Dynamic Multi Component Genetic Merit Index A Novel Statistical Approach for Livestock Breeding

This paper introduces the Dynamic Multi Component Genetic Merit Index (DMCGMI), a novel statistical approach for estimating genetic merit in livestock breeding. Traditional genetic evaluation methods predominantly rely on additive genetic models with static economic weights, limiting their ability to capture the complex genetic architecture of economically important traits and adapt to changing market conditions. The DMCGMI addresses these limitations by integrating multiple genetic components additive, dominance, and gene by environment interactions while incorporating temporal dynamics in both genetic expression and economic valuation. The model employs a network based approach to genomic clustering and non linear interaction modeling, providing a more comprehensive prediction of genetic potential across varying environmental conditions. A two-stage estimation procedure balances computational feasibility with model complexity, making the DMCGMI practical for implementation in commercial breeding programs. Case studies across dairy cattle, pig, and beef cattle breeding demonstrate significant improvements in prediction accuracy (10–30%), genetic progress (10–20%), and economic returns ($5-200 per animal) compared to traditional methods. The DMCGMI represents a significant advancement in livestock breeding methodology, offering enhanced genetic progress, economic optimization, and adaptability to environmental challenges while maintaining the interpretability necessary for practical breeding decisions.