Genome‐based prediction of multiple wheat quality traits in multiple years. Issue 3 (25th July 2020)

• Record Type: Journal Article
• Authors: Ibba, Maria Itria; Crossa, Jose; Montesinos‐López, Osval A.; Montesinos‐López, Abelardo; Juliana, Philomin; Guzman, Carlos; Delorean, Emily; Dreisigacker, Susanne; Poland, Jesse
• Is Part Of: œplant genome. Volume 13:Issue 3(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-07-25
• Subjects: Plant genomes -- Periodicals; Plant genome mapping -- Periodicals; 572.862
• DOI: 10.1002/tpg2.20034
• Languages: English
• ISSNs: 1940-3372

Abstract

Abstract: Wheat quality improvement is an important objective in all wheat breeding programs. However, due to the cost, time and quantity of seed required, wheat quality is typically analyzed only in the last stages of the breeding cycle on a limited number of samples. The use of genomic prediction could greatly help to select for wheat quality more efficiently by reducing the cost and time required for this analysis. Here were evaluated the prediction performances of 13 wheat quality traits under two multi‐trait models (Bayesian multi‐trait multi‐environment [BMTME] and multi‐trait ridge regression [MTR]) using five data sets of wheat lines evaluated in the field during two consecutive years. Lines in the second year (testing) were predicted using the quality information obtained in the first year (training). For most quality traits were found moderate to high prediction accuracies, suggesting that the use of genomic selection could be feasible. The best predictions were obtained with the BMTME model in all traits and the worst with the MTR model. The best predictions with the BMTME model under the mean arctangent absolute percentage error (MAAPE) were for test weight across the five data sets, whereas the worst predictions were for the alveograph trait ALVPL. In contrast, under Pearson's correlation, the best predictions depended on the data set. The results obtained suggest that the BMTME model should be preferred for multi‐trait prediction analyses. This model allows to obtain not only the correlation among traits, but also the correlation among environments, helping to increase the prediction accuracy.