Genomic selection (GS) based recurrent selection methods were developed to accelerate the domestication of intermediate wheatgrass [IWG, Thinopyrum intermedium (Host) Barkworth & D.R. Dewey]. A subset of the breeding population phenotyped at multiple environments is used to train GS models and then predict trait values of the breeding population. In this study, we implemented several GS models that investigated the use of additive and dominance effects and G×E interaction effects to understand how they affected trait predictions in intermediate wheatgrass. We evaluated 451 genotypes from the University of Minnesota IWG breeding program for nine agronomic and domestication traits at two Minnesota locations during 2017–2018. Genet‐mean based heritabilities for these traits ranged from 0.34 to 0.77. Using four‐fold cross validation, we observed the highest predictive abilities (correlation of 0.67) in models that considered G×E effects. When G×E effects were fitted in GS models, trait predictions improved by 18%, 15%, 20%, and 23% for yield, spike weight, spike length, and free threshing, respectively. Genomic selection models with dominance effects showed only modest increases of up to 3% and were trait‐dependent. Cross‐environment predictions were better for high heritability traits such as spike length, shatter resistance, free threshing, grain weight, and seed length than traits with low heritability and large environmental variance such as spike weight, grain yield, and seed width. Our results confirm that GS can accelerate IWG domestication by increasing genetic gain per breeding cycle and assist in selection of genotypes with promise of better performance in diverse environments.

中文翻译：

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优势和G×E交互作用提高了中间小麦草（Thinopyrum intermedium）的基因组预测和遗传增益

基因组选择（GS）基于递归选择方法被开发以加速中间小麦草的驯化[IWG，中间偃麦草（主持人）巴克沃斯和杜威博士]。在多种环境中表型化的繁殖种群的子集用于训练GS模型，然后预测繁殖种群的特征值。在这项研究中，我们实施了几个GS模型，研究了加性和显性效应以及G×E相互作用效应的使用，以了解它们如何影响中间小麦草的性状预测。我们评估了明尼苏达大学IWG育种计划中的451个基因型，以了解其在2017-2018年期间在明尼苏达州两个地点的9个农学和驯养性状。这些特征基于遗传均值的遗传力范围为0.34至0.77。使用四重交叉验证，我们在考虑了G×E效应的模型中观察到了最高的预测能力（相关系数为0.67）。在GS模型中安装G×E效果时，对产量，穗重，穗长和自由脱粒的性状预测分别提高了18％，15％，20％和23％。具有显性作用的基因组选择模型仅显示出不超过3％的适度增长，并且是与性状相关的。与高遗传力性状（如穗长，抗碎性，自由脱粒，籽粒重和种子长）相比，跨环境预测要好于遗传力低且环境方差较大（如穗重，籽粒产量和种子宽度）的性状。我们的结果证实，GS可以通过增加每个育种周期的遗传增益来加速IWG驯化，并有助于选择基因型，并有望在不同环境中获得更好的性能。具有显性作用的基因组选择模型仅显示出不超过3％的适度增长，并且是与性状相关的。与高遗传力性状（如穗长，抗碎性，自由脱粒，谷粒重量和种子长度）相比，跨环境预测要好于遗传力低且环境变异较大（如穗重，籽粒产量和种子宽度）的性状。我们的结果证实，GS可以通过增加每个育种周期的遗传增益来加速IWG驯化，并有助于选择基因型，并有望在不同环境中获得更好的性能。具有显性作用的基因组选择模型仅显示出不超过3％的适度增长，并且是与性状相关的。与高遗传力性状（如穗长，抗碎性，自由脱粒，籽粒重和种子长）相比，跨环境预测要好于遗传力低且环境方差较大（如穗重，籽粒产量和种子宽度）的性状。我们的结果证实，GS可以通过增加每个育种周期的遗传增益来加速IWG驯化，并有助于选择基因型，并有望在不同环境中获得更好的性能。以及种子长度要比遗传力低和环境变异大（例如穗重，籽粒产量和种子宽度）大的特征。我们的结果证实，GS可以通过增加每个育种周期的遗传增益来加速IWG驯化，并有助于选择基因型，并有望在不同环境中获得更好的性能。以及种子长度要比遗传力低和环境变异大（例如穗重，籽粒产量和种子宽度）大的特征。我们的结果证实，GS可以通过增加每个育种周期的遗传增益来加速IWG驯化，并有助于选择基因型，并有望在不同环境中获得更好的性能。