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Digital phenotyping and genotype-to-phenotype (G2P) models to predict complex traits in cereal crops

Code: 9781801463126
Nicolas Virlet, Rothamsted Research, UK; Danilo H. Lyra, Biometrics and Breeding Research, BASF, Belgium; and Malcolm J. Hawkesford, Rothamsted Research, UK

Chapter synopsis: The revolution in digital phenotyping combined with the new layers of omics and envirotyping tools offers great promise to improve selection and accelerate genetic gains for crop improvement. This chapter examines the latest methods involving digital phenotyping tools to predict complex traits in cereals crops. The chapter has two parts. In the first part, entitled “Digital phenotyping as a tool to support breeding programs”, the secondary phenotypes measured by high-throughput plant phenotyping that are potentially useful for breeding are reviewed. In the second part, “Implementing complex G2P models in breeding programs”, the integration of data from digital phenotyping into genotype to phenotype (G2P) models to improve the prediction of complex traits using genomic information is discussed. The current status of statistical models to incorporate secondary traits in univariate and multivariate models, as well as how to better handle longitudinal (for example light interception, biomass accumulation, canopy height) traits, is reviewed.

DOI: 10.19103/AS.2022.0102.12

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Table of contents 1 Introduction 2 Digital phenotyping as a tool to support breeding programs 3 Genotype-to-phenotyping (G2P) models: integrating data from phenomics and envirotyping in predictive breeding 4 Conclusion 5 Acknowledgements 6 Where to look for further information 7 Abbreviations 8 References

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