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Genome optimization for improvement of maize breeding

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Key message

We propose a new model to improve maize breeding that incorporates doubled haploid production, genomic selection, and genome optimization.

Abstract

Breeding 4.0 has been considered the next era of plant breeding. It is clear that the Breeding 4.0 era for maize will feature the integration of multi-disciplinary technologies including genomics and phenomics, gene editing and synthetic biology, and Big Data and artificial intelligence. The breeding approach of passively selecting ideal genotypes from designated genetic pools must soon evolve to virtual design of optimized genomes by pyramiding superior alleles using computational simulation. An optimized genome expressing optimal phenotypes, which may never actually be created, can function as a blueprint for breeding programs to use minimal materials and hybridizations to achieve maximum genetic gain. We propose a new breeding pipeline, “genomic design breeding,” that incorporates doubled haploid production, genomic selection, and genome optimization and is facilitated by different scales of trait predictions and decision-making models. Successful implementation of the proposed model will facilitate the evolution of maize breeding from “art” to “science” and eventually to “intelligence,” in the Breeding 4.0 era.

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Acknowledgements

This work was supported by the Key Research and Development Program of China (2018YFA0901003) and the National Science Foundation of China (31871706).

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Authors and Affiliations

  1. National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100913, China

    Shuqin Jiang, Jun Yan, Ran Fu & Xiangfeng Wang

  2. Key Laboratory of Biology and Genetics Improvement of Maize in Arid Area of Northwest Region, Ministry of Agriculture, Northwest A&F University, Shaanxi, China

    Qian Cheng

Authors
  1. Shuqin Jiang
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  2. Qian Cheng
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  3. Jun Yan
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  4. Ran Fu
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  5. Xiangfeng Wang
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Contributions

JY performed the comparative analysis of machine learning methods. QC and SJ performed the rrBLUP analysis of population stratification and population size. SJ, RF, and XW wrote the manuscript.

Corresponding author

Correspondence to Xiangfeng Wang.

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Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Communicated by Mingliang Xu.

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Cite this article

Jiang, S., Cheng, Q., Yan, J. et al. Genome optimization for improvement of maize breeding. Theor Appl Genet 133, 1491–1502 (2020). https://doi.org/10.1007/s00122-019-03493-z

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