Computational anatomy and geometric shape analysis enables analysis of complex craniofacial phenotypes in zebrafish

Computational anatomy (CA) approaches start by estimating a canonical ‘template’ from a sample of images. This template is then used as a basis for statistical analysis to quantify structural differences among groups of interest. Due to the complexity of fish skulls, previous attempts to classify craniofacial phenotypes have relied on qualitative features or 2D landmarks. In this work we aim to identify and quantify differences in 3D craniofacial phenotypes in adult zebrafish mutants. We first estimate a synthetic ‘normative’ zebrafish template using microCT scans from a sample pool of wildtype animals using the Advanced Normalization Tools (ANTs). To validate the accuracy of the template and our CA pipeline, we compared the otolith volumes derived from the CA approach to manually segmented volumes of the same set of zebrafish. Our overall CA based segmentation volumes are statistically similar to our manual segmentations and both results show that mutants have larger otoliths than their wildtype controls. We apply CA to zebrafish with somatic mutations in bmp1a, whose human ortholog when disrupted is associated with Osteogenesis Imperfecta. Our Generalized Procrustes Analysis separated the bmp1a and wildtype fish along the first two principal components which collectively explained 31.8% of the variation in the dataset. Compared to controls, the phenotypic differences in bmp1a fish are concentrated around the operculum and the orbit. Our CA approach seems to offer a potential pipeline for high throughput screening of complex fish craniofacial phenotypes, especially those of zebrafish which are an important model system for testing genome to phenome relationships in the study of development, evolution, and human diseases.