Genomic Prediction of Manganese Efficiency in Winter Barley

Manganese efficiency is a quantitative abiotic stress trait controlled by several genes each with a small effect. Manganese deficiency leads to yield reduction in winter barley ( Hordeum vulgare L.). Breeding new cultivars for this trait remains difficult because of the lack of visual symptoms and the polygenic features of the trait. Hence, Mn efficiency is a potential suitable trait for a genomic selection (GS) approach. A collection of 248 winter barley varieties was screened for Mn efficiency using Chlorophyll a (Chl a ) fluorescence in six environments prone to induce Mn deficiency. Two models for genomic prediction were implemented to predict future performance and breeding value of untested varieties. Predictions were obtained using multivariate mixed models: best linear unbiased predictor (BLUP) and genomic best linear unbiased predictor (G‐BLUP). In the first model, predictions were based on the phenotypic evaluation, whereas both phenotypic and genomic marker data were included in the second model. Accuracy of predicting future phenotype, r ( g ^ , y ^ ) , and accuracy of predicting true breeding values, r ( g ^ , g ) , were calculated and compared for both models using six cross‐validation (CV) schemes; these were designed to mimic plant breeding programs. Overall, the CVs showed that prediction accuracies increased when using the G‐BLUP model compared with the prediction accuracies using the BLUP model. Furthermore, the accuracies [ r ( g ^ , g ) ] of predicting breeding values were more accurate than accuracy of predicting future phenotypes [ r ( g ^ , y ^ ) ]. The study confirms that genomic data may enhance the prediction accuracy. Moreover it indicates that GS is a suitable breeding approach for quantitative abiotic stress traits.