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Liu H, Zhu C, Wang Y, Wang Z, Zou K, Song W, Tao Z, Xu W, Zhang S, Wang Z, Li H. Effects of residual feed intake on the economic traits of fast-growing meat ducks. Poult Sci 2024; 103:103879. [PMID: 38833748 PMCID: PMC11190701 DOI: 10.1016/j.psj.2024.103879] [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: 03/21/2024] [Revised: 05/12/2024] [Accepted: 05/14/2024] [Indexed: 06/06/2024] Open
Abstract
Feed efficiency (FE) is a crucial economic indicator of meat duck production. The objective of this study was to assess the impact of residual feed intake (RFI), defined as the difference between the actual and expected feed intake based on animal's production and maintenance requirements, on the growth performance (GP), slaughter and internal organ characteristics of fast-growing meat ducks. In total, 1,300 healthy 14-day-old male fast-growing meat ducks were housed in individual cages until slaughter at the age of 35 d. The characteristics of the carcass and internal organs of 30 ducks with the highest RFI (HRFI) and the lowest RFI (LRFI) were respectively determined. RFI, the feed conversion ratio (FCR), and average day feed intake (ADFI) were significantly lower in the LRFI group than the HRFI group (P < 0.001), while there were no significant differences in marketing BW or BW gain (BWG) (P > 0.05). The thigh muscle and lean meat yields were higher, and the abdominal fat content was lower (P < 0.001) in the LRFI group, while there were no significant differences in other carcass traits between the groups (P > 0.05). The liver and gizzard yields were significantly higher (P < 0.001) in the LRFI group, while there were no significant differences (P > 0.05) in intestinal length between the groups. RFI was highly positively correlate with FCR and ADFI (P < 0.01), but negatively correlated the yields of thigh muscle, lean meat, liver, and gizzard, and positively correlated with abdominal fat content. These results indicate that selection for low RFI could improve the FE of fast-growing meat ducks without affecting the marketing BW and BWG, while increasing yields of thigh muscle and lean meat and reducing abdominal fat content. These findings offer useful insights into the biological processes that influence FE of fast-growing meat ducks.
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Affiliation(s)
- Hongxiang Liu
- Jiangsu Institute of Poultry Sciences, Yangzhou 225125, China
| | - Chunhong Zhu
- Jiangsu Institute of Poultry Sciences, Yangzhou 225125, China
| | - Yifei Wang
- Jiangsu Institute of Poultry Sciences, Yangzhou 225125, China
| | - Zhen Wang
- Shandong Hekangyuan Group Co., Ltd, Jinan, 250000, China
| | - Kexin Zou
- Shandong Hekangyuan Group Co., Ltd, Jinan, 250000, China
| | - Weitao Song
- Jiangsu Institute of Poultry Sciences, Yangzhou 225125, China
| | - Zhiyun Tao
- Jiangsu Institute of Poultry Sciences, Yangzhou 225125, China
| | - Wenjuan Xu
- Jiangsu Institute of Poultry Sciences, Yangzhou 225125, China
| | - Shuangjie Zhang
- Jiangsu Institute of Poultry Sciences, Yangzhou 225125, China
| | - Zhicheng Wang
- Jiangsu Institute of Poultry Sciences, Yangzhou 225125, China
| | - Huifang Li
- Jiangsu Institute of Poultry Sciences, Yangzhou 225125, China.
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2
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Chang Y, Guo R, Gu T, Zong Y, Sun H, Xu W, Chen L, Tian Y, Li G, Lu L, Zeng T. Integrated transcriptome and microbiome analyses of residual feed intake in ducks during high production period. Poult Sci 2024; 103:103726. [PMID: 38636203 PMCID: PMC11031780 DOI: 10.1016/j.psj.2024.103726] [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: 12/22/2023] [Revised: 03/12/2024] [Accepted: 03/31/2024] [Indexed: 04/20/2024] Open
Abstract
Residual feed intake (RFI) is a crucial parameter for assessing the feeding efficiency of poultry. Minimizing RFI can enhance feed utilization and reduce costs. In this study, 315 healthy female ducks were individually housed in cages. Growth performance was monitored during the high laying period, from 290 to 325 d of age. The cecal transcriptome and microbiome of 12 ducks with high RFI and 12 with low residual feed intake (LRFI) were analyzed. Regarding growth performance, the LRFI group exhibited significantly lower RFI, feed conversion ratio (FCR), and feed intake (Fi) compared to the HRFI group (p < 0.01). However, there were no significant differences observed in body weight (BW), body weight gain (BWG), and egg mass (EML) between the groups (p > 0.05). Microbiome analysis demonstrated that RFI impacted gut microbial abundance, particularly affecting metabolism and disease-related microorganisms such as Romboutsia, Enterococcus, and Megamonas funiformis. Transcriptome analysis revealed that varying RFI changed the expression of genes related to glucose metabolism and lipid metabolism, including APOA1, G6PC1, PCK1, and PLIN1. The integrated analysis indicated that host genes were closely linked to the microbiota and primarily function in lipid metabolism, which may enhance feeding efficiency by influencing metabolism and maintaining gut homeostasis.
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Affiliation(s)
- Yuguang Chang
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-Products; Key Laboratory of Livestock and Poultry Resources (Poultry) Evaluation and Utilization, Ministry of Agriculture and Rural Affairs; Institute of Animal Husbandry and Veterinary Medicine, Zhejiang Academy of Agricultural Sciences, Hangzhou, 310021, China
| | - Rongbing Guo
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-Products; Key Laboratory of Livestock and Poultry Resources (Poultry) Evaluation and Utilization, Ministry of Agriculture and Rural Affairs; Institute of Animal Husbandry and Veterinary Medicine, Zhejiang Academy of Agricultural Sciences, Hangzhou, 310021, China; College of Animal Science, Zhejiang A&F University, Hangzhou, China
| | - Tiantian Gu
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-Products; Key Laboratory of Livestock and Poultry Resources (Poultry) Evaluation and Utilization, Ministry of Agriculture and Rural Affairs; Institute of Animal Husbandry and Veterinary Medicine, Zhejiang Academy of Agricultural Sciences, Hangzhou, 310021, China
| | - Yibo Zong
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-Products; Key Laboratory of Livestock and Poultry Resources (Poultry) Evaluation and Utilization, Ministry of Agriculture and Rural Affairs; Institute of Animal Husbandry and Veterinary Medicine, Zhejiang Academy of Agricultural Sciences, Hangzhou, 310021, China
| | - Hanxue Sun
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-Products; Key Laboratory of Livestock and Poultry Resources (Poultry) Evaluation and Utilization, Ministry of Agriculture and Rural Affairs; Institute of Animal Husbandry and Veterinary Medicine, Zhejiang Academy of Agricultural Sciences, Hangzhou, 310021, China; College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, 430064, China
| | - Wenwu Xu
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-Products; Key Laboratory of Livestock and Poultry Resources (Poultry) Evaluation and Utilization, Ministry of Agriculture and Rural Affairs; Institute of Animal Husbandry and Veterinary Medicine, Zhejiang Academy of Agricultural Sciences, Hangzhou, 310021, China
| | - Li Chen
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-Products; Key Laboratory of Livestock and Poultry Resources (Poultry) Evaluation and Utilization, Ministry of Agriculture and Rural Affairs; Institute of Animal Husbandry and Veterinary Medicine, Zhejiang Academy of Agricultural Sciences, Hangzhou, 310021, China
| | - Yong Tian
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-Products; Key Laboratory of Livestock and Poultry Resources (Poultry) Evaluation and Utilization, Ministry of Agriculture and Rural Affairs; Institute of Animal Husbandry and Veterinary Medicine, Zhejiang Academy of Agricultural Sciences, Hangzhou, 310021, China
| | - Guoqin Li
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-Products; Key Laboratory of Livestock and Poultry Resources (Poultry) Evaluation and Utilization, Ministry of Agriculture and Rural Affairs; Institute of Animal Husbandry and Veterinary Medicine, Zhejiang Academy of Agricultural Sciences, Hangzhou, 310021, China
| | - Lizhi Lu
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-Products; Key Laboratory of Livestock and Poultry Resources (Poultry) Evaluation and Utilization, Ministry of Agriculture and Rural Affairs; Institute of Animal Husbandry and Veterinary Medicine, Zhejiang Academy of Agricultural Sciences, Hangzhou, 310021, China
| | - Tao Zeng
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-Products; Key Laboratory of Livestock and Poultry Resources (Poultry) Evaluation and Utilization, Ministry of Agriculture and Rural Affairs; Institute of Animal Husbandry and Veterinary Medicine, Zhejiang Academy of Agricultural Sciences, Hangzhou, 310021, China.
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3
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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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Beer MA, Proft KM, Veillet A, Kozakiewicz CP, Hamilton DG, Hamede R, McCallum H, Hohenlohe PA, Burridge CP, Margres MJ, Jones ME, Storfer A. Disease-driven top predator decline affects mesopredator population genomic structure. Nat Ecol Evol 2024; 8:293-303. [PMID: 38191839 DOI: 10.1038/s41559-023-02265-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 11/02/2023] [Indexed: 01/10/2024]
Abstract
Top predator declines are pervasive and often have dramatic effects on ecological communities via changes in food web dynamics, but their evolutionary consequences are virtually unknown. Tasmania's top terrestrial predator, the Tasmanian devil, is declining due to a lethal transmissible cancer. Spotted-tailed quolls benefit via mesopredator release, and they alter their behaviour and resource use concomitant with devil declines and increased disease duration. Here, using a landscape community genomics framework to identify environmental drivers of population genomic structure and signatures of selection, we show that these biotic factors are consistently among the top variables explaining genomic structure of the quoll. Landscape resistance negatively correlates with devil density, suggesting that devil declines will increase quoll genetic subdivision over time, despite no change in quoll densities detected by camera trap studies. Devil density also contributes to signatures of selection in the quoll genome, including genes associated with muscle development and locomotion. Our results provide some of the first evidence of the evolutionary impacts of competition between a top predator and a mesopredator species in the context of a trophic cascade. As top predator declines are increasing globally, our framework can serve as a model for future studies of evolutionary impacts of altered ecological interactions.
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Affiliation(s)
- Marc A Beer
- School of Biological Sciences, Washington State University, Pullman, WA, USA
| | - Kirstin M Proft
- School of Natural Sciences, University of Tasmania, Hobart, Tasmania, Australia
| | - Anne Veillet
- Hilo Core Genomics Facility, University of Hawaii at Hilo, Hilo, HI, USA
| | - Christopher P Kozakiewicz
- Department of Integrative Biology, Michigan State University, W.K. Kellogg Biological Station, Hickory Corners, MI, USA
| | - David G Hamilton
- School of Natural Sciences, University of Tasmania, Hobart, Tasmania, Australia
| | - Rodrigo Hamede
- School of Natural Sciences, University of Tasmania, Hobart, Tasmania, Australia
- CANECEV, Centre de Recherches Ecologiques et Evolutives sur le Cancer, Montpellier, France
| | - Hamish McCallum
- Environmental Futures Research Institute, Griffith University, Nathan, Queensland, Australia
| | - Paul A Hohenlohe
- Institute for Bioinformatics and Evolutionary Studies, University of Idaho, Moscow, ID, USA
| | | | - Mark J Margres
- Department of Integrative Biology, University of South Florida, Tampa, FL, USA
| | - Menna E Jones
- School of Natural Sciences, University of Tasmania, Hobart, Tasmania, Australia
| | - Andrew Storfer
- School of Biological Sciences, Washington State University, Pullman, WA, USA.
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5
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Guo S, Liu Y, Xu Y, Gai K, Cong B, Xing K, Qi X, Wang X, Xiao L, Long C, Guo Y, Chen L, Sheng X. Identification of key genes affecting sperm motility in chicken based on whole-transcriptome sequencing. Poult Sci 2023; 102:103135. [PMID: 37856906 PMCID: PMC10590750 DOI: 10.1016/j.psj.2023.103135] [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: 07/18/2023] [Revised: 09/15/2023] [Accepted: 09/18/2023] [Indexed: 10/21/2023] Open
Abstract
Sperm motility is an important index for the evaluation of semen quality. Improving sperm motility is important to improve reproductive performance, promote breeding process, and reduce production cost. However, the molecular mechanisms regulating sperm motility in chickens remain unclear. In this study, histological observation and whole-transcriptome analysis were performed on testicular tissue of chickens with high and low sperm motility. Histological observations showed that roosters with high sperm motility exhibited better semen quality than those with low sperm motility. In addition, the germinal epithelial cells of roosters with low sperm motility were loosely arranged and contained many vacuoles. RNA-seq results revealed the expression of 23,033 mRNAs, 2,893 lncRNAs, and 515 miRNAs in chicken testes. Among them, there were 417 differentially expressed mRNAs (DEmRNAs), 106 differentially expressed lncRNAs (DElncRNAs), and 15 differentially expressed miRNAs (DEmiRNAs) between high and low sperm motility testes. These differentially expressed genes were involved in the G protein-coupled receptor signaling pathway, cilia structure, Wnt signaling, MAPK signaling, GnRH signaling, and mTOR signaling. By integrating the competitive relationships between DEmRNAs, DElncRNAs, and DEmiRNAs, we identified the regulatory pathway of MSTRG.3077.3/MSTRG.9085.1-gga-miR-138-5p-CADM1 and MSTRG.2290.1-gga-miR-142-3p-GNAQ/PPP3CA as crucial in the modulation of chicken sperm motility. This study provides new insights into the function and mechanism of ceRNAs in regulating sperm motility in chicken testes.
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Affiliation(s)
- Shihao Guo
- Animal Science and Technology College, Beijing University of Agriculture, Beijing 102206, China
| | - Yizheng Liu
- Animal Science and Technology College, Beijing University of Agriculture, Beijing 102206, China
| | - Yaxi Xu
- Animal Science and Technology College, Beijing University of Agriculture, Beijing 102206, China
| | - Kai Gai
- Animal Science and Technology College, Beijing University of Agriculture, Beijing 102206, China
| | - Bailin Cong
- Animal Science and Technology College, Beijing University of Agriculture, Beijing 102206, China
| | - Kai Xing
- Animal Science and Technology College, Beijing University of Agriculture, Beijing 102206, China
| | - Xiaolong Qi
- Animal Science and Technology College, Beijing University of Agriculture, Beijing 102206, China
| | - Xiangguo Wang
- Animal Science and Technology College, Beijing University of Agriculture, Beijing 102206, China
| | - Longfei Xiao
- Animal Science and Technology College, Beijing University of Agriculture, Beijing 102206, China
| | - Cheng Long
- Animal Science and Technology College, Beijing University of Agriculture, Beijing 102206, China
| | - Yong Guo
- Animal Science and Technology College, Beijing University of Agriculture, Beijing 102206, China
| | - Li Chen
- College of Food Science and Engineering, Beijing University of Agriculture, Beijing 102206, China
| | - Xihui Sheng
- Animal Science and Technology College, Beijing University of Agriculture, Beijing 102206, China.
