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Zhao D, Liu R, Tan X, Kang H, Wang J, Ma Z, Zhao H, Xiang H, Zhang Z, Li H, Zhao G. Large-scale transcriptomic and genomic analyses reveal a novel functional gene SERPINB6 for chicken carcass traits. J Anim Sci Biotechnol 2024; 15:70. [PMID: 38730308 DOI: 10.1186/s40104-024-01026-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Accepted: 03/18/2024] [Indexed: 05/12/2024] Open
Abstract
BACKGROUND Carcass traits are crucial indicators of meat production efficiency. However, the molecular regulatory mechanisms associated with these traits remain unclear. RESULTS In this study, we conducted comprehensive transcriptomic and genomic analyses on 399 Tiannong partridge chickens to identify key genes and variants associated with carcass traits and to elucidate the underlying regulatory mechanisms. Based on association analyses with the elastic net (EN) model, we identified 12 candidate genes (AMY1A, AP3B2, CEBPG, EEF2, EIF4EBP1, FGFR1, FOXD3, GOLM1, LOC107052698, PABPC1, SERPINB6 and TBC1D16) for 4 carcass-related traits, namely live weight, dressed weight, eviscerated weight, and breast muscle weight. SERPINB6 was identified as the only overlapping gene by 3 analyses, EN model analysis, weighted gene co-expression network analysis and differential expression analysis. Cell-level experiments confirmed that SERPINB6 promotes the proliferation of chicken DF1 cells and primary myoblasts. Further expression genome-wide association study and association analysis indicated that rs317934171 is the critical site that enhances SERPINB6 expression. Furthermore, a dual-luciferase reporter assay proved that gga-miR-1615 targets the 3'UTR of SERPINB6. CONCLUSIONS Collectively, our findings reveal that SERPINB6 serves as a novel gene for chicken carcass traits by promoting fibroblast and myoblast proliferation. Additionally, the downstream variant rs317934171 regulates SERPINB6 expression. These results identify a new target gene and molecular marker for the molecular mechanisms of chicken carcass traits.
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Affiliation(s)
- Di Zhao
- Guangdong Provincial Key Laboratory of Animal Molecular Design and Precise Breeding, School of Life Science and Engineering, Foshan University, Foshan, China
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Ranran Liu
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Xiaodong Tan
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Huimin Kang
- Guangdong Provincial Key Laboratory of Animal Molecular Design and Precise Breeding, School of Life Science and Engineering, Foshan University, Foshan, China
| | - Jie Wang
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Zheng Ma
- Guangdong Provincial Key Laboratory of Animal Molecular Design and Precise Breeding, School of Life Science and Engineering, Foshan University, Foshan, China
| | - Haiquan Zhao
- Guangdong Provincial Key Laboratory of Animal Molecular Design and Precise Breeding, School of Life Science and Engineering, Foshan University, Foshan, China
| | - Hai Xiang
- Guangdong Provincial Key Laboratory of Animal Molecular Design and Precise Breeding, School of Life Science and Engineering, Foshan University, Foshan, China
| | - Zhengfen Zhang
- Guangdong Tinoo's Foods Group Co., Ltd., Qingyuan, China
| | - Hua Li
- Guangdong Provincial Key Laboratory of Animal Molecular Design and Precise Breeding, School of Life Science and Engineering, Foshan University, Foshan, China.
- Guangdong Tinoo's Foods Group Co., Ltd., Qingyuan, China.
| | - Guiping Zhao
- Guangdong Provincial Key Laboratory of Animal Molecular Design and Precise Breeding, School of Life Science and Engineering, Foshan University, Foshan, China.
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China.
