Breed assignment test in four Italian beef cattle breeds

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.

Breed identification using breed-informative SNPs and machine learning based on whole genome sequence data and SNP chip data

BACKGROUND

Breed identification is useful in a variety of biological contexts. Breed identification usually involves two stages, i.e., detection of breed-informative SNPs and breed assignment. For both stages, there are several methods proposed. However, what is the optimal combination of these methods remain unclear. In this study, using the whole genome sequence data available for 13 cattle breeds from Run 8 of the 1,000 Bull Genomes Project, we compared the combinations of three methods (Delta, F_ST, and I_n) for breed-informative SNP detection and five machine learning methods (KNN, SVM, RF, NB, and ANN) for breed assignment with respect to different reference population sizes and difference numbers of most breed-informative SNPs. In addition, we evaluated the accuracy of breed identification using SNP chip data of different densities.

RESULTS

We found that all combinations performed quite well with identification accuracies over 95% in all scenarios. However, there was no combination which performed the best and robust across all scenarios. We proposed to integrate the three breed-informative detection methods, named DFI, and integrate the three machine learning methods, KNN, SVM, and RF, named KSR. We found that the combination of these two integrated methods outperformed the other combinations with accuracies over 99% in most cases and was very robust in all scenarios. The accuracies from using SNP chip data were only slightly lower than that from using sequence data in most cases.

CONCLUSIONS

The current study showed that the combination of DFI and KSR was the optimal strategy. Using sequence data resulted in higher accuracies than using chip data in most cases. However, the differences were generally small. In view of the cost of genotyping, using chip data is also a good option for breed identification.

Evaluating the use of statistical and machine learning methods for estimating breed composition of purebred and crossbred animals in thirteen cattle breeds using genomic information

Introduction: The ability to accurately predict breed composition using genomic information has many potential uses including increasing the accuracy of genetic evaluations, optimising mating plans and as a parameter for genotype quality control. The objective of the present study was to use a database of genotyped purebred and crossbred cattle to compare breed composition predictions using a freely available software, Admixture, with those from a single nucleotide polymorphism Best Linear Unbiased Prediction (SNP-BLUP) approach; a supplementary objective was to determine the accuracy and general robustness of low-density genotype panels for predicting breed composition. Methods: All animals had genotype information on 49,213 autosomal single nucleotide polymorphism (SNPs). Thirteen breeds were included in the analysis and 500 purebred animals per breed were used to establish the breed training populations. Accuracy of breed composition prediction was determined using a separate validation population of 3,146 verified purebred and 4,330 two and three-way crossbred cattle. Results: When all 49,213 autosomal SNPs were used for breed prediction, a minimal absolute mean difference of 0.04 between Admixture vs. SNP-BLUP breed predictions was evident. For crossbreds, the average absolute difference in breed prediction estimates generated using SNP-BLUP and Admixture was 0.068 with a root mean square error of 0.08. Breed predictions from low-density SNP panels were generated using both SNP-BLUP and Admixture and compared to breed prediction estimates using all 49,213 SNPs (representing the gold standard). Breed composition estimates of crossbreds required more SNPs than predicting the breed composition of purebreds. SNP-BLUP required ≥3,000 SNPs to predict crossbred breed composition, but only 2,000 SNPs were required to predict purebred breed status. The absolute mean (standard deviation) difference across all panels <2,000 SNPs was 0.091 (0.054) and 0.315 (0.316) when predicting the breed composition of all animals using Admixture and SNP-BLUP, respectively compared to the gold standard prediction. Discussion: Nevertheless, a negligible absolute mean (standard deviation) difference of 0.009 (0.123) in breed prediction existed between SNP-BLUP and Admixture once ≥3,000 SNPs were considered, indicating that the prediction of breed composition could be readily integrated into SNP-BLUP pipelines used for genomic evaluations thereby avoiding the necessity for a stand-alone software.

