Abstract
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 × 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 × 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.
Highlights
The pea, Pisum sativum L., is an important cool-season legume crop grown in temperate climates, with a global annual production of 17 million tons (FAOSTAT, 2014)
Principle component analysis of the genotypic data showed that the first principle component separated most of the lines from the Plant and Food Research Limited (PFR) breeding program from most of the lines from other sources, while the second principle component separated a small group of the PFR lines from the remaining lines (Figure 1)
The greatest mean prediction accuracy achieved for a disease severity score (ASC) was 0.56, obtained using genomic best linear unbiased prediction (GBLUP) analysis with a mean value for ASC and data quality threshold of 70%
Summary
The pea, Pisum sativum L., is an important cool-season legume crop grown in temperate climates, with a global annual production of 17 million tons (FAOSTAT, 2014). It is nutritionally valuable, providing a rich source of protein and starch, as well as a range of other nutrients. The symbiotic relationship with nitrogen-fixing bacteria makes the pea an important component of sustainable cropping systems (Tayeh et al, 2015a). It takes several years to produce genetically and phenotypically stable lines, and to produce enough seed for field trials and commercialization. Increasing the efficiency of the breeding process by detecting elite lines earlier, using high-throughput genotyping or phenotyping, would be valuable
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