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Genome Wide Analysis of Flowering Time Trait in Multiple Environments via High-Throughput Genotyping Technique in Brassica napus L

Author

Listed:
  • Lun Li
  • Yan Long
  • Libin Zhang
  • Jessica Dalton-Morgan
  • Jacqueline Batley
  • Longjiang Yu
  • Jinling Meng
  • Maoteng Li

Abstract

The prediction of the flowering time (FT) trait in Brassica napus based on genome-wide markers and the detection of underlying genetic factors is important not only for oilseed producers around the world but also for the other crop industry in the rotation system in China. In previous studies the low density and mixture of biomarkers used obstructed genomic selection in B. napus and comprehensive mapping of FT related loci. In this study, a high-density genome-wide SNP set was genotyped from a double-haploid population of B. napus. We first performed genomic prediction of FT traits in B. napus using SNPs across the genome under ten environments of three geographic regions via eight existing genomic predictive models. The results showed that all the models achieved comparably high accuracies, verifying the feasibility of genomic prediction in B. napus. Next, we performed a large-scale mapping of FT related loci among three regions, and found 437 associated SNPs, some of which represented known FT genes, such as AP1 and PHYE. The genes tagged by the associated SNPs were enriched in biological processes involved in the formation of flowers. Epistasis analysis showed that significant interactions were found between detected loci, even among some known FT related genes. All the results showed that our large scale and high-density genotype data are of great practical and scientific values for B. napus. To our best knowledge, this is the first evaluation of genomic selection models in B. napus based on a high-density SNP dataset and large-scale mapping of FT loci.

Suggested Citation

  • Lun Li & Yan Long & Libin Zhang & Jessica Dalton-Morgan & Jacqueline Batley & Longjiang Yu & Jinling Meng & Maoteng Li, 2015. "Genome Wide Analysis of Flowering Time Trait in Multiple Environments via High-Throughput Genotyping Technique in Brassica napus L," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-18, March.
    • Handle: RePEc:plo:pone00:0119425
    DOI: 10.1371/journal.pone.0119425
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    References listed on IDEAS

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    1. Ben J Hayes & Jennie Pryce & Amanda J Chamberlain & Phil J Bowman & Mike E Goddard, 2010. "Genetic Architecture of Complex Traits and Accuracy of Genomic Prediction: Coat Colour, Milk-Fat Percentage, and Type in Holstein Cattle as Contrasting Model Traits," PLOS Genetics, Public Library of Science, vol. 6(9), pages 1-11, September.
    2. Park, Trevor & Casella, George, 2008. "The Bayesian Lasso," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 681-686, June.
    3. Robert Makowsky & Nicholas M Pajewski & Yann C Klimentidis & Ana I Vazquez & Christine W Duarte & David B Allison & Gustavo de los Campos, 2011. "Beyond Missing Heritability: Prediction of Complex Traits," PLOS Genetics, Public Library of Science, vol. 7(4), pages 1-9, April.
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    Cited by:

    1. Jun Zou & Yusheng Zhao & Peifa Liu & Lei Shi & Xiaohua Wang & Meng Wang & Jinling Meng & Jochen Christoph Reif, 2016. "Seed Quality Traits Can Be Predicted with High Accuracy in Brassica napus Using Genomic Data," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-22, November.
    2. Alberto Cargnelutti Filho & Cleiton Antonio Wartha & Jéssica Andiara Kleinpaul & Ismael Mario Marcio Neu & Daniela Lixinski Silveira, 2024. "Sample Size to Estimate the Mean and Median of Traits in Canola," Journal of Agricultural Science, Canadian Center of Science and Education, vol. 10(11), pages 123-123, April.

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