Genomic Prediction of Grain Yield in a Barley MAGIC Population Modeling Genotype per Environment Interaction

• DOI: 10.3389/fpls.2021.664148
• Publication Date: 2021-05-24T05:31:52Z
• Authors: Damiano Puglisi; Stefano Delbono; Andrea Visioni; Hakan Ozkan; İbrahim Kara; Ana M. Casas; Ernesto Igartua; Giampiero Valè; Angela Roberta Lo...; Luigi Cattivelli; Alessandro Tondelli; Agostino Fricano
• Keywords: 0301 basic medicine; 570; Population; Plant Science; genotype x environment interaction; Gene; 630; SB1-1110; Agricultural and Biological Sciences; Effective population size; 03 medical and health sciences; Selection (genetic algorithm); Cultivar Evaluation and Mega-Environment Investigation; Genetic Value Prediction; Sociology; Barley; Biochemistry, Genetics and Molecular Biology; Machine learning; Genetics; FOS: Mathematics; Genetic variation; Biology; genomic prediction; Demography; 2. Zero hunger; Genomic prediction; Genotype x environment interactions; Statistics; Predictive modelling; barley; Plant culture; Life Sciences; 15. Life on land; MAGIC; Computer science; FOS: Sociology; Rice Genomics; Genetic Mapping; Maize Domestication; Plant Breeding; Genetic Architecture of Quantitative Traits; Best linear unbiased prediction; FOS: Biological sciences; GBLUP; Genomic Selection in Plant and Animal Breeding; Epistasis; Mathematics

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.