Genetic diversity and breed identification of Chinese and Vietnamese local chicken breeds based on microsatellite analysis.

South Asia and Southeast Asia are the origins of domestic chickens and are rich in poultry genetic resources, resulting in many unique local chicken breeds. However, with the rapid intensification of poultry farming worldwide, many local chicken breeds are threatened with extinction. In response to China's "One Belt, One Road" policy, it is imperative to strengthen the conservation and breeding of local chicken breeds in China and Vietnam. This study characterized 18 microsatellite molecular genetic markers to analyze the genetic diversity of 21 local chicken populations in southern China (Yunnan and Guangxi Provinces) and Vietnam, breed identification tags for microsatellite loci were constructed. The results showed that a total of 377 alleles were detected in all breeds, and the most alleles (44) and the highest polymorphic information content (0.7820) were detected at the LEI0094 locus. The average polymorphic information content (PIC) content of the whole population was 0.65, indicating moderate polymorphism. The genetic diversity of the whole population was rich, except for two loci MCW0111 and MCW0016, that showed heterozygote excess at microsatellite loci, and the population had high genetic differentiation. The Vietnamese breeds showed low pairwise fixation coefficient (FST) and Nei's standard genetic distance (DS) between them. According to the neighbor-joining dendrogram constructed by DS and the analysis of population genetic structure using the structure program, Longshengfeng chicken, Yunlong dwarf chicken, Tengchong white chicken, Xiayan chicken, and Daweishan mini chicken are similar, and Xishuangbanna game fowl, Wuding chicken, and Lanping silky chicken are similar to Yanjin black-bone chicken. In addition, excluding Dongtao chicken, other Vietnamese breeds are clustered together, indicating that the southern chicken breeds are closely related and have experienced better breeding. Overall, the whole population is rich in genetic resources, and the chicken breeds in the three regions are genetically close because of geographical factors and human activities. Dongtao chicken in Vietnamese, Chinese Yunnan local chicken breeds (Gallus gallus spadiceus), and red jungle fowl chickens (Gallus gallus) may have the same origin. We also constructed unique microsatellite molecular markers for 20 cultivars using 15 microsatellite loci. This study provides valuable insights to facilitate breed identification, improve cultivar protection, and new germplasm construction.

China's Yunnan, Guangxi, and Vietnam are rich in biodiversity, and a wide variety of local chicken breeds exist. Due to the rapid development of intensive farming, the biodiversity of chicken breeds in these two regions has gradually decreased. To protect the diversity of local breeds, and to promote the exchange of germplasm resources and the creation of new germplasm between China and Vietnam, this study analyzed the genetic diversity of some chicken breeds in Yunnan, Guangxi, and Vietnam by using microsatellite molecular markers. Studies have shown that the entire population has rich genetic resources, and Vietnamese chickens have a strong kinship with some local chicken breeds in Yunnan Province, China, and there is also a high degree of hybridization between Vietnamese local chickens. In addition, since some chicken breeds have more similarities in appearance, this study constructed unique molecular tags for different breed-specific alleles as one of the methods to accurately distinguish different breeds for breed protection and subsequent breeding.

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, FST, and In) 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.

Evaluation of six machine learning classification algorithms in pig breed identification using SNPs array data.

Breed identification utilizing multiple information sources and methods is widely applicated in the field of animal genetics and breeding. Simultaneously, with the development of artificial intelligence, the integration of high-throughput genomic data and machine learning techniques is increasingly used for breed identification. In this context, we used 654 individuals from 15 pig breeds, evaluating the performance of machine learning and stacking ensemble learning classifiers, as well as the function of feature selection and anomaly detection in different scenarios. Our results showed that, when using a training set of 16 individuals per breed and 32 features (SNPs), the accuracy of breed identification with feature selection (eXtreme Gradient Boosting, XGBoost) could exceed 95.00% (nine breeds), and was improved by 7.04% over the results with random selection. For stacking ensemble learning, feature selection methods (including random selection method) were used before different base learners. When these base learners' training set had 16 individuals per breed and 32 features, the accuracy of stacking ensemble learning improved by 9.24% over the best base learner (nine breeds), but did not significantly increase the advantage over the models with XGBoost feature selection. When using a training set of 16 individuals and 512 features per breed, breed identification with anomaly detection (local outlier factor, LOF) and random selection could achieve an accuracy of 89.06% (15 breeds). These results show that machine learning could be an effective tool for breed identification and this study will also provide useful information for the application of machine learning in animal genetics and breeding.