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Wang X, Li D, Xu Y, Ding X, Liang S, Xie L, Wang Y, Zhan X. Xylanase Supplement Enhances the Growth Performance of Broiler by Modulating Serum Metabolism, Intestinal Health, Short-Chain Fatty Acid Composition, and Microbiota. Animals (Basel) 2024; 14:1182. [PMID: 38672330 PMCID: PMC11047501 DOI: 10.3390/ani14081182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2024] [Revised: 04/01/2024] [Accepted: 04/06/2024] [Indexed: 04/28/2024] Open
Abstract
This study aimed to investigate the effects of different levels of xylanase supplementation in a wheat-based diet on growth performance, short-chain fatty acids, intestinal health, microbial composition, and serum metabolism. A total of 1200 male chicks were randomly assigned to four wheat-based diet treatments: Group C (adding 0 mg/kg of xylanase), Group L (adding 50 mg/kg of xylanase), Group M (adding 100 mg/kg of xylanase), and Group H (adding 150 mg/kg of xylanase). The experiment lasted for 56 days. The results indicated that Group H broilers experienced a decreased feed-to-gain ratio throughout the study period. Additionally, dietary supplementation with xylanase led to an increase in the physical barrier, as indicated by increased VH and VH/CD in the gut (p < 0.05). Furthermore, levels of D-lactic acid and endotoxin were reduced. Xylanase supplementation also increased the abundance of Muc-2, ZO-1, and Occludin (p < 0.05). Moreover, xylanase supplementation enhanced the activity of sucrase and maltase in the duodenum (p < 0.05), which may be attributable to the upregulation of the abundance of SI and MGA (p < 0.05). Furthermore, xylanase addition promoted propionic acid produced by specific bacteria, such as Phascolarctobacterium, and influenced the microbial composition to some extent, promoting intestinal health. Additionally, 150 mg/kg of xylanase supplementation increased the amino acid, peptide, and carbohydrate content and upregulated the metabolism of amino acids related to histidine, cysteine, methionine, and other pathways (p < 0.05). These findings suggest adequate xylanase supplementation can enhance nutritional digestibility and absorption, improve growth performance, stimulate endogenous enzyme activity, optimize intestinal morphology and barrier function, and positively influence acid-producing bacteria and amino acid metabolic pathways.
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Affiliation(s)
- Xiaoli Wang
- State Key Laboratory of Institute of Agro-Product Safety and Nutrition, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China;
| | - Danlei Li
- Key Laboratory of Animal Nutrition and Feed Science in East China, Ministry of Agriculture, College of Animal Sciences, Zhejiang University, Hangzhou 310058, China; (D.L.); (Y.X.); (X.D.); (S.L.); (L.X.)
| | - Yibin Xu
- Key Laboratory of Animal Nutrition and Feed Science in East China, Ministry of Agriculture, College of Animal Sciences, Zhejiang University, Hangzhou 310058, China; (D.L.); (Y.X.); (X.D.); (S.L.); (L.X.)
| | - Xiaoqing Ding
- Key Laboratory of Animal Nutrition and Feed Science in East China, Ministry of Agriculture, College of Animal Sciences, Zhejiang University, Hangzhou 310058, China; (D.L.); (Y.X.); (X.D.); (S.L.); (L.X.)
| | - Shuang Liang
- Key Laboratory of Animal Nutrition and Feed Science in East China, Ministry of Agriculture, College of Animal Sciences, Zhejiang University, Hangzhou 310058, China; (D.L.); (Y.X.); (X.D.); (S.L.); (L.X.)
| | - Lingyu Xie
- Key Laboratory of Animal Nutrition and Feed Science in East China, Ministry of Agriculture, College of Animal Sciences, Zhejiang University, Hangzhou 310058, China; (D.L.); (Y.X.); (X.D.); (S.L.); (L.X.)
| | - Yongxia Wang
- Technology on Green-Eco-Healthy Animal Husbandry of Zhejiang Province, College of Animal Science and Technology, College of Veterinary Medicine, Zhejiang A&F University, Hangzhou 311300, China;
| | - Xiuan Zhan
- Key Laboratory of Animal Nutrition and Feed Science in East China, Ministry of Agriculture, College of Animal Sciences, Zhejiang University, Hangzhou 310058, China; (D.L.); (Y.X.); (X.D.); (S.L.); (L.X.)
