In livestock and poultry production, the production and consumption of mutton has always occupied an important position. As the global population grows and living standards improve, the demand for high-quality livestock and poultry products is increasing day by day. In the livestock industry, mutton is an important source of protein and its production and quality have received widespread attention. The optimization of mutton production traits has become a key factor in improving the economic benefits of the livestock and poultry industry and meeting consumer demand. However, traditional breeding methods have certain limitations in solving the problem of genetic improvement of complex traits. Therefore, it is particularly important to find an efficient and accurate genetic improvement strategy (Li et al., 2023). In recent years, with the rapid development of genomics and bioinformatics, genome-wide association analysis (GWAS), as a powerful genetic analysis tool, has been widely used in animal genetics and genetic improvement research.

The research background of genome-wide association analysis began in the field of human genetics, aiming to explore the relationship between genes and phenotypes to analyze the genetic basis of complex traits (Uffelmann et al., 2021). The introduction of this method provides new ideas and technical means for research on genetic improvement of livestock and poultry. Through statistical analysis of genotype and phenotypic data of large-scale individuals, genome-wide association analysis can discover key genotypes and loci that affect trait performance, thereby providing an important reference for further genetic improvement.

Optimization of mutton production traits has important theoretical and practical significance for animal husbandry. As one of the important livestock and poultry products, the yield and quality of mutton directly affect the economic and social benefits of animal husbandry. In mutton production, traits that affect yield and quality include but are not limited to body shape characteristics, muscle tissue composition, fat content, and disease resistance. Therefore, in-depth research on the genetic basis of mutton production traits can provide scientific basis and technical support for breeding and selection, accelerate the process of genetic improvement of livestock and poultry, and improve the efficiency and quality of mutton production (Safari and Fogarty, 2019). The optimization of mutton production traits can also promote the sustainable development of animal husbandry, promote the transformation and upgrading of animal husbandry, and meet social demand for high-quality livestock and poultry products.

The purpose of this study was to optimize mutton production traits using genome-wide association analysis. By collecting genotype and trait performance data of large-scale individuals, using advanced statistical models and analysis methods, we can mine the genetic basis of mutton production traits and identify key genetic markers to provide scientific basis and technical support for the genetic improvement of mutton production. This study hopes to provide new genetic improvement strategies for mutton production, promote the sustainable development of the livestock and poultry industry, meet the growing consumer demand, and promote the prosperity and progress of the livestock industry.

1 Challenges and Prospects of Optimizing Mutton production traits

Mutton production traits refer to various characteristics and properties that affect the quality and quantity of mutton production. It includes characteristics such as meat quality, yield, and body shape. Optimization of mutton production traits is of great significance for improving mutton production efficiency and improving meat quality.

1.1 Definition of mutton production traits

Mutton production traits cover many aspects. These include the quality of the meat, the yield and quality of the meat, and the body shape of the mutton sheep (Li et al., 2024). In terms of meat quality, it mainly includes meat tenderness, fat-to-lean ratio, meat color, etc. In terms of yield and quality, it involves the growth rate and slaughter weight of mutton sheep. In terms of body shape, it mainly includes the weight and body proportion of the mutton sheep.

1.2 Importance of mutton production traits

Mutton is an important part of people's daily diet, and the quality of mutton directly affects consumers' taste and health. Good mutton production traits can ensure that the mutton tastes fresh and tender, is rich in nutrients, and is conducive to improving breeding efficiency. As people pay more attention to healthy diet, high-quality, low-fat mutton is becoming more and more popular among consumers.

The optimization of mutton production traits is not only related to the economic interests of producers, but also closely related to the health of consumers. By optimizing the production traits of mutton, it can not only improve breeding efficiency and reduce production costs, but also produce healthier and higher-quality mutton products to meet people's needs for food safety and nutritional health.

1.3 Current challenges in optimizing mutton production traits

Although the optimization of mutton production traits is of great significance, there are still some challenges faced in actual production. First of all, mutton production traits are affected by multiple factors such as genetics, environment and management, so it is not easy to effectively optimize them. Traditional genetic improvement methods have problems such as long time and high cost, which limit the efficiency and speed of optimization of mutton production traits. Therefore, how to use advanced genetic technology to speed up the pace of optimization of mutton production traits has become an urgent problem to be solved.