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6
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Zheng M, Liao J, Li Z, Xu Z, Jiang Z, Tan L, Fu R, Xu H, Li Z, Zhang X, Nie Q. Evaluation of the selection of key individuals for genotype imputation in Chinese yellow-feathered chicken. Poult Sci 2023; 102:102901. [PMID: 37499612 PMCID: PMC10393784 DOI: 10.1016/j.psj.2023.102901] [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: 04/09/2023] [Revised: 06/02/2023] [Accepted: 06/24/2023] [Indexed: 07/29/2023] Open
Abstract
Genotype imputation is a powerful technique employed by next-generation sequencing (NGS) and genotyping arrays, which can significantly enhance the cost-effectiveness and efficiency of genomic selection. The accuracy of imputation is largely determined by the choice of reference panel, with previous studies generally demonstrating that a closely related population as a reference panel leads to greater accuracy than a more distantly related population. Various strategies have been proposed for selecting desirable individuals via targeted resequencing, but their efficiencies need further improvement. In this study, we present a practical broiler selection methodology for a local Chinese chicken line that integrates established methods based on pedigree, genomics, and random sampling, and leverages genotype and pedigree information from the yellow-plumage dwarf chicken line. The efficacy of these selection strategies was assessed by evaluating their ability to accurately impute masked genotypes from data obtained using a 600K chip. Our findings reveal that the pedigree-based method yields superior accuracy in genotype imputation, whereas the haplotype-based method exhibits greater stability. Nonetheless, the impact of these targeted methods for selecting key individuals is slightly different when initiating a new sequencing project in a production context. Overall, this study highlights the advantages of using the pedigree-based approach as the preferred method for optimizing genotype imputation in broiler chickens.
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Affiliation(s)
- Ming Zheng
- Lingnan Guangdong Laboratory of Modern Agriculture, College of Animal Science, South China Agricultural University, Guangzhou 510642, Guangdong, China; Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, and Key Laboratory of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou 510642, Guangdong, China; State Key Laboratory of Livestock and Poultry Breeding, South China Agricultural University, Guangzhou 510642, Guangdong, China
| | - Jiahao Liao
- Lingnan Guangdong Laboratory of Modern Agriculture, College of Animal Science, South China Agricultural University, Guangzhou 510642, Guangdong, China; Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, and Key Laboratory of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou 510642, Guangdong, China; State Key Laboratory of Livestock and Poultry Breeding, South China Agricultural University, Guangzhou 510642, Guangdong, China
| | - Zhuohang Li
- Lingnan Guangdong Laboratory of Modern Agriculture, College of Animal Science, South China Agricultural University, Guangzhou 510642, Guangdong, China; Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, and Key Laboratory of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou 510642, Guangdong, China; State Key Laboratory of Livestock and Poultry Breeding, South China Agricultural University, Guangzhou 510642, Guangdong, China
| | - Zhenqiang Xu
- Guangdong Wens Nanfang Poultry Breeding Co., Ltd., Xinxing 527439, China
| | - Ziqin Jiang
- Guangdong Wens Nanfang Poultry Breeding Co., Ltd., Xinxing 527439, China
| | - Liangtian Tan
- Lingnan Guangdong Laboratory of Modern Agriculture, College of Animal Science, South China Agricultural University, Guangzhou 510642, Guangdong, China; Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, and Key Laboratory of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou 510642, Guangdong, China; State Key Laboratory of Livestock and Poultry Breeding, South China Agricultural University, Guangzhou 510642, Guangdong, China
| | - Rong Fu
- Lingnan Guangdong Laboratory of Modern Agriculture, College of Animal Science, South China Agricultural University, Guangzhou 510642, Guangdong, China; Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, and Key Laboratory of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou 510642, Guangdong, China; State Key Laboratory of Livestock and Poultry Breeding, South China Agricultural University, Guangzhou 510642, Guangdong, China
| | - Haiping Xu
- Lingnan Guangdong Laboratory of Modern Agriculture, College of Animal Science, South China Agricultural University, Guangzhou 510642, Guangdong, China; Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, and Key Laboratory of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou 510642, Guangdong, China; State Key Laboratory of Livestock and Poultry Breeding, South China Agricultural University, Guangzhou 510642, Guangdong, China
| | - Zhenhui Li
- Lingnan Guangdong Laboratory of Modern Agriculture, College of Animal Science, South China Agricultural University, Guangzhou 510642, Guangdong, China; Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, and Key Laboratory of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou 510642, Guangdong, China; State Key Laboratory of Livestock and Poultry Breeding, South China Agricultural University, Guangzhou 510642, Guangdong, China
| | - Xiquan Zhang
- Lingnan Guangdong Laboratory of Modern Agriculture, College of Animal Science, South China Agricultural University, Guangzhou 510642, Guangdong, China; Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, and Key Laboratory of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou 510642, Guangdong, China; State Key Laboratory of Livestock and Poultry Breeding, South China Agricultural University, Guangzhou 510642, Guangdong, China
| | - Qinghua Nie
- Lingnan Guangdong Laboratory of Modern Agriculture, College of Animal Science, South China Agricultural University, Guangzhou 510642, Guangdong, China; Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, and Key Laboratory of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou 510642, Guangdong, China; State Key Laboratory of Livestock and Poultry Breeding, South China Agricultural University, Guangzhou 510642, Guangdong, China.
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7
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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: 1] [Impact Index Per Article: 1.0] [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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8
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Kaewsatuan P, Poompramun C, Kubota S, Yongsawatdigul J, Molee W, Uimari P, Molee A. Thigh muscle metabolic response is linked to feed efficiency and meat characteristics in slow-growing chicken. Poult Sci 2023; 102:102741. [PMID: 37186966 DOI: 10.1016/j.psj.2023.102741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 04/16/2023] [Accepted: 04/18/2023] [Indexed: 05/17/2023] Open
Abstract
The Korat chicken (KR) is a slow-growing Thai chicken breed with relatively poor feed efficiency (FE) but very tasty meat with high protein and low fat contents, and a unique texture. To enhance the competitiveness of KR, its FE should be improved. However, selecting for FE has an unknown effect on meat characteristics. Thus, understanding the genetic basis underlying FE traits and meat characteristics is needed. In this study, 75 male KR birds were raised up to 10 wk of age. For each bird, the feed conversion ratio (FCR), residual feed intake (RFI), and physicochemical properties, flavor precursors, and biological compounds in the thigh meat were evaluated. At 10 wk of age, thigh muscle samples from 6 birds (3 with high FCR and 3 with low FCR values) were selected, and their proteomes were investigated using a label-free proteomic method. Weighted gene coexpression network analysis (WGCNA) was used to screen the key protein modules and pathways. The WGCNA results revealed that FE and meat characteristics significantly correlated with the same protein module. However, the correlation was unfavorable; improving FE may result in a decrease in meat quality through the alteration in biological processes including glycolysis/gluconeogenesis, metabolic pathway, carbon metabolism, biosynthesis of amino acids, pyruvate metabolism, and protein processing in the endoplasmic reticulum. The hub proteins of the significant module (TNNT1, TNNT3, TNNI2, TNNC2, MYLPF, MYH10, GADPH, PGK1, LDHA, and GPI) were also identified to be associated with energy metabolism, and muscle growth and development. Given that the same proteins and pathways are present in FE and meat characteristics but in opposite directions, selection practices for KR should simultaneously consider both trait groups to maintain the high meat quality of KR while improving FE.
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Affiliation(s)
- Pramin Kaewsatuan
- School of Animal Technology and Innovation, Institute of Agricultural Technology, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand
| | - Chotima Poompramun
- School of Animal Technology and Innovation, Institute of Agricultural Technology, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand
| | - Satoshi Kubota
- School of Animal Technology and Innovation, Institute of Agricultural Technology, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand
| | - Jirawat Yongsawatdigul
- School of Food Technology, Institute of Agricultural Technology, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand
| | - Wittawat Molee
- School of Animal Technology and Innovation, Institute of Agricultural Technology, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand
| | - Pekka Uimari
- Department of Agricultural Sciences, Faculty of Agriculture and Forestry, University of Helsinki, Helsinki 00790, Finland
| | - Amonrat Molee
- School of Animal Technology and Innovation, Institute of Agricultural Technology, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand.
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9
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Ye H, Xu Z, Bello SF, Zhu Q, Kong S, Zheng M, Fang X, Jia X, Xu H, Zhang X, Nie Q. Haplotype analysis of genomic prediction by incorporating genomic pathway information based on high-density SNP marker in Chinese yellow-feathered chicken. Poult Sci 2023; 102:102549. [PMID: 36907129 PMCID: PMC10024239 DOI: 10.1016/j.psj.2023.102549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Revised: 01/16/2023] [Accepted: 01/27/2023] [Indexed: 02/09/2023] Open
Abstract
Genomic selection using single nucleotide polymorphism (SNP) markers is now intensively investigated in breeding and has been widely utilized for genetic improvement. Currently, several studies have used haplotype (consisting of multiallelic SNPs) for genomic prediction and revealed its performance advantage. In this study, we comprehensively evaluated the performance of haplotype models for genomic prediction in 15 traits, including 6 growth, 5 carcass, and 4 feeding traits in a Chinese yellow-feathered chicken population. We adopted 3 methods to define haplotypes from high-density SNP panels, and our strategy included combining Kyoto Encyclopedia of Genes and Genomes pathway information and considering linkage disequilibrium (LD) information. Our results showed an increase in prediction accuracy due to haplotypes ranging from -0.04∼27.16% in all traits, where the significant improvements were found in 12 traits. The estimates of haplotype epistasis heritability were strongly correlated with the accuracy increase by haplotype models. In addition, incorporating genomic annotation information could further increase the accuracy of the haplotype model, where the further increase in accuracy is significantly relative to the increase of relative haplotype epistasis heritability. The genomic prediction using LD information for constructing haplotypes has the best prediction performance among the 4 traits. These results uncovered that haplotype methods were beneficial for genomic prediction, and the accuracy could be further increased by incorporating genomic annotation information. Moreover, using LD information would potentially improve the performance of genomic prediction.
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Affiliation(s)
- Haoqiang Ye
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, 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, Guangzhou, 510642 China
| | - Zhenqiang Xu
- Wen's Nanfang Poultry Breeding Co. Ltd, Guangdong Province, Yunfu 527400, China
| | - Semiu Folaniyi Bello
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, 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, Guangzhou, 510642 China
| | - Qianghui Zhu
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou, 510642 China
| | - Shaofen Kong
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, 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, Guangzhou, 510642 China
| | - Ming Zheng
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, 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, Guangzhou, 510642 China
| | - Xiang Fang
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, 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, Guangzhou, 510642 China
| | - Xinzheng Jia
- Guangdong Provincial Key Laboratory of Animal Molecular Design and Precise Breeding, Foshan University, Foshan, 528225 China
| | - Haiping Xu
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, 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, Guangzhou, 510642 China
| | - Xiquan Zhang
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, 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, Guangzhou, 510642 China
| | - Qinghua Nie
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, 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, Guangzhou, 510642 China.
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10
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Cai D, Wang Z, Zhou Z, Lin D, Ju X, Nie Q. Integration of transcriptome sequencing and whole genome resequencing reveal candidate genes in egg production of upright and pendulous-comb chickens. Poult Sci 2023; 102:102504. [PMID: 36739803 PMCID: PMC9932115 DOI: 10.1016/j.psj.2023.102504] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 12/30/2022] [Accepted: 01/10/2023] [Indexed: 01/18/2023] Open
Abstract
Egg production performance plays an important role in the poultry industry across the world. Previous studies have shown a great difference in egg production performance between pendulous-comb (PC) and upright-comb (UC) chickens. However, there are no reports to identify potential candidate genes for egg production in PC and UC chickens. In the present study, 1,606 laying chickens were raised, and the egg laid by individual chicken was collected for 100 d. Moreover, the expression level of estrogen and progesterone hormones was measured at the start-laying and peak-laying periods of hens. Besides, 4 PC and 4 UC chickens were selected at 217 d of age to perform transcriptome sequencing (RNA-seq) and whole genome resequencing (WGS) to screen the potential candidate genes of egg production. The results showed that PC chicken demonstrated better egg production performance (P < 0.05) and higher estrogen and progesterone hormone expression levels than UC chicken (P < 0.05). RNA-seq analysis showed that 341 upregulated and 1,036 downregulated differentially expressed genes (DEGs) were identified in the ovary tissues of PC and UC chickens. These DEGs were mainly enriched in protein-related, lipid-related, and nucleic acids-related biological processes including ribosome, peptide biosynthetic process, lipid transport terms, and catalytic activity acting on RNA which can significantly affect egg production in chicken. The enrichment results of WGS analysis were consistent with RNA-seq. Further, joint analysis of WGS and RNA-seq data was utilized to screen 30 genes and CAMK1D, CLSTN2, MAST2, PIK3C2G, TBC1D1, STK3, ADGRB3, and PPARGC1A were identified as potential candidate genes for egg production in PC and UC chickens. In summary, our study provides a wealth of information for a better understanding of the genetic and molecular mechanism for the future breeding of PC and UC chickens for egg production.
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Affiliation(s)
- Danfeng Cai
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, Lingnan Guangdong Laboratory of Agriculture, College of Animal Science, South China Agricultural University, Guangzhou 510642, Guangdong, China,Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, and Key Laboratory of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou 510642, Guangdong, China
| | - Zhijun Wang
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, Lingnan Guangdong Laboratory of Agriculture, College of Animal Science, South China Agricultural University, Guangzhou 510642, Guangdong, China,Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, and Key Laboratory of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou 510642, Guangdong, China,College of Animal Science and Technology, Zhejiang Agriculture and Forestry University, Lin'an 311300, China
| | - Zhen Zhou
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, Lingnan Guangdong Laboratory of Agriculture, College of Animal Science, South China Agricultural University, Guangzhou 510642, Guangdong, China,Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, and Key Laboratory of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou 510642, Guangdong, China
| | - Duo Lin
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, Lingnan Guangdong Laboratory of Agriculture, College of Animal Science, South China Agricultural University, Guangzhou 510642, Guangdong, China,Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, and Key Laboratory of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou 510642, Guangdong, China
| | - Xing Ju
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, Lingnan Guangdong Laboratory of Agriculture, College of Animal Science, South China Agricultural University, Guangzhou 510642, Guangdong, China,Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, and Key Laboratory of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou 510642, Guangdong, China
| | - Qinghua Nie
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, Lingnan Guangdong Laboratory of Agriculture, College of Animal Science, South China Agricultural University, Guangzhou 510642, Guangdong, China; Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, and Key Laboratory of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou 510642, Guangdong, China.
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11
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Ren T, Lin W, Yang X, Zhang Z, He S, Li W, Li Z, Zhang X. QPCTL Affects the Daily Weight Gain of the F2 Population and Regulates Myogenic Cell Proliferation and Differentiation in Chickens. Animals (Basel) 2022; 12:ani12243535. [PMID: 36552455 PMCID: PMC9774964 DOI: 10.3390/ani12243535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 12/10/2022] [Accepted: 12/11/2022] [Indexed: 12/16/2022] Open
Abstract
Molecular breeding can accelerate the process of animal breeding and improve the breeding efficiency. To date, many Indel molecular markers have been identified in livestock and poultry, but how Indels affect economic traits is not well understood. For molecular breeding, it is crucial to reveal the mechanism of action of Indels and to provide more accurate information. The purpose of this study was to investigate how the 52/224-bp multiallelic Indels of the chicken QPCTL promoter area affect the daily weight gain of chickens and the potential regulatory mechanism of the QPCTL gene. The analysis was conducted by association analysis, qPCR, dual-fluorescence assay and Western blotting. The results showed that Indels in the QPCTL promoter region were significantly associated with the daily weight gain in chickens and that QPCTL expression showed a decreasing trend in embryonic breast muscle tissues. Furthermore, QPCTL expression was significantly higher in breast muscle tissues of the AC genotype than in those of the AB and BB genotypes. Based on the transcriptional activity results, the pGL3-C vector produced more luciferase activity than pGL3-A and pGL3-B. In addition, overexpression of QPCTL promoted chicken primary myoblast (CPM) proliferation and inhibited differentiation. The results of this study suggest that Indels in the promoter region of the QPCTL gene may regulate the proliferation and differentiation of CPMs by affecting the expression of QPCTL, which ultimately affects the growth rate of chickens. These Indels have important value for the molecular breeding of chickens, and QPCTL can be used as a candidate gene to regulate and improve chicken growth and development.
