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Optimizing the allocation of resources for genomic selection in one breeding cycle

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Abstract

Key message

We developed a universally applicable planning tool for optimizing the allocation of resources for one cycle of genomic selection in a biparental population. The framework combines selection theory with constraint numerical optimization and considers genotype  ×  environment interactions.

Abstract

Genomic selection (GS) is increasingly implemented in plant breeding programs to increase selection gain but little is known how to optimally allocate the resources under a given budget. We investigated this problem with model calculations by combining quantitative genetic selection theory with constraint numerical optimization. We assumed one selection cycle where both the training and prediction sets comprised double haploid (DH) lines from the same biparental population. Grain yield for testcrosses of maize DH lines was used as a model trait but all parameters can be adjusted in a freely available software implementation. An extension of the expected selection accuracy given by Daetwyler et al. (2008) was developed to correctly balance between the number of environments for phenotyping the training set and its population size in the presence of genotype × environment interactions. Under small budget, genotyping costs mainly determine whether GS is superior over phenotypic selection. With increasing budget, flexibility in resource allocation increases greatly but selection gain leveled off quickly requiring balancing the number of populations with the budget spent for each population. The use of an index combining phenotypic and GS predicted values in the training set was especially beneficial under limited resources and large genotype × environment interactions. Once a sufficiently high selection accuracy is achieved in the prediction set, further selection gain can be achieved most efficiently by massively expanding its size. Thus, with increasing budget, reducing the costs for producing a DH line becomes increasingly crucial for successfully exploiting the benefits of GS.

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Acknowledgments

This publication is dedicated to Professor Chris-Carolin Schön, TU München, to acknowledge her outstanding role in initiating and coordinating the “Synbreed” project and promoting genomic selection in plant breeding. Funding for this research came from the German Federal Ministry of Education and Research (BMBF) within the AgroClustEr “Synbreed—Synergistic Plant and Animal Breeding” (grant 0315528D) as well as from DuPont Pioneer under a Ph.D. fellowship for C.R.

Conflict of interest

The authors declare that they have no conflict of interest.

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The authors declare that all experiments comply with the current laws in Germany.

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Authors and Affiliations

  1. Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, 70593, Stuttgart, Germany

    Christian Riedelsheimer & Albrecht E. Melchinger

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  1. Christian Riedelsheimer
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  2. Albrecht E. Melchinger
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Correspondence to Albrecht E. Melchinger.

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Communicated by M. Frisch.

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Riedelsheimer, C., Melchinger, A.E. Optimizing the allocation of resources for genomic selection in one breeding cycle. Theor Appl Genet 126, 2835–2848 (2013). https://doi.org/10.1007/s00122-013-2175-9

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  • DOI: https://doi.org/10.1007/s00122-013-2175-9

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