Breed Identification Research Articles

Comparative analysis of marker selection approaches for population stratification in Garole and Deccani sheep

The present study was undertaken to identify ancestry-informative single nucleotide polymorphism (SNP) markers from medium-density Ovine50K genotyping data for the development of low-density SNP panels in Deccani and Garole sheep breeds of India. These panels can be used for efficient and cost-effective genetic analysis and breed identification. Genotyping data from 49 individuals (25 Garole, and 24 Deccani) was analyzed using the OvineSNP50 BeadChip array. Quality control measures were applied to ensure data accuracy and improve reliability and reproducibility of results. Admixture analysis was performed to assess genetic structure and identify potential marker sets for low-density panels. Three different methods (distance, frequency, and index) were used to select markers based on their distribution and allele frequencies. The low-density panels were designed at pre-set sizes of 500, 1000, 2000, 3000, and 5000. The results revealed that low-density panels, particularly with 3K and 5K markers, can effectively differentiate between Deccani and Garole sheep. In conclusion, the 0.5K dense SNP panel developed via index method (followed by frequency method) can be recommended for better genomic clustering (~90%) for both the breeds. The developed SNP panels provide a valuable resource for genetic analysis and breed identification in these sheep populations, potentially facilitating conservation efforts and genetic improvement programs.

Classification of Dog Breeds Using Convolutional Neural Network Models and Support Vector Machine.

When classifying breeds of dogs, the accuracy of classification significantly affects breed identification and dog research. Using images to classify dog breeds can improve classification efficiency; however, it is increasingly challenging due to the diversities and similarities among dog breeds. Traditional image classification methods primarily rely on extracting simple geometric features, while current convolutional neural networks (CNNs) are capable of learning high-level semantic features. However, the diversity of dog breeds continues to pose a challenge to classification accuracy. To address this, we developed a model that integrates multiple CNNs with a machine learning method, significantly improving the accuracy of dog images classification. We used the Stanford Dog Dataset, combined image features from four CNN models, filtered the features using principal component analysis (PCA) and gray wolf optimization algorithm (GWO), and then classified the features with support vector machine (SVM). The classification accuracy rate reached 95.24% for 120 breeds and 99.34% for 76 selected breeds, respectively, demonstrating a significant improvement over existing methods using the same Stanford Dog Dataset. It is expected that our proposed method will further serve as a fundamental framework for the accurate classification of a wider range of species.

Cow Milk Fatty Acid and Protein Composition in Different Breeds and Regions in China.

Cow milk is rich in proteins, fats, carbohydrates, and minerals; however, its precise nutrient content varies based on various factors. In the current study, we evaluated the differences in the fatty acid and protein contents of milk and the factors associated with these differences. To achieve this, samples were collected from seven types of cows in different regions. These included samples from three dairy breeds: Chinese Holstein milk from Beijing, China (BH), Chinese Holstein milk (HH) and Jersey milk (JS) from Hebei province, China; and four dairy/meat breeds: Sanhe milk (SH) from Inner Mongolia Autonomous Region, China, Xinjiang brown milk (XH) and Simmental milk (SI) from Xinjiang Uygur Autonomous Region, China, and Shu Xuanhua milk (SX) from Sichuan province, China. Breed significantly affects total fat, fatty acid, and protein contents. Additionally, geographic region significantly affects the contents of different fatty acids, α-lactalbumin, and lactoferrin. JS has the highest total fat and casein contents. XH samples contain significantly higher unsaturated fatty acid content than BH samples and do not differ significantly from JS. Additionally, the low β-lactoglobulin and high lactoferrin contents in XH samples may be favorable for the growth and development of infants. Our results may inform the development of dairy products from different cow breeds and advance the process of accurate breed identification.

Machine Learning-Aided Ultra-Low-Density Single Nucleotide Polymorphism Panel Helps to Identify the Tharparkar Cattle Breed: Lessons for Digital Transformation in Livestock Genomics.

