Theoretical and Applied Genetics

, Volume 126, Issue 11, pp 2835–2848 | Cite as

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

  • Christian Riedelsheimer
  • Albrecht E. Melchinger
Original Paper

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.

Notes

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.

Ethical standards

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

Supplementary material

122_2013_2175_MOESM1_ESM.cdf (56 kb)
Supplementary material 1 (CDF 56 kb)

References

  1. Bernardo R (2008) Molecular markers and selection for complex traits in plants: learning from the last 20 years. Crop Sci 48:1649–1664CrossRefGoogle Scholar
  2. Bernardo R, Yu J (2007) Prospects for genome-wide selection for quantitative traits in maize. Crop Sci 47:1082–1090CrossRefGoogle Scholar
  3. Calus MPL, Veerkamp RF (2011) Accuracy of multi-trait genomic selection using different methods. Genet Sel Evol 43:26PubMedCrossRefGoogle Scholar
  4. Combs E, Bernardo R (2013) Accuracy of genomewide selection for different traits with constant population size, heritability, and number of markers. Plant Genome 6:1–7CrossRefGoogle Scholar
  5. Daetwyler HD, Villanueva B, Woolliams JA (2008) Accuracy of predicting the genetic risk of disease using a genome-wide approach. PLoS ONE 3:e3395PubMedCrossRefGoogle Scholar
  6. Daetwyler HD, Calus MPL, Pong-Wong R, de los Campos G, Hickey JM (2013) Genomic prediction in animals and plants: simulation of data, validation, reporting, and benchmarking. Genetics 193:347–365PubMedCrossRefGoogle Scholar
  7. de los Campos G, Hickey JM, Pong-Wong R, Daetwyler HD, Calus MPL (2013) Whole-genome regression and prediction methods applied to plant and animal breeding. Genetics 193:327–345PubMedCrossRefGoogle Scholar
  8. Dekkers JCM, Hospital F (2002) The use of molecular genetics in the improvement of agricultural populations. Nat Rev Genet 3:22–32PubMedCrossRefGoogle Scholar
  9. Elshire RJ, Glaubitz JC, Sun Q, Poland JA, Kawamoto K, Buckler ES, Mitchell SE (2011) A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS ONE 6:e19379PubMedCrossRefGoogle Scholar
  10. Endelman JB, Atlin GN, Beyene Y, Semagn K, Zhang X, Sorrells ME, Jannink JL (2013) Optimal design of preliminary yield trials with genome-wide markers. Crop Sci. doi: 10.2135/cropsci2013.03.0154 Google Scholar
  11. Falconer DS, Mackay TFC (1996) Introduction to quantitative genetics. Pearson, EssexGoogle Scholar
  12. Fisher RA (1918) The correlation between relatives on the supposition of mendelian inheritance. Phil Trans R Soc Edinb 52:399–433CrossRefGoogle Scholar
  13. Goddard M (2009) Genomic selection: prediction of accuracy and maximization of long term response. Genetica 136:245–257PubMedCrossRefGoogle Scholar
  14. Goddard ME, Hayes BJ, Meuwissen THE (2011) Using the genomic relationship matrix to predict the accuracy of genomic selection. J Anim Breed Genet 128:409–421PubMedCrossRefGoogle Scholar
  15. Habier D, Fernando RL, Dekkers JCM (2007) The impact of genetic relationship information on genome-assisted breeding values. Genetics 177:2389–2397PubMedGoogle Scholar
  16. Heffner EL, Sorrells ME, Jannink JL (2009) Genomic selection for crop improvement. Crop Sci 49:1–12CrossRefGoogle Scholar
  17. Heslot N, Yang H-P, Sorrells ME, Jannink JL (2012) Genomic selection in plant breeding: a comparison of models. Crop Sci 52:146–160Google Scholar
  18. Jannink J-L (2010) Dynamics of long-term genomic selection. Genet Sel Evol 42:35PubMedCrossRefGoogle Scholar
  19. Jia Y, Jannink J-L (2012) Multiple-trait genomic selection methods increase genetic value prediction accuracy. Genetics 192:1513–1522PubMedCrossRefGoogle Scholar
  20. Knapp SJ, Bridges WC (1990) Using molecular markers to estimate quantitative trait locus parameters: power and genetic variances for unreplicated and replicated progeny. Genetics 126:769–777PubMedGoogle Scholar
  21. Lande R, Thompson R (1990) Efficiency of marker-assisted selection in the improvement of quantitative traits. Genetics 124:743–756PubMedGoogle Scholar
  22. Lorenz AJ (2013) Resource allocation for maximizing prediction accuracy and genetic gain of genomic selection in plant breeding: a simulation experiment. G3: Genes, Genomes, Genetics 3:481–491Google Scholar
  23. Lorenz AJ, Chao S, Asoro FG, Heffner EL, Hayashi T, Iwata H, Smith KP, Sorrells ME, Jannink JL (2011) Genomic selection in plant breeding: knowledge and prospects. Adv Agron 110:77–123CrossRefGoogle Scholar
  24. Lorenzana RE, Bernardo R (2008) Genetic correlation between corn performance in organic and conventional production systems. Crop Sci 48:903–910CrossRefGoogle Scholar
