Fig. 1. Evolution of Genetic Selection in Livestock.

2           Traditional Genetic Selection

The concept of improving farm animals through breeding started long before DNA was discovered. Animal owners simply looked at the animals and chose the ones with the best traits. They wanted bigger size, more milk, stronger body, or better fertility. By letting these animals reproduce, they managed to pass on the good qualities to the next generations (Nicholas, 1987). This method is known as phenotypic selection. It was reasonably effective when the traits were easy to measure and strongly inherited, but it was less reliable for other traits.

In the early 1900s, the situation began to change. The principles of Mendel and the rise of statistical genetics gave breeders better tools. They could now measure heritability and predict the amount of progress. This advancement led to the development of selection indexes. These indexes used information from several traits at once, which made decisions more balanced (Falconer & Mackay, 1996).

A real turning point was the development of Best Linear Unbiased Prediction, or BLUP. This method separated the genetic signal from the noise of the environment. It gave more accurate breeding values, which was a big improvement (Henderson, 1975). BLUP quickly became the standard, especially in dairy cattle. It improved milk yield, fertility, and also how long animals stayed productive.

Nevertheless, traditional selection methods had several limitations. Progress was slow, as breeders often had to wait years to observe results. In addition, these methods were ineffective for traits with low heritability, such as fertility and disease resistance, which are strongly influenced by environmental factors (Hill, 2010). Because of these problems, researchers started to look for new methods. This search led to molecular tools and genomic technologies, which opened the door to a new phase in animal breeding.

2.1         Phenotypic selection in early livestock

Phenotypic selection, in the simplest sense, refers to choosing animals for breeding because of the way they look or how they perform. Farmers would often select the bigger cow, the faster-growing calf, or the one that gave more milk, without having any genetic data or fancy records in hand (Bourdon, 2000). Basically, if an animal looked good and worked well, it was considered a good parent.

The challenge is that appearance can be misleading. For example, a calf may grow quickly due to better nutrition rather than superior genetics. Moreover, traits such as fertility or lifespan, which manifest later in life or are strongly influenced by environmental factors, were particularly difficult to improve using this method alone (Falconer & Mackay, 1996). Even with all these challenges, phenotypic selection formed the early stages of animal breeding. It gave us the traditional breeds and set the first standards for what people wanted in livestock. Interestingly, it’s still around today. In places where advanced tools like genomics are too expensive or unavailable, farmers still rely on it (Van Eenennaam, 2018). At the end of the day, even the most advanced technologies build on the same old idea: pick the animals that seem to do best, and hope the next generation carries those traits forward (Fig. 2).

Fig. 2. Cycle of Phenotypic Selection.

2.2         Selection indexes in genetic

Back in the early 1900s, breeders started experimenting with what we now call the selection index. The idea was quite practical: rather than picking animals for just one feature, like milk yield, they looked at a mix of traits and combined them into a single score (Hazel, 1943). In everyday practice, that meant a cow wasn’t judged only on how much milk she gave but also on things like fertility, fat levels in the milk, or udder health.

Think of it like this: each trait is given a certain weight, depending on how important it is, how strongly it’s inherited, and how it ties to other traits. Add them together and you get one number. That number makes comparisons easier. A cow with only average milk yield but excellent fertility and healthy udders could still rank higher than one that produces more milk but struggle to reproduce (Nicholas, 1987).

Over time, as farm records and pedigrees got better, indexes became more accurate and ended up forming the base of many national breeding programs. Even today, they haven’t disappeared. Instead, modern genomic data are often plugged straight into index systems, which makes them sharper but still grounded in the same old idea (VanRaden, 2008) (Fig. 3).

Fig. 3. Selection Index in Animal Breeding.

2.3         Best Linear Unbiased Prediction (BLUP)

‏The introduction of Best Linear Unbiased Prediction (BLUP) was a turning point in animal breeding. Developed in the mid-20^th century, it offered breeders a stronger statistical tool to estimate breeding values with much greater accuracy (Henderson, 1975). The key idea behind BLUP is separating what comes from genes and what results from the environment, which makes the estimates closer to the animal’s real genetic potential.

What sets BLUP apart is how it brings several factors together. It can adjust for fixed effects such as herd, year, or management practices while also considering random effects like genetic variation within a population. Pedigree information is part of the model as well, so even animals that are still young or have not yet shown a trait can be evaluated using family data (Mrode, 2014). This way, decisions become more reliable and genetic progress happens faster across generations.