Cattle breed identification is crucial for livestock research and sustainable food systems, and advances in genomics and artificial intelligence present new opportunities to address these challenges. This study investigates the identification of the Tharparkar cattle breed using genomics tools combined with machine learning (ML) techniques. By leveraging data from the Bovine SNP 50K chip, we developed a breed-specific panel of single nucleotide polymorphisms (SNPs) for Tharparkar cattle and integrated data from seven other Indian cattle populations to enhance panel robustness. Genome-wide association studies (GWAS) and principal component analysis were employed to identify 500 SNPs, which were then refined using ML models-AdaBoost, bagging tree, gradient boosting machines, and random forest-to determine the minimal number of SNPs needed for accurate breed identification. Panels of 23 and 48 SNPs achieved accuracy rates of 95.2-98.4%. Importantly, the identified SNPs were associated with key productive and adaptive traits, thus attesting to the value and potentials of digital transformation in livestock genomics. The ML-aided ultra-low-density SNP panel approach reported here not only facilitates breed identification but also contributes to preserving genetic diversity and guiding future breeding programs.

Development and validation of a 5K low-density SNP chip for Hainan cattle

BackgroundThis study aimed to design and develop a 5K low-density liquid chip for Hainan cattle utilizing targeted capture sequencing technology. The chip incorporates a substantial number of functional single nucleotide polymorphism (SNP) loci derived from public literature, including SNP loci significantly associated with immunity, heat stress, meat quality, reproduction, and other traits. Additionally, SNPs located in the coding regions of immune-related genes from the Bovine Genome Variation Database (BGVD) and Hainan cattle-specific SNP loci were included.ResultsA total of 5,293 SNPs were selected, resulting in 9,837 DNA probes with a coverage rate of 85.69%, thereby creating a Hainan cattle-specific 5K Genotyping by Target Sequencing (GBTS) liquid chip. Evaluation with 152 cattle samples demonstrated excellent clustering performance and a detection rate ranging from 96.60 to 99.07%, with 94.5% of SNP sites exhibiting polymorphism. The chip achieved 100% gender coverage and displayed a heterozygosity rate between 14.20% and 29.65%, with a repeatability rate of 99.65–99.85%. Analyses using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) revealed the potential regulatory roles of exonic SNPs in immune response pathways.ConclusionThe development and validation of the 5K GBTS liquid chip for Hainan cattle represent a valuable tool for genome analysis and genetic diversity assessment. Furthermore, it facilitates breed identification, gender determination, and kinship analysis, providing a foundation for the efficient utilization and development of local cattle genetic resources.

Population structure and breed identification of Chinese indigenous sheep breeds using whole genome SNPs and InDels

BackgroundAccurate breed identification is essential for the conservation and sustainable use of indigenous farm animal genetic resources. In this study, we evaluated the phylogenetic relationships and genomic breed compositions of 13 sheep breeds using SNP and InDel data from whole genome sequencing. The breeds included 11 Chinese indigenous and 2 foreign commercial breeds. We compared different strategies for breed identification with respect to different marker types, i.e. SNPs, InDels, and a combination of SNPs and InDels (named SIs), different breed-informative marker detection methods, and different machine learning classification methods.ResultsUsing WGS-based SNPs and InDels, we revealed the phylogenetic relationships between 11 Chinese indigenous and two foreign sheep breeds and quantified their purities through estimated genomic breed compositions. We found that the optimal strategy for identifying these breeds was the combination of DFI_union for breed-informative marker detection, which integrated the methods of Delta, Pairwise Wright's FST, and Informativeness for Assignment (namely DFI) by merging the breed-informative markers derived from the three methods, and KSR for breed assignment, which integrated the methods of K-Nearest Neighbor, Support Vector Machine, and Random Forest (namely KSR) by intersecting their results. Using SI markers improved the identification accuracy compared to using SNPs or InDels alone. We achieved accuracies over 97.5% when using at least the 1000 most breed-informative (MBI) SI markers and even 100% when using 5000 SI markers.ConclusionsOur results provide not only an important foundation for conservation of these Chinese local sheep breeds, but also general approaches for breed identification of indigenous farm animal breeds.

Comparison of Skull Morphometric Characteristics of Simmental and Holstein Cattle Breeds.

