Animal Molecular Breeding 2025, Vol.15, No.2 http://animalscipublisher.com/index.php/amb © 2025 AnimalSci Publisher, registered at the publishing platform that is operated by Sophia Publishing Group, founded in British Columbia of Canada. All Rights Reserved.
Animal Molecular Breeding 2025, Vol.15, No.2 http://animalscipublisher.com/index.php/amb © 2025 AnimalSci Publisher, registered at the publishing platform that is operated by Sophia Publishing Group, founded in British Columbia of Canada. All Rights Reserved. Publisher AnimalSci Publisher Editedby Editorial Team of Animal Molecular Breeding Email: edit@amb.animalscipublisher.com Website: http://animalscipublisher.com/index.php/amb Address: 11388 Stevenston Hwy, PO Box 96016, Richmond, V7A 5J5, British Columbia Canada Animal Molecular Breeding (ISSN 1927-5609) is an open access, peer reviewed journal published online by AnimalSci Publisher. The journal is publishing all the latest and outstanding research articles, letters and reviews in all areas of animal molecular breeding, containing transgenic breeding and marker assisted breeding, particularly publishing innovative research findings in the basic and applied fields of molecular genetics and novel techniques for improvement, applications of molecular enhanced products, as well as the significant evaluation of their related application field. AnimalSci Publisher is an international Open Access publisher specializing in animal molecular breeding, including genetics, breeding, as well as the related field registered at the publishing platform that is operated by Sophia Publishing Group (SPG), founded in British Columbia of Canada. All the articles published in Animal Molecular Breeding are Open Access, and are distributed 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. AnimalSci Publisher uses CrossCheck service to identify academic plagiarism through the world’s leading plagiarism prevention tool, iParadigms, and to protect the original authors’ copyrights.
Animal Molecular Breeding (online), 2025, Vol. 15, No.2 ISSN 1927-5609 http://animalscipublisher.com/index.php/amb © 2025 AnimalSci Publisher, registered at the publishing platform that is operated by Sophia Publishing Group, founded in British Columbia of Canada. All Rights Reserved. Latest Content Integrating AI-Driven Genomic Selection and Gene Editing for Precision Goat Breeding Yanlin Wang, Xiaofang Lin Animal Molecular Breeding, 2025, Vol. 15, No. 2, 49-59 Epigenetic Regulation of Growth and Feather Development in Ducks Jingya Li, Mengyue Chen Animal Molecular Breeding, 2025, Vol. 15, No. 2, 60-71 Advances in Disease Control and Immunity in Goats: A Comprehensive Review Xuezhong Zhang, Xinghao Li Animal Molecular Breeding, 2025, Vol. 15, No. 2, 72-81 Marker-Assisted Selection for Fast-Growth and High-Yield Tilapia Breeds Qiong Wang, Jinni Wu Animal Molecular Breeding, 2025, Vol. 15, No. 2, 82-90 Genetic Variability and Breeding Strategies for Key Traits During Channa Domestication Xuelian Jiang, Manman Li Animal Molecular Breeding, 2025, Vol. 15, No. 2, 91-101
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.
Animal Molecular Breeding, 2025, Vol.15, No.2, 49-59 http://animalscipublisher.com/index.php/amb 50 2 Genomic Selection in Goat Breeding 2.1 Concept and mechanism of genomic selection (GS) Lei et al. (2024) demonstrated that GS uses numerous markers across the entire genome and combines the expression data of animals to estimate their genetic breeding values. The 52K SNP chips specially designed for goats have also been widely used with the development of next-generation sequencing technology, and they can provide a lot of useful genetic information. Rupp et al. (2016) and Zhang et al. (2024) found that GS is more accurate and efficient than traditional methods because it takes into account both phenotypic data and genetic relationships among animals. GS is particularly practical for some goat populations with relatively small breeding scales and immature systems. 2.2 Applications in goat traits of interest Genomic selection (GS) has been used to improve many important traits of goats, such as milk production, wool quality and meat yield. Scientists used SNP data from the whole genome to identify many DNA regions and genes related to these traits. These traits include fur color, adaptation to high altitudes, growth rate, fertility, milk protein content, etc. (Wang et al., 2016; Brito et al., 2017; Guo et al., 2018; Yan et al., 2022). For example, the KITLGand ASIP genes are related to fur color, while the EPAS1 gene is related to the ability to adapt to high altitudes. GS technology has also helped identify the “selection imprints” related to milk production and climate adaptation, which is very helpful for directed breeding (Figure 1) (Wang et al., 2016; Guo et al., 2018; Ghanatsaman et al., 2023). Some simulation studies have also found that if medium-density SNP chips, such as 45K chips, are used in combination with a reference population of approximately 1 500 goats, the genomic breeding values (GEBV) of traits such as fiber thickness and body weight can be predicted relatively accurately (Yan et al., 2022). Figure 1 A: putative sweep area (chr. 10, 55.02~55.04 Mb) is approved by π test (The figure was drawn using VCFtools commands (version 0.1.17) and R software environment). B: The patterns of haplotype distribution for VPS13C loci in all 140 goats. The existence of homozygosity and heterozygosity is colored in brown and intermediate brown, respectively. The absence of the derived allele is shown in white. Missing- genotyped regions or individuals are shown in gray (The figure was drawn using Beagle (version 4.0), R software environment and python scripts (our in-home script was used)) (Adopted from Ghanatsaman et al., 2023)
