The present study aimed to identify causative loci and genes enriched in pathways associated with canine obesity using a genome-wide association study (GWAS). The GWAS was first performed to identify candidate single-nucleotide polymorphisms (SNPs) associated with obesity and obesity-related traits including body weight and blood sugar in 18 different breeds of 153 dogs. A total of 10 and 2 SNPs were found to be significantly (p < 3.74 × 10−7) associated with body weight and blood sugar, respectively. None of the SNPs were identified to be significantly associated with obesity trait. We subsequently followed up the GWAS analysis with gene-set enrichment and pathway analyses. A gene-set with 1057, 1409, and 1243 SNPs annotated to 449, 933 and 820 genes for obesity, body weight, and blood sugar, respectively was created by sub-setting the GWAS result at a threshold of p < 0.01 for the gene-set enrichment analysis. In total, 84 GO and 21 KEGG pathways for obesity, 114 GO and 44 KEGG pathways for blood sugar, 120 GO and 24 KEGG pathways for body weight were found to be enriched. Among the pathways and GO terms, we highlighted five enriched pathways (Wnt signaling pathway, adherens junction, pathways in cancer, axon guidance, and insulin secretion) and seven GO terms (fat cell differentiation, calcium ion binding, cytoplasm, nucleus, phospholipid transport, central nervous system development, and cell surface) that were found to be shared among all the traits. Our data provide insights into the genes and pathways associated with obesity and obesity-related traits.

Meat from Korean native chickens (KNCs) has high consumer demand; however, slow growth performance and high variation in body weight (BW) of KNCs remain an issue. Genome-wide association study (GWAS) is a powerful method to identify quantitative trait-associated genomic loci. A GWAS, based on a large-scale KNC population, is needed to identify underlying genetic mechanisms related to its growth traits. To identify BW-associated genomic regions, we performed a GWAS using the chicken 60K single nucleotide polymorphism (SNP) panel for 1328 KNCs. BW was measured at 8 weeks of age, from 2018 to 2020. Twelve SNPs were associated with BW at the suggestive significance level (p < 2.95 × 10−5) and located near or within 11 candidate genes, including WDR37, KCNIP4, SLIT2, PPARGC1A, MYOCD and ADGRA3. Gene set enrichment analysis based on the GWAS results at p < 0.05 (1680 SNPs) showed that 32 Gene Ontology terms and two Kyoto Encyclopedia of Genes and Genomes pathways, including regulation of transcription, motor activity, the mitogen-activated protein kinase signaling pathway, and tight junction, were significantly enriched (p < 0.05) for BW-associated genes. These pathways are involved in cell growth and development, related to BW gain. The identified SNPs are potential biomarkers in KNC breeding.

Objective: The highly pathogenic avian influenza virus (HPAIV) is a threat to the poultry industry as well as the economy and remains a potential source of pandemic infection in humans. Antiviral genes are considered a potential factor for HPAIV resistance. Therefore, in this study, we investigated gene expression related to cytokine-cytokine receptor interactions by comparing resistant and susceptible Ri chicken lines for avian influenza virus infection.Methods: Ri chickens of resistant (Mx/A; BF2/B21) and susceptible (Mx/G; BF2/B13) lines were selected by genotyping the Mx and BF2 genes. These chickens were then infected with HPAIV H5N1, and their lung tissues were collected for RNA sequencing.Results: In total, 972 differentially expressed genes (DEGs) were observed between resistant and susceptible Ri chickens, according to the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. In particular, DEGs associated with cytokinecytokine receptor interactions were most abundant. The expression levels of cytokines (IL-1β, IL-6, IL-8, and IL-18), chemokines (CCL4 and CCL17), interferons (IFN-γ), and IFNstimulated genes (Mx1, CCL19, OASL, and PRK) were higher in H5N1-resistant chickens than in H5N1-susceptible chickens. Conclusion:Resistant chickens show stronger immune responses and antiviral activity (cytokines, chemokines, and IFN-stimulated genes) than those of susceptible chickens against HPAIV infection.

