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Guo X, Li J, Li X, Sun J, Zou X, Ji J, Qu H, Shu D, Luo C. Synergy of genetics and lipid metabolism driving feed utilization efficiency in chickens. Poult Sci 2025; 104:104885. [PMID: 39978204 PMCID: PMC11880708 DOI: 10.1016/j.psj.2025.104885] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2024] [Revised: 01/20/2025] [Accepted: 02/04/2025] [Indexed: 02/22/2025] Open
Abstract
Residual feed intake (RFI) is a key indicator of feed efficiency, critical for enhancing the economic sustainability of poultry production. However, the genetic and metabolic regulatory mechanisms of RFI remain unclear. This study analyzed the genome, liver transcriptome, metabolome, and lipidome of hens with low and high feed efficiency (N = 60) from the previously established RFI divergent broiler lines (F15). Our results revealed pronounced genetic differentiation between low RFI (LRFI) and high RFI (HRFI) lines and identified genomic signatures of selection associated with feed efficiency. Transcriptomic analysis showed differential expression of genes involved in neural regulation and lipid metabolism. Notably, LRFI chickens exhibited reduced hepatic lipid accumulation, which was associated with decreased fatty acid metabolism and increased cholesterol metabolism (P < 0.05). The lipidomic analysis uncovered distinct profiles of glycerophospholipids (e.g., PE-P and PC-O) and sphingolipids (e.g., ceramides), which were more abundant in LRFI chickens (P < 0.05) and strongly correlated with key lipid metabolism processes (P < 0.05). Despite improved feed efficiency, LRFI chickens demonstrated signs of increased oxidative stress. Moreover, integrative analyses revealed that genes such as MGAT5, GABRA4, and LRRC4C, exhibiting strong selection signatures and higher expression in the LRFI line (P < 0.05), were identified as key regulators of lipid metabolism, potentially contributing to the observed differences in feed efficiency. This comprehensive study highlights the synergistic effect of genetics and lipid metabolism in driving feed utilization efficiency in chickens, establishing a scientific foundation for breeding strategies aimed at improving feed efficiency in poultry production.
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Affiliation(s)
- Xiaoli Guo
- State Key Laboratory of Swine and Poultry Breeding Industry & Guangdong Key Laboratory of Animal Breeding and Nutrition & Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, PR China
| | - Jianbo Li
- State Key Laboratory of Swine and Poultry Breeding Industry, Agro-Biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, PR China
| | - Xiaochang Li
- State Key Laboratory of Animal Biotech Breeding and Frontier Science Center for Molecular Design Breeding, China Agricultural University, Beijing, 100193, PR China
| | - Jia Sun
- State Key Laboratory of Swine and Poultry Breeding Industry & Guangdong Key Laboratory of Animal Breeding and Nutrition & Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, PR China
| | - Xian Zou
- State Key Laboratory of Swine and Poultry Breeding Industry & Guangdong Key Laboratory of Animal Breeding and Nutrition & Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, PR China
| | - Jian Ji
- State Key Laboratory of Swine and Poultry Breeding Industry & Guangdong Key Laboratory of Animal Breeding and Nutrition & Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, PR China
| | - Hao Qu
- State Key Laboratory of Swine and Poultry Breeding Industry & Guangdong Key Laboratory of Animal Breeding and Nutrition & Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, PR China
| | - Dingming Shu
- State Key Laboratory of Swine and Poultry Breeding Industry & Guangdong Key Laboratory of Animal Breeding and Nutrition & Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, PR China
| | - Chenglong Luo
- State Key Laboratory of Swine and Poultry Breeding Industry & Guangdong Key Laboratory of Animal Breeding and Nutrition & Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, PR China.
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Xiong X, Yu C, Qiu M, Zhang Z, Hu C, Zhu S, Yang L, Peng H, Song X, Chen J, Xia B, Wang J, Qing Y, Yang C. Genomic and Gut Microbiome Evaluations of Growth and Feed Efficiency Traits in Broilers. Animals (Basel) 2024; 14:3615. [PMID: 39765519 PMCID: PMC11672845 DOI: 10.3390/ani14243615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2024] [Revised: 12/10/2024] [Accepted: 12/13/2024] [Indexed: 01/11/2025] Open
Abstract
In this study, we combined genomic and gut microbiome data to evaluate 13 economically important growth and feed efficiency traits in 407 Dahen broilers, including body weight (BW) at four, six, nine, and ten weeks of age (BW4, BW6, BW9, and BW10), as well as the average daily gain (ADG6, ADG9, and ADG10), feed conversion ratio (FCR6, FCR9, and FCR10), and residual feed intake (RFI6, RFI9, and RFI10) for the three growing ages. The highest ADG and lowest FCR were observed at nine and six weeks of age, respectively. We obtained 47,872 high-quality genomic single-nucleotide polymorphisms (SNPs) by sequencing the genomes and 702 amplicon sequence variants (ASVs) of the gut microbiome by sequencing the 16S rRNA gene, both of which were used for analyses of linear mixed models. The heritability estimates (± standard error, SE) ranged from 0.103 ± 0.072 to 0.156 ± 0.079 for BW, 0.154 ± 0.074 to 0.276 ± 0.079 for the ADG, 0.311 ± 0.076 to 0.454 ± 0.076 for the FCR, and 0.413 ± 0.077 to 0.609 ± 0.076 for the RFI traits. We consistently observed moderate and low negative genetic correlations between the BW traits and the FCR and RFI traits (r = -0.562 to -0.038), whereas strong positive correlations were observed between the FCR and RFI traits (r = 0.564 to 0.979). For the FCR and RFI traits, strong positive correlations were found between the measures at the three ages. In contrast to the genomic contribution, we did not detect a gut microbial contribution to all of these traits, as the estimated microbiabilities did not confidently deviate from zero. We systematically evaluated the contributions of host genetics and gut microbes to several growth and feed efficiency traits in Dahen broilers, and the results show that only the host genetics had significant effects on the phenotypic variations in a flock. The parameters obtained in this study, based on the combined use of genomic and gut microbiota data, may facilitate the implementation of efficient breeding schemes in Dahen broilers.
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Affiliation(s)
- Xia Xiong
- Animal Breeding and Genetics Key Laboratory of Sichuan Province, Sichuan Animal Science Academy, Chengdu 610066, China; (X.X.); (C.Y.); (M.Q.); (Z.Z.); (C.H.); (S.Z.); (L.Y.); (H.P.); (X.S.); (J.C.); (B.X.); (J.W.)
| | - Chunlin Yu
- Animal Breeding and Genetics Key Laboratory of Sichuan Province, Sichuan Animal Science Academy, Chengdu 610066, China; (X.X.); (C.Y.); (M.Q.); (Z.Z.); (C.H.); (S.Z.); (L.Y.); (H.P.); (X.S.); (J.C.); (B.X.); (J.W.)
| | - Mohan Qiu
- Animal Breeding and Genetics Key Laboratory of Sichuan Province, Sichuan Animal Science Academy, Chengdu 610066, China; (X.X.); (C.Y.); (M.Q.); (Z.Z.); (C.H.); (S.Z.); (L.Y.); (H.P.); (X.S.); (J.C.); (B.X.); (J.W.)
| | - Zengrong Zhang
- Animal Breeding and Genetics Key Laboratory of Sichuan Province, Sichuan Animal Science Academy, Chengdu 610066, China; (X.X.); (C.Y.); (M.Q.); (Z.Z.); (C.H.); (S.Z.); (L.Y.); (H.P.); (X.S.); (J.C.); (B.X.); (J.W.)
| | - Chenming Hu
- Animal Breeding and Genetics Key Laboratory of Sichuan Province, Sichuan Animal Science Academy, Chengdu 610066, China; (X.X.); (C.Y.); (M.Q.); (Z.Z.); (C.H.); (S.Z.); (L.Y.); (H.P.); (X.S.); (J.C.); (B.X.); (J.W.)
| | - Shiliang Zhu
- Animal Breeding and Genetics Key Laboratory of Sichuan Province, Sichuan Animal Science Academy, Chengdu 610066, China; (X.X.); (C.Y.); (M.Q.); (Z.Z.); (C.H.); (S.Z.); (L.Y.); (H.P.); (X.S.); (J.C.); (B.X.); (J.W.)
| | - Li Yang
- Animal Breeding and Genetics Key Laboratory of Sichuan Province, Sichuan Animal Science Academy, Chengdu 610066, China; (X.X.); (C.Y.); (M.Q.); (Z.Z.); (C.H.); (S.Z.); (L.Y.); (H.P.); (X.S.); (J.C.); (B.X.); (J.W.)