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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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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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Dadfar MJ, Torshizi RV, Maghsoudi A, Ehsani A, Masoudi AA. Trade-off between feed efficiency and immunity in specialized high-performing chickens. Poult Sci 2023; 102:102703. [PMID: 37141810 DOI: 10.1016/j.psj.2023.102703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Revised: 04/03/2023] [Accepted: 04/03/2023] [Indexed: 05/06/2023] Open
Abstract
Based on resource allocation theory, ignoring importance of immunity, and focus on growth and feed efficiency (FE) traits in breeding plans may lead to serious weakness in immune system performance. However, in poultry the adverse effects of selection for FE on the immune system are unclear. Therefore, an experiment was conducted to study the trade-off between FE and immunity using a total of 180 high-performing specialized male chickens from a commercial broiler line which were selected over 30 generations for growth (body weight gain, BWG) and FE (residual feed intake, RFI). Birds were reared for 42 d and 5 FE-related traits of the birds in the last week were considered including daily feed intake (DFI), feed conversion ratio (FCR), residual feed intake (RFI), residual BW gain (RG), and residual intake and gain (RIG). For all 180 chickens, immune system performance including humoral immune response, cell-mediated immunity (CMI), and the activity of lysozyme enzyme (L. activity) as innate immunity was measured. After ascending sort of each FE records, 10% of higher records (H-FE: N = 18) and 10% of lower records (L-FE: N = 18) were determined, and immunity between L-FE and H-FE groups were compared. Moreover, L-BWG and H-BWG were analyzed because BWG is one of components in the FE formula. Performance of the immune system was not statistically different for CMI in none of the studied FE groups. Moreover, high and low groups for DFI and BWG were not different regarding the immunity of the birds. Antibody titers against Newcastle disease virus (NDV) were different between low and high groups of FCR, RG, and RIG. Likewise, SRBC-derived antibodies were significantly different between RFI groups. Rather than humoral immunity, RIG had adversely effect on the innate immunity. Results of the present study showed that although RIG is a more appropriate indicator for FE, choosing for high RIG can weaken the performance of the both humoral and innate immune systems, while RFI had fewer adverse effects.
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Affiliation(s)
- Mohammad-Javad Dadfar
- Department of Animal Science, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran
| | - Rasoul Vaez Torshizi
- Department of Animal Science, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran.
| | - Ali Maghsoudi
- Department of Animal Science, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran
| | - Alireza Ehsani
- Department of Animal Science, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran
| | - Ali Akbar Masoudi
- Department of Animal Science, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran
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Genome-Wide Association Study of Growth Traits in a Four-Way Crossbred Pig Population. Genes (Basel) 2022; 13:genes13111990. [DOI: 10.3390/genes13111990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Revised: 10/28/2022] [Accepted: 10/28/2022] [Indexed: 11/04/2022] Open
Abstract
Growth traits are crucial economic traits in the commercial pig industry and have a substantial impact on pig production. However, the genetic mechanism of growth traits is not very clear. In this study, we performed a genome-wide association study (GWAS) based on the specific-locus amplified fragment sequencing (SLAF-seq) to analyze ten growth traits on 223 four-way intercross pigs. A total of 227,921 highly consistent single nucleotide polymorphisms (SNPs) uniformly dispersed throughout the entire genome were used to conduct GWAS. A total of 53 SNPs were identified for ten growth traits using the mixed linear model (MLM), of which 18 SNPs were located in previously reported quantitative trait loci (QTL) regions. Two novel QTLs on SSC4 and SSC7 were related to average daily gain from 30 to 60 kg (ADG30–60) and body length (BL), respectively. Furthermore, 13 candidate genes (ATP5O, GHRHR, TRIM55, EIF2AK1, PLEKHA1, BRAP, COL11A2, HMGA1, NHLRC1, SGSM1, NFATC2, MAML1, and PSD3) were found to be associated with growth traits in pigs. The GWAS findings will enhance our comprehension of the genetic architecture of growth traits. We suggested that these detected SNPs and corresponding candidate genes might provide a biological foundation for improving the growth and production performance of pigs in swine breeding.
