Theoretical and Applied Genetics

, Volume 126, Issue 11, pp 2907–2920 | Cite as

Selfing for the design of genomic selection experiments in biparental plant populations

  • Benjamin McClosky
  • Jason LaCombe
  • Steven D. Tanksley
Original Paper

Abstract

Self-fertilization (selfing) is commonly used for population development in plant breeding, and it is well established that selfing increases genetic variance between lines, thus increasing response to phenotypic selection. Furthermore, numerous studies have explored how selfing can be deployed to maximal benefit in the context of traditional plant breeding programs (Cornish in Heredity 65:201–211,1990a, Heredity 65:213–220,1990b; Liu et al. in Theor Appl Genet 109:370–376, 2004; Pooni and Jinks in Heredity 54:255–260, 1985). However, the impact of selfing on response to genomic selection has not been explored. In the current study we examined how selfing impacts the two key aspects of genomic selection—GEBV prediction (training) and selection response. We reach the following conclusions: (1) On average, selfing increases genomic selection gains by more than 70 %. (2) The gains in genomic selection response attributable to selfing hold over a wide range population sizes (100–500), heritabilities (0.2–0.8), and selection intensities (0.01–0.1). However, the benefits of selfing are dramatically reduced as the number of QTLs drops below 20. (3) The major cause of the improved response to genomic selection with selfing is through an increase in the occurrence of superior genotypes and not through improved GEBV predictions. While performance of the training population improves with selfing (especially with low heritability and small population sizes), the magnitude of these improvements is relatively small compared with improvements observed in the selection population. To illustrate the value of these insights, we propose a practical genomic selection scheme that substantially shortens the number of generations required to fully capture the benefits of selfing. Specifically, we provide simulation evidence that indicates the proposed scheme matches or exceeds the selection gains observed in advanced populations (i.e. F 8 and doubled haploid) across a broad range of heritability and QTL models. Without sacrificing selection gains, we also predict that fully inbred candidates for potential commercialization can be identified as early as the F 4 generation.

Notes

Conflict of interest

The authors declare no conflict of interest.

