Sabag Idan, Bi Ye, Peleg Zvi, Morota Gota
The Robert H. Smith Institute of Plant Sciences and Genetics in Agriculture, The Hebrew University of Jerusalem, Rehovot, Israel.
School of Animal Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, United States.
Front Genet. 2023 Mar 13;14:1108416. doi: 10.3389/fgene.2023.1108416. eCollection 2023.
Sesame is an ancient oilseed crop containing many valuable nutritional components. The demand for sesame seeds and their products has recently increased worldwide, making it necessary to enhance the development of high-yielding cultivars. One approach to enhance genetic gain in breeding programs is genomic selection. However, studies on genomic selection and genomic prediction in sesame have yet to be conducted. In this study, we performed genomic prediction for agronomic traits using the phenotypes and genotypes of a sesame diversity panel grown under Mediterranean climatic conditions over two growing seasons. We aimed to assess prediction accuracy for nine important agronomic traits in sesame using single- and multi-environment analyses. In single-environment analysis, genomic best linear unbiased prediction, BayesB, BayesC, and reproducing kernel Hilbert spaces models showed no substantial differences. The average prediction accuracy of the nine traits across these models ranged from 0.39 to 0.79 for both growing seasons. In the multi-environment analysis, the marker-by-environment interaction model, which decomposed the marker effects into components shared across environments and environment-specific deviations, improved the prediction accuracies for all traits by 15%-58% compared to the single-environment model, particularly when borrowing information from other environments was made possible. Our results showed that single-environment analysis produced moderate-to-high genomic prediction accuracy for agronomic traits in sesame. The multi-environment analysis further enhanced this accuracy by exploiting marker-by-environment interaction. We concluded that genomic prediction using multi-environmental trial data could improve efforts for breeding cultivars adapted to the semi-arid Mediterranean climate.
芝麻是一种古老的油料作物，含有许多有价值的营养成分。最近，全球对芝麻及其产品的需求有所增加，因此有必要加强高产栽培品种的开发。提高育种计划中遗传增益的一种方法是基因组选择。然而，尚未开展关于芝麻基因组选择和基因组预测的研究。在本研究中，我们利用在地中海气候条件下两个生长季节种植的芝麻多样性群体的表型和基因型，对农艺性状进行了基因组预测。我们旨在通过单环境和多环境分析评估芝麻九个重要农艺性状的预测准确性。在单环境分析中，基因组最佳线性无偏预测、贝叶斯B、贝叶斯C和再生核希尔伯特空间模型没有显著差异。在两个生长季节中，这些模型对九个性状的平均预测准确性在0.39至0.79之间。在多环境分析中，标记与环境互作模型将标记效应分解为跨环境共享的成分和特定于环境的偏差，与单环境模型相比，所有性状的预测准确性提高了15%-58%，特别是在能够从其他环境借用信息时。我们的结果表明，单环境分析对芝麻农艺性状产生了中等至高的基因组预测准确性。多环境分析通过利用标记与环境互作进一步提高了这种准确性。我们得出结论，使用多环境试验数据进行基因组预测可以改进适应半干旱地中海气候的品种的育种工作。
