Michel Sebastian, Kummer Christian, Gallee Martin, Hellinger Jakob, Ametz Christian, Akgöl Batuhan, Epure Doru, Güngör Huseyin, Löschenberger Franziska, Buerstmayr Hermann
Department for Agrobiotechnology (IFA-Tulln), Institute for Biotechnology in Plant Production, University of Natural Resources and Life Sciences, Vienna (BOKU), Konrad-Lorenz-Str. 20, 3430, Tulln, Austria.
Versuchsanstalt für Getreideverarbeitung, Österreichische Mühlenvereinigung e.V, Prinz-Eugen-Straße 14/1/4, 1040, Vienna, Austria.
Theor Appl Genet. 2018 Feb;131(2):477-493. doi: 10.1007/s00122-017-2998-x. Epub 2017 Oct 23.
Genomic selection shows great promise for pre-selecting lines with superior bread baking quality in early generations, 3 years ahead of labour-intensive, time-consuming, and costly quality analysis. The genetic improvement of baking quality is one of the grand challenges in wheat breeding as the assessment of the associated traits often involves time-consuming, labour-intensive, and costly testing forcing breeders to postpone sophisticated quality tests to the very last phases of variety development. The prospect of genomic selection for complex traits like grain yield has been shown in numerous studies, and might thus be also an interesting method to select for baking quality traits. Hence, we focused in this study on the accuracy of genomic selection for laborious and expensive to phenotype quality traits as well as its selection response in comparison with phenotypic selection. More than 400 genotyped wheat lines were, therefore, phenotyped for protein content, dough viscoelastic and mixing properties related to baking quality in multi-environment trials 2009-2016. The average prediction accuracy across three independent validation populations was r = 0.39 and could be increased to r = 0.47 by modelling major QTL as fixed effects as well as employing multi-trait prediction models, which resulted in an acceptable prediction accuracy for all dough rheological traits (r = 0.38-0.63). Genomic selection can furthermore be applied 2-3 years earlier than direct phenotypic selection, and the estimated selection response was nearly twice as high in comparison with indirect selection by protein content for baking quality related traits. This considerable advantage of genomic selection could accordingly support breeders in their selection decisions and aid in efficiently combining superior baking quality with grain yield in newly developed wheat varieties.
基因组选择在早期世代预选出具有卓越面包烘焙品质的品系方面显示出巨大潜力，比劳动强度大、耗时且成本高昂的品质分析提前3年。烘焙品质的遗传改良是小麦育种中的重大挑战之一，因为相关性状的评估通常涉及耗时、费力且成本高昂的测试，迫使育种者将复杂的品质测试推迟到品种培育的最后阶段。众多研究已表明基因组选择对于诸如谷物产量等复杂性状的前景，因此它也可能是一种选择烘焙品质性状的有趣方法。因此，在本研究中，我们聚焦于基因组选择对难以表型鉴定且成本高昂的品质性状的准确性，以及与表型选择相比其选择响应。为此，在2009 - 2016年的多环境试验中，对400多个基因分型的小麦品系进行了与烘焙品质相关的蛋白质含量、面团粘弹性和混合特性的表型鉴定。在三个独立验证群体中的平均预测准确性为r = 0.39，通过将主要QTL建模为固定效应以及采用多性状预测模型，可将其提高到r = 0.47，这对于所有面团流变学性状产生了可接受的预测准确性（r = 0.38 - 0.63）。此外，基因组选择可比直接表型选择提前2 - 3年应用，并且与通过蛋白质含量对烘焙品质相关性状进行间接选择相比，估计的选择响应几乎高出一倍。基因组选择的这一显著优势因此可以支持育种者做出选择决策，并有助于在新培育的小麦品种中有效地将卓越的烘焙品质与谷物产量相结合。