References

  1. Albrecht T, Wimmer V, Auinger H, Erbe M, Knaak C, Ouzunova M et al. (2011) Genome-based prediction of testcross values in maize. Theor Appl Genet 123: 339–350PubMedCrossRefGoogle Scholar
  2. Allard R (1999) Principles of Plant Breeding, 2nd edn. John Wiley & Sons, New YorkGoogle Scholar
  3. Bernardo R (1996) Best linear unbiased prediction of maize single-cross performance. Crop Sci 36: 50–56CrossRefGoogle Scholar
  4. Bernardo R (2010) Genomewide selection with minimal crossing in self-pollinated crops. Crop Sci 50: 624–627CrossRefGoogle Scholar
  5. Bernardo R, Yu J (2007) Prospects for genomewide selection for quantitative traits in maize. Crop Sci 47: 1082–1090CrossRefGoogle Scholar
  6. Bordes J, Charmet G, de Vaulx RD, Lapierre A, Pollacsek M, Beckert M et al. (2007) Doubled-haploid versus single-seed descent and S1-family variation for testcross performance in a maize population. Euphytica 154: 41–51CrossRefGoogle Scholar
  7. Brachi B, Faure N, Horton M, Flahauw E, Vazquez A, Nordborg M, et al. (2010) Linkage and association mapping of Arabidopsis thaliana flowering time in nature. PLoS Genet 6(5):e1000940PubMedCrossRefGoogle Scholar
  8. Buckler E, Holland J, Bradbury P, Acharya C, Brown P, Browne C, et al. (2009) The genetic architecture of maize flowering time. Science 325: 714–718PubMedCrossRefGoogle Scholar
  9. Choo T, Reinbergs E, Park S (1982) Comparison of frequency distributions of doubled haploid and single seed descent lines in barley. Theor Appl Genet 61: 215–218Google Scholar
  10. Cornish M (1990) Selection during a selfing programme. I. The effects of a single round of selection. Heredity 65: 201–211PubMedCrossRefGoogle Scholar
  11. Cornish M (1990) Selection during a selfing programme. II. The effects of two or more rounds of selection. Heredity 65: 213–220PubMedCrossRefGoogle Scholar
  12. Courtois B (1993) Comparison of single seed descent and anther culture-derived lines of three single crosses of rice. Theor Appl Genet 85: 625–631CrossRefGoogle Scholar
  13. Daetwyler H, Pong-Wong R, Villanueva B, Woolliams J (2010) The impact of genetic architecture on genome-wide evaluation methods. Genetics 185: 1021–1031PubMedCrossRefGoogle Scholar
  14. Daetwyler H, Villanueva B, Woolliams J (2008) Accuracy of predicting the genetic risk of disease using a genome-wide approach. PLoS One 3(10):e3395PubMedCrossRefGoogle Scholar
  15. Falconer D, Mackay T (1996) Introduction to quantitative genetics, 4th edn, Longman, Harlow, Essex, UK.Google Scholar
  16. Farrar D, Glauber R (1967) Multicollinearity in regression analysis: the problem revisited. Rev Econ Stat 49: 92–107CrossRefGoogle Scholar
  17. Fess T, Kotcon J, Benedito V (2011) Crop breeding for low input agriculture: a sustainable response to feed a growing world population. Sustainability 3: 1742–1772CrossRefGoogle Scholar
  18. Goddard M, Hayes B (2007) Genomic selection. J Anim Breed Genet 124: 323–330PubMedCrossRefGoogle Scholar
  19. Guo Z, Tucker D, Lu J, Kishore V, Gay G (2012) Evaluation of genome-wide selection efficiency in maize nested association mapping populations. Theor Appl Genet 124: 261–275PubMedCrossRefGoogle Scholar
  20. Hallauer A, Miranda J (1988) Qunatitative genetics in maize breeding. Iowa State University Press, USAGoogle Scholar
  21. Hastings W (1970) Monte carlo sampling methods using Markov chains and their applications. Biometrika 57: 97–109CrossRefGoogle Scholar
  22. Heffner E, Jannink J, Iwata H, Souzad E, Sorrells M (2011) Genomic selection accuracy for grain quality traits in biparental wheat populations. Crop Sci 51:2597–2606CrossRefGoogle Scholar
  23. Heffner E, Lorenz A, Jannink J, Sorrells M (2010) Plant breeding with genomic selection: gain per unit time and cost. Crop Sci 50:1681–1690CrossRefGoogle Scholar
  24. Heffner E, Sorrells M, Jannink J (2009) Genomic selection for crop improvement. Crop Sci 49:1–12CrossRefGoogle Scholar
  25. Hoeting J, Madigan D, Raftery A, Volinsky C (1999) Bayesian model averaging : a tutorial. Stat Sci 14:382–417CrossRefGoogle Scholar
  26. Iyamabo O, Hayes PM (1995) Effects of selection and opportunities for recombination in doubled-haploid populations of barley (hordeum vulgare l). Plant Breed. 114:131–136CrossRefGoogle Scholar
  27. Kearsey M, Sturley S (1984) A model for the incorporation of epistasis into a computer simulation for three experimental designs. Heredity 52: 373–382CrossRefGoogle Scholar
