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Genomic Cross Prediction for Linseed Improvement

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Accelerated Plant Breeding, Volume 4

Abstract

Crossing between two or more parents is a fundamental way to generate superior genetic variants through genetic recombination and transgressive segregation in modern crop breeding. The selection of parents and crosses is the first key step for the success of crop breeding. The traditional method for screening parents and crosses is primarily based on phenotypic performance and genetic differences between parents and the breeders’ empirical expertise. With the availability of genome-wide molecular markers and other genomic information, computer simulation offers a computational approach to simulate genetic recombination events between parents and progeny segregation of crosses and to generate segregation populations of any virtual crosses for various breeding schemes. Genomic selection (GS) enables to estimate breeding values (BVs) of the segregation individuals of crosses. Therefore, the integration of computer simulation and GS leads to an advanced genomic tool, named genomic cross prediction, to predict the genetic performance of different types of crosses by evaluating BVs and genetic variances of their segregation populations, enhancing the potential of success in crossbreeding. This chapter overviews the strategies and methods of genomic cross prediction and illustrates its application potential in crops, especially flax linseed breeding.

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Acknowledgments

This work is a part of the 4DWheat project, and was also supported by the total Utilization Flax GENomics (TUFGEN) project. The two projects were funded by Agriculture and Agri-Food Canada, Genome Canada and co-funders from the wheat and flax industries in Canada including producer groups. The authors also recognize the support provided by Genome Prairie.

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Authors and Affiliations

  1. Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON, Canada

    Frank M. You, Chunfang Zheng, Sampurna Bartaula, Nadeem Khan & Sylvie Cloutier

  2. National Key Facility for Crop Gene Resources and Genetic Improvement, and Institute of Crop Sciences, Beijing, China

    Jiankang Wang

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  1. Frank M. You
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  2. Chunfang Zheng
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  3. Sampurna Bartaula
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  4. Nadeem Khan
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  5. Jiankang Wang
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  6. Sylvie Cloutier
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Correspondence to Frank M. You .

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Editors and Affiliations

  1. Punjab Agricultural University, Ludhiana, Punjab, India

    Satbir Singh Gosal

  2. Mountain Research Center for Field Crops, Sher-e-Kashmir University of Agricultural Sciences and Technology, Srinagar, Jammu and Kashmir, India

    Shabir Hussain Wani

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You, F.M., Zheng, C., Bartaula, S., Khan, N., Wang, J., Cloutier, S. (2022). Genomic Cross Prediction for Linseed Improvement. In: Gosal, S.S., Wani, S.H. (eds) Accelerated Plant Breeding, Volume 4. Springer, Cham. https://doi.org/10.1007/978-3-030-81107-5_13

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