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Affiliation(s)
- Tuanhui Ren
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou 510642, China
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, and Key Laboratory of Chicken Genetics Breeding and Reproduction, Ministry of Agriculture, Guangzhou 510642, China
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou 450046, China
| | - Wujian Lin
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou 510642, China
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, and Key Laboratory of Chicken Genetics Breeding and Reproduction, Ministry of Agriculture, Guangzhou 510642, China
| | - Xiuxian Yang
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou 510642, China
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, and Key Laboratory of Chicken Genetics Breeding and Reproduction, Ministry of Agriculture, Guangzhou 510642, China
| | - Zihao Zhang
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou 510642, China
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, and Key Laboratory of Chicken Genetics Breeding and Reproduction, Ministry of Agriculture, Guangzhou 510642, China
| | - Shizi He
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou 510642, China
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, and Key Laboratory of Chicken Genetics Breeding and Reproduction, Ministry of Agriculture, Guangzhou 510642, China
| | - Wangyu Li
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou 510642, China
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, and Key Laboratory of Chicken Genetics Breeding and Reproduction, Ministry of Agriculture, Guangzhou 510642, China
| | - Zhuanjian Li
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou 450046, China
- Correspondence: (Z.L.); (X.Z.)
| | - Xiquan Zhang
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou 510642, China
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, and Key Laboratory of Chicken Genetics Breeding and Reproduction, Ministry of Agriculture, Guangzhou 510642, China
- Correspondence: (Z.L.); (X.Z.)
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12
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Su Z, Bai X, Wang H, Wang S, Chen C, Xiao F, Guo H, Gao H, Leng L, Li H. Identification of biomarkers associated with the feed efficiency by metabolomics profiling: results from the broiler lines divergent for high or low abdominal fat content. J Anim Sci Biotechnol 2022; 13:122. [PMID: 36352447 PMCID: PMC9647982 DOI: 10.1186/s40104-022-00775-3] [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: 05/04/2022] [Accepted: 09/05/2022] [Indexed: 11/11/2022] Open
Abstract
Background Improving feed efficiency (FE) is one of the main objectives in broiler breeding. It is difficult to directly measure FE traits, and breeders hence have been trying to identify biomarkers for the indirect selection and improvement of FE traits. Metabolome is the "bridge" between genome and phenome. The metabolites may potentially account for more of the phenotypic variation and can suitably serve as biomarkers for selecting FE traits. This study aimed to identify plasma metabolite markers for selecting high-FE broilers. A total of 441 birds from Northeast Agricultural University broiler lines divergently selected for abdominal fat content were used to analyze plasma metabolome and estimate the genetic parameters of differentially expressed metabolites. Results The results identified 124 differentially expressed plasma metabolites (P < 0.05) between the lean line (high-FE birds) and the fat line (low-FE birds). Among these differentially expressed plasma metabolites, 44 were found to have higher positive or negative genetic correlations with FE traits (|rg| ≥ 0.30). Of these 44 metabolites, 14 were found to display moderate to high heritability estimates (h2 ≥ 0.20). However, among the 14 metabolites, 4 metabolites whose physiological functions have not been reported were excluded. Ultimately, 10 metabolites were suggested to serve as the potential biomarkers for breeding the high-FE broilers. Based on the physiological functions of these metabolites, reducing inflammatory and improving immunity were proposed to improve FE and increase production efficiency. Conclusions According to the pipeline for the selection of the metabolite markers established in this study, it was suggested that 10 metabolites including 7-ketocholesterol, dimethyl sulfone, epsilon-(gamma-glutamyl)-lysine, gamma-glutamyltyrosine, 2-oxoadipic acid, L-homoarginine, testosterone, adenosine 5'-monophosphate, adrenic acid, and calcitriol could be used as the potential biomarkers for breeding the "food-saving broilers".
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13
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Bai H, Guo Q, Yang B, Dong Z, Li X, Song Q, Jiang Y, Wang Z, Chang G, Chen G. Effects of residual feed intake divergence on growth performance, carcass traits, meat quality, and blood biochemical parameters in small-sized meat ducks. Poult Sci 2022; 101:101990. [PMID: 35841639 PMCID: PMC9289854 DOI: 10.1016/j.psj.2022.101990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 05/10/2022] [Accepted: 05/29/2022] [Indexed: 11/28/2022] Open
Abstract
Feed efficiency (FE) is a major economic trait of meat duck. This study aimed to evaluate the effects of residual feed intake (RFI) divergence on growth performance, carcass traits, meat quality, and blood biochemical parameters in small-sized meat ducks. A total of 500 healthy 21-day-old male ducks were housed in individual cages until slaughter at 63 d of age. The growth performance was determined for all the ducks. The carcass yield, meat quality, and blood biochemical parameters were determined for the selected 30 high-RFI (HRFI) and 30 low-RFI (LRFI) ducks. In terms of growth performance, the RFI, feed conversion ratio (FCR), and average daily feed intake (ADFI) were found to be significantly lower in the LRFI group (P < 0.01), whereas no differences were observed in the BW and body weight gain (P > 0.05). For slaughter performance, no differences were observed in the carcass traits between the LRFI and HRFI groups (P > 0.05). For meat quality, the shear force of breast muscle was significantly lower in the LRFI group (P < 0.05), while the other meat quality traits of breast and thigh muscles demonstrated no differences (P > 0.05). For blood biochemical parameters, the serum concentrations of triglycerides (TG) and glucose (GLU) were significantly lower in the LRFI group (P < 0.05), while the other parameters showed no differences (P > 0.05). The correlation analysis demonstrated a high positive correlation between RFI, FCR, and ADFI (P < 0.01). The RFI demonstrated a negative effect on the breast muscle and lean meat yields, but a positive effect on the shear force of breast muscle (P < 0.05). Further, the RFI demonstrated a positive effect on the TG and GLU levels (P < 0.05). These results indicate that the selection for low RFI could improve the FE of small-sized meat ducks without affecting the production performance. This study provides valuable insight into the biological processes underlying the variations in FE in small-sized meat ducks.
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Affiliation(s)
- H Bai
- Joint International Research Laboratory of Agriculture and Agri-Product Safety, The Ministry of Education of China, Institutes of Agricultural Science and Technology Development, Yangzhou University, Jiangsu Yangzhou 225009, China
| | - Q Guo
- Key Laboratory of Animal Genetics and Breeding and Molecular Design of Jiangsu Province, Yangzhou University, Yangzhou 225009, China
| | - B Yang
- Key Laboratory of Animal Genetics and Breeding and Molecular Design of Jiangsu Province, Yangzhou University, Yangzhou 225009, China
| | - Z Dong
- Key Laboratory of Animal Genetics and Breeding and Molecular Design of Jiangsu Province, Yangzhou University, Yangzhou 225009, China
| | - X Li
- Key Laboratory of Animal Genetics and Breeding and Molecular Design of Jiangsu Province, Yangzhou University, Yangzhou 225009, China
| | - Q Song
- Key Laboratory of Animal Genetics and Breeding and Molecular Design of Jiangsu Province, Yangzhou University, Yangzhou 225009, China
| | - Y Jiang
- Key Laboratory of Animal Genetics and Breeding and Molecular Design of Jiangsu Province, Yangzhou University, Yangzhou 225009, China
| | - Z Wang
- Key Laboratory of Animal Genetics and Breeding and Molecular Design of Jiangsu Province, Yangzhou University, Yangzhou 225009, China
| | - G Chang
- Joint International Research Laboratory of Agriculture and Agri-Product Safety, The Ministry of Education of China, Institutes of Agricultural Science and Technology Development, Yangzhou University, Jiangsu Yangzhou 225009, China; Key Laboratory of Animal Genetics and Breeding and Molecular Design of Jiangsu Province, Yangzhou University, Yangzhou 225009, China
| | - G Chen
- Joint International Research Laboratory of Agriculture and Agri-Product Safety, The Ministry of Education of China, Institutes of Agricultural Science and Technology Development, Yangzhou University, Jiangsu Yangzhou 225009, China; Key Laboratory of Animal Genetics and Breeding and Molecular Design of Jiangsu Province, Yangzhou University, Yangzhou 225009, China.
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14
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Xiao C, Sun T, Yang Z, Zou L, Deng J, Yang X. Whole transcriptome RNA Sequencing Reveals the Global Molecular Responses and circRNA/lncRNA-miRNA-mRNA ceRNA Regulatory Network in Chicken Fat Deposition. Poult Sci 2022; 101:102121. [PMID: 36116349 PMCID: PMC9485216 DOI: 10.1016/j.psj.2022.102121] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 03/21/2022] [Accepted: 08/03/2022] [Indexed: 11/29/2022] Open
Abstract
Fat deposition is a vital factor affecting the economics of poultry production. Numerous studies on fat deposition have been done. However, the molecular regulatory mechanism is still unclear. In the present study, the whole-transcriptome RNA sequencing in abdominal fat, back skin, and liver both high- and low-abdominal fat groups was used to uncover the competitive endogenous RNA (ceRNA) regulation network related to chicken fat deposition. The results showed that differentially expressed (DE) genes in abdominal fat, back skin, liver were 1207(784 mRNAs, 330 lncRNAs, 41 circRNAs, 52 miRNAs), 860 (607 mRNAs, 166 lncRNAs, 26 circRNAs, 61 miRNAs), and 923 (501 mRNAs, 262 lncRNAs, 15 circRNAs, 145 miRNAs), respectively. The ceRNA regulatory network analysis indicated that the fatty acid metabolic process, monocarboxylic acid metabolic process, carboxylic acid metabolic process, glycerolipid metabolism, fatty acid metabolism, and peroxisome proliferator-activated receptor (PPAR) signaling pathway took part in chicken fat deposition. Meanwhile, we scan the important genes, FADS2, HSD17B12, ELOVL5, AKR1E2, DGKQ, GPAM, PLIN2, which were regulated by gga-miR-460b-5p, gga-miR-199-5p, gga-miR-7470-3p, gga-miR-6595-5p, gga-miR-101-2-5p. While these miRNAs were competitive combined by lncRNAs including MSTRG.18043, MSTRG.7738, MSTRG.21310, MSTRG.19577, and circRNAs including novel_circ_PTPN2, novel_circ_CTNNA1, novel_circ_PTPRD. This finding provides new insights into the regulatory mechanism of mRNA, miRNA, lncRNA, and circRNA in chicken fat deposition.
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Affiliation(s)
- Cong Xiao
- College of Animal Science and Technology, Guangxi University, Nanning 530004, China
| | - Tiantian Sun
- College of Animal Science and Technology, Guangxi University, Nanning 530004, China
| | - Zhuliang Yang
- College of Animal Science and Technology, Guangxi University, Nanning 530004, China
| | - Leqin Zou
- College of Animal Science and Technology, Guangxi University, Nanning 530004, China
| | - Jixian Deng
- College of Animal Science and Technology, Guangxi University, Nanning 530004, China
| | - Xiurong Yang
- College of Animal Science and Technology, Guangxi University, Nanning 530004, China.
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15
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Yang R, Xu Z, Wang Q, Zhu D, Bian C, Ren J, Huang Z, Zhu X, Tian Z, Wang Y, Jiang Z, Zhao Y, Zhang D, Li N, Hu X. Genome‑wide association study and genomic prediction for growth traits in yellow-plumage chicken using genotyping-by-sequencing. Genet Sel Evol 2021; 53:82. [PMID: 34706641 PMCID: PMC8555081 DOI: 10.1186/s12711-021-00672-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Accepted: 09/08/2021] [Indexed: 12/25/2022] Open
Abstract
Background Growth traits are of great importance for poultry breeding and production and have been the topic of extensive investigation, with many quantitative trait loci (QTL) detected. However, due to their complex genetic background, few causative genes have been confirmed and the underlying molecular mechanisms remain unclear, thus limiting our understanding of QTL and their potential use for the genetic improvement of poultry. Therefore, deciphering the genetic architecture is a promising avenue for optimising genomic prediction strategies and exploiting genomic information for commercial breeding. The objectives of this study were to: (1) conduct a genome-wide association study to identify key genetic factors and explore the polygenicity of chicken growth traits; (2) investigate the efficiency of genomic prediction in broilers; and (3) evaluate genomic predictions that harness genomic features. Results We identified five significant QTL, including one on chromosome 4 with major effects and four on chromosomes 1, 2, 17, and 27 with minor effects, accounting for 14.5 to 34.1% and 0.2 to 2.6% of the genomic additive genetic variance, respectively, and 23.3 to 46.7% and 0.6 to 4.5% of the observed predictive accuracy of breeding values, respectively. Further analysis showed that the QTL with minor effects collectively had a considerable influence, reflecting the polygenicity of the genetic background. The accuracy of genomic best linear unbiased predictions (BLUP) was improved by 22.0 to 70.3% compared to that of the conventional pedigree-based BLUP model. The genomic feature BLUP model further improved the observed prediction accuracy by 13.8 to 15.2% compared to the genomic BLUP model. Conclusions A major QTL and four minor QTL were identified for growth traits; the remaining variance was due to QTL effects that were too small to be detected. The genomic BLUP and genomic feature BLUP models yielded considerably higher prediction accuracy compared to the pedigree-based BLUP model. This study revealed the polygenicity of growth traits in yellow-plumage chickens and demonstrated that the predictive ability can be greatly improved by using genomic information and related features. Supplementary Information The online version contains supplementary material available at 10.1186/s12711-021-00672-9.
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Affiliation(s)
- Ruifei Yang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China.,College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Zhenqiang Xu
- Wen's Nanfang Poultry Breeding Co. Ltd, Yunfu, 527400, Guangdong Province, China
| | - Qi Wang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China
| | - Di Zhu
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China
| | - Cheng Bian
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China
| | - Jiangli Ren
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China
| | - Zhuolin Huang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China
| | - Xiaoning Zhu
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China
| | - Zhixin Tian
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China
| | - Yuzhe Wang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China
| | - Ziqin Jiang
- Wen's Nanfang Poultry Breeding Co. Ltd, Yunfu, 527400, Guangdong Province, China
| | - Yiqiang Zhao
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China
| | - Dexiang Zhang
- Wen's Nanfang Poultry Breeding Co. Ltd, Yunfu, 527400, Guangdong Province, China.
| | - Ning Li
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China
| | - Xiaoxiang Hu
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China.