This study aimed to reveal the morphological characteristics of pure Holstein and Simmental skulls and to obtain reference values for morphometric analysis. Moreover, 54 skulls from 12- to 14-month-old male Holstein (n = 25) and Simmental (n = 29) cattle were collected from Turkey's Southeastern Anatolia Region between 2023 and 2024. Linear measurements indicated that Holsteins had longer skulls compared to Simmentals. Holsteins exhibited significantly higher values for the greatest length of nasals and the shortest skull length. The facial breadth was wider in Holsteins and statistically distinctive between the breeds. Holsteins had a more oval orbital bony roof, while Simmentals exhibited a wider orbital structure. The orbital index was higher in Holsteins, distinguishing between the two breeds. It was observed that Simmental cattle had a wider occipital region. This difference is likely due to the larger lateral appearance of the Simmental skull, which has more body weight and provides a larger surface area for muscle attachment. These differences not only aid in breed identification but also offer insights into the functional adaptations of each breed. Future research should explore the genetic and environmental factors contributing to these morphological traits, further enriching our knowledge of cattle morphology and its implications for breeding and conservation efforts.

Characterizing the Phan Rang Sheep: A First Look at the Y Chromosome, Mitochondrial DNA, and Morphometrics.

The Phan Rang sheep, considered the sole indigenous breed of Vietnam, are primarily concentrated in the two central provinces of Ninh Thuan and Binh Thuan, with Ninh Thuan accounting for more than 90% of the country's sheep population. These provinces are known for their high temperatures and frequent droughts. The long-standing presence of the Phan Rang sheep in these regions suggests their potential resilience to heat stress-a trait of increasing interest in the face of global climate change. Despite the breed's significance, a critical knowledge gap hinders conservation and breeding programs. To address this, our study employed a two-pronged approach. First, we collected body conformational data to aid in breed identification. Second, we analyzed mitochondrial DNA (D-loop) and Y chromosome markers (SRY and SRYM18) to elucidate the maternal and paternal lineages. Among the 68 Phan Rang sheep analyzed for their D-loop, 19 belonged to mitochondrial haplogroup A, while 49 belonged to haplogroup B. The haplogroups can be subdivided into 16 unique haplotypes. All 19 rams surveyed for their paternal lineages belonged to haplotypes H5 and H6. These findings strongly support the hypothesis of dual origins for the Phan Rang sheep. This study presents the first genetic data for the Phan Rang breed, providing crucial insights for future research and conservation efforts.

Attention module incorporated transfer learning empowered deep learning-based models for classification of phenotypically similar tropical cattle breeds (Bos indicus).

Accurate breed identification in dairy cattle is essential for optimizing herd management and improving genetic standards. A smart method for correctly identifying phenotypically similar breeds can empower farmers to enhance herd productivity. A convolutional neural network (CNN) based model was developed for the identification of Sahiwal and Red Sindhi cows. To increase the classification accuracy, first, cows's pixels were segmented from the background using CNN model. Using this segmented image, a masked image was produced by retaining cows' pixels from the original image while eliminating the background. To improve the classification accuracy, models were trained on four different images of each cow: front view, side view, grayscale front view, and grayscale side view. The masked images of these views were fed to the multi-input CNN model which predicts the class of input images. The segmentation model achieved intersection-over-union (IoU) and F1-score values of 81.75% and 85.26%, respectively with an inference time of 296ms. For the classification task, multiple variants of MobileNet and EfficientNet models were used as the backbone along with pre-trained weights. The MobileNet model achieved 80.0% accuracy for both breeds, while MobileNetV2 and MobileNetV3 reached 82.0% accuracy. CNN models with EfficientNet as backbones outperformed MobileNet models, with accuracy ranging from 84.0% to 86.0%. The F1-scores for these models were found to be above 83.0%, indicating effective breed classification with fewer false positives and negatives. Thus, the present study demonstrates that deep learning models can be used effectively to identify phenotypically similar-looking cattle breeds. To accurately identify zebu breeds, this study will reduce the dependence of farmers on experts.

Study of <i>ASIP</i> and <i>MC1R</i> gene polymorphism horses of the kabardian breed