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A significant quantitative trait locus on chromosome Z and its impact on egg production traits in seven maternal lines of meat-type chicken. J Anim Sci Biotechnol 2022; 13:96. [PMID: 35941697 PMCID: PMC9361671 DOI: 10.1186/s40104-022-00744-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Accepted: 06/09/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Egg production is economically important in the meat-type chicken industry. To better understand the molecular genetic mechanism of egg production in meat-type chicken, genetic parameter estimation, genome-wide association analyses combined with meta-analyses, Bayesian analyses, and selective sweep analyses were performed to screen single nucleotide polymorphisms (SNPs) and other genetic loci that were significantly associated with egg number traits in 11,279 chickens from seven material lines. RESULTS Yellow-feathered meat-type chickens laid 115 eggs at 43 weeks of age and white-feathered chickens laid 143 eggs at 60 weeks of age, with heritability ranging from 0.034-0.258. Based on meta-analyses and selective sweep analyses, one region (10.81-13.05 Mb) on chromosome Z was associated with egg number in all lines. Further analyses using the W2 line was also associated with the same region, and 29 SNPs were identified that significantly affected estimation of breeding value of egg numbers. The 29 SNPs were identified as having a significant effect on the egg number EBV in 3194 birds in line W2. There are 36 genes in the region, with glial cell derived neurotrophic factor, DAB adaptor protein 2, protein kinase AMP-activated catalytic subunit alpha 1, NAD kinase 2, mitochondrial, WD repeat domain 70, leukemia inhibitory factor receptor alpha, complement C6, and complement C7 identified as being potentially affecting to egg number. In addition, three SNPs (rs318154184, rs13769886, and rs313325646) associated with egg number were located on or near the prolactin receptor gene. CONCLUSION Our study used genomic information from different chicken lines and populations to identify a genomic region (spanning 2.24 Mb) associated with egg number. Nine genes and 29 SNPs were identified as the most likely candidate genes and variations for egg production. These results contribute to the identification of candidate genes and variants for egg traits in poultry.
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Ogawa S, Darhan H, Suzuki K. Genetic and genomic analysis of oxygen consumption in mice. J Anim Breed Genet 2022; 139:596-610. [PMID: 35608337 DOI: 10.1111/jbg.12721] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 05/07/2022] [Indexed: 12/16/2022]
Abstract
We estimated genetic parameters for oxygen consumption (OC), OC per metabolic body weight (OCMBW) and body weight at three through 8 weeks of age in divergently selected mice populations, with an animal model considering maternal genetic, common litter environmental and cytoplasmic inheritance effects. Cytoplasmic inheritance was considered based on maternal lineage information. With respect to OC, estimated direct heritability was moderate (0.32) and the estimated proportion of the variance of cytoplasmic inheritance effects to the phenotypic variance was very low (0.01), implying that causal genes for OC could be located on autosomes. To assess this hypothesis, we attempted to identify possible candidate causal genes through selective signature detection with the results of pooled whole-genome resequencing using pooled DNA samples from high and low OC mice. We made a list of possible candidate causal genes for OC, including those relating to electron transport chain and ATP-binding proteins (Ndufa12, Sdhc, Atp10b, etc.), Prr16 encoding Largen protein, Cry1 encoding a key component of the circadian core oscillator and so on. The results, although careful interpretation must be required, could contribute to elucidate the genetic mechanism of OC, an indicator for maintenance energy requirement, and therefore feed efficiency.