| | - Han Peng
- Animal Breeding and Genetics Key Laboratory of Sichuan Province, Sichuan Animal Science Academy, Chengdu 610066, China; (X.X.); (C.Y.); (M.Q.); (Z.Z.); (C.H.); (S.Z.); (L.Y.); (H.P.); (X.S.); (J.C.); (B.X.); (J.W.)
| | - Xiaoyan Song
- Animal Breeding and Genetics Key Laboratory of Sichuan Province, Sichuan Animal Science Academy, Chengdu 610066, China; (X.X.); (C.Y.); (M.Q.); (Z.Z.); (C.H.); (S.Z.); (L.Y.); (H.P.); (X.S.); (J.C.); (B.X.); (J.W.)
| | - Jialei Chen
- Animal Breeding and Genetics Key Laboratory of Sichuan Province, Sichuan Animal Science Academy, Chengdu 610066, China; (X.X.); (C.Y.); (M.Q.); (Z.Z.); (C.H.); (S.Z.); (L.Y.); (H.P.); (X.S.); (J.C.); (B.X.); (J.W.)
| | - Bo Xia
- Animal Breeding and Genetics Key Laboratory of Sichuan Province, Sichuan Animal Science Academy, Chengdu 610066, China; (X.X.); (C.Y.); (M.Q.); (Z.Z.); (C.H.); (S.Z.); (L.Y.); (H.P.); (X.S.); (J.C.); (B.X.); (J.W.)
| | - Jiangxian Wang
- Animal Breeding and Genetics Key Laboratory of Sichuan Province, Sichuan Animal Science Academy, Chengdu 610066, China; (X.X.); (C.Y.); (M.Q.); (Z.Z.); (C.H.); (S.Z.); (L.Y.); (H.P.); (X.S.); (J.C.); (B.X.); (J.W.)
| | - Yi Qing
- Chengdu Livestock and Poultry Genetic Resources Protection Center, Chengdu 610081, China
| | - Chaowu Yang
- Animal Breeding and Genetics Key Laboratory of Sichuan Province, Sichuan Animal Science Academy, Chengdu 610066, China; (X.X.); (C.Y.); (M.Q.); (Z.Z.); (C.H.); (S.Z.); (L.Y.); (H.P.); (X.S.); (J.C.); (B.X.); (J.W.)
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Liu P, Luo N, Liu D, Ying F, Zhu D, Wen J, Zhao G, An B. Integrating GWAS and transcriptomics to identify candidate genes conferring relative growth rate trait in white-feathered broiler. Poult Sci 2024; 103:104338. [PMID: 39426221 PMCID: PMC11536000 DOI: 10.1016/j.psj.2024.104338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2024] [Revised: 09/12/2024] [Accepted: 09/14/2024] [Indexed: 10/21/2024] Open
Abstract
Broilers are a globally significant resource for food production, and their relative growth rate (RGR) has attracted increasing attention for improving broiler monitoring, feed management and feed conversion. The main objectives of this study were to identify key candidate genes affecting the RGR in white-feathered broiler by integrating genomic and transcriptomic datasets. This study reports a meta-analysis of genome-wide association studies (GWAS) using 3 purebred lines (n = 3,727) and 5,841,467 input SNPs to understand the genetic control of the RGR. A total of 101 associated SNPs located on 6 chromosomes were identified, 16 of which were common in the GWAS and meta cohorts. Fine mapping of a significant peak with 7 linked SNP (r2 > 0.94) located within the coding region of RAP2C revealed that chr4:3474286 (C > G) among these SNPs was a highly putative causal variant (PIP = 19%) and explained 2.26% of the RGR variation. Further analyses indicated that the surface expression level of the RAP2C gene in the blood, macrophage, lung tissue, and cecum tissue of commercial broiler breed (Ross) was higher than in the corresponding tissues of other egg-laying hens and local breeds. In addition, there was a significant difference in the expression of the RAP2C gene between the high (H, n = 5) and low (L, n = 4) RGR groups. A total of 301 differentially expressed genes (DEGs) related to the RGR in white-feathered broiler were identified by transcriptome differential analysis between the H and L populations, among which NFKBIA, CSF1R and TLR2A were important hub genes. Furthermore, the candidate genes identified based on GWASs, meta-analysis and DEGs analysis were significantly enriched for gene ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways involved in the growth cone, integrated-mediated signaling pathway, and MAPK signaling pathway. Overall, the RAP2C, NFKBIA, CSF1R and TLR2A genes are considered the most important candidate genes influencing RGR trait in white-feathered broiler. These findings provide valuable insights into the complex system that regulates broiler growth.
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Affiliation(s)
- Peihao Liu
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Na Luo
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Dawei Liu
- MiLe Xinguang Agricultural and Animal Industrials Corporation, MiLe, 652300, China
| | - Fan Ying
- MiLe Xinguang Agricultural and Animal Industrials Corporation, MiLe, 652300, China
| | - Dan Zhu
- MiLe Xinguang Agricultural and Animal Industrials Corporation, MiLe, 652300, China
| | - Jie Wen
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Guiping Zhao
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Bingxing An
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China; Center for Quantitative Genetics and Genomics (QGG), Aarhus University, Aarhus, 8000, Denmark.
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Sun J, Yang X, Zhao G, He Z, Xing W, Chen Y, Tan X, Wang M, Li W, An B, Pan Z, Zhou Z, Wen J, Liu R. Protein phosphatase 1 catalytic subunit gamma is a causative gene for meat lightness and redness. PLoS Genet 2024; 20:e1011467. [PMID: 39565795 PMCID: PMC11616877 DOI: 10.1371/journal.pgen.1011467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 12/04/2024] [Accepted: 10/23/2024] [Indexed: 11/22/2024] Open
Abstract
The quality of meat is important to the consumer. Color is a primary indicator of meat quality and is characterized mainly into lightness, redness, and yellowness. Here, we used the genome-wide association study (GWAS) and gene-based association analysis with whole-genome resequencing of 230 fast-growing white-feathered chickens to map genes related to meat lightness and redness to a 6.24 kb QTL region (GGA15: 6298.34-6304.58 kb). This analysis revealed that only the protein phosphatase 1 catalytic subunit gamma (PPP1CC) was associated with meat color (P = 8.65E-08). The causal relationships between PPP1CC expression and meat lightness/redness were further validated through Mendelian randomization analyses (P < 2.9E-12). Inducible skeletal muscle-specific PPP1CC knockout (PPP1CC-SSKO) mice were generated and these mice showed increased lightness and decreased myoglobin content in the limb muscles. In addition, the predominant myofiber shifted from slow-twitch to fast-twitch myofibers. Through transcriptome and targeted metabolome evidence, we found that inhibition of PPP1CC decreased the expression of typical slow-twitch myofiber and myofiber-type specification genes and enhanced the glycolysis pathway. Functional validation through a plasmid reporter assay revealed that a SNP (rs315520807, C > T) located in the intron of PPP1CC could regulate the gene transcription activity. The differences in meat color phenotypes, myoglobin content, frequency of rs315520807 variant, expression of PPP1CC and fast-twitch fiber marker genes were detected between fast-growing white-feathered chickens and local chickens. In this study, PPP1CC was identified as the causative gene for meat color, and the novel target gene and variant that can aid in the innovation of meat improvement technology were detected.
