Computational Molecular Biology 2024, Vol.14, No.4, 145-154 http://bioscipublisher.com/index.php/cmb 149 leverages the effects of all markers across the genome, providing a more comprehensive and unbiased estimate of genetic values (Bradshaw et al., 2016). This approach captures the cumulative effect of numerous small-effect QTLs, which are often overlooked in MAS. As a result, WGP offers higher accuracy in predicting breeding values, leading to more effective selection and faster genetic gains (Meuwissen et al., 2016). 4.2.2 Application in complex trait prediction The application of WGP in predicting complex traits has shown significant promise in both plant and animal breeding. By incorporating all available marker information, WGP models can predict the genetic potential of individuals with high accuracy, even for traits with low heritability (Bradshaw et al., 2016; Varshney et al., 2017). This has been particularly beneficial in livestock breeding, where traits such as milk production and disease resistance are influenced by many genes with small effects (Meuwissen et al., 2016). In crop breeding, WGP has enabled the selection of lines with superior agronomic performance, accelerating the breeding cycle and enhancing genetic gains per unit time (Unêda-Trevisoli et al., 2017). 4.3 Use of multi-trait and multi-environment models The integration of multi-trait and multi-environment models in genomic selection has further improved the accuracy and robustness of predictions. These models account for the genetic correlations between traits and the interactions between genotypes and environments, providing a more holistic view of an individual's genetic potential. By leveraging data from multiple traits and environments, breeders can make more informed selection decisions, optimizing genetic gains across diverse conditions (Figure 2). This approach is particularly valuable in plant breeding, where environmental variability can significantly impact trait expression and selection outcomes (Merrick et al., 2022). The advancements in high-density genotyping, whole-genome prediction, and the use of multi-trait and multi-environment models have significantly enhanced the effectiveness of genomic selection. These innovations have addressed the limitations of traditional MAS, providing more accurate and comprehensive predictions of genetic values, and ultimately accelerating the pace of genetic improvement in both plant and animal breeding (Wang et al., 2016; Meuwissen et al., 2016; Varshney et al., 2017). Merrick et al. (2022) demonstrated the use of multi-trait and multi-environment models in genomic selection, showing how different methods integrate data from multiple traits and environments to improve prediction accuracy. Figure 2 highlights how the integration of environmental variables and multiple traits enhances model accuracy, confirming that multi-trait and multi-environment models can provide more robust prediction results in complex breeding environments. 5 Challenges and Limitations of Genomic Selection 5.1 Genotype-environment interactions Genotype-environment interactions (GEI) present a significant challenge in genomic selection (GS) as they can drastically affect the prediction accuracy of GS models. Traditional models often struggle to account for the complexity of GEI, leading to poor phenotype predictions in unobserved environments. To address this, novel models such as the 3GS model have been developed, which integrate genotype plus genotype × environment (GGE) analysis with GS. This model has shown higher prediction accuracy, especially in environments with low to negative correlations to other environments, and can predict new genotypes in unobserved environments with high accuracy. Additionally, the computational complexity of 3GS increases linearly with the number of environments and population size, making it significantly faster than standard models for large datasets (Jighly et al., 2021). Other approaches, such as the BGGE package, also aim to improve computational efficiency while accounting for GEI by using Bayesian linear mixed models and special genetic covariance matrices (Granato et al., 2018). These advancements highlight the importance of incorporating GEI into GS models to enhance prediction accuracy and computational efficiency.
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