The coat color of horses is of considerable interest due to its aesthetic appeal and significance in breed identification. Understanding the genetic mechanisms underlying coat color variation is important for horse breeders and geneticists. The purpose of the study is to study the influence of polymorphism of the MC1R and ASIP genes on the coat color of Kabardian horses, a unique domestic breed. The proposal is not agreed upon, the idea is not clear. Why was the research done? Based on the results of genotyping 127 horses of the Kabardian breed using the MC1R and ASIP genes, the frequencies of various alleles and genotypes on which the color of the animals depends were determined. Statistical analysis was carried out to determine the relationship between them. Kabardian horses are characterized by a wide range of colors, including karak, black, bay, dark bay and gray. The majority of the horses studied (96.9 %) were found to have a dominant allele E of the MC1R gene, associated with the synthesis of eumelanin and darker coat color. The frequency of the A/A genotype of the ASIP gene increased as the coat color lightened: black – 0.0 %, karak – 9.5 %, dark bay – 56.6 %, bay – 84.8 %. The frequency of the mutant allele a was 0.280. In general, it contributed to the formation of black coat color in Kabardian horses. The results of the study provide insight into the relationship of the MC1R and ASIP genes with the formation of coat colors in Kabardian horses. These results have practical implications for horse breeders when selecting individuals for crossbreeding and contribute to the understanding of the genetic mechanisms underlying variation in coat color within horse populations.

Dog Breed Identification Using Deep Learning

Deep learning has revolutionized computer vision tasks, including image classification. In this study, we propose a deep learning approach for the identification of dog breeds from images. Leveraging transfer learning with the ResNet50V2 model, our methodology involves preprocessing and augmenting a dataset of dog images to enhance model generalization. Through rigorous experimentation and training, our model achieves competitive accuracy rates in identifying over 60 unique dog breeds. Results indicate the efficacy of our approach in breed classification tasks, with potential applications in pet identification systems and animal welfare initiatives. Keywords: Deep learning, Dog breed identification, Transfer learning, Computer vision.

Markers for meat provenance and authenticity with an account of its defining factors and quality characteristics - a review.

Provenance is becoming increasingly important in meat supply chains as it lends products higher perceived quality. However, its precise definition and interpretation along with its associated characteristics factors have remained somewhat elusive. This review meticulously defines meat provenance while dissecting the essential factors and associated quality attributes that constitute its essence and are subsequently employed to establish pertinent markers for provenance. Meat provenance emerges as a multi-dimensional construct stemming from the adept management of a constellation of factors relating to geographical origin, farm production system, traceability, and authenticity. Through intricate interactions, these factors unveil innate originality that not only forges a distinct reputation but also imparts a unique typicity to the meat product. Gaining insights into a meat product's provenance becomes attainable by scrutinizing its pertinent composition and organoleptic quality traits. Trace elements and stable isotopes stand out as provenance markers, forging a direct connection to both geographical origin and dietary sources. While somewhat less direct in linkage, other markers such as plant biomarkers, fatty acid composition, pH levels, flavour and aromatic compounds along with organoleptic characteristics contribute to the overall understanding of provenance. Additionally, the identification of animal species and breeds serves as key markers, particularly in the context of protected geographical indications. The study findings are useful for the various stakeholders of how the information for meat provenance can be linked with intrinsic and extrinsic factors for meat quality and protecting the integrity of the supply chain with special reference to traceability and authenticity. © 2024 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Optimizing Bangkaew dog breed identification using DNA technology.

The Bangkaew dog is an indigenous dog breed in the Phitsanulok province of Thailand. This breed is recognized by the Fédération Cynologique Internationale (FCI), a global canine organization. The unique traits of the Bangkaew breed lead to purebred selection for breeding, while only their traits and pedigree from parental history are recorded. Determination of the risk of inbreeding depression and the origin of unknown DNA profiles is essential due to the challenges in predicting puppy characteristics, which are crucial for breed management and conservation. This study aimed to emphasize that current allelic frequency data for the Bangkaew dog breed must be considered for precise individual identification. Approximately 82 Bangkaew dogs from various Thai localities were studied using 15 microsatellite markers for genotypic monitoring and individual identification. Maternal genetic inheritance was assessed via mtDNA D-loop analysis. The results revealed high genetic diversity in the Bangkaew breed, indicating low potential for inbreeding. We also found that using a 15 loci microsatellite panel was effective for the identification of Bangkaew dogs. The optimized 10 loci microsatellite genotyping panel developed in this study presents improved identification testing efficiency, promoting both time- and cost-effectiveness. Analysis of microsatellite DNA markers in Bangkaew dogs using an optimized panel of 10 loci selected from 15 loci effectively facilitated individual identification. This approach not only enhances time and cost efficiency, but also provides accurate allelic frequency estimates, which are crucial for the realistic evaluation of DNA evidence.