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Affiliation(s)
- Jiahong Sun
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Xinting Yang
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Guiping Zhao
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Zhengxiao He
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Wenhao Xing
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Yanru Chen
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Xiaodong Tan
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Mengjie Wang
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Wei Li
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Bingxing An
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Zhangyuan Pan
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Zhengkui Zhou
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Jie Wen
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Ranran Liu
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Beijing, China
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Mthana MS, Mthiyane DMN. High dietary Mucuna pruriens utilis seed meal compromises growth performance, carcass traits, haemato-biochemistry, and meat quality of broilers. Trop Anim Health Prod 2024; 56:310. [PMID: 39352513 PMCID: PMC11445329 DOI: 10.1007/s11250-024-04120-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2024] [Accepted: 09/11/2024] [Indexed: 10/04/2024]
Abstract
Usage of soyabean meal (SBM) in broiler diets is economically and environmentally unsustainable thus necessitating investigation of alternative protein sources. Therefore, this study investigated effects of incremental inclusion levels of Mucuna pruriens utilis seed meal (MSM) for partial substitution of SBM in broiler diets. In a completely randomized design (CRD), 400 day-old Ross 308 chicks were allotted to 5 iso-caloric-nitrogenous MSM-containing (0, 5, 10, 15, and 20%) dietary treatments. Each treatment was replicated 8 times, with each pen having 10 birds, during starter (d1 - 14), grower (d15 - 28), and finisher (d29 - 42) phases. Results showed that dietary MSM decreased feed intake (FI: quadratic: P < 0.001), body weight gain (BWG: linear: P < 0.001), and feed conversion efficiency (FCE: linear: P < 0.001) as it linearly decreased slaughter weight (SW: P < 0.001), hot carcass weight (HCW: P < 0.001), cold carcass weight (CCW: P < 0.001), dressing percentage (P < 0.001), and breast weight (P < 0.05). In contrast, dietary MSM linearly increased the weights of the liver (P < 0.01), proventriculus (P < 0.001), gizzard (P < 0.001), duodenum (P = 0.01), jejunum (P < 0.001), ileum (P < 0.001), caecum (P < 0.01), and colon (P < 0.01). Also, dietary MSM quadratically increased blood heterophils (P < 0.05) and alkaline phosphatase activity (P < 0.05) of the chickens whilst linearly increasing their serum amylase (P = 0.001) and lipase (P = 0.001) activities and linearly decreasing their serum symmetric dimethylarginine (SDMA: P = 0.001) and cholesterol (P < 0.05). Further, dietary MSM linearly decreased chicken breast meat ultimate pH (P < 0.05) whilst linearly increasing its cooking loss (P < 0.01), drip loss (P < 0.05) and shear force (P < 0.01). In conclusion, dietary MSM compromised growth performance, carcass characteristics, and meat quality of broilers as it increased the weights of their digestive-metabolic organs.
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Affiliation(s)
- Makiwa Simeon Mthana
- Department of Animal Science, School of Agricultural Sciences, Faculty of Natural and Agricultural Sciences, North-West University (Mahikeng Campus), Private Bag X 2046, Mmabatho, 2735, South Africa
| | - Doctor Mziwenkosi Nhlanhla Mthiyane
- Department of Animal Science, School of Agricultural Sciences, Faculty of Natural and Agricultural Sciences, North-West University (Mahikeng Campus), Private Bag X 2046, Mmabatho, 2735, South Africa.
- Food Security and Safety Focus Area, Faculty of Natural and Agricultural Sciences, North-West University (Mahikeng Campus), Mmabatho, 2735, South Africa.
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Gu S, Huang Q, Jie Y, Sun C, Wen C, Yang N. Transcriptomic and epigenomic landscapes of muscle growth during the postnatal period of broilers. J Anim Sci Biotechnol 2024; 15:91. [PMID: 38961455 PMCID: PMC11223452 DOI: 10.1186/s40104-024-01049-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Accepted: 05/12/2024] [Indexed: 07/05/2024] Open
Abstract
BACKGROUND Broilers stand out as one of the fastest-growing livestock globally, making a substantial contribution to animal meat production. However, the molecular and epigenetic mechanisms underlying the rapid growth and development of broiler chickens are still unclear. This study aims to explore muscle development patterns and regulatory networks during the postnatal rapid growth phase of fast-growing broilers. We measured the growth performance of Cornish (CC) and White Plymouth Rock (RR) over a 42-d period. Pectoral muscle samples from both CC and RR were randomly collected at day 21 after hatching (D21) and D42 for RNA-seq and ATAC-seq library construction. RESULTS The consistent increase in body weight and pectoral muscle weight across both breeds was observed as they matured, with CC outpacing RR in terms of weight at each stage of development. Differential expression analysis identified 398 and 1,129 genes in the two dimensions of breeds and ages, respectively. A total of 75,149 ATAC-seq peaks were annotated in promoter, exon, intron and intergenic regions, with a higher number of peaks in the promoter and intronic regions. The age-biased genes and breed-biased genes of RNA-seq were combined with the ATAC-seq data for subsequent analysis. The results spotlighted the upregulation of ACTC1 and FDPS at D21, which were primarily associated with muscle structure development by gene cluster enrichment. Additionally, a noteworthy upregulation of MUSTN1, FOS and TGFB3 was spotted in broiler chickens at D42, which were involved in cell differentiation and muscle regeneration after injury, suggesting a regulatory role of muscle growth and repair. CONCLUSIONS This work provided a regulatory network of postnatal broiler chickens and revealed ACTC1 and MUSTN1 as the key responsible for muscle development and regeneration. Our findings highlight that rapid growth in broiler chickens triggers ongoing muscle damage and subsequent regeneration. These findings provide a foundation for future research to investigate the functional aspects of muscle development.
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Affiliation(s)
- Shuang Gu
- State Key Laboratory of Animal Biotech Breeding and Frontier Science Center for Molecular Design Breeding, China Agricultural University, Beijing, 100193, China
- National Engineering Laboratory for Animal Breeding and Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Department of Animal Genetics and Breeding, College of Animal Science and Technology China Agricultural University, Beijing, 100193, China
| | - Qiang Huang
- State Key Laboratory of Animal Biotech Breeding and Frontier Science Center for Molecular Design Breeding, China Agricultural University, Beijing, 100193, China
- National Engineering Laboratory for Animal Breeding and Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Department of Animal Genetics and Breeding, College of Animal Science and Technology China Agricultural University, Beijing, 100193, China
| | - Yuchen Jie
- State Key Laboratory of Animal Biotech Breeding and Frontier Science Center for Molecular Design Breeding, China Agricultural University, Beijing, 100193, China
- National Engineering Laboratory for Animal Breeding and Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Department of Animal Genetics and Breeding, College of Animal Science and Technology China Agricultural University, Beijing, 100193, China
| | - Congjiao Sun
- State Key Laboratory of Animal Biotech Breeding and Frontier Science Center for Molecular Design Breeding, China Agricultural University, Beijing, 100193, China
- National Engineering Laboratory for Animal Breeding and Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Department of Animal Genetics and Breeding, College of Animal Science and Technology China Agricultural University, Beijing, 100193, China
- Sanya Institute of China Agricultural University, Hainan, 572025, China
| | - Chaoliang Wen
- State Key Laboratory of Animal Biotech Breeding and Frontier Science Center for Molecular Design Breeding, China Agricultural University, Beijing, 100193, China
- National Engineering Laboratory for Animal Breeding and Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Department of Animal Genetics and Breeding, College of Animal Science and Technology China Agricultural University, Beijing, 100193, China
- Sanya Institute of China Agricultural University, Hainan, 572025, China
| | - Ning Yang
- State Key Laboratory of Animal Biotech Breeding and Frontier Science Center for Molecular Design Breeding, China Agricultural University, Beijing, 100193, China.
- National Engineering Laboratory for Animal Breeding and Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Department of Animal Genetics and Breeding, College of Animal Science and Technology China Agricultural University, Beijing, 100193, China.
- Sanya Institute of China Agricultural University, Hainan, 572025, China.
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Cai K, Liu R, Wei L, Wang X, Cui H, Luo N, Wen J, Chang Y, Zhao G. Genome-wide association analysis identify candidate genes for feed efficiency and growth traits in Wenchang chickens. BMC Genomics 2024; 25:645. [PMID: 38943081 PMCID: PMC11212279 DOI: 10.1186/s12864-024-10559-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Accepted: 06/24/2024] [Indexed: 07/01/2024] Open
Abstract
BACKGROUND Wenchang chickens are one of the most popular local chicken breeds in the Chinese chicken industry. However, the low feed efficiency is the main shortcoming of this breed. Therefore, there is a need to find a more precise breeding method to improve the feed efficiency of Wenchang chickens. In this study, we explored important candidate genes and variants for feed efficiency and growth traits through genome-wide association study (GWAS) analysis. RESULTS Estimates of genomic heritability for growth and feed efficiency traits, including residual feed intake (RFI) of 0.05, average daily food intake (ADFI) of 0.21, average daily weight gain (ADG) of 0.24, body weight (BW) at 87, 95, 104, 113 days of age (BW87, BW95, BW104 and BW113) ranged from 0.30 to 0.44. Important candidate genes related to feed efficiency and growth traits were identified, such as PLCE1, LAP3, MED28, QDPR, LDB2 and SEL1L3 genes. CONCLUSION The results identified important candidate genes for feed efficiency and growth traits in Wenchang chickens and provide a theoretical basis for the development of new molecular breeding technology.
