G × EBLUP: A novel method for exploring genotype by environment interactions and genomic prediction

Area of Science:

• Quantitative genetics
• Genomics
• Animal and plant breeding

Background:

• Genotype by environment (G × E) interaction is fundamental to complex traits and diseases.
• Most genomic prediction methods overlook G × E interaction (GEI), limiting prediction accuracy.
• Accurate genomic prediction requires incorporating GEI for complex trait and disease analysis.

Purpose of the Study:

• To propose a novel genomic prediction method, G × EBLUP, that explicitly considers GEI.
• To enhance the accuracy of genomic breeding value estimation by incorporating GEI.
• To identify genome-wide single nucleotide polymorphisms (SNPs) susceptible to GEI.

Main Methods:

• Development of the G × EBLUP method for genomic prediction incorporating GEI.
• Utilizing comprehensive simulations to evaluate G × EBLUP performance.
• Analyzing real-world data from pigs and maize to validate the method's efficacy.

Main Results:

• G × EBLUP demonstrated higher prediction accuracy and efficiency in mapping GEI SNPs compared to existing methods like GBLUP.
• The method showed significant improvements in prediction accuracy for traits like pig backfat thickness (3%) and maize grain weight (~5.0%) and water content (~7.7%).
• G × EBLUP is robust across different environment levels and can identify favorable environments for breeding.

Conclusions:

• G × EBLUP is a robust and effective method for genomic prediction, particularly when GEI variance is substantial.
• The method enhances breeding strategies by providing more accurate genomic breeding values and identifying GEI-susceptible SNPs.
• G × EBLUP offers a valuable tool for market-specific animal and plant breeding programs aiming to optimize trait performance across diverse environments.