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Advances in integrated genomic selection for rapid genetic gain in crop improvement: a review

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Abstract

Main conclusion

Genomic selection and its importance in crop breeding. Integration of GS with new breeding tools and developing SOP for GS to achieve maximum genetic gain with low cost and time.

Abstract

The success of conventional breeding approaches is not sufficient to meet the demand of a growing population for nutritious food and other plant-based products. Whereas, marker assisted selection (MAS) is not efficient in capturing all the favorable alleles responsible for economic traits in the process of crop improvement. Genomic selection (GS) developed in livestock breeding and then adapted to plant breeding promised to overcome the drawbacks of MAS and significantly improve complicated traits controlled by gene/QTL with small effects. Large-scale deployment of GS in important crops, as well as simulation studies in a variety of contexts, addressed G × E interaction effects and non-additive effects, as well as lowering breeding costs and time. The current study provides a complete overview of genomic selection, its process, and importance in modern plant breeding, along with insights into its application. GS has been implemented in the improvement of complex traits including tolerance to biotic and abiotic stresses. Furthermore, this review hypothesises that using GS in conjunction with other crop improvement platforms accelerates the breeding process to increase genetic gain. The objective of this review is to highlight the development of an appropriate GS model, the global open source network for GS, and trans-disciplinary approaches for effective accelerated crop improvement. The current study focused on the application of data science, including machine learning and deep learning tools, to enhance the accuracy of prediction models. Present study emphasizes on developing plant breeding strategies centered on GS combined with routine conventional breeding principles by developing GS-SOP to achieve enhanced genetic gain.

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Data availability

The datasets generated or analyzed during the current study are presented in the article.

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Acknowledgements

Authors thankful to ICAR for providing support for research in the area of genomic selection.

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  1. ICAR-National Rice Research Institute, Cuttack, India

    C. Anilkumar

  2. University of Agricultural Sciences, Bangalore, India

    N. C. Sunitha & S. Ramesh

  3. ICAR-Indian Agriculture Research Institute, New Delhi, India

    Harikrishna & Narayana Bhat Devate

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Anilkumar, C., Sunitha, N.C., Harikrishna et al. Advances in integrated genomic selection for rapid genetic gain in crop improvement: a review. Planta 256, 87 (2022). https://doi.org/10.1007/s00425-022-03996-y

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