Computational Molecular Biology 2024, Vol.14, No.2, 54-63 http://bioscipublisher.com/index.php/cmb 54 Research Report Open Access Genome-Wide Prediction and Selection in Plant and Animal Breeding: A Systematic Review of Current Techniques Xiaoya Zhang, Jianquan Li Hainan Key Laboratory of Crop Molecular Breeding, Sanya, 572025, China Corresponding author: jianquan.li @hitar.org Computational Molecular Biology, 2024, Vol.14, No.2 doi: 10.5376/cmb.2024.14.0007 Received: 03 Feb., 2024 Accepted: 14 Mar., 2024 Published: 01 Apr., 2024 Copyright © 2024 Zhang and Li, 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: Zhang X.Y., and Li J.Q., 2024, Genome-wide prediction and selection in plant and animal breeding: a systematic review of current techniques, Computational Molecular Biology, 14(2): 54-63 (doi: 10.5376/cmb.2024.14.0007) Abstract With the advancement of genomics technology, whole genome prediction (GWP) and genome selection (GS) have become important tools in plant and animal breeding. Genomic selection utilizes whole genome marker information to select target traits through predictive models, improving breeding efficiency and accuracy. This study comprehensively reviews the application of whole genome prediction technology in plant and animal breeding, with a focus on exploring its role in improving breeding efficiency. Analyzing current genome selection models and methods, exploring the potential application of GS in improving important agronomic and economic traits, as well as its prospects in different fields. Research has shown that GS technology has greatly improved selection efficiency in multiple breeding projects, particularly in enhancing plant disease resistance and increasing crop yield. In animal breeding, genome selection has been widely applied to improve the reproductive traits, health, and productivity of livestock. Keywords Genomic selection; Plant breeding; Animal breeding; Machine learning; Genotype-environment interaction 1 Introduction Genome-wide prediction and selection (GWPS) have revolutionized the fields of plant and animal breeding by enabling the prediction of complex traits through the use of dense genomic markers. This approach involves the implementation of whole-genome regression (WGR) models, where phenotypes are regressed on thousands of markers concurrently, allowing for the accurate prediction of genetic values (Campos et al., 2013). The advent of high-throughput sequencing technologies has facilitated the capture of both additive and non-additive genetic effects, thereby enhancing the prediction of genetic gains from selection (He et al., 2023). Various statistical models, such as genomic best linear unbiased predictor (G-BLUP) and Bayesian least absolute shrinkage and selection operator (BLASSO), have been developed to address the high dimensionality and multicollinearity challenges inherent in GWPS (Lima et al., 2019a). Additionally, non-parametric methods like Delta-p have been proposed to further improve prediction accuracy (Lima et al., 2019b). The integration of GWPS into modern breeding programs has significantly increased the efficiency and speed of genetic evaluations, leading to higher genetic gains per unit of time (Alkimim et al., 2020). This is particularly crucial for perennial species, where traditional breeding cycles are lengthy. By leveraging genomic estimated breeding values (GEBVs), breeders can identify superior genotypes early in the breeding cycle, thus accelerating the selection process (Lima et al., 2019a). The application of GWPS has shown promising results in various crops, including cassava, Coffea canephora, and Asian rice, demonstrating its potential to enhance breeding outcomes across diverse species (Lima et al., 2019a; Lima et al., 2019b; Alkimim et al., 2020). Moreover, the use of deep learning models in GWPS has further improved prediction accuracy for complex traits, making it a valuable tool in large-scale breeding programs (Sandhu et al., 2021). This study provides a comprehensive overview of the current technologies and methods used in genome-wide prediction and selection (GWPS) in plant and animal breeding. It summarizes the various statistical models and methods employed in GWPS, including both parametric and non-parametric approaches, and evaluates their effectiveness and efficiency in different breeding programs and species. The study also discusses the challenges
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