Enhancing genomic selection by fitting large-effect SNPs as fixed effects and a genotype-by-environment effect using...

Area of Science:

• Plant Breeding
• Quantitative Genetics
• Genomics

Background:

• Genomic selection (GS) is increasingly vital for commercial crop breeding.
• Enhancing GS efficiency is crucial for maximizing its impact.
• Understanding genetic architecture and genotype-by-environment (G × E) interactions is key.

Purpose of the Study:

• To investigate methods for enhancing genomic selection (GS) accuracy in maize.
• To explore the utility of large-effect single nucleotide polymorphisms (SNPs) and G × E interactions in GS models.
• To improve prediction accuracy for efficient crop breeding.

Main Methods:

• Evaluated a maize BC1F3:4 population (481 families) for days to anthesis across four environments.
• Genotyped the population using DNA chips with 55,000 SNPs.
• Applied GS models incorporating large-effect SNPs as fixed effects and G × E components.

Main Results:

• Fitting the top four large-effect SNPs as fixed effects increased prediction accuracy.
• This increase in accuracy was associated with a decrease in genetic variance.
• GS models integrating large-effect SNPs and G × E effects generally showed enhanced performance.

Conclusions:

• Fitting large-effect markers as fixed effects can improve GS prediction accuracy.
• Incorporating genotype-by-environment (G × E) interactions further enhances GS models.
• These approaches offer a pathway for more accurate phenotypic predictions and efficient plant selection in crop breeding programs.