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Optimization of Multi-Generation Multi-location Genomic Prediction Models for Recurrent Genomic Selection in an

Hugues de Verdal1,2, Cédric Baertschi3,4, Julien Frouin3,4

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Summary

Genomic selection in rice breeding can be improved by integrating multi-generation and multi-location data. This approach enhances predictive ability for traits like grain zinc concentration, potentially accelerating breeding cycles.

Keywords:
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Area of Science:

  • Plant breeding
  • Genomics
  • Quantitative genetics

Background:

  • Genomic selection (GS) is crucial for enhancing genetic gain in recurrent selection breeding schemes.
  • Shuttle breeding programs can benefit from integrating multi-generation and multi-location data to improve genomic prediction models.
  • The Cirad-CIAT upland rice breeding program aims to optimize recurrent genomic selection for increased genetic gain and reduced phenotyping.

Purpose of the Study:

  • To evaluate the impact of multi-generation and multi-location data on genomic prediction models in an upland rice breeding program.
  • To compare different genomic prediction scenarios and models for optimizing breeding schemes.
  • To assess the potential for increasing selection intensity and accelerating the breeding cycle.

Main Methods:

  • Utilized a synthetic rice population (PCT27) advanced through multiple generations (S0:2 to S0:4).
  • Phenotyped progenies in two distinct locations: Santa Rosa (target) and Palmira (surrogate).
  • Performed genomic prediction using five scenarios and 24 models, with calibration and validation sets defined by generation and location.

Main Results:

  • Training models with data from the target location (Santa Rosa) yielded predictive abilities from 0.19 (grain zinc concentration) to 0.30 (grain yield).
  • Expanding the training set with multi-generation data (PCT27A) improved predictive abilities for most traits, notably 61% for grain zinc concentration.
  • Multi-location phenotyping improved prediction accuracy, especially when considering genotype-by-environment (GxE) interactions, with specific models showing higher accuracy for flowering, grain zinc concentration, plant height, and grain yield.

Conclusions:

  • While the optimal genomic prediction scenario varied by trait, integrating multi-location and multi-generation data offered low gains in predictive ability.
  • This approach, however, holds potential for increasing selection intensity, accelerating the breeding cycle, and providing economic benefits to rice breeding programs.
  • Further research into GxE interactions can refine prediction models for specific traits and environments.