Optimizing Plant Breeding Programs for Genomic Selection

Plant geneticists and breeders have used marker technology since the 1980s in quantitative trait locus (QTL) identification. Marker-assisted selection is effective for large-effect QTL but has been challenging to use with traits controlled by multiple minor effect alleles. Therefore, genomic (GS) was proposed estimate all markers simultaneously, thereby capturing their effects. However, breeding programs are still struggling identify best strategy implement it into programs. Traditional need be optimized GS effectively. This review explores optimization of variety release based on aspects breeder’s equation. Optimizations include reorganizing field designs, training populations, increasing number lines evaluated, leveraging large amount phenotypic data collected across different growing seasons environments increase heritability estimates, intensity, accuracy. Breeding can leverage genotypic maximize genetic gain accuracy through methods utilizing multi-trait and, multi-environment models, high-throughput phenotyping, deep learning approaches. Overall, this describes various that plant utilize gains effectively breeding.