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      Multi-trait genomic-enabled prediction enhances accuracy in multi-year wheat breeding trials


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          Implementing genomic-based prediction models in genomic selection requires an understanding of the measures for evaluating prediction accuracy from different models and methods using multi-trait data. In this study, we compared prediction accuracy using six large multi-trait wheat data sets (quality and grain yield). The data were used to predict 1 year (testing) from the previous year (training) to assess prediction accuracy using four different prediction models. The results indicated that the conventional Pearson’s correlation between observed and predicted values underestimated the true correlation value, whereas the corrected Pearson’s correlation calculated by fitting a bivariate model was higher than the division of the Pearson’s correlation by the squared root of the heritability across traits, by 2.53–11.46%. Across the datasets, the corrected Pearson’s correlation was higher than the uncorrected by 5.80–14.01%. Overall, we found that for grain yield the prediction performance was highest using a multi-trait compared to a single-trait model. The higher the absolute genetic correlation between traits the greater the benefits of multi-trait models for increasing the genomic-enabled prediction accuracy of traits.

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          Most cited references26

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          Efficient methods to compute genomic predictions.

          P VanRaden (2008)
          Efficient methods for processing genomic data were developed to increase reliability of estimated breeding values and to estimate thousands of marker effects simultaneously. Algorithms were derived and computer programs tested with simulated data for 2,967 bulls and 50,000 markers distributed randomly across 30 chromosomes. Estimation of genomic inbreeding coefficients required accurate estimates of allele frequencies in the base population. Linear model predictions of breeding values were computed by 3 equivalent methods: 1) iteration for individual allele effects followed by summation across loci to obtain estimated breeding values, 2) selection index including a genomic relationship matrix, and 3) mixed model equations including the inverse of genomic relationships. A blend of first- and second-order Jacobi iteration using 2 separate relaxation factors converged well for allele frequencies and effects. Reliability of predicted net merit for young bulls was 63% compared with 32% using the traditional relationship matrix. Nonlinear predictions were also computed using iteration on data and nonlinear regression on marker deviations; an additional (about 3%) gain in reliability for young bulls increased average reliability to 66%. Computing times increased linearly with number of genotypes. Estimation of allele frequencies required 2 processor days, and genomic predictions required <1 d per trait, and traits were processed in parallel. Information from genotyping was equivalent to about 20 daughters with phenotypic records. Actual gains may differ because the simulation did not account for linkage disequilibrium in the base population or selection in subsequent generations.
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            MCMC Methods for Multi-Response Generalized Linear Mixed Models: TheMCMCglmmRPackage

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              Ridge Regression and Other Kernels for Genomic Selection with R Package rrBLUP


                Author and article information

                Role: Editor
                G3 (Bethesda)
                G3: Genes|Genomes|Genetics
                Oxford University Press
                October 2021
                30 July 2021
                30 July 2021
                : 11
                : 10
                : jkab270
                [1 ] Departamento de Matemáticas, Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara , Guadalajara 44430, Mexico
                [2 ] Department of Plant Sciences, College of Agricultural & Environmental Sciences, University of California Davis , Davis CA 95616, USA
                [3 ] International Maize and Wheat Improvement Center (CIMMYT) , Carretera México-Veracruz, México
                [4 ] Colegio de Postgraduados (COLPOS) , Montecillos, Edo. de México, México
                [5 ] Facultad de Telemática, Universidad de Colima , Colima, México
                Author notes
                Corresponding author: International Maize and Wheat Improvement Center (CIMMYT), Km 45 Carretera Mexico-Veracruz, CP 52640, Mexico; Colegio de Postgraduados (COLPOS), CP 56230, Montecillos, Edo. de México, Mexico. Email j.crossa@ 123456cgiar.org (J.C.); Facultad de Telemática, Universidad de Colima, Colima, Mexico. Email: oamontes2@ 123456hotmail.com (O.A.M.-L.)
                Author information
                © The Author(s) 2021. Published by Oxford University Press on behalf of Genetics Society of America.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                : 01 May 2021
                : 25 July 2021
                : 22 September 2021
                Page count
                Pages: 12
                Funded by: Bill and Melinda Gates Foundation, DOI 10.13039/100000865;
                Award ID: INV-003439
                Funded by: FCDO Accelerating Genetic Gains in Maize and Wheat for Improved Livelihoods;
                Funded by: USAID, DOI 10.13039/100000200;
                Award ID: 9 MTO 069033
                Funded by: USAID-CIMMYT Wheat;
                Funded by: Stress Tolerant Maize for Africa;
                Funded by: Foundations for Research Levy on Agricultural Products;
                Funded by: Agricultural Agreement Research Fund;
                Award ID: 267806
                Funded by: USDA National Institute of Food and Agriculture;
                Award ID: 2020-67013-30904
                Award ID: 2018-67015-27957

                wheat,wheat quality,multi-trait analysis,multi-environment analysis,genomic prediction,genpred,shared data resource


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