Screening Discriminating SNPs for Chinese Indigenous Pig Breeds Identification Using a Random Forests Algorithm.

Chinese indigenous pig breeds have unique genetic characteristics and a rich diversity; however, effective breed identification methods have not yet been well established. In this study, a genotype file of 62,822 single-nucleotide polymorphisms (SNPs), which were obtained from 1059 individuals of 18 Chinese indigenous pig breeds and 5 cosmopolitan breeds, were used to screen the discriminating SNPs for pig breed identification. After linkage disequilibrium (LD) pruning filtering, this study excluded 396 SNPs on non-constant chromosomes and retained 20.92~-27.84% of SNPs for each of the 18 autosomes, leaving a total of 14,823 SNPs. The principal component analysis (PCA) showed the largest differences between cosmopolitan and Chinese pig breeds (PC1 = 10.452%), while relatively small differences were found among the 18 indigenous pig breeds from the Yangtze River Delta region of China. Next, a random forest (RF) algorithm was used to filter these SNPs and obtain the optimal number of decision trees (ntree = 1000) using corresponding out-of-bag (OOB) error rates. By comparing two different SNP ranking methods in the RF analysis, the mean decreasing accuracy (MDA) and mean decreasing Gini index (MDG), the effects of panels with different numbers of SNPs on the assignment accuracy, and the statistics of SNP distribution on each chromosome in the panels, a panel of 1000 of the most breed-discriminative tagged SNPs were finally selected based on the MDA screening method. A high accuracy (>99.3%) was obtained by the breed prediction of 318 samples in the RF test set; thus, a machine learning classification method was established for the multi-breed identification of Chinese indigenous pigs based on a low-density panel of SNPs.

Single nucleotide polymorphism marker combinations for classifying Yeonsan Ogye chicken using a machine learning approach.

Genetic analysis has great potential as a tool to differentiate between different species and breeds of livestock. In this study, the optimal combinations of single nucleotide polymorphism (SNP) markers for discriminating the Yeonsan Ogye chicken (Gallus gallus domesticus) breed were identified using high-density 600K SNP array data. In 3,904 individuals from 198 chicken breeds, SNP markers specific to the target population were discovered through a case-control genome-wide association study (GWAS) and filtered out based on the linkage disequilibrium blocks. Significant SNP markers were selected by feature selection applying two machine learning algorithms: Random Forest (RF) and AdaBoost (AB). Using a machine learning approach, the 38 (RF) and 43 (AB) optimal SNP marker combinations for the Yeonsan Ogye chicken population demonstrated 100% accuracy. Hence, the GWAS and machine learning models used in this study can be efficiently utilized to identify the optimal combination of markers for discriminating target populations using multiple SNP markers.

[Review of genetic diversity and breed identification of black-bone silky fowl].

Black-bone silky fowl, sweet, pungent, and hot-natured, is one of the valuable domesticated birds with special economic value in China's genebank of poultry breed, which has a long history of medicinal and edible uses. It has the effects of tonifying liver and kidney, replenishing Qi and blood, nourishing yin, clearing heat, regulating menstruation, invigorating spleen, and securing essence. Therefore, it has remarkable efficacy of enhancing physical strength, tonifying blood, and treating diabetes and gynecological diseases. Various local black-bone silky fowl breeds have been generated due to the differences in environmental conditions, breed selection, and rearing conditions in different areas of China, which are mainly concentrated in Taihe, Wan'an, and Ji'an in Jiangxi province and Putian, Jinjiang, and Yongchun in Fujian province. The indigenous chicken breeds in China have different body sizes, appearance, coat colors, etc. The complex lineages lead to extremely unstable genetic traits. The diverse breeds similar in appearance result in the confusion in the market of silky fowl breeds. With the rapid development of molecular biological technology, the genetics of black-bone silky fowls has been intensively studied. This article reviews the research progress of the germplasm resources, genetic diversity, and breed identification of black-bone silky fowl in China at the morphology, chromosome, protein, and DNA levels. Further, it introduces the principles, application status, and limitations of DNA markers such as mitochondrial DNA, microsatellite markers, and SNPs. This review provides a theoretical basis for the mining of elite trait genes and the protection and utilization of local black-bone silky fowl germplasm resources in China.

