AUTHOR=Carpenter Margaret A. , Goulden David S. , Woods Carmel J. , Thomson Susan J. , Kenel Fernand , Frew Tonya J. , Cooper Rebecca D. , Timmerman-Vaughan Gail M. 

TITLE=Genomic Selection for Ascochyta Blight Resistance in Pea

JOURNAL=Frontiers in Plant Science

VOLUME=Volume 9 - 2018

YEAR=2018

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2018.01878

DOI=10.3389/fpls.2018.01878

ISSN=1664-462X

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, 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. Finally, the inclusion of marker x environment interactions in a GBLUP model was evaluated. The best prediction accuracy achieved for 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% (i.e. missing SNP data in <30% of lines). However, the differences in prediction accuracy among the GS models, missing data thresholds, and treatments of trait data were minimal. 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 enviroments. 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.