Vitale Paolo, Montesinos-López Osval, Gerard Guillermo, Velu Govindan, Tadesse Zerihun, Montesinos-López Abelardo, Dreisigacker Susanne, Pacheco Angela, Toledo Fernando, Saint Pierre Carolina, Pérez-Rodríguez Paulino, Gardner Keith, Crespo-Herrera Leonardo, Crossa José
International Maize and Wheat Improvement Center (CIMMYT), Km 45 Carretera México-Veracruz, El Batan, Edo. de México 5623, Mexico.
Facultad de Telemática, Universidad de Colima, Colima 28040, Mexico.
G3 (Bethesda). 2025 Apr 17;15(4). doi: 10.1093/g3journal/jkaf038.
Genomic selection is an essential tool to improve genetic gain in wheat breeding. This study aimed to enhance prediction accuracy for grain yield across various selection environments using CIMMYT's (International Maize and Wheat Improvement Center) historical dataset. Ten years of grain yield data from 6 selection environments were analyzed, with the populations of 5 years (2018-2023) as the validation population and earlier years (back to 2013-2014) as the training population. Generally, we observed that as the number of training years increased, the prediction accuracy tended to improve or stabilize. For instance, in the late heat stress selection environment (beds late heat stress), prediction accuracy increased from 0.11 (1 training year) to 0.23 (5 years), stabilizing at 0.26. Similar trends were observed in the intermediate drought selection environment (beds with 2 irrigations), with prediction accuracy rising from 0.12 (1 year) to 0.21 (4 years) but minimal improvement beyond that. Conversely, some selection environments, such as flat 5 irrigations (flat optimal environment), did not significantly increase, with the prediction accuracy fluctuating around 0.09-0.14 regardless of the number of training years used. Additionally, average genetic diversity within the training population and the validation population influenced prediction accuracy. Indeed, a negative correlation between prediction accuracy and the genetic distance was observed. This highlights the need to balance genetic diversity to enhance the predictive power of genomic selection models. These findings exhibit the benefits of using an extended historical dataset while considering genetic diversity to maximize prediction accuracy in genomic selection strategies for wheat breeding, ultimately supporting the development of high-yielding varieties.
基因组选择是提高小麦育种遗传增益的重要工具。本研究旨在利用国际玉米和小麦改良中心（CIMMYT）的历史数据集，提高不同选择环境下小麦产量的预测准确性。分析了来自6个选择环境的10年小麦产量数据，将2018 - 2023年这5年的群体作为验证群体，更早年份（追溯到2013 - 2014年）作为训练群体。总体而言，我们观察到随着训练年份的增加，预测准确性趋于提高或稳定。例如，在后期热胁迫选择环境（苗床后期热胁迫）中，预测准确性从0.11（1个训练年）提高到0.23（5年），并稳定在0.26。在中度干旱选择环境（两次灌溉的苗床）中也观察到类似趋势，预测准确性从0.12（1年）提高到0.21（4年），但此后提升极小。相反，一些选择环境，如五次灌溉平地（平地最优环境），预测准确性没有显著提高，无论使用的训练年份数量如何，预测准确性都在0.09 - 0.14左右波动。此外，训练群体和验证群体内部的平均遗传多样性影响预测准确性。实际上，观察到预测准确性与遗传距离之间存在负相关。这突出了平衡遗传多样性以增强基因组选择模型预测能力的必要性。这些发现展示了在小麦育种的基因组选择策略中，使用扩展的历史数据集并考虑遗传多样性以最大化预测准确性的益处，最终支持高产小麦品种的培育。
