Computational Molecular Biology 2024, Vol.14, No.2, 54-63 http://bioscipublisher.com/index.php/cmb 59 5.3 Yield and quality traits Improving yield and quality traits in crops is a primary goal of plant breeding, and GS has proven to be a valuable tool in this regard. By utilizing genome-wide markers, GS can predict complex traits influenced by multiple genes, such as yield and quality, with greater accuracy (Jannink et al., 2010; Wang et al., 2018). The method's ability to capture the effects of small QTL and incorporate them into prediction models allows for more comprehensive selection decisions (Desta and Ortiz, 2014; Wang et al., 2018). Empirical studies have shown that GS can lead to significant genetic gains in yield and quality traits, making it a critical component of modern breeding programs (Jannink et al., 2010; Crossa et al., 2017). Additionally, the integration of multi-trait genomic selection methods, which optimize selection decisions across multiple traits, further enhances the effectiveness of GS in improving yield and quality (Figure 2) (Moeinizade et al., 2020). This multi-objective optimization approach ensures that breeding programs can achieve balanced improvements in various economically important traits, ultimately leading to the development of superior crop varieties. Figure 2 Comparison of multi-trait linear weighted selection (MT-LAS), single-trait linear weighted selection (ST-LAS), and Index selection methods (Adopted from Moeinizade et al., 2020) Image Caption: This figure illustrates the performance of MT-LAS, ST-LAS, and different index selection methods over 10 generations in a simulation; Each small box represents the distribution of genetic estimated breeding values (GEBVs) for two traits across each generation, with the gray bars indicating the constraint boundaries; The three numbers in each box represent the standard deviations (SD) of trait 1 and trait 2, followed by the correlation between the two traits (Adopted from Moeinizade et al., 2020) Moeinizade et al. (2020) studied the genetic performance and correlations of traits in multi-trait selection (MT-LAS) and single-trait selection (ST-LAS). Under genomic selection (GS), selecting multiple traits simultaneously (such as yield and quality) allows for better balancing of improvement goals and more effective selection of target traits. Index selection, on the other hand, influences the direction of selection through different weighting coefficients. Overall, MT-LAS performed excellently in balancing improvements across multiple traits, demonstrating significant potential for applications in improving crop yield and quality. 6 Applications in Animal Breeding 6.1. Genetic improvement of livestock Genomic selection (GS) has revolutionized the genetic improvement of livestock by enabling more accurate predictions of breeding values. Traditional marker-assisted selection (MAS) was limited by the complexity of traits in livestock, which are influenced by thousands of genes with small effects. GS overcomes this by
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