Department of Bioindustry and Bioresource Engineering, Sejong University, Seoul, Republic of Korea.
Plant Engineering Research Institute, Sejong University, Seoul, Republic of Korea.
BMC Plant Biol. 2024 Mar 27;24(1):222. doi: 10.1186/s12870-024-04934-8.
Genomic selection (GS) is an efficient breeding strategy to improve quantitative traits. It is necessary to calculate genomic estimated breeding values (GEBVs) for GS. This study investigated the prediction accuracy of GEBVs for five fruit traits including fruit weight, fruit width, fruit height, pericarp thickness, and Brix. Two tomato germplasm collections (TGC1 and TGC2) were used as training populations, consisting of 162 and 191 accessions, respectively.
Large phenotypic variations for the fruit traits were found in these collections and the 51K Axiom SNP array generated confident 31,142 SNPs. Prediction accuracy was evaluated using different cross-validation methods, GS models, and marker sets in three training populations (TGC1, TGC2, and combined). For cross-validation, LOOCV was effective as k-fold across traits and training populations. The parametric (RR-BLUP, Bayes A, and Bayesian LASSO) and non-parametric (RKHS, SVM, and random forest) models showed different prediction accuracies (0.594-0.870) between traits and training populations. Of these, random forest was the best model for fruit weight (0.780-0.835), fruit width (0.791-0.865), and pericarp thickness (0.643-0.866). The effect of marker density was trait-dependent and reached a plateau for each trait with 768-12,288 SNPs. Two additional sets of 192 and 96 SNPs from GWAS revealed higher prediction accuracies for the fruit traits compared to the 31,142 SNPs and eight subsets.
Our study explored several factors to increase the prediction accuracy of GEBVs for fruit traits in tomato. The results can facilitate development of advanced GS strategies with cost-effective marker sets for improving fruit traits as well as other traits. Consequently, GS will be successfully applied to accelerate the tomato breeding process for developing elite cultivars.
基因组选择（GS）是一种提高数量性状的有效育种策略。需要计算 GS 的基因组估计育种值（GEBV）。本研究调查了 GEBV 对五个果实性状（包括果实重量、果实宽度、果实高度、果皮厚度和 Brix）的预测准确性。两个番茄种质资源收集（TGC1 和 TGC2）用作训练群体，分别包含 162 和 191 个个体。
在这些收集物中发现了果实性状的大表型变异，并且 51K Axiom SNP 阵列产生了可靠的 31,142 个 SNP。使用不同的交叉验证方法、GS 模型和标记集在三个训练群体（TGC1、TGC2 和组合）中评估了预测准确性。对于交叉验证，LOOCV 在跨性状和训练群体方面非常有效。参数（RR-BLUP、Bayes A 和贝叶斯 LASSO）和非参数（RKHS、SVM 和随机森林）模型显示了不同的预测准确性（0.594-0.870），不同性状和训练群体之间。其中，随机森林是果实重量（0.780-0.835）、果实宽度（0.791-0.865）和果皮厚度（0.643-0.866）的最佳模型。标记密度的影响是依赖于性状的，对于每个性状，768-12,288 SNP 达到了一个平台。来自 GWAS 的另外两组 192 和 96 个 SNP 显示了比 31,142 SNP 和八个子集更高的果实性状预测准确性。
本研究探讨了一些因素，以提高番茄果实性状的 GEBV 预测准确性。结果可以促进开发具有成本效益的标记集的先进 GS 策略，以提高果实性状以及其他性状。因此，GS 将成功应用于加速番茄的育种过程，以开发优秀的品种。