Because of these strengths, BLUP was quickly adopted in national genetic evaluation programs, especially for dairy and beef cattle. It had a significant practical impact. There was a noticeable and significant economic return from these statistical advances, as evidenced by the U.S. dairy industry's extensive use of BLUP based animal models, which helped to rushing the average annual genetic gain for milk yield to over 100 kg per cow by the late 20th century (Shook, 2006). Its ability to process large and complex datasets made it ideal for modern breeding. Rather than being replaced, it evolved further. Today it forms the basis of genomic BLUP (GBLUP), where pedigree-based predictions are enhanced with genomic data for even sharper evaluations (VanRaden, 2008) (Fig. 4).

Fig. 4. BLUP Impact on Animal Breeding.

3           Genomic selection and modern

Over the past two decades, the process of genetic selection in farm animals has progressed into a new stage characterized by rapid technological growth and the application of molecular techniques. At the center of this is genomic selection (GS), a method that applies dense genome wide marker data to estimate an animal’s genetic potential with greater accuracy and at an earlier age (Meuwissen et al., 2001). Unlike conventional selection strategies that rely on visible traits and pedigree details, GS uses DNA information to calculate breeding values, improving efficiency and shortening the generation interval.

In dairy cattle, GS has become a core element of breeding programs. Through the use of thousands of single nucleotide polymorphisms (SNPs) spread across the genome, young calves can be evaluated shortly after birth, even in the absence of performance records (Hayes et al., 2009). This advancement has delivered faster genetic progress, higher prediction accuracy, and significant economic gains for the dairy industry worldwide (Pryce & Daetwyler, 2012).

Another important approach to support genetic selection is marker assisted selection (MAS). MAS focuses on specific genes or genomic regions linked to traits of interest such growth rate, carcass quality, or resistance to disease. It has proven effective in animals with long generation intervals, including cattle and sheep, where early selection is valuable (Dekkers, 2004).

In addition, the emergence of genome editing technologies, particularly CRISPR/Cas9, has introduced new possibilities for targeted genetic changes (Van Eenennaam, 2019). For example, pigs edited with this technique have been produced with resistance to PRRS (Porcine Reproductive and Respiratory Syndrome), showing how such methods can address major issues in animal health and welfare (Whitworth et al., 2016).

Moreover, the combination of big data, bioinformatics, and machine learning is transferring the ability to analyze large datasets involving genomic, phenotypic, and environmental factors. These tools enable precision breeding by supporting real-time decision-making and revealing new associations between traits (Wray et al., 2019).

However, these developments have improved both accuracy and efficiency of genetic selection, they also present challenges such as high implementation costs, the requirements for technical skills, and ongoing ethical debates surrounding genetic modification and data controls.

3.1         Genomic selection

‏Genomic selection (GS) is one of the major advances in animal breeding because it makes it possible to estimate breeding values using many molecular markers spread across the genome (Meuwissen, Hayes, & Goddard, 2001). Unlike older methods that depend on pedigree records or physical traits, GS uses genomic information directly, which improves accuracy and allows selection at a younger age. This speeds up genetic improvement (VanRaden, 2008). The method has been especially important in dairy cattle, where genomic data have led to stronger selection and shorter generation intervals (Wiggans, VanRaden, & Cooper, 2011). The results of implementing GS in the dairy industry were noticeable. A landmark study on the U.S. Holstein population by García-Ruiz et al. (2016) reported that after the adoption of genomics, the annual rate of genetic gain for milk yield more than doubled. Concurrently, the generation interval for sires of sons was nearly halved. This unprecedented acceleration of genetic progress, especially for historically difficult-to-improve traits like daughter pregnancy rate, characterizes one of the most significant achievements in the history of animal breeding. Beyond the dairy industry, the use of GS has expanded significantly into swine and poultry breeding. For example, in pigs, genomic selection has been shown to enhance the rate of genetic gain by approximately 9–56% in dam lines and 3.5–27% in sire lines, depending on the reference population size and selection strategy. These enhancements are seriously evident for complex traits such as feed efficiency, disease resistance, and reproductive performance (Sharif-Islam et al.,2024). Besides, the field is advancing over standard SNP chips towards using whole-genome sequence (WGS) data. while arithmetic intensive, WGS-based prediction models have the potential to reap the effects of unique genetic variants and costive mutations directly, with studies showing they can improve prediction accuracy by an additional 5–9% for specific traits compared to standard SNP panels (Al Kalaldeh et al., 2019). The idea behind GS is to use statistical models that link single nucleotide polymorphisms (SNPs) with traits of economic or biological value. In this way, genomic estimated breeding values (GEBVs) can be predicted even for young animals without phenotypic records (Habier, et al., 2007). This is useful for traits with low heritability, such as fertility and disease resistance, which were hard to improve with traditional selection (Goddard & Hayes, 2009). GS also helps in cases where collecting phenotypic data is costly or takes too much time.