Dog Breeds Classifier

This paper introduces a novel approach to dog breed detection using deep learning, focusing on the InceptionV5 architecture. It addresses the need for accurate breed identification crucial in pet care, veterinary diagnostics, and animal welfare studies. By training a fine-tuned InceptionV5 model on a Kaggle dataset pre-processed via Google Colab, the system achieves classification based on standard metrics like accuracy and F1-score. Integration into a Flask web app enables real-time breed detection from uploaded images, enhancing accessibility. The study highlights the efficacy of deep learning, particularly InceptionV5, in automating breed identification tasks, offering practical solutions across various applications. Moreover, it discusses potential implications for pet care, veterinary diagnostics, and animal welfare enhancement. Top of Form Key Words — dog breed detection, deep learning, InceptionV5 architecture, pet identification

Pawsome Detect: Harnessing Pattern Recognition and Data Mining for Canine Age and Breed Detection

PAWSOMEDETECT leverages pattern recognition to revolutionize canine age and breed detection. Determining a dog's age and breed from photos has become vital due to rising popularity of dogs as pets and the demand for personalized, data-driven pet care. Traditional methods rely on manual inspection and judgment, prone to errors. Furthermore, mating compatibility among breeds is a consideration for dog owners. This aids users in identifying compatible breeds for mating, increasing successful mating chances. To achieve accurate age and breed, deep learning models are trained on a vast collection of annotated dog photos. These models use Convolutional Neural Networks (CNNs) and transfer learning to improve performance and generalize knowledge, revealing age and breed patterns. Researchers, breeders, and owners can benefit from this data. Following ISO-25010 guidelines, the study confirms the system's accuracy in age and breed determination, as well as its ability to detect user locations for successful mating. It meets response time and resource utilization requirements, ensuring timely operations. In conclusion, "PAWSOMEDETECT" a groundbreaking pattern recognition-based method for accurate age and breed identification in dogs. It provides data-driven solutions for pet care, mating compatibility, and more, benefiting users and dogs. The study's continued enhancements promise even greater accuracy and functionality.

The use of SNP markers for cattle breed identification.

A potential application of single nucleotide polymorphisms (SNPs) in animal husbandry and production is identification of the animal breed. In this study, using chosen marker selection methods and genotypic data obtained with the use of Illumina Bovine SNP50 BeadChip for individuals belonging to ten cattle breeds, the reduced panels containing the most informative SNP markers were developed. The suitability of selected SNP panels for the effective and reliable assignment of the studied individuals to the breed of origin was checked by three allocation algorithms implemented in GeneClass 2. The studied breeds set included both Polish-native breeds under the genetic resources conservation programs and highly productive breeds with a global range. For all of the tested marker selection methods ("delta" and two FST-based variants), two separate methodological approaches of marker assortment were used and three marker panels were created with 96, 192, and 288 SNPs respectively, to determine the minimum number of markers required for effective differentiation of the studied breeds. Moreover, the usefulness of the most effective panels of markers to assess the population structure and genetic diversity of the analyzed breeds was examined. The conducted analyses showed the possibility of using SNP subsets from medium-density genotypic microarrays to distinguish breeds of cattle kept in Poland and to analyze their genetic structure.

Utilizing EfficientNet for sheep breed identification in low-resolution images

Automatically recognizing sheep breeds is highly valuable for the sheep farming industry, allowing farmers to pinpoint their specific business needs. Accurately distinguishing between sheep breeds poses a challenge for numerous farmers with limited expertise. Although biometric-based identification offers a feasible solution, its application becomes impractical when assessing large numbers of sheep in real-time. Therefore, the implementation of an automatic sheep classification model that can replicate the breed identification skills of a sheep breed expert can come in handy. This would be particularly beneficial for novice farmers who could utilize handheld devices for breed classification. To address this objective, we propose employing a convolutional neural network (CNN) model capable of rapidly and accurately identifying sheep breeds from low-resolution images. Our experiment utilized a dataset of 1680 facial images representing four distinct sheep breeds. We conducted experiments on the dataset using various EfficientNet models and found that EfficientNetB5 achieved the highest performance with 97.62 % accuracy on a 10 % test split. The classification model we developed has the potential to assist sheep farmers in efficiently distinguishing between different breeds, facilitating more precise assessments and sector-specific classification for various businesses within the industry.