Review of genetic diversity and breed identification of black-bone silky fowl / 中国中药杂志

Black-bone silky fowl, sweet, pungent, and hot-natured, is one of the valuable domesticated birds with special economic value in China's genebank of poultry breed, which has a long history of medicinal and edible uses. It has the effects of tonifying liver and kidney, replenishing Qi and blood, nourishing yin, clearing heat, regulating menstruation, invigorating spleen, and securing essence. Therefore, it has remarkable efficacy of enhancing physical strength, tonifying blood, and treating diabetes and gynecological diseases. Various local black-bone silky fowl breeds have been generated due to the differences in environmental conditions, breed selection, and rearing conditions in different areas of China, which are mainly concentrated in Taihe, Wan'an, and Ji'an in Jiangxi province and Putian, Jinjiang, and Yongchun in Fujian province. The indigenous chicken breeds in China have different body sizes, appearance, coat colors, etc. The complex lineages lead to extremely unstable genetic traits. The diverse breeds similar in appearance result in the confusion in the market of silky fowl breeds. With the rapid development of molecular biological technology, the genetics of black-bone silky fowls has been intensively studied. This article reviews the research progress of the germplasm resources, genetic diversity, and breed identification of black-bone silky fowl in China at the morphology, chromosome, protein, and DNA levels. Further, it introduces the principles, application status, and limitations of DNA markers such as mitochondrial DNA, microsatellite markers, and SNPs. This review provides a theoretical basis for the mining of elite trait genes and the protection and utilization of local black-bone silky fowl germplasm resources in China.

Omics-Based Analytical Approaches for Assessing Chicken Species and Breeds in Food Authentication.

Chicken is known to be the most common meat type involved in food mislabeling and adulteration. Establishing a method to authenticate chicken content precisely and identifying chicken breeds as declared in processed food is crucial for protecting consumers' rights. Categorizing the authentication method into their respective omics disciplines, such as genomics, transcriptomics, proteomics, lipidomics, metabolomics, and glycomics, and the implementation of bioinformatics or chemometrics in data analysis can assist the researcher in improving the currently available techniques. Designing a vast range of instruments and analytical methods at the molecular level is vital for overcoming the technical drawback in discriminating chicken from other species and even within its breed. This review aims to provide insight and highlight previous and current approaches suitable for countering different circumstances in chicken authentication.

Genomic diversity and population structure of three autochthonous Greek sheep breeds assessed with genome-wide DNA arrays.

In the present study, genome-wide genotyping was applied to characterize the genetic diversity and population structure of three autochthonous Greek breeds: Boutsko, Karagouniko and Chios. Dairy sheep are among the most significant livestock species in Greece numbering approximately 9 million animals which are characterized by large phenotypic variation and reared under various farming systems. A total of 96 animals were genotyped with the Illumina's OvineSNP50K microarray beadchip, to study the population structure of the breeds and develop a specialized panel of single-nucleotide polymorphisms (SNPs), which could distinguish one breed from the others. Quality control on the dataset resulted in 46,125 SNPs, which were used to evaluate the genetic structure of the breeds. Population structure was assessed through principal component analysis (PCA) and admixture analysis, whereas inbreeding was estimated based on runs of homozygosity (ROHs) coefficients, genomic relationship matrix inbreeding coefficients (FGRM) and patterns of linkage disequilibrium (LD). Associations between SNPs and breeds were analyzed with different inheritance models, to identify SNPs that distinguish among the breeds. Results showed high levels of genetic heterogeneity in the three breeds. Genetic distances among breeds were modest, despite their different ancestries. Chios and Karagouniko breeds were more genetically related to each other compared to Boutsko. Analysis revealed 3802 candidate SNPs that can be used to identify two-breed crosses and purebred animals. The present study provides, for the first time, data on the genetic background of three Greek indigenous dairy sheep breeds as well as a specialized marker panel that can be applied for traceability purposes as well as targeted genetic improvement schemes and conservation programs.

