International Maize and Wheat Improvement Center (CIMMYT), Km 45, Carretera México-Veracruz, Texcoco CP 52640, Estado de México, Mexico.
Colegio de Postgraduados, Montecillos CP 56230, Estado de México, Mexico.
Genes (Basel). 2024 Jul 29;15(8):995. doi: 10.3390/genes15080995.
This study presents a novel approach for the optimization of genomic parental selection in breeding programs involving categorical and continuous-categorical multi-trait mixtures (CMs and CCMMs). Utilizing the Bayesian decision theory (BDT) and latent trait models within a multivariate normal distribution framework, we address the complexities of selecting new parental lines across ordinal and continuous traits for breeding. Our methodology enhances precision and flexibility in genetic selection, validated through extensive simulations. This unified approach presents significant potential for the advancement of genetic improvements in diverse breeding contexts, underscoring the importance of integrating both categorical and continuous traits in genomic selection frameworks.
本研究提出了一种新的方法，用于优化涉及分类和连续分类多性状混合物（CM 和 CCMM）的育种计划中的基因组亲本选择。本研究利用贝叶斯决策理论（BDT）和潜在特征模型，在多元正态分布框架内，解决了在分类和连续性状方面选择新亲本系的复杂性。我们的方法通过广泛的模拟验证了提高遗传选择精度和灵活性的方法。这种统一的方法为不同育种背景下的遗传改良提供了重要的潜力，强调了在基因组选择框架中整合分类和连续性状的重要性。
