Thirty-four generations of divergent selection for 8-week body weight in chickens

Nondestructive estimation method of live chicken leg weight based on deep learning

In the broiler-breeding industry, phenotype determination is critical. Leg weight is a fundamental indicator for breeding, and noninvasive testing technology can reduce damage to animals. This study proposes a broiler leg weight estimation system comprising a weight-estimation model and computed tomography (CT) acquisition equipment. The weight-estimation model can automatically process the scan results of live broiler chickens from the CT acquisition equipment. The weight-estimation model comprises an improved you-only-look-once (YOLOv5) segmentation algorithm and a random forest fitting network. The segmentation head was introduced into the YOLOv5 network, combined with a multiscale attention mechanism and an atrous spatial pyramid pooling architecture, and a new network model, YOLO- measuring chicken leg weight (YOLO-MCLW), was proposed to improve segmentation efficiency and accuracy. Morphological parameters were extracted from the obtained mask image, and a random forest network was used for fitting. The experiments show that the system exhibited an average absolute error of 7.27 g and an average percentage error of 4.82% in tests on 50 individual legs of 25 broiler chickens. The prediction R2 of broiler chicken legs can reaches 88.98%, the segmentation intersection over union result reaches 95.45%, and 37.04 images are processed per second. This system provides technical support for the part determination of broiler chickens in commercial breeding.

Identification of Major Loci and Candidate Genes for Meat Production-Related Traits in Broilers

Background

Carcass traits are crucial characteristics of broilers. However, the underlying genetic mechanisms are not well understood. In the current study, significant loci and major-effect candidate genes affecting nine carcass traits related to meat production were analyzed in 873 purebred broilers using an imputation-based genome-wide association study.

Results

The heritability estimates of nine carcass traits, including carcass weight, thigh muscle weight, and thigh muscle percentage, were moderate to high and ranged from 0.21 to 0.39. Twelve genome-wide significant SNPs and 118 suggestively significant SNPs of 546,656 autosomal variants were associated with carcass traits. All SNPs for six weight traits (body weight at 42 days of age, carcass weight, eviscerated weight, whole thigh weight, thigh weight, and thigh muscle weight) were clustered around the 24.08 Kb region (GGA24: 5.73–5.75 Mb) and contained only one candidate gene (DRD2). The most significant SNP, rs15226023, accounted for 4.85–7.71% of the estimated genetic variance of the six weight traits. The remaining SNPs for carcass composition traits (whole thigh percentage and thigh percentage) were clustered around the 42.52 Kb region (GGA3: 53.03–53.08 Mb) and contained only one candidate gene (ADGRG6). The most significant SNP in this region, rs13571431, accounted for 11.89–13.56% of the estimated genetic variance of two carcass composition traits. Some degree of genetic differentiation in ADGRG6 between large and small breeds was observed.

Conclusion

We identified one 24.08 Kb region for weight traits and one 42.52 Kb region for thigh-related carcass traits. DRD2 was the major-effect candidate gene for weight traits, and ADGRG6 was the major-effect candidate gene for carcass composition traits. Our results supply essential information for causative mutation identification of carcass traits in broilers.

Comparison of early growth and survivability in indigenous guinea fowls from Northern Ghana

Three guinea fowl populations from Northern Ghana were compared in terms of their body weight, growth rates, and survivability during the first 11 weeks of life. Keets (n = 865) were hatched from eggs collected from 32 sampling areas divided into eleven subpopulations within three populations in Northern Ghana. Together with an experimental flock maintained at Animal Research Institute (ARI flock), these birds were raised and appraised for weekly body weights, weekly growth rates, and survivability. Weekly body weights did not differ significantly (p > 0.05) among the three populations, although ARI flock were significantly heavier (p ˂ 0.05) compared to the main populations until the fourth week. In contrast, among the subpopulations, significant differences emerged in body weights from the second week and were more pronounced from the sixth week. Growth rates measured as weekly weight gains also differed significantly among subpopulations beyond the second week, although differences in growth rates were not significantly different among whole populations. The mean values for total feed intake, daily feed intake, and feed conversion ratio (FCR) did not vary significantly (p > 0.05) between the populations. Therefore, although the variations in body weight and growth rates were limited among the populations, there existed significant variations among subpopulations, creating opportunities to establish genetically divergent populations for growth rate and to improve early growth rates and body weights in local guinea fowls by selection. High survivability observed in the ARI flock compared to keets from the three populations of Northern Ghana was likely due to good breeder stock management practices despite their common ancestry.

Genotype frequency distributions of 28 SNP markers in two commercial lines and five Chinese native chicken populations

Background

Modern breeding in the poultry industry mainly aims to produce high-performance poultry lines and breeds in two main directions of productivity, meat and eggs. To understand more about the productive potential of lowly selected Chinese native chicken populations, we selected 14 representative SNP markers strongly associated with growth traits or carcass traits and 14 SNP markers strongly associated with egg laying traits through previous reports. By using the MassArray technology, we detected the genotype frequency distributions of these 28 SNP markers in seven populations including four lowly selected as well as one moderately selected Sichuan native chicken populations, one commercial broiler line and one commercial layer line.

