Genotype and Environment interaction in Genomic Selection

In this poster I’m going to discuss about how Genomic Selection (GS) employs genomic data to enhance breeding accuracy by predicting individual breeding values. It utilizes a training population to develop models that estimate genetic potential based on genomic markers. GS is significant for increasing accuracy, reducing phenotyping costs, and accelerating breeding cycles.
Genotype x Environment Interaction (GEI) describes how different genotypes respond variably to environmental conditions, impacting traits like yield. Understanding GEI is crucial for improving predictive accuracy in GS models, enhancing breeding efficiency and resource allocation.
Challenges in conventional GS models include low predictive power for complex GEI and computational difficulties. Improved models like 3GS integrate GEI with GS for better predictions and efficiency. Future directions involve using environmental data and advanced modeling techniques to enhance selection outcomes in diverse conditions.