Recent improvements in sequencing and bioinformatics have made GS more practical, even for small populations or breeding programs in developing nations (García-Ruiz et al., 2016). Still, there are challenges, such as the cost of genotyping, the need for large reference populations, and the difficulty of modeling complex gene interaction. Even so, GS has changed the way animal breeding is done and has become a key approach in genetic improvement programs around the world.

3.2         Marker-assisted selection (MAS)

‏Marker-assisted selection (MAS) is a molecular breeding approach that utilizes genetic markers closely linked to genes of interest to assist in the selection process. Unlike traditional selection methods that depend primarily on observable traits (phenotypes), MAS enables breeders to make decisions based on genotypic information, particularly for traits that are difficult, expensive, or time consuming to measure (Dekkers, 2004). This approach is especially valuable for improving traits with low heritability, such as disease resistance, fertility, and meat quality (Andersson & Georges, 2004). A classic success story for MAS is the control of scrapie in sheep. By identifying genetic markers for the PRNP gene associated with resistance, national breeding programs in countries like the United Kingdom and France successfully selected for resistant animals. This strategy led to a dramatic reduction in the prevalence of the disease, providing an example of how MAS can be used effectively to manage a major health issue in a livestock population (Agrimi et al., 2003). MAS relies on the identification of quantitative trait loci (QTL) or specific genes associated with economical important traits.  Reliable markers are identified; they can be used to select animals carrying favorable alleles even before the traits are expressed, thereby accelerating genetic gain and increasing selection efficiency (Collard & Mackill, 2008). This is useful in early selection of young animals and for traits with late expression or those that require animals sacrifice for measurements, such as carcass quality. Despite its potential, the implementation of MAS in livestock has faced challenges, such as the limited availability of robust for many traits and the costs of genotyping. However, as genotyping becomes more affordable and accurate, MAS is used in combination with genomic selection to improve the power of genetic improvement programs (Hayes et al., 2006) (Fig. 5).

Fig. 5. Marker-Assisted Selection Process.

4           Gene editing, AI, and sustainable selection

As the demand for animal products grows and issues such as climate change, food security, and animal welfare become more urgent, the future of genetic selection in farm animals is expected to be more advanced and carefully designed. A key direction is the use of gene editing tools like CRISPR/Cas9 in breeding programs. Traditional selection that depends on natural genetic variation, gene editing makes it possible to add or remove specific mutations with high accuracy (Van Eenennaam, 2019). This can help improve traits as disease resistance, heat tolerance, feed efficiency, and meat quality, leading to major changes in livestock genetics within only a few generations (Whitelaw & Lillico, 2022).

One example is the development of heat-tolerant dairy cattle through targeted edits, which could help keep production stable under rising global temperatures (Cuellar et al., 2024). In pigs and poultry, gene knockout approaches are also being used to improve resistance to diseases, which reduces antibiotic use and supports animal welfare (Whitelaw et al., 2016).

Besides gene editing, artificial intelligence (AI) and machine learning are becoming important tools in breeding. They can handle large genomic, phenotypic, and environmental datasets to detect complex trait interactions and improve selection decisions (Chafai et al., 2023). When combined with precision livestock farming, including sensor-based monitoring, AI allows real-time breeding decisions, especially in large herds (Si, Q., 2024).

Another important future goal is sustainable breeding, which focuses on balancing productivity with environmental and social needs. Programs now aim to lower methane emissions, improve feed conversion, and increase animal resilience, helping ensure that breeding progress supports sustainability (Boichard & Brochard, 2012). Concomitantly, genomic tools are being used to protect local and indigenous breeds, which hold valuable genetic diversity and adaptation traits.

Still, these future steps face challenges. Ethical concerns about gene editing, differences in regulations across countries, public opinion on genetically modified animals, and unequal access to advanced breeding tools between developed and developing regions remain major barriers (Van Eenennaam, 2019; Wolt et al., 2015).  The future of genetic selection is moving toward greater precision, faster progress, and stronger focus on sustainability. To reach its full potential, investment in research, infrastructure, and public awareness will be crucial.

4.1         CRISPR/Cas9 technology

‏CRISPR/Cas9 is now considered one of the most important tools in gene editing, with a major impact on animal breeding and genetics. This system makes it possible to change DNA in a way that is relatively low cost. By directly adding, deleting, or modifying genes, it provides a powerful way to improve traits in farm animals (Jinek et al., 2012). For example, CRISPR/Cas9 has been used to improve growth, feed efficiency, and resistance to diseases in livestock (Tan et al., 2016).

