Current knowledge on the epidemiology and prevention of Avian leukosis virus in China

Avian leukosis virus (ALV) is an enveloped retrovirus with a single-stranded RNA genome, belonging to the genus Alpharetrovirus within the family Retroviridae. The disease (Avian leukosis, AL) caused by ALV is mainly characterized by tumor development and immunosuppression in chickens, which increases susceptibility to other pathogens and leads to significant economic losses in the Chinese poultry industry. The government and poultry industry have made lots of efforts to eradicate ALV, but the threat of which remains not vanished. This review provides a summary of the updated understanding of ALV in China, which mainly focuses on genetic and molecular biology, epidemiology, and diagnostic methods. Additionally, promising antiviral agents and ALV eradication strategies performed in China are also included.

Genetic parameters and genomic prediction of growth and breast morphological traits in a crossbreed duck population

Genomic selection (GS) has great potential to increase genetic gain in poultry breeding. However, the performance of genomic prediction in duck growth and breast morphological (BM) traits remains largely unknown. The objective of this study was to evaluate the benefits of genomic prediction for duck growth and BM traits using methods such as GBLUP, single-step GBLUP, Bayesian models, and different marker densities. This study collected phenotypic data for 14 growth and BM traits in a crossbreed population of 1893 Pekin duck × mallard, which included 941 genotyped ducks. The estimation of genetic parameters indicated high heritabilities for body weight (0.54-0.72), whereas moderate-to-high heritabilities for average daily gain (0.21-0.57) traits. The heritabilities of BM traits ranged from low to moderate (0.18-0.39). The prediction ability of GS on growth and BM traits increased by 7.6% on average compared to the pedigree-based BLUP method. The single-step GBLUP outperformed GBLUP in most traits with an average of 0.3% higher reliability in our study. Most of the Bayesian models had better performance on predictive reliability, except for BayesR. BayesN emerged as the top-performing model for genomic prediction of both growth and BM traits, exhibiting an average increase in reliability of 3.0% compared to GBLUP. The permutation studies revealed that 50 K markers had achieved ideal prediction reliability, while 3 K markers still achieved 90.8% predictive capability would further reduce the cost for duck growth and BM traits. This study provides promising evidence for the application of GS in improving duck growth and BM traits. Our findings offer some useful strategies for optimizing the predictive ability of GS in growth and BM traits and provide theoretical foundations for designing a low-density panel in ducks.

Identification of genes related to growth and amino acid metabolism from the transcriptome profile of the liver of growing laying hens

The growing period is a critical period for the growth and development of hens and affects their production performance during the laying period. During the early stage of growing, bone and muscle growth is rapid, making it necessary to provide sufficient amino acids (AA) to support the growth and development of laying hens. In this experiment, RNA-Sequencing (RNA-Seq) was applied to compare the liver tissues from 6- to 12-wk-old growing laying hens to identify candidate genes related to growth and AA transport and metabolism. In the liver tissues, 596 differentially expressed genes (DEG) were identified, of which 424 genes were up-regulated and 172 were down-regulated. Through enrichment analysis and DEGs analysis, some DEGs and pathways related to AA transport and metabolism were identified. Additionally, there were significantly increased activities in the liver of glutamate dehydrogenase (GDH), glutamic oxaloacetic transaminase (GOT), and glutamate pyruvate transaminase (GPT). Meanwhile, the level of serum insulin-like growth factor binding protein-5 (IGFBP-5) significantly elevated, and insulin-like growth factor-1 (IGF-1) levels significantly reduced at 12 wk compared to 6 wk. The AA contents in the breast muscle were not significantly altered, while the levels of the free AA in the serum underwent significant changes. This study discovered that the transport and metabolism of AAs in growing laying hens at different ages changed, which influenced the growth and development of growing laying hens. This contributes to future research on the mechanisms of growth and AA metabolism during the growing period of laying hens.

