Genomic Prediction of Grain Yield in a Barley MAGIC Population Modeling Genotype per Environment Interaction
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 base...
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Frontiers Media S.A.
2021-05-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2021.664148/full |
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| author | Damiano Puglisi Stefano Delbono Andrea Visioni Hakan Ozkan İbrahim Kara Ana M. Casas Ernesto Igartua Giampiero Valè Angela Roberta Lo Piero Luigi Cattivelli Alessandro Tondelli Agostino Fricano |
| author_facet | Damiano Puglisi Stefano Delbono Andrea Visioni Hakan Ozkan İbrahim Kara Ana M. Casas Ernesto Igartua Giampiero Valè Angela Roberta Lo Piero Luigi Cattivelli Alessandro Tondelli Agostino Fricano |
| author_sort | Damiano Puglisi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-21T20:05:10Z |
| format | Article |
| id | doaj.art-01e62e84d23e41d5b2fdf47e0d0c7672 |
| institution | Directory Open Access Journal |
| issn | 1664-462X |
| language | English |
| last_indexed | 2024-12-21T20:05:10Z |
| publishDate | 2021-05-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Plant Science |
| spelling | doaj.art-01e62e84d23e41d5b2fdf47e0d0c76722022-12-21T18:51:51ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2021-05-011210.3389/fpls.2021.664148664148Genomic Prediction of Grain Yield in a Barley MAGIC Population Modeling Genotype per Environment InteractionDamiano Puglisi0Stefano Delbono1Andrea Visioni2Hakan Ozkan3İbrahim Kara4Ana M. Casas5Ernesto Igartua6Giampiero Valè7Angela Roberta Lo Piero8Luigi Cattivelli9Alessandro Tondelli10Agostino Fricano11Dipartimento di Agricoltura, Alimentazione e Ambiente (Di3A), Università di Catania, Catania, ItalyCouncil for Agricultural Research and Economics–Research Centre for Genomics and Bioinformatics, Fiorenzuola d’Arda, ItalyBiodiversity and Crop Improvement Program, International Center for Agricultural Research in the Dry Areas, Avenue Hafiane Cherkaoui, Rabat, MoroccoDepartment of Field Crops, Faculty of Agriculture, University of Cukurova, Adana, TurkeyBahri Dagdas International Agricultural Research Institute, Konya, TurkeyAula Dei Experimental Station (EEAD-CSIC), Spanish Research Council, Zaragoza, SpainAula Dei Experimental Station (EEAD-CSIC), Spanish Research Council, Zaragoza, SpainDiSIT, Dipartimento di Scienze e Innovazione Tecnologica, Università del Piemonte Orientale, Vercelli, ItalyDipartimento di Agricoltura, Alimentazione e Ambiente (Di3A), Università di Catania, Catania, ItalyCouncil for Agricultural Research and Economics–Research Centre for Genomics and Bioinformatics, Fiorenzuola d’Arda, ItalyCouncil for Agricultural Research and Economics–Research Centre for Genomics and Bioinformatics, Fiorenzuola d’Arda, ItalyCouncil for Agricultural Research and Economics–Research Centre for Genomics and Bioinformatics, Fiorenzuola d’Arda, ItalyMulti-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.https://www.frontiersin.org/articles/10.3389/fpls.2021.664148/fullgenomic predictionMAGICbarleyGBLUPgenotype x environment interaction |
| spellingShingle | Damiano Puglisi Stefano Delbono Andrea Visioni Hakan Ozkan İbrahim Kara Ana M. Casas Ernesto Igartua Giampiero Valè Angela Roberta Lo Piero Luigi Cattivelli Alessandro Tondelli Agostino Fricano Genomic Prediction of Grain Yield in a Barley MAGIC Population Modeling Genotype per Environment Interaction Frontiers in Plant Science genomic prediction MAGIC barley GBLUP genotype x environment interaction |
| title | Genomic Prediction of Grain Yield in a Barley MAGIC Population Modeling Genotype per Environment Interaction |
| title_full | Genomic Prediction of Grain Yield in a Barley MAGIC Population Modeling Genotype per Environment Interaction |
| title_fullStr | Genomic Prediction of Grain Yield in a Barley MAGIC Population Modeling Genotype per Environment Interaction |
| title_full_unstemmed | Genomic Prediction of Grain Yield in a Barley MAGIC Population Modeling Genotype per Environment Interaction |
| title_short | Genomic Prediction of Grain Yield in a Barley MAGIC Population Modeling Genotype per Environment Interaction |
| title_sort | genomic prediction of grain yield in a barley magic population modeling genotype per environment interaction |
| topic | genomic prediction MAGIC barley GBLUP genotype x environment interaction |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2021.664148/full |
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