In mutton production, there are significant genetic differences between different breeds and individuals, which provides a basis for the optimization of mutton production traits. However, in order to achieve rapid and accurate optimization, relying solely on traditional selection methods is no longer suitable. Therefore, the introduction of advanced genetic technologies such as genome-wide association analysis has become an important way to solve the problem of optimizing mutton production traits. By deeply exploring the genetic information of sheep breeds, finding gene loci related to target traits, and using this information for precise selection, we can effectively improve the optimization efficiency of mutton production traits, accelerate the genetic process, and thus promote the sustainability of mutton production. develop.

2 Principles and Applications of Genome-Wide Association Analysis

Genome-wide association study (GWAS), as an important method in the field of genetics research, plays a key role in revealing the connection between genes and traits. Its principle is to find the correlation between genotype and phenotype through the analysis of large-scale genotype data and phenotypic data, thereby exploring the genetic basis of trait formation (Cano-Gamez and Trynka, 2020). In the field of animal genetics, genome-wide association analysis is widely used to analyze the genetic basis of various economic traits, providing an important means for genetic improvement of livestock and poultry.

2.1 Principles of genome-wide association analysis

Genome-wide association analysis is based on the concept of common variations, namely Single Nucleotide Polymorphisms (SNPs). In the study of mutton production traits, these SNP sites may be related to genetic variations in muscle tissue growth rate, fat content, disease resistance, etc. By comparing SNP genotype data and phenotypic data of large-scale individuals, the correlation between certain SNP sites and specific traits can be found (Xu and Taylor, 2009). This method does not rely on pre-set hypothetical genes or pathways, so it can comprehensively and efficiently discover the association between genes and traits.

2.2 Application of genome-wide association analysis in animal genetics

In the genetic improvement of sheep, genome-wide association analysis is widely used to analyze the genetic basis of various economic traits. For example, by analyzing sheep genome data, key genes or gene regions related to production traits such as meat quality, growth rate, and coat color can be discovered. These findings not only help to understand the molecular mechanisms of trait formation, but also provide candidate markers for selective breeding, accelerating the process of genetic improvement of sheep.

2.3 The value of genome-wide association analysis in genetic improvement of livestock and poultry

In the genetic improvement of livestock and poultry, genome-wide association analysis provides important technical support for precision breeding. By identifying genetic markers related to meat quality, growth rate, disease resistance, etc., breeders can more accurately select excellent individuals for breeding, thus speeding up the breeding process and improving the efficiency of genetic improvement. Genome-wide association analysis can also reveal new relationships between genetic variants and traits, providing breeders with more options. These research results are of great significance for optimizing the production traits of livestock and poultry, improving meat quality, and enhancing the disease resistance of livestock and poultry.

As an efficient genetic analysis method, genome-wide association analysis has important value in the genetic improvement of livestock and poultry. By revealing the correlation between genes and traits, this method provides a scientific basis for precision breeding and promotes the optimization and improvement of livestock and poultry production traits. With the continuous advancement of technology and in-depth research, it is believed that genome-wide association analysis will play an increasingly important role in the field of livestock and poultry genetic improvement and make greater contributions to the sustainable development of the livestock and poultry industry.

3 Genome-Wide Association Analysis Method for Optimization of Mutton Production Traits

3.1 Sample collection and data processing

Before conducting genome-wide association analysis, it is first necessary to collect a sufficient number and representative samples and effectively process the data (Asif et al., 2021). The selection of samples should take into account factors such as sheep breed, region, environment, etc. to ensure the reliability and representativeness of the results. For example, when selecting samples, you can consider collecting samples from sheep in different regions, different growth stages, and different breeds to fully reflect the diversity of mutton production traits. At the same time, preliminary processing is performed on the collected sample data, including data cleaning, removal of outliers, etc., to reduce noise and interference in the data and improve the accuracy and credibility of subsequent analysis.

3.2 Acquisition and quality control of genotype data

Obtaining high-quality genotype data is one of the key steps for conducting genome-wide association analysis. Commonly used methods for obtaining genotype data include gene chip technology and whole-genome sequencing technology. Gene chip technology has the advantages of high throughput and low cost, and is suitable for genotype analysis of large-scale samples; while whole-genome sequencing technology can provide more comprehensive and accurate genotype information, but the cost is higher. After genotype data is obtained, strict quality control is required, including checking the completeness, consistency, and accuracy of the data, and removing low-quality data and erroneous labels to ensure the reliability and validity of subsequent analysis.