  28. Laurie C, Chasalow S, LeDeaux J, McCarroll R, Bush D, Hauge B, et al (2004) The genetic architecture of response to long-term artificial selection for oil concentration in the maize kernel. Genetics 168:2141–2155PubMedCrossRefGoogle Scholar
  29. Lee S, van der Werf J, Hayes B, Goddard M, Visscher P (2008) Predicting unobserved phenotypes for complex traits from whole-genome SNP data. PLoS Genet 4(10):e1000231PubMedCrossRefGoogle Scholar
  30. Legarra A (2008) Performance of genomic selection in mice. Genetics 180: 611–618PubMedCrossRefGoogle Scholar
  31. Liu P, Zhu J, Lu Y (2004) Marker-assisted selection in segregating generations of self-fertilizing crops. Theor Appl Genet 109: 370–376PubMedGoogle Scholar
  32. Lorenzana R, Bernardo R (2009) Accuracy of genotypic value predictions for marker-based selection in biparental plant populations. Theor Appl Genet 120: 151–161PubMedCrossRefGoogle Scholar
  33. Luan T, Woolliams J, Lien S, Kent M, Svendsen M, Meuwissen T (2009) The accuracy of genomic selection in Norwegian red cattle assessed by cross-validation. Genetics 183: 1119–1126PubMedCrossRefGoogle Scholar
  34. Lynch M, Walsh B (1998) Genetics and analysis of quantitative traits. Sinauer Associates, SunderlandGoogle Scholar
  35. Mayor P, Bernardo R (2009) Genomewide selection and marker-assisted recurrent selection in doubled haploid versus F2 populations. Crop Sci 49:1719–1725CrossRefGoogle Scholar
  36. McMullen M, Kresovich S, Villeda H, Bradbury P, Li H, Sun Q (2009) Genetic properties of the maize nested association mapping population. Science 325:737–740PubMedCrossRefGoogle Scholar
  37. Meuwissen T, Hayes B, Goddard M (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819–1829PubMedGoogle Scholar
  38. Murigneux A, Baud S, Beckert M (1993) Molecular and morphological evaluation of doubled-haploid lines in maize. 2. comparison with single-seed-descent lines. Theor Appl Genet 87:278–287CrossRefGoogle Scholar
  39. Otto S, Jones C (2000) Detecting the undetected: estimating the total number of loci underlying a quantitative trait. Genetics 156:2093–2107PubMedGoogle Scholar
  40. Park S, Walsh E, Reinbergs E, Song L, Kasha K (1976) Field perfomance of doubled haploid barley lines in comparison with lines developed by the pedigree and SSD methods. Can J Plant Sci 56:467–474CrossRefGoogle Scholar
  41. Piepho H (2009) Ridge regression and extensions for genomewide selection in maize. Crop Sci 49:1165–1176CrossRefGoogle Scholar
  42. Piyasatian N, Fernando R, Dekkers J (2007) Genomic selection for marker assisted improvement in line crosses. Theor Appl Genet 115(5):665–74PubMedCrossRefGoogle Scholar
  43. Pooni H, Jinks J (1985) Retrospective selection and sources of superior inbreds amongst pedigree inbred families of Nicotiana rustica. Heredity 54:255–260CrossRefGoogle Scholar
  44. Riggs T, Snape J (1977) Effects of linkage and interaction in a comparison of theoretical populations derived by diploidized haploid and single seed descent methods. Theor Appl Genet 49:111–115CrossRefGoogle Scholar
  45. Schaeffer L (2006) Strategy for applying genome-wide selection in dairy cattle. J Anim Breed Genet 123:218–223PubMedCrossRefGoogle Scholar
  46. Simmonds N (1979) Principles of crop improvement. Longman London, LondonGoogle Scholar
  47. Snape J (1976) A theoretical comparison of diploidized haploid and single seed descent populations. Heredity 36:275–277CrossRefGoogle Scholar
  48. Verbyla K, Bowman P, Hayes B, Goddard M (2010) Sensitivity of genomic selection to using different prior distributions. BMC Proc 4(Suppl 1):S5. PubMedCrossRefGoogle Scholar
  49. Verbyla K, Hayes B, Bowman P, Goddard M (2009) Accuracy of genomic selection using stochastic search variable selection in Australian holstein friesian dairy cattle. Genet Res 91:301–311CrossRefGoogle Scholar
  50. Wells W, Weiser G (1989) Additive genetic variance within populations derived by single-seed descent and pod-bulk descent. Theor Appl Genet 78:365–368CrossRefGoogle Scholar
  51. Wong C, Bernardo R (2008) Genomewide selection in oil plam: increasing selection gain per unit time and cost with small populations. Theor Appl Genet 116:815–824PubMedCrossRefGoogle Scholar
  52. Zhong S, Dekkers J, Fernando R, Jannink J (2009) Factors affecting accuracy from genomic selection in populations derived from multiple inbred lines: a barley case study. Genetics 182:355–364PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Benjamin McClosky
    • 1
  • Jason LaCombe
    • 1
  • Steven D. Tanksley
    • 1
  1. 1.Nature Source GeneticsIthacaUSA

Personalised recommendations