Facial shape and allometry quantitative trait locus intervals in the Diversity Outbred mouse are enriched for known skeletal and facial development genes

Abstract

The biology of how faces are built and come to differ from one another is complex. Discovering normal variants that contribute to differences in facial morphology is one key to untangling this complexity, with important implications for medicine and evolutionary biology. This study maps quantitative trait loci (QTL) for skeletal facial shape using Diversity Outbred (DO) mice. The DO is a randomly outcrossed population with high heterozygosity that captures the allelic diversity of eight inbred mouse lines from three subspecies. The study uses a sample of 1147 DO animals (the largest sample yet employed for a shape QTL study in mouse), each characterized by 22 three-dimensional landmarks, 56,885 autosomal and Xchromosome markers, and sex and age classifiers. We identified 37 facial shape QTL across 20 shape principal components (PCs) using a mixed effects regression that accounts for kinship among observations. The QTL include some previously identified intervals as well as new regions that expand the list of potential targets for future experimental study. Three QTL characterized shape associations with size (allometry). Median support interval size was 3.5 Mb. Narrowing additional analysis to QTL for the five largest magnitude shape PCs, we found significant overrepresentation of genes with known roles in growth, skeletal and facial development, and sensory organ development. For most intervals, one or more of these genes lies within 0.25 Mb of the QTL's peak. QTL effect sizes were small, with none explaining more than 0.5% of facial shape variation. Thus, our results are consistent with a model of facial diversity that is influenced by key genes in skeletal and facial development and, simultaneously, is highly polygenic.

A landmark-free morphometrics pipeline for high-resolution phenotyping: application to a mouse model of Down syndrome

“…Heads were prepared for µCT by fixation in paraformaldehyde and then scanned at a 25 µm resolution using a µCT 50 (Scanco). DO mice (Katz et al, 2020) were bred at the Jackson Laboratory. DO mice are specimens derived from eight inbred founder lines that included three mouse subspecies, resulting in a population with high genetic diversity.…”

“…To evaluate the landmark-free approach in a larger sample, we quantified subtle patterns of shape variation in a relatively larger sample of Diversity Outbred (DO) mice. DO mice are derived from the same eight founder strains as the Collaborative Cross (CC) inbred strains (Churchill et al, 2012), which included three mouse subspecies, resulting in a population with relatively high genetic and morphological diversity that resembles the diversity found in natural populations (Churchill et al, 2012;Katz et al, 2020).…”

HDAC9structural variants disruptingTWIST1transcriptional regulation lead to craniofacial and limb malformations

“…The total sample size for this analysis was 170 skulls (25 Wildtype, 38 Hdac ∆/+ , 44 CTCF ∆/∆ , 18 eTw5-7 ∆/+ and 45 eTw5-7 ∆/∆ ). To quantify craniofacial shape, we used the standard set of 68 3D landmarks used in previous work 35,36 . We used geometric morphometric methods to perform quantitative analyses to statistically evaluate and visualize patterns of variation in craniofacial shape and size, using the software R. We performed a Generalized Procrustes Superimposition Analysis (GPA) to extract the aligned Procrustes shape coordinates from the 3D landmark data, using the R package geomorph 37 .…”

MusMorph, a database of standardized mouse morphology data for morphometric meta-analyses

“…Less emphasis has been placed on µCT imaging and analysis of adults and mid-gestation (E10 to E11) mutants, where critical developmental events, like fusion of the craniofacial prominences, occur. Mouse lines with normal (non-pathological) levels of variation, such as recombinant inbred strains and outbred strains with high heterozygosity [21][22][23] , have also been poorly characterized. Quantifying such variation is important, because it drives disease susceptibility and course of disease in humans.…”

“…strains with high heterozygosity [21][22][23] , have also been poorly characterized. Quantifying such variation is important, because it drives disease susceptibility and course of disease in humans.…”