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.

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 KITLG and 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).

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 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 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.

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 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).

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 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.

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.

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.

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