Genome optimization for improvement of maize breeding

Area of Science:

• Plant breeding
• Genomics
• Computational biology

Background:

• The next era of plant breeding, Breeding 4.0, necessitates integrating advanced technologies like genomics, phenomics, gene editing, synthetic biology, Big Data, and AI.
• Traditional maize breeding relies on passive selection, which is becoming insufficient for maximizing genetic gain.

Purpose of the Study:

• To propose a novel maize breeding model, termed "genomic design breeding."
• To outline a pipeline that integrates doubled haploid production, genomic selection, and genome optimization.
• To facilitate the transition of maize breeding from an empirical art to a data-driven science and intelligence.

Main Methods:

• Incorporation of doubled haploid production for rapid genetic advancement.
• Application of genomic selection for predicting breeding values.
• Utilizing genome optimization through computational simulation for virtual design of superior genotypes.

Main Results:

• The proposed genomic design breeding model enables the virtual design of optimized genomes.
• This model allows for the pyramiding of superior alleles to achieve maximum genetic gain with minimal resources.
• Trait predictions and decision-making models at various scales facilitate the breeding pipeline.

Conclusions:

• Genomic design breeding represents a significant advancement in maize improvement strategies.
• The model supports the evolution towards an intelligent Breeding 4.0 era for maize.
• Successful implementation will enhance efficiency and effectiveness in developing superior maize varieties.