Genomic Prediction of Rust Resistance in Tetraploid Wheat under Field and Controlled Environment Conditions

被引:7
|
作者
Azizinia, Shiva [1 ]
Bariana, Harbans [2 ]
Kolmer, James [3 ]
Pasam, Raj [1 ]
Bhavani, Sridhar [4 ]
Chhetri, Mumta [2 ]
Toor, Arvinder [2 ]
Miah, Hanif [2 ]
Hayden, Matthew J. [1 ,5 ]
Pino del Carpio, Dunia [1 ]
Bansal, Urmil [2 ]
Daetwyler, Hans D. [1 ,5 ]
机构
[1] Agr Victoria, AgriBio, Ctr AgriBiosci, Bundoora, Vic 3083, Australia
[2] Univ Sydney, Fac Sci, Sch Life & Environm Sci, Plant Breeding Inst, 107 Cobbitty Rd, Cobbitty, NSW 2570, Australia
[3] USDA ARS, Cereal Dis Lab, St Paul, MN 55108 USA
[4] Int Maize & Wheat Improvement Ctr CIMMYT, Texcoco 56237, Mexico
[5] La Trobe Univ, Sch Appl Syst Biol, Bundoora, Vic 3083, Australia
来源
AGRONOMY-BASEL | 2020年 / 10卷 / 11期
关键词
tetraploid wheat; genomic selection; multivariate analysis; adult plant resistance; all stage resistance; FUSARIUM HEAD BLIGHT; F-SP TRITICI; GENETIC VALUE; SELECTION METHODS; TRAITS; OPTIMIZATION; PEDIGREE; QUALITY; VALUES; STEM;
D O I
10.3390/agronomy10111843
中图分类号
S3 [农学(农艺学)];
学科分类号
0901 ;
摘要
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 x environment (G x 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.
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页数:17
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