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
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