The shape of pig scapula is complex and is important for sow robustness and health. To better understand the relationship between 3D shape of the scapula and functional traits, it is necessary to build a model that explains most of the morphological variation between animals. This requires point correspondence, i.e. a map that explains which points represent the same piece of tissue among individuals. The objective of this study was to further develop an automated computational pipeline for the segmentation of computed tomography (CT) scans to incorporate 3D modelling of the scapula, and to develop a genetic prediction model for 3D morphology. The surface voxels of the scapula were identified on 2143 CT-scanned pigs, and point correspondence was established by predicting the coordinates of 1234 semi-landmarks on each animal, using the coherent point drift algorithm. A subsequent principal component analysis showed that the first 10 principal components covered more than 80% of the total variation in 3D shape of the scapula. Using principal component scores as phenotypes in a genetic model, estimates of heritability ranged from 0.4 to 0.8 (with standard errors from 0.07 to 0.08). To validate the entire computational pipeline, a statistical model was trained to predict scapula shape based on marker genotype data. The mean prediction reliability averaged over the whole scapula was equal to 0.18 (standard deviation = 0.05) with a higher reliability in convex than in concave regions. Estimates of heritability of the principal components were high and indicated that the computational pipeline that processes CT data to principal component phenotypes was associated with little error. Furthermore, we showed that it is possible to predict the 3D shape of scapula based on marker genotype data. Taken together, these results show that the proposed computational pipeline closes the gap between a point cloud representing the shape of an animal and its underlying genetic components.

中文翻译：

---

模拟猪肩胛骨的形状。

猪肩胛骨的形状复杂，对母猪的健壮和健康很重要。为了更好地理解肩胛骨的 3D 形状与功能特征之间的关系，有必要建立一个模型来解释动物之间的大部分形态变化。这需要点对应，即解释哪些点代表个体中同一块组织的地图。本研究的目的是进一步开发用于计算机断层扫描 (CT) 扫描分割的自动化计算管道，以纳入肩胛骨的 3D 建模，并开发 3D 形态的遗传预测模型。在 2143 头 CT 扫描猪上识别了肩胛骨的表面体素，并使用相干点漂移算法预测每只动物上 1234 个半标志的坐标，建立点对应关系。随后的主成分分析表明，前 10 个主成分覆盖了肩胛骨 3D 形状总变化的 80% 以上。使用主成分得分作为遗传模型中的表型，遗传力的估计范围为 0.4 至 0.8（标准误差为 0.07 至 0.08）。为了验证整个计算流程，训练了统计模型以根据标记基因型数据预测肩胛骨形状。整个肩胛骨的平均预测可靠性等于 0.18（标准差 = 0.05），凸区域的可靠性高于凹区域。主成分遗传力的估计值很高，表明将 CT 数据处理为主成分表型的计算流程几乎没有误差。此外，我们表明可以根据标记基因型数据预测肩胛骨的 3D 形状。总而言之，这些结果表明，所提出的计算管道缩小了代表动物形状的点云与其潜在遗传成分之间的差距。