Optimizing genomic prediction for complex traits via investigating multiple factors in switchgrass.

Area of Science:

• Plant breeding and genetics
• Genomics and bioinformatics
• Biofuel feedstock development

Background:

• Genomic prediction accelerates crop improvement and reveals genetic underpinnings of complex traits.
• Switchgrass (Panicum virgatum L.) is a key perennial biofuel feedstock, but its complex genetic architecture presents challenges for breeding.
• Optimizing genomic prediction requires understanding how various genomic factors influence prediction accuracy.

Purpose of the Study:

• To assess the impact of genome assemblies, genotyping approaches, variant types, allelic complexities, ploidy levels, and population structures on genomic prediction accuracy for 20 complex traits in switchgrass.
• To identify optimal genomic strategies for enhancing selective breeding in switchgrass and other polyploid species.

Main Methods:

• Evaluated prediction accuracy using different genome assemblies (short-read vs. long-read).
• Compared genotyping approaches (exome capture vs. genotyping-by-sequencing) and variant types (multi-allelic vs. bi-allelic).
• Assessed the influence of polyploidy levels (tetraploid vs. octoploid) and population structures on prediction outcomes for 20 complex traits.

Main Results:

• Short-read genome assemblies performed comparably or better than long-read assemblies.
• Exome capture and multi-allelic variants showed superior performance over genotyping-by-sequencing and bi-allelic variants, respectively.
• Tetraploid models generally yielded higher prediction accuracy than octoploid models, suggesting benefits from greater genetic distances.

Conclusions:

• The choice of genome assembly, genotyping method, and variant type significantly impacts genomic prediction accuracy in switchgrass.
• Integrating different variant types is crucial for optimizing predictions depending on the specific trait.
• Findings provide essential guidance for improving genomic prediction strategies and accelerating the breeding of agronomic traits in switchgrass and other polyploid crops.