Cappetta Elisa, Andolfo Giuseppe, Di Matteo Antonio, Barone Amalia, Frusciante Luigi, Ercolano Maria Raffaella
Department of Agricultural Sciences, University of Naples Federico II, Via Università 100, 80055 Naples, Italy.
Plants (Basel). 2020 Sep 18;9(9):1236. doi: 10.3390/plants9091236.
Genomic selection (GS) is a predictive approach that was built up to increase the rate of genetic gain unit of time and reduce the generation interval by utilizing genome-wide markers in breeding programs. It has emerged as a valuable method for improving complex traits that are controlled by many genes with small effects. GS enables the prediction of the breeding value of candidate genotypes for selection. In this work, we address important issues related to GS and its implementation in the plant context with special emphasis on tomato breeding. Genomic constraints and critical parameters affecting the accuracy of prediction such as the number of markers, statistical model, phenotyping and complexity of trait, training population size and composition should be carefully evaluated. The comparison of GS approaches for facilitating the selection of tomato superior genotypes during breeding programs is also discussed. GS applied to tomato breeding has already been shown to be feasible. We illustrated how GS can improve the rate of gain in elite line selection, and descendent and backcross schemes. The GS schemes have begun to be delineated and computer science can provide support for future selection strategies. A new promising breeding framework is beginning to emerge for optimizing tomato improvement procedures.
基因组选择（GS）是一种预测方法，通过在育种计划中利用全基因组标记来提高单位时间内的遗传增益率并缩短世代间隔。它已成为改良由多个微效基因控制的复杂性状的一种有价值的方法。基因组选择能够预测用于选择的候选基因型的育种值。在这项工作中，我们探讨了与基因组选择及其在植物领域（特别着重于番茄育种）的实施相关的重要问题。应仔细评估基因组限制以及影响预测准确性的关键参数，如标记数量、统计模型、表型分析和性状复杂性、训练群体大小和组成。还讨论了在育种计划中促进番茄优良基因型选择的基因组选择方法的比较。应用于番茄育种的基因组选择已被证明是可行的。我们说明了基因组选择如何提高优良品系选择、后代和回交方案中的增益率。基因组选择方案已开始被描绘出来，并且计算机科学可为未来的选择策略提供支持。一个新的有前景的育种框架正在出现，以优化番茄改良程序。