  25. Lorenzana RE, Bernardo R (2009) Accuracy of genotypic value predictions for marker-based selection in biparental plant populations. Theor Appl Genet 120:151–161PubMedCrossRefGoogle Scholar
  26. Massman JM, Jung HJG, Bernardo R (2012) Genomewide selection versus marker-assisted recurrent selection to improve grain yield and stover-quality traits for cellulosic ethanol in maize. Crop Sci 53:58–66Google Scholar
  27. Melchinger AE, Utz HF, Schön CC (1998) Quantitative trait locus (QTL) mapping using different testers and independent population samples in maize reveals low power of QTL detection and large bias in estimates of QTL effects. Genetics 149:383–403PubMedGoogle Scholar
  28. Melchinger AE, Schipprack W, Würschum T, Chen S, Technow F (2013) Rapid and accurate identification of in vivo induced haploid seeds based on oil content provides a new tool for maize genetics and breeding. Sci Rep 3:2129PubMedCrossRefGoogle Scholar
  29. Meuwissen THE, Goddard M (2010) Accurate prediction of genetic values for complex traits by whole-genome resequencing. Genetics 185:623–631PubMedCrossRefGoogle Scholar
  30. Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819–1829PubMedGoogle Scholar
  31. Moreau L, Charcosset A, Hospital F, Galais A (1998) Marker-assisted selection efficiency in populations of finite size. Genetics 148:1353–1365PubMedGoogle Scholar
  32. Moreau L, Lemarié S, Charcosset A, Gallais A (2000) Economic efficiency of one cycle of marker-assisted selection. Crop Sci 40:329–337CrossRefGoogle Scholar
  33. Moreau L, Charcosset A, Gallais A (2004) Experimental evaluation of several cycles of marker-assisted selection in maize. Euphytica 137:111–118CrossRefGoogle Scholar
  34. Poland JA, Rife TW (2012) Genotyping-by-sequencing for plant breeding and genetics. Plant Genome 5:92–102CrossRefGoogle Scholar
  35. Prigge V, Schipprack W, Mahuku G, Atlin GN, Melchinger AE (2012) Development of in vivo haploid inducers for tropical maize breeding programs. Euphytica 185:482–490Google Scholar
  36. Riedelsheimer C, Czedik-Eysenberg A, Grieder C, Lisec J, Technow F, Sulpice R, Altmann T, Stitt M, Willmitzer L, Melchinger AE (2012a) Genomic and metabolic prediction of complex heterotic traits in hybrid maize. Nat Genet 44:217–220PubMedCrossRefGoogle Scholar
  37. Riedelsheimer C, Technow F, Melchinger AE (2012b) Comparison of whole-genome prediction models for traits with contrasting genetic architecture in a diversity panel of maize inbred lines. BMC Genomics 13:452PubMedCrossRefGoogle Scholar
  38. Riedelsheimer C, Endelmann JB, Stange M, Sorrells ME, Jannink JL, Melchinger AE (2013) Genomic predictability of interconnected biparental maize populations. Genetics 194:493–503PubMedCrossRefGoogle Scholar
  39. Schön CC, Utz HF, Groh S, Truberg B, Openshaw S, Melchinger AE (2004) Quantitative trait locus mapping based on resampling in a vast maize testcross experiment and its relevance to quantitative genetics for complex traits. Genetics 167:485–498PubMedCrossRefGoogle Scholar
  40. Technow F, Riedelsheimer C, Schrag TA, Melchinger AE (2012) Genomic prediction of hybrid performance in maize with models incorporating dominance and population specific marker effects. Theor Appl Genet 125:1181–1194PubMedCrossRefGoogle Scholar
  41. Tomerius AM (2001) Optimizing the development of seed-parent lines in hybrid rye breeding, Dissertation, University of HohenheimGoogle Scholar
  42. Utz HF (1969) Mehrstufenselektion in der Pflanzenzüchtung. Dissertation, University of HohenheimGoogle Scholar
  43. Villanueva B, Dekkers JCM, Woolliams JA, Settar P (2004) Maximizing genetic gain over multiple generations with quantitative trait locus selection and control of inbreeding. J Anim Sci 82:1305–1314PubMedGoogle Scholar
  44. Xu YB, Crouch JH (2008) Marker-assisted selection in plant breeding: from publications to practice. Crop Sci 48:391–407CrossRefGoogle Scholar
  45. Xu Y, Lu Y, Xie C, Gao S, Wan J, Prasanna BM (2012) Whole-genome strategies for marker-assisted plant breeding. Mol Breeding 29:833–854CrossRefGoogle Scholar
  46. Yabe S, Ohsawa R, Iwata H (2013) Potential of genomic selection for mass selection breeding in annual allogamous crops. Crop Sci 53:95–105Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Christian Riedelsheimer
    • 1
  • Albrecht E. Melchinger
    • 1
  1. 1.Institute of Plant Breeding, Seed Science and Population Genetics, University of HohenheimStuttgartGermany

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