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.

Korean native chickens (KNCs) comprise an indigenous chicken breed of South Korea that was restored through a government project in the 1990s. The KNC population has not been developed well and has mostly been used to maintain purebred populations in the government research institution. We investigated the genetic features of the KNC population in a selection signal study for the efficient improvement of this breed. We used 600K single nucleotide polymorphism data sampled from 191 KNCs (NG, 38; NL, 29; NR, 52; NW, 39; and NY, 33) and 54 commercial chickens (Hy-line Brown, 10; Lohmann Brown, 10; Arbor Acres, 10; Cobb, 12; and Ross, 12). Haplotype phasing was performed using EAGLE software as the initial step for the primary data analysis. Pre-processed data were analyzed to detect selection signals using the ‘rehh’ package in R software. A few common signatures of selection were identified in KNCs. Most quantitative trait locus regions identified as candidate regions were associated with traits related to reproductive organs, eggshell characteristics, immunity, and organ development. Block patterns with high linkage disequilibrium values were observed for LPP, IGF11, LMNB2, ERBB4, GABRB2, NTM, APOO, PLOA1, CNTN1, NTSR1, DEF3, CELF1, and MEF2D genes, among regions with confirmed selection signals. NL and NW lines contained a considerable number of selective sweep regions related to broilers and layers, respectively. We recommend focusing on improving the egg and meat traits of KNC NL and NW lines, respectively, while improving multiple traits for the other lines.

The major histocompatibility complex (MHC) is a highly polymorphic gene region that regulates cellular communication in all specific immune responses. In this study, we investigated 11 microsatellite (MS) markers in the MHC-B region of chicken populations from four countries: Sri Lanka, Bangladesh, South Korea, and Nigeria. The MS markers were divided into two sets. Set 1 included five novel MS markers, which we assessed using 192 samples from 21 populations. Set 2 included six previously reported markers, which we assessed using 881 samples from 29 populations. The Set 1 MS markers had lower polymorphism (polymorphic information content (PIC) < 0.5) than the Set 2 markers (PIC = 0.4–0.9). In all populations, the LEI0258 marker was the most polymorphic, with a total of 38 alleles (PIC = 0.912, expected heterozygosity (H_e) = 0.918). Local populations from Sri Lanka, Bangladesh, and Nigeria had higher allele diversity and more haplotypes for Set 2 MS markers than Korean and commercial populations. The Sri Lankan Karuwalagaswewa village population had the highest MHC diversity (mean allele number = 8.17, H_e = 0.657), whereas the white leghorn population had the lowest (mean allele number = 2.33, H_e = 0.342). A total of 409 haplotypes (89 shared and 320 unique), with a range of 4 (Rhode Island red) to 46 (Karuwalagaswewa village (TA)), were identified. Among the shared haplotypes, the B21-like haplotype was identified in 15 populations. The genetic relationship observed in a neighbour-joining tree based on the D_A distance agreed with the breeding histories and geographic separations. The results indicated high MHC diversity in the local chicken populations. The difference in the allelic pattern among populations presumably reflects the effects of different genotypes, environments, geographic variation, and breeding policies in each country. The selection of MHC allele in domestic poultry can vary due to intensification of poultry production. Preserved MHC diversity in local chicken provides a great opportunity for future studies that address the relationships between MHC polymorphisms and differential immune responses.