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Genomic Prediction of Rust Resistance in Tetraploid Wheat under Field and Controlled Environment Conditions
Agronomy ( IF 3.949 ) Pub Date : 2020-11-23 , DOI: 10.3390/agronomy10111843
Shiva Azizinia , Harbans Bariana , James Kolmer , Raj Pasam , Sridhar Bhavani , Mumta Chhetri , Arvinder Toor , Hanif Miah , Matthew J. Hayden , Dunia Pino del Carpio , Urmil Bansal , Hans D. Daetwyler

Genomic selection can increase the rate of genetic gain in crops through accumulation of positive alleles and reduce phenotyping costs by shortening the breeding cycle time. We performed genomic prediction for resistance to wheat rusts in tetraploid wheat accessions using three cross-validation with the objective of predicting: (1) rust resistance when individuals are not tested in all environments/locations, (2) the performance of lines across years, and (3) adult plant resistance (APR) of lines with bivariate models. The rationale for the latter is that seedling assays are faster and could increase prediction accuracy for APR. Predictions were derived from adult plant and seedling responses for leaf rust (Lr), stem rust (Sr) and stripe rust (Yr) in a panel of 391 accessions grown across multiple years and locations and genotyped using 16,483 single nucleotide polymorphisms. Different Bayesian models and genomic best linear unbiased prediction yielded similar accuracies for all traits. Site and year prediction accuracies for Lr and Yr ranged between 0.56–0.71 for Lr and 0.51–0.56 for Yr. While prediction accuracy for Sr was variable across different sites, accuracies for Yr were similar across different years and sites. The changes in accuracies can reflect higher genotype × environment (G × E) interactions due to climate or pathogenic variation. The use of seedling assays in genomic prediction was underscored by significant positive genetic correlations between all stage resistance (ASR) and APR (Lr: 0.45, Sr: 0.65, Yr: 0.50). Incorporating seedling phenotypes in the bivariate genomic approach increased prediction accuracy for all three rust diseases. Our work suggests that the underlying plant-host response to pathogens in the field and greenhouse screens is genetically correlated, but likely highly polygenic and therefore difficult to detect at the individual gene level. Overall, genomic prediction accuracies were in the range suitable for selection in early generations of the breeding cycle.

中文翻译:

四倍体小麦在田间和受控环境下抗锈性的基因组预测。

基因组选择可通过积累正等位基因来提高农作物的遗传增益速率,并通过缩短育种周期时间降低表型成本。我们使用三项交叉验证对四倍体小麦种质的小麦锈病抗性进行了基因组预测,其目的是预测:(1)未在所有环境/位置进行个体测试时的抗锈性;(2)多年以来品系的表现, (3)具有双变量模型的品系的成年植物抗性(APR)。后者的理由是,幼苗测定速度更快,并且可以提高APR的预测准确性。预测是根据成年植物和幼苗对391种在多年和不同地点生长的种的叶锈病(Lr),茎锈病(Sr)和条锈病(Yr)的反应得出的,并使用16种基因分型 483个单核苷酸多态性。不同的贝叶斯模型和基因组最佳线性无偏预测对所有性状产生相似的准确度。Lr和Yr的站点和年份预测精度介于Lr的0.56-0.71和Yr的0.51-0.56之间。尽管不同地点对Sr的预测准确性存在差异,但不同年份和不同地点的Yr准确性却相似。精度的变化可以反映由于气候或病原体变异而引起的更高的基因型×环境(G×E)相互作用。所有阶段抗性(ASR)和APR之间的显着正遗传相关性(Lr:0.45,Sr:0.65,Yr:0.50)强调了在基因组预测中使用幼苗试验。在双变量基因组方法中纳入幼苗表型可以提高对所有三种锈病的预测准确性。我们的工作表明,田间和温室筛选对病原体的潜在植物宿主反应具有遗传相关性,但可能是高度多基因的,因此很难在单个基因水平上检测到。总的来说,基因组预测的准确性在适合早期繁殖周期选择的范围内。
更新日期:2020-11-23
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