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Abstract
Implementing genomic-based prediction models in genomic selection requires an understanding of the measures for evaluating prediction accuracy from different models and methods using multi-trait data. In this study, we compared prediction accuracy using six large multi-trait wheat data sets (quality and grain yield). The data were used to predict 1 year (testing) from the previous year (training) to assess prediction accuracy using four different prediction models. The results indicated that the conventional Pearson’s correlation between observed and predicted values underestimated the true correlation value, whereas the corrected Pearson’s correlation calculated by fitting a bivariate model was higher than the division of the Pearson’s correlation by the squared root of the heritability across traits, by 2.53–11.46%. Across the datasets, the corrected Pearson’s correlation was higher than the uncorrected by 5.80–14.01%. Overall, we found that for grain yield the prediction performance was highest using a multi-trait compared to a single-trait model. The higher the absolute genetic correlation between traits the greater the benefits of multi-trait models for increasing the genomic-enabled prediction accuracy of traits.
Highlights
Wheat is one of the most important cultivated crops in the world and is a major source of energy and protein in the human diet
Spring wheat lines selected for quality and grain yield analyses from CIMMYT first year yield trials (YT) were used as the training population to predict the quality of lines selected from elite yield trials (EYT) for quality and grain yield analyses in a second year
The average Pearson’s correlation (APC) for all methods across the 5-testing set configurations are shown in Figures 1–6, for the 6 pairs of years
Summary
Wheat is one of the most important cultivated crops in the world and is a major source of energy and protein in the human diet. It is used to produce a diversity of foods with specific end-use requirements including protein quantity, quality, and kernel hardness (Pen~a et al 2002). In the context of wheat breeding, a major objective is selection of lines with high grain yield performance and good grain quality. Initial breeding cycles select on grain yield performance, while quality traits are improved in later stages due to the scale of seed requirements and the financial and time cost of quality assessments. GP could improve selection accuracy in both early and later breeding stages by improving the overall grain yield of lines in the first stage and considerably reducing the cost of the screening process in the second multi-trait selection stage (Ibba et al 2020)
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