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      Increased Predictive Accuracy of Multi-Environment Genomic Prediction Model for Yield and Related Traits in Spring Wheat ( Triticum aestivum L.)

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          Abstract

          Genomic selection (GS) has the potential to improve the selection gain for complex traits in crop breeding programs from resource-poor countries. The GS model performance in multi-environment (ME) trials was assessed for 141 advanced breeding lines under four field environments via cross-predictions. We compared prediction accuracy (PA) of two GS models with or without accounting for the environmental variation on four quantitative traits of significant importance, i.e., grain yield (GRYLD), thousand-grain weight, days to heading, and days to maturity, under North and Central Indian conditions. For each trait, we generated PA using the following two different ME cross-validation (CV) schemes representing actual breeding scenarios: (1) predicting untested lines in tested environments through the ME model (ME_CV1) and (2) predicting tested lines in untested environments through the ME model (ME_CV2). The ME predictions were compared with the baseline single-environment (SE) GS model (SE_CV1) representing a breeding scenario, where relationships and interactions are not leveraged across environments. Our results suggested that the ME models provide a clear advantage over SE models in terms of robust trait predictions. Both ME models provided 2–3 times higher prediction accuracies for all four traits across the four tested environments, highlighting the importance of accounting environmental variance in GS models. While the improvement in PA from SE to ME models was significant, the CV1 and CV2 schemes did not show any clear differences within ME, indicating the ME model was able to predict the untested environments and lines equally well. Overall, our results provide an important insight into the impact of environmental variation on GS in smaller breeding programs where these programs can potentially increase the rate of genetic gain by leveraging the ME wheat breeding trials.

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          Most cited references70

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          FactoMineR: AnRPackage for Multivariate Analysis

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            TASSEL: software for association mapping of complex traits in diverse samples.

            Association analyses that exploit the natural diversity of a genome to map at very high resolutions are becoming increasingly important. In most studies, however, researchers must contend with the confounding effects of both population and family structure. TASSEL (Trait Analysis by aSSociation, Evolution and Linkage) implements general linear model and mixed linear model approaches for controlling population and family structure. For result interpretation, the program allows for linkage disequilibrium statistics to be calculated and visualized graphically. Database browsing and data importation is facilitated by integrated middleware. Other features include analyzing insertions/deletions, calculating diversity statistics, integration of phenotypic and genotypic data, imputing missing data and calculating principal components.
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              Prediction of Total Genetic Value Using Genome-Wide Dense Marker Maps

              Recent advances in molecular genetic techniques will make dense marker maps available and genotyping many individuals for these markers feasible. Here we attempted to estimate the effects of ∼50,000 marker haplotypes simultaneously from a limited number of phenotypic records. A genome of 1000 cM was simulated with a marker spacing of 1 cM. The markers surrounding every 1-cM region were combined into marker haplotypes. Due to finite population size (Ne = 100), the marker haplotypes were in linkage disequilibrium with the QTL located between the markers. Using least squares, all haplotype effects could not be estimated simultaneously. When only the biggest effects were included, they were overestimated and the accuracy of predicting genetic values of the offspring of the recorded animals was only 0.32. Best linear unbiased prediction of haplotype effects assumed equal variances associated to each 1-cM chromosomal segment, which yielded an accuracy of 0.73, although this assumption was far from true. Bayesian methods that assumed a prior distribution of the variance associated with each chromosome segment increased this accuracy to 0.85, even when the prior was not correct. It was concluded that selection on genetic values predicted from markers could substantially increase the rate of genetic gain in animals and plants, especially if combined with reproductive techniques to shorten the generation interval.
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                Author and article information

                Contributors
                Journal
                Front Plant Sci
                Front Plant Sci
                Front. Plant Sci.
                Frontiers in Plant Science
                Frontiers Media S.A.
                1664-462X
                08 October 2021
                2021
                : 12
                : 720123
                Affiliations
                [1] 1Borlaug Institute for South Asia , Ludhiana, India
                [2] 2Department of Biological Sciences and Biotechnology, Institute of Advanced Research , Gandhinagar, India
                [3] 3International Maize and Wheat Improvement Center , New Delhi, India
                [4] 4Department of Plant Pathology, Kansas State University , Manhattan, KS, United States
                [5] 5Department of Biotechnology, Thapar Institute of Engineering & Technology , Patiala, India
                [6] 6Department of Plant Resources and Environment, Jeju National University , Jeju-si, South Korea
                [7] 7Global Wheat Program, International Maize and Wheat Improvement Center , Texcoco, Mexico
                Author notes

                Edited by: Valentin Wimmer, KWS Saat, Germany

                Reviewed by: Pedro José Martínez-García, Spanish National Research Council, Spain; Mian Abdur Rehman Arif, Nuclear Institute for Agriculture and Biology, Pakistan

                *Correspondence: Vipin Tomar viomics@ 123456gmail.com

                This article was submitted to Plant Breeding, a section of the journal Frontiers in Plant Science

                †Present address: Daljit Singh, The Climate Corporation, Bayer Crop Science, Creve Coeur, MO, United States

                Article
                10.3389/fpls.2021.720123
                8531512
                34691100
                ec4a318d-a7f7-4bc2-908a-67ff05295b6e
                Copyright © 2021 Tomar, Singh, Dhillon, Chung, Poland, Singh, Joshi, Gautam, Tiwari and Kumar.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 03 June 2021
                : 03 September 2021
                Page count
                Figures: 4, Tables: 4, Equations: 4, References: 72, Pages: 12, Words: 8035
                Funding
                Funded by: United States Agency for International Development, doi 10.13039/100000200;
                Categories
                Plant Science
                Original Research

                Plant science & Botany
                single-environment,multi-environments,genotyping by sequencing,genomic selection (gs),genomics predictions,best linear unbiased predictions,wheat

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