3.3 Statistical models for genome-wide association analysis

Statistical models for genome-wide association analysis are key tools for determining associations between genotypes and traits. Commonly used genome-wide association analysis methods include linear models, mixed models, Bayesian methods, etc. The linear model is more convenient to calculate under simplified conditions and is suitable for situations with a small sample size; the mixed model can take into account the impact of population structure and kinship on the results and is suitable for situations with a large sample size; the Bayesian method can Provides the posterior distribution of gene effects, which is more suitable for situations where the sample size is small but more precise results are required. When selecting an appropriate statistical model, factors such as the number of samples, the genetic background of the trait, and the population structure need to be taken into consideration, and adjusted and optimized based on the actual situation to improve the efficiency and accuracy of association analysis.

3.4 Identification and verification of key trait markers

Following genome-wide association analysis, the identified key trait markers need to be further identified and validated. This includes conducting verification experiments, functional verification, etc. on the association between markers and traits to confirm the true association between markers and traits, and further explore their mechanism of action and influencing factors. For example, the function and impact of markers can be verified through transgenic animal models (Hou et al., 2021); the expression patterns and biological functions of markers in sheep can also be studied through histological, biochemical and other methods. At the same time, cross-validation, re-validation and other analyzes are also needed to ensure the stability and reliability of the markers.

Through the application of the above whole-genome association analysis methods, mutton production traits can be effectively optimized, the quality and yield of mutton can be improved, and strong support can be provided for the genetic improvement of livestock and poultry and the development of the breeding industry. The continuous development and improvement of genome-wide association analysis methods will further promote the research and practice of optimization of mutton production traits and contribute to the sustainable development of the mutton industry.

4 Application of Genome-Wide Association Analysis in Mutton Production Traits

Optimization of mutton production is a comprehensive topic, involving many aspects of body shape, muscle tissue, fat tissue and other traits. Through genome-wide association analysis, people can deeply study the genetic basis of these traits and provide precise improvement plans for mutton production.

4.1 Optimization of body shape-related traits

Optimization of sheep body size is critical to lamb production. Body shape directly affects the amount and quality of meat, so it is of great significance for farmers to breed meat sheep with good body shape. Through genome-wide association analysis, Jiang et al. (2021) found some genes closely related to sheep body size. These genes may affect the growth rate, weight growth rate, height, etc. of sheep. Through in-depth study of these genes, humans can understand their mechanism of action in the process of body shape formation, thereby accurately selecting breeding objects and formulating corresponding breeding strategies to achieve precise control of sheep body shape. This will help improve the efficiency and quality of lamb production.

4.2 Optimization of muscle tissue-related traits

The quality of mutton is mainly affected by the muscle tissue. High-quality muscle tissue can improve the taste, tenderness and nutritional value of mutton. Genome-wide association analysis can help researchers identify key genes that influence muscle tissue growth and development (Figure 1). These genes may affect aspects such as muscle fiber growth, muscle fiber type distribution, and muscle fat content. Through the study of these genes, people can gain an in-depth understanding of the genetic mechanism in the formation of muscle tissue, and then conduct targeted breeding efforts to improve mutton production traits.

4.3 Optimization of adipose tissue-related traits

Fat tissue is one of the important factors affecting mutton quality. The distribution and content of fat directly affect the taste and flavor of mutton. Genome-wide association analysis can help people gain a deeper understanding of the mechanism of adipose tissue formation and development (Zhang et al., 2021). Some genes are closely related to processes such as adipocyte proliferation, fatty acid synthesis, and fat metabolism. By studying these genes, we will find the key genes that regulate adipose tissue, and take corresponding breeding measures to optimize the fat content and fat tissue distribution of mutton, and improve the taste and flavor of mutton.

4.4 Analysis and optimization of other key traits

In addition to traits such as body size, muscle tissue and adipose tissue, there are many other key traits affecting lamb production that need to be analyzed and optimized. For example, wool quality, disease resistance, reproductive performance, etc. are all important factors affecting mutton production (Figure 2). Through genome-wide association analysis, people can deeply study the genetic mechanisms behind these traits and provide more comprehensive optimization solutions for mutton production.

Through the application of genome-wide association analysis in mutton production traits, people can have a deeper understanding of the genetic background of sheep, accurately select breeding objects, and optimize breeding strategies, thereby improving the production efficiency and quality of mutton and satisfying people's demand for high-quality meat. needs. The application of this method not only contributes to the development of the breeding industry, but also provides consumers with better quality meat products.

5 Challenges of Genome-Wide Association Analysis in Optimization of Mutton Production Traits

Optimization of mutton production traits is an important topic in animal husbandry, and genome-wide association analysis is considered an effective method. However, in practice, it faces many challenges, including data acquisition and processing, quality control of genotype data, and selection and optimization of statistical models.