Animal Molecular Breeding, 2025, Vol.15, No.2, 49-59 http://animalscipublisher.com/index.php/amb 51 2.3 Challenges and opportunities in goat genomic selection Yan et al.’s research in 2022 found that the cost of genotyping is relatively high, the number of reference sheep is insufficient, and in many cases, complete performance data and DNA information are lacking. The prediction accuracy of GEBV is affected by factors such as the number of markers, the size of the reference population, and whether the trait itself is prone to inheritance. If more female goats can be added as reference individuals in the study, the accuracy rate will be improved to some extent. However, if the total number of reference goats is insufficient, this improvement will become limited. The research conducted by Brito et al. (2017) and Bertolini et al. (2018) pointed out that due to the complex genetic structure of goats, they are easily influenced by human breeding and the selection of the natural environment. Therefore, such situations also need to be taken into account during breeding. The research by Rupp et al. (2016) and Zhang et al. (2024) indicates that sequencing technology is developing rapidly nowadays, and the world is also collaborating to develop SNP chips with unified standards. These advancements may bring new opportunities for the application of GS in goat breeding. 3 Gene Editing Tools for Trait Improvement 3.1 Mainstream gene editing technologies With the development of various precise genome editing technologies, rapid progress has been made in the genetic improvement of domestic animals (including goats). The commonly used editing tools at present include zinc finger nucleases (ZFNs), TALENs and the CRISPR/Cas9 system. Although ZFNs and TALENs were the first to achieve site-directed gene modification, they were less applied due to their complex operation and high cost. In contrast, CRISPR/Cas9 has become the most commonly used gene editing tool at present due to its simplicity, high efficiency, low cost and wide application range, and can be used to achieve gene knockout, insertion and base editing, etc. (Menchaca et al., 2016; Bhat et al., 2017; Ruan et al., 2017). In recent years, some new methods have emerged, such as the ISDra2-TnpB system (for site-specific integration of regulatory sequences), base editing and prime editing. These techniques have further expanded the means of improving livestock traits (Dhakate et al., 2022; Feng et al., 2024; Lu et al., 2024). 3.2 Current status of gene editing in caprine species Menchaca et al. (2016) knocked out the MSTN and FGF5 genes using CRISPR/Cas9 technology and bred goats with more developed muscles or changed hair characteristics. The success rate of editing a single gene is approximately 21%, and the success rate of knocking two genes simultaneously can also reach 10%. In the same year, Feng et al. (2024) and Lu et al. (2024) used a new tool called ISDra2-TnpB. They precisely inserted DNA fragments that regulate inflammation into a gene promoter called lysozyme. The dairy goats they raised would be more resistant to mastitis and have better health conditions. 3.3 Ethical and regulatory considerations The application of gene editing technology in goats and other domestic animals may lead to "off-target effects", and there are also concerns regarding animal welfare. If these animals are released into the natural environment, it may bring ecological risks. In many regions, management is still not comprehensive enough. Issues such as "How to classify gene-edited animals" and "whether they can be used for commercial purposes" remain undetermined. From an ethical perspective, it is also debatable whether people can accept non-therapeutic improvements (such as appearance). Bhat et al. (2017), Ruan et al. (2017), and Lu et al. (2024) all hold that in the future, in addition to technological progress, risk assessment and communication with the public must also advance simultaneously. 4 Artificial Intelligence in Livestock Genomics 4.1 Machine learning (ML) and deep learning (DL) approaches Common machine learning methods such as random forest, support vector machine, and convolutional neural network have been used to predict many important livestock traits such as carcass characteristics and susceptibility to diseases (Liang et al., 2020; Chafai et al., 2023; Hay, 2024). Deep learning is very good at identifying complex and less intuitive patterns from genomic data and performance data. In the research of Novakovsky et al. (2022), a new technology called "explainable Artificial Intelligence" (xAI) is also being
Animal Molecular Breeding, 2025, Vol.15, No.2, 49-59 http://animalscipublisher.com/index.php/amb 52 developed. It can help researchers understand more clearly how these complex models make judgments and extract biologically significant explanations. 4.2 AI applications in genotype-phenotype prediction At present, many kinds of machine learning algorithms have been used to predict the carcass characteristics, growth rate and health status of livestock. The effects of these AI models are sometimes even better than those of traditional linear models (Liang et al., 2020; Srivastava et al., 2021; Chafai et al., 2023). Morota et al. (2022) and Dórea and Menezes (2024) demonstrated that AI is also frequently employed in the collection of phenotypic data. Researchers can monitor the growth, behavior and health of animals in real time through computer vision technology and wearable sensors, and can collect high-quality data quickly and accurately. Ferreira et al. (2024) found that AI can also integrate various data from different sources, such as sensor information and images, to enhance the accuracy of predictions and facilitate the early detection of health issues. 4.3 Advantages of AI in handling complex data sets Machine learning and deep learning models can well capture various relationships in data, whether they are simple linear relationships or more complex nonlinear patterns. They can extract useful genetic information from very large SNP data and also cope with some non-additive genetic influences (Liang et al., 2020; Srivastava et al., 2021; Hay, 2024). Ferreira et al. (2024) demonstrated that AI-driven data fusion technology can integrate data from different sources, enabling more accurate and timely analysis. AI also makes it possible to collect large-scale phenotypic data of animals without disturbing them, as it can automatically complete many links, such as checking data quality and selecting key features, improving the efficiency and sustainability of livestock breeding (Morota et al., 2022; Dórea and Menezes, 2024; Spangler, 2024). 