A well-known application is the creation of pigs resistant to porcine reproductive and respiratory syndrome virus (PRRSV) by knocking out the CD163 gene, which the virus needs to enter the cells (Whitworth et al., 2016). In cattle, gene edits have also been applied to produce naturally hornless animals, which avoids the need for dehorning and improves animal welfare (Carlson et al., 2016).

 Also, A noticeable recent application focuses on climate resilience. Researchers have successfully used CRISPR to introduce a specific mutation in the PMEL17 gene in cattle, resulting in a short, sleek hair coat known as the slick phenotype. This single edit has been proven to enhance heat tolerance, reducing core body temperature in hot climates and thereby preventing a drop in milk production and fertility (Osei-Amponsah et al., 2019). This work has moved over the research phase, with the U.S. Food and Drug Administration (FDA) making a low-risk determination in 2022 for the marketing of products from gene-edited slick cattle, paving the way for commercialization. Similarly, in early 2024, the FDA approved PRRS-resistant pigs developed by Genus PLC for human consumption, marking a critical regulatory milestone for the commercial adoption of gene-edited livestock (FDA, 2024). These examples represent tangible successes where genome editing has solved long-standing industry challenges, moving from theoretical potential to market-ready application.

Unlike traditional selection or marker-assisted breeding, CRISPR makes it possible to introduce useful genetic changes in a much shorter time, without waiting for many generations. Still, there are challenges, such as the risk of off-target edits, strict regulations, and ethical debates that must be resolved before large-scale use (Van Eenennaam, 2017). Even so, CRISPR/Cas9 is seen as a key tool for speeding up genetic progress, improving sustainability, and supporting better welfare in livestock production.

4.2         Gene knockout strategies in farm animals

‏Gene knockout refers to switching off or removing certain genes in order to understand their role or to improve traits in animals. In livestock breeding, this approach has been used to remove unwanted characteristics and to add advantages such as stronger disease resistance, better meat quality, or higher reproductive efficiency (Niemann & Kues, 2007).

Different tools are available for gene knockout, including zinc-finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs), and, more recently, CRISPR/Cas9. Among these, CRISPR has become the most widely used because it is easier to apply, more efficient, and less costly (Doudna & Charpentier, 2014). For instance, when the myostatin (MSTN) gene is knocked out in cattle or pigs, the animals develop more muscle, which increases lean meat production (Cyranoski, 2015).

This strategy can also help with animal welfare. An example is editing out the genes that cause horn growth in cattle, which avoids the painful process of dehorning. Similarly, fertility-related genes in males can be targeted to produce sterile animals, which can help manage populations or limit the spread of disease (Lillico et al., 2013).

Even though gene knockout has many benefits, it still carries risks. Unexpected effects may appear, and strict regulations exist to make sure the methods are safe for both animals and consumers. Because of this, careful evaluation is always needed before using it on a wide scale (Fig. 6).

Fig. 6. CRISPR/Cas Technology, Gene Knockout Strategies, and AI Machine Learning.

4.3         Artificial intelligence and machine learning in animal breeding

‏Artificial Intelligence (AI) and Machine Learning (ML) are bringing major changes to animal breeding by making it possible to analyze large and complex datasets. These tools help in evaluating genetics, predicting traits, and improving breeding programs (González-Recio et al., 2014).

With methods such as random forests, support vector machines, and deep learning, ML can combine genomic, phenotypic, and environmental data at the same time. This gives more accurate predictions of breeding values than traditional statistical models like BLUP, especially for traits controlled by many genes and influenced by the environment (Meuwissen et al., 2016). For example, ML has been successfully applied to predict resistance to diseases, milk production, and reproductive performance (Pérez-Enciso & Zingaretti, 2019).

AI is also being used in precision livestock farming, where data from sensors, cameras, and wearable devices provide real-time information to guide breeding choices, monitor health, and boost farm efficiency. When combined with genomic selection, AI can speed up genetic progress and support more sustainable livestock systems (Brito et al., 2020).

Recent studies have supplied quantitative proof of their superiority for specific traits. For example, a latest study examining reproductive traits in pigs found that a machine learning model (Random Forest) improved predictive accuracy by 4–6% compared to the traditional GBLUP model (Wang et al., 2025). The advantage of ML models lies in their ability to capture complex, non-linear, and epistatic interactions between genes that linear models like GBLUP often miss. This enhanced accuracy is particularly valuable for predicting novel phenotypes derived from high-output sensor data in precision livestock farming, such as activity levels or rumination time, which are often powerful predictors of health and welfare (Morota et al., 2018). Still, some challenges exist, such as ensuring reliable data, understanding how algorithms make decisions, and managing high computing costs. For this reason, close collaboration between breeders, computer experts, and geneticists is needed to take full advantage of AI and ML in animal breeding (Fig. 7).

