Genomic index selection provides a pragmatic framework for setting and refining multi-objective breeding targets in...

Area of Science:

• Plant breeding and genetics
• Biotechnology
• Agricultural science

Background:

• Miscanthus is a promising biomass crop, but breeding superior varieties is challenging due to complex genetic traits and genotype-by-environment interactions.
• Traditional marker-assisted selection is limited for complex traits like biomass yield; genomic selection offers an alternative with moderate prediction accuracies.
• Genomic selection shows promise for Miscanthus breeding, but prediction accuracies for biomass yield itself remain relatively low.

Purpose of the Study:

• To extend a combined index selection and genomic prediction approach for simultaneous multiple breeding targets in Miscanthus.
• To evaluate the simultaneous achievement of increased biomass yield with altered flowering time or improved cell wall composition (lignin and cellulose).
• To monitor correlated selection responses for non-target traits without prior economic weighting.

Main Methods:

• Utilized a previously proposed combination of index selection and genomic prediction.
• Evaluated two hypothetical scenarios for increasing biomass yield by 20% in a single selection round.
• Scenario 1: Increased yield with delayed flowering; Scenario 2: Increased yield with reduced lignin and increased cellulose content.

Main Results:

• Both breeding scenarios efficiently achieved their primary objectives using high selection intensities (20% and 4% of genotypes).
• Significant differences in correlated responses were observed between the two scenarios.
• The relative economic values of secondary traits varied considerably, impacting overall selection index outcomes.

Conclusions:

• In silico evaluation of breeding objectives and correlated responses is crucial before extensive resource commitment.
• The proposed integrated approach is broadly applicable for complex breeding programs.
• The methodology can readily incorporate high-throughput phenotyping data into breeding platforms.