Isidro Y Sánchez Julio, Akdemir Deniz
Centro de Biotecnologia y Genómica de Plantas, Instituto Nacional de Investigación y Tecnologia Agraria y Alimentaria, Universidad Politécnica de Madrid, Campus de Montegancedo, Madrid, Spain.
Animal and Crop Science Division, Agriculture and Food Science Centre, University College Dublin, Dublin, Ireland.
Front Plant Sci. 2021 Sep 9;12:715910. doi: 10.3389/fpls.2021.715910. eCollection 2021.
Genomic selection (GS) is becoming an essential tool in breeding programs due to its role in increasing genetic gain per unit time. The design of the training set (TRS) in GS is one of the key steps in the implementation of GS in plant and animal breeding programs mainly because (i) TRS optimization is critical for the efficiency and effectiveness of GS, (ii) breeders test genotypes in multi-year and multi-location trials to select the best-performing ones. In this framework, TRS optimization can help to decrease the number of genotypes to be tested and, therefore, reduce phenotyping cost and time, and (iii) we can obtain better prediction accuracies from optimally selected TRS than an arbitrary TRS. Here, we concentrate the efforts on reviewing the lessons learned from TRS optimization studies and their impact on crop breeding and discuss important features for the success of TRS optimization under different scenarios. In this article, we review the lessons learned from training population optimization in plants and the major challenges associated with the optimization of GS including population size, the relationship between training and test set (TS), update of TRS, and the use of different packages and algorithms for TRS implementation in GS. Finally, we describe general guidelines to improving the rate of genetic improvement by maximizing the use of the TRS optimization in the GS framework.
由于基因组选择（GS）在提高单位时间内的遗传增益方面发挥着作用，它正成为育种计划中的一项重要工具。GS中训练集（TRS）的设计是在植物和动物育种计划中实施GS的关键步骤之一，主要原因如下：（i）TRS优化对于GS的效率和有效性至关重要；（ii）育种者在多年和多地试验中测试基因型，以选择表现最佳的基因型。在此框架下，TRS优化有助于减少待测试的基因型数量，从而降低表型分析成本和时间；（iii）与任意选择的TRS相比，从优化选择的TRS中我们可以获得更高的预测准确性。在此，我们集中精力回顾从TRS优化研究中吸取的经验教训及其对作物育种的影响，并讨论在不同情况下TRS优化成功的重要特征。在本文中，我们回顾了从植物训练群体优化中吸取的经验教训以及与GS优化相关的主要挑战，包括群体大小、训练集与测试集（TS）的关系、TRS的更新，以及在GS中使用不同的软件包和算法来实施TRS。最后，我们描述了通过在GS框架中最大限度地利用TRS优化来提高遗传改良率的一般指导原则。
