CMB_2024v14n4

Computational Molecular Biology 2024, Vol.14, No.4, 145-154 http://bioscipublisher.com/index.php/cmb 151 practices but also introduced substantial computational challenges. Non-linear Bayesian models, while accurate, are computationally intensive, necessitating strategies to improve efficiency (Wang et al., 2016). The development of ensemble learning algorithms, such as Gradient Boosted Decision Trees (GBDT), offers a promising alternative by providing high computational efficiency and competitive prediction accuracy compared to traditional Bayesian models (Yu et al., 2022). Additionally, the use of sparse covariance matrices and block diagonal matrices in models like BGGE can reduce computational time significantly (Granato et al., 2018). These innovations are crucial for managing the large-scale, high-dimensional data typical in modern breeding programs and ensuring that GS remains a viable and efficient tool for genetic improvement. 6 Case Studies and Practical Applications 6.1 Successful implementation in crop breeding Genomic selection (GS) has revolutionized crop breeding by enabling the rapid selection of superior genotypes and accelerating breeding cycles. The concept, initially proposed by Meuwissen et al. in 2001, has been widely adopted in crop breeding programs, particularly for crops like maize and wheat, due to large international efforts by organizations such as the International Maize and Wheat Improvement Center (CIMMYT) (Koning, 2016). GS has shown significant promise in improving quantitative traits controlled by multiple genes with small effects, which traditional marker-assisted selection (MAS) struggled to address (Varshney et al., 2017; Budhlakoti et al., 2022). The integration of GS with other breeding tools and platforms has further enhanced genetic gain. For instance, refining field management to improve heritability estimation and prediction accuracy, and developing optimum GS models that consider genotype-by-environment interactions and non-additive effects, have been crucial (Xu et al., 2019). Additionally, the use of high-throughput genotyping and phenotyping technologies has accelerated the breeding process, making GS a powerful tool for developing climate-resilient crop varieties (Wang et al., 2018; Krishnappa et al., 2021). 6.2 Application in livestock improvement In livestock breeding, GS has led to unprecedented advances, particularly in dairy cattle, where it has almost entirely replaced traditional selection methods based on progeny testing. This shift has resulted in a doubling of genetic improvement per generation compared to traditional methods (Koning, 2016). The success of GS in livestock is attributed to the significant reduction in generation intervals and the higher individual values of livestock, which make the investment in GS more economically viable (Xu et al., 2019). The application of GS in livestock has been facilitated by the development of medium-density SNP chips, which became routinely available for main livestock species around 2006. This technological advancement allowed for the widespread adoption of GS in the industry, leading to remarkable improvements in genetic gain and selection accuracy . Current methods for GS in livestock include linear regression, Best Linear Unbiased Selection (BLUP), and Bayesian approaches, with the latter being extensively refined over the years (Meuwissen et al., 2016). 6.3 Insights from emerging research Emerging research in GS continues to push the boundaries of what is possible in both crop and livestock breeding. For instance, studies have shown that integrating GS with speed breeding and other novel technologies can significantly enhance the efficiency and pace of breeding programs. Additionally, the development of improved statistical models that leverage genomic information to increase prediction accuracies is critical for the effectiveness of GS-enabled breeding programs (Budhlakoti et al., 2022). In the context of human genetics, insights into the genetic architecture of complex traits are informing GS approaches in livestock. For example, understanding the genetic mechanisms underlying variation in complex traits, such as height, can help improve the accuracy of genomic predictions in livestock (Kemper, 2021). This cross-disciplinary approach highlights the potential for GS to benefit from advances in other fields of genetics, further enhancing its application in agricultural breeding programs.

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