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Affiliation(s)
- Keqi Cai
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518124, P.R. China
- State Key Laboratory of Animal Nutrition, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, P.R. China
| | - Ranran Liu
- State Key Laboratory of Animal Nutrition, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, P.R. China
| | - Limin Wei
- The Sanya Research Institute, Hainan Academy of Agricultural Sciences, Sanya, 572025, P.R. China
| | - Xiuping Wang
- Hainan (Tan Niu) Wenchang Chicken Co., LTD, Haikou, 570100, P.R. China
| | - Huanxian Cui
- State Key Laboratory of Animal Nutrition, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, P.R. China
| | - Na Luo
- State Key Laboratory of Animal Nutrition, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, P.R. China
| | - Jie Wen
- State Key Laboratory of Animal Nutrition, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, P.R. China
| | - Yuxiao Chang
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518124, P.R. China.
| | - Guiping Zhao
- State Key Laboratory of Animal Nutrition, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, P.R. China.
- The Sanya Research Institute, Hainan Academy of Agricultural Sciences, Sanya, 572025, P.R. China.
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8
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Zhao D, Liu R, Tan X, Kang H, Wang J, Ma Z, Zhao H, Xiang H, Zhang Z, Li H, Zhao G. Large-scale transcriptomic and genomic analyses reveal a novel functional gene SERPINB6 for chicken carcass traits. J Anim Sci Biotechnol 2024; 15:70. [PMID: 38730308 PMCID: PMC11571647 DOI: 10.1186/s40104-024-01026-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Accepted: 03/18/2024] [Indexed: 05/12/2024] Open
Abstract
BACKGROUND Carcass traits are crucial indicators of meat production efficiency. However, the molecular regulatory mechanisms associated with these traits remain unclear. RESULTS In this study, we conducted comprehensive transcriptomic and genomic analyses on 399 Tiannong partridge chickens to identify key genes and variants associated with carcass traits and to elucidate the underlying regulatory mechanisms. Based on association analyses with the elastic net (EN) model, we identified 12 candidate genes (AMY1A, AP3B2, CEBPG, EEF2, EIF4EBP1, FGFR1, FOXD3, GOLM1, LOC107052698, PABPC1, SERPINB6 and TBC1D16) for 4 carcass-related traits, namely live weight, dressed weight, eviscerated weight, and breast muscle weight. SERPINB6 was identified as the only overlapping gene by 3 analyses, EN model analysis, weighted gene co-expression network analysis and differential expression analysis. Cell-level experiments confirmed that SERPINB6 promotes the proliferation of chicken DF1 cells and primary myoblasts. Further expression genome-wide association study and association analysis indicated that rs317934171 is the critical site that enhances SERPINB6 expression. Furthermore, a dual-luciferase reporter assay proved that gga-miR-1615 targets the 3'UTR of SERPINB6. CONCLUSIONS Collectively, our findings reveal that SERPINB6 serves as a novel gene for chicken carcass traits by promoting fibroblast and myoblast proliferation. Additionally, the downstream variant rs317934171 regulates SERPINB6 expression. These results identify a new target gene and molecular marker for the molecular mechanisms of chicken carcass traits.
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Affiliation(s)
- Di Zhao
- Guangdong Provincial Key Laboratory of Animal Molecular Design and Precise Breeding, School of Life Science and Engineering, Foshan University, Foshan, China
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Ranran Liu
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Xiaodong Tan
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Huimin Kang
- Guangdong Provincial Key Laboratory of Animal Molecular Design and Precise Breeding, School of Life Science and Engineering, Foshan University, Foshan, China
| | - Jie Wang
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Zheng Ma
- Guangdong Provincial Key Laboratory of Animal Molecular Design and Precise Breeding, School of Life Science and Engineering, Foshan University, Foshan, China
| | - Haiquan Zhao
- Guangdong Provincial Key Laboratory of Animal Molecular Design and Precise Breeding, School of Life Science and Engineering, Foshan University, Foshan, China
| | - Hai Xiang
- Guangdong Provincial Key Laboratory of Animal Molecular Design and Precise Breeding, School of Life Science and Engineering, Foshan University, Foshan, China
| | - Zhengfen Zhang
- Guangdong Tinoo's Foods Group Co., Ltd., Qingyuan, China
| | - Hua Li
- Guangdong Provincial Key Laboratory of Animal Molecular Design and Precise Breeding, School of Life Science and Engineering, Foshan University, Foshan, China.
- Guangdong Tinoo's Foods Group Co., Ltd., Qingyuan, China.
| | - Guiping Zhao
- Guangdong Provincial Key Laboratory of Animal Molecular Design and Precise Breeding, School of Life Science and Engineering, Foshan University, Foshan, China.
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China.
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9
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Wang X, Li D, Xu Y, Ding X, Liang S, Xie L, Wang Y, Zhan X. Xylanase Supplement Enhances the Growth Performance of Broiler by Modulating Serum Metabolism, Intestinal Health, Short-Chain Fatty Acid Composition, and Microbiota. Animals (Basel) 2024; 14:1182. [PMID: 38672330 PMCID: PMC11047501 DOI: 10.3390/ani14081182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2024] [Revised: 04/01/2024] [Accepted: 04/06/2024] [Indexed: 04/28/2024] Open
Abstract
This study aimed to investigate the effects of different levels of xylanase supplementation in a wheat-based diet on growth performance, short-chain fatty acids, intestinal health, microbial composition, and serum metabolism. A total of 1200 male chicks were randomly assigned to four wheat-based diet treatments: Group C (adding 0 mg/kg of xylanase), Group L (adding 50 mg/kg of xylanase), Group M (adding 100 mg/kg of xylanase), and Group H (adding 150 mg/kg of xylanase). The experiment lasted for 56 days. The results indicated that Group H broilers experienced a decreased feed-to-gain ratio throughout the study period. Additionally, dietary supplementation with xylanase led to an increase in the physical barrier, as indicated by increased VH and VH/CD in the gut (p < 0.05). Furthermore, levels of D-lactic acid and endotoxin were reduced. Xylanase supplementation also increased the abundance of Muc-2, ZO-1, and Occludin (p < 0.05). Moreover, xylanase supplementation enhanced the activity of sucrase and maltase in the duodenum (p < 0.05), which may be attributable to the upregulation of the abundance of SI and MGA (p < 0.05). Furthermore, xylanase addition promoted propionic acid produced by specific bacteria, such as Phascolarctobacterium, and influenced the microbial composition to some extent, promoting intestinal health. Additionally, 150 mg/kg of xylanase supplementation increased the amino acid, peptide, and carbohydrate content and upregulated the metabolism of amino acids related to histidine, cysteine, methionine, and other pathways (p < 0.05). These findings suggest adequate xylanase supplementation can enhance nutritional digestibility and absorption, improve growth performance, stimulate endogenous enzyme activity, optimize intestinal morphology and barrier function, and positively influence acid-producing bacteria and amino acid metabolic pathways.
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Affiliation(s)
- Xiaoli Wang
- State Key Laboratory of Institute of Agro-Product Safety and Nutrition, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China;
| | - Danlei Li
- Key Laboratory of Animal Nutrition and Feed Science in East China, Ministry of Agriculture, College of Animal Sciences, Zhejiang University, Hangzhou 310058, China; (D.L.); (Y.X.); (X.D.); (S.L.); (L.X.)
| | - Yibin Xu
- Key Laboratory of Animal Nutrition and Feed Science in East China, Ministry of Agriculture, College of Animal Sciences, Zhejiang University, Hangzhou 310058, China; (D.L.); (Y.X.); (X.D.); (S.L.); (L.X.)
| | - Xiaoqing Ding
- Key Laboratory of Animal Nutrition and Feed Science in East China, Ministry of Agriculture, College of Animal Sciences, Zhejiang University, Hangzhou 310058, China; (D.L.); (Y.X.); (X.D.); (S.L.); (L.X.)
| | - Shuang Liang
- Key Laboratory of Animal Nutrition and Feed Science in East China, Ministry of Agriculture, College of Animal Sciences, Zhejiang University, Hangzhou 310058, China; (D.L.); (Y.X.); (X.D.); (S.L.); (L.X.)
| | - Lingyu Xie
- Key Laboratory of Animal Nutrition and Feed Science in East China, Ministry of Agriculture, College of Animal Sciences, Zhejiang University, Hangzhou 310058, China; (D.L.); (Y.X.); (X.D.); (S.L.); (L.X.)
| | - Yongxia Wang
- Technology on Green-Eco-Healthy Animal Husbandry of Zhejiang Province, College of Animal Science and Technology, College of Veterinary Medicine, Zhejiang A&F University, Hangzhou 311300, China;
| | - Xiuan Zhan
- Key Laboratory of Animal Nutrition and Feed Science in East China, Ministry of Agriculture, College of Animal Sciences, Zhejiang University, Hangzhou 310058, China; (D.L.); (Y.X.); (X.D.); (S.L.); (L.X.)
