Computational Molecular Biology 2024, Vol.14, No.4, 145-154 http://bioscipublisher.com/index.php/cmb 145 Research Insight Open Access The Evolving Landscape of Genomic Selection: Insights and Innovations in Quantitative Genetics Xiaojun Li , Shuiji Zhang Biotechnology Research Center, Cuixi Academy of Biotechnology, Zhuji, 311800, Zhejiang, China Corresponding author: xiaojun.li@cuixi.org Computational Molecular Biology, 2024, Vol.14, No.4 doi: 10.5376/cmb.2024.14.0017 Received: 20 May, 2024 Accepted: 30 Jun., 2024 Published: 12 Jul., 2024 Copyright © 2024 Li and Zhang, This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Preferred citation for this article: Li X.J., and Zhang S.J., 2024, The evolving landscape of genomic selection: insights and innovations in quantitative genetics, Computational Molecular Biology, 14(4): 145-154 (doi: 10.5376/cmb.2024.14.0017) Abstract Genomic selection (GS), as a key technology in modern breeding programs, has significantly advanced crop and livestock breeding. By integrating quantitative genetics and genome prediction models, GS has improved the accuracy of predicting complex traits and accelerated the cultivation of high-yield and stress resistant varieties. This study explores the historical evolution, technological innovation, and practical applications of genome selection in breeding. It analyzes the advantages brought by innovative technologies such as high-density genotyping and whole genome prediction, especially their widespread application in multi trait and multi environment models. Although GS has great potential in modern breeding, it still faces challenges such as genotype environment interaction, prediction accuracy, and data complexity. I hope to summarize the latest progress of GS through case analysis and provide direction for future research, in order to promote the application of quantitative genetics and genome selection in a wider range of fields, and provide support for global food security and sustainable agricultural development. Keywords Genomic selection; Quantitative genetics; Genomic prediction; Marker-assisted selection; Complex traits 1 Introduction Genomic Selection (GS) has emerged as a significant breakthrough in the field of breeding in recent years. Unlike traditional marker-assisted selection, which relies on a limited number of markers associated with specific traits, GS utilizes genome-wide marker data to predict the breeding values of individuals. By estimating the effects of all markers comprehensively, GS captures the small-effect alleles that influence complex traits, thereby improving breeding efficiency and accuracy (Meuwissen et al., 2016; Crossa et al., 2017). With advances in high-density genotyping technologies, GS has been widely applied in both plant and animal breeding, significantly accelerating genetic improvement (Heslot et al., 2015; Rice and Lipka, 2021). GS plays a crucial role in modern breeding programs. By integrating genome-wide marker information, GS significantly increases selection accuracy, shortens breeding cycles, and enhances genetic gains per unit time. This method is particularly effective in improving quantitative traits controlled by multiple genes, especially in addressing challenges related to climate change and enhancing crop yields and livestock production (Liu et al., 2019; Merrick et al., 2022). Additionally, GS reduces the need for large-scale phenotyping, lowers breeding costs, and, through advanced statistical models and high-throughput phenotyping technologies, improves breeding efficiency (Larkin et al., 2019; Cappetta et al., 2020). This study systematically reviews the latest developments and innovations in the field of GS. By analyzing the development history, various application models, and methods of GS, this study explores the actual effects of GS in different breeding programs and evaluates its impact on genetic gain and breeding efficiency. In addition, challenges and limitations in the implementation of GS were identified, and possible solutions to address these issues were proposed. In the future, GS is expected to continue promoting the sustainable development of global agriculture by integrating emerging technologies and improving prediction accuracy. 2 Evolution of Genomic Selection 2.1 Historical development of GS The concept of genomic selection (GS) was first introduced by Meuwissen et al. in 2001, marking a significant departure from traditional marker-assisted selection (MAS) methods. Prior to this, agricultural genomics primarily
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