Animal Molecular Breeding, 2025, Vol.15, No.2, 49-59 http://animalscipublisher.com/index.php/amb 49 Review and Progress Open Access Integrating AI-Driven Genomic Selection and Gene Editing for Precision Goat Breeding Yanlin Wang1, Xiaofang Lin 2 1 Tropical Animal Resources Research Center, Hainan Institute of Tropical Agricultural Resources, Sanya, 572025, Hainan, China 2 Tropical Animal Medicine Research Center, Hainan Institute of Tropical Agricultural Resources, Sanya, 572025, Hainan, China Corresponding author: xiaofang.lin@hitar.org Animal Molecular Breeding, 2025, Vol.15, No.2 doi: 10.5376/amb.2025.15.0006 Received: 10 Jan., 2025 Accepted: 22 Feb., 2025 Published: 10 Mar., 2025 Copyright © 2025 Wang and Lin, 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: Wang Y.L., and Lin X.F., 2025, Integrating AI-driven genomic selection and gene editing for precision goat breeding, Animal Molecular Breeding, 15(2): 49-59 (doi: 10.5376/amb.2025.15.0006) Abstract This study reviews the application progress of AI-driven genome selection (GS) and gene editing technologies in precision goat breeding. By analyzing the application of high-density molecular markers, whole-genome sequencing and AI algorithms in the improvement of important traits in goats, this study summarized the effect of genomic selection in enhancing genetic progression and selection accuracy, and explored the potential of gene editing technologies such as CRISPR/Cas9 in precisely improving traits. And the key role of AI in phenotypic prediction, target gene screening and editing strategy design was evaluated. This study aims to provide a scientific reference for accelerating the precise improvement of goat populations in terms of productivity, disease resistance and environmental adaptability, and to help the livestock industry develop in a sustainable and efficient direction. Keywords Precision breeding; Genomic selection (GS); Gene editing; Artificial intelligence (AI); Goat genetic improvement 1 Introduction Genomic technology has developed rapidly in recent years. Scientists have begun to use some new tools such as high-density DNA labeling or whole-genome sequencing to identify genes related to goat milk production, muscle growth, and stress resistance (Wang et al., 2016; Yang et al., 2021; Ghanatsaman et al., 2023). Negro et al. (2024) indicates that genomic selection technology is becoming increasingly common. It can combine DNA information and phenotypic data to estimate which goats are more suitable for breeding, accelerate the pace of breed improvement, and achieve more accurate selection. Gore et al. (2021) and Zhang et al. (2024) found that the GS technology was more effective in breeding dairy goats and meat goats, not only improving the accuracy of predictions but also accelerating genetic progress. The application of artificial intelligence in the aquaculture industry is bringing about significant changes. Gore et al. (2021) and Zhang et al. (2024) demonstrated in their research that AI can integrate a large amount of different data, such as DNA information and animal expression, to help us better formulate breeding plans. It can also predict some complex traits and identify new genes related to good traits. The research by Zhang et al. (2018), Zhang et al. (2019), and Feng et al. (2024) all indicate that gene editing technologies like CRISPR/Cas9 are becoming increasingly advanced. They can directly modify the genes of objects, especially those related to yield and disease resistance, enabling scientists to more purposefully improve the genetic characteristics of goats. This study reviewed the current development status and latest progress of goat genome selection and gene editing technologies, and evaluated the application potential of artificial intelligence in enhancing genetic improvement and breeding efficiency. This study proposes an application framework integrating genomic selection, gene editing and AI technology, aiming to provide a scientific basis for achieving precise genetic improvement of goat populations in terms of productivity, disease resistance and environmental adaptability.
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