Dongwon Seo

Single nucleotide polymorphism (SNP) genotyping panels for domesticated ducks are currently lacking, despite their importance for genetic diversity studies and genomic selection. We analyzed whole-genome sequencing data from 74 ducks. To obtain a 40K SNP marker set, we performed SNP calling and applied minor allele frequency (MAF) and linkage disequilibrium (LD) criteria. SNPs from 33 genes associated with Quantitative Trait Loci (QTL) were manually selected and added to the panel. The performance of the resulting SNP panel was validated using genotyping data from 28 additional duck samples. We developed a 40K SNP genotyping panel for ducks. Applying criteria of call rate, MAF, and LD, 37,386 SNP markers were selected. After excluding SNPs near repeats and indels, and incorporating 591 SNPs from 33 duck QTL-associated genes, the final set consisted of 35,613 SNP markers. When evaluating the performance of the SNP panel, genotypes were successfully called for 35,209 out of 35,613 SNPs, achieving a call rate of 98.9%. After applying QC filters, 33,870 SNPs were confirmed to be usable. Our SNP panel design process successfully generated a dataset at a 40K scale without compromising the genetic distances of the whole genome. By minimizing variation at the SNP probe alignment sites, we achieved a high call rate. The panel demonstrated reasonable robustness and suitability for further genomic studies, making it a valuable tool for genetic improvement and trait selection in duck populations.

Objective: Chickens and ducks represent the most important poultry species within the livestock industry. However, the availability of single nucleotide polymorphism (SNP) chips for these species is limited, and development of separate chips for each species requires considerable expense. To address this limitation, we developed the first poultry SNP chip applicable to both species simultaneously. The 60K SNP chip comprises 30,816 SNPs for chickens and 35,209 SNPs for ducks. The performance of the chip was evaluated using non-pooled datasets (chicken only and duck only) and a pooled dataset combining chicken and duck DNA to verify that genotyping accuracy was maintained without crossspecies interference.Methods: DNA was extracted from 28 Korean native chickens and 28 Korean native ducks. Genotyping was performed using the Illumina platform, employing chicken-only, duck-only, and pooled chicken-duck SNP panels. Genotype accuracy and concordance were evaluated with PLINK. Sample and SNP quality were assessed in accordance with the Illumina genotyping protocol. Principal component analysis (PCA) was conducted to evaluate and compare the chicken-duck pooled dataset with the non-pooled datasets.Results: The mean genotype concordance of non-pooled (chicken only and duck only) datasets exceeded 99%, while concordance between non-pooled and chicken-duck pooled datasets surpassed 97.8%. All datasets satisfied quality thresholds for call rate, GenCall score, 10% GenCall score, and cluster separation. PCA revealed consistent clustering patterns, with no significant differences observed between non-pooled and pooled datasets.Conclusion: The first, at the best of our knowledge, poultry SNP chip incorporating chicken-duck pooled samples, has been successfully developed and validated. This chip provides reliable genotyping performance for both species and presents a cost-effective option for large-scale SNP chip development in the livestock industry.

The objectives of this paper were to assess the genetic diversity, population structure, genetic differentiation, and phylogenetic relationships among seven duck populations using 14 microsatellite (MS) markers. This paper included 176 individuals representing seven duck populations of Bangladesh: indigenous duck (BLD), Nageswari (NAG), Rupali (RUP), Jinding (JIN), Pekin (PEK), BAU Black and White (BWC), and BAU White (WHC). A total of 133 alleles were observed with a mean of 9.50 alleles per locus. Genetic diversity was evaluated using measures such as allele frequency, observed and expected heterozygosity, and Shannon’s information index with average values of 5.44 ± 0.31, 0.59 ± 0.02, 0.64 ± 0.02, and 1.28 ± 0.05, respectively. Population differentiation and inbreeding analysis (F-statistics) indicated moderate genetic diversity and a slight degree of inbreeding across populations. Analysis of molecular variance indicated that 75% of the total genetic diversity was attributable to the within-population variation, whereas 9% and 16% were attributed to the variation among individuals and population differentiation, respectively. Indigenous duck populations (BLD, NAG, and RUP) had a close genetic relationship with JIN ducks and an intermediate relationship with two crossbreds (BWC and WHC), and the highest genetic distance was observed with PEK ducks. Neighbor-joining phylogenetic analysis revealed that the Bangladeshi indigenous duck populations formed a single cluster, while the two crossbreds (BWC and WHC) and PEK exhibited their distinct genetic identities in separate clusters. Furthermore, structure analysis at K = 2 to 5 confirmed the distinct genetic architecture (ΔK = 4.00) of the studied duck populations. This paper provides important insights into genetic diversity measures and population differentiation that will be helpful in future genetic improvement, conservation initiatives, and the design of appropriate breeding programs.

