Daetwyler Hans D, Bansal Urmil K, Bariana Harbans S, Hayden Matthew J, Hayes Ben J
School of Applied Systems Biology, La Trobe University, Bundoora, VIC, 3083, Australia,
Theor Appl Genet. 2014 Aug;127(8):1795-803. doi: 10.1007/s00122-014-2341-8. Epub 2014 Jun 26.
We have demonstrated that genomic selection in diverse wheat landraces for resistance to leaf, stem and strip rust is possible, as genomic breeding values were moderately accurate. Markers with large effects in the Bayesian analysis confirmed many known genes, while also discovering many previously uncharacterised genome regions associated with rust scores. Genomic selection, where selection decisions are based on genomic estimated breeding values (GEBVs) derived from genome-wide DNA markers, could accelerate genetic progress in plant breeding. In this study, we assessed the accuracy of GEBVs for rust resistance in 206 hexaploid wheat (Triticum aestivum) landraces from the Watkins collection of phenotypically diverse wheat genotypes from 32 countries. The landraces were genotyped for 5,568 SNPs using an Illumina iSelect 9 K bead chip assay and phenotyped for field-based leaf rust (Lr), stem rust (Sr) and stripe rust (Yr) responses across multiple years. Genomic Best Linear Unbiased Prediction (GBLUP) and a Bayesian Regression method (BayesR) were used to predict GEBVs. Based on fivefold cross-validation, the accuracy of genomic prediction averaged across years was 0.35, 0.27 and 0.44 for Lr, Sr and Yr using GBLUP and 0.33, 0.38 and 0.30 for Lr, Sr and Yr using BayesR, respectively. Inclusion of PCR-predicted genotypes for known rust resistance genes increased accuracy more substantially when the marker was diagnostic (Lr34/Sr57/Yr18) for the presence-absence of the gene rather than just linked (Sr2). Investigation of the impact of genetic relatedness between validation and reference lines on accuracy of genomic prediction showed that accuracy will be higher when each validation line had at least one close relationship to the reference lines. Overall, the prediction accuracies achieved in this study are encouraging, and confirm the feasibility of genomic selection in wheat. In several instances, estimated marker effects were confirmed by published literature and results of mapping experiments using Watkins accessions.
我们已经证明，在不同的小麦地方品种中进行叶锈病、条锈病和秆锈病抗性的基因组选择是可行的，因为基因组育种值具有一定的准确性。贝叶斯分析中效应较大的标记证实了许多已知基因，同时还发现了许多以前未鉴定的与锈病评分相关的基因组区域。基因组选择是指基于全基因组DNA标记得出的基因组估计育种值（GEBV）来做出选择决策，它可以加速植物育种的遗传进展。在本研究中，我们评估了来自沃特金斯收集的206份六倍体小麦（普通小麦）地方品种的锈病抗性GEBV的准确性，这些地方品种代表了来自32个国家的表型多样的小麦基因型。使用Illumina iSelect 9 K芯片检测法对这些地方品种进行了5568个单核苷酸多态性（SNP）的基因分型，并对多年来基于田间的叶锈病（Lr）、秆锈病（Sr）和条锈病（Yr）反应进行了表型分析。使用基因组最佳线性无偏预测（GBLUP）和贝叶斯回归方法（BayesR）来预测GEBV。基于五重交叉验证，使用GBLUP时，Lr、Sr和Yr多年平均的基因组预测准确性分别为0.35、0.27和0.44，使用BayesR时，Lr、Sr和Yr的准确性分别为0.33、0.38和0.30。当标记对已知锈病抗性基因的存在与否具有诊断性（Lr34/Sr57/Yr18）而非仅仅是连锁（Sr2）时，纳入PCR预测的已知锈病抗性基因的基因型能更显著地提高准确性。对验证系和参考系之间的遗传相关性对基因组预测准确性的影响进行的研究表明，当每个验证系与参考系至少有一个密切关系时，准确性会更高。总体而言，本研究中获得的预测准确性令人鼓舞，并证实了小麦基因组选择的可行性。在一些情况下，估计的标记效应得到了已发表文献以及使用沃特金斯种质进行的定位实验结果的证实。
