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Complex morphological traits are the product of many genes with transient or lasting developmental effects that interact in anatomical context. Mouse models are a key resource for disentangling such effects, because they offer myriad tools for manipulating the genome in a controlled environment. Unfortunately, phenotypic data are often obtained using laboratory-specific protocols, resulting in self-contained datasets that are difficult to relate to one another for larger scale analyses. To enable meta-analyses of morphological variation, particularly in the craniofacial complex and brain, we created MusMorph, a database of standardized mouse morphology data spanning numerous genotypes and developmental stages, including E10.5, E11.5, E14.5, E15.5, E18.5, and adulthood. To standardize data collection, we implemented an atlas-based phenotyping pipeline that combines techniques from image registration, deep learning, and morphometrics. Alongside stage-specific atlases, we provide aligned micro-computed tomography images, dense anatomical landmarks, and segmentations (if available) for each specimen (N = 10,056). Our workflow is open-source to encourage transparency and reproducible data collection. The MusMorph data and scripts are available on FaceBase (www.facebase.org, https://doi.org/10.25550/3-HXMC) and GitHub (https://github.com/jaydevine/MusMorph).

Knowledge of bone structure-function relationships in mice has been based on relatively small sample sets that limit generalizability. We sought to investigate structure-function relationships of long bones from a large population of genetically diverse mice. Therefore, we analyzed previously published data from the femur and radius of male and female mice from the F34 generation of the Large-by-Small advanced intercross line (LGXSM AI), which have over a two-fold continuous spread of bone and body sizes (Silva et al. 2019 JBMR).Morphological traits, mechanical properties, and estimated material properties were collected from the femur and radius from 1113 LGXSM AI adult mice (avg. age 25 wks). Males and females fed a low-fat or high-fat diet were evaluated to increase population variation. The data were analyzed using principal component analysis (PCA), Pearson's correlation, and multivariate linear regression.Using PCA groupings and hierarchical clustering, we identified a reduced set of traits that span the population variation and are relatively independent of each other. These include three morphometry parameters (cortical area, medullary area, and length), two mechanical properties (ultimate force and post-yield displacement), and one material property (ultimate stress). When comparing traits of the femur to the radius, morphological traits are moderately well correlated (rOur results indicate that variation in cortical bone phenotype in the F34 LGXSM AI mouse population can be adequately described by a reduced set of bone traits. These traits include cortical area, medullary area, bone length, ultimate force, post-yield displacement, and ultimate stress. The weak correlation of mechanical and material properties between the femur and radius indicates that the results from routine three-point bending tests of one long bone (e.g., femur) may not be generalizable to another long bone (e.g., radius). Additionally, these properties could not be fully predicted from bone morphology alone, confirming the importance of mechanical testing. Finally, material properties of the femur estimated based on beam theory equations showed a strong dependence on geometry that was not seen in the radius, suggesting that differences in femur size within a study may confound interpretation of estimated material properties.