Teodoro João Vitor, Mourão Gerson Barreto, Carvalho Rachel Santos Bueno, de Mattos Elisângela Chicaroni, Ferraz José Bento Sterman, Eler Joanir Pereira
Universidade Federal do Triângulo Mineiro, Iturama, Brazil.
Universidade de São Paulo, Pirassununga, Brazil.
J Anim Breed Genet. 2025 Nov;142(6):669-674. doi: 10.1111/jbg.12936. Epub 2025 Mar 11.
Evaluating optimal mating combinations in large populations poses significant combinatorial and computational challenges. To address this, we propose a method to optimise mating combinations in composite cattle populations, incorporating heterosis and genetic variability. Leveraging integer linear programming, our approach maximises expected offspring merit, outperforming random mating systems. A robust mathematical model and specialised software were developed to implement the method, demonstrating its effectiveness on a real dataset. Notably, results reveal a 14.8% superiority over random mating averages and a 12.4% advantage over random mating maxima. The method's flexibility and adaptability enable constraint inclusion and application to diverse species and genomic data, making it an indispensable tool for enhancing mating selection efficiency and effectiveness in composite beef cattle breeding programmes.
在大型群体中评估最优交配组合面临着重大的组合和计算挑战。为解决这一问题，我们提出了一种在复合牛群中优化交配组合的方法，该方法纳入了杂种优势和遗传变异性。利用整数线性规划，我们的方法能使预期后代的优良性状最大化，优于随机交配系统。我们开发了一个强大的数学模型和专门的软件来实施该方法，并在一个真实数据集上证明了其有效性。值得注意的是，结果显示该方法比随机交配平均值高出14.8%，比随机交配最大值高出12.4%。该方法的灵活性和适应性使其能够纳入约束条件，并应用于不同物种和基因组数据，成为提高复合肉牛育种计划中交配选择效率和效果的不可或缺的工具。