5 Framework for Integrating AI with Genomic Selection 5.1 AI-enhanced genomic prediction pipelines New advancements in AI make it easier for us to identify complex genetic structures that are difficult to understand with traditional linear models, such as nonlinear relationships and interactions between genes. Adding AI methods to the genome selection process can combine a large amount of genomic and phenotypic data and improve the prediction accuracy of breeding values. With deep learning and other advanced machine learning methods, AI models can handle ultra-large-scale and multi-dimensional data, making genomic breeding faster and more accurate. This method is particularly useful for traits that are controlled by many genes and are easily affected by the environment. It is very likely to break the traditional breeding methods and accelerate the improvement speed of domestic animal breeds such as goats (Figure 2) (Bhat et al., 2023). 5.2 Data sources and preprocessing strategies Efficient AI-driven genome selection (GS) relies on diverse and high-quality data resources, such as high-density SNP chips, whole-genome sequencing data, and rich phenotypic records. Take goats as an example. The 52K SNP chip developed by the International Goat Genome Consortium has been widely used in genome-wide association studies (GWAS) and GS studies (Rupp et al., 2016). Before modeling, data preprocessing is very crucial, usually including the quality control of genotype data, filling in missing values, standardization of phenotypic data, and integration of multi-omics information. Simulation studies show that using medium-density SNP panels (such as 45K SNPs) combined with a moderate-sized reference population (approximately 1 500) can effectively improve the prediction accuracy of goat GEBV. The accuracy of GEBV is also related to the heritability of the trait and the number of RAMS in the reference population. For traits with medium heritability, if the reference population size is large, the prediction effect will be better (Yan et al., 2022). 5.3 Comparison with traditional BLUP and GBLUP models BLUP uses family and phenotypic data, while GBLUP adds DNA information on this basis. GBLUP is particularly suitable when there is not much phenotypic data, and its predictive effect will be more accurate. The later emerged single-step GBLUP (ssGBLUP) integrates genotype, phenotype and lineage data all together, and its predictive accuracy is slightly higher than that of GBLUP. The research results of Yan et al. (2022) on dairy goats
Animal Molecular Breeding, 2025, Vol.15, No.2, 49-59 http://animalscipublisher.com/index.php/amb 53 show that compared with the traditional BLUP model, the accuracy of predicting yield-related traits with GBLUP and ssGBLUP has increased by 10% to 13%. Bhat et al. (2023) and Negro et al. (2024) hold that although AI models are better at handling complex gene interactions and have the potential to further enhance predictive performance, these models have high requirements for computing power and still need to undergo rigorous verification before truly replacing traditional methods. Figure 2 Potential of artificial intelligence (AI)-based machine-learning (ML) and deep-learning (DL) models in genome-wide associationstudies (GWAS) and genomic selection (GS) analyses. The AI-based models capture linear and nonlinear interactions in GWAS and GS for use incrop breeding; MTAs represent the marker-trait associations (Adopted from Bhat et al., 2023) 6 Framework for Integrating AI with Gene Editing 6.1 AI for target site identification and efficiency prediction AI has been used in plant breeding to predict what impact a certain genetic variation will have on external performance, helping scientists better design gene editing. Farooq et al. (2024) hold that this method is actually also applicable to many animals such as goats. AI is very strong in pattern recognition and big data analysis. Researchers can use it to predict more accurately which parts will be edited correctly and also discover potential "off-target" problems. AI can also help optimize the design of gRNA in CRISPR/Cas9, improve editing efficiency and reduce the occurrence of unexpected mutations (Zhang et al., 2018; Zhang et al., 2019). 6.2 Functional genomics and AI With the development of high-throughput sequencing technology and SNP chips, scientists have obtained a large amount of genetic data. Subsequently, AI can conduct in-depth analysis of these data to help identify which genetic variations may be related to important traits such as disease resistance and reproductive ability (Rupp et al., 2016; Farooq et al., 2024). AI algorithms can also narrow the gap between genotypes and phenotypes, and identify candidate genes and regulatory elements that may affect traits. 6.3 Designing gene editing strategies with AI assistance Artificial intelligence (AI) can integrate genomic, expression and functional data to help design and optimize gene editing programs. AI can prioritize the selection of appropriate editing targets based on the influence of target traits and safety requirements, which is of great value for breeding more disease-resistant or high-yielding goats (Li et al., 2024). AI models can also simulate different gene editing schemes, helping researchers select the most