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Mohammadigheisar M, Shouldice VL, Torrey S, Widowski TM, Ward NE, Kiarie EG. Growth performance, organ attributes, nutrient and caloric utilization in broiler chickens differing in growth rates when fed a corn-soybean meal diet with multienzyme supplement containing phytase, protease and fiber degrading enzymes. Poult Sci 2021; 100:101362. [PMID: 34358957 PMCID: PMC8350502 DOI: 10.1016/j.psj.2021.101362] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 05/20/2021] [Accepted: 06/22/2021] [Indexed: 11/16/2022] Open
Abstract
Growth performance, organ weight, ceca digesta short chain fatty acids (SCFA), jejunal histomorphometry, tibia ash, apparent retention (AR) of components and caloric efficiency were investigated in broiler chicken strains differing in growth rate fed diets with multienzyme supplement (MES). The strains differed in estimated time to reach 2.1 kg BW: 37, 43, 47, and 50 d and were designated C, F, J, and N, respectively. A corn-soybean meal diet was formulated for 2-phase program (starter and grower) and fed without or with MES containing phytase, protease and fiber-degrading enzymes. A total of 640-day-old chicks (42.3 + 0.01 g/bird) were housed in cages (5 cockerels and 5 pullets/cage) and allocated to give 8 replicates/ strain and diet combination. Equal amount of feed was fed based on observed ad-libitum intake of C strain in the starter (d 0-14) and grower (d 15-28). Body weight was monitored, grab excreta samples taken and at completion of allocated feed one bird per cage necropsied for samples. With exception of P, apparent metabolizable energy corrected for nitrogen (AMEn) and ceca digesta acetic acid, there was no (P > 0.05) interaction between strain and MES on examined responses. Strains differed (P < 0.01) on growth, FCR, gizzard weight, tibia ash, breast weight, ceca digesta concentration of lactic, propionic, and isobutyric acid and caloric efficiency. The final body weight (BW) was 1,344, 1,134, 959, and 916 g/bird for C, F, J, and N, respectively. Corresponding caloric efficiency was 4,930, 5,807, 6,680 and 7,199 kcal/kg BW gain, respectively. Birds fed MES had higher BW gain (P < 0.05) in grower phase, larger gizzard, higher AR of CP, crude fat, neutral detergent fiber, and Ca than non-MES birds. In conclusion, growth rate influenced organ attributes, nutrient, and caloric utilization. Enzyme supplementation improved growth in grower phase and nutrient utilization independent of strain, suggesting that effects of feed enzymes are not influenced by inherent growth rate.
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Affiliation(s)
| | - Victoria L Shouldice
- Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada, N1G2W1
| | - Stephanie Torrey
- Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada, N1G2W1
| | - Tina M Widowski
- Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada, N1G2W1
| | - Nelson E Ward
- DSM Nutritional Products Inc., Parsippany, NJ, 07054, USA
| | - Elijah G Kiarie
- Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada, N1G2W1.
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17
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Kang H, Zhao D, Xiang H, Li J, Zhao G, Li H. Large-scale transcriptome sequencing in broiler chickens to identify candidate genes for breast muscle weight and intramuscular fat content. Genet Sel Evol 2021; 53:66. [PMID: 34399688 PMCID: PMC8369645 DOI: 10.1186/s12711-021-00656-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 07/15/2021] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND In broiler production, breast muscle weight and intramuscular fat (IMF) content are important economic traits. Understanding the genetic mechanisms that underlie these traits is essential to implement effective genetic improvement programs. To date, genome-wide association studies (GWAS) and gene expression analyses have been performed to identify candidate genes for these traits. However, GWAS mainly detect associations at the DNA level, while differential expression analyses usually have low power because they are typically based on small sample sizes. To detect candidate genes for breast muscle weight and IMF contents (intramuscular fat percentage and relative content of triglycerides, cholesterol, and phospholipids), we performed association analyses based on breast muscle transcriptomic data on approximately 400 Tiannong partridge chickens at slaughter age. RESULTS First, by performing an extensive simulation study, we evaluated the statistical properties of association analyses of gene expression levels and traits based on the linear mixed model (LMM) and three regularized linear regression models, i.e., least absolute shrinkage and selection operator (LASSO), ridge regression (RR), and elastic net (EN). The results show that LMM, LASSO and EN with tuning parameters that are determined based on the one standard error rule exhibited the lowest type I error rates. Using results from all three models, we detected 43 candidate genes with expression levels that were associated with breast muscle weight. In addition, candidate genes were detected for intramuscular fat percentage (1), triglyceride content (2), cholesterol content (1), and phospholipid content (1). Many of the identified genes have been demonstrated to play roles in the development and metabolism of skeletal muscle or adipocyte. Moreover, weighted gene co-expression network analyses revealed that many candidate genes were harbored by gene co-expression modules, which were also significantly correlated with the traits of interest. The results of Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses indicated that these modules are involved in muscle development and contraction, and in lipid metabolism. CONCLUSIONS Our study provides valuable insight into the transcriptomic bases of breast muscle weight and IMF contents in Chinese indigenous yellow broilers. Our findings could be useful for the genetic improvement of these traits in broiler chickens.
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Affiliation(s)
- Huimin Kang
- Guangdong Provincial Key Laboratory of Animal Molecular Design and Precise Breeding; Key Laboratory of Animal Molecular Design and Precise Breeding of Guangdong Higher Education Institutes; School of Life Science and Engineering, Foshan University, #33 Guang-yun-lu, Shishan, Nanhai, Foshan, 528231, Guangdong, People's Republic of China
| | - Di Zhao
- Guangdong Provincial Key Laboratory of Animal Molecular Design and Precise Breeding; Key Laboratory of Animal Molecular Design and Precise Breeding of Guangdong Higher Education Institutes; School of Life Science and Engineering, Foshan University, #33 Guang-yun-lu, Shishan, Nanhai, Foshan, 528231, Guangdong, People's Republic of China
| | - Hai Xiang
- Guangdong Provincial Key Laboratory of Animal Molecular Design and Precise Breeding; Key Laboratory of Animal Molecular Design and Precise Breeding of Guangdong Higher Education Institutes; School of Life Science and Engineering, Foshan University, #33 Guang-yun-lu, Shishan, Nanhai, Foshan, 528231, Guangdong, People's Republic of China
| | - Jing Li
- Guangdong Provincial Key Laboratory of Animal Molecular Design and Precise Breeding; Key Laboratory of Animal Molecular Design and Precise Breeding of Guangdong Higher Education Institutes; School of Life Science and Engineering, Foshan University, #33 Guang-yun-lu, Shishan, Nanhai, Foshan, 528231, Guangdong, People's Republic of China
| | - Guiping Zhao
- Guangdong Provincial Key Laboratory of Animal Molecular Design and Precise Breeding; Key Laboratory of Animal Molecular Design and Precise Breeding of Guangdong Higher Education Institutes; School of Life Science and Engineering, Foshan University, #33 Guang-yun-lu, Shishan, Nanhai, Foshan, 528231, Guangdong, People's Republic of China. .,Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, No. 2 Yuanmingyuan West Road, Beijing, 100193, People's Republic of China.
| | - Hua Li
- Guangdong Provincial Key Laboratory of Animal Molecular Design and Precise Breeding; Key Laboratory of Animal Molecular Design and Precise Breeding of Guangdong Higher Education Institutes; School of Life Science and Engineering, Foshan University, #33 Guang-yun-lu, Shishan, Nanhai, Foshan, 528231, Guangdong, People's Republic of China. .,Guangdong Tinoo's Foods Group Co., Ltd, Jiangkou, Feilaixia, Qingcheng, Qingyuan, 511827, Guangdong, People's Republic of China.
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18
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Wen C, Yan W, Mai C, Duan Z, Zheng J, Sun C, Yang N. Joint contributions of the gut microbiota and host genetics to feed efficiency in chickens. MICROBIOME 2021; 9:126. [PMID: 34074340 PMCID: PMC8171024 DOI: 10.1186/s40168-021-01040-x] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Accepted: 02/22/2021] [Indexed: 05/19/2023]
Abstract
BACKGROUND Feed contributes most to livestock production costs. Improving feed efficiency is crucial to increase profitability and sustainability for animal production. Host genetics and the gut microbiota can both influence the host phenotype. However, the association between the gut microbiota and host genetics and their joint contribution to feed efficiency in chickens is largely unclear. RESULTS Here, we examined microbial data from the duodenum, jejunum, ileum, cecum, and feces in 206 chickens and their host genotypes and confirmed that the microbial phenotypes and co-occurrence networks exhibited dramatic spatial heterogeneity along the digestive tract. The correlations between host genetic kinship and gut microbial similarities within different sampling sites were weak, with coefficients ranging from - 0.07 to 0.08. However, microbial genome-wide analysis revealed that genetic markers near or inside the genes MTHFD1L and LARGE1 were associated with the abundances of cecal Megasphaera and Parabacteroides, respectively. The effect of host genetics on residual feed intake (RFI) was 39%. We further identified three independent genetic variations that were related to feed efficiency and had a modest effect on the gut microbiota. The contributions of the gut microbiota from the different parts of the intestinal tract on RFI were distinct. The cecal microbiota accounted for 28% of the RFI variance, a value higher than that explained by the duodenal, jejunal, ileal, and fecal microbiota. Additionally, six bacteria exhibited significant associations with RFI. Specifically, lower abundances of duodenal Akkermansia muciniphila and cecal Parabacteroides and higher abundances of cecal Lactobacillus, Corynebacterium, Coprobacillus, and Slackia were related to better feed efficiency. CONCLUSIONS Our findings solidified the notion that both host genetics and the gut microbiota, especially the cecal microbiota, can drive the variation in feed efficiency. Although host genetics has a limited effect on the entire microbial community, a small fraction of gut microorganisms tends to interact with host genes, jointly contributing to feed efficiency. Therefore, the gut microbiota and host genetic variations can be simultaneously targeted by favoring more-efficient taxa and selective breeding to improve feed efficiency in chickens. Video abstract.
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Affiliation(s)
- Chaoliang Wen
- National Engineering Laboratory for Animal Breeding and Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing, 100193, China
| | - Wei Yan
- National Engineering Laboratory for Animal Breeding and Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing, 100193, China
| | - Chunning Mai
- National Engineering Laboratory for Animal Breeding and Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing, 100193, China
| | - Zhongyi Duan
- National Engineering Laboratory for Animal Breeding and Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing, 100193, China
- National Animal Husbandry Service, Beijing, 100125, China
| | - Jiangxia Zheng
- National Engineering Laboratory for Animal Breeding and Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing, 100193, China
| | - Congjiao Sun
- National Engineering Laboratory for Animal Breeding and Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing, 100193, China.
| | - Ning Yang
- National Engineering Laboratory for Animal Breeding and Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing, 100193, China.
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19
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Yang X, Sun J, Zhao G, Li W, Tan X, Zheng M, Feng F, Liu D, Wen J, Liu R. Identification of Major Loci and Candidate Genes for Meat Production-Related Traits in Broilers. Front Genet 2021; 12:645107. [PMID: 33859671 PMCID: PMC8042277 DOI: 10.3389/fgene.2021.645107] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 03/02/2021] [Indexed: 12/30/2022] Open
Abstract
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.
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Affiliation(s)
- Xinting Yang
- 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, China
| | - Jiahong Sun
- 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, 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, 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, China
| | - Xiaodong Tan
- 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, 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, China
| | - Furong Feng
- Foshan Gaoming Xinguang Agricultural and Animal Industrials Corporation, Foshan, China
| | - Dawei Liu
- Foshan Gaoming Xinguang Agricultural and Animal Industrials Corporation, Foshan, 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, 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, China
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20
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Li W, Zheng M, Zhao G, Wang J, Liu J, Wang S, Feng F, Liu D, Zhu D, Li Q, Guo L, Guo Y, Liu R, Wen J. Identification of QTL regions and candidate genes for growth and feed efficiency in broilers. Genet Sel Evol 2021; 53:13. [PMID: 33549052 PMCID: PMC7866652 DOI: 10.1186/s12711-021-00608-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2019] [Accepted: 01/26/2021] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND Feed accounts for about 70% of the total cost of poultry meat production. Residual feed intake (RFI) has become the preferred measure of feed efficiency because it is phenotypically independent of growth rate and body weight. In this study, our aim was to estimate genetic parameters and identify quantitative trait loci (QTL) for feed efficiency in 3314 purebred broilers using a genome-wide association study. Broilers were genotyped using a custom 55 K single nucleotide polymorphism (SNP) array. RESULTS Estimates of genomic heritability for seven growth and feed efficiency traits, including body weight at 28 days of age (BW28), BW42, average daily feed intake (ADFI), RFI, and RFI adjusted for weight of abdominal fat (RFIa), ranged from 0.12 to 0.26. Eleven genome-wide significant SNPs and 15 suggestively significant SNPs were detected, of which 19 clustered around two genomic regions. A region on chromosome 16 (2.34-2.66 Mb) was associated with both BW28 and BW42, and the most significant SNP in this region, AX_101003762, accounted for 7.6% of the genetic variance of BW28. The other region, on chromosome 1 (91.27-92.43 Mb) was associated with RFI and ADFI, and contains the NSUN3 and EPHA6 as candidate genes. The most significant SNP in this region, AX_172588157, accounted for 4.4% of the genetic variance of RFI. In addition, a genomic region containing the gene AGK on chromosome 1 was found to be associated with RFIa. The NSUN3 and AGK genes were found to be differentially expressed in breast muscle, thigh muscle, and abdominal fat between male broilers with high and low RFI. CONCLUSIONS We identified QTL regions for BW28 and BW42 (spanning 0.32 Mb) and RFI (spanning 1.16 Mb). The NSUN3, EPHA6, and AGK were identified as the most likely candidate genes for these QTL. These genes are involved in mitochondrial function and behavioral regulation. These results contribute to the identification of candidate genes and variants for growth and feed efficiency in poultry.
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Affiliation(s)
- 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
- College of Animal Science and Technology, China Agricultural University, 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
| | - 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
| | - Jie 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
| | - Jie 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
| | - Shunli 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
| | - Furong Feng
- Foshan Gaoming Xinguang Agricultural and Animal Industrials Corporation, Foshan, 528515 China
| | - Dawei Liu
- Foshan Gaoming Xinguang Agricultural and Animal Industrials Corporation, Foshan, 528515 China
| | - Dan Zhu
- Foshan Gaoming Xinguang Agricultural and Animal Industrials Corporation, Foshan, 528515 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
| | - Liping Guo
- 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
- College of Animal Science and Technology, China Agricultural University, Beijing, 100193 China
| | - Yuming Guo
- College of Animal Science and Technology, China Agricultural University, 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
| | - 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
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21
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Zhang DY, Zhang XX, Li GZ, Li XL, Zhang YK, Zhao Y, Song QZ, Wang WM. Transcriptome analysis of long noncoding RNAs ribonucleic acids from the livers of Hu sheep with different residual feed intake. Animal 2020; 15:100098. [PMID: 33573993 DOI: 10.1016/j.animal.2020.100098] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Revised: 09/23/2020] [Accepted: 09/24/2020] [Indexed: 11/24/2022] Open
Abstract
Long noncoding RNAs (LncRNAs), as key regulators, have vital functions in various biological activities. However, in sheep, little has been reported concerning the genetic mechanism of LncRNA regulation of feed efficiency. In the present study, we explored the genome-wide expression of LncRNAs and transcripts of uncertain coding potential (TUCPs) in the livers of sheep with extreme residual feed intake (RFI) using RNA sequencing. We identified 1 523 TUCPs and 1 996 LncRNAs, among which 10 LncRNAs and 16 TUCPs were identified as being differentially expressed between the High-RFI and Low-RFI groups. Co-expression and co-localization methods were used to search for LncRNA and TUCP target genes, which identified 970/1 538 and 23/27 genes, respectively. Ontology and pathways analysis revealed that the LncRNAs/TUCPs that were highly expressed in the Low-RFI group are mostly concentrated in energy metabolism pathways. For example, LNC_000890 and TUCP_000582 might regulate liver tissue metabolic efficiency. The LncRNAs/TUCPs that were highly expressed in the High-RFI group are mostly enriched in immune function pathways. For example, TUCP_000832 might regulate animal health, thereby affecting feed efficiency. Subsequently, a co-expression network was established by applying the expression information of both the differentially expressed LncRNAs and TUCPs and their target mRNAs. The network indicated that differentially expressed genes targeted by the upregulated LncRNAs and TUCPs were mainly related to energy metabolism, while those genes targeted by the downregulated LncRNAs and TUCPs were mainly related to immune response. These results provide the basis for further study of LncRNA/TUCP-mediated regulation of feed efficiency.