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10
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Volkova NA, Romanov MN, Abdelmanova AS, Larionova PV, German NY, Vetokh AN, Shakhin AV, Volkova LA, Sermyagin AA, Anshakov DV, Fisinin VI, Griffin DK, Sölkner J, Brem G, McEwan JC, Brauning R, Zinovieva NA. Genome-Wide Association Study Revealed Putative SNPs and Candidate Genes Associated with Growth and Meat Traits in Japanese Quail. Genes (Basel) 2024; 15:294. [PMID: 38540354 PMCID: PMC10970133 DOI: 10.3390/genes15030294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 02/08/2024] [Accepted: 02/23/2024] [Indexed: 06/14/2024] Open
Abstract
The search for SNPs and candidate genes that determine the manifestation of major selected traits is one crucial objective for genomic selection aimed at increasing poultry production efficiency. Here, we report a genome-wide association study (GWAS) for traits characterizing meat performance in the domestic quail. A total of 146 males from an F2 reference population resulting from crossing a fast (Japanese) and a slow (Texas White) growing breed were examined. Using the genotyping-by-sequencing technique, genomic data were obtained for 115,743 SNPs (92,618 SNPs after quality control) that were employed in this GWAS. The results identified significant SNPs associated with the following traits at 8 weeks of age: body weight (nine SNPs), daily body weight gain (eight SNPs), dressed weight (33 SNPs), and weights of breast (18 SNPs), thigh (eight SNPs), and drumstick (three SNPs). Also, 12 SNPs and five candidate genes (GNAL, DNAJC6, LEPR, SPAG9, and SLC27A4) shared associations with three or more traits. These findings are consistent with the understanding of the genetic complexity of body weight-related traits in quail. The identified SNPs and genes can be used in effective quail breeding as molecular genetic markers for growth and meat characteristics for the purpose of genetic improvement.
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Affiliation(s)
- Natalia A. Volkova
- L. K. Ernst Federal Research Center for Animal Husbandry, Dubrovitsy, Podolsk 142132, Moscow Oblast, Russia; (N.A.V.); (A.S.A.); (P.V.L.); (N.Y.G.); (A.N.V.); (A.V.S.); (L.A.V.); (A.A.S.)
| | - Michael N. Romanov
- L. K. Ernst Federal Research Center for Animal Husbandry, Dubrovitsy, Podolsk 142132, Moscow Oblast, Russia; (N.A.V.); (A.S.A.); (P.V.L.); (N.Y.G.); (A.N.V.); (A.V.S.); (L.A.V.); (A.A.S.)
- School of Biosciences, University of Kent, Canterbury CT2 7NJ, Kent, UK;
| | - Alexandra S. Abdelmanova
- L. K. Ernst Federal Research Center for Animal Husbandry, Dubrovitsy, Podolsk 142132, Moscow Oblast, Russia; (N.A.V.); (A.S.A.); (P.V.L.); (N.Y.G.); (A.N.V.); (A.V.S.); (L.A.V.); (A.A.S.)
| | - Polina V. Larionova
- L. K. Ernst Federal Research Center for Animal Husbandry, Dubrovitsy, Podolsk 142132, Moscow Oblast, Russia; (N.A.V.); (A.S.A.); (P.V.L.); (N.Y.G.); (A.N.V.); (A.V.S.); (L.A.V.); (A.A.S.)
| | - Nadezhda Yu. German
- L. K. Ernst Federal Research Center for Animal Husbandry, Dubrovitsy, Podolsk 142132, Moscow Oblast, Russia; (N.A.V.); (A.S.A.); (P.V.L.); (N.Y.G.); (A.N.V.); (A.V.S.); (L.A.V.); (A.A.S.)
| | - Anastasia N. Vetokh
- L. K. Ernst Federal Research Center for Animal Husbandry, Dubrovitsy, Podolsk 142132, Moscow Oblast, Russia; (N.A.V.); (A.S.A.); (P.V.L.); (N.Y.G.); (A.N.V.); (A.V.S.); (L.A.V.); (A.A.S.)
| | - Alexey V. Shakhin
- L. K. Ernst Federal Research Center for Animal Husbandry, Dubrovitsy, Podolsk 142132, Moscow Oblast, Russia; (N.A.V.); (A.S.A.); (P.V.L.); (N.Y.G.); (A.N.V.); (A.V.S.); (L.A.V.); (A.A.S.)
| | - Ludmila A. Volkova
- L. K. Ernst Federal Research Center for Animal Husbandry, Dubrovitsy, Podolsk 142132, Moscow Oblast, Russia; (N.A.V.); (A.S.A.); (P.V.L.); (N.Y.G.); (A.N.V.); (A.V.S.); (L.A.V.); (A.A.S.)
| | - Alexander A. Sermyagin
- L. K. Ernst Federal Research Center for Animal Husbandry, Dubrovitsy, Podolsk 142132, Moscow Oblast, Russia; (N.A.V.); (A.S.A.); (P.V.L.); (N.Y.G.); (A.N.V.); (A.V.S.); (L.A.V.); (A.A.S.)
| | - Dmitry V. Anshakov
- Breeding and Genetic Center “Zagorsk Experimental Breeding Farm”—Branch of the Federal Research Center “All-Russian Poultry Research and Technological Institute”, Russian Academy of Sciences, Sergiev Posad 141311, Moscow Oblast, Russia;
| | - Vladimir I. Fisinin
- Federal Research Center “All-Russian Poultry Research and Technological Institute” of the Russian Academy of Sciences, Sergiev Posad 141311, Moscow Oblast, Russia;
| | - Darren K. Griffin
- School of Biosciences, University of Kent, Canterbury CT2 7NJ, Kent, UK;
| | - Johann Sölkner
- Institute of Livestock Sciences (NUWI), University of Natural Resources and Life Sciences Vienna, 1180 Vienna, Austria;
| | - Gottfried Brem
- Institute of Animal Breeding and Genetics, University of Veterinary Medicine, 1210 Vienna, Austria;
| | - John C. McEwan
- AgResearch, Invermay Agricultural Centre, Mosgiel 9053, New Zealand; (J.C.M.); (R.B.)
| | - Rudiger Brauning
- AgResearch, Invermay Agricultural Centre, Mosgiel 9053, New Zealand; (J.C.M.); (R.B.)
| | - Natalia A. Zinovieva
- L. K. Ernst Federal Research Center for Animal Husbandry, Dubrovitsy, Podolsk 142132, Moscow Oblast, Russia; (N.A.V.); (A.S.A.); (P.V.L.); (N.Y.G.); (A.N.V.); (A.V.S.); (L.A.V.); (A.A.S.)
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11
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Pan R, Qi L, Xu Z, Zhang D, Nie Q, Zhang X, Luo W. Weighted single-step GWAS identified candidate genes associated with carcass traits in a Chinese yellow-feathered chicken population. Poult Sci 2024; 103:103341. [PMID: 38134459 PMCID: PMC10776626 DOI: 10.1016/j.psj.2023.103341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Revised: 11/26/2023] [Accepted: 11/28/2023] [Indexed: 12/24/2023] Open
Abstract
Carcass traits in broiler chickens are complex traits that are influenced by multiple genes. To gain deeper insights into the genetic mechanisms underlying carcass traits, here we conducted a weighted single-step genome-wide association study (wssGWAS) in a population of Chinese yellow-feathered chicken. The objective was to identify genomic regions and candidate genes associated with carcass weight (CW), eviscerated weight with giblets (EWG), eviscerated weight (EW), breast muscle weight (BMW), drumstick weight (DW), abdominal fat weight (AFW), abdominal fat percentage (AFP), gizzard weight (GW), and intestine length (IL). A total of 1,338 broiler chickens with phenotypic and pedigree information were included in this study. Of these, 435 chickens were genotyped using a 600K single nucleotide polymorphism chip for association analysis. The results indicate that the most significant regions for 9 traits explained 2.38% to 5.09% of the phenotypic variation, from which the region of 194.53 to 194.63Mb on chromosome 1 with the gene RELT and FAM168A identified on it was significantly associated with CW, EWG, EW, BMW, and DW. Meanwhile, the 5 traits have a strong genetic correlation, indicating that the region and the genes can be used for further research. In addition, some candidate genes associated with skeletal muscle development, fat deposition regulation, intestinal repair, and protection were identified. Gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses suggested that the genes are involved in processes such as vascular development (CD34, FGF7, FGFR3, ITGB1BP1, SEMA5A, LOXL2), bone formation (FGFR3, MATN1, MEF2D, DHRS3, SKI, STC1, HOXB1, HOXB3, TIPARP), and anatomical size regulation (ADD2, AKT1, CFTR, EDN3, FLII, HCLS1, ITGB1BP1, SEMA5A, SHC1, ULK1, DSTN, GSK3B, BORCS8, GRIP2). In conclusion, the integration of phenotype, genotype, and pedigree information without creating pseudo-phenotype will facilitate the genetic improvement of carcass traits in chickens, providing valuable insights into the genetic architecture and potential candidate genes underlying carcass traits, enriching our understanding and contributing to the breeding of high-quality broiler chickens.
