Genomic selection for salinity tolerance in japonica rice

Area of Science:

• Plant breeding
• Genetics
• Agronomy

Background:

• Improving crop performance in saline conditions is crucial for global food security.
• Genomic selection (GS) is a powerful tool for enhancing selection accuracy and reducing breeding cycle length.
• Understanding genotype-by-environment interactions is key for developing salt-tolerant varieties.

Purpose of the Study:

• To evaluate the efficiency of genomic selection for predicting salt stress tolerance in rice.
• To assess the predictive ability of GS models across different traits and conditions.
• To validate GS model performance using an independent breeding population.

Main Methods:

• A rice reference panel (241 accessions) was genotyped with 20,255 SNPs and phenotyped for morphological traits and ion content under control and mild salinity.
• Genomic prediction models were developed using cross-validation within the reference panel.
• Model performance was validated on an independent breeding population (393 accessions).

Main Results:

• Genotype-by-condition interactions were observed for most traits.
• Predictive abilities for multi-environment models ranged from 0.25–0.64 for morphological traits and 0.05–0.40 for stress response indices.
• Validation on the breeding population showed predictive abilities from 0.00–0.66, correlating strongly with cross-validation results (r = 0.69).

Conclusions:

• Genomic selection is efficient for predicting salt stress tolerance in rice breeding lines.
• GS can significantly improve the efficiency of breeding programs targeting salinity-prone environments.
• This approach facilitates the development of more resilient rice varieties.