RAPD/SCAR Approaches for Identification of Adulterant Breeds' Milk in Dairy Products.

Food safety and quality are nowadays a major consumers' concern. In the dairy industry the fraudulent addition of cheaper/lower-quality milks from nonlegitimate species/breeds compromises the quality and value of the final product. Despite the already existing approaches for identification of the species origin of milk, there is little information regarding differentiation at an intra-species level. In this protocol we describe a low-cost, sensitive, fast, and reliable analytical technique-Random Amplified Polymorphic DNA/Sequence Characterized Amplified Region (RAPD/SCAR)-capable of an efficient detection of adulterant breeds in milk mixtures used for fraudulent manufacturing of dairy products and suitable for the detection of milk adulteration in processed dairy foods.

Human hospitalisations due to dog bites in Ireland (1998-2013): Implications for current breed specific legislation.

The aim of this study was to examine the efficacy of the current breed specific legislation in Ireland by investigating all dog bite hospital admissions throughout Ireland since that legislation was introduced. Data for statistical analyses were acquired through the National Hospital In-Patient Enquiry Scheme. In years 1998-2013, a total of 3164 human hospitalisations (admissions for dog bite) occurred in Ireland. Incidence of hospitalisations increased over this period (P <0.001). Male humans were at greater risk than females of dog bite hospitalisation (P = 0.015). Children under 10 years were identified as an at-risk group. The present legislation is not effective as a dog bite mitigation strategy in Ireland and may be contributing to a rise in hospitalisations.

A SNP test to identify Africanized honeybees via proportion of 'African' ancestry.

The honeybee, Apis mellifera, is the world's most important pollinator and is ubiquitous in most agricultural ecosystems. Four major evolutionary lineages and at least 24 subspecies are recognized. Commercial populations are mainly derived from subspecies originating in Europe (75-95%). The Africanized honeybee is a New World hybrid of A. m. scutellata from Africa and European subspecies, with the African component making up 50-90% of the genome. Africanized honeybees are considered undesirable for bee-keeping in most countries, due to their extreme defensiveness and poor honey production. The international trade in honeybees is restricted, due in part to bans on the importation of queens (and semen) from countries where Africanized honeybees are extant. Some desirable strains from the United States of America that have been bred for traits such as resistance to the mite Varroa destructor are unfortunately excluded from export to countries such as Australia due to the presence of Africanized honeybees in the USA. This study shows that a panel of 95 single nucleotide polymorphisms, chosen to differentiate between the African, Eastern European and Western European lineages, can detect Africanized honeybees with a high degree of confidence via ancestry assignment. Our panel therefore offers a valuable tool to mitigate the risks of spreading Africanized honeybees across the globe and may enable the resumption of queen and bee semen imports from the Americas.

Is that dog a pit bull? A cross-country comparison of perceptions of shelter workers regarding breed identification.

Bull breeds are commonly kept as companion animals, but the pit bull terrier is restricted by breed-specific legislation (BSL) in parts of the United States and throughout the United Kingdom. Shelter workers must decide which breed(s) a dog is. This decision may influence the dog's fate, particularly in places with BSL. In this study, shelter workers in the United States and United Kingdom were shown pictures of 20 dogs and were asked what breed each dog was, how they determined each dog's breed, whether each dog was a pit bull, and what they expected the fate of each dog to be. There was much variation in responses both between and within the United States and United Kingdom. UK participants frequently labeled dogs commonly considered by U.S. participants to be pit bulls as Staffordshire bull terriers. UK participants were more likely to say their shelters would euthanize dogs deemed to be pit bulls. Most participants noted using dogs' physical features to determine breed, and 41% affected by BSL indicated they would knowingly mislabel a dog of a restricted breed, presumably to increase the dog's adoption chances.