Animal Molecular Breeding, 2025, Vol.15, No.2, 49-59 http://animalscipublisher.com/index.php/amb 54 suitable tools, such as CRISPR/Cas9 or ISDra2-TnpB, and predict their editing efficiency and accuracy (Zhang et al., 2019; Farooq et al., 2024). Doing so can arrange experiments more scientifically, such as conducting gene knockout and knock-in simultaneously, to improve some specific traits, such as making goats more disease-resistant, or enabling them to produce more of a certain protein (Figure 3) (Zhang et al., 2018; Feng et al., 2024; Li et al., 2024). Figure 3 Cas9-mediated HNP1 knock-in in goats at the CSN2locus (Adopted from Li et al., 2024) Image caption: (a) Schematic diagram of experiment design for HNP1 insertion into goat CSN2. sgRNA left and right were designed to target exon 7 of CSN2. The plasmid PUC57-HNP1 with the left arm (LA), 2A-HNP1, and the right arm (RA) was used as a homologous repair template for HNP1 insertion. Four primer sets were utilized for genome editing detection. In wild type (WT) and CSN2 KO goats (repaired through the NHEJ method), only a 420 bp fragment could be amplified using the T7E1 primer sets. In HNP1-inserted goats (repaired through HDR method), an additional 813 bp fragment could be amplified. The other three primer sets, HDR_LA, HDR_RA, and HNP1, only amplified the expected fragments in HNP1-inserted goats. (b) PCR analysis using the four primer sets. (c) The two goats (H2 and P2) with HNP1 insertion at CSN2 locus. (d) TA clone sequencing results of HDR_LA and HDR_RA amplicons. * following the T2A-HNP1, indicating the stop codon (Adopted from Li et al., 2024) 7 Case Study: AI-Assisted Genomic Selection in Dairy Goat Breeding 7.1 Background and breeding objectives The breeding of dairy goats is mainly aimed at enhancing economic traits such as milk production and milk protein, as well as body shape characteristics that affect the lifespan and production efficiency of goats. Most traditional methods rely on pedigree and expression data for seed selection. However, if the heritability of some
Animal Molecular Breeding, 2025, Vol.15, No.2, 49-59 http://animalscipublisher.com/index.php/amb 55 traits is low or there is a lack of expression records, it is difficult to select accurately and the progress will also slow down. Combining genomic selection with reproductive techniques such as artificial insemination is now regarded as a relatively effective approach, as it can not only accelerate the breeding speed, especially in complex environments, but also improve economic benefits (Gore et al., 2021; Massender et al., 2022; Negro et al., 2024). 7.2 Model implementation and results The latest research has found that after integrating lineage, phenotype and genomic data with single-step genomic BLUP and genomic BLUP, the breeding value (EBVs) can be estimated more accurately. The research results of Negro et al. (2024) show that in the dairy goats of Saanen and Alpine in Italy, the breeding values predicted by ssGBLUP have increased by approximately 10% to 13% compared with the traditional BLUP. The results calculated by the genomic method are also highly consistent with the traditional method in terms of milk production traits. Massender et al. (2022) demonstrated that in Canada, after genomic selection of similar goat breeds using single-breed or multi-breed models, the prediction accuracy of body shape traits increased by an average of 32% to 41%, and the improvement was more significant in individuals without expression data. Gore et al. (2021) conducted a simulation study on dairy goats in tropical regions, which demonstrated that combining genomic selection with artificial insemination significantly enhanced annual genetic progress, economic benefits, and overall profits. The greatest genetic improvement was achieved when the core breeding population accounted for 14% to 16% of the total population. 7.3 Lessons learned and practical takeaways The application of AI-driven genome selection methods in dairy goat breeding not only improves the accuracy of breeding value prediction, but also accelerates the speed of genetic improvement and brings better economic benefits. Research by Massender et al. (2022) and Negro et al. (2024) indicates that it is particularly suitable for situations where there is a lack of performance data or where the animals themselves do not exhibit certain traits. Gore et al. (2021) stated that if combined with artificial insemination and other reproductive techniques, the breeding efficiency and profitability could be further enhanced, which would be more beneficial for regions with less abundant resources. Massender et al. (2022) hold that in niche varieties with a small sample size, multi-variety genomic models also have advantages. They can reduce prediction errors and make the results more accurate. 8 Socioeconomic and Ethical Implications 8.1 Impact on smallholder farming and genetic diversity Manirakiza et al. (2020) found that in some community breeding projects, small-scale farmers, due to the need to sell sheep for money, were unable to persist in long-term participation in breeding programs, and they might also lack the experience and resources to manage large breeding groups. In order to make these projects more sustainable, it is necessary to strengthen the construction of breeders' associations and help small-scale farmers broaden their income sources at the same time. In this way, they will be more motivated to participate in the long term and can truly benefit from it. Wang et al. (2016) and Ncube et al. (2025) hold that although genomic selection helps to enhance disease resistance, adaptability, etc., if only a few economic traits are focused on, it may lead to a deterioration of the genetic diversity of the entire variety. 8.2 Public perception and consumer trust Many people have concerns about animal welfare, food safety, and whether gene-edited animals are natural. These concerns can also affect their attitudes towards gene editing and AI breeding technologies, as well as whether the market accepts these products. To make the public accept these new technologies, it becomes very important to communicate openly and transparently. The benefits of these technologies, possible risks, and existing regulatory measures all need to be clearly explained, which is conducive to building public trust. Nielsen (2022) indicates that some independent ethical institutions point out that when promoting the application of new technologies to farm animals, both the technology itself and whether it conforms to social values and whether it is truly beneficial to the public need to be taken into account.
