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Genomic prediction and selection response for grain yield in safflower.
Huanhuan Zhao1,2, Zibei Lin2, Majid Khansefid1,2
1School of Applied Systems Biology, La Trobe University, Bundoora, VIC, Australia.
Frontiers in Genetics
|April 13, 2023
Summary
Genomic selection improves prediction accuracy for safflower grain yield by incorporating correlated traits like plant height. This breeding approach enhances selection for high grain yield, oil content, and adaptability across environments.
Area of Science:
- Plant breeding
- Genetics
- Agricultural science
Background:
- Agronomic traits in safflower are often genetically correlated.
- Correlated traits can enhance genomic selection (GS) accuracy, especially for low-heritability traits.
- Understanding these correlations is crucial for developing improved safflower varieties.
Purpose of the Study:
- To investigate genetic correlations between key agronomic traits in safflower.
- To evaluate the impact of incorporating correlated traits into GS models for grain yield prediction.
- To assess selection responses for grain yield and oil content using different selection indices and considering genotype-by-environment interactions.
Main Methods:
- Analysis of genetic correlations between grain yield (GY), plant height (PH), and days to flowering (DF).
- Implementation of multivariate genomic selection models incorporating PH to predict GY.
- Evaluation of selection responses using various selection indices and genotype-by-environment (g×E) interactions.
Main Results:
- Moderate genetic correlations were found between GY and PH (0.272-0.531), and low correlations between GY and DF (-0.157-0.201).
- Including PH in multivariate GS models improved GY prediction accuracy by 4%-20%.
- Simultaneous selection for GY and seed oil content (OL) with equal weights yielded positive gains across all sites; incorporating g×E interactions led to more balanced site-specific responses.
Conclusions:
- Genomic selection is effective for improving GY and OL in safflower.
- Multivariate GS models incorporating correlated traits like PH enhance prediction accuracy.
- Considering g×E interactions in GS provides more robust and adaptable safflower varieties.
