Genome optimization via virtual simulation to accelerate maize hybrid breeding

Area of Science:

• Plant breeding
• Genomics
• Computational biology

Background:

• Doubled-haploid (DH) technology accelerates maize inbred line development.
• Genomic selection (GS) is crucial for selecting superior lines due to high phenotyping costs.
• Efficient genotype-based screening of numerous inbred lines remains a challenge.

Purpose of the Study:

• To implement and evaluate a novel 'genome optimization via virtual simulation' (GOVS) toolbox.
• To assist in the selection of superior maize lines based on genomic contributions to a virtual genome.
• To optimize the strategy for doubled-haploid (DH) line production by identifying complementary elite lines.

Main Methods:

• Utilized genotype and phenotype data from 1404 maize lines and their F1 progeny.
• Developed GOVS to simulate a virtual genome with abundant optimal genotypes.
• Assessed line superiority based on contributions of genomic fragments to the simulated genome.

Main Results:

• GOVS facilitates selection of superior lines by identifying those contributing more optimal genotype fragments.
• GOVS-assisted selection avoids arbitrary thresholds common in traditional GS.
• Selected lines demonstrated complementary advantageous alleles, aiding DH production strategy.

Conclusions:

• GOVS provides a novel approach to complement traditional genomic selection in maize.
• The GOVS toolbox can guide breeding decisions and optimize DH line development.
• Integrating DH production, GS, and genome optimization enhances genomically designed breeding in maize.