Animal Molecular Breeding, 2025, Vol.15, No.2, 49-59 http://animalscipublisher.com/index.php/amb 56 8.3 Regulatory pathways and international guidelines The regulatory systems related to the application of AI-driven genome selection and gene editing in animal breeding that are gradually being established and improved by various countries and international organizations play a key role in promoting the rational and safe use of technologies. A good regulatory framework, while encouraging technological innovation, should also take into account ethical issues, biosecurity and public interests, and find a balance point among them. Feng et al. (2024) used precise gene editing to breed disease-resistant goats, which further demonstrates the importance of formulating clear regulatory guidelines, not only to ensure safety but also to address ethical concerns in society. Nielsen (2022) indicates that it is hoped that international organizations and regulatory authorities of various countries can take the lead in promoting the unification of standards, encourage more communication and cooperation among researchers, farmers and consumers, and further guide the promotion and application of cutting-edge technologies in animal husbandry in a more responsible way. 9 Future Prospects and Research Directions 9.1 Precision livestock farming and real-time genomics Jones and Wilson (2022) demonstrated that genomic sequencing, gene annotation, and editing technologies are becoming increasingly advanced. Coupled with the support of AI analysis and cloud computing platforms, this not only enhances production efficiency but also improves animal health and welfare, while reducing environmental impact. McLean et al. (2020) found that AI-supported real-time genomic technology can quickly identify and select good traits, accelerate the genetic improvement process, make breeding more precise, and also superimpose multiple useful genetic variations in a generation of goats to enhance breeding effectiveness. 9.2 AI and gene editing synergies in developing countries With the development of technologies such as next-generation sequencing and genome-wide association studies, scientists have begun to identify genes related to important traits such as meat and milk production in local goat breeds. Ncube et al. (2025) suggests that integrating AI and gene editing technology into traditional breeding methods is expected to optimize the entire goat production system, not only improving meat quality but also enabling goats to better adapt to harsh climates such as heat or drought. But at present, researchers do not know enough about the genetic diversity of local varieties, and more investment is needed for research to ensure that these technologies can truly benefit small-scale farmers (Bishop and Van Eenennaam, 2020; Ncube et al., 2025). Bishop and Van Eenennaam (2020) hold that the significant differences in regulatory rules among different countries have affected the fair promotion of technologies. Moreover, in order to promote the wider and better use of these technologies in developing regions, it becomes extremely important to strengthen technical training and local capacity building. 9.3 Education, policy, and public engagement If these new technologies can be considered from multiple aspects such as ethics, society and regulation together, it will be easier for society to accept them and ensure their responsible use. The concept of “Sociotechnical imaginaries” is very important. It refers to the vision and values of the future of technology jointly held by the industry, scientific researchers and the public, which will affect the development direction and management methods of gene editing. The previous research indicates that to achieve these goals, it is necessary to maintain transparent communication, widely listen to the voices of different groups, involve all stakeholders, promote the coordination and unification of international standards, build public trust, and strengthen ethical supervision. And provide guidance for policy-making. Acknowledgments The authors appreciates the comments from two anonymous peer reviewers on the manuscript of this study and thank the team members for helping to sort out the literature materials.
Animal Molecular Breeding, 2025, Vol.15, No.2, 49-59 http://animalscipublisher.com/index.php/amb 57 Conflict of Interest Disclosure The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Bertolini F., Servin B., Talenti A., Rochat E., Kim E., Oget C., Palhière I., Crisà A., Catillo G., Steri R., Amills M., Colli L., Marras G., Milanesi M., Nicolazzi E., Rosen B., Van Tassell C., Guldbrandtsen B., Sonstegard T., Tosser-Klopp G., Stella A., Rothschild M., Joost S., and Crepaldi P., 2018, Signatures of selection and environmental adaptation across the goat genome post-domestication, Genetics, Selection, Evolution, 50: 57. https://doi.org/10.1186/s12711-018-0421-y Bhat J., Feng X., Mir Z., Raina A., and Siddique K., 2023, Recent advances in artificial intelligence, mechanistic models and speed breeding offer exciting opportunities for precise and accelerated genomics-assisted breeding, Physiologia Plantarum, 175(4): e13969. https://doi.org/10.1111/ppl.13969 Bhat S., Malik A., Ahmad S., Shah R., Ganai N., Shafi S., and Shabir N., 2017, Advances in genome editing for improved animal breeding: a review, Veterinary World, 10(11): 1361-1366. https://doi.org/10.14202/vetworld.2017.1361-1366 Bishop T., and Van Eenennaam A., 2020, Genome editing approaches to augment livestock breeding programs, Journal of Experimental Biology, 223: jeb207159. https://doi.org/10.1242/jeb.207159 Brito L., Kijas J., Ventura R., Sargolzaei M., Porto-Neto L., Cánovas Á., Feng Z., Jafarikia M., and Schenkel F., 2017, Genetic diversity and signatures of selection in various goat breeds revealed by genome-wide SNP markers, BMC Genomics, 18: 229. https://doi.org/10.1186/s12864-017-3610-0 Chafai N., Hayah I., Houaga I., and Badaoui B., 2023, A review of machine learning models applied to genomic prediction in animal breeding, Frontiers in Genetics, 14: 