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Affiliation(s)
- D Y Zhang
- College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, Gansu 730070, China
| | - X X Zhang
- College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, Gansu 730070, China; Engineering Laboratory of Sheep Breeding and Reproduction Biotechnology in Gansu Province, Minqin Zhongtian Sheep Industry Co. Ltd, Minqin, Gansu 733300, China
| | - G Z Li
- College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, Gansu 730070, China
| | - X L Li
- College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, Gansu 730070, China
| | - Y K Zhang
- College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, Gansu 730070, China
| | - Y Zhao
- College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, Gansu 730070, China
| | - Q Z Song
- College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, Gansu 730070, China
| | - W M Wang
- College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, Gansu 730070, China.
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Chen C, Su Z, Li Y, Luan P, Wang S, Zhang H, Xiao F, Guo H, Cao Z, Li H, Leng L. Estimation of the genetic parameters of traits relevant to feed efficiency: result from broiler lines divergent for high or low abdominal fat content. Poult Sci 2020; 100:461-466. [PMID: 33518097 PMCID: PMC7858006 DOI: 10.1016/j.psj.2020.10.028] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 09/25/2020] [Accepted: 10/22/2020] [Indexed: 11/25/2022] Open
Abstract
Feed consumption represents a major cost in poultry production and improving feed efficiency is one of the important goals in breeding strategies. The present study aimed to analyze the relationship between feed efficiency and relevant traits and find the proper selection method for improving feed efficiency by using the Northeast Agricultural University High and Low Fat broiler lines that were divergently selected for abdominal fat content. A total of 899 birds were used to measure the feed intake (FI), abdominal fat weight (AFW), and body weight traits. The abdominal fat percentage (AFP), feed conversion ratio (FCR), and the residual feed intake (RFI) were calculated for each individual broiler. The differences in the AFW, AFP, and in traits relevant to feed efficiency, such as FCR and RFI, between the fat line and the lean line were analyzed, and the genetic parameters were estimated for AFW, AFP, and feed efficiency relevant traits. The results showed that AFW, AFP, body weight gain (BWG), FI, FCR, and RFI were significantly higher in the fat line compared with the lean line. The heritability of FI, BWG, FCR, RFI, AFW, and AFP were 0.45, 0.28, 0.36, 0.38, 0.33, and 0.30, respectively. Both FCR and RFI showed high positive genetic correlations with FI, AFW, and AFP and relatively low, negative genetic correlations with BWG. The RFI showed much higher positive genetic correlation with the abdominal fat traits than FCR. In addition, the FCR showed negative genetic correlation with body weight of 4 wk (BW4) and 7 wk (BW7), whereas RFI showed positive genetic correlation with BW4 and BW7. The results showed that both RFI and FCR could be used for improving feed efficiency. When selecting against RFI, the AFP could be significantly reduced, and by selecting against FCR, the body weight could be improved simultaneously.
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Affiliation(s)
- Chong Chen
- Key Laboratory of Chicken Genetics and Breeding, Ministry of Agriculture and Rural Affairs, Harbin 150030, P. R. China; Key Laboratory of Animal Genetics, Breeding and Reproduction, Education Department of Heilongjiang Province, Harbin 150030, P. R. China; College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, P. R. China
| | - Zhiyong Su
- Key Laboratory of Chicken Genetics and Breeding, Ministry of Agriculture and Rural Affairs, Harbin 150030, P. R. China; Key Laboratory of Animal Genetics, Breeding and Reproduction, Education Department of Heilongjiang Province, Harbin 150030, P. R. China; College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, P. R. China
| | - Yumao Li
- Key Laboratory of Chicken Genetics and Breeding, Ministry of Agriculture and Rural Affairs, Harbin 150030, P. R. China; Key Laboratory of Animal Genetics, Breeding and Reproduction, Education Department of Heilongjiang Province, Harbin 150030, P. R. China; College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, P. R. China
| | - Peng Luan
- Key Laboratory of Chicken Genetics and Breeding, Ministry of Agriculture and Rural Affairs, Harbin 150030, P. R. China; Key Laboratory of Animal Genetics, Breeding and Reproduction, Education Department of Heilongjiang Province, Harbin 150030, P. R. China; College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, P. R. China
| | - Shouzhi Wang
- Key Laboratory of Chicken Genetics and Breeding, Ministry of Agriculture and Rural Affairs, Harbin 150030, P. R. China; Key Laboratory of Animal Genetics, Breeding and Reproduction, Education Department of Heilongjiang Province, Harbin 150030, P. R. China; College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, P. R. China
| | - Hui Zhang
- Key Laboratory of Chicken Genetics and Breeding, Ministry of Agriculture and Rural Affairs, Harbin 150030, P. R. China; Key Laboratory of Animal Genetics, Breeding and Reproduction, Education Department of Heilongjiang Province, Harbin 150030, P. R. China; College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, P. R. China
| | - Fan Xiao
- Fujian Sunzer Biotechnology Development Co., Ltd., Guangze 354100, Fujian Province, P. R. China
| | - Huaishun Guo
- Fujian Sunzer Biotechnology Development Co., Ltd., Guangze 354100, Fujian Province, P. R. China
| | - Zhiping Cao
- Key Laboratory of Chicken Genetics and Breeding, Ministry of Agriculture and Rural Affairs, Harbin 150030, P. R. China; Key Laboratory of Animal Genetics, Breeding and Reproduction, Education Department of Heilongjiang Province, Harbin 150030, P. R. China; College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, P. R. China
| | - Hui Li
- Key Laboratory of Chicken Genetics and Breeding, Ministry of Agriculture and Rural Affairs, Harbin 150030, P. R. China; Key Laboratory of Animal Genetics, Breeding and Reproduction, Education Department of Heilongjiang Province, Harbin 150030, P. R. China; College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, P. R. China
| | - Li Leng
- Key Laboratory of Chicken Genetics and Breeding, Ministry of Agriculture and Rural Affairs, Harbin 150030, P. R. China; Key Laboratory of Animal Genetics, Breeding and Reproduction, Education Department of Heilongjiang Province, Harbin 150030, P. R. China; College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, P. R. China.
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Li W, Liu R, Zheng M, Feng F, Liu D, Guo Y, Zhao G, Wen J. New insights into the associations among feed efficiency, metabolizable efficiency traits and related QTL regions in broiler chickens. J Anim Sci Biotechnol 2020; 11:65. [PMID: 32607230 PMCID: PMC7318453 DOI: 10.1186/s40104-020-00469-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Accepted: 05/01/2020] [Indexed: 12/30/2022] Open
Abstract
Background Improving the feed efficiency would increase profitability for producers while also reducing the environmental footprint of livestock production. This study was conducted to investigate the relationships among feed efficiency traits and metabolizable efficiency traits in 180 male broilers. Significant loci and genes affecting the metabolizable efficiency traits were explored with an imputation-based genome-wide association study. The traits measured or calculated comprised three growth traits, five feed efficiency related traits, and nine metabolizable efficiency traits. Results The residual feed intake (RFI) showed moderate to high and positive phenotypic correlations with eight other traits measured, including average daily feed intake (ADFI), dry excreta weight (DEW), gross energy excretion (GEE), crude protein excretion (CPE), metabolizable dry matter (MDM), nitrogen corrected apparent metabolizable energy (AMEn), abdominal fat weight (AbF), and percentage of abdominal fat (AbP). Greater correlations were observed between growth traits and the feed conversion ratio (FCR) than RFI. In addition, the RFI, FCR, ADFI, DEW, GEE, CPE, MDM, AMEn, AbF, and AbP were lower in low-RFI birds than high-RFI birds (P < 0.01 or P < 0.05), whereas the coefficients of MDM and MCP of low-RFI birds were greater than those of high-RFI birds (P < 0.01). Five narrow QTLs for metabolizable efficiency traits were detected, including one 82.46-kb region for DEW and GEE on Gallus gallus chromosome (GGA) 26, one 120.13-kb region for MDM and AMEn on GGA1, one 691.25-kb region for the coefficients of MDM and AMEn on GGA5, one region for the coefficients of MDM and MCP on GGA2 (103.45–103.53 Mb), and one 690.50-kb region for the coefficient of MCP on GGA14. Linkage disequilibrium (LD) analysis indicated that the five regions contained high LD blocks, as well as the genes chromosome 26 C6orf106 homolog (C26H6orf106), LOC396098, SH3 and multiple ankyrin repeat domains 2 (SHANK2), ETS homologous factor (EHF), and histamine receptor H3-like (HRH3L), which are known to be involved in the regulation of neurodevelopment, cell proliferation and differentiation, and food intake. Conclusions Selection for low RFI significantly decreased chicken feed intake, excreta output, and abdominal fat deposition, and increased nutrient digestibility without changing the weight gain. Five novel QTL regions involved in the control of metabolizable efficiency in chickens were identified. These results, combined through nutritional and genetic approaches, should facilitate novel insights into improving feed efficiency in poultry and other species.
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Affiliation(s)
- 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.,College of Animal Science and Technology, China Agricultural University, 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
| | - 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
| | - Furong Feng
- Foshan Gaoming Xinguang Agricultural and animal Industrials Corporation, Foshan, 528515 China
| | - Dawei Liu
- Foshan Gaoming Xinguang Agricultural and animal Industrials Corporation, Foshan, 528515 China
| | - Yuming Guo
- College of Animal Science and Technology, China Agricultural University, 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
| | - 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
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24
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Ye S, Chen ZT, Zheng R, Diao S, Teng J, Yuan X, Zhang H, Chen Z, Zhang X, Li J, Zhang Z. New Insights From Imputed Whole-Genome Sequence-Based Genome-Wide Association Analysis and Transcriptome Analysis: The Genetic Mechanisms Underlying Residual Feed Intake in Chickens. Front Genet 2020; 11:243. [PMID: 32318090 PMCID: PMC7147382 DOI: 10.3389/fgene.2020.00243] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2019] [Accepted: 02/28/2020] [Indexed: 12/26/2022] Open
Abstract
Poultry feed constitutes the largest cost in poultry production, estimated to be up to 70% of the total cost. Moreover, there is pressure on the poultry industry to increase production to meet the protein demand of humans and simultaneously reduce emissions to protect the environment. Therefore, improving feed efficiency plays an important role to improve profits and the environmental footprint in broiler production. In this study, using imputed whole-genome sequencing data, genome-wide association analysis (GWAS) was performed to identify single-nucleotide polymorphisms (SNPs) and genes associated with residual feed intake (RFI) and its component traits. Furthermore, a transcriptomic analysis between the high-RFI and the low-RFI groups was performed to validate the candidate genes from GWAS. The results showed that the heritability estimates of average daily gain (ADG), average daily feed intake (ADFI), and RFI were 0.29 (0.004), 0.37 (0.005), and 0.38 (0.004), respectively. Using imputed sequence-based GWAS, we identified seven significant SNPs and five candidate genes [MTSS I-BAR domain containing 1, folliculin, COP9 signalosome subunit 3, 5′,3′-nucleotidase (mitochondrial), and gametocyte-specific factor 1] associated with RFI, 20 significant SNPs and one candidate gene (inositol polyphosphate multikinase) associated with ADG, and one significant SNP and one candidate gene (coatomer protein complex subunit alpha) associated with ADFI. After performing a transcriptomic analysis between the high-RFI and the low-RFI groups, both 38 up-regulated and 26 down-regulated genes were identified in the high-RFI group. Furthermore, integrating regional conditional GWAS and transcriptome analysis, ras-related dexamethasone induced 1 was the only overlapped gene associated with RFI, which also suggested that the region (GGA14: 4767015–4882318) is a new quantitative trait locus associated with RFI. In conclusion, using imputed sequence-based GWAS is an efficient method to identify significant SNPs and candidate genes in chicken. Our results provide valuable insights into the genetic mechanisms of RFI and its component traits, which would further improve the genetic gain of feed efficiency rapidly and cost-effectively in the context of marker-assisted breeding selection.
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Affiliation(s)
- Shaopan Ye
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, China
| | - Zi-Tao Chen
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, China
| | - Rongrong Zheng
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, China
| | - Shuqi Diao
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, China
| | - Jinyan Teng
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, China
| | - Xiaolong Yuan
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, China
| | - Hao Zhang
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, China
| | - Zanmou Chen
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, China
| | - Xiquan Zhang
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, China
| | - Jiaqi Li
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, China
| | - Zhe Zhang
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, China
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25
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Zhang D, Zhang X, Li F, Li C, La Y, Mo F, Li G, Zhang Y, Li X, Song Q, Zhao Y, Wang W. Transcriptome Analysis Identifies Candidate Genes and Pathways Associated With Feed Efficiency in Hu Sheep. Front Genet 2019; 10:1183. [PMID: 31798641 PMCID: PMC6878960 DOI: 10.3389/fgene.2019.01183] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2019] [Accepted: 10/24/2019] [Indexed: 12/20/2022] Open
Abstract
In the genetic improvement of livestock and poultry, residual feed intake (RFI) is an important economic trait. However, in sheep, the genetic regulatory mechanisms of RFI are unclear. In the present study, we measured the feed efficiency (FE)-related phenotypes of 137 male Hu lambs, and selected six lambs with very high (n = 3) and very low (n = 3) RFI values and analyzed their liver transcriptomes. A total of 101 differentially expressed genes were identified, of which 40 were upregulated and 61 were downregulated in the low-RFI group compared with that in the high-RFI group. The downregulated genes were mainly concentrated in immune function pathways, while the upregulated genes were mainly involved in energy metabolism pathways. Two differentially expressed genes, ADRA2A (encoding adrenoceptor alpha 2A) and RYR2 (ryanodine receptor 2), were selected as candidate genes for FE and subjected to single nucleotide polymorphism scanning and association analysis. Two synonymous mutations, ADRA2A g.1429 C > A and RYR2 g.1117 A > C, were detected, which were both significantly associated with the feed conversion rate. These findings provide a deeper understanding of the molecular mechanisms regulating FE, and reveal key genes and genetic variants that could be used to genetically improve FE in sheep.
