Application of genomic big data to analyze the genetic diversity and population structure of Korean domestic chickens

Genetic diversity analysis is crucial for maintaining and managing genetic resources. Several studies have examined the genetic diversity of Korean domestic chicken (KDC) populations using microsatellite markers, but it is difficult to capture the characteristics of the whole genome in this manner. Hence, this study analyzed the genetic diversity of several KDC populations using high-density single nucleotide polymorphism (SNP) genotype data. We examined 935 birds from 21 KDC populations, including indigenous and adapted Korean native chicken (KNC), Hyunin and Jeju KDC, and Hanhyup commercial KDC populations. A total of 212,420 SNPs of 21 KDC populations were used for calculating genetic distances and fixation index, and for ADMIXTURE analysis. As a result of the analysis, the indigenous KNC groups were genetically closer and more fixed than the other groups. Furthermore, Hyunin and Jeju KDC were similar to the indigenous KNC. In comparison, adapted KNC and Hanhyup KDC populations derived from the same original species were genetically close to each other, but had different genetic structures from the others. In conclusion, this study suggests that continuous evaluation and management are required to prevent a loss of genetic diversity in each group. Basic genetic information is provided that can be used to improve breeds quickly by utilizing the various characteristics of native chickens.

Identification of Target Chicken Populations by Machine Learning Models Using the Minimum Number of SNPs

A marker combination capable of classifying a specific chicken population could improve commercial value by increasing consumer confidence with respect to the origin of the population. This would facilitate the protection of native genetic resources in the market of each country. In this study, a total of 283 samples from 20 lines, which consisted of Korean native chickens, commercial native chickens, and commercial broilers with a layer population, were analyzed to determine the optimal marker combination comprising the minimum number of markers, using a 600 k high-density single nucleotide polymorphism (SNP) array. Machine learning algorithms, a genome-wide association study (GWAS), linkage disequilibrium (LD) analysis, and principal component analysis (PCA) were used to distinguish a target (case) group for comparison with control chicken groups. In the processing of marker selection, a total of 47,303 SNPs were used for classifying chicken populations; 96 LD-pruned SNPs (50 SNPs per LD block) served as the best marker combination for target chicken classification. Moreover, 36, 44, and 8 SNPs were selected as the minimum numbers of markers by the AdaBoost (AB), Random Forest (RF), and Decision Tree (DT) machine learning classification models, which had accuracy rates of 99.6%, 98.0%, and 97.9%, respectively. The selected marker combinations increased the genetic distance and fixation index (Fst) values between the case and control groups, and they reduced the number of genetic components required, confirming that efficient classification of the groups was possible by using a small number of marker sets. In a verification study including additional chicken breeds and samples (12 lines and 182 samples), the accuracy did not significantly change, and the target chicken group could be clearly distinguished from the other populations. The GWAS, PCA, and machine learning algorithms used in this study can be applied efficiently, to determine the optimal marker combination with the minimum number of markers that can distinguish the target population among a large number of SNP markers.

Genetic diversity and population structure of indigenous chicken of Bangladesh using microsatellite markers

Objective

The objectives of this study were to investigate the genetic diversity, population structure and relatedness among the five chicken populations of Bangladesh using microsatellite markers.

Methods

A total of 161 individuals representing 5 chicken populations (non-descript Deshi [ND], naked neck [NN], hilly [HI], Aseel [AS], and red jungle fowl [JF]) were included in this study to investigate genetic diversity measures, population structure, genetic distance and phylogenetic relationships. Genotyping was performed using 16 selected polymorphic microsatellite markers distributed across 10 chromosomes.

Results

The average observed and expected heterozygosity, mean number of alleles and polymorphic information content were found to be 0.67±0.01, 0.70±0.01, 10.7 and 0.748, respectively in the studied populations. The estimated overall fixation index across the loci (F), heterozygote deficiency within (F_IS) and among (F_IT) chicken populations were 0.04±0.02, 0.05 and 0.16, respectively. Analysis of molecular variance analysis revealed 88.07% of the total genetic diversity was accounted for within population variation and the rest 11.93% was incurred with population differentiation (F_ST). The highest pairwise genetic distance (0.154) was found between ND and AS while the lowest distance was between JF and AS (0.084). Structure analysis depicted that the studied samples can be categorized into four distinct types or varieties (ΔK = 3.74) such as ND, NN, and HI where AS and JF clustered together as an admixed population. The Neighbor-Joining phylogenetic tree and discriminant analysis of principal component also showed close relatedness among three chicken varieties namely AS, HI, and JF.

Conclusion

The results reflected that indigenous chicken of Bangladesh still possess rich genetic diversity but weak differentiation among the studied populations. This finding provides some important insight on genetic diversity measures that could support the designing and implementing of future breeding plans for indigenous chickens of Bangladesh.

Microsatellite based genetic diversity study in indigenous chicken ecotypes of Karnataka

Aim:

The current study was the first of its kind taken upon indigenous ecotypes of the Karnataka in order to unravel the diversity details at 20 chicken microsatellite regions.

Materials and Methods:

210 indigenous chicken belonging to six districts of Bangalore and Mysore division formed the target sample for the present study. The genomic deoxyribonucleic acid was isolated by phenol chloroform isoamyl alcohol method. A panel of 20 microsatellite regions, including 14 recommended by FAO and six identified from published scientific literature became the targeted chicken genomic region. 27-33 samples were successfully genotyped in each of the six ecotypes through simplex or multiplex polymerase chain reactions, polyacrylamide gel electrophoresis and silver staining for the selected microsatellite panel.

Results:

The chickens of Ramanagara and Chamrajnagara were most distant with a Nei’s genetic distance value of 0.22. The chickens of Bangalore rural and Mysore were least distant with a value of 0.056. The Ramanagara and Chamrajnagara pair had Nei’s genetic identity value of 0.802, which is least among all pairs of ecotypes. There were five main nodes from which the six ecotypes evolved on the basis 20 microsatellite markers used in this study. This study indicates that the four ecotypes Ramnagara, Bangalore Rural, Chickaballapura and Mysore are genetically identical due to their common ancestral evolution while, Mandya and Chamrajnagara ecotypes formed a relatively different cluster due to a separate common ancestral chicken population and less number of generations since drifting from bifurcation node.

Conclusion:

Twenty microsatellite markers based genetic diversity study on six indigenous ecotypes indicated lower genetic distances as well as lower F_ST values compared to the distinguished breeds reported. There were two main clusters, which differentiated into six ecotypes. They may differentiate into more distinct varieties if bred in isolation for a longer number of generations.