1150596. https://doi.org/10.3389/fgene.2023.1150596 Dhakate P., Sehgal D., Vaishnavi S., Chandra A., Singh A., Raina S., and Rajpal V., 2022, Comprehending the evolution of gene editing platforms for crop trait improvement, Frontiers in Genetics, 13: 876987. https://doi.org/10.3389/fgene.2022.876987 Dórea J., and Menezes G., 2024, 462 artificial intelligence and machine learning to improve livestock farming, Journal of Animal Science, 102: 296. https://doi.org/10.1093/jas/skae234.338 Farooq M., Gao S., Hassan M., Huang Z., Rasheed A., Hearne S., Prasanna B., Li X., and Li H., 2024, Artificial intelligence in plant breeding, Trends in Genetics, 40(10): 891-908. https://doi.org/10.1016/j.tig.2024.07.001 Feng R., Zhao J., Zhang Q., Zhu Z., Zhang J., Liu C., Zheng X., Wang F., Su J., Ma X., Mi X., Guo L., Yan X., Liu Y., Li H., Chen X., Deng Y., Wang G., Zhang Y., Liu X., and Liu J., 2024, Generation of anti-mastitis gene-edited dairy goats with enhancing lysozyme expression by inflammatory regulatory sequence using ISDra2-TnpB system, Advanced Science, 11(38): 2404408. https://doi.org/10.1002/advs.202404408 Ferreira R., Balaguer M., Bresolin T., Chandra R., Rosa G., White H., and Dórea J., 2024, Multi-modal machine learning for the early detection of metabolic disorder in dairy cows using a cloud computing framework, Comput. Electron. Agric., 227: 109563. https://doi.org/10.1016/j.compag.2024.109563 Ghanatsaman Z., Mehrgardi A., Nanaei A., and Esmailizadeh A., 2023, Comparative genomic analysis uncovers candidate genes related with milk production and adaptive traits in goat breeds, Scientific Reports, 13: 8722. https://doi.org/10.1038/s41598-023-35973-0 Gore D., Okeno T., Muasya T., and Mburu J., 2021, Improved response to selection in dairy goat breeding programme through reproductive technology and genomic selection in the tropics, Small Ruminant Research, 200: 106397. https://doi.org/10.1016/j.smallrumres.2021.106397 Guo J., Tao H., Li P., Li L., Zhong T., Wang L., Ma J., Chen X., Song T., and Zhang H., 2018, Whole-genome sequencing reveals selection signatures associated with important traits in six goat breeds, Scientific Reports, 8: 10405. https://doi.org/10.1038/s41598-018-28719-w Hay E., 2024, Machine learning for the genomic prediction of growth traits in a composite beef cattle population, Animals, 14(20): 3014. https://doi.org/10.3390/ani14203014 Jones H., and Wilson P., 2022, Progress and opportunities through use of genomics in animal production, Trends in Genetics, 38(12): 1228-1252. https://doi.org/10.1016/j.tig.2022.06.014 Lei J., Xu Z.W., Shao X.W., Jiang H., and Zhang Y.M., 2024, Integrating GWAS and genomic selection to enhance soybean breeding, Legume Genomics and Genetics, 15(6): 270-279. https://doi.org/10.5376/lgg.2024.15.0026
Animal Molecular Breeding, 2025, Vol.15, No.2, 49-59 http://animalscipublisher.com/index.php/amb 58 Li D., Guo R., Chen F., Wang J., Wang F., and Wan Y., 2024, Genetically engineered goats as efficient mammary gland bioreactors for production of recombinant human neutrophil peptide 1 using CRISPR/Cas9, Biology, 13(6): 367. https://doi.org/10.3390/biology13060367 Liang M., Miao J., Wang X., Chang T., An B., Duan X., Xu L., Gao X., Zhang L., Li J., and Gao H., 2020, Application of ensemble learning to genomic selection in chinese simmental beef cattle, Journal of Animal Breeding and Genetics, 138(3): 291-299. https://doi.org/10.1111/jbg.12514 Lu Z., Zhang L., Mu Q., Liu J., Chen Y., Wang H., Zhang Y., Su R., Wang R., Wang Z., Lv Q., Liu Z., Liu J., Li Y., and Zhao Y., 2024, Progress in research and prospects for application of precision gene-editing technology based on CRISPR-Cas9 in the genetic improvement of sheep and goats, Agriculture, 14(3): 487. https://doi.org/10.3390/agriculture14030487 Manirakiza J., Moula N., Detilleux J., Hatungumukama G., and Antoine-Moussiaux N., 2020, Socioeconomic assessment of the relevance of a community-based goat breeding project in smallholding systems, Animal, 15(1): 100042. https://doi.org/10.1016/j.animal.2020.100042 Massender E., Brito L., Maignel L., Oliveira H., Jafarikia M., Baes C., Sullivan B., and Schenkel F., 2022, Single- and multiple-breed genomic evaluations for conformation traits in Canadian Alpine and Saanen dairy goats, Journal of Dairy Science, 105(7): 5985-6000. https://doi.org/10.3168/jds.2021-21713 McLean Z., Oback B., and Laible G., 2020, Embryo-mediated genome editing for accelerated genetic improvement of livestock, Frontiers of Agricultural Science and Engineering, 7: 148-160. https://doi.org/10.15302/j-fase-2019305 Menchaca A., Anegon I., Whitelaw C., Baldassarre H., and Crispo M., 2016, New insights and current tools for genetically engineered (GE) sheep and goats, Theriogenology, 86(1): 160-169. https://doi.org/10.1016/j.theriogenology.2016.04.028 Middelveld S., and Macnaghten P., 2021, Gene editing of livestock, Elementa: Science of the Anthropocene, 9(1): 00073. https://doi.org/10.1525/elementa.2020.00073 Morota G., Ha D., and Chen J., 2022, 19 how can artificial intelligence accelerate phenotyping efforts in animal breeding, Journal of Animal Science, 100: 11-12. https://doi.org/10.1093/jas/skac247.020 Ncube K., Nephawe K., Mpofu T., Monareng N., Mofokeng M., and Mtileni B., 2025, Genomic advancements in assessing growth performance, meat quality, and carcass characteristics of goats in Sub-Saharan Africa: a systematic review, International Journal of Molecular Sciences, 26(5): 2323. https://doi.org/10.3390/ijms26052323 Negro A., Cesarani A., Cortellari M., Bionda A., Fresi P., Macciotta N., Grande S., Biffani S., and Crepaldi P., 2024, A comparison of genetic and genomic breeding values in Saanen and Alpine goats, Animal, 18(4): 101118. https://doi.org/10.1016/j.animal.2024.101118 Nielsen B., 2022, Genome editing and farmed animal breeding: social and ethical issues, Animal Welfare, 31(1): 156. https://doi.org/10.1017/s0962728600009878 Novakovsky G., Dexter N., Libbrecht M., Wasserman W., and Mostafavi S., 2022, Obtaining genetics insights from deep learning via explainable artificial intelligence, Nature