5.1 Difficulties in data collection and processing

The optimization of mutton production traits requires a large amount of data support, including information on body shape, growth rate, muscle tissue structure, etc. However, there are many difficulties in the actual data collection process. First, sheep herds are widely distributed and numerous, making it difficult to collect and standardize data. Differences in breeding conditions and management levels in different regions and environments will also challenge the consistency of data, posing difficulties for the effective use of data. Human interference may occur during the collection process, such as inaccurate data recording, sampling errors, etc., which further increases the complexity of data processing.

5.2 Challenges in genotype data quality control

In genome-wide association analysis, the quality of genotype data directly affects the accuracy and reliability of the analysis. However, the quality of genotype data is affected by many factors, including sample source, DNA extraction method, sequencing technology, etc. These factors may lead to problems such as missing values and incorrect genotype assignments in the data, thus affecting the credibility of the results of association analysis. Therefore, how to effectively control the quality of genotype data has become a major challenge in genome-wide association analysis. In addition, for large-scale sample data, efficient quality control methods are also particularly important to ensure data quality and the credibility of analysis results.

5.3 Selection and optimization of statistical models

In genome-wide association analysis, choosing an appropriate statistical model is critical to the accuracy and interpretability of the results. However, traditional statistical models may not be able to adequately explain the complex relationships in mutton production traits because these traits are affected by polygenic genetic and environmental factors. Therefore, it is necessary to design and optimize statistical models suitable for mutton production traits to improve the effect and accuracy of correlation analysis. With the continuous development of computer technology, the introduction of new methods such as machine learning provides new ideas and methods for the optimization of statistical models (Sun and Zhao, 2020). However, this also brings more challenges to the selection of statistical models, which requires comprehensive consideration of factors such as model complexity, computational resource requirements, and model interpretability.

In the face of the above challenges, researchers need to comprehensively use methods such as data collection and processing, genotype data quality control, and statistical model selection and optimization to continuously improve the effect and efficiency of genome-wide association analysis in the optimization of mutton production traits, and provide a better way for mutton. Provide more powerful support for the sustainable development of the industry.

6 Achievements and Prospects

Genome-wide association analysis methods for optimization of mutton production traits have achieved a series of remarkable results. In terms of sample collection and data processing, the researchers established a large and diverse sheep breed resource library, covering sheep breeds with various geographical, environmental and genetic backgrounds. This provides sufficient data support for subsequent genome-wide association analysis. In terms of acquisition and quality control of genotype data, advanced sequencing technology and strict quality control standards are used to ensure the accuracy and reliability of the data. In terms of statistical models for genome-wide association analysis, researchers continue to improve and optimize the models, improving the accuracy and efficiency of analysis. Most importantly, in terms of identification and verification of key trait markers, researchers have successfully discovered multiple genetic loci closely related to mutton production traits, providing a basis and guidance for the optimization of mutton production traits (Li et al., 2023).

Although certain results have been achieved, there are still some problems and challenges faced in genome-wide association analysis for optimization of mutton production traits (Su et al., 2023). Due to the diversity of sheep resources and the complexity of genetic background, there are still certain limitations in sample selection and data processing, and it is necessary to further expand the sample size and improve data analysis methods. In terms of identification and verification of key trait markers, since mutton production traits are affected by multiple factors, such as environment, management, and nutrition, more comprehensive research and verification are needed to ensure the accuracy and stability of markers. The genome-wide association analysis method itself also has certain limitations. For example, the detection ability of rare variants is weak, and it needs to be combined with other methods for comprehensive analysis and verification.

People will continue to work hard on genome-wide association analysis for the optimization of mutton production traits, focusing on solving existing problems and challenges, further deepening research results, and exploring new research directions in future. Researchers will strengthen the collection and integration of sheep resources, establish a more complete sheep resource library, and provide richer and more diversified data support for genome-wide association analysis; they will continue to improve and optimize data processing and analysis methods to improve data The quality and precision of analysis to cope with the diversity of sheep resources and the complexity of genetic backgrounds. Researchers will strengthen cooperation with other disciplines and fields, conduct more comprehensive research, explore the internal mechanisms and regulatory networks of mutton production traits, and provide more in-depth and comprehensive theoretical support for the optimization of mutton production traits; and will also actively Explore new research methods and technologies, such as artificial intelligence and gene editing, to open up new ways and methods for the optimization of mutton production traits. This study is full of confidence in the genome-wide association analysis for the optimization of mutton production traits. It is believed that in the near future, humans will achieve more significant research results and make greater contributions to the development and growth of the mutton industry.

