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Island-Model Genomic Selection for Long-Term Genetic Improvement of Autogamous Crops

Author

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  • Shiori Yabe
  • Masanori Yamasaki
  • Kaworu Ebana
  • Takeshi Hayashi
  • Hiroyoshi Iwata

Abstract

Acceleration of genetic improvement of autogamous crops such as wheat and rice is necessary to increase cereal production in response to the global food crisis. Population and pedigree methods of breeding, which are based on inbred line selection, are used commonly in the genetic improvement of autogamous crops. These methods, however, produce a few novel combinations of genes in a breeding population. Recurrent selection promotes recombination among genes and produces novel combinations of genes in a breeding population, but it requires inaccurate single-plant evaluation for selection. Genomic selection (GS), which can predict genetic potential of individuals based on their marker genotype, might have high reliability of single-plant evaluation and might be effective in recurrent selection. To evaluate the efficiency of recurrent selection with GS, we conducted simulations using real marker genotype data of rice cultivars. Additionally, we introduced the concept of an “island model” inspired by evolutionary algorithms that might be useful to maintain genetic variation through the breeding process. We conducted GS simulations using real marker genotype data of rice cultivars to evaluate the efficiency of recurrent selection and the island model in an autogamous species. Results demonstrated the importance of producing novel combinations of genes through recurrent selection. An initial population derived from admixture of multiple bi-parental crosses showed larger genetic gains than a population derived from a single bi-parental cross in whole cycles, suggesting the importance of genetic variation in an initial population. The island-model GS better maintained genetic improvement in later generations than the other GS methods, suggesting that the island-model GS can utilize genetic variation in breeding and can retain alleles with small effects in the breeding population. The island-model GS will become a new breeding method that enhances the potential of genomic selection in autogamous crops, especially bringing long-term improvement.

Suggested Citation

  • Shiori Yabe & Masanori Yamasaki & Kaworu Ebana & Takeshi Hayashi & Hiroyoshi Iwata, 2016. "Island-Model Genomic Selection for Long-Term Genetic Improvement of Autogamous Crops," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-21, April.
  • Handle: RePEc:plo:pone00:0153945
    DOI: 10.1371/journal.pone.0153945
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    References listed on IDEAS

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    1. Alexandratos, Nikos & Bruinsma, Jelle, 2012. "World agriculture towards 2030/2050: the 2012 revision," ESA Working Papers 288998, Food and Agriculture Organization of the United Nations, Agricultural Development Economics Division (ESA).
    2. Steve O'Hagan & Joshua Knowles & Douglas B Kell, 2012. "Exploiting Genomic Knowledge in Optimising Molecular Breeding Programmes: Algorithms from Evolutionary Computing," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-14, November.
    3. Tweeten, Luther G. & Thompson, Stanley R., 2008. "Long-term Global Agricultural Output Supply-Demand Balance and Real Farm and Food Prices," Working Papers 46009, Ohio State University, Department of Agricultural, Environmental and Development Economics.
    4. Jennifer Spindel & Hasina Begum & Deniz Akdemir & Parminder Virk & Bertrand Collard & Edilberto Redoña & Gary Atlin & Jean-Luc Jannink & Susan R McCouch, 2015. "Genomic Selection and Association Mapping in Rice (Oryza sativa): Effect of Trait Genetic Architecture, Training Population Composition, Marker Number and Statistical Model on Accuracy of Rice Genomic," PLOS Genetics, Public Library of Science, vol. 11(2), pages 1-25, February.
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