Reviews Genetics, 24: 125-137. https://doi.org/10.1038/s41576-022-00532-2 Raza S., Hassanin A., Pant S., Bing S., Sitohy M., Abdelnour S., Alotaibi M., Al-Hazani T., El-Aziz A., Cheng G., and Zan L., 2021, Potentials, prospects and applications of genome editing technologies in livestock production, Saudi Journal of Biological Sciences, 29: 1928-1935. https://doi.org/10.1016/j.sjbs.2021.11.037 Ruan J., Xu J., Chen-Tsai R., and Li K., 2017, Genome editing in livestock: are we ready for a revolution in animal breeding industry, Transgenic Research, 26: 715-726. https://doi.org/10.1007/s11248-017-0049-7 Rupp R., Mucha S., Larroque H., McEwan J., and Conington J., 2016, Genomic application in sheep and goat breeding, Animal Frontiers, 6: 39-44. https://doi.org/10.2527/AF.2016-0006 Spangler M., 2024, 461 machine learning and AI to improve genetic prediction in beef cattle: potential uses and misuses, Journal of Animal Science, 102: 296-297. https://doi.org/10.1093/jas/skae234.339 Srivastava S., Lopez B., Kumar H., Jang M., Chai H., Park W., Park J., and Lim D., 2021, Prediction of Hanwoo cattle phenotypes from genotypes using machine learning methods, Animals, 11(7): 2066. https://doi.org/10.3390/ani11072066 Wang X., Liu J., Zhou G., Guo J., Yan H., Niu Y., Li Y., Yuan C., Geng R., Lan X., An X., Tian X., Zhou H., Song J., Jiang Y., and Chen Y., 2016, Whole-genome sequencing of eight goat populations for the detection of selection signatures underlying production and adaptive traits, Scientific Reports, 6: 38932. https://doi.org/10.1038/srep38932
Animal Molecular Breeding, 2025, Vol.15, No.2, 49-59 http://animalscipublisher.com/index.php/amb 59 Yan X., Zhang T., Liu L., Yu Y., Yang G., Han Y., Gong G., Wang F., Zhang L., Liu H., Li W., Yan X., Mao H., Li Y., Du C., Li J., Zhang Y., Wang R., Lv Q., Wang Z., Zhang J., Liu Z., Wang Z., and Su R., 2022, Accuracy of genomic selection for important economic traits of cashmere and meat goats assessed by simulation study, Frontiers in Veterinary Science, 9: 770539. https://doi.org/10.3389/fvets.2022.770539 Yang B., Yuan Y., Zhou D., Ma Y., Mahrous K., Wang S., He Y., Duan X., Zhang W., and E G., 2021, Genome-wide selection signal analysis of Australian Boer goat reveals artificial selection imprinting on candidate genes related to muscle development, Animal Genetics, 52(4): 550-555. https://doi.org/10.1111/age.13092 Zhang J., Cui M., Nie Y., Dai B., Li F., Liu D., Liang H., and Cang M., 2018, CRISPR/Cas9-mediated specific integration of fat-1 at the goat MSTNlocus, The FEBS Journal, 285(15): 2828-2839. https://doi.org/10.1111/febs.14520 Zhang J., Liu J., Yang W., Cui M., Dai B., Dong Y., Yang J., Zhang X., Liu D., Liang H., and Cang M., 2019, Comparison of gene editing efficiencies of CRISPR/Cas9 and TALEN for generation of MSTNknock-out cashmere goats, Theriogenology, 132: 1-11. https://doi.org/10.1016/j.theriogenology.2019.03.029 Zhang L., Duan Y., Zhao S., Xu N., and Zhao Y., 2024, Caprine and ovine genomic selection- progress and application, Animals, 14(18): 2659. https://doi.org/10.3390/ani14182659
Animal Molecular Breeding, 2025, Vol.15, No.2, 60-71 http://animalscipublisher.com/index.php/amb 60 Review Article Open Access Epigenetic Regulation of Growth and Feather Development in Ducks Jingya Li, Mengyue Chen Animal Science Research Center, Cuixi Academy of Biotechnology, Zhuji, 311800, Zhejiang, China Corresponding author: mengyue.chen@cuixi.org Animal Molecular Breeding, 2025, Vol.15, No.2 doi: 10.5376/amb.2025.15.0007 Received: 01 Feb., 2025 Accepted: 05 Mar., 2025 Published: 20 Mar., 2025 Copyright © 2025 Li and Chen, 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 J.Y., and Chen M.Y., 2025, Epigenetic regulation of growth and feather development in ducks, Animal Molecular Breeding, 15(2): 60-71 (doi: 10.5376/amb.2025.15.0007) Abstract Duck is an important economic poultry, and its growth rate and feather development are directly related to meat and poultry yield and feather quality. This study reviews the latest progress in recent years on epigenetic mechanisms such as DNA methylation, histone modification and non-coding RNA during duck growth and feather development. Literature shows that DNA methylation plays an important regulatory role in the muscle growth and metabolism of duck embryos, and environmental factors such as temperature and nutrition can affect the growth performance of ducks by changing the methylation state. In terms of feather development, epigenetic mechanisms mediate gene expression reprogramming during the transition from primary down feather to mature feathers, and miRNA and lncRNA are involved in complex regulatory networks of feather follicle formation and feather growth cycle. In summary, duck growth and feather development are regulated by multi-level epigenetic regulation, including DNA methylation remodeling development-related gene expression, histone modifications to alter chromatin status, and non-coding RNA-mediated post-transcriptional regulation. A deep understanding of these mechanisms helps to reveal the molecular basis of differences in duck growth and feather morphology, and provides new ideas for improving poultry breeding and production performance. Keywords Duck feather development; DNA methylation; Histone modification; Non-coding RNA; Epigenetic regulation 1 Introduction Duck occupies an important position in the global livestock and poultry industry and is one of the main sources of meat and down products. With the development of modern breeding, breeding work has significantly improved the growth rate and meat production performance of ducks, but it also brought about certain physiological development problems, such as shortening of the growth period accompanied by incomplete muscle development and incomplete feathers during slaughter (Chen et al., 2017). Therefore, on the basis of ensuring the results of genetic selection and breeding, it is of great significance to conduct in-depth research on the molecular mechanisms that affect duck growth and feather development. Recent studies have found that epigenetics