Fig. 7. From MAS to Gene Editing and AI for Sustainable Selection.

5           ‏Challenges and ethical considerations

‏Although genetic selection has achieved remarkable progress, its use in livestock still faces many technical, economic, ethical, and legal challenges.

A central issue is the ethical concern surrounding gene editing. While tools like CRISPR allow accurate genetic changes, they also raise doubts about animal well-being, natural balance, and possible side effects. For example, selecting for faster growth or larger muscle size may unintentionally cause health or behavioral problems (Shapiro, 2018). Many consumers remain cautious about food from genetically modified animals due to safety, environmental, and tampering with nature concerns (Frewer et al., 2013).

Differences in regulation also play a role. Countries such as the USA and Brazil apply more flexible rules for gene edited animals, while the European Union enforces stricter regulations, treating them (Waltz, 2022). These differences restrict trade, slow down innovation, and complicate the marketing of such livestock.

From a practical point of view, modern genetic technologies demand costly infrastructure, skilled experts, and reliable data systems. This is especially difficult for developing countries, where limited access to these resources slows adoption (Marshall et al., 2019). As a result, the gap between advanced breeding systems and traditional ones may increase.

Loss of genetic diversity is another concern. Intense focus on a few traits in commercial breeds reduces variation and raises inbreeding risks, which may limit adaptability in the future (FAO, 2015). Preserving native breeds and setting balanced goals is therefore crucial.

Finally, as breeding programs become increasingly data-driven, questions of ownership and fair access to genomic information are growing. Smallholder farmers, in particular, may not benefit equally from these advances (Burrow et al., 2021).

In conclusion, genetic selection holds enormous promise, but its future depends on transparent regulations, equal access to technology, and careful attention to ethics and animal welfare (Fig. 8).

Fig. 8. Sustainable Livestock Production via Genetics.

6           ‏Conclusion

Genetic selection has evolved remarkably from simple phenotypic observations to complex genomic and biotechnological tools. Historically, breeders relied on observable traits and pedigrees to make improvements in productivity, fertility, and disease resistance. The advent of genomic selection and gene-editing tools such as CRISPR has transformed livestock breeding into a highly precise and efficient discipline, capable of addressing both economic and environmental challenges. ‏Today, genetic selection contributes significantly to enhanced productivity, reduced disease burden, and improved animal welfare, especially when combined with advanced reproductive technologies and data-driven decision-making. At the same time, the emergence of AI, machine learning, and real-time phenotyping tools promises to further accelerate genetic gains and optimize breeding programs. ‏Looking to the future, genetic selection must prioritize sustainability and equity. This includes not only maximizing production efficiency but also preserving genetic diversity, adapting to climate change, and ensuring fair access to biotechnological advances across countries and farming systems. Policymakers, scientists, and stakeholders must work collaboratively to develop transparent regulatory frameworks, public communication strategies, and capacity-building programs that support ethical and responsible genetic progress. ‏While challenges related to ethics, regulation, and social acceptance remain, the opportunities for transforming livestock production through genetics are unprecedented. By embracing innovation while addressing risks and public concerns, the future of animal breeding can be sustainable, resilient, and socially aligned with global food system goals.

Acknowledgement

‏I would like to express my sincere appreciation to the Department of Animal and Poultry Production, Faculty of Agriculture, Sohag University, for the continuous guidance and knowledge I have gained throughout my academic journey so far.

Authors contribution

Mina Boktar: Conceptualization, Methodology, Software, Validation, Formalanalysis, Investigation, Writing-Original Draft, Writing-Review & Editing.

Funding:

No funding available for this study.

Ethical approval

Not applicable.

Informed consent

Not available.

Conflicts of interest

There is no conflict of interest to declare.

Data availability statement

Data are available upon request.

Declaration of AI Technology Use

The authors declare that they used ChatGPT to rephrase text in the manuscript to improve clarity and readability. Additionally, the figures were prepared using Napkin AI (https://www.napkin.ai).The authors reviewed and edited all AI-generated content as needed and take full responsibility for the publication's content.