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Affiliation(s)
- Rongyang Pan
- State Key Laboratory of Livestock and Poultry Breeding, & Lingnan Guangdong Laboratory of Agriculture, South China Agricultural University, Guangzhou 510642, China; Guangdong Xugang Yellow Poultry Seed Industry Group Co., Ltd, Jiangmen City, Guangdong Province, China; Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, and Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affair, South China Agricultural University, Guangzhou 510642, China; Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou 510642, China
| | - Lin Qi
- State Key Laboratory of Livestock and Poultry Breeding, & Lingnan Guangdong Laboratory of Agriculture, South China Agricultural University, Guangzhou 510642, China; Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, and Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affair, South China Agricultural University, Guangzhou 510642, China; Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou 510642, China
| | - Zhenqiang Xu
- State Key Laboratory of Livestock and Poultry Breeding, & Lingnan Guangdong Laboratory of Agriculture, South China Agricultural University, Guangzhou 510642, China; Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, and Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affair, South China Agricultural University, Guangzhou 510642, China; Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou 510642, China
| | - Dexiang Zhang
- State Key Laboratory of Livestock and Poultry Breeding, & Lingnan Guangdong Laboratory of Agriculture, South China Agricultural University, Guangzhou 510642, China; Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, and Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affair, South China Agricultural University, Guangzhou 510642, China; Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou 510642, China
| | - Qinghua Nie
- State Key Laboratory of Livestock and Poultry Breeding, & Lingnan Guangdong Laboratory of Agriculture, South China Agricultural University, Guangzhou 510642, China; Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, and Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affair, South China Agricultural University, Guangzhou 510642, China; Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou 510642, China
| | - Xiquan Zhang
- State Key Laboratory of Livestock and Poultry Breeding, & Lingnan Guangdong Laboratory of Agriculture, South China Agricultural University, Guangzhou 510642, China; Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, and Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affair, South China Agricultural University, Guangzhou 510642, China; Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou 510642, China
| | - Wen Luo
- State Key Laboratory of Livestock and Poultry Breeding, & Lingnan Guangdong Laboratory of Agriculture, South China Agricultural University, Guangzhou 510642, China; Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, and Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affair, South China Agricultural University, Guangzhou 510642, China; Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou 510642, China.
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12
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He Z, Liu R, Wang M, Wang Q, Zheng J, Ding J, Wen J, Fahey AG, Zhao G. Combined effect of microbially derived cecal SCFA and host genetics on feed efficiency in broiler chickens. MICROBIOME 2023; 11:198. [PMID: 37653442 PMCID: PMC10472625 DOI: 10.1186/s40168-023-01627-6] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 07/18/2023] [Indexed: 09/02/2023]
Abstract
BACKGROUND Improving feed efficiency is the most important goal for modern animal production. The regulatory mechanisms of controlling feed efficiency traits are extremely complex and include the functions related to host genetics and gut microbiota. Short-chain fatty acids (SCFAs), as significant metabolites of microbiota, could be used to refine the combined effect of host genetics and gut microbiota. However, the association of SCFAs with the gut microbiota and host genetics for regulating feed efficiency is far from understood. RESULTS In this study, 464 broilers were housed for RFI measuring and examining the host genome sequence. And 300 broilers were examined for cecal microbial data and SCFA concentration. Genome-wide association studies (GWAS) showed that four out of seven SCFAs had significant associations with genome variants. One locus (chr4: 29414391-29417189), located near or inside the genes MAML3, SETD7, and MGST2, was significantly associated with propionate and had a modest effect on feed efficiency traits and the microbiota. The genetic effect of the top SNP explained 8.43% variance of propionate. Individuals with genotype AA had significantly different propionate concentrations (0.074 vs. 0.131 μg/mg), feed efficiency (FCR: 1.658 vs. 1.685), and relative abundance of 14 taxa compared to those with the GG genotype. Christensenellaceae and Christensenellaceae_R-7_group were associated with feed efficiency, propionate concentration, the top SNP genotypes, and lipid metabolism. Individuals with a higher cecal abundance of these taxa showed better feed efficiency and lower concentrations of caecal SCFAs. CONCLUSION Our study provides strong evidence of the pathway that host genome variants affect the cecal SCFA by influencing caecal microbiota and then regulating feed efficiency. The cecal taxa Christensenellaceae and Christensenellaceae_R-7_group were identified as representative taxa contributing to the combined effect of host genetics and SCFAs on chicken feed efficiency. These findings provided strong evidence of the combined effect of host genetics and gut microbial SCFAs in regulating feed efficiency traits. Video Abstract.
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Affiliation(s)
- Zhengxiao He
- State Key Laboratory of Animal Nutrition; Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
- School of Agriculture and Food Science, University College Dublin, Dublin, Ireland
| | - Ranran Liu
- State Key Laboratory of Animal Nutrition; Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Mengjie Wang
- State Key Laboratory of Animal Nutrition; Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Qiao Wang
- State Key Laboratory of Animal Nutrition; Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Jumei Zheng
- State Key Laboratory of Animal Nutrition; Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Jiqiang Ding
- State Key Laboratory of Animal Nutrition; Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Jie Wen
- State Key Laboratory of Animal Nutrition; Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Alan G Fahey
- School of Agriculture and Food Science, University College Dublin, Dublin, Ireland.
| | - Guiping Zhao
- State Key Laboratory of Animal Nutrition; Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China.
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Dadfar MJ, Torshizi RV, Maghsoudi A, Ehsani A, Masoudi AA. Trade-off between feed efficiency and immunity in specialized high-performing chickens. Poult Sci 2023; 102:102703. [PMID: 37141810 DOI: 10.1016/j.psj.2023.102703] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Revised: 04/03/2023] [Accepted: 04/03/2023] [Indexed: 05/06/2023] Open
Abstract
Based on resource allocation theory, ignoring importance of immunity, and focus on growth and feed efficiency (FE) traits in breeding plans may lead to serious weakness in immune system performance. However, in poultry the adverse effects of selection for FE on the immune system are unclear. Therefore, an experiment was conducted to study the trade-off between FE and immunity using a total of 180 high-performing specialized male chickens from a commercial broiler line which were selected over 30 generations for growth (body weight gain, BWG) and FE (residual feed intake, RFI). Birds were reared for 42 d and 5 FE-related traits of the birds in the last week were considered including daily feed intake (DFI), feed conversion ratio (FCR), residual feed intake (RFI), residual BW gain (RG), and residual intake and gain (RIG). For all 180 chickens, immune system performance including humoral immune response, cell-mediated immunity (CMI), and the activity of lysozyme enzyme (L. activity) as innate immunity was measured. After ascending sort of each FE records, 10% of higher records (H-FE: N = 18) and 10% of lower records (L-FE: N = 18) were determined, and immunity between L-FE and H-FE groups were compared. Moreover, L-BWG and H-BWG were analyzed because BWG is one of components in the FE formula. Performance of the immune system was not statistically different for CMI in none of the studied FE groups. Moreover, high and low groups for DFI and BWG were not different regarding the immunity of the birds. Antibody titers against Newcastle disease virus (NDV) were different between low and high groups of FCR, RG, and RIG. Likewise, SRBC-derived antibodies were significantly different between RFI groups. Rather than humoral immunity, RIG had adversely effect on the innate immunity. Results of the present study showed that although RIG is a more appropriate indicator for FE, choosing for high RIG can weaken the performance of the both humoral and innate immune systems, while RFI had fewer adverse effects.
