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      Simulation‐based decision‐making and implementation of tools in hybrid crop breeding pipelines

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          Abstract

          New technologies have been developed over the last few years aiming to support breeding pipeline optimization for long‐term genetic gains. However, the implementation of these new tools and their impact on any breeding program's budget are not well studied. Here, we compare multiple breeding pipeline strategies accounting for genomic selection and high‐throughput phenotyping (HTP) by means of hybrid gain and cost‐effectiveness. We simulated a hybrid crop breeding program through coalescent theory. We compared two strategies for parental updates and four breeding pipelines: conventional breeding pipeline; conventional breeding pipeline with HTP; conventional breeding pipeline with genomic selection; conventional breeding pipeline with genomic selection and HTP. All analyses were implemented under three different levels of genotype‐by‐environment interaction (G×E) and two trait heritabilities (0.3 and 0.7). Overall, the results show that scenarios with early parental selection perform better than the others. In addition, the implementation of HTP delivered the highest hybrid gain in the long‐term, whereas the implementation of genomic selection seems to be more cost‐effective. We suggest, considering breeding programs with complex trait inheritance and accounting for higher levels of G×E, investing in breeding pipelines accounting for genomic selection as a strategy to create and maintain long‐term hybrid gain. Moreover, considering an unconstrained budget, the investment in both, genomic selection and HTP, represents the best strategy. Hence, these results provide strategies that may aid breeders in optimizing self‐pollination breeding programs.

          Core Ideas

          • Early parental selection has the potential to increase long‐term genetic performance.

          • High‐throughput phenotyping (HTP) can aid breeding program improvement.

          • Breeding pipelines focusing on complex traits should consider the implementation of genomic selection and HTP.

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          Most cited references56

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          Prediction of Total Genetic Value Using Genome-Wide Dense Marker Maps

          T Meuwissen, B. Hayes, M. E. Goddard (2001)
          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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            Genomic Selection in Plant Breeding: Methods, Models, and Perspectives.

            Jaime Cuevas, Osval Antonio Montesinos-López, Diego Jarquín … (2017)
            Genomic selection (GS) facilitates the rapid selection of superior genotypes and accelerates the breeding cycle. In this review, we discuss the history, principles, and basis of GS and genomic-enabled prediction (GP) as well as the genetics and statistical complexities of GP models, including genomic genotype×environment (G×E) interactions. We also examine the accuracy of GP models and methods for two cereal crops and two legume crops based on random cross-validation. GS applied to maize breeding has shown tangible genetic gains. Based on GP results, we speculate how GS in germplasm enhancement (i.e., prebreeding) programs could accelerate the flow of genes from gene bank accessions to elite lines. Recent advances in hyperspectral image technology could be combined with GS and pedigree-assisted breeding.
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              A reaction norm model for genomic selection using high-dimensional genomic and environmental data

              Diego Jarquín, Jose Crossa, Xavier Lacaze … (2013)
              Key message New methods that incorporate the main and interaction effects of high-dimensional markers and of high-dimensional environmental covariates gave increased prediction accuracy of grain yield in wheat across and within environments. Abstract In most agricultural crops the effects of genes on traits are modulated by environmental conditions, leading to genetic by environmental interaction (G × E). Modern genotyping technologies allow characterizing genomes in great detail and modern information systems can generate large volumes of environmental data. In principle, G × E can be accounted for using interactions between markers and environmental covariates (ECs). However, when genotypic and environmental information is high dimensional, modeling all possible interactions explicitly becomes infeasible. In this article we show how to model interactions between high-dimensional sets of markers and ECs using covariance functions. The model presented here consists of (random) reaction norm where the genetic and environmental gradients are described as linear functions of markers and of ECs, respectively. We assessed the proposed method using data from Arvalis, consisting of 139 wheat lines genotyped with 2,395 SNPs and evaluated for grain yield over 8 years and various locations within northern France. A total of 68 ECs, defined based on five phases of the phenology of the crop, were used in the analysis. Interaction terms accounted for a sizable proportion (16 %) of the within-environment yield variance, and the prediction accuracy of models including interaction terms was substantially higher (17–34 %) than that of models based on main effects only. Breeding for target environmental conditions has become a central priority of most breeding programs. Methods, like the one presented here, that can capitalize upon the wealth of genomic and environmental information available, will become increasingly important.
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                Author and article information

                Contributors
                Marco Antônio Peixoto: (View ORCID Profile)
                Igor Ferreira Coelho: (View ORCID Profile)
                Leonardo L. Bhering: (View ORCID Profile)
                Márcio F. R. Resende: (View ORCID Profile)
                Journal
                Title: Crop Science
                Abbreviated Title: Crop Science
                Publisher: Wiley
                ISSN (Print): 0011-183X
                ISSN (Electronic): 1435-0653
                Publication date Created: January 2024
                Publication date (Electronic): November 21 2023
                Publication date (Print): January 2024
                Volume: 64
                Issue: 1
                Pages: 110-125
                Affiliations
                [1 ] Laboratório de Biometria Universidade Federal de Viçosa Viçosa Minas Gerais Brazil
                [2 ] Sweet Corn Breeding and Genomics Lab University of Florida Gainesville Florida USA
                Article
                DOI: 10.1002/csc2.21139
                SO-VID: 7c78795e-f208-4f46-be7d-2b61cd870601
                Copyright © © 2024
                License:

                http://creativecommons.org/licenses/by/4.0/

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