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Bones in the vertebrate cranial base and limb skeleton grow by endochondral ossification, under the control of growth plates. Mechanisms of endochondral ossification are conserved across growth plates, which increases covariation in size and shape among bones, and in turn may lead to correlated changes in skeletal traits not under direct selection. We used micro-CT and geometric morphometrics to characterize shape changes in the cranium of the Longshanks mouse, which was selectively bred for longer tibiae. We show that Longshanks skulls became longer, flatter, and narrower in a stepwise process. Moreover, we show that these morphological changes likely resulted from developmental changes in the growth plates of the Longshanks cranial base, mirroring changes observed in its tibia. Thus, indirect and non-adaptive morphological changes can occur due to developmental overlap among distant skeletal elements, with important implications for interpreting the evolutionary history of vertebrate skeletal form.

The long legs and short arms of humans are distinctive for a primate, the result of selection acting in opposite directions on each limb at different points in our evolutionary history. This mosaic pattern challenges our understanding of the relationship of development and evolvability because limbs are serially homologous and genetic correlations should act as a significant constraint on their independent evolution. Here we test a developmental model of limb covariation in anthropoid primates and demonstrate that both humans and apes exhibit significantly reduced integration between limbs when compared to quadrupedal monkeys. This result indicates that fossil hominins likely escaped constraints on independent limb variation via reductions to genetic pleiotropy in an ape-like last common ancestor (LCA). This critical change in integration among hominoids, which is reflected in macroevolutionary differences in the disparity between limb lengths, facilitated selection for modern human limb proportions and demonstrates how development helps shape evolutionary change.

Transcriptional regulation exhibits extensive robustness, but human genetics indicates sensitivity to transcription factor (TF) dosage. Reconciling such observations requires quantitative studies of TF dosage effects at trait-relevant ranges, largely lacking so far. TFs play central roles in both normal-range and disease-associated variation in craniofacial morphology; we therefore developed an approach to precisely modulate TF levels in human facial progenitor cells and applied it to SOX9, a TF associated with craniofacial variation and disease (Pierre Robin sequence (PRS)). Most SOX9-dependent regulatory elements (REs) are buffered against small decreases in SOX9 dosage, but REs directly and primarily regulated by SOX9 show heightened sensitivity to SOX9 dosage; these RE responses partially predict gene expression responses. Sensitive REs and genes preferentially affect functional chondrogenesis and PRS-like craniofacial shape variation. We propose that such REs and genes underlie the sensitivity of specific phenotypes to TF dosage, while buffering of other genes leads to robust, nonlinear dosage-to-phenotype relationships. SOX9 titration in neural crest cells identifies regulatory elements and genes with sensitive or buffered responses. Sensitive genes are enriched for craniofacial disorder genes phenocopying SOX9, suggesting differential sensitivity contributes to phenotypic specificity.

Geometric morphometrics is the statistical analysis of landmark-based shape variation and its covariation with other variables. Over the past two decades, the gold standard of landmark data acquisition has been manual detection by a single observer. This approach has proven accurate and reliable in small-scale investigations. However, big data initiatives are increasingly common in biology and morphometrics. This requires fast, automated, and standardized data collection. We combine techniques from image registration, geometric morphometrics, and deep learning to automate and optimize anatomical landmark detection. We test our method on high-resolution, micro-computed tomography images of adult mouse skulls. To ensure generalizability, we use a morphologically diverse sample and implement fundamentally different deformable registration algorithms. Compared to landmarks derived from conventional image registration workflows, our optimized landmark data show up to a 39.1% reduction in average coordinate error and a 36.7% reduction in total distribution error. In addition, our landmark optimization produces estimates of the sample mean shape and variance–covariance structure that are statistically indistinguishable from expert manual estimates. For biological imaging datasets and morphometric research questions, our approach can eliminate the time and subjectivity of manual landmark detection whilst retaining the biological integrity of these expert annotations.

Robustness to perturbation is a fundamental feature of complex organisms. Mutations are the raw material for evolution, yet robustness to their effects is required for species survival. The mechanisms that produce robustness are poorly understood. Nonlinearities are a ubiquitous feature of development that may link variation in development to phenotypic robustness. Here, we manipulate the gene dosage of a signaling molecule, Fgf8 , a critical regulator of vertebrate development. We demonstrate that variation in Fgf8 expression has a nonlinear relationship to phenotypic variation, predicting levels of robustness among genotypes. Differences in robustness are not due to gene expression variance or dysregulation, but emerge from the nonlinearity of the genotype–phenotype curve. In this instance, embedded features of development explain robustness differences. How such features vary in natural populations and relate to genetic variation are key questions for unraveling the origin and evolvability of this feature of organismal development. Developmental processes often involve nonlinearities, but the consequences for translating genotype to phenotype are not well characterized. Here, Green et al. vary Fgf8 signaling across allelic series of mice and show that phenotypic robustness in craniofacial shape is explained by a nonlinear effect of Fgf8 expression.

Human craniofacial shape is highly variable yet highly heritable with numerous 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 general population. We compare three-dimensional facial scans from 43 individuals with achondroplasia and 8246 controls to calculate achondroplasia-like facial scores. Multivariate GWAS of the control scores reveals a polygenic basis for facial variation along an achondroplasia-specific shape axis, identifying genes primarily involved in skeletal development. Jointly modeling these genes in two independent control samples, both human and mouse, shows 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 insights into the genetic intersection of complex traits and Mendelian disorders. Vanneste et al. introduce a syndrome-informed approach to GWAS, identifying a polygenic basis for achondroplasia-like facial shape variation in the general population, and showing that complex traits and Mendelian disorders share genetic pathways.