7           Reference

Agrimi, U., Conte, M., Morelli, L., Di Bari, M. A., Di Guardo, G., Ligios, C., Antonucci, G., Aufiero, G. M., Pozzato, N., Mutinelli, F., Nonno, R., & Vaccari, G. (2003). Animal transmissible spongiform encephalopathies and genetics. Veterinary research communications, 27 Suppl 1, 31–38. https://doi.org/10.1023/b:verc.0000014115.18327.26

Al-Gebouri, F. G., & Eidan, S. M. (2024). Metabolites and semen characteristics in different bulls fertility. Iraqi Journal of Agricultural Sciences, 55(Special), 206-216. https://doi.org/10.36103/ijas.v55iSpecial.1899

Al Kalaldeh, M., Gibson, J., Duijvesteijn, N. et al. (2019). Using imputed whole-genome sequence data to improve the accuracy of genomic prediction for parasite resistance in Australian sheep. Genet Sel Evol 51, 32. https://doi.org/10.1186/s12711-019-0476-4

Andersson, L., & Georges, M. (2004). Domestic-animal genomics: Deciphering the genetics of complex traits. Nature Reviews Genetics, 5(3), 202–212. https://doi.org/10.1038/nrg1294

Boichard, D., & Brochard, M. (2012). New phenotypes for new breeding goals in dairy cattle. Animal, 6(4), 544–550. https://doi.org/10.1017/S1751731112000018

Bourdon, R. M. (2000). Understanding animal breeding (2nd ed.). Upper Saddle River, NJ, USA:  Prentice Hall.

Brito, L. F., Oliveira, H. R., McConn, B. R., Schinckel, A. P., Arrazola, A., Marchant-Forde, J. N., & Johnson, J. S. (2020). Large-scale phenotyping of livestock welfare in commercial production systems: A new frontier in animal breeding. Frontiers in Genetics, 11, 793. https://doi.org/10.3389/fgene.2020.00793

Burrow, H. M., Mrode, R., Mwai, A. O., Coffey, M. P., & Hayes, B. J. (2021). Challenges and Opportunities in Applying Genomic Selection to Ruminants Owned by Smallholder Farmers. Agriculture, 11(11), 1172. https://doi.org/10.3390/agriculture11111172

Carlson, D. F., Lancto, C. A., Zang, B., Kim, E. S., Walton, M., Oldeschulte, D., ... & Fahrenkrug, S. C. (2016). Production of hornless dairy cattle from genome-edited cell lines. Nature Biotechnology, 34(5), 479–481. https://doi.org/10.1038/nbt.3560

Chafai, N., Hayah, I., Houaga, I., & 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

Clark, M., & Tilman, D. (2017). Comparative analysis of environmental impacts of agricultural production systems, agricultural input efficiency, and food choice. Environmental Research Letters, 12(6), 064016. https://doi.org/10.1088/1748-9326/aa6cd7

Collard, B. C. Y., & Mackill, D. J. (2008). Marker-assisted selection: An approach for precision plant breeding in the twenty-first century. Philosophical Transactions of the Royal Society B: Biological Sciences, 363(1491), 557–572. https://doi.org/10.1098/rstb.2007.2170

Cuellar, C. J., et al. (2024). Consequences of gene editing of PRLR on thermotolerance, growth, and male reproduction in cattle. FASEB BioAdvances, 6(8), 223–234. https://doi.org/10.1096/fba.2024-00029

Cyranoski, D. (2016). CRISPR gene-editing tested in a person for the first time. Nature, 539(7630), 479. https://doi.org/10.1038/nature.2016.20988

Dekkers J. C. (2004). Commercial application of marker- and gene-assisted selection in livestock: strategies and lessons. Journal of animal science, 82 E-Suppl, E313–E328. https://doi.org/10.2527/2004.8213_supplE313x

Doudna, J. A., & Charpentier, E. (2014). The new frontier of genome engineering with CRISPR-Cas9. Science, 346(6213), 1258096. https://doi.org/10.1126/science.1258096

Eidan, S. M., & Khudhir, S. A. (2023). Association between atp1a1 gene polymorphisms with semen characteristics in Holstein bulls. Iraqi Journal of Agricultural Sciences, 54(2), 330-337. https://doi.org/10.36103/ijas.v54i2.1706

Falconer, D. S., & Mackay, T. F. C. (1996). Introduction to quantitative genetics (4th ed.). Harlow, Essex, United Kingdom:  Longman.

FAO. (2015). The Second Report on the State of the World’s Animal Genetic Resources for Food and Agriculture. Food and Agriculture Organization of the United Nations.

FDA. (2024). FDA Approves Gene-Edited Pigs for Food and Biomedical Use. U.S. Food and Drug Administration.