Study on the Evolution of Genetic Resources of Domestic Animals in Romania

Abstract The paper proposes the identification of animal breeds at risk of abandonment and the analysis of animal genetic resources from the perspective of the evolution of total herds and genetic quality determination programs. Data from the National Institute of Statistics show that in the period 2015–2022, Romania recorded a decline in the total livestock of cattle (−12.34%) and pigs (−32.44%) and slight increases in the total sheep and goats. On the other hand, the National Agency for Animal Husbandry indicates significant decreases in the number of breeding females, both in cattle (−14.7%) and pigs (−32.5%). Within the 4 species of animals analysed (cattle, sheep, goats and pigs), 14 breeds in danger of abandonment were identified, generally native breeds with lower production potential than that of improved breeds. For the identified animal breeds, genetic quality determination programs have been approved, which are implemented by animal breeders’ associations accredited for the establishment and management of Genealogical Registries. Maintaining the genetic diversity of local breeds in danger of abandonment can contribute to the diversification of existing populations, to restoring the vitality of selected lines, to increasing the productivity and disease resistance of animals.

Genomic insights into shank and eggshell color in Italian local chickens

Eggshell and shank color in poultry is an intriguing topic of research due to the roles in selection, breed recognition, and environmental adaptation. This study delves into the genomics foundations of shank and eggshell pigmentation in Italian local chickens through genome-wide association studies analysis to uncover the mechanisms governing these phenotypes. To this purpose, 483 animals from 20 local breeds (n = 466) and 2 commercial lines (n = 17) were considered and evaluated for shank and eggshell color. All animals were genotyped using the Affymetrix Axiom 600 K Chicken Genotyping Array. As regards shank color, the most interesting locus was detected on chromosome Z, close to the TYRP1 gene, known to play a key role in avian pigmentation. Additionally, several novel loci and genes associated with shank pigmentation, skin pigmentation, UV protection, and melanocyte regulation were identified (e.g., MTAP, CDKN2A, CDKN2B). In eggshell, fewer significant loci were identified, including SLC7A11 and MITF on chromosomes 4 and 12, respectively, associated with melanocyte processes and pigment synthesis. This comprehensive study shed light on the genetic architecture underlying shank and eggshell color in Italian native chicken breeds, contributing to a better understanding of this phenomenon which plays a role in breed identification and conservation, and has ecological and economic implications.

Genomic structure analysis and construction of DNA fingerprint for four sheep populations

The Yongdeng Qishan sheep (QS) is a sheep population found locally in China. To gain in-depth knowledge of its population characteristics, three control groups were chosen, comprising the Lanzhou fat-tailed sheep (LFT), TAN sheep (TAN), and Minxian black fur sheep (MBF), inhabiting the nearby environments. This study genotyped a total of 120 individuals from four sheep populations: QS, LFT, TAN, and MBF. Using Specific-Locus Amplified Fragment Sequencing, we conducted genetic diversity, population structure, and selective sweep analysis, and constructed the fingerprint of each population. In total, there were 782 535 single nucleotide polymorphism (SNP) variations identified, with most being situated within regions that are intergenic or intronic. The genetic diversity analysis revealed that the QS population exhibited lower genetic diversity compared to the other three populations. Consistent results were obtained from the principal component, phylogenetic tree, and population structure analysis, indicating significant genetic differences between QS and the other three populations. However, a certain degree of differentiation was observed within the QS population. The linkage disequilibrium (LD) patterns among the four populations showed clear distinctions, with the QS group demonstrating the most rapid LD decline. Kinship analysis supported the findings of population structure, dividing the 90 QS individuals into two subgroups consisting of 23 and 67 individuals. Selective sweep analysis identified a range of genes associated with reproduction, immunity, and adaptation to high-altitude hypoxia. These genes hold potential as candidate genes for marker-assisted selection breeding. Additionally, a total of 86 523 runs of homozygosity (ROHs) were detected, showing non-uniform distribution across chromosomes, with chromosome 1 having the highest coverage percentage and chromosome 26 the lowest. In the high-frequency ROH islands, 79 candidate genes were associated with biological processes such as reproduction and fat digestion and absorption. Furthermore, a DNA fingerprint was constructed for the four populations using 349 highly polymorphic SNPs. In summary, our research delves into the genetic diversity and population structure of QS population. The construction of DNA fingerprint profiles for each population can provide valuable references for the identification of sheep breeds both domestically and internationally.