Development of a genomic tool for breed assignment by comparison of different classification models: Application to three local cattle breeds

Assignment of individual cattle to a specific breed can often not rely on pedigree information. This is especially the case for local breeds for which the development of genomic assignment tools is required to allow individuals of unknown origin to be included to their herd books. A breed assignment model can be based on two specific stages: (a) the selection of breed-informative markers and (b) the assignment of individuals to a breed with a classification method. However, the performance of combination of methods used in these two stages has been rarely studied until now. In this study, the combination of 16 different SNP panels with four classification methods was developed on 562 reference genotypes from 12 cattle breeds. Based on their performances, best models were validated on three local breeds of interest. In cross-validation, 14 models had a global cross-validation accuracy higher than 90%, with a maximum of 98.22%. In validation, best models used 7,153 or 2,005 SNPs, based on a partial least squares-discriminant analysis (PLS-DA) and assigned individuals to breeds based on nearest shrunken centroids. The average validation sensitivity of the first two best models for the three local breeds of interest were 98.33% and 97.5%. Moreover, results reported in this study suggest that further studies should consider the PLS-DA method when selecting breed-informative SNPs.

SNPs with intermediate minor allele frequencies facilitate accurate breed assignment of Indian Tharparkar cattle

Tharparkar cattle breed is widely known for its superior milch quality and hardiness attributes. This study aimed to develop an ultra-low density breed-specific single nucleotide polymorphism (SNP) genotype panel to accurately quantify Tharparkar populations in biological samples. In this study, we selected and genotyped 72 Tharparkar animals randomly from Cattle & Buffalo Farm of IVRI, India. This Bovine SNP50 BeadChip genotypic datum was merged with the online data from six indigenous cattle breeds and five taurine breeds. Here, we used a combination of pre-selection statistics and the MAF-LD method developed in our laboratory to analyze the genotypic data obtained from 317 individuals of 12 distinct breeds to identify breed-informative SNPs for the selection of Tharparkar cattle. This methodology identified 63 unique Tharparkar-specific SNPs near intermediate gene frequencies. We report several informative SNPs in genes/QTL regions affecting phenotypes or production traits that might differentiate the Tharparkar breed.

KDM2B-associated paunch calf syndrome in Marchigiana cattle

BACKGROUND

Chianina, Romagnola, and Marchigiana are the 3 most important Italian breeds of cattle raised in the Apennine Mountains. Inherited disorders have been reported in the Chianina and Romagnola breeds but not in the Marchigiana breed. Recently, a case resembling recessively inherited KDM2B-associated paunch calf syndrome (PCS) in Romagnola cattle was identified in Marchigiana cattle.

HYPOTHESIS/OBJECTIVES

To characterize the features of the observed congenital anomaly, evaluate its possible genetic etiology, and determine the prevalence of the deleterious allele in the Marchigiana population.

ANIMALS

A single stillborn Marchigiana calf was referred for clinicopathological examination because of the presence of PCS-like morphological lesions.

METHODS

The animal was necropsied and the calf and its parents were genotyped. A PCR-based direct gene test was applied to determine the KDM2B genotype and 114 Marchigiana bulls were genotyped.

RESULTS

The pathological phenotype included facial deformities, enlarged fluid-filled abdomen, and hepatic fibrosis. The affected animal was the offspring of consanguineous mating and homozygous presence of the KDM2B missense variant was confirmed. Both parents were heterozygous for KDM2B and the prevalence of carriers in a selected population of Marchigiana bulls was <2%.

CONCLUSIONS AND CLINICAL IMPORTANCE

The characteristic malformations and genetic findings were consistent with the diagnosis of PCS and provide evidence that the deleterious KDM2B variant initially detected in Romagnola cattle also occurs in the Marchigiana breed.

Comparing assignment-based approaches to breed identification within a large set of horses

Considering the extensive data sets and statistical techniques, animal breeding embodies a branch of machine learning that has a constantly increasing impact on breeding. In our study, information regarding the potential of machine learning and data mining within a large set of horses and breeds is presented. The individual assignment methods and factors influencing the success rate of the procedure are compared at the Czech population scale. The fixation index values ranged from 0.057 (HMS1) to 0.144 (HTG6), and the overall genetic differentiation amounted to 8.9% among the breeds. The highest genetic divergence (F_ST = 0.378) was established between the Friesian and Equus przewalskii; the highest degree of gene migration was obtained between the Czech and Bavarian Warmblood (N_m = 14,302); and the overall global heterozygote deficit across the populations was 10.4%. The eight standard methods (Bayesian, frequency, and distance) using GeneClass software and almost all mainstream classification algorithms (Bayes Net, Naive Bayes, IB1, IB5, KStar, JRip, J48, Random Forest, Random Tree, PART, MLP, and SVM) from the WEKA machine learning workbench were compared by utilizing 314,874 real allelic data sets. The Bayesian method (GeneClass, 89.9%) and Bayesian network algorithm (WEKA, 84.8%) outperformed the other techniques. The breed genomic prediction accuracy reached the highest value in the cold-blooded horses. The overall proportion of individuals correctly assigned to a population depended mainly on the breed number and genetic divergence. These statistical tools could be used to assess breed traceability systems, and they exhibit the potential to assist managers in decision-making as regards breeding and registration.