Results

Based on the genotype frequency distributions of these 28 SNP markers in 5 native chicken populations and 2 commercial lines, the results suggested that these Chinese indigenous chicken populations have a relatively close relationship with the commercial broiler line but a marked distinction from the commercial layer line. Two native chicken breeds, Shimian Caoke Chicken and Daheng Broilers, share similar genetic structure with the broiler line.

Conclusions

Our observations may help us to better select and breed superior domestic chickens and provide new clues for further study of breeding programs in local chicken populations.

Unraveling genomic associations with feed efficiency and body weight traits in chickens through an integrative approach

Background

Feed efficiency and growth rate have been targets for selection to improve chicken production. The incorporation of genomic tools may help to accelerate selection. We genotyped 529 individuals using a high-density SNP chip (600 K, Affymetrix®) to estimate genomic heritability of performance traits and to identify genomic regions and their positional candidate genes associated with performance traits in a Brazilian F₂ Chicken Resource population. Regions exhibiting selection signatures and a SNP dataset from resequencing were integrated with the genomic regions identified using the chip to refine the list of positional candidate genes and identify potential causative mutations.

Results

Feed intake (FI), feed conversion ratio (FC), feed efficiency (FE) and weight gain (WG) exhibited low genomic heritability values (i.e. from 0.0002 to 0.13), while body weight at hatch (BW1), 35 days-of-age (BW35), and 41 days-of-age (BW41) exhibited high genomic heritability values (i.e. from 0.60 to 0.73) in this F₂ population. Twenty unique 1-Mb genomic windows were associated with BW1, BW35 or BW41, located on GGA1–4, 6–7, 10, 14, 24, 27 and 28. Thirty-eight positional candidate genes were identified within these windows, and three of them overlapped with selection signature regions. Thirteen predicted deleterious and three high impact sequence SNPs in these QTL regions were annotated in 11 positional candidate genes related to osteogenesis, skeletal muscle development, growth, energy metabolism and lipid metabolism, which may be associated with body weight in chickens.

Conclusions

The use of a high-density SNP array to identify QTL which were integrated with whole genome sequence signatures of selection allowed the identification of candidate genes and candidate causal variants. One novel QTL was detected providing additional information to understand the genetic architecture of body weight traits. We identified QTL for body weight traits, which were also associated with fatness in the same population. Our findings form a basis for further functional studies to elucidate the role of specific genes in regulating body weight and fat deposition in chickens, generating useful information for poultry breeding programs.

Circular RNA circHIPK3 Promotes the Proliferation and Differentiation of Chicken Myoblast Cells by Sponging miR-30a-3p

Circular RNAs and microRNAs widely exist in various species and play crucial roles in multiple biological processes. It is essential to study their roles in myogenesis. In our previous sequencing data, both miR-30a-3p and circular HIPK3 (circHIPK3) RNA, which are produced by the third exon of the HIPK3 gene, were differentially expressed among chicken skeletal muscles at 11 embryo age (E11), 16 embryo age (E16), and 1-day post-hatch (P1). Here, we investigated their potential roles in myogenesis. Proliferation experiment showed that miR-30a-3p could inhibit the proliferation of myoblast. Through dual-luciferase assay and Myosin heavy chain (MYHC) immunofluorescence, we found that miR-30a-3p could inhibit the differentiation of myoblast by binding to Myocyte Enhancer Factor 2 C (MEF2C), which could promote the differentiation of myoblast. Then, we found that circHIPK3 could act as a sponge of miR-30a-3p and exerted a counteractive effect of miR-30a-3p by promoting the proliferation and differentiation of myoblasts. Taking together, our data suggested that circHIPK3 could promote the chicken embryonic skeletal muscle development by sponging miR-30a-3p.

Transcriptome profile analysis of leg muscle tissues between slow- and fast-growing chickens

Chicken is widely favored by consumers because of some unique features. The leg muscles occupy an important position in the market. However, the specific mechanism for regulating muscle growth speed is not clear. In this experiment, we used Jinghai yellow chickens with different body weights at 300 days as research subjects. The chickens were divided into fast- and slow-growing groups, and we collected leg muscles after slaughtering for use in RNA-seq. After comparing the two groups, 87 differentially expressed genes (DEGs) were identified (fold change ≥ 2 and FDR < 0.05). The fast-growing group had 42 up-regulated genes and 45 down-regulated genes among these DEGs compared to the slow-growing group. Six items were significantly enriched in the biological process: embryo development ending in birth or egg hatching, chordate embryonic development, embryonic skeletal system development, and embryo development as well as responses to ketones and the sulfur compound biosynthetic process. Two significantly enriched pathways were found in the KEGG pathway analysis (P-value < 0.05): the insulin signaling pathway and the adipocytokine signaling pathway. This study provides a theoretical basis for the molecular mechanism of chicken growth and for improving the production of Jinghai yellow chicken.

