Genomic Selection for Ascochyta Blight Resistance in Pea

被引:14
|
作者
Carpenter, Margaret A. [1 ]
Goulden, David S. [1 ]
Woods, Carmel J. [1 ]
Thomson, Susan J. [1 ]
Kenel, Fernand [1 ]
Frew, Tonya J. [1 ]
Cooper, Rebecca D. [1 ]
Timmerman-Vaughan, Gail M. [1 ]
机构
[1] New Zealand Inst Plant & Food Res Ltd, Christchurch, New Zealand
来源
关键词
genomic selection; ascochyta blight; pea; disease resistance; genotyping-by-sequencing; QUANTITATIVE TRAIT LOCI; PISUM-SATIVUM L; PREDICTION; ACCURACY; IDENTIFICATION; POPULATIONS; REGRESSION; SOFTWARE; MARKERS; GENES;
D O I
10.3389/fpls.2018.01878
中图分类号
Q94 [植物学];
学科分类号
071001 ;
摘要
Genomic selection GS) is a breeding tool, which is rapidly gaining popularity for plant breeding, particularly for traits that are difficult to measure. One such trait is ascochyta blight resistance in pea Pisum sativum L.), which is difficult to assay because it is strongly influenced by the environment and depends on the natural occurrence of multiple pathogens. Here we report a study of the efficacy of GS for predicting ascochyta blight resistance in pea, as represented by ascochyta blight disease score ASC), and using nucleotide polymorphism data acquired through genotyping-by-sequencing. The effects on prediction accuracy of different GS models and different thresholds for missing genotypic data which modified the number of single nucleotide polymorphisms used in the analysis) were compared using cross-validation. Additionally, the inclusion of marker x environment interactions in a genomic best linear unbiased prediction GBLUP) model was evaluated. Finally, different ways of combining trait data from two field trials using bivariate, spatial, and single-stage analyses were compared to results obtained using a mean value. The best prediction accuracy achieved for ASC was 0.56, obtained using GBLUP analysis with a mean value for ASC and data quality threshold of 70% i.e., missing SNP data in < 30% of lines). GBLUP and Bayesian Reproducing kernel Hilbert spaces regression RKHS) performed slightly better than the other models trialed, whereas different missing data thresholds made minimal differences to prediction accuracy. The prediction accuracies of individual, randomly selected, testing/training partitions were highly variable, highlighting the effect that the choice of training population has on prediction accuracy. The inclusion of marker x environment interactions did not increase the prediction accuracy for lines which had not been phenotyped, but did improve the results of prediction across environments. GS is potentially useful for pea breeding programs pursuing ascochyta blight resistance, both for predicting breeding values for lines that have not been phenotyped, and for providing enhanced estimated breeding values for lines for which trait data is available.
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页数:13
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