Mechanisms of resource allocation are essential for maternal and fetal survival, particularly when the availability of nutrients is limited. We investigated the responses of feto-placental development to maternal chronic protein malnutrition to test the hypothesis that maternal low protein diet produces differential growth restriction of placental and fetal tissues, and adaptive changes in the placenta that may mitigate impacts on fetal growth. C57BL/6J female mice were fed either a low-protein diet (6% protein) or control isocaloric diet (20% protein). On embryonic days E10.5, 17.5 and 18.5 tissue samples were prepared for morphometric, histological and quantitative RT-PCR analyses, which included markers of trophoblast cell subtypes. Potential endocrine adaptations were assessed by the expression of Prolactin-related hormone genes. In the low protein group, placenta weight was significantly lower at E10.5, followed by reduction of maternal weight at E17.5, while the fetuses became significantly lighter no earlier than at E18.5. Fetal head at E18.5 in the low protein group, though smaller than controls, was larger than expected for body size. The relative size and shape of the cranial vault and the flexion of the cranial base was affected by E17.5 and more severely by E18.5. The junctional zone, a placenta layer rich in endocrine and energy storing glycogen cells, was smaller in low protein placentas as well as the expression of Pcdh12, a marker of glycogen trophoblast cells. Placental hormone gene Prl3a1 was altered in response to low protein diet: expression was elevated at E17.5 when fetuses were still growing normally, but dropped sharply by E18.5 in parallel with the slowing of fetal growth. This model suggests that nutrients are preferentially allocated to sustain fetal and brain growth and suggests the placenta as a nutrient sensor in early gestation with a role in mitigating impacts of poor maternal nutrition on fetal growth.

Fitness-related traits tend to have low heritabilities. Conversely, morphology tends to be highly heritable. Yet, many fitness-related performance traits such as running speed or bite force depend critically on morphology. Craniofacial morphology correlates with bite performance in several groups including rodents. However, within species, this relationship is less clear, and the genetics of performance, morphology and function are rarely analyzed in combination. Here, we use a half-sib design in outbred wild-derived Mus musculus to study the morphology-bite force relationship and determine whether there is additive genetic (co-)variance for these traits. Results suggest that bite force has undetectable additive genetic variance and heritability in this sample, while morphological traits related mechanically to bite force exhibit varying levels of heritability. The most heritable traits include the length of the mandible which relates to bite force. Despite its correlation with morphology, realized bite force was not heritable, which suggests it is less responsive to selection in comparison to its morphological determinants. We explain this paradox with a non-additive, many-to-one mapping hypothesis of heritable change in complex traits. We furthermore propose that performance traits could evolve if pleiotropic relationships among the determining traits are modified.

3D facial landmarks are known to be diagnostically relevant biometrics for many genetic syndromes. The objective of this study was to extend a state-of-the-art image-based 2D facial landmarking algorithm for the challenging task of 3D landmark identification on subjects with genetic syndromes, who often have moderate to severe facial dysmorphia. The automatic 3D facial landmarking algorithm presented here uses 2D image-based facial detection and landmarking models to identify 12 landmarks on 3D facial surface scans. The landmarking algorithm was evaluated using a test set of 444 facial scans with ground truth landmarks identified by two different human observers. Three hundred and sixty nine of the subjects in the test set had a genetic syndrome that is associated with facial dysmorphology. For comparison purposes, the manual landmarks were also used to initialize a non-linear surface-based registration of a non-syndromic atlas to each subject scan. Compared to the average intra- and inter-observer landmark distances of 1.1 mm and 1.5 mm respectively, the average distance between the manual landmark positions and those produced by the automatic image-based landmarking algorithm was 2.5 mm. The average error of the registration-based approach was 3.1 mm. Comparing the distributions of Procrustes distances from the mean for each landmarking approach showed that the surface registration algorithm produces a systemic bias towards the atlas shape. In summary, the image-based automatic landmarking approach performed well on this challenging test set, outperforming a semi-automatic surface registration approach, and producing landmark errors that are comparable to state-of-the-art 3D geometry-based facial landmarking algorithms evaluated on non-syndromic subjects.

Human hands and feet have longer, more robust first digits, and shorter lateral digits compared to African apes. These similarities are often assumed to be independently evolved adaptations for manipulative activities and bipedalism, respectively. However, hands and feet are serially homologous structures that share virtually identical developmental blueprints, raising the possibility that digital proportions coevolved in human hands and feet because of underlying developmental linkages that increase phenotypic covariation between them. Here we show that phenotypic covariation between serially homologous fingers and toes in Homo and Pan is not only higher than expected, it also causes these digits to evolve along highly parallel trajectories under episodes of simulated directional selection, even when selection pressures push their means in divergent directions. Further, our estimates of the selection pressures required to produce human-like fingers and toes from an African ape-like ancestor indicate that selection on the toes was substantially stronger, and likely led to parallel phenotypic changes in the hands. Our data support the hypothesis that human hands and feet coevolved, and suggest that the evolution of long robust big toes and short lateral toes for bipedalism led to changes in hominin fingers that may have facilitated the emergence of stone tool technology.