Effectiveness of a 95 SNP panel for the screening of breed label fraud in the Chinese meat market

Breed assignment has proved to be useful to control meat trade and protect the value of special productions. Meat-related frauds have been detected in China; therefore, 95 SNPs selected from the ISAG core panel were evaluated to develop an automated and technologically updated tool to screen breed label fraud in the Chinese meat market. A total of 271 animals from four Chinese yellow cattle (CYC) populations, six Bos taurus breeds, two Bos indicus and one composite were used. The allocation test distinguished European, Japanese and Zebu breeds, and two Chinese genetic components. It correctly allocated Japanese Black, Zebu and British breeds in 100, 90 and 89% of samples, respectively. CYC evidenced the Zebu, Holstein and Limousin introgression. The test did not detect CYC components in any of the 25 samples from Argentinean butchers. The method could be useful to certify Angus, Hereford and Japanese Black meat, but a modification in the panel would be needed to differentiate other breeds.

The evaluation of the usefulness of pedigree verification-dedicated SNPs for breed assignment in three polish cattle populations

The breed assignment in cattle is one of the issues of molecular genetics which needs further testing and development. Although several statistical approaches have been developed to enable such application, the obtained results strongly depend on specific populations differentiation and power of markers discrimination or their informativeness. Currently, all breeding animals are being tested for parentage with the use of panel of 12 microsatellite markers, which in near future probably will be replaced by about 100 single nucleotide polymorphisms (SNPs). Despite the fact that SNPs are mainly bi-allelic, the multilocus genotypes can reach the level of polymorphism of a panel of microsatellite markers. In this study we attempted to determine the breed of origin of 741 cattle by using 120 SNPs dedicated for parentage testing and included in the BovineSNP50 BeadChip genotyping assay (Illumina). The applied Bayesian and frequency-based methods allowed such differentiation, however, the reliability of the results was not completely satisfying, suggesting that the studied markers are not the best tool for breed assignment.

Molecular traceability of beef from synthetic Mexican bovine breeds

Traceability ensures a link between carcass, quarters or cuts of beef and the individual animal or the group of animals from which they are derived. Meat traceability is an essential tool for successful identification and recall of contaminated products from the market during a food crisis. Meat traceability is also extremely important for protection and value enhancement of good-quality brands. Molecular meat traceability would allow verification of conventional methods used for beef tracing in synthetic Mexican bovine breeds. We evaluated a set of 11 microsatellites for their ability to identify animals belonging to these synthetic breeds, Brangus and Charolais/Brahman (78 animals). Seven microsatellite markers allowed sample discrimination with a match probability, defined as the probability of finding two individuals sharing by chance the same genotypic profile, of 10(-8). The practical application of the marker set was evaluated by testing eight samples from carcasses and pieces of meat at the slaughterhouse and at the point of sale. The DNA profiles of the two samples obtained at these two different points in the production-commercialization chain always proved that they came from the same animal.

Authentication of meat and meat products

In recent years, interest in meat authenticity has increased. Many consumers are concerned about the meat they eat and accurate labelling is important to inform consumer choice. Authentication methods can be categorised into the areas where fraud is most likely to occur: meat origin, meat substitution, meat processing treatment and non-meat ingredient addition. Within each area the possibilities for fraud can be subcategorised as follows: meat origin-sex, meat cuts, breed, feed intake, slaughter age, wild versus farmed meat, organic versus conventional meat, and geographic origin; meat substitution-meat species, fat, and protein; meat processing treatment-irradiation, fresh versus thawed meat and meat preparation; non-meat ingredient addition-additives and water. Analytical methods used in authentication are as diverse as the authentication problems, and include a diverse range of equipment and techniques. This review is intended to provide an overview of the possible analytical methods available for meat and meat products authentication. In areas where no authentication methods have been published, possible strategies are suggested.