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Affiliation(s)
- Shinichiro Ogawa
- Graduate School of Agricultural Science, Tohoku University, Sendai, Miyagi, Japan
| | - Hongyu Darhan
- Graduate School of Agricultural Science, Tohoku University, Sendai, Miyagi, Japan
| | - Keiichi Suzuki
- Graduate School of Agricultural Science, Tohoku University, Sendai, Miyagi, Japan
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He Z, Li S, Li W, Ding J, Zheng M, Li Q, Fahey AG, Wen J, Liu R, Zhao G. Comparison of genomic prediction methods for residual feed intake in broilers. Anim Genet 2022; 53:466-469. [PMID: 35292985 DOI: 10.1111/age.13186] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 02/24/2022] [Accepted: 02/28/2022] [Indexed: 11/30/2022]
Abstract
Residual feed intake (RFI) is a measure of the feed efficiency of animals. Previous studies have identified SNPs associated with RFI. The objective of this study was to compare the GBLUP model with the GA-BLUP model including previously identified associated SNPs. The nine associated SNPs were obtained from the genome-wide association study on a discovery population as preselection information. These models were analysed using ASREML software using a 5-fold cross-validation method on a validation population. With the genetic architecture (GA) matrix used, which was conducted with the nine RFI-associated SNPs, the prediction accuracy of RFI was improved compared with the original GBLUP model. The calculated optimal ω was 0.981 for RFI, which is in line with the optimal range from 0.9 to 1.0 in the gradient test. The prediction accuracy increased by 2% in the GA-BLUP model with ω being 0.981 compared with the GBLUP model. In conclusion, the GA-BLUP with the nine RFI-associated SNPs and an optimal ω can improve the prediction accuracy for a specific trait compared with GBLUP.
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Affiliation(s)
- Zhengxiao He
- State Key Laboratory of Animal Nutrition, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China.,School of Agriculture and Food Science, University College Dublin, Dublin, Ireland
| | - Sen Li
- State Key Laboratory of Animal Nutrition, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Wei Li
- State Key Laboratory of Animal Nutrition, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Jiqiang Ding
- State Key Laboratory of Animal Nutrition, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Maiqing Zheng
- State Key Laboratory of Animal Nutrition, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Qinghe Li
- State Key Laboratory of Animal Nutrition, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Alan G Fahey
- School of Agriculture and Food Science, University College Dublin, Dublin, Ireland
| | - Jie Wen
- State Key Laboratory of Animal Nutrition, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Ranran Liu
- State Key Laboratory of Animal Nutrition, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Guiping Zhao
- State Key Laboratory of Animal Nutrition, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
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Tan X, Liu R, Li W, Zheng M, Zhu D, Liu D, Feng F, Li Q, Liu L, Wen J, Zhao G. Assessment the effect of genomic selection and detection of selective signature in broilers. Poult Sci 2022; 101:101856. [PMID: 35413593 PMCID: PMC9018145 DOI: 10.1016/j.psj.2022.101856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 02/01/2022] [Accepted: 02/28/2022] [Indexed: 12/02/2022] Open
Abstract
Due to high selection advances and shortened generation interval, genomic selection (GS) is now an effective animal breeding scheme. In broilers, many studies have compared the accuracy of different GS prediction methods, but few reports have demonstrated phenotypic or genetic changes using GS. In this study, the paternal chicken line B underwent continuous selection for 3 generations. The chicken 55 k SNP chip was used to estimate the genetic parameters and detect genomic response regions by selective sweep analysis. The heritability for body weight (BW), meat production, and abdominal fat traits were ranged from 0.12 to 0.38. A high genetic correlation was found between BW and meat production traits, while a low genetic correlation (<0.1) was found between meat production and abdominal fat traits. Selection resulted in an increase of about 516 g in BW and 140 g in breast muscle weight. Percentage of breast muscle and whole thigh were increased 0.8 to 1.5%. No change was observed in abdominal fat percentage. The genomic estimated breeding value advances was positive for BW and meat production (except whole thigh percentage), while negative for abdominal fat percentage. By selective sweep analysis, 39 common chromosomal regions and 102 protein coding genes were found to be influenced, including MYH1A, MYH1B, and MYH1D of the MYH gene family. Tight junction pathway as well as myosin complex related terms were enriched. This study demonstrates the effective use of GS for improvements in BW and meat production in chicken line B. Further, genomic regions, responsive to intensive genetic selection, were identified to contain genes of the MYH family.