This study evaluated the genetic diversity and population structure of four Korean native chicken lines using 30K SNP chip data. A total of 192 birds from the four lines were analyzed to assess heterozygosity, inbreeding coefficients, linkage disequilibrium (LD), effective population size (Ne), and inter-line relationships. All lines exhibited similar levels of genetic diversity, with slightly negative inbreeding coefficient values indicating minimal inbreeding. LD decay patterns were comparable across lines, and Ne values suggested stable long-term genetic variability. Principle component analysis, population structure analysis, and phylogenetic analyses consistently showed clear separation among the four lines, with the Ogye line most distinct and the grey-brown and white line pair genetically closest. Overall, these results demonstrate that the current breeding and mating scheme effectively maintains within-line genetic diversity while preserving distinct line identities.

Objective: This study aimed to identify the optimal single nucleotide polymorphism (SNP) panel density for accurate imputation in the Korean native chicken (KNC) and Yeonsan Ogye (YO) populations. The primary focus was on evaluating how the reference population size and SNP density influence imputation performance and accuracy.Methods: Data were collected from five purebred lines of KNC and the YO population, comprising a total of 256 KNC and 199 YO chickens. Imputed genotype ratio and accuracy were evaluated across various scenarios using SNP densities of 2.5K, 5K, 10K, and 50K in both populations. Additionally, for the YO dataset, reference population sizes of 50, 100, and 150 were analyzed to assess their impact on imputation outcomes.Results: Higher SNP densities notably improved imputation performance. Specifically, when SNP panel density reached 10K or greater, the ratio of imputed SNPs exceeded 70% and the accuracy increased substantially, regardless of the reference population size. However, imputation efficiency decreased markedly when either the reference or test population size was around 50 individuals.Conclusion: A test SNP density of at least 10K was determined to be essential for accurate genotype imputation. Additionally, imputation efficiency was observed to decline when either the reference or test population included around 50 individuals. These findings provide important data that can guide the genetic improvement of indigenous livestock populations.

In swine breeding programs, it has now become critically important to emphasize selection for resilience to external environmental stress factors that have negatively impacted the productivity of pigs, such as those due to climate change induced temperature increases, or the intensification of housing environments. Secretion of cortisol, a neurophysiological change mediated by the hypothalamic-pituitary-adrenal axis, is a central mechanism in the biological stress response. This hormone is closely related to pig robustness and health and can serve as an informative indicator of stress resistance and robustness in pigs. To identify positional candidate genes and their genetic variants influencing blood cortisol levels, we conducted genome-wide association study (GWAS), joint linkage and linkage disequilibrium (LALD) mapping and Bayesian fine-mapping analysis in an F2 resource population generated by crossing Duroc pigs with Korean native pigs. The data used in the study included 243 F2 animals. We utilized imputed whole-genome sequencing data for our analyses. GWAS results revealed a genome-wide significant quantitative trait locus (q-value < 0.05) located within a ~2.46 Mb region between single nucleotide polymorphisms 7:114031215 and 7:116497417 on pig chromosome 7, which accounted for 12.65% of the phenotypic variation. LALD mapping analysis was performed to narrow down the confidence interval (CI) of the quantitative trait locus which resulted in a CI of 2.39 Mb (7:114409266~116803751). Further, to identify candidate causal genes within the 2.39 Mb region, fine-mapping analysis was performed within the region. The fine-mapping analysis identified SERPINA1, ITPK1, CLMN, SERPINA12, and PRIMA1, in addition to SERPINA6, which was previously shown to be associated with blood cortisol levels. Our results identified positional candidate genes and genetic variants associated with serum cortisol concentrations that can be included in marker panels for genomic prediction to improve selection for robustness in pigs.

This study investigated copy number variations (CNVs) in 2112 pigs from five populations: Korean Duroc (DUC), Korean Native Pig (KNP), and their crossbred offspring (F₁, F₂, and WRH). CNVs were detected using PennCNV and QuantiSNP, with CNVRuler identifying 698 CNV regions (CNVRs), covering 109 Mb (4.83%) of the porcine genome. Comparison with previous CNV studies on swine revealed CNVR overlap rates ranging from 31.12% (French Yorkshire) to 81.27% (Xiang), and 9.06% newly identified CNVRs. DUC showed the most CNVRs (n = 384), followed by WRH (n = 225). Meanwhile, F₁ and F₂ exhibited far fewer CNVRs (five and seven, respectively). Functional enrichment analysis highlighted various genes overlapping with the CNVRs, including 1236 genes in DUC and 572 genes in WRH, linked to biological processes. The quantitative trait loci (QTLs), overlapping with CNVRs, exhibited particular overlapping with traits such as average daily gain (4.24% of QTLs in DUC, 4.51% of QTLs in WRH). In contrast, KNP, F₁, and F₂ populations exhibited a higher frequency of CNVRs containing QTLs overlapped with drip loss. These findings indicate that WRH may inherit growth traits from DUC. This study provides a better understanding of CNVs in the pigs, which can potentially be used in improving genetic merits of pig populations.

한국가금학회지(The Journal of Curriculum Evaluation)

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.

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.