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Affiliation(s)
- Mohammad-Javad Dadfar
- Department of Animal Science, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran
| | - Rasoul Vaez Torshizi
- Department of Animal Science, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran.
| | - Ali Maghsoudi
- Department of Animal Science, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran
| | - Alireza Ehsani
- Department of Animal Science, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran
| | - Ali Akbar Masoudi
- Department of Animal Science, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran
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Wang H, Wang X, Li M, Sun H, Chen Q, Yan D, Dong X, Pan Y, Lu S. Genome-Wide Association Study of Growth Traits in a Four-Way Crossbred Pig Population. Genes (Basel) 2022; 13:1990. [PMID: 36360227 PMCID: PMC9689869 DOI: 10.3390/genes13111990] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Revised: 10/28/2022] [Accepted: 10/28/2022] [Indexed: 04/29/2025] Open
Abstract
Growth traits are crucial economic traits in the commercial pig industry and have a substantial impact on pig production. However, the genetic mechanism of growth traits is not very clear. In this study, we performed a genome-wide association study (GWAS) based on the specific-locus amplified fragment sequencing (SLAF-seq) to analyze ten growth traits on 223 four-way intercross pigs. A total of 227,921 highly consistent single nucleotide polymorphisms (SNPs) uniformly dispersed throughout the entire genome were used to conduct GWAS. A total of 53 SNPs were identified for ten growth traits using the mixed linear model (MLM), of which 18 SNPs were located in previously reported quantitative trait loci (QTL) regions. Two novel QTLs on SSC4 and SSC7 were related to average daily gain from 30 to 60 kg (ADG30-60) and body length (BL), respectively. Furthermore, 13 candidate genes (ATP5O, GHRHR, TRIM55, EIF2AK1, PLEKHA1, BRAP, COL11A2, HMGA1, NHLRC1, SGSM1, NFATC2, MAML1, and PSD3) were found to be associated with growth traits in pigs. The GWAS findings will enhance our comprehension of the genetic architecture of growth traits. We suggested that these detected SNPs and corresponding candidate genes might provide a biological foundation for improving the growth and production performance of pigs in swine breeding.
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Affiliation(s)
- Huiyu Wang
- Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China
- Faculty of Animal Science, Xichang University, Xichang 615000, China
| | - Xiaoyi Wang
- Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China
| | - Mingli Li
- Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China
| | - Hao Sun
- Faculty of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Qiang Chen
- Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China
| | - Dawei Yan
- Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China
| | - Xinxing Dong
- Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China
| | - Yuchun Pan
- Faculty of Animal Science, Zhejiang University, Hangzhou 310058, China
| | - Shaoxiong Lu
- Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China
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15
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A significant quantitative trait locus on chromosome Z and its impact on egg production traits in seven maternal lines of meat-type chicken. J Anim Sci Biotechnol 2022; 13:96. [PMID: 35941697 PMCID: PMC9361671 DOI: 10.1186/s40104-022-00744-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Accepted: 06/09/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Egg production is economically important in the meat-type chicken industry. To better understand the molecular genetic mechanism of egg production in meat-type chicken, genetic parameter estimation, genome-wide association analyses combined with meta-analyses, Bayesian analyses, and selective sweep analyses were performed to screen single nucleotide polymorphisms (SNPs) and other genetic loci that were significantly associated with egg number traits in 11,279 chickens from seven material lines. RESULTS Yellow-feathered meat-type chickens laid 115 eggs at 43 weeks of age and white-feathered chickens laid 143 eggs at 60 weeks of age, with heritability ranging from 0.034-0.258. Based on meta-analyses and selective sweep analyses, one region (10.81-13.05 Mb) on chromosome Z was associated with egg number in all lines. Further analyses using the W2 line was also associated with the same region, and 29 SNPs were identified that significantly affected estimation of breeding value of egg numbers. The 29 SNPs were identified as having a significant effect on the egg number EBV in 3194 birds in line W2. There are 36 genes in the region, with glial cell derived neurotrophic factor, DAB adaptor protein 2, protein kinase AMP-activated catalytic subunit alpha 1, NAD kinase 2, mitochondrial, WD repeat domain 70, leukemia inhibitory factor receptor alpha, complement C6, and complement C7 identified as being potentially affecting to egg number. In addition, three SNPs (rs318154184, rs13769886, and rs313325646) associated with egg number were located on or near the prolactin receptor gene. CONCLUSION Our study used genomic information from different chicken lines and populations to identify a genomic region (spanning 2.24 Mb) associated with egg number. Nine genes and 29 SNPs were identified as the most likely candidate genes and variations for egg production. These results contribute to the identification of candidate genes and variants for egg traits in poultry.
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16
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Ogawa S, Darhan H, Suzuki K. Genetic and genomic analysis of oxygen consumption in mice. J Anim Breed Genet 2022; 139:596-610. [PMID: 35608337 DOI: 10.1111/jbg.12721] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 05/07/2022] [Indexed: 12/16/2022]
Abstract
We estimated genetic parameters for oxygen consumption (OC), OC per metabolic body weight (OCMBW) and body weight at three through 8 weeks of age in divergently selected mice populations, with an animal model considering maternal genetic, common litter environmental and cytoplasmic inheritance effects. Cytoplasmic inheritance was considered based on maternal lineage information. With respect to OC, estimated direct heritability was moderate (0.32) and the estimated proportion of the variance of cytoplasmic inheritance effects to the phenotypic variance was very low (0.01), implying that causal genes for OC could be located on autosomes. To assess this hypothesis, we attempted to identify possible candidate causal genes through selective signature detection with the results of pooled whole-genome resequencing using pooled DNA samples from high and low OC mice. We made a list of possible candidate causal genes for OC, including those relating to electron transport chain and ATP-binding proteins (Ndufa12, Sdhc, Atp10b, etc.), Prr16 encoding Largen protein, Cry1 encoding a key component of the circadian core oscillator and so on. The results, although careful interpretation must be required, could contribute to elucidate the genetic mechanism of OC, an indicator for maintenance energy requirement, and therefore feed efficiency.
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Affiliation(s)
- Shinichiro Ogawa
- Graduate School of Agricultural Science, Tohoku University, Sendai, Miyagi, Japan
| | - Hongyu Darhan
- Graduate School of Agricultural Science, Tohoku University, Sendai, Miyagi, Japan
| | - Keiichi Suzuki
- Graduate School of Agricultural Science, Tohoku University, Sendai, Miyagi, Japan
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He Z, Li S, Li W, Ding J, Zheng M, Li Q, Fahey AG, Wen J, Liu R, Zhao G. Comparison of genomic prediction methods for residual feed intake in broilers. Anim Genet 2022; 53:466-469. [PMID: 35292985 DOI: 10.1111/age.13186] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 02/24/2022] [Accepted: 02/28/2022] [Indexed: 11/30/2022]
Abstract
Residual feed intake (RFI) is a measure of the feed efficiency of animals. Previous studies have identified SNPs associated with RFI. The objective of this study was to compare the GBLUP model with the GA-BLUP model including previously identified associated SNPs. The nine associated SNPs were obtained from the genome-wide association study on a discovery population as preselection information. These models were analysed using ASREML software using a 5-fold cross-validation method on a validation population. With the genetic architecture (GA) matrix used, which was conducted with the nine RFI-associated SNPs, the prediction accuracy of RFI was improved compared with the original GBLUP model. The calculated optimal ω was 0.981 for RFI, which is in line with the optimal range from 0.9 to 1.0 in the gradient test. The prediction accuracy increased by 2% in the GA-BLUP model with ω being 0.981 compared with the GBLUP model. In conclusion, the GA-BLUP with the nine RFI-associated SNPs and an optimal ω can improve the prediction accuracy for a specific trait compared with GBLUP.
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Affiliation(s)
- Zhengxiao He
- State Key Laboratory of Animal Nutrition, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China.,School of Agriculture and Food Science, University College Dublin, Dublin, Ireland
| | - Sen Li
- State Key Laboratory of Animal Nutrition, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Wei Li
- State Key Laboratory of Animal Nutrition, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Jiqiang Ding
- State Key Laboratory of Animal Nutrition, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Maiqing Zheng
- State Key Laboratory of Animal Nutrition, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Qinghe Li
- State Key Laboratory of Animal Nutrition, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Alan G Fahey
- School of Agriculture and Food Science, University College Dublin, Dublin, Ireland
| | - Jie Wen
- State Key Laboratory of Animal Nutrition, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Ranran Liu
- State Key Laboratory of Animal Nutrition, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Guiping Zhao
- State Key Laboratory of Animal Nutrition, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
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18
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Tan X, Liu R, Li W, Zheng M, Zhu D, Liu D, Feng F, Li Q, Liu L, Wen J, Zhao G. Assessment the effect of genomic selection and detection of selective signature in broilers. Poult Sci 2022; 101:101856. [PMID: 35413593 PMCID: PMC9018145 DOI: 10.1016/j.psj.2022.101856] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 02/01/2022] [Accepted: 02/28/2022] [Indexed: 12/02/2022] Open
Abstract
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.
