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Genotype‐by‐environment interaction for turfgrass quality in bermudagrass across the southeastern United States
Crop Science ( IF 2.0 ) Pub Date : 2020-07-16 , DOI: 10.1002/csc2.20260
Beatriz Tomé Gouveia 1 , Esteban Fernando Rios 2 , José Airton Rodrigues Nunes 1 , Salvador A. Gezan 3 , Patricio R. Munoz 4 , Kevin E. Kenworthy 2 , J. Bryan Unruh 5 , Grady L. Miller 6 , Susana R. Milla‐Lewis 6 , Brian M. Schwartz 7 , Paul L. Raymer 8 , Ambika Chandra 9 , Benjamin G. Wherley 10 , Yanqi Wu 11 , Dennis Martin 12 , Justin Q. Moss 12
Affiliation  

Estimation of genotype‐by‐environment interaction (GEI) is important in breeding programs because it provides critical information to guide selection decisions. In general, multienvironment trials exhibit heterogeneity of variances and covariances at several levels. Thus, the objectives of this study were (a) to find the best genetic covariance matrix to model GEI and compare changes in genotypic rankings between the best covariance structure against a compound symmetry structure, (b) to define mega‐environments for turfgrass performance across the southeastern United States, and (c) to estimate genetic correlations between drought or nondrought and growing or nongrowing conditions to determine the extent of GEI under specific environments. Three nurseries with 165, 164, and 154 genotypes were evaluated in 2011–2012, 2012–2013, and 2013–2014, respectively. These nurseries were conducted at eight locations (Citra, FL; Hague, FL; College Station, TX; Dallas, TX; Griffin, GA; Tifton, GA; Stillwater, OK; and Jackson Springs, NC). The response variables were averaged turfgrass quality (TQ), TQ under drought (TQD), nondrought TQ (TQND), TQ under actively growing months (TQG), and TQ under nongrowing months (TQNG). This study demonstrated that (a) the best variance structure varied among traits and seasons, and changes in genotype rankings were dependent on GEI; (b) considering TQ and TQND, mega‐environments formed between Jackson Springs and College Station, and between Citra, Dallas, and Griffin, whereas Stillwater, Hague, and Tifton represented unique environments across the southeastern United States; and (c) genetic correlations between drought or nondrought and growing or nongrowing conditions suggested that indirect selection can be efficient in multienvironment trials for contrasting environmental conditions.

中文翻译:

美国东南部百慕大草中草皮质量的基因-环境相互作用

基因型-环境相互作用(GEI)的估计在育种计划中很重要,因为它提供了指导选择决策的关键信息。通常,多环境试验在几个级别上表现出方差和协方差的异质性。因此,本研究的目标是(a)找到最佳遗传协方差矩阵以对GEI建模,并比较最佳协方差结构与复合对称结构之间的基因型排名变化;(b)定义整个草坪草性能的大环境。 (c)估计干旱或非干旱与生长或不生长条件之间的遗传相关性,以确定在特定环境下GEI的程度。在2011–2012年,2012–2013年和2013–2014年对3个具有165、164和154基因型的苗圃进行了评估,分别。这些苗圃在八个地点(佛罗里达州西特拉;佛罗里达州海牙;德克萨斯州大学城;德克萨斯州达拉斯;乔治亚州格里芬;乔治亚州蒂夫顿;俄克拉何马州斯蒂芬沃特和北卡罗来纳州杰克逊斯普林斯)进行。响应变量为平均草皮质量(TQ),干旱下的TQ(TQD),非干旱下的TQ(TQND),活跃生长月份下的TQ(TQG)和非生长月份下的TQ(TQNG)。这项研究表明:(a)最佳的变异结构在性状和季节之间变化,并且基因型排名的变化取决于GEI;(b)考虑到TQ和TQND,杰克逊温泉和大学城之间以及Citra,达拉斯和格里芬之间形成了巨大的环境,而斯蒂尔沃特,海牙和蒂夫顿则代表了美国东南部的独特环境;
更新日期:2020-07-16
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