Genomic Prediction of Grain Yield in a Barley MAGIC Population Modeling Genotype per Environment Interaction
18 Pags.- 7 Figs.- 4 Tabls. © 2021 Puglisi, Delbono, Visioni, Ozkan, Kara, Casas, Igartua, Valè, Piero, Cattivelli, Tondelli and Fricano. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). ; Multi-parent Advanced Generation Inter-crosses (MAGIC) lines have mosaic genomes that are generated shuffling the genetic material of the founder parents following pre-defined crossing schemes. In cereal crops, these experimental populations have been extensively used to investigate the genetic bases of several traits and dissect the genetic bases of epistasis. In plants, genomic prediction models are usually fitted using either diverse panels of mostly unrelated accessions or individuals of biparental families and several empirical analyses have been conducted to evaluate the predictive ability of models fitted to these populations using different traits. In this paper, we constructed, genotyped and evaluated a barley MAGIC population of 352 individuals developed with a diverse set of eight founder parents showing contrasting phenotypes for grain yield. We combined phenotypic and genotypic information of this MAGIC population to fit several genomic prediction models which were cross-validated to conduct empirical analyses aimed at examining the predictive ability of these models varying the sizes of training populations. Moreover, several methods to optimize the composition of the training population were also applied to this MAGIC population and cross-validated to estimate the resulting predictive ability. Finally, extensive phenotypic data generated in field trials organized across an ample range of water regimes and climatic conditions in the Mediterranean were used to fit and cross-validate multi-environment genomic prediction models including G×E interaction, using both genomic best linear unbiased prediction and reproducing kernel Hilbert space along with a non-linear Gaussian Kernel. Overall, our empirical analyses showed that genomic prediction models trained with a limited number of MAGIC lines can be used to predict grain yield with values of predictive ability that vary from 0.25 to 0.60 and that beyond QTL mapping and analysis of epistatic effects, MAGIC population might be used to successfully fit genomic prediction models. We concluded that for grain yield, the single-environment genomic prediction models examined in this study are equivalent in terms of predictive ability while, in general, multi-environment models that explicitly split marker effects in main and environmental-specific effects outperform simpler multi-environment models. ; This research was carried out in the framework of the iBarMed project, which has been funded through the ARIMNet2 initiative and the Italian "Ministry of Agricultural, Food and Forestry Policies" under grant agreement "DM n. 20120." ARIMNet2 has received funding from the EU 7th Framework Programme for research, technological development and demonstration under grant agreement no. 618127. The work was also supported by YSTEMIC_1063 (An integrated approach to the challenge of sustainable food systems: adaptive and mitigatory strategies to address climate change and malnutrition: From cereal diversity to plant breeding), a research project funded by Italian "Ministry of Agricultural, Food and Forestry Policies" in the frame of the Knowledge Hub on Food and Nutrition Security. ; Peer reviewed