Genomic prediction can be advantageous in barley breeding for traits such as yield and malting quality to increase selection accuracy and minimize expensive phenotyping. In this paper, we investigate the possibilities of genomic selection for malting quality traits using a limited training population. The size of the training population is an important factor in determining the prediction accuracy of a trait. We investigated the potential for genomic prediction of malting quality within breeding cycles with leave one out (LOO) cross-validation, and across breeding cycles with leave set out (LSO) cross-validation. In addition, we investigated the effect of training population size on prediction accuracy by random two, four, and ten-fold cross-validation. The material used in this study was a population of 1329 spring barley lines from four breeding cycles. We found medium to high narrow sense heritabilities of the malting traits ( 0.31 to 0.65). Accuracies of predicting breeding values from LOO tests ranged from 0.6 to 0.9 making it worth the effort to use genomic prediction within breeding cycles. Accuracies from LSO tests ranged from 0.39 to 0.70 showing that genomic prediction across the breeding cycles were possible as well. Accuracy of prediction increased when the size of the training population increased. Therefore, prediction accuracy might be increased both within and across breeding cycle by increasing size of the training population.

中文翻译：

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实用大麦育种计划中麦芽品质性状的基因组预测

基因组预测在大麦育种中具有优势，例如产量和麦芽品质，可提高选择准确性并最大程度地减少昂贵的表型。在本文中，我们研究了使用有限的培训人群进行麦芽品质性状基因组选择的可能性。训练种群的大小是确定性状预测准确性的重要因素。我们调查了育种质量基因组预测在育种周期内进行留一法（LOO）交叉验证的潜力，并在整个育种周期中进行留​​休法（LSO）交叉验证。另外，我们通过随机的二，四和十倍交叉验证研究了训练人口规模对预测准确性的影响。本研究中使用的材料是来自四个育种周期的1329株春季大麦品系。我们发现麦芽性状的中等至高度狭义遗传力（0.31至0.65）。根据LOO测试预测育种值的准确性介于0.6到0.9之间，因此值得在育种周期内使用基因组预测。LSO测试的准确性范围为0.39至0.70，这表明整个育种周期的基因组预测也是可能的。当训练人数增加时，预测的准确性会提高。因此，通过增加训练种群的数量，可以在育种周期内和整个育种周期提高预测准确性。根据LOO测试预测育种值的准确性介于0.6到0.9之间，因此值得在育种周期内使用基因组预测。LSO测试的准确性范围为0.39至0.70，这表明跨育种周期的基因组预测也是可能的。当训练人数增加时，预测的准确性会提高。因此，通过增加训练种群的数量，可以在育种周期内和整个育种周期提高预测准确性。根据LOO测试预测育种值的准确性介于0.6到0.9之间，因此值得在育种周期内使用基因组预测。LSO测试的准确性范围为0.39至0.70，这表明跨育种周期的基因组预测也是可能的。当训练人数增加时，预测的准确性会提高。因此，通过增加训练种群的数量，可以在育种周期内和整个育种周期提高预测准确性。