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Affiliation(s)
- Deyin Zhang
- College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, China
| | - Xiaoxue Zhang
- College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, China.,Engineering Laboratory of Sheep Breeding and Reproduction Biotechnology in Gansu Province, Minqin Zhongtian Sheep Industry Co. Ltd., Minqin, China
| | - Fadi Li
- College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, China.,Engineering Laboratory of Sheep Breeding and Reproduction Biotechnology in Gansu Province, Minqin Zhongtian Sheep Industry Co. Ltd., Minqin, China.,The State Key Laboratory of Grassland Agro-ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, China
| | - Chong Li
- College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, China
| | - Yongfu La
- College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, China
| | - Futao Mo
- College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, China
| | - Guoze Li
- College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, China
| | - Yukun Zhang
- College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, China
| | - Xiaolong Li
- College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, China
| | - Qizhi Song
- College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, China
| | - Yuan Zhao
- College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, China
| | - Weimin Wang
- College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, China
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Host transcriptome and microbiome interaction modulates physiology of full-sibs broilers with divergent feed conversion ratio. NPJ Biofilms Microbiomes 2019; 5:24. [PMID: 31552140 PMCID: PMC6754422 DOI: 10.1038/s41522-019-0096-3] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2019] [Accepted: 08/12/2019] [Indexed: 12/31/2022] Open
Abstract
Efficient livestock production relies on effective conversion of feed into body weight gain (BWG). High levels of feed conversion are especially important in production of broiler chickens, birds reared for meat, where economic margins are tight. Traits associated with improved broiler growth and feed efficiency have been subjected to intense genetic selection, but measures such as feed conversion ratio (FCR) remain variable, even between full siblings (sibs). Non-genetic factors such as the composition and function of microbial populations within different enteric compartments have been recognized to influence FCR, although the extent of interplay between hosts and their microbiomes is unclear. To examine host–microbiome interactions we investigated variation in the composition and functions of host intestinal-hepatic transcriptomes and the intestinal microbiota of full-sib broilers with divergent FCR. Progeny from 300 broiler families were assessed for divergent FCR set against shared genetic backgrounds and exposure to the same environmental factors. The seven most divergent full-sib pairs were chosen for analysis, exhibiting marked variation in transcription of genes as well as gut microbial diversity. Examination of enteric microbiota in low FCR sibs revealed variation in microbial community structure and function with no difference in feed intake compared to high FCR sibs. Gene transcription in low and high FCR sibs was significantly associated with the abundance of specific microbial taxa. Highly intertwined interactions between host transcriptomes and enteric microbiota are likely to modulate complex traits like FCR and may be amenable to selective modification with relevance to improving intestinal homeostasis and health.
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27
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Ramayo-Caldas Y, Mármol-Sánchez E, Ballester M, Sánchez JP, González-Prendes R, Amills M, Quintanilla R. Integrating genome-wide co-association and gene expression to identify putative regulators and predictors of feed efficiency in pigs. Genet Sel Evol 2019; 51:48. [PMID: 31477014 PMCID: PMC6721172 DOI: 10.1186/s12711-019-0490-6] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2018] [Accepted: 08/19/2019] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Feed efficiency (FE) has a major impact on the economic sustainability of pig production. We used a systems-based approach that integrates single nucleotide polymorphism (SNP) co-association and gene-expression data to identify candidate genes, biological pathways, and potential predictors of FE in a Duroc pig population. RESULTS We applied an association weight matrix (AWM) approach to analyse the results from genome-wide association studies (GWAS) for nine FE associated and production traits using 31K SNPs by defining residual feed intake (RFI) as the target phenotype. The resulting co-association network was formed by 829 SNPs. Additive effects of this SNP panel explained 61% of the phenotypic variance of RFI, and the resulting phenotype prediction accuracy estimated by cross-validation was 0.65 (vs. 0.20 using pedigree-based best linear unbiased prediction and 0.12 using the 31K SNPs). Sixty-eight transcription factor (TF) genes were identified in the co-association network; based on the lossless approach, the putative main regulators were COPS5, GTF2H5, RUNX1, HDAC4, ESR1, USP16, SMARCA2 and GTF2F2. Furthermore, gene expression data of the gluteus medius muscle was explored through differential expression and multivariate analyses. A list of candidate genes showing functional and/or structural associations with FE was elaborated based on results from both AWM and gene expression analyses, and included the aforementioned TF genes and other ones that have key roles in metabolism, e.g. ESRRG, RXRG, PPARGC1A, TCF7L2, LHX4, MAML2, NFATC3, NFKBIZ, TCEA1, CDCA7L, LZTFL1 or CBFB. The most enriched biological pathways in this list were associated with behaviour, immunity, nervous system, and neurotransmitters, including melatonin, glutamate receptor, and gustation pathways. Finally, an expression GWAS allowed identifying 269 SNPs associated with the candidate genes' expression (eSNPs). Addition of these eSNPs to the AWM panel of 829 SNPs did not improve the accuracy of genomic predictions. CONCLUSIONS Candidate genes that have a direct or indirect effect on FE-related traits belong to various biological processes that are mainly related to immunity, behaviour, energy metabolism, and the nervous system. The pituitary gland, hypothalamus and thyroid axis, and estrogen signalling play fundamental roles in the regulation of FE in pigs. The 829 selected SNPs explained 61% of the phenotypic variance of RFI, which constitutes a promising perspective for applying genetic selection on FE relying on molecular-based prediction.
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Affiliation(s)
- Yuliaxis Ramayo-Caldas
- 0000 0001 1943 6646grid.8581.4Animal Breeding and Genetics Program, Institute for Research and Technology in Food and Agriculture (IRTA), Torre Marimon, 08140 Caldes de Montbui, Spain
| | - Emilio Mármol-Sánchez
- grid.7080.fDepartment of Animal Genetics, Centre for Research in Agricultural Genomics (CRAG), CSCIC-IRTA-UAB-UB, Campus de LA Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain
| | - Maria Ballester
- 0000 0001 1943 6646grid.8581.4Animal Breeding and Genetics Program, Institute for Research and Technology in Food and Agriculture (IRTA), Torre Marimon, 08140 Caldes de Montbui, Spain
| | - Juan Pablo Sánchez
- 0000 0001 1943 6646grid.8581.4Animal Breeding and Genetics Program, Institute for Research and Technology in Food and Agriculture (IRTA), Torre Marimon, 08140 Caldes de Montbui, Spain
| | - Rayner González-Prendes
- grid.7080.fDepartment of Animal Genetics, Centre for Research in Agricultural Genomics (CRAG), CSCIC-IRTA-UAB-UB, Campus de LA Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain
| | - Marcel Amills
- grid.7080.fDepartment of Animal Genetics, Centre for Research in Agricultural Genomics (CRAG), CSCIC-IRTA-UAB-UB, Campus de LA Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain
- grid.7080.fDepartament de Ciència Animal i dels Aliments, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain
| | - Raquel Quintanilla
- 0000 0001 1943 6646grid.8581.4Animal Breeding and Genetics Program, Institute for Research and Technology in Food and Agriculture (IRTA), Torre Marimon, 08140 Caldes de Montbui, Spain
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Ye S, Gao N, Zheng R, Chen Z, Teng J, Yuan X, Zhang H, Chen Z, Zhang X, Li J, Zhang Z. Strategies for Obtaining and Pruning Imputed Whole-Genome Sequence Data for Genomic Prediction. Front Genet 2019; 10:673. [PMID: 31379929 PMCID: PMC6650575 DOI: 10.3389/fgene.2019.00673] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2019] [Accepted: 06/27/2019] [Indexed: 11/13/2022] Open
Abstract
Genomic prediction with imputed whole-genome sequencing (WGS) data is an attractive approach to improve predictive ability with low cost. However, high accuracy has not been realized using this method in livestock. In this study, we imputed 435 individuals from 600K single nucleotide polymorphism (SNP) chip data to WGS data using different reference panels. We also investigated the prediction accuracy of genomic best linear unbiased prediction (GBLUP) using imputed WGS data from different reference panels, linkage disequilibrium (LD)-based marker pruning, and pre-selected variants based on Genome-wide association society (GWAS) results. Results showed that the imputation accuracies from 600K to WGS data were 0.873 ± 0.038, 0.906 ± 0.036, and 0.979 ± 0.010 for the internal, external, and combined reference panels, respectively. In most traits of chickens, the prediction accuracy of imputed WGS data obtained from the internal reference panel was greater than or equal to that of the combined reference panel; the external reference panel had the lowest prediction accuracy. Compared with 600K chip data, GBLUP with imputed WGS data had only a small increase (1-3%) in prediction accuracy. Using only variants selected from imputed WGS data based on GWAS results resulted in almost no increase for most traits and even increased the bias of the regression coefficient. The impact of the degree of LD of selected and remaining variants on prediction accuracy was different. For average daily gain (ADG), residual feed intake (RFI), intestine length (IL), and body weight in 91 days (BW91), the accuracy of GBLUP increased as the degree of LD of selected variants decreased, but the opposite relationship occurred for the remaining variants. But for breast muscle weight (BMW) and average daily feed intake (ADFI), the accuracy of GBLUP increased as the degree of LD of selected variants increased, and the degree of LD of remaining variants had a small effect on prediction accuracy. Overall, the optimal imputation strategy to obtain WGS data for genomic prediction should consider the relationship between selected individuals and target population individuals to avoid heterogeneity of imputation. LD-based marker pruning can be used to improve the accuracy of genomic prediction using imputed WGS data.
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Affiliation(s)
- Shaopan Ye
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, China
| | - Ning Gao
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | - Rongrong Zheng
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, China
| | - Zitao Chen
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, China
| | - Jinyan Teng
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, China
| | - Xiaolong Yuan
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, China
| | - Hao Zhang
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, China
| | - Zanmou Chen
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, China
| | - Xiquan Zhang
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, China
| | - Jiaqi Li
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, China
| | - Zhe Zhang
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, China
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Association analysis between feed efficiency and expression of key genes of the avTOR signaling pathway in meat-type ducks. Mol Biol Rep 2019; 46:3537-3544. [PMID: 31140048 DOI: 10.1007/s11033-019-04720-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2018] [Accepted: 02/07/2019] [Indexed: 10/26/2022]
Abstract
Genes involved in the target of rapamycin (TOR) signaling pathway are implicated in nutrient translation, cell proliferation and differentiation, and anabolism, which can affect both growth and feed intake. However, the role of TOR signaling in the regulation of feed intake and feed efficiency in poultry is not clear. In the present study, a total of 1000 ducks, of similar initial weight, were chosen and transferred to individual cages to determine their residual feed intake (RFI) from the age of 21 to 42 days. Subsequently, 60 ducks, which were divided into high (HRFI) and low (LRFI) groups according to their RFI, were chosen to analyze the TOR signaling activities in the liver. The differential expression level of genes involved in the TOR signaling pathway was assayed by the real-time polymerase chain reaction. In the liver, the expression of AKT, avTOR, avLST8, and S6K1 was significantly higher in LRFI ducks than in HRFI ducks; avTOR and AKT were negatively associated with the feed conversion ratio and RFI. Furthermore, PI3K was moderately positively associated with AKT; AKT was strongly positively associated with PI3K, avTOR, avLST8, and S6K1; and avTOR was strongly positively associated with S6K1. In conclusion, the activation of avTOR signaling in the liver of LRFI ducks might be ascribed to higher energy state or more active nutrient transport (amino acids), or both, than those in the liver of HRFI ducks. The results of the present study indicate that AKT and avTOR of TOR signaling might be used as candidate genes to assess molecular regulation of feed efficiency.
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Yan W, Sun C, Wen C, Ji C, Zhang D, Yang N. Relationships between feeding behaviors and performance traits in slow-growing yellow broilers. Poult Sci 2019; 98:548-555. [PMID: 30239851 DOI: 10.3382/ps/pey424] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2018] [Accepted: 08/18/2018] [Indexed: 12/29/2022] Open
Abstract
This study was conducted to investigate the feeding behaviors of slow-growing yellow broilers and the relationships of feeding behaviors with performance traits. With the help of automatic recording systems in floor houses, feeding events from a pure line of slow-growing yellow broilers were recorded from 57 to 77 d of age. After data quality control, a total of 116,477 feeding records from 319 birds were used for analyses. Feeding behaviors including number of visits per day (18.74), feeding duration per day (71.17 min/d), feeding duration per visit (262.00 s/visit), feeding rate (2.19 g/min), and feed intake per visit (8.52 g/visit) were calculated according to feeding records. Correlation analyses and comparisons between divergent efficiency groups were performed to examine the relationships between feed efficiency and feeding behaviors. Absolute correlations between residual feed intake (RFI) and feeding behaviors (except for feed intake per visit) were significant but weak (r = 0.18 to 0.34, P < 0.05), whereas feed conversion ratio (FCR) was not significantly correlated with any feeding behaviors. All of the weight-associated traits were positively correlated with feeding rate and feed intake per visit (r = 0.19 to 0.25, P < 0.05). Compared with the inefficient birds with the 20% highest RFI or FCR (HRFI or HFCR), the efficient ones with the 20% lowest RFI or FCR (LRFI or LFCR) ate faster (P < 0.05), spent shorter eating duration (P < 0.05) and had similar feed intakes per visit (P > 0.05). However, number of visits per day and the feeding duration per day were lower in the LRFI group than in the HRFI group (P < 0.05) but were not significantly different between the LFCR and HFCR groups (P > 0.05). In summary, this study shows the feeding behaviors of group-housed slow-growing yellow broilers and observed that RFI has closer relationships with feeding behaviors than FCR does, and the selection for birds with improved RFI may result in fewer visits, shorter duration and faster feeding rate.
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Affiliation(s)
- Wei Yan
- National Engineering Laboratory for Animal Breeding and MOA Key Laboratory of Animal Genetics and Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Congjiao Sun
- National Engineering Laboratory for Animal Breeding and MOA Key Laboratory of Animal Genetics and Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Chaoliang Wen
- National Engineering Laboratory for Animal Breeding and MOA Key Laboratory of Animal Genetics and Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Congliang Ji
- Wen's Nanfang Poultry Breeding Co. Ltd, Guangdong Province, Yunfu 527400, China
| | - Dexiang Zhang
- Wen's Nanfang Poultry Breeding Co. Ltd, Guangdong Province, Yunfu 527400, China
| | - Ning Yang
- National Engineering Laboratory for Animal Breeding and MOA Key Laboratory of Animal Genetics and Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
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Liu R, Liu J, Zhao G, Li W, Zheng M, Wang J, Li Q, Cui H, Wen J. Relevance of the intestinal health-related pathways to broiler residual feed intake revealed by duodenal transcriptome profiling. Poult Sci 2019; 98:1102-1110. [DOI: 10.3382/ps/pey506] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2018] [Accepted: 10/13/2018] [Indexed: 12/13/2022] Open
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Wang J, Yuan X, Ye S, Huang S, He Y, Zhang H, Li J, Zhang X, Zhang Z. Genome wide association study on feed conversion ratio using imputed sequence data in chickens. ASIAN-AUSTRALASIAN JOURNAL OF ANIMAL SCIENCES 2018; 32:494-500. [PMID: 30381748 PMCID: PMC6409457 DOI: 10.5713/ajas.18.0319] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/23/2018] [Accepted: 09/20/2018] [Indexed: 01/11/2023]
Abstract
Objective Feed consumption contributes a large percentage for total production costs in the poultry industry. Detecting genes associated with feeding traits will be of benefit to improve our understanding of the molecular determinants for feed efficiency. The objective of this study was to identify candidate genes associated with feed conversion ratio (FCR) via genome-wide association study (GWAS) using sequence data imputed from single nucleotide polymorphism (SNP) panel in a Chinese indigenous chicken population. Methods A total of 435 Chinese indigenous chickens were phenotyped for FCR and were genotyped using a 600K SNP genotyping array. Twenty-four birds were selected for sequencing, and the 600K SNP panel data were imputed to whole sequence data with the 24 birds as the reference. The GWAS were performed with GEMMA software. Results After quality control, 8,626,020 SNPs were used for sequence based GWAS, in which ten significant genomic regions were detected to be associated with FCR. Ten candidate genes, ubiquitin specific peptidase 44, leukotriene A4 hydrolase, ETS transcription factor, R-spondin 2, inhibitor of apoptosis protein 3, sosondowah ankyrin repeat domain family member D, calmodulin regulated spectrin associated protein family member 2, zinc finger and BTB domain containing 41, potassium sodium-activated channel subfamily T member 2, and member of RAS oncogene family were annotated. Several of them were within or near the reported FCR quantitative trait loci, and others were newly reported. Conclusion Results from this study provide valuable prior information on chicken genomic breeding programs, and potentially improve our understanding of the molecular mechanism for feeding traits.
