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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 Selection in Elite, Tropical Rice Breeding Lines

Author

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  • Jennifer Spindel
  • Hasina Begum
  • Deniz Akdemir
  • Parminder Virk
  • Bertrand Collard
  • Edilberto Redoña
  • Gary Atlin
  • Jean-Luc Jannink
  • Susan R McCouch

Abstract

Genomic Selection (GS) is a new breeding method in which genome-wide markers are used to predict the breeding value of individuals in a breeding population. GS has been shown to improve breeding efficiency in dairy cattle and several crop plant species, and here we evaluate for the first time its efficacy for breeding inbred lines of rice. We performed a genome-wide association study (GWAS) in conjunction with five-fold GS cross-validation on a population of 363 elite breeding lines from the International Rice Research Institute's (IRRI) irrigated rice breeding program and herein report the GS results. The population was genotyped with 73,147 markers using genotyping-by-sequencing. The training population, statistical method used to build the GS model, number of markers, and trait were varied to determine their effect on prediction accuracy. For all three traits, genomic prediction models outperformed prediction based on pedigree records alone. Prediction accuracies ranged from 0.31 and 0.34 for grain yield and plant height to 0.63 for flowering time. Analyses using subsets of the full marker set suggest that using one marker every 0.2 cM is sufficient for genomic selection in this collection of rice breeding materials. RR-BLUP was the best performing statistical method for grain yield where no large effect QTL were detected by GWAS, while for flowering time, where a single very large effect QTL was detected, the non-GS multiple linear regression method outperformed GS models. For plant height, in which four mid-sized QTL were identified by GWAS, random forest produced the most consistently accurate GS models. Our results suggest that GS, informed by GWAS interpretations of genetic architecture and population structure, could become an effective tool for increasing the efficiency of rice breeding as the costs of genotyping continue to decline.Author Summary: Genomic selection is a promising breeding technique that aims to improve the efficiency and speed of the breeding process. While it has been shown to be effective in crops such as wheat and corn, it has not yet been applied to rice breeding. Genome-wide association studies (GWAS), by contrast, are used to identify genes or QTLs that underlie traits of importance to breeding such as yield, flowering time, or plant height, and has been performed successfully in rice. Here, we experiment with applying genomic selection in conjunction with GWAS to a rice breeding program at the International Rice Research Institute in the Philippines and show that genomic selection can result in more accurate predictions of breeding line performance than pedigree data alone and that GWAS results can inform the results of GS. Our results suggest that GS could be an effective tool for increasing the efficiency of rice breeding.

Suggested Citation

  • 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.
  • Handle: RePEc:plo:pgen00:1004982
    DOI: 10.1371/journal.pgen.1004982
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    Cited by:

    1. Aditi Bhandari & Jérôme Bartholomé & Tuong-Vi Cao-Hamadoun & Nilima Kumari & Julien Frouin & Arvind Kumar & Nourollah Ahmadi, 2019. "Selection of trait-specific markers and multi-environment models improve genomic predictive ability in rice," PLOS ONE, Public Library of Science, vol. 14(5), pages 1-21, May.
    2. Marco Scutari & Ian Mackay & David Balding, 2016. "Using Genetic Distance to Infer the Accuracy of Genomic Prediction," PLOS Genetics, Public Library of Science, vol. 12(9), pages 1-19, September.
    3. Laviola, Bruno Galvêas & Rodrigues, Erina Vitório & Teodoro, Paulo Eduardo & Peixoto, Leonardo de Azevedo & Bhering, Leonardo Lopes, 2017. "Biometric and biotechnology strategies in Jatropha genetic breeding for biodiesel production," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 894-904.
    4. Yusuke Toda & Hitomi Wakatsuki & Toru Aoike & Hiromi Kajiya-Kanegae & Masanori Yamasaki & Takuma Yoshioka & Kaworu Ebana & Takeshi Hayashi & Hiroshi Nakagawa & Toshihiro Hasegawa & Hiroyoshi Iwata, 2020. "Predicting biomass of rice with intermediate traits: Modeling method combining crop growth models and genomic prediction models," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-21, June.
    5. Ramin Rayee & Hoang-Dung Tran & Tran Dang Xuan & Tran Dang Khanh, 2018. "Imposed Water Deficit after Anthesis for the Improvement of Macronutrients, Quality, Phytochemicals, and Antioxidants in Rice Grain," Sustainability, MDPI, vol. 10(12), pages 1-12, December.
    6. Charles‐Elie Rabier & Simona Grusea, 2021. "Prediction in high‐dimensional linear models and application to genomic selection under imperfect linkage disequilibrium," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(4), pages 1001-1026, August.
    7. Md. S. Islam & Per McCord & Quentin D. Read & Lifang Qin & Alexander E. Lipka & Sushma Sood & James Todd & Marcus Olatoye, 2022. "Accuracy of Genomic Prediction of Yield and Sugar Traits in Saccharum spp. Hybrids," Agriculture, MDPI, vol. 12(9), pages 1-22, September.
    8. Muhammad Junaid Zaghum & Kashir Ali & Sheng Teng, 2022. "Integrated Genetic and Omics Approaches for the Regulation of Nutritional Activities in Rice ( Oryza sativa L.)," Agriculture, MDPI, vol. 12(11), pages 1-17, October.
    9. Shin-Fu Tsai & Chih-Chien Shen & Chen-Tuo Liao, 2021. "Bayesian Optimization Approaches for Identifying the Best Genotype from a Candidate Population," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(4), pages 519-537, December.
    10. Cécile Grenier & Tuong-Vi Cao & Yolima Ospina & Constanza Quintero & Marc Henri Châtel & Joe Tohme & Brigitte Courtois & Nourollah Ahmadi, 2015. "Accuracy of Genomic Selection in a Rice Synthetic Population Developed for Recurrent Selection Breeding," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-25, August.
    11. 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.
    12. Julien Frouin & Axel Labeyrie & Arnaud Boisnard & Gian Attilio Sacchi & Nourollah Ahmadi, 2019. "Genomic prediction offers the most effective marker assisted breeding approach for ability to prevent arsenic accumulation in rice grains," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-22, June.
    13. Charles-Elie Rabier & Philippe Barre & Torben Asp & Gilles Charmet & Brigitte Mangin, 2016. "On the Accuracy of Genomic Selection," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-23, June.
    14. Prabin Bajgain & James A. Anderson, 2021. "Multi-Allelic Haplotype-Based Association Analysis Identifies Genomic Regions Controlling Domestication Traits in Intermediate Wheatgrass," Agriculture, MDPI, vol. 11(7), pages 1-15, July.

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