Evaluation of the selection of key individuals for genotype imputation in Chinese yellow-feathered chicken

Genotype imputation is a powerful technique employed by next-generation sequencing (NGS) and genotyping arrays, which can significantly enhance the cost-effectiveness and efficiency of genomic selection. The accuracy of imputation is largely determined by the choice of reference panel, with previous studies generally demonstrating that a closely related population as a reference panel leads to greater accuracy than a more distantly related population. Various strategies have been proposed for selecting desirable individuals via targeted resequencing, but their efficiencies need further improvement. In this study, we present a practical broiler selection methodology for a local Chinese chicken line that integrates established methods based on pedigree, genomics, and random sampling, and leverages genotype and pedigree information from the yellow-plumage dwarf chicken line. The efficacy of these selection strategies was assessed by evaluating their ability to accurately impute masked genotypes from data obtained using a 600K chip. Our findings reveal that the pedigree-based method yields superior accuracy in genotype imputation, whereas the haplotype-based method exhibits greater stability. Nonetheless, the impact of these targeted methods for selecting key individuals is slightly different when initiating a new sequencing project in a production context. Overall, this study highlights the advantages of using the pedigree-based approach as the preferred method for optimizing genotype imputation in broiler chickens.

Molecular Design-Based Breeding: A Kinship Index-Based Selection Method for Complex Traits in Small Livestock Populations

Genomic selection (GS) techniques have improved animal breeding by enhancing the prediction accuracy of breeding values, particularly for traits that are difficult to measure and have low heritability, as well as reducing generation intervals. However, the requirement to establish genetic reference populations can limit the application of GS in pig breeds with small populations, especially when small populations make up most of the pig breeds worldwide. We aimed to propose a kinship index based selection (KIS) method, which defines an ideal individual with information on the beneficial genotypes for the target trait. Herein, the metric for assessing selection decisions is a beneficial genotypic similarity between the candidate and the ideal individual; thus, the KIS method can overcome the need for establishing genetic reference groups and continuous phenotype determination. We also performed a robustness test to make the method more aligned with reality. Simulation results revealed that compared to conventional genomic selection methods, the KIS method is feasible, particularly, when the population size is relatively small.

Serum bone remodeling parameters and transcriptome profiling reveal abnormal bone metabolism associated with keel bone fractures in laying hens

Keel bone fractures affect welfare, health, and production performance in laying hens. A total of one hundred and twenty 35-wk-old Hy-line Brown laying hens with normal keel (NK) bone were housed in furnished cages and studied for ten weeks to investigate the underlying mechanism of keel bone fractures. At 45 wk of age, the keel bone state of birds was assessed by palpation and X-ray, and laying hens were recognized as NK and fractured keel (FK) birds according to the presence or absence of fractures in keel bone. The serum samples of 10 NK and 10 FK birds were collected to determine bone metabolism-related indexes and slaughtered to collect keel bones for RNA-sequencing (RNA-seq), Micro-CT, and histopathological staining analyses. The results showed that the concentrations of Ca, phosphorus, calcitonin, 25-hydroxyvitamin D₃, and osteocalcin and activities of alkaline phosphatase and tartrate-resistant acid phosphatase (TRAP) in serum samples of FK birds were lower than those of NK birds (P < 0.05), but the concentrations of parathyroid hormone, osteoprotegerin, and corticosterone in serum samples of FK birds were higher than those of NK birds (P < 0.05). TRAP staining displayed that FK bone increased the number of osteoclasts (P < 0.05). Micro-CT analysis indicated that FK bone decreased bone mineral density (P < 0.05). Transcriptome sequencing analysis of NK and FK bones identified 214 differentially expressed genes (DEGs) (|log2FoldChange| > 1, P < 0.05), among which 88 were upregulated and 126 downregulated. Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) analysis indicated that 14 DEGs related to skeletal muscle movement and bone Ca transport (COL6A1, COL6A2, COL6A3, PDGFA, MYLK2, EGF, CAV3, ADRA1D, BDKRB1, CACNA1S, TNN, TNNC1, TNNC2, and RYR3) were enriched in focal adhesion and Ca signaling pathway, regulating bone quality. This study suggests that abnormal bone metabolism related to keel bone fractures is possibly responded to fracture healing in laying hens.