Frewer, L. J., van der Lans, I. A., Fischer, A. R., Reinders, M. J., Menozzi, D., Zhang, X., van den Berg, I., & Zimmermann, K. L. (2013). Public perceptions of agri-food applications of genetic modification – a systematic review and meta-analysis. Trends in Food Science & Technology, 30(2), 142–152. https://doi.org/10.1016/j.tifs.2013.01.003

García-Ruiz, A., Cole, J. B., VanRaden, P. M., Wiggans, G. R., Ruiz-López, F. J., & Van Tassell, C. P. (2016). Changes in genetic selection differentials and generation intervals in US Holstein dairy cattle as a result of genomic selection. Proceedings of the National Academy of Sciences, 113(28), E3995–E4004. https://doi.org/10.1073/pnas.1519061113

Goddard, M. E., & Hayes, B. J. (2009). Mapping genes for complex traits in domestic animals and their use in breeding programmes. Nature Reviews Genetics, 10(6), 381–391. https://doi.org/10.1038/nrg2575

Gonzalez-Recio, O., Rosa, G. J. M., & Gianola, D. (2014). Machine learning methods and predictive ability metrics for genome-wide prediction of complex traits. Livestock Science, 166, 217–231. https://doi.org/10.1016/j.livsci.2014.05.036

Habier, D., Fernando, R. L., & Dekkers, J. C. M. (2007). The impact of genetic relationship information on genome-assisted breeding values. Genetics, 177(4), 2389–2397. https://doi.org/10.1534/genetics.107.081190

Hayes, B. J., Bowman, P. J., Chamberlain, A. J., & Goddard, M. E. (2009). Invited review: Genomic selection in dairy cattle: progress and challenges. Journal of Dairy Science, 92(2), 433–443. https://doi.org/10.3168/jds.2008-1646

Hayes, B. J., Lewin, H. A., & Goddard, M. E. (2013). The future of livestock breeding: Genomic selection for efficiency, reduced emissions intensity, and adaptation. Trends in Genetics, 29(4), 206–214. https://doi.org/10.1016/j.tig.2012.11.009

Hazel, L. N. (1943). The genetic basis for constructing selection indexes. Genetics, 28(6), 476–490. https://doi.org/10.1093/genetics/28.6.476

Henderson, C. R. (1975). Best linear unbiased estimation and prediction under a selection model. Biometrics, 31(2), 423–447. https://doi.org/10.2307/2529430

Hill, W. G. (2010). Understanding and using quantitative genetic variation. Philosophical Transactions of the Royal Society B: Biological Sciences, 365(1537), 73–85. https://doi.org/10.1098/rstb.2009.0203

Hill W. G. (2014). Applications of population genetics to animal breeding, from wright, fisher and lush to genomic prediction. Genetics, 196(1), 1–16. https://doi.org/10.1534/genetics.112.147850

Jinek, M., Chylinski, K., Fonfara, I., Hauer, M., Doudna, J. A., & Charpentier, E. (2012). A programmable dual-RNA–guided DNA endonuclease in adaptive bacterial immunity. Science, 337(6096), 816–821. https://doi.org/10.1126/science.1225829

Lillico, S. G., Proudfoot, C., Carlson, D. F., Stverakova, D., Neil, C., Blain, C., ... & Whitelaw, C. B. (2013). Live pigs produced from genome edited zygotes. Scientific Reports, 3, 2847. https://doi.org/10.1038/srep02847

Marshall, K., Gibson, J. P., Mwai, O., Mwacharo, J. M., Haile, A., Getachew, T., Mrode, R., & Kemp, S. J. (2019). Livestock genomics for developing countries: African examples in practice. Frontiers in Genetics, 10, 297. https://doi.org/10.3389/fgene.2019.00297

Meuwissen, T. H. E., Hayes, B. J., & Goddard, M. E. (2001). Prediction of total genetic value using genome-wide dense marker maps. Genetics, 157(4), 1819–1829. https://doi.org/10.1093/genetics/157.4.1819

Meuwissen, T., Hayes, B., & Goddard, M. (2016). Genomic selection: A paradigm shift in animal breeding. Animal Frontiers, 6(1), 6–14. https://doi.org/10.2527/af.2016-0002

Morota, G., Ventura, R. V., Silva, F. F., Koyama, M., & Fernando, S. C. (2018). Big Data Analytics and Precision Animal Agriculture Symposium: Machine learning and data mining advance predictive big data analysis in precision animal agriculture. Journal of Animal Science, 96(4), 1540-1550. https://doi.org/10.1093/jas/sky014

Mrode, R. A. (2014). Linear models for the prediction of animal breeding values (3rd ed.). CABI Publishing. https://doi.org/10.1079/9781780643915.0000

Nicholas, F. W. (1987). Veterinary genetics. Oxford, United Kingdom: Oxford University Press.