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Affiliation(s)
- Jiaying Wang
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
| | - Xiaolong Yuan
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
| | - Shaopan Ye
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
| | - Shuwen Huang
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
| | - Yingting He
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
| | - Hao Zhang
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
| | - Jiaqi Li
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
| | - Xiquan Zhang
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
| | - Zhe Zhang
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
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Wen C, Yan W, Zheng J, Ji C, Zhang D, Sun C, Yang N. Feed efficiency measures and their relationships with production and meat quality traits in slower growing broilers. Poult Sci 2018; 97:2356-2364. [PMID: 29669019 DOI: 10.3382/ps/pey062] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2017] [Indexed: 11/20/2022] Open
Abstract
Feed consumption accounts for the major cost of broiler production. Improving the efficiency of feed utilization is a primary goal in breeding strategies, although few studies have focused on slower growing broilers. Here, we recorded the feed intake (FI) during the fast-growing period (d 56 to 76) and measured the live weight, body measurements, carcass characteristics, and intramuscular fat (IMF) content of Chinese yellow broilers. Then, the residual feed intake (RFI) and feed conversion ratio (FCR) were calculated for each individual. Pair-wise phenotypic correlations were subsequently calculated between feed efficiency traits and others. Finally, we separately selected the more efficient individuals based on RFI and FCR values to evaluate the impacts on the traits of FI, growth, carcass characteristics, and meat quality. The results showed higher correlations between FCR and production traits than with RFI, while RFI showed a moderate and positive phenotypic correlation with abdominal fat. FCR was weakly correlated with FI and slightly positively correlated with IMF content. The correlation coefficient between RFI and FI was 0.62, and that between RFI and IMF content was close to zero. Without increasing FI, decreasing FCR could effectively enhance the growth rate and market weight with no adverse effect on meat quality. In contrast, by improving RFI, FI and abdominal fat mass were significantly reduced and thus increased the yield with no unfavorable effects on meat quality. In consideration of consumer preference and overall economical benefits, RFI is a more suitable index to improve feed efficiency in slower growing broilers.
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Affiliation(s)
- Chaoliang Wen
- National Engineering Laboratory for Animal Breeding and MOA Key Laboratory of Animal Genetics and Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Wei Yan
- National Engineering Laboratory for Animal Breeding and MOA Key Laboratory of Animal Genetics and Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Jiangxia Zheng
- National Engineering Laboratory for Animal Breeding and MOA Key Laboratory of Animal Genetics and Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Congliang Ji
- Guangdong Wen's Nanfang Poultry Breeding Co. Ltd, Yunfu 527400, Guangdong Province, China
| | - Dexiang Zhang
- Guangdong Wen's Nanfang Poultry Breeding Co. Ltd, Yunfu 527400, Guangdong Province, China
| | - Congjiao Sun
- National Engineering Laboratory for Animal Breeding and MOA Key Laboratory of Animal Genetics and Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Ning Yang
- National Engineering Laboratory for Animal Breeding and MOA Key Laboratory of Animal Genetics and Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
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Izadnia HR, Tahmoorespur M, Bakhtiarizadeh MR, Nassiri M, Esmaeilkhanien S. Gene expression profile analysis of residual feed intake for Isfahan native chickens using RNA-SEQ data. ITALIAN JOURNAL OF ANIMAL SCIENCE 2018. [DOI: 10.1080/1828051x.2018.1507625] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Affiliation(s)
- Hamid Reza Izadnia
- Animal Science Improvement Research Department, Agricultural and Natural Resources Research and Education Center, Safiabad AREEO, Dezful, Iran
| | - Mojtaba Tahmoorespur
- Faculty of Agriculture, Department of Animal Science, Ferdowsi University of Mashhad, Mashhad, Iran
| | | | - Mohammadreza Nassiri
- Faculty of Agriculture, Department of Animal Science, Ferdowsi University of Mashhad, Mashhad, Iran
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Yi Z, Li X, Luo W, Xu Z, Ji C, Zhang Y, Nie Q, Zhang D, Zhang X. Feed conversion ratio, residual feed intake and cholecystokinin type A receptor gene polymorphisms are associated with feed intake and average daily gain in a Chinese local chicken population. J Anim Sci Biotechnol 2018; 9:50. [PMID: 29942508 PMCID: PMC6000933 DOI: 10.1186/s40104-018-0261-1] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2017] [Accepted: 04/25/2018] [Indexed: 11/25/2022] Open
Abstract
Background The feed conversion ratio (FCR) and residual feed intake (RFI) are common indexes in measuring feed efficiency for livestock. RFI is a feed intake adjusted for requirements for maintenance and production so these two traits are related. Similarly, FCR is related to feed intake and weight gain because it is their ratio. Cholecystokinin type A receptor (CCKAR) plays an important role in animal digestive process. We examined the interplay of these three parameters in a local Chinese chicken population. Results The feed intake (FI) and body weights (BW) of 1,841 individuals were monitored on a daily basis from 56 to 105 d of age. There was a strong correlation between RFI and average daily feed intake (ADFI) and a negative correlation between the FCR and daily gain (rg = − 0.710). Furthermore, we identified 51 single nucleotide polymorphisms (SNPs) in the CCKAR and 4 of these resulted in amino acid mutations. The C334A mutation was specifically associated with FI and the expected feed intake (EFI) (P < 0.01) and significantly associated with the average daily gain (ADG) (P < 0.05). G1290A was significantly associated with FI and EFI (P < 0.05). Conclusion FCR is apply to weight selecting, and RFI is more appropriate if the breeding focus is feed intake. And C334A and G1290A of the CCKAR gene can be deemed as candidate markers for feed intake and weight gain. Electronic supplementary material The online version of this article (10.1186/s40104-018-0261-1) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Zhenhua Yi
- 1Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou, 510642 Guangdong China.,2Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding and Key Laboratory of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou, 510642 Guangdong China
| | - Xing Li
- 1Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou, 510642 Guangdong China.,2Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding and Key Laboratory of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou, 510642 Guangdong China
| | - Wen Luo
- 1Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou, 510642 Guangdong China.,2Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding and Key Laboratory of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou, 510642 Guangdong China
| | - Zhenqiang Xu
- Wen's Nanfang Poultry Breeding Co. Ltd, Yunfu, 527400 Guangdong China
| | - Congliang Ji
- Wen's Nanfang Poultry Breeding Co. Ltd, Yunfu, 527400 Guangdong China
| | - Yan Zhang
- Wen's Nanfang Poultry Breeding Co. Ltd, Yunfu, 527400 Guangdong China
| | - Qinghua Nie
- 1Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou, 510642 Guangdong China.,2Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding and Key Laboratory of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou, 510642 Guangdong China
| | - Dexiang Zhang
- 1Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou, 510642 Guangdong China.,2Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding and Key Laboratory of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou, 510642 Guangdong China.,Wen's Nanfang Poultry Breeding Co. Ltd, Yunfu, 527400 Guangdong China
| | - Xiquan Zhang
- 1Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou, 510642 Guangdong China.,2Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding and Key Laboratory of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou, 510642 Guangdong China
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Chen M, Wang J, Wang Y, Wu Y, Fu J, Liu JF. Genome-wide detection of selection signatures in Chinese indigenous Laiwu pigs revealed candidate genes regulating fat deposition in muscle. BMC Genet 2018; 19:31. [PMID: 29776331 PMCID: PMC5960162 DOI: 10.1186/s12863-018-0622-y] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2017] [Accepted: 04/30/2018] [Indexed: 12/31/2022] Open
Abstract
Background Currently, genome-wide scans for positive selection signatures in commercial breed have been investigated. However, few studies have focused on selection footprints of indigenous breeds. Laiwu pig is an invaluable Chinese indigenous pig breed with extremely high proportion of intramuscular fat (IMF), and an excellent model to detect footprint as the result of natural and artificial selection for fat deposition in muscle. Result In this study, based on GeneSeek Genomic profiler Porcine HD data, three complementary methods, FST, iHS (integrated haplotype homozygosity score) and CLR (composite likelihood ratio), were implemented to detect selection signatures in the whole genome of Laiwu pigs. Totally, 175 candidate selected regions were obtained by at least two of the three methods, which covered 43.75 Mb genomic regions and corresponded to 1.79% of the genome sequence. Gene annotation of the selected regions revealed a list of functionally important genes for feed intake and fat deposition, reproduction, and immune response. Especially, in accordance to the phenotypic features of Laiwu pigs, among the candidate genes, we identified several genes, NPY1R, NPY5R, PIK3R1 and JAKMIP1, involved in the actions of two sets of neurons, which are central regulators in maintaining the balance between food intake and energy expenditure. Conclusions Our results identified a number of regions showing signatures of selection, as well as a list of functionally candidate genes with potential effect on phenotypic traits, especially fat deposition in muscle. Our findings provide insights into the mechanisms of artificial selection of fat deposition and further facilitate follow-up functional studies. Electronic supplementary material The online version of this article (10.1186/s12863-018-0622-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Minhui Chen
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Jiying Wang
- Shandong Provincial Key Laboratory of Animal Disease Control and Breeding, Institute of Animal Science and Veterinary Medicine, Shandong Academy of Agricultural Sciences, Jinan, 250100, China
| | - Yanping Wang
- Shandong Provincial Key Laboratory of Animal Disease Control and Breeding, Institute of Animal Science and Veterinary Medicine, Shandong Academy of Agricultural Sciences, Jinan, 250100, China
| | - Ying Wu
- Shandong Provincial Key Laboratory of Animal Disease Control and Breeding, Institute of Animal Science and Veterinary Medicine, Shandong Academy of Agricultural Sciences, Jinan, 250100, China
| | - Jinluan Fu
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China.
| | - Jian-Feng Liu
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China.
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Fischer A, Delagarde R, Faverdin P. Identification of biological traits associated with differences in residual energy intake among lactating Holstein cows. J Dairy Sci 2018; 101:4193-4211. [DOI: 10.3168/jds.2017-12636] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2017] [Accepted: 12/04/2017] [Indexed: 11/19/2022]
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Liu J, Liu R, Wang J, Zhang Y, Xing S, Zheng M, Cui H, Li Q, Li P, Cui X, Li W, Zhao G, Wen J. Exploring Genomic Variants Related to Residual Feed Intake in Local and Commercial Chickens by Whole Genomic Resequencing. Genes (Basel) 2018; 9:genes9020057. [PMID: 29364149 PMCID: PMC5852553 DOI: 10.3390/genes9020057] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2017] [Revised: 12/20/2017] [Accepted: 01/02/2018] [Indexed: 02/01/2023] Open
Abstract
Improving feed efficiency is a major goal in poultry production to reduce production costs and increase profitability. The genomic variants and possible molecular mechanisms responsible for residual feed intake (RFI) in chickens, however, remain poorly understood. In this study, using both local and commercial breeds, genome re-sequencing of low RFI and high RFI chickens was performed to elucidate the genomic variants underlying RFI. Results showed that 8,505,214 and 8,479,041 single nucleotide polymorphisms (SNPs) were detected in low and high RFI Beijing-You chickens, respectively; 8,352,008 and 8,372,769 SNPs were detected in low- and high-RFI Cobb chickens, respectively. Through a series of filtering processes, 3746 candidate SNPs assigned to 1137 genes in Beijing-You chickens and 575 candidate SNPs (448 genes) in Cobb chickens were found. The validation of the selected 191 SNPs showed that 46 SNPs were significantly associated with the RFI in an independent population of 779 Cobb chickens, suggesting that the method of screening associated SNPs with whole genome sequencing (WGS) strategy was reasonable. Functions annotation of RFI-related genes indicated that genes in Beijing-You were enriched in lipid and carbohydrate metabolism, as well as the phosphatase and tensin homolog (PTEN) signaling pathway. In Cobb, however, RFI-related genes were enriched in the feed behavior process and cAMP responsive element binding protein (CREB) signaling pathway. For both breeds, organismal development physiological processes were enriched. Correspondingly, NOS1, PHKG1, NEU3 and PIP5K1B were differentially expressed in Beijing-You, while CDC42, CSK, PIK3R3, CAMK4 and PLCB4 were differentially expressed in Cobb, suggesting that these might be key genes that contribute to RFI. The results of the present study identified numerous novel SNPs for RFI, which provide candidate biomarkers for use in the genetic selection for RFI. The study has improved knowledge of the genomic variants and potential biological pathways underlying RFI in chickens.
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Affiliation(s)
- Jie Liu
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China.
- State Key Laboratory of Animal Nutrition, Beijing 100193, China.
| | - Ranran Liu
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China.
- State Key Laboratory of Animal Nutrition, Beijing 100193, China.
| | - Jie Wang
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China.
- State Key Laboratory of Animal Nutrition, Beijing 100193, China.
| | - Yonghong Zhang
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China.
- College of Animal Science, Jilin University, Changchun 130062, China.
| | - Siyuan Xing
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China.
- State Key Laboratory of Animal Nutrition, Beijing 100193, China.
| | - Maiqing Zheng
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China.
- State Key Laboratory of Animal Nutrition, Beijing 100193, China.
| | - Huanxian Cui
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China.
- State Key Laboratory of Animal Nutrition, Beijing 100193, China.
| | - Qinghe Li
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China.
- State Key Laboratory of Animal Nutrition, Beijing 100193, China.
| | - Peng Li
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China.
- State Key Laboratory of Animal Nutrition, Beijing 100193, China.
| | - Xiaoyan Cui
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China.
- State Key Laboratory of Animal Nutrition, Beijing 100193, China.
| | - Wei Li
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China.
- State Key Laboratory of Animal Nutrition, Beijing 100193, China.
| | - Guiping Zhao
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China.
- State Key Laboratory of Animal Nutrition, Beijing 100193, China.
| | - Jie Wen
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China.
- State Key Laboratory of Animal Nutrition, Beijing 100193, China.
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Lin S, Lin X, Zhang Z, Jiang M, Rao Y, Nie Q, Zhang X. Copy Number Variation in SOX6 Contributes to Chicken Muscle Development. Genes (Basel) 2018; 9:genes9010042. [PMID: 29342086 PMCID: PMC5793193 DOI: 10.3390/genes9010042] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2017] [Revised: 01/11/2018] [Accepted: 01/12/2018] [Indexed: 11/16/2022] Open
Abstract
Copy number variations (CNVs), which cover many functional genes, are associated with complex diseases, phenotypic diversity and traits that are economically important to raising chickens. The sex-determining region Y-box 6 (Sox6) plays a key role in fast-twitch muscle fiber differentiation of zebrafish and mice, but it is still unknown whether SOX6 plays a role in chicken skeletal muscle development. We identified two copy number polymorphisms (CNPs) which were significantly related to different traits on the genome level in chickens by AccuCopy® and CNVplex® analyses. Notably, five white recessive rock (CN = 1, CN = 3) variant individuals and two Xinghua (CN = 3) variant individuals contain a CNP13 (chromosome5: 10,500,294-10,675,531) which overlaps with SOX6. There is a disordered region in SOX6 proteins 265-579 aa coded by a partial CNV overlapping region. A quantitative real-time polymerase chain reaction showed that the expression level of SOX6 mRNA was positively associated with CNV and highly expressed during the skeletal muscle cell differentiation in chickens. After the knockdown of the SOX6, the expression levels of IGFIR1, MYF6, SOX9, SHOX and CCND1 were significantly down-regulated. All of them directly linked to muscle development. These results suggest that the number of CNVs in the CNP13 is positively associated with the expression level of SOX6, which promotes the proliferation and differentiation of skeletal muscle cells by up-regulating the expression levels of the muscle-growth-related genes in chickens as in other animal species.
