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Genetic mouse models of skeletal abnormalities have demonstrated promise in the identification of phenotypes relevant to human skeletal diseases. Traditionally, phenotypes are assessed by manually examining radiographs, a tedious and potentially error-prone process. In response, this study developed a deep learning-based model that streamlines the measurement of murine bone lengths from radiographs in an accurate and reproducible manner. A bone detection and measurement pipeline utilizing the Keypoint R-CNN algorithm with an EfficientNet-B3 feature extraction backbone was developed to detect murine bone positions and measure their lengths. The pipeline was developed utilizing 94 X-ray images with expert annotations on the start and end position of each murine bone. The accuracy of our pipeline was evaluated on an independent dataset test with 592 images, and further validated on a previously published dataset of 21,300 mouse radiographs. The results showed that our model performed comparably to humans in measuring tibia and femur lengths (R2 > 0.92, p-value = 0) and significantly outperformed humans in measuring pelvic lengths in terms of precision and consistency. Furthermore, the model improved the precision and consistency of genetic association mapping results, identifying significant associations between genetic mutations and skeletal phenotypes with reduced variability. This study demonstrates the feasibility and efficiency of automated murine bone length measurement in the identification of mouse models of abnormal skeletal phenotypes. [ABSTRACT FROM AUTHOR]

Proper embryonic and postnatal skeletal development require coordination of myriad complex molecular mechanisms. Disruption of these processes, through genetic mutation, contributes to variation in skeletal development. We developed a high‐throughput N‐ethyl‐N‐nitrosourea (ENU)‐induced saturation mutagenesis skeletal screening approach in mice to identify genes required for proper skeletal development. Here, we report initial results from live‐animal X‐ray and dual‐energy X‐ray absorptiometry (DXA) imaging of 27,607 G3 mice from 806 pedigrees, testing the effects of 32,198 coding/splicing mutations in 13,020 genes. A total of 39.7% of all autosomal genes were severely damaged or destroyed by mutations tested twice or more in the homozygous state. Results from our study demonstrate the feasibility of in vivo mutagenesis to identify mouse models of skeletal disease. Furthermore, our study demonstrates how ENU mutagenesis provides opportunities to create and characterize putative hypomorphic mutations in developmentally essential genes. Finally, we present a viable mouse model and case report of recessive skeletal disease caused by mutations in FAM20B. Results from this study, including engineered mouse models, are made publicly available via the online Mutagenetix database. © 2021 American Society for Bone and Mineral Research (ASBMR). [ABSTRACT FROM AUTHOR]

Explores the relation between former Head Start parents' self-efficacy beliefs, beliefs about their children's academic abilities, their affective state of depression, their perceptions of the school climate, and their reported level of involvement in their children's education at the end of the kindergarten year. Impact of an environmental school climate; Personal characteristics associated with parent involvement.