Genetically selected modern broiler chickens have acquired outstanding production efficiency through rapid growth and improved feed efficiency compared to unselected chicken breeds. Recently, we analyzed the transcriptome of breast muscle tissues obtained from modern pedigree male (PeM) broilers (rapid growth and higher efficiency) and foundational Barred Plymouth Rock (BPR) chickens (slow growth and poorer efficiency). This study was designed to investigate microRNAs that play role in rapid growth of the breast muscles in modern broiler chickens. In this study, differential abundance of microRNA (miRNA) was analyzed in breast muscle of PeM and BPR chickens and the results were integrated with differentially expressed (DE) mRNA in the same tissues. A total of 994 miRNA were identified in PeM and BPR chicken lines from the initial analysis of small RNA sequencing data. After filtering and statistical analyses, the results showed miR-2131-5p, miR-221-5p, miR-126-3p, miR-146b-5p, miR-10a-5p, let-7b, miR-125b-5p, and miR-146c-5p up-regulated whereas miR-206 down-regulated in PeM compared to BPR breast muscle. Based on inhibitory regulations of miRNAs on the mRNA abundance, our computational analysis using miRDB, an online software, predicated that 118 down-regulated mRNAs may be targeted by the up-regulated miRNAs, while 35 up-regulated mRNAs appear to be due to a down-regulated miRNA (i.e., miR-206). Functional network analyses of target genes of DE miRNAs showed their involvement in calcium signaling, axonal guidance signaling, and NRF2-mediated oxidative stress response pathways suggesting their involvement in breast muscle growth in chickens. From the integrated analyses of differentially expressed miRNA-mRNA data, we were able to identify breast muscle specific miRNAs and their target genes whose concerted actions can contribute to rapid growth and higher feed efficiency in modern broiler chickens. This study provides foundation data for elucidating molecular mechanisms that govern muscle growth in chickens.

The aim of this study was to investigate the effect of single nucleotide polymorphisms (SNPs) of the melanogenesis associated transcription factor (MITF) and dopachrome tautomerase (DCT) genes on plumage coloration in Asian native duck breeds. MITF encodes a protein for microphthalmia-associated transcription factor, which regulates the development and function of melanocytes for pigmentation of skin, hair, and eyes. Among the tyrosinase-related family genes, DCT is a pigment cell-specific gene that plays important roles in the melanin synthesis pathway and the expression of skin, feather, and retina color. Five Asian duck varieties (black Korean native, white Korean native, commercial Peking, Nageswari, and Bangladeshi Deshi white ducks) were investigated to examine the polymorphisms associated with plumage colors. Among previously identified SNPs, three synonymous SNPs and one indel of MITF and nine SNPs in exon regions of DCT were genotyped. The allele frequencies for SNPs of the black and white plumage color populations were estimated and Fisher's exact test was conducted to assess the association between the allele frequencies of these two populations. Two synonymous SNPs (c.114T>G and c.147T>C) and a 14-bp indel (GCTGCAAAC AGATG) in intron 7 of MITF were significantly associated with the black- and white-colored breeds (pG (p.His313Arg)] in DCT, was highly significantly associated (pG) was significantly associated (p<0.05) with black and white color plumage in the studied duck populations. The results of this study provide a basis for further investigations of the associations between polymorphisms and plumage color phenotypes in Asian duck breeds.

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.

Journal of Animal Science and Technology (JAST) is a peer-reviewed, open access journal publishing original research, review articles and notes in all fields of animal science. Topics covered by the journal include: genetics and breeding, physiology, nutrition of monogastric animals, nutrition of ruminants, animal products (milk, meat, eggs and their by-products) and their processing, grasslands and roughages, livestock environment, animal biotechnology, animal behavior and welfare.

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 (FIS) and among (FIT) 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 (FST). 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.

Objective: Mongolia is one of a few countries that supports over 25 million goats, but genetic diversity, demographic history, and the origin of goat populations in Mongolia have not been well studied. This study was conducted to assess the genetic diversity, phylogenetic status and population structure of Mongolian native goats, as well as to discuss their origin together with other foreign breeds from different countries using hypervariable region 1 (HV1) in mtDNA.Methods: In this study, we examined the genetic diversity and phylogenetic status of Mongolian native goat populations using a 452 base-pair long fragment of HVI of mitochondrial DNA from 174 individuals representing 12 populations. In addition, 329 previously published reference sequences from different regions were included in our phylogenetic analyses.Results: Investigated native Mongolian goats displayed relatively high genetic diversities. After sequencing, we found a total of 109 polymorphic sites that defined 137 haplotypes among investigated populations. Of these, haplotype and nucleotide diversities of Mongolian goats were calculated as 0.997±0.001 and 0.0283±0.002, respectively. These haplotypes clearly clustered into four haplogroups (A, B, C, and D), with the predominance of haplogroup A (90.8%). Estimates of pairwise differences (Fst) and the analysis of molecular variance values among goat populations in Mongolia showed low genetic differentiation and weak geographical structure. In addition, Kazakh, Chinese (from Huanghuai and Leizhou), and Arabian (Turkish and Baladi breeds) goats had smaller genetic differentiation compared to Mongolian goats.Conclusion: In summary, we report novel information regarding genetic diversity, population structure, and origin of Mongolian goats. The findings obtained from this study reveal that abundant haplogroups (A to D) occur in goat populations in Mongolia, with high levels of haplotype and nucleotide diversity.