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Shen L, Yu J, Ge Y, Li H, Li Y, Cao Z, Luan P, Xiao F, Gao H, Zhang H. Associations of Transcription Factor 21 Gene Polymorphisms with the Growth and Body Composition Traits in Broilers. Animals (Basel) 2022; 12:ani12030393. [PMID: 35158719 PMCID: PMC8833368 DOI: 10.3390/ani12030393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 02/04/2022] [Accepted: 02/06/2022] [Indexed: 11/21/2022] Open
Abstract
Simple Summary The functional SNPs discovered in this work will give helpful information on the crucial molecular markers that may be employed in breeding efforts to improve the heart development of broiler chickens. Abstract This study aims to identify molecular marker loci that could be applied in broiler breeding programs. In this study, we used public databases to locate the Transcription factor 21 (TCF21) gene that affected the economically important traits in broilers. Ten single nucleotide polymorphisms were detected in the TCF21 gene by monoclonal sequencing. The polymorphisms of these 10 SNPs in the TCF21 gene were significantly associated (p < 0.05) with multiple growth and body composition traits. Furthermore, the TT genotype of g.-911T>G was identified to significantly increase the heart weight trait without affecting the negative traits, such as abdominal fat and reproduction by multiple methods. Thus, it was speculated that the g.-911T>G identified in the TCF21 gene might be used in marker-assisted selection in the broiler breeding program.
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Affiliation(s)
- Linyong Shen
- Key Laboratory of Chicken Genetics and Breeding, Ministry of Agriculture and Rural Affairs, Key Laboratory of Animal Genetics, Breeding and Reproduction, Education Department of Heilongjiang Province, College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, China; (L.S.); (Y.G.); (H.L.); (Y.L.); (Z.C.); (P.L.)
| | - Jiaqiang Yu
- Forest Investigating and Planning Institute of Daxinganling, Yakshi 022150, China;
| | - Yaowen Ge
- Key Laboratory of Chicken Genetics and Breeding, Ministry of Agriculture and Rural Affairs, Key Laboratory of Animal Genetics, Breeding and Reproduction, Education Department of Heilongjiang Province, College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, China; (L.S.); (Y.G.); (H.L.); (Y.L.); (Z.C.); (P.L.)
| | - Hui Li
- Key Laboratory of Chicken Genetics and Breeding, Ministry of Agriculture and Rural Affairs, Key Laboratory of Animal Genetics, Breeding and Reproduction, Education Department of Heilongjiang Province, College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, China; (L.S.); (Y.G.); (H.L.); (Y.L.); (Z.C.); (P.L.)
| | - Yumao Li
- Key Laboratory of Chicken Genetics and Breeding, Ministry of Agriculture and Rural Affairs, Key Laboratory of Animal Genetics, Breeding and Reproduction, Education Department of Heilongjiang Province, College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, China; (L.S.); (Y.G.); (H.L.); (Y.L.); (Z.C.); (P.L.)
| | - Zhiping Cao
- Key Laboratory of Chicken Genetics and Breeding, Ministry of Agriculture and Rural Affairs, Key Laboratory of Animal Genetics, Breeding and Reproduction, Education Department of Heilongjiang Province, College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, China; (L.S.); (Y.G.); (H.L.); (Y.L.); (Z.C.); (P.L.)
| | - Peng Luan
- Key Laboratory of Chicken Genetics and Breeding, Ministry of Agriculture and Rural Affairs, Key Laboratory of Animal Genetics, Breeding and Reproduction, Education Department of Heilongjiang Province, College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, China; (L.S.); (Y.G.); (H.L.); (Y.L.); (Z.C.); (P.L.)
| | - Fan Xiao
- Fujian Sunnzer Biotechnology Development Co., Ltd., Nanping 354100, China; (F.X.); (H.G.)
| | - Haihe Gao
- Fujian Sunnzer Biotechnology Development Co., Ltd., Nanping 354100, China; (F.X.); (H.G.)
| | - Hui Zhang
- Key Laboratory of Chicken Genetics and Breeding, Ministry of Agriculture and Rural Affairs, Key Laboratory of Animal Genetics, Breeding and Reproduction, Education Department of Heilongjiang Province, College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, China; (L.S.); (Y.G.); (H.L.); (Y.L.); (Z.C.); (P.L.)
- Correspondence: ; Tel.: +86-451-55191486
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20
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Identifying the key genes and functional enrichment pathways associated with feed efficiency in cattle. Gene 2022; 807:145934. [PMID: 34478820 DOI: 10.1016/j.gene.2021.145934] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 08/21/2021] [Accepted: 08/27/2021] [Indexed: 12/22/2022]
Abstract
Residual feed intake (RFI) is a measurement of feed efficiency, and is inversely correlated with feed efficiency. The differentially expressed genes (DEGs) associated with RFI vary substantially among studies, posing great challenges in finding the RFI-related marker genes. This study attempted to resolve this issue by integrating and comparing the multiple transcriptome sequencing data associated with RFI in the cattle liver, using differential, functional enrichment, protein-protein interaction (PPI) network, weighted co-expression network (WGCNA), and gene set enrichment analyses (GSEA) to identify the candidate genes and functional enrichment pathways that are closely associated with RFI. Four candidate genes namely SHC1, GPX4, ACADL, and IGF1 were identified and validated as the marker genes for RFI. Four functional enrichment pathways, namely the fatty acid metabolism, sugar metabolism, energy metabolism, and protein ubiquitination were also found to be closely related to RFI. This study identified several genes and signaling pathways with shared characteristics, which will provide new insights into the molecular mechanisms related to the regulation of feed efficiency, and provide basis for molecular markers related to feed efficiency in beef cattle.
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Zhang X, Luan P, Cao D, Hu G. A High-Density Genetic Linkage Map and Fine Mapping of QTL For Feed Conversion Efficiency in Common Carp ( Cyprinus carpio). Front Genet 2021; 12:778487. [PMID: 34868267 PMCID: PMC8633483 DOI: 10.3389/fgene.2021.778487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 10/22/2021] [Indexed: 12/02/2022] Open
Abstract
Feed conversion efficiency (FCE) is an economically crucial trait in fish, however, little progress has been made in genetics and genomics for this trait because phenotypes of the trait are difficult to measure. In this study, we constructed a high-density and high-resolution genetic linkage map with 28,416 SNP markers for common carp (Cyprinus carpio) based on high throughput genotyping with the carp 250K single nucleotide polymorphism (SNP) array in a full-sib F1 family of mirror carp (Cyprinus carpio) consisting of 141 progenies. The linkage map contained 11,983 distinct loci and spanned 3,590.09 cM with an average locus interval of 0.33 cM. A total of 17 QTL for the FCE trait were detected on four LGs (LG9, LG20, LG28, and LG32), explaining 8.9-15.9% of the phenotypic variations. One major cluster containing eight QTL (qFCE1-28, qFCE2-28, qFCE3-28, qFCE4-28, qFCE5-28, qFCE6-28, qFCE7-28, and qFCE8-28) was detected on LG28. Two clusters consisting of four QTL (qFCE1-32, qFCE2-32, qFCE3-32, and qFCE4-32) and three QTL (qFCE1-20, qFCE2-20, and qFCE3-20) were detected on LG32 and LG20, respectively. Nine candidate genes (ACACA, SCAF4, SLC2A5, TNMD, PCDH1, FOXO, AGO1, FFAR3, and ARID1A) underlying the feed efficiency trait were also identified, the biological functions of which may be involved in lipid metabolism, carbohydrate metabolism, energy deposition, fat accumulation, digestion, growth regulation, and cell proliferation and differentiation according to GO (Gene Ontology). As an important tool, high-density and high-resolution genetic linkage maps play a crucial role in the QTL fine mapping of economically important traits. Our novel findings provided new insights that elucidate the genetic basis and molecular mechanism of feed efficiency and the subsequent marker-assisted selection breeding in common carp.
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Affiliation(s)
- Xiaofeng Zhang
- National and Local United Engineering Laboratory for Freshwater Fish Breeding, Heilongjiang River Fisheries Research Institute, Chinese Academy of Fishery Sciences, Harbin, China
| | | | | | - Guo Hu
- National and Local United Engineering Laboratory for Freshwater Fish Breeding, Heilongjiang River Fisheries Research Institute, Chinese Academy of Fishery Sciences, Harbin, China
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