Maximizing genetic gains across agronomic and consumer preference traits in St. Augustinegrass breeding

IF 2 3区 农林科学 Q2 AGRONOMY Crop Science Pub Date : 2024-10-09 DOI:10.1002/csc2.21374
Susana R. Milla‐Lewis, Beatriz Tome Gouveia, Kevin Kenworthy, Jing Zhang, Ambika Chandra, Grady L. Miller, Esdras M. Carbajal, Brian Schwartz, Paul Raymer, Marta Pudzianowska, James H. Beard, J. Bryan Unruh
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Abstract

Combining large multi‐environment trial (MET) datasets to decide which genotypes to move forward in the breeding process can be challenging, especially when dealing with negatively correlated traits. The use of a selection index has long been identified as an effective strategy in these situations. However, the method has found limited application in turfgrass breeding. The objective of this study was to use MET data for St. Augustinegrass [Stenotaphrum secundatum (Walt.) Kuntze] breeding lines evaluated across the southern United States to compare genetic gains achieved with the additive additive genetic index (AI) versus the turf performance index (TPI) incorporating agronomic as well as consumer preference traits. The use of either selection index produced more positive genetic gains across traits than direct selection even in the presence of negative correlations. However, the higher genetic gains obtained with AI versus TPI indicate that the use of an index that weighs traits according to their importance is a better approach for selection. Moreover, under a more stringent selection intensity, none of the best lines identified with AI would have been selected with TPI emphasizing the importance of choosing selection criteria that provide a more nuanced ranking of lines. Additionally, higher heritability values and gains from selection were obtained for turfgrass quality under stress (drought and shade) than under normal conditions indicating that selection under stress environments might be more efficient. Most of the evaluated St. Augustinegrass lines outperformed the checks, further supporting the value of cross‐institutional breeding collaborations.
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在圣奥古斯丁草育种中最大限度地提高农艺性状和消费者偏好性状的遗传收益
结合大型多环境试验(MET)数据集来决定在育种过程中采用哪些基因型是一项挑战,尤其是在处理负相关性状时。在这种情况下,使用选择指数早已被认为是一种有效的策略。然而,这种方法在草坪育种中的应用却很有限。本研究的目的是利用在美国南部评估的圣奥古斯丁草(Stenotaphrum secundatum (Walt.) Kuntze)育种品系的 MET 数据,比较使用加性遗传指数(AI)与结合农艺学和消费者偏好性状的草坪性能指数(TPI)所获得的遗传收益。与直接选择相比,即使存在负相关,使用任一选择指数都能在各性状上产生更多的正遗传增益。然而,人工合成指数比 TPI 获得的遗传增益更高,这表明使用根据性状重要性进行权衡的指数是更好的选择方法。此外,在更严格的选择强度下,用 AI 选出的最佳品系都不会用 TPI 选出,这就强调了选择能提供更细致的品系排序的选择标准的重要性。此外,在胁迫(干旱和遮荫)条件下,草坪草质量的遗传率值和选择收益均高于正常条件下,这表明在胁迫环境下进行选择可能更有效。大多数经过评估的圣奥古斯丁草品系的表现都优于对照品系,这进一步证明了跨机构育种合作的价值。
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来源期刊
Crop Science
Crop Science 农林科学-农艺学
CiteScore
4.50
自引率
8.70%
发文量
197
审稿时长
3 months
期刊介绍: Articles in Crop Science are of interest to researchers, policy makers, educators, and practitioners. The scope of articles in Crop Science includes crop breeding and genetics; crop physiology and metabolism; crop ecology, production, and management; seed physiology, production, and technology; turfgrass science; forage and grazing land ecology and management; genomics, molecular genetics, and biotechnology; germplasm collections and their use; and biomedical, health beneficial, and nutritionally enhanced plants. Crop Science publishes thematic collections of articles across its scope and includes topical Review and Interpretation, and Perspectives articles.
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