Schulthess Albert Wilhelm, Wang Yu, Miedaner Thomas, Wilde Peer, Reif Jochen C, Zhao Yusheng
Department of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466, Gatersleben, Germany.
State Plant Breeding Institute, University of Hohenheim, 70593, Stuttgart, Germany.
Theor Appl Genet. 2016 Feb;129(2):273-87. doi: 10.1007/s00122-015-2626-6. Epub 2015 Nov 3.
Exploiting the benefits from multiple-trait genomic selection for protein content prediction relying on additional grain yield information within training sets is a realistic genomic selection approach in rye breeding.
Multiple-trait genomic selection (MTGS) was specially designed to benefit from the information of genetically correlated indicator traits in order to improve genomic prediction accuracies. Two segregating F3:4 rye testcross populations genotyped using diversity array technology markers and evaluated for grain yield (GY) and protein content (PC) were considered. The aims of our study were to explore the benefits of MTGS over single-trait genomic selection (STGS) for GY and PC prediction and to apply GS to predict different selection indices (SIs) for GY and PC improvement. Our results using a two-trait model (2TGS) empirically confirm that the ideal scenario to exploit the benefits of MTGS would be when the predictions of a relatively low heritable target trait with scarce phenotypic records are supported by an intensively phenotyped genetically correlated indicator trait which has higher heritability. This ideal scenario is expected for PC in practice. According to our GS implementation, MTGS can be performed in order to achieve more cycles of selection by unit of time. If the aim is to exclusively improve the prediction accuracy of a scarcely phenotyped trait, 2TGS will be a more accurate approach than a three-trait model which incorporates an additional correlated indicator trait. In general for balanced phenotypic information, we recommend to perform GS considering SIs as single traits, this method being a simple, direct and efficient way of prediction.
在黑麦育种中，利用训练集中额外的籽粒产量信息进行多性状基因组选择以预测蛋白质含量，是一种切实可行的基因组选择方法。
多性状基因组选择（MTGS）专门设计用于利用遗传相关指示性状的信息，以提高基因组预测准确性。本研究考虑了两个使用多样性阵列技术标记进行基因分型，并对籽粒产量（GY）和蛋白质含量（PC）进行评估的分离F3:4黑麦测交群体。我们研究的目的是探索多性状基因组选择相对于单性状基因组选择（STGS）在预测GY和PC方面的优势，并应用基因组选择来预测用于提高GY和PC的不同选择指数（SIs）。我们使用双性状模型（2TGS）的结果通过实证证实，利用多性状基因组选择优势的理想情况是，具有较高遗传力且经过密集表型分析的遗传相关指示性状能够支持对具有稀缺表型记录的低遗传力目标性状的预测。在实际中，PC有望出现这种理想情况。根据我们的基因组选择实施情况，进行多性状基因组选择可以在单位时间内实现更多轮次的选择。如果目标是专门提高难以进行表型分析的性状的预测准确性，2TGS将比纳入额外相关指示性状的三性状模型更准确。总体而言，对于平衡的表型信息，我们建议将选择指数作为单性状进行基因组选择，这种方法是一种简单、直接且有效的预测方式。