A genome-wide association study reveals novel genomic regions and positional candidate genes for fat deposition in broiler chickens

Background

Excess fat content in chickens has a negative impact on poultry production. The discovery of QTL associated with fat deposition in the carcass allows the identification of positional candidate genes (PCGs) that might regulate fat deposition and be useful for selection against excess fat content in chicken’s carcass. This study aimed to estimate genomic heritability coefficients and to identify QTLs and PCGs for abdominal fat (ABF) and skin (SKIN) traits in a broiler chicken population, originated from the White Plymouth Rock and White Cornish breeds.

Results

ABF and SKIN are moderately heritable traits in our broiler population with estimates ranging from 0.23 to 0.33. Using a high density SNP panel (355,027 informative SNPs), we detected nine unique QTLs that were associated with these fat traits. Among these, four QTL were novel, while five have been previously reported in the literature. Thirteen PCGs were identified that might regulate fat deposition in these QTL regions: JDP2, PLCG1, HNF4A, FITM2, ADIPOR1, PTPN11, MVK, APOA1, APOA4, APOA5, ENSGALG00000000477, ENSGALG00000000483, and ENSGALG00000005043. We used sequence information from founder animals to detect 4843 SNPs in the 13 PCGs. Among those, two were classified as potentially deleterious and two as high impact SNPs.

Conclusions

This study generated novel results that can contribute to a better understanding of fat deposition in chickens. The use of high density array of SNPs increases genome coverage and improves QTL resolution than would have been achieved with low density. The identified PCGs were involved in many biological processes that regulate lipid storage. The SNPs identified in the PCGs, especially those predicted as potentially deleterious and high impact, may affect fat deposition. Validation should be undertaken before using these SNPs for selection against carcass fat accumulation and to improve feed efficiency in broiler chicken production.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-4779-6) contains supplementary material, which is available to authorized users.

Whole genomic prediction of growth and carcass traits in a Chinese quality chicken population

By incorporating high-density markers into breeding value prediction models, the whole genomic prediction (WGP) method can effectively accelerate genetic improvement in livestock breeding. However, the performance of WGP varies across species and populations and is affected by the underlying genetic architecture. In particular, very little is known about the performance of WGP for many chicken breeds. Here we estimate the genetic parameters and evaluate the performance of WGP for 18 growth and carcass traits in a Chinese quality chicken population. In total, 435 chickens were systematically phenotyped and genotyped using a 600K genotyping array. Two variance component estimation scenarios, 3 breeding value prediction methods, and 2 validation procedures were compared. The results showed that the heritability of these 18 traits was medium to high (ranging from 0.28 to 0.60) and that deviations existed between the heritability estimated from pedigrees and markers. Compared with conventional breeding methods, WGP could potentially increase the selection accuracy by 20% or more depending on the prediction model used, the trait under consideration, and the genetic connectedness between the training and validation individuals. Our results showed the potential of implementing genomic selection in small breeding herds.

A Genome-Wide mRNA Screen and Functional Analysis Reveal FOXO3 as a Candidate Gene for Chicken Growth

Chicken growth performance provides direct economic benefits to the poultry industry. However, the underlying genetic mechanisms are unclear. The objective of this study was to identify candidate genes associated with chicken growth and investigate their potential mechanisms. We used RNA-Seq to study the breast muscle transcriptome in high and low tails of Recessive White Rock (WRR_h, WRR_l) and Xinghua chickens (XH_h, XH_l). A total of 60, 23, 153 and 359 differentially expressed genes were detected in WRR_h vs. WRR_l, XH_h vs. XH_l, WRR_h vs. XH_h and WRR_l vs. XH_l, respectively. GO, KEGG pathway and gene network analyses showed that CEBPB, FBXO32, FOXO3 and MYOD1 played key roles in growth. The functions of FBXO32 and FOXO3 were validated. FBXO32 was predominantly expressed in leg muscle, heart and breast muscle. After decreased FBXO32 expression, growth-related genes such as PDK4, IGF2R and IGF2BP3 were significantly down-regulated (P < 0.05). FBXO32 was significantly (P < 0.05) associated with carcass and meat quality traits, but not growth traits. FOXO3 was predominantly expressed in breast and leg muscle. In both of these tissues, the FOXO3 mRNA level in XH was significantly higher than that in WRR chickens with normal body weight (P < 0.05). In DF-1 cells, siRNA knockdown of FOXO3 significantly (P < 0.01) inhibited the MYOD expression and significantly up-regulated (P < 0.01 or P < 0.05) the expression of growth-related genes including CEBPB, FBXO32, GH, GHR, IGF1R, IGF2R, IGF2BP1, IGF2BP3, INSR, PDK1 and PDK4. Moreover, 18 SNPs were identified in FOXO3. G66716193A was significantly (P < 0.05) associated with growth traits. The sites C66716002T, C66716195T and A66716179G were significantly (P < 0.05) associated with growth or carcass traits. These results demonstrated that FOXO3 is a candidate gene influencing chicken growth. Our observations provide new clues to understand the molecular basis of chicken growth.