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Extension of a haplotype-based genomic prediction model to manage multi-environment wheat data using environmental covariates

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A multi-environment genomic prediction model incorporating environmental covariates increased the prediction accuracy of wheat grain protein content. The advantage of the haplotype-based model was dependent upon the trait of interest.

Abstract

The inclusion of environment covariates (EC) in genomic prediction models has the potential to precisely model environmental effects and genotype-by-environment interactions. Together with EC, a haplotype-based genomic prediction approach, which is capable of accommodating the interaction between local epistasis and environment, may increase the prediction accuracy. The main objectives of our study were to evaluate the potential of EC to portray the relationship between environments and the relevance of local epistasis modelled by haplotype-based approaches in multi-environment prediction. The results showed that among five traits: grain yield (GY), plant height, protein content, screenings percentage (SP) and thousand kernel weight, protein content exhibited a 2.1% increase in prediction accuracy when EC was used to model the environmental relationship compared to treatment of the environment as a regular random effect without a variance–covariance structure. The approach used a Gaussian kernel to characterise the relationship among environments that displayed no advantage in contrast to the use of a genomic relationship matrix. The prediction accuracies of haplotype-based approaches for SP were consistently higher than the genotype-based model when the numbers of single-nucleotide polymorphisms (SNP) in a haplotype were from three to ten. In contrast, for GY, haplotype-based models outperformed genotype-based methods when two to four SNPs were used to construct the haplotype.

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Acknowledgements

This study is funded by the Grain Research Development Corporation (GRDC, US00081), the University of Sydney and Agriculture Victoria. The authors would like to thank Dr. Yong Jiang for his input on genomic prediction models and the manuscript and Dr. Dunia Pino del Carpio for helping to revise the manuscript.

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Authors and Affiliations

  1. Agriculture Victoria, Centre for AgriBioscience, AgriBio, Bundoora, VIC, Australia

    Sang He, Kerrie Forrest, Fan Shi, Matthew J. Hayden & Hans D. Daetwyler

  2. School of Life and Environmental Sciences, Plant Breeding Institute, Sydney Institute of Agriculture, The University of Sydney, Narrabri, NSW, Australia

    Rebecca Thistlethwaite & Richard Trethowan

  3. School of Applied Systems Biology, La Trobe University, Bundoora, VIC, Australia

    Matthew J. Hayden & Hans D. Daetwyler

  4. School of Life and Environmental Sciences, Plant Breeding Institute, Sydney Institute of Agriculture, The University of Sydney, Cobbitty, NSW, Australia

    Richard Trethowan

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  1. Sang He
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Contributions

SH, HDD and RTr designed the study. SH conducted genomic prediction analyses. FS performed genotype quality control and imputation. RTr and RTh developed the plant populations and collected the phenotypes. KF and MJH genotyped the population. All authors wrote and approved the final manuscript.

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Correspondence to Sang He.

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Communicated by Albrecht E. Melchinger.

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He, S., Thistlethwaite, R., Forrest, K. et al. Extension of a haplotype-based genomic prediction model to manage multi-environment wheat data using environmental covariates. Theor Appl Genet 132, 3143–3154 (2019). https://doi.org/10.1007/s00122-019-03413-1

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