provides a new perspective for understanding how genes and the environment work together on traits. Epigenetic mechanisms can regulate gene expression without changing DNA base sequences, resulting in phenotypic differences under conditions of constant genotype (Sepers et al., 2019). Epigenetics mainly includes DNA methylation, histone covalent modification (such as acetylation, methylation, etc.), and non-coding RNA-mediated regulation of gene expression (Huang et al., 2025). DNA methylation usually occurs on the CpG dinucleotides in the promoter region of the gene, and the increased methylation level is often associated with gene silencing; post-translational modification of histones changes the degree of chromatin tightness, thereby affecting the accessibility of transcription factors to genes. In addition, non-coding RNAs such as microRNAs (miRNAs), long-chain non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) can finely regulate gene function at the post-transcriptional or translational level by interacting with target mRNAs or proteins (Chen et al., 2017). Together, these epigenetic mechanisms form a complex regulatory network, which plays an important role in the processes of duck muscle growth, fat deposition, gonad development, and feather morphology construction.
Animal Molecular Breeding, 2025, Vol.15, No.2, 60-71 http://animalscipublisher.com/index.php/amb 61 Based on the above background, this study will review important research progress in the field of epigenetic regulation in recent years based on the two themes of duck growth and feather development. We will explore the role of epigenetic mechanisms in the growth process of ducks (including muscle and skeletal development, metabolic regulation, etc.) in the embryonic and growth fattening stages; focus on explaining the epigenetic regulation of duck feather development (hair follicle generation, feather differentiation and replacement); aim to systematically summarize the current research status of epigenetic regulation of duck growth and feather development, and provide a scientific basis for the future application of epigenetic principles in poultry production. 2 Overview of Duck Growth and Feather Development 2.1 Characteristics of growth and development stages Ducks grow and develop through multiple stages in the embryonic and postnatal periods. Duck embryos can hatch out of the shell after developing in fertilized eggs for about 28 days. The embryo development speed and tissue differentiation of the fertilized eggs are affected together with the nutrition of the fertilized eggs and the external hatching environment. After being released from the shell, the meat duck can reach the market weight at about 58 weeks of age. This rapid growth is due to breeding improvement and efficient feeding, but is also closely related to endocrine regulation and epigenetic mechanisms. During the growth of ducks, the number of skeletal muscle fibers is mainly determined during the embryonic stage, while the growth and hypertrophy of muscle fibers are achieved after birth through satellite cell proliferation and fusion. The study found that the critical period of skeletal muscle development in the embryonic stage of duck (such as embryonic age of 13 to 19 days) corresponds to specific microRNA and gene expression peaks, indicating a developmental stage-specific gene regulation network. After the shelling, the muscle and adipose tissue of the duck chicks will change significantly with the increase of day age: the growth rate is the fastest before 4 weeks of age, and then the weight gain gradually slows down and matures (Chen and Li, 2024). In this process, endocrine factors such as the growth hormone (GH)-IGF axis play a role, and the epigenetic state of the gene (such as the methylation level of the growth-related gene promoter) is also changing dynamically (Cong et al., 2023). 2.2 Feather formation and periodic changes The feather development of ducks has its own unique rules. Ducklings are covered with down feathers when they come out of their shells, which are mainly used for insulation. In subsequent growth, ducks will undergo a replacement process from down feather to child feather and then to adult feather, which is equivalent to the hair replacement cycle of mammals. Studies have shown that ducklings gradually grow primary flying feathers and body covering feathers within a few weeks after birth, and the primary down feathers are replaced with more functional juvenile feathers for simple flight and stronger insulation (Lu et al., 2024). As sexually mature, ducks also develop reproductive plumes, showing seasonal or gender-different feather morphology and color. Periodic growth and defecation of feathers (feathering) usually occurs 1 to 2 times a year in waterfowls such as ducks and others, and is mostly performed after the breeding season. Feather formation is driven by stem cell proliferation and differentiation within feather follicles (Figure 1). Feather development includes processes such as feather bud formation, feather axis and feather sheet differentiation, keratin deposition, etc., which are precisely controlled by a series of gene regulatory pathways, such as Wnt/β-catenin signal promoting feather primordial formation, BMP signal limiting feather spacing, and Shh signal mediating feather branch branches, etc. Feather development processes are also affected by epigenetic regulation. For example, the chromatin open state of the promoter of specific genes of feather stem cells is closely related to its differentiation potential. The color pattern of feathers also depends on the distribution of melanocytes in the embryonic stage and the expression of related genes, which may be affected by epigenetic factors (such as pigment gene promoter methylation, non-coding RNA regulation, etc.) (Twumasi et al., 2024). Therefore, feather development is a dynamic and complex process with clear stages and periodicity, and behind it involves the exquisite regulation of developmental biology and epigenetics.
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