References 

Asif H., Alliey-Rodriguez N., Keedy S., Tamminga C.A., Sweeney J.A., Pearlson G., Clementz B.A., Keshavan M.S., Buckley P., Liu C.Y., Neale B., and Gershon E.S., 2021, GWAS significance thresholds for deep phenotyping studies can depend upon minor allele frequencies and sample size, Molecular psychiatry, 26(6): 2048-2055.

https://doi.org/10.1038/s41380-020-0670-3
PMid:32066829 PMCid:PMC7429341

Cano-Gamez E., and Trynka G., 2020, From GWAS to function: using functional genomics to identify the mechanisms underlying complex diseases, Frontiers in genetics, 11: 424.

https://doi.org/10.3389/fgene.2020.00424
PMid:32477401 PMCid:PMC7237642

Hou Y., Wei Y., Lautrup S., Yang B., Wang Y., Cordonnier S., Mattson M.P., Croteau D.L., and Bohr V.A., 2021, NAD+ supplementation reduces neuroinflammation and cell senescence in a transgenic mouse model of Alzheimer’s disease via cGAS–STING, Proceedings of the National Academy of Sciences, 118(37): e2011226118.

https://doi.org/10.1073/pnas.2011226118
PMid:34497121 PMCid:PMC8449423

Jiang J., Cao Y., Shan H., and Song X., 2021, The GWAS analysis of body size and population verification of related SNPs in Hu sheep, Frontiers in genetics, 12: 642552.

https://doi.org/10.3389/fgene.2021.642552
PMid:34093644 PMCid:PMC8173124

Li C.L., Guo T.T., and Yue Y.J., 2023, Research advances on genomic selection and its application in sheep breeding, China Animal Husbandry & Veterinary Medicine, 50(2): 616-625.

Li P., Li X.M., Long Q.M., Zeng J., Yao M., Tang M.Z., Gong F., and Zhang J.W., 2024, Effects of different feeding modes on meat quality characteristics in guizhou black goat, China Herbivore Science, 44(1): 80-84.

Li X.J., Han J.G., Liang B.M., Ye S.H., Jiang L., and Ma Y.H., 2023, Research advance on available genetic markers for important economic traits in sheep, China Animal Husbandry & Veterinary Medicine, 50(8): 3142-3156.

Lu Z., Yue Y., Yuan C., Liu J., Chen Z., Niu C., Sun P., Zhu S., Zhao H., Guo T., and Yang B., 2020, Genome-wide association study of body weight traits in chinese fine-wool sheep, Animals, 10(1): 170.

https://doi.org/10.3390/ani10010170
PMid:31963922 PMCid:PMC7022301

Mohammadabadi M., Bordbar F., Jensen J., Du M., and Guo W., 2021, Key genes regulating skeletal muscle development and growth in farm animals, Animals, 11(3): 835.

https://doi.org/10.3390/ani11030835
PMid:33809500 PMCid:PMC7999090

Safari A., and Fogarty N.M., 2019, Genetic parameters for sheep production traits.

Su P., Huang Y.M., Xu T.Q., Lin C.J., Pan C.Y., Zhang Q.F., and Huang X.Y., 2023, Application of genome-wide association study in sheep genetics and breeding, Chinese Journal of Animal Science, 59(10): 74-85.

Sun N., and Zhao H., 2020, Statistical methods in genome-wide association studies, Annual Review of Biomedical Data Science, 3: 265-288.

https://doi.org/10.1146/annurev-biodatasci-030320-041026

Uffelmann E., Huang Q.Q., Munung N.S., De Vries J., Okada Y., Martin A.R., Martin H.C., Lappalainen T., and Posthuma D., 2021, Genome-wide association studies, Nature Reviews Methods Primers, 1(1): 59.

https://doi.org/10.1038/s43586-021-00056-9

Xu Z., and Taylor J.A., 2009, SNPinfo: integrating GWAS and candidate gene information into functional SNP selection for genetic association studies, Nucleic acids research, 37(suppl_2): W600-W605.

https://doi.org/10.1093/nar/gkp290
PMid:19417063 PMCid:PMC2703930

Zhang W., Xu M., Wang J., and Gan S., 2021, Comparative transcriptome analysis of key genes and pathways activated in response to fat deposition in two sheep breeds with distinct tail phenotype, Frontiers in genetics, 12: 639030.

https://doi.org/10.3389/fgene.2021.639030
PMid:33897762 PMCid:PMC8060577