Polymorphisms of CRELD1 and DNAJC30 and their relationship with chicken carcass traits

Carcass traits play important roles in the broiler industry and single nucleotide polymorphism (SNP) can be efficient molecular markers for marker-assisted breeding of chicken carcass traits. Based on our previous RNA-seq data (accession number GSE58755), cysteine rich with epidermal growth factor like domains 1 (CRELD1) and DnaJ heat shock protein family member C30 (DNAJC30) are differentially expressed in breast muscle between white recessive rock chicken (WRR) and Xinghua chicken (XH). In this study, we further characterize the potential function and SNP mutation of CRELD1 and DNAJC30 in chicken for the first time. According to protein interaction network and enrichment analysis, CRELD1 and DNAJC30 may play some roles in chicken muscle development and fat deposition. In WRR and XH, the results of the relative tissue expression pattern demonstrated that CRELD1 and DNAJC30 are not only differentially expressed in breast muscle but also leg muscle and abdominal fat. Therefore, we identified 5 SNP sites of CRELD1 and 7 SNP sites of DNAJC30 and genotyped them in an F₂ chicken population. There are 4 sites of CRELD1 and 3 sites of DNAJC30 are associated with chicken carcass traits like breast muscle weight, body weight, dressed weight, leg weight percentage, eviscerated weight with giblet percentage, intermuscular adipose width, shank length, and girth. These results suggest that the SNP sites of CRELD1 and DNAJC30 can be potential molecular markers to improve the chicken carcass traits and lay the foundation for marker-assisted selection.

Grade follicles transcriptional profiling analysis in different laying stages in chicken

During follicular development, a series of key events such as follicular recruitment and selection are crucially governed by strict complex regulation. However, its molecular mechanisms remain obscure. To identify the dominant genes controlling chicken follicular development, the small white follicle (SWF), the small yellow follicle (SYF), and the large yellow follicle (LYF) in different laying stages (W22, W31, W51) were collected for RNA sequencing and bioinformatics analysis. There were 1866, 1211, and 1515 differentially expressed genes (DEGs) between SWF and SYF in W22, W31, and W51, respectively. 4021, 2295, and 2902 DEGs were respectively identified between SYF and LYF in W22, W31, and W51. 5618, 4016, and 4809 DEGs were respectively identified between SWF and LYF in W22, W31, and W51. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis indicated that extracellular matrix, extracellular region, extracellular region part, ECM-receptor interaction, collagen extracellular matrix, and collagen trimer were significantly enriched (P < 0.05). Protein–protein interaction analysis revealed that COL4A2, COL1A2, COL4A1, COL5A2, COL12A1, ELN, ALB, and MMP10 might be key candidate genes for follicular development in chicken. The current study identified dominant genes and pathways contributing to our understanding of chicken follicular development. 

Assessment the effect of genomic selection and detection of selective signature in broilers

Due to high selection advances and shortened generation interval, genomic selection (GS) is now an effective animal breeding scheme. In broilers, many studies have compared the accuracy of different GS prediction methods, but few reports have demonstrated phenotypic or genetic changes using GS. In this study, the paternal chicken line B underwent continuous selection for 3 generations. The chicken 55 k SNP chip was used to estimate the genetic parameters and detect genomic response regions by selective sweep analysis. The heritability for body weight (BW), meat production, and abdominal fat traits were ranged from 0.12 to 0.38. A high genetic correlation was found between BW and meat production traits, while a low genetic correlation (<0.1) was found between meat production and abdominal fat traits. Selection resulted in an increase of about 516 g in BW and 140 g in breast muscle weight. Percentage of breast muscle and whole thigh were increased 0.8 to 1.5%. No change was observed in abdominal fat percentage. The genomic estimated breeding value advances was positive for BW and meat production (except whole thigh percentage), while negative for abdominal fat percentage. By selective sweep analysis, 39 common chromosomal regions and 102 protein coding genes were found to be influenced, including MYH1A, MYH1B, and MYH1D of the MYH gene family. Tight junction pathway as well as myosin complex related terms were enriched. This study demonstrates the effective use of GS for improvements in BW and meat production in chicken line B. Further, genomic regions, responsive to intensive genetic selection, were identified to contain genes of the MYH family.

