Computational Molecular Biology 2024, Vol.14, No.4, 145-154 http://bioscipublisher.com/index.php/cmb 150 Figure 2 Optimization of the traditional breeding pipeline and product development based on an 11–year breeding program from parental crossing to variety release. The effect of each component of optimization (genomic selection, training population design, inbred line development, field design, high-throughput phenotyping (HTP) on different aspects of the breeder’s equation (selection intensity, selection accuracy, genetic variance, and cycle time) is shown by the coverage of the method of optimization within the respective column of the different factors of the breeder’s equation. For example, for Years 1-3 of the breeding cycle, the composition and structure of the training population (purple) affect both selection accuracy and genetic variance, whereas the choice of genomic selection models affects the intensity of selection, prediction accuracy, and genetic variance (Adopted from Merrick et al., 2022) 5.2 Accuracy of genomic predictions The accuracy of genomic predictions is a critical factor in the success of GS. The correlation between predicted and true breeding values is influenced by several factors, including the density of markers and the size of the reference population. Increasing the size of the reference set and using denser markers can improve prediction accuracy. However, this often comes at the cost of increased computational burden, particularly with non-linear Bayesian models, which, while providing higher accuracy for some traits, require significant computational resources (Wang et al., 2016). Theoretical advancements have introduced new proxies for accuracy that outperform existing ones, particularly in configurations of linkage disequilibrium (LD) between quantitative trait loci (QTLs) and markers (Rabier et al., 2016). Despite these improvements, challenges remain in maintaining the stability of genomic predictions, as fluctuations in evaluations can lead to a crisis of confidence in GS (Misztal et al., 2021). Therefore, ongoing research is needed to develop models that balance accuracy and computational efficiency while ensuring stable predictions. 5.3 Data complexity and computational demands The complexity of data and the computational demands associated with GS are significant limitations. The integration of high-density SNP and whole-genome sequence data into GS models has transformed breeding
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