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Related Experiment Video

Updated: Oct 17, 2025

Genetic Mapping of Thermotolerance Differences Between Species of Saccharomyces Yeast via Genome-Wide Reciprocal Hemizygosity Analysis
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Reciprocal Recurrent Genomic Selection Is Impacted by Genotype-by-Environment Interactions.

Maximilian Rembe1, Jochen Christoph Reif1, Erhard Ebmeyer2

  • 1Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Seeland, Germany.

Frontiers in Plant Science
|October 11, 2021
PubMed
Summary

Reciprocal recurrent genomic selection can improve hybrid wheat performance. This study demonstrated its potential, achieving a selection gain of 1.0 dt ha⁻¹ for grain yield.

Keywords:
abiotic stressgenotype-times-year interactiongrain yieldhybrid breedinglong-term selection gain

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Area of Science:

  • Plant breeding
  • Quantitative genetics
  • Agricultural science

Background:

  • Reciprocal recurrent genomic selection (RRGS) is a strategy to enhance hybrid performance in two parent populations.
  • Wheat hybrid breeding can benefit significantly from advanced breeding techniques like RRGS.

Purpose of the Study:

  • To empirically assess the potential and limitations of reciprocal recurrent genomic selection.
  • To evaluate selection gain for grain yield in a wheat breeding program using RRGS.

Main Methods:

  • Developed genome-wide prediction equations using genomic and phenotypic data from 1,604 single crosses.
  • Implemented one cycle of RRGS on the female pool with selection at F2 and F5 stages.
  • Evaluated grain yield selection gain across six environments.

Main Results:

  • Pronounced genotype-by-environment interactions were observed.
  • Excluding two outlier environments, a selection gain of 1.0 dt ha⁻¹ was achieved compared to original parental lines.
  • RRGS demonstrated potential for improving hybrid wheat, contingent on robust prediction equations.

Conclusions:

  • Reciprocal recurrent genomic selection shows promise for advancing hybrid wheat breeding.
  • The development of robust genome-wide predictive equations is crucial for effective RRGS implementation.
  • Genotype-by-environment interactions necessitate careful consideration in prediction model development.