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Genomic selection efficiency and a priori estimation of accuracy in a structured dent maize panel

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Abstract

Key message

Population structure affects genomic selection efficiency as well as the ability to forecast accuracy using standard GBLUP.

Abstract

Genomic prediction models usually assume that the individuals used for calibration belong to the same population as those to be predicted. Most of the a priori indicators of precision, such as the coefficient of determination (CD), were derived from those same models. But genetic structure is a common feature in plant species, and it may impact genomic selection efficiency and the ability to forecast prediction accuracy. We investigated the impact of genetic structure in a dent maize panel (“Amaizing Dent”) using different scenarios including within- or across-group predictions. For a given training set size, the best accuracies were achieved when predicting individuals using a model calibrated on the same genetic group. Nevertheless, a diverse training set representing all the groups had a certain predictive efficiency for all the validation sets, and adding extra-group individuals was almost always beneficial. It underlines the potential of such a generic training set for dent maize genomic selection applications. Alternative prediction models, taking genetic structure explicitly into account, did not improve the prediction accuracy compared to GBLUP. We also investigated the ability of different indicators of precision to forecast accuracy in the within- or across-group scenarios. There was a global encouraging trend of the CD to differentiate scenarios, although there were specific combinations of target populations and traits where the efficiency of this indicator proved to be null. One hypothesis to explain such erratic performances is the impact of genetic structure through group-specific allele diversity at QTLs rather than group-specific allele effects.

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Notes

  1. https://github.com/QuantGen/MTM.

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Acknowledgements

This research was supported by the “Investissement d’Avenir” project “Amaizing”. S. Rio is jointly funded by the program AdmixSel of the INRA metaprogram SelGen and by the breeding companies partners of the Amaizing project: Caussade-Semences, Euralis, KWS, Limagrain, Maisadour, RAGT and Syngenta. We thank Valerie Combes, Delphine Madur and Stephane Nicolas for DNA extraction, analysis and assembly of genotypic data. We thank Cyril Bauland and Carine Palaffre (INRA Saint-Martin de Hinx) for the panel assembly and the coordination of seed production, all breeding companies partners of the Amaizing project and Biogemma for field trials and Pierre Dubreuil (Biogemma) for the assembly and the analysis of phenotypic data.

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Authors and Affiliations

  1. GQE - Le Moulon, INRA, Univ. Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, 91190, Gif-sur-Yvette, France

    Simon Rio, Tristan Mary-Huard, Laurence Moreau & Alain Charcosset

  2. MIA, INRA, AgroParisTech, Université Paris-Saclay, 75005, Paris, France

    Tristan Mary-Huard

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  1. Simon Rio
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Correspondence to Alain Charcosset.

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Communicated by Benjamin Stich.

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Rio, S., Mary-Huard, T., Moreau, L. et al. Genomic selection efficiency and a priori estimation of accuracy in a structured dent maize panel. Theor Appl Genet 132, 81–96 (2019). https://doi.org/10.1007/s00122-018-3196-1

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