The Nosology Committee of the International Skeletal Dysplasia Society currently recognizes 461 skeletal disorders and 437 genes that are associated with 425 of these disorders (Mortier et al., 2019). As these associations are identified, methods such as Next Generation Sequencing and genetic modification of model organisms are used to parse out the underlying genetic mechanisms of skeletal diseases. CRISPR-modified mouse and zebrafish models have been used to understand the mechanisms for skeletal diseases such as sclerosteosis, osteoporosis, and osteogenesis imperfecta, as well as more rare variations of these disorders (Garg et al., 2022;Kaya et al., 2022;Kwon et al., 2019). Using model organisms to understand the genetic mechanism of these disorders presents opportunities to develop and test potential treatments (Kague & Karasik, 2022). However, for these models to be useful, the homologous genes in model organisms must present observable phenotypes that parallel the symptoms of human diseases. Current best practices use measures such as tissue mineral density and bone thickness to identify phenotypes that are common between model

Genetic diseases affecting the skeletal system present with a wide range of symptoms that make diagnosis and treatment difficult. Genome-wide association and sequencing studies have identified genes linked to human skeletal diseases. Gene editing of zebrafish models allows researchers to further examine the link between genotype and phenotype, with the long-term goal of improving diagnosis and treatment. While current automated tools enable rapid and in-depth phenotyping of the axial skeleton, characterizing the effects of mutations on the craniofacial skeleton has been more challenging. The objective of this study was to evaluate a semi-automated screening tool can be used to quantify craniofacial variations in zebrafish models using four genes that have been associated with human skeletal diseases (meox1, plod2, sost, and wnt16) as test cases. We used traditional landmarks to ground truth our dataset and pseudolandmarks to quantify variation across the 3D cranial skeleton between the groups (somatic crispant, germline mutant, and control fish). The proposed pipeline identified variation between the crispant or mutant fish and control fish for four genes. Variation in phenotypes parallel human craniofacial symptoms for two of the four genes tested. This study demonstrates the potential as well as the limitations of our pipeline as a screening tool to examine multi-dimensional phenotypes associated with the zebrafish craniofacial skeleton.