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Affiliation(s)
- Shudai Lin
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, Key Laboratory of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science of South China Agricultural University, Guangzhou 510642, China.
| | - Xiran Lin
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, Key Laboratory of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science of South China Agricultural University, Guangzhou 510642, China.
| | - Zihao Zhang
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, Key Laboratory of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science of South China Agricultural University, Guangzhou 510642, China.
| | - Mingya Jiang
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, Key Laboratory of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science of South China Agricultural University, Guangzhou 510642, China.
| | - Yousheng Rao
- Department of Biological Technology, Nanchang Normal University, Nanchang 330029, China.
| | - Qinghua Nie
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, Key Laboratory of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science of South China Agricultural University, Guangzhou 510642, China.
| | - Xiquan Zhang
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, Key Laboratory of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science of South China Agricultural University, Guangzhou 510642, China.
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Integrative approach using liver and duodenum RNA-Seq data identifies candidate genes and pathways associated with feed efficiency in pigs. Sci Rep 2018; 8:558. [PMID: 29323241 PMCID: PMC5764994 DOI: 10.1038/s41598-017-19072-5] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2017] [Accepted: 12/21/2017] [Indexed: 12/11/2022] Open
Abstract
This study aims identifying candidate genes and pathways associated with feed efficiency (FE) in pigs. Liver and duodenum transcriptomes of 37 gilts showing high and low residual feed intake (RFI) were analysed by RNA-Seq. Gene expression data was explored through differential expression (DE) and weighted gene co-expression network analyses. DE analysis revealed 55 and 112 differentially regulated genes in liver and duodenum tissues, respectively. Clustering genes according to their connectivity resulted in 23 (liver) and 25 (duodenum) modules of genes with a co-expression pattern. Four modules, one in liver (with 444 co-expressed genes) and three in duodenum (gathering 37, 126 and 41 co-expressed genes), were significantly associated with FE indicators. Intra-module analyses revealed tissue-specific candidate genes; 12 of these genes were also identified as DE between individuals with high and low RFI. Pathways enriched by the list of genes showing DE and/or belonging to FE co-expressed modules included response to oxidative stress, inflammation, immune response, lipid metabolism and thermoregulation. Low overlapping between genes identified in duodenum and liver tissues was observed but heat shock proteins were associated to FE in both tissues. Our results suggest tissue-specific rather than common transcriptome regulatory processes associated with FE in pigs.
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A genome-wide study to identify genes responsible for oviduct development in chickens. PLoS One 2017; 12:e0189955. [PMID: 29281706 PMCID: PMC5744973 DOI: 10.1371/journal.pone.0189955] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2017] [Accepted: 12/05/2017] [Indexed: 12/16/2022] Open
Abstract
Molecular genetic tools provide a method for improving the breeding selection of chickens (Gallus gallus). Although some studies have identified genes affecting egg quality, little is known about the genes responsible for oviduct development. To address this issue, here we used a genome-wide association (GWA) study to detect genes or genomic regions that are related to oviduct development in a chicken F2 resource population by employing high-density 600 K single-nucleotide polymorphism (SNP) arrays. For oviduct length and weight, which exhibited moderate heritability estimates of 0.35 and 0.39, respectively, chromosome 1 (GGA1) explained 9.45% of the genetic variance, while GGA4 to GGA8 and GGA11 explained over 1% of the variance. Independent univariate genome-wide screens for oviduct length and weight detected 69 significant SNPs on GGA1 and 49 suggestive SNPs on GGA1, GGA4, and GGA8. One hundred and fourteen suggestive SNPs were associated with oviduct length, while 73 SNPs were associated with oviduct weight. The significant genomic regions affecting oviduct weight ranged from 167.79–174.29 Mb on GGA1, 73.16–75.70 Mb on GGA4, and 4.88–4.92 Mb on GGA8. The genes CKAP2, CCKAR, NCAPG, IGFBP3, and GORAB were shown to have potential roles in oviduct development. These genes are involved in cell survival, appetite, and growth control. Our results represent the first GWA analysis of genes controlling oviduct weight and length. The identification of genomic loci and potential candidate genes affecting oviduct development greatly increase our understanding of the genetic basis underlying oviduct development, which could have an impact on the selection of egg quality.
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Sell-Kubiak E, Wimmers K, Reyer H, Szwaczkowski T. Genetic aspects of feed efficiency and reduction of environmental footprint in broilers: a review. J Appl Genet 2017; 58:487-498. [PMID: 28342159 PMCID: PMC5655602 DOI: 10.1007/s13353-017-0392-7] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2016] [Revised: 01/18/2017] [Accepted: 03/08/2017] [Indexed: 11/28/2022]
Abstract
Currently, optimization of feed efficiency is one of the main challenges in improvement programs of livestock and poultry genetics. The objective of this review is to present the genetic aspects of feed efficiency related traits in meat-type chicken and possible ways to reduce the environmental impact of poultry meat production with effective breeding. Basic measures of feed efficiency are defined and the genetic background of these traits, including a review of heritabilities is described. Moreover, a number of genomic regions and candidate genes determining feed efficiency traits of broilers that were detected over the past decades are described. Classical and genomic selection strategies for feed efficiency in the context of its relationships with other performance traits are discussed as well. Finally, future strategies to improve feed digestibility are described as it is expected that they will decrease wastes and greenhouse gas emission. Further genetic improvement of feed efficiency, should be examined jointly with appropriate feeding strategies in broilers.
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Affiliation(s)
- Ewa Sell-Kubiak
- Departament of Genetics and Animal Breeding, Poznan University of Life Sciences, Wolynska st. 33, 60-637, Poznan, Poland
| | - Klaus Wimmers
- Institute of Genome Biology, Leibniz Institute of Farm Animal Biology, Wilhelm-Stahl-Allee 2, 18196, Dummerstorf, Germany
| | - Henry Reyer
- Institute of Genome Biology, Leibniz Institute of Farm Animal Biology, Wilhelm-Stahl-Allee 2, 18196, Dummerstorf, Germany
| | - Tomasz Szwaczkowski
- Departament of Genetics and Animal Breeding, Poznan University of Life Sciences, Wolynska st. 33, 60-637, Poznan, Poland.
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Li Z, Abdalla BA, Zheng M, He X, Cai B, Han P, Ouyang H, Chen B, Nie Q, Zhang X. Systematic transcriptome-wide analysis of mRNA-miRNA interactions reveals the involvement of miR-142-5p and its target (FOXO3) in skeletal muscle growth in chickens. Mol Genet Genomics 2017; 293:69-80. [PMID: 28866851 DOI: 10.1007/s00438-017-1364-7] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2017] [Accepted: 08/29/2017] [Indexed: 11/29/2022]
Abstract
The goal of this study was to perform a systematic transcriptome-wide analysis of mRNA-miRNA interactions and to identify candidates involved in the interplay between miRNAs and mRNAs that regulate chicken muscle growth. We used our previously published mRNA (GSE72424) and miRNA (GSE62971) deep sequencing data from two-tailed samples [i.e., the highest (h) and lowest (l) body weights] of Recessive White Rock (WRR) and Xinghua (XH) chickens to conduct integrative analyses of the miRNA-mRNA interactions involved in chicken skeletal muscle growth. A total of 162, 15, 173, and 27 miRNA-mRNA pairs with negatively correlated expression patterns were identified in miRNA-mRNA networks constructed on the basis of the WRRh vs. XHh, WRRh vs. WRRl, WRRl vs. XHl, and XHh vs. XHl comparisons, respectively. Ingenuity Pathway Analysis revealed that gene networks identified for the WRRh vs. XHh contrast were associated with developmental disorders. Importantly, the WRRh vs. XHh contrast miRNA-mRNA network was enriched in IGF-1 signaling pathway genes, including FOXO3. A dual-luciferase reporter assay showed that FOXO3 was a target of miR-142-5p. Furthermore, miR-142-5p overexpression significantly decreased FOXO3 mRNA levels and promoted the expression of growth-related genes. These data demonstrated that miR-142-5p targets FOXO3 and promotes growth-related gene expression and regulates skeletal muscle growth in chicken. Comprehensive analysis facilitated the identification of miRNAs and target genes that might contribute to the regulation of skeletal muscle development. Our results provide new clues for understanding the molecular basis of chicken growth.
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Affiliation(s)
- Zhenhui Li
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Wushan Street, Tianhe District, Guangzhou, 510642, Guangdong, China.,Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding and Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou, Guangdong, China
| | - Bahareldin Ali Abdalla
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Wushan Street, Tianhe District, Guangzhou, 510642, Guangdong, China.,Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding and Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou, Guangdong, China
| | - Ming Zheng
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Wushan Street, Tianhe District, Guangzhou, 510642, Guangdong, China.,Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding and Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou, Guangdong, China
| | - Xiaomei He
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Wushan Street, Tianhe District, Guangzhou, 510642, Guangdong, China.,Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding and Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou, Guangdong, China
| | - Bolin Cai
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Wushan Street, Tianhe District, Guangzhou, 510642, Guangdong, China.,Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding and Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou, Guangdong, China
| | - Peigong Han
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Wushan Street, Tianhe District, Guangzhou, 510642, Guangdong, China.,Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding and Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou, Guangdong, China
| | - Hongjia Ouyang
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Wushan Street, Tianhe District, Guangzhou, 510642, Guangdong, China.,Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding and Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou, Guangdong, China
| | - Biao Chen
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Wushan Street, Tianhe District, Guangzhou, 510642, Guangdong, China.,Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding and Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou, Guangdong, China
| | - Qinghua Nie
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Wushan Street, Tianhe District, Guangzhou, 510642, Guangdong, China. .,Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding and Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou, Guangdong, China.
| | - Xiquan Zhang
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Wushan Street, Tianhe District, Guangzhou, 510642, Guangdong, China.,Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding and Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou, Guangdong, China
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Zhang J, Li X, Zhou Y, Cui L, Li J, Wu C, Wan Y, Li J, Wang Y. The interaction of MC3R and MC4R with MRAP2, ACTH, α-MSH and AgRP in chickens. J Endocrinol 2017; 234:155-174. [PMID: 28512117 DOI: 10.1530/joe-17-0131] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2017] [Accepted: 05/15/2017] [Indexed: 02/05/2023]
Abstract
The interaction of melanocortin-4 (MC4R) and melanocortin-3 (MC3R) receptors with proopiomelanocortin (POMC)-derived peptides (e.g. α-MSH), agouti-related protein (AgRP) and melanocortin-2 receptor accessory protein 2 (MRAP2) is suggested to play critical roles in energy balance of vertebrates. However, evidence on their interaction in birds remains scarce. Our study aims to reveal their interaction in chickens and the results showed that (1) chicken (c-)MC3R and cMC4R expressed in Chinese hamster ovary (CHO) cells can be activated by α-MSH and ACTH1-39 equipotently, monitored by a pGL3-CRE-luciferase reporter system; (2) cMC3R and cMC4R, when co-expressed with cMRAP2 (or cMRAP, a cMRAP2 homolog), show increased sensitivity to ACTH treatment and thus likely act as ACTH-preferring receptors, and the interaction between cMC3R/cMC4R and cMRAP2 was demonstrated by co-immunoprecipitation assay; (3) both cMC3R and cMC4R display constitutive activity when expressed in CHO cells, as monitored by dual-luciferase reporter assay, and cMRAP2 (and cMRAP) can modulate their constitutive activity; (4) AgRP inhibits the constitutive activity of cMC3R/cMC4R, and it also antagonizes ACTH/α-MSH action on cMC4R/cMC3R, indicating that AgRP functions as the inverse agonist and antagonist for both receptors. These findings, together with the co-expression of cMC4R, cMC3R, cMRAP2, cAgRP and cPOMC in chicken hypothalamus detected by quantitative real-time PCR, suggest that within the hypothalamus, α-MSH/ACTH, AgRP and MRAP2 may interact at the MC4R(/MC3R) interface to control energy balance. Furthermore, our data provide novel proof for the involvement of MRAP2 (and MRAP) in fine-tuning the constitutive activity and ligand sensitivity and selectivity of both MC3R and MC4R in vertebrates.
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MESH Headings
- Adrenocorticotropic Hormone/genetics
- Adrenocorticotropic Hormone/metabolism
- Agouti-Related Protein/genetics
- Agouti-Related Protein/metabolism
- Amino Acid Sequence
- Animals
- CHO Cells
- Carrier Proteins/genetics
- Carrier Proteins/metabolism
- Chickens/genetics
- Chickens/metabolism
- Cloning, Molecular
- Cricetinae
- Cricetulus
- DNA, Complementary
- Gene Expression Regulation/physiology
- Receptor, Melanocortin, Type 3/genetics
- Receptor, Melanocortin, Type 3/metabolism
- Receptor, Melanocortin, Type 4/genetics
- Receptor, Melanocortin, Type 4/metabolism
- alpha-MSH/genetics
- alpha-MSH/metabolism
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Affiliation(s)
- Jiannan Zhang
- Key Laboratory of Bio-resources and Eco-environment of Ministry of EducationCollege of Life Sciences, Sichuan University, Chengdu, People's Republic of China
| | - Xin Li
- Key Laboratory of Bio-resources and Eco-environment of Ministry of EducationCollege of Life Sciences, Sichuan University, Chengdu, People's Republic of China
| | - Yawei Zhou
- Key Laboratory of Bio-resources and Eco-environment of Ministry of EducationCollege of Life Sciences, Sichuan University, Chengdu, People's Republic of China
| | - Lin Cui
- Key Laboratory of Bio-resources and Eco-environment of Ministry of EducationCollege of Life Sciences, Sichuan University, Chengdu, People's Republic of China
| | - Jing Li
- Key Laboratory of Bio-resources and Eco-environment of Ministry of EducationCollege of Life Sciences, Sichuan University, Chengdu, People's Republic of China
| | - Chenlei Wu
- Key Laboratory of Bio-resources and Eco-environment of Ministry of EducationCollege of Life Sciences, Sichuan University, Chengdu, People's Republic of China
| | - Yiping Wan
- Key Laboratory of Bio-resources and Eco-environment of Ministry of EducationCollege of Life Sciences, Sichuan University, Chengdu, People's Republic of China
| | - Juan Li
- Key Laboratory of Bio-resources and Eco-environment of Ministry of EducationCollege of Life Sciences, Sichuan University, Chengdu, People's Republic of China
| | - Yajun Wang
- Key Laboratory of Bio-resources and Eco-environment of Ministry of EducationCollege of Life Sciences, Sichuan University, Chengdu, People's Republic of China
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45
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Zhang Z, Xu ZQ, Luo YY, Zhang HB, Gao N, He JL, Ji CL, Zhang DX, Li JQ, Zhang XQ. Whole genomic prediction of growth and carcass traits in a Chinese quality chicken population. J Anim Sci 2017; 95:72-80. [PMID: 28177394 DOI: 10.2527/jas.2016.0823] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
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.
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