Modelling the shape of the pig scapula

Area of Science:

• Animal morphology
• Quantitative genetics
• Biomedical imaging

Background:

• Pig scapula shape is crucial for sow health and robustness.
• Understanding 3D scapula morphology requires accurate point correspondence between individuals.
• Automated computational pipelines are needed for 3D modeling and genetic prediction of complex bone structures.

Purpose of the Study:

• To develop an automated computational pipeline for segmenting computed tomography (CT) scans of pig scapulae.
• To incorporate 3D modeling of the scapula into the pipeline.
• To develop a genetic prediction model for 3D scapula morphology.

Main Methods:

• Identified scapula surface voxels from 2143 CT-scanned pigs.
• Established point correspondence using the coherent point drift algorithm to predict 1234 semi-landmarks.
• Performed principal component analysis (PCA) on 3D shape data and used PCA scores as phenotypes in a genetic model.

Main Results:

• The first 10 principal components explained over 80% of the 3D scapula shape variation.
• Heritability estimates for principal components ranged from 0.4 to 0.8.
• A statistical model predicted scapula shape from marker genotype data with an average reliability of 0.18.

Conclusions:

• High heritability estimates indicate low error in the computational pipeline.
• The study demonstrates the feasibility of predicting 3D pig scapula shape from genetic marker data.
• The developed pipeline effectively links 3D animal shape data to its genetic underpinnings.