of phenotypic effects, with some impacting the full face and others affecting localized regions. Our recent GWAS identified 17,612 common genetic variants associated with facial variation at 138 loci[13] The success of this GWAS was attributed in part to an innovative data-driven phenotyping approach, in which the 3D facial surfaces were partitioned into hierarchically organized regions, each defined by multiple axes of shape variation. This approach allows for testing of genetic variants on facial morphology at multiple levels of scale—from. These results enhance our understanding of the genetic architecture of human facial variation
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