Until recently, genome-scale phasing was limited due to the short read sizes of sequence data. Though the use of long-read sequencing can overcome this limitation, they require extensive error correction. The emergence of technologies such as 10X genomics linked read sequencing and Hi-C which uses short-read sequencers along with library preparation protocols that facilitates long-read assemblies have greatly reduced the complexities of genome scale phasing. Moreover, it is possible to accurately assemble phased genome of individual samples using these methods. Therefore, in this study, we compared three phasing strategies which included two sample preparation methods along with the Long Ranger pipeline of 10X genomics and HapCut2 software, namely 10X-LG, 10X-HapCut2, and HiC-HapCut2 and assessed their performance and accuracy. We found that the 10X-LG had the best phasing performance amongst the method analyzed. They had the highest phasing rate (89.6%), longest adjusted N50 (1.24 Mb), and lowest switch error rate (0.07%). Moreover, the phasing accuracy and yield of the 10X-LG stayed over 90% for distances up to 4 Mb and 550 Kb respectively, which were considerably higher than 10X-HapCut2 and Hi-C Hapcut2. The results of this study will serve as a good reference for future benchmarking studies and also for reference-based imputation in Hanwoo.

Objective: A genomic region associated with a particular phenotype is called QTL. To detect the optimal F 2 population size associated with QTLs in native chicken, we performed a simulation study on F 2 population derived from crosses between two different breeds.Methods: A total of 15 males and 150 females were randomly selected from the last generation of each F 1 population which was composed of different breed to create two different F 2 populations.The progenies produced from these selected individuals were simulated for six more generations.Their marker genotypes were simulated with a density of 50K at three different heritability levels for the traits such as 0.1, 0.3 and 0.5. Our study is that to compare 100, 500, 1000 TPreference population (RP) groups to each other with three different heritability levels. And a total of 35 QTLs were used, and their locations were randomly created.Results: With a TPRP size of 100, no QTL was detected to satisfy Bonferroni value at three different heritability levels. In a TPRP size of 500, two QTLs were detected when the heritability was 0.5. With a TPRP size of 1,000, 0.1 heritability was detected only one QTL, and 0.5 heritability shows that five QTLs were detected. To sum up, TPRP size and heritability are playing a key role to detect QTLs in QTL study. The larger TPRP size and greater heritability value, the higher the probability of detection of QTLs.With a TPRP size of 100, some QTLs were found, even though the number of QTLs were somewhat similar for h 2 = 0.1, 0.3, and 0.5, respectively. This result indicates an increased in h 2 did not improve number of QTLs at TPRP size of 100. With a TPRP size of 1000, many QTLs were detected at different h 2 levels of traits, even at the h 2 value of 0.1 Conclusion:Our study suggests that the use of a large TPRP and heritability can improve QTL detection in an F 2 chicken population.

Genome-wide association study for the free amino acid and nucleotide components of breast meat in an F2 crossbred chicken population Running Title (within 10 words)GWAS for free amino acid and nucleotide of chicken meat

No abstract

Chickens are a species of vertebrate with varying colors. Various colors of chickens must be classified to find color-related genes. In the past, color scoring was performed based on human visual observation. Therefore, chicken colors have not been measured with precise standards. In order to solve this problem, a computer vision approach was used in this study. Image quantization based on k-means clustering for all pixels of RGB values can objectively distinguish inherited colors that are expressed in various ways. This study was also conducted to determine whether plumage color differences exist in the reciprocal cross lines between two breeds: black Yeonsan Ogye (YO) and White Leghorn (WL). Line B is a crossbred line between YO males and WL females while Line L is a reciprocal crossbred line between WL males and YO females. One male and ten females were selected for each F 1 line, and full-sib mating was conducted to generate 883 F 2 birds. The results indicate that the distribution of light and dark colors of k-means clustering converged to 7:3. Additionally, the color of Line B was lighter than that of Line L (P<0.01). This study suggests that the genes underlying plumage colors can be identified using quantification values from the computer vision approach described in this study.