Genomic selection for meat quality traits in Pekin duck

Genomic selection uses genome-wide molecular marker data to predict an animal's genetic value in the breeding program. This study's objective was to present heritability estimates and accuracy of genomic prediction using different methods for meat quality traits in Pekin duck. There were two kinds of ducks in the genomic selection training population: 639 fat-type ducks and 540 lean-type ducks. A single-trait animal model was used to estimate heritability and adjust the phenotype. GBLUP and BayesR methods were performed to estimate the SNP effects. The accuracy of genomic prediction was calculated using 5-fold cross-validation. The accuracy varied from 0.235 to 0.501 with the lowest accuracy estimated for traits associated with abdominal fat weight in the combined population and the most remarkable accuracy observed for abdominal fat percentage traits in the lean-type duck population. Overall, BayesR can achieve the highest prediction accuracy, while the combined population strategy could be used to increase the accuracy of prediction only when the two populations have the same breeding aim for a certain trait.

Optimizing genomic prediction model given causal genes in a dairy cattle population

As genotypic data are moving from SNP chip toward whole-genome sequence, the accuracy of genomic prediction (GP) exhibits a marginal gain, although all genetic variation, including causal genes, are contained in whole-genome sequence data. Meanwhile, genetic analyses on complex traits, such as genome-wide association studies, have identified an increasing number of genomic regions, including potential causal genes, which would be reliable prior knowledge for GP. Many studies have tried to improve the performance of GP by modifying the prediction model to incorporate prior knowledge. Although several plausible results have been obtained from model modification or strategy optimization, most of them were validated in a specific empirical population with a limited variety of genetic architecture for complex traits. An alternative approach is to use simulated genetic architecture with known causal genes (e.g., simulated causative SNP) to evaluate different GP models with given causal genes. Our objectives were to (1) evaluate the performance of GP under a variety of genetic architectures with a subset of known causal genes and (2) compare different GP models modified by highlighting causal genes and different strategies to weight causal genes. In this study, we simulated pseudo-phenotypes under a variety of genetic architectures based on the real genotypes and phenotypes of a dairy cattle population. Besides classical genomic best linear unbiased prediction, we evaluated 3 modified GP models that highlight causal genes as follows: (1) by treating them as fixed effects, (2) by treating them as a separate random component, and (3) by combining them into the genomic relationship matrix as random effects. Our results showed that highlighting the known causal genes, which explained a considerable proportion of genetic variance in the GP models, increased the predictive accuracy. Combining all given causal genes into the genomic relationship matrix was the optimal strategy under all the scenarios validated, and treating causal genes as a separate random component is also recommended, when more than 20% of genetic variance was explained by known causal genes. Moreover, assigning differential weights to each causal gene further improved the predictive accuracy.

Transcriptome profiling analysis of underlying regulation of growing follicle development in the chicken

Large ovarian follicles are primary characteristics of oviparous species. The development of such follicles is crucially governed by strict intrinsic complex regulation. Many aspects of the genetic basis of this regulation remain obscure. To identify the dominant genes controlling follicular development in the chicken, growing follicles (400–1,600 μm in diameter) were selected for RNA sequencing and bioinformatics analysis. Comparing the 400-μm follicles with 800-μm follicles identified a total of 3,627 differentially expressed genes (1,792 upregulated and 1,835 downregulated genes). Comparing the 400-μm follicles with 1,600-μm follicles revealed 9,650 differentially expressed genes (including 4,848 upregulated and 4,802 downregulated genes). Comparing 800-μm with 1,600-μm follicles revealed a total of 6,779 differentially expressed genes (3,427 upregulated and 3,352 downregulated genes). Transcriptome analysis revealed that genes related to the extracellular matrix–receptor interactions, steroid biosynthesis, cell adhesion, and phagosomes displayed remarkable differential expressions. Relative to 400-μm follicles, collagen content, production of steroid hormones, cell adhesion, and phagocytic factors were significantly increased in the 1,600-μm follicles. This study identifies the dominant genes involved in the promotion of follicular development in oviparous vertebrates and represents the extraordinary gene regulation pattern related to development of the growing follicles in poultry.