Niemann, H., & Kues, W. A. (2007). Transgenic farm animals: An update. Reproduction, Fertility and Development, 19(6), 762–770. https://doi.org/10.1071/RD07040

Osei-Amponsah, R., Chauhan, S. S., Leury, B. J., Cheng, L., Cullen, B., Clarke, I. J., & Dunshea, F. R. (2019). Genetic Selection for Thermotolerance in Ruminants. Animals, 9(11), 948. https://doi.org/10.3390/ani9110948

Pérez-Enciso, M., & Zingaretti, L. M. (2019). A guide on deep learning for complex trait genomic prediction. Genes, 10(7), 553. https://doi.org/10.3390/genes10070553

Pryce, J. E., & Daetwyler, H. D. (2012). Designing dairy cattle breeding schemes under genomic selection: A review of international research. Animal Production Science, 52(3), 107–114. https://doi.org/10.1071/AN11098

Shapiro, P. (2018). Clean meat: How growing meat without animals will revolutionize dinner and the world. New York, NY, USA: Gallery Books.

Sharif-Islam, M., van der Werf, J. H. J., Wood, B. J., & Hermesch, S. (2024). The predicted benefits of genomic selection on pig breeding objectives. Journal of Animal Breeding and Genetics. https://doi.org/10.1111/jbg.12873

Shook, G. E. (2006). Major advances in determining appropriate selection goals. Journal of Dairy Science, 89(4), 1349–1361. https://doi.org/10.3168/jds.S0022-0302(06)72202-0

Si, Q. (2024). Advancements in Precision Livestock Farming: Technologies and Applications. Animal Molecular Breeding, 14(2), 1–8. https://doi.org/10.5376/amb.2024.14.0020

Tan, W., Proudfoot, C., Lillico, S. G., & Whitelaw, C. B. (2016). Gene targeting, genome editing: from Dolly to editors. Transgenic research, 25(3), 273–287. https://doi.org/10.1007/s11248-016-9932-x

U.S. Food and Drug Administration. (2022). FDA Makes Low-Risk Determination for Marketing of Products from Genome-Edited Beef Cattle. Retrieved from https://www.fda.gov/animal-veterinary/cvm-updates/fda-announces-animal-biotechnology-webinar-low-risk-intentional-genomic-alterations-animals-food-use

Van Eenennaam, A. L. (2017). Genetic modification of food animals. Current Opinion in Biotechnology, 44, 27–34. https://doi.org/10.1016/j.copbio.2016.10.007

Van Eenennaam, Alison. (2018). The Uses of Biotechnology to Improve Animal Welfare. CRC Press.  https://doi.org/10.1201/b21911-8

Van Eenennaam A. L. (2019). Application of genome editing in farm animals: cattle. Transgenic research, 28(Suppl 2), 93–100. https://doi.org/10.1007/s11248-019-00141-6

VanRaden, P. M. (2008). Efficient methods to compute genomic predictions. Journal of Dairy Science, 91(11), 4414–4423. https://doi.org/10.3168/jds.2007-0980

Waltz, E. (2022). How Europe’s gene-edited-food rules could change. Nature, 606(7912), 20-21.

Wang, J., Chai, J., Chen, L., Zhang, T., Long, X., Diao, S., Chen, D., Guo, Z., Tang, G., & Wu, P. (2025). Enhancing genomic prediction accuracy of reproduction traits in Rongchang pigs through machine learning. Animals, 15(4), 525. https://doi.org/10.3390/ani15040525

Whitelaw, C. B. A., Sheets, T. P., Lillico, S. G., & Telugu, B. P. (2016). Engineering large animal models of human disease. Journal of Pathology, 238(2), 247 256. https://doi.org/10.1002/path.4648

Whitelaw, B., & Lillico, S. (2022). Increasing livestock farming sustainability using genome editing technology. Biochemical Society Transactions, 44(3), 9–12. https://doi.org/10.1042/bio_2022_114

Whitworth, K. M., Rowland, R. R. R., Ewen, C. L., Trible, B. R., Kerrigan, M. A., Cino-Ozuna, A. G., ... & Prather, R. S. (2016). Gene-edited pigs are protected from porcine reproductive and respiratory syndrome virus. Nature Biotechnology, 34(1), 20–22. https://doi.org/10.1038/nbt.3434

Wiggans, G. R., VanRaden, P. M., & Cooper, T. A. (2011). The genomic evaluation system in the United States: Past, present, future. Journal of Dairy Science, 94(6), 3202–3211. https://doi.org/10.3168/jds.2010-3866

Wolt, J. D., Wang, K., & Yang, B. (2015). The regulatory status of genome-edited crops. Plant Biotechnology Journal, 14(2), 510–518. https://doi.org/10.1111/pbi.12444

Wray, N. R., Lin, T., Austin, J., McGrath, J. J., Hickie, I. B., Murray, G. K., & Visscher, P. M. (2021). From Basic Science to Clinical Application of Polygenic Risk Scores: A Primer. JAMA Psychiatry, 78(1), 101–109. https://doi.org/10.1001/jamapsychiatry.2020.3049