Research progress on genomic selection breeding technology for crops.

Area of Science:

• Plant breeding
• Quantitative genetics
• Bioinformatics

Background:

• Genomic selection (GS) leverages genome-wide markers and phenotypic data for predicting breeding values in crops.
• GS is advantageous for polygenic traits, offering higher accuracy and efficiency than traditional methods.

Purpose of the Study:

• This review critically examines factors influencing genomic selection efficacy in crop improvement.
• It highlights challenges and proposes future directions for practical implementation.

Main Methods:

• The review synthesizes current knowledge on training population design, modeling non-additive effects, and integrating multi-trait/multi-environment data.
• It compares various predictive models, including linear, Bayesian, and machine learning approaches.
• Current status of breeding chip development for major crops is summarized.

Main Results:

• Genomic selection offers significant potential for accelerating crop breeding cycles and enhancing efficiency.
• Key challenges include managing genotype-by-environment interactions, modeling complex genetic effects, and the cost/interpretability of advanced methods.
• Integration of multi-omics data and development of crop-specific resources are crucial.

Conclusions:

• Addressing challenges in training population design, genetic modeling, and data integration is vital for maximizing GS utility.
• Future research should focus on developing user-friendly, interpretable analytical platforms and cost-effective breeding chips.
• Bridging the gap between advanced methodologies and practical breeding applications is essential for future crop improvement.