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Shen L, Yu J, Ge Y, Li H, Li Y, Cao Z, Luan P, Xiao F, Gao H, Zhang H. Associations of Transcription Factor 21 Gene Polymorphisms with the Growth and Body Composition Traits in Broilers. Animals (Basel) 2022; 12:ani12030393. [PMID: 35158719 PMCID: PMC8833368 DOI: 10.3390/ani12030393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 02/04/2022] [Accepted: 02/06/2022] [Indexed: 11/21/2022] Open
Abstract
Simple Summary The functional SNPs discovered in this work will give helpful information on the crucial molecular markers that may be employed in breeding efforts to improve the heart development of broiler chickens. Abstract This study aims to identify molecular marker loci that could be applied in broiler breeding programs. In this study, we used public databases to locate the Transcription factor 21 (TCF21) gene that affected the economically important traits in broilers. Ten single nucleotide polymorphisms were detected in the TCF21 gene by monoclonal sequencing. The polymorphisms of these 10 SNPs in the TCF21 gene were significantly associated (p < 0.05) with multiple growth and body composition traits. Furthermore, the TT genotype of g.-911T>G was identified to significantly increase the heart weight trait without affecting the negative traits, such as abdominal fat and reproduction by multiple methods. Thus, it was speculated that the g.-911T>G identified in the TCF21 gene might be used in marker-assisted selection in the broiler breeding program.
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Affiliation(s)
- Linyong Shen
- Key Laboratory of Chicken Genetics and Breeding, Ministry of Agriculture and Rural Affairs, Key Laboratory of Animal Genetics, Breeding and Reproduction, Education Department of Heilongjiang Province, College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, China; (L.S.); (Y.G.); (H.L.); (Y.L.); (Z.C.); (P.L.)
| | - Jiaqiang Yu
- Forest Investigating and Planning Institute of Daxinganling, Yakshi 022150, China;
| | - Yaowen Ge
- Key Laboratory of Chicken Genetics and Breeding, Ministry of Agriculture and Rural Affairs, Key Laboratory of Animal Genetics, Breeding and Reproduction, Education Department of Heilongjiang Province, College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, China; (L.S.); (Y.G.); (H.L.); (Y.L.); (Z.C.); (P.L.)
| | - Hui Li
- Key Laboratory of Chicken Genetics and Breeding, Ministry of Agriculture and Rural Affairs, Key Laboratory of Animal Genetics, Breeding and Reproduction, Education Department of Heilongjiang Province, College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, China; (L.S.); (Y.G.); (H.L.); (Y.L.); (Z.C.); (P.L.)
| | - Yumao Li
- Key Laboratory of Chicken Genetics and Breeding, Ministry of Agriculture and Rural Affairs, Key Laboratory of Animal Genetics, Breeding and Reproduction, Education Department of Heilongjiang Province, College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, China; (L.S.); (Y.G.); (H.L.); (Y.L.); (Z.C.); (P.L.)
| | - Zhiping Cao
- Key Laboratory of Chicken Genetics and Breeding, Ministry of Agriculture and Rural Affairs, Key Laboratory of Animal Genetics, Breeding and Reproduction, Education Department of Heilongjiang Province, College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, China; (L.S.); (Y.G.); (H.L.); (Y.L.); (Z.C.); (P.L.)
| | - Peng Luan
- Key Laboratory of Chicken Genetics and Breeding, Ministry of Agriculture and Rural Affairs, Key Laboratory of Animal Genetics, Breeding and Reproduction, Education Department of Heilongjiang Province, College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, China; (L.S.); (Y.G.); (H.L.); (Y.L.); (Z.C.); (P.L.)
| | - Fan Xiao
- Fujian Sunnzer Biotechnology Development Co., Ltd., Nanping 354100, China; (F.X.); (H.G.)
| | - Haihe Gao
- Fujian Sunnzer Biotechnology Development Co., Ltd., Nanping 354100, China; (F.X.); (H.G.)
| | - Hui Zhang
- Key Laboratory of Chicken Genetics and Breeding, Ministry of Agriculture and Rural Affairs, Key Laboratory of Animal Genetics, Breeding and Reproduction, Education Department of Heilongjiang Province, College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, China; (L.S.); (Y.G.); (H.L.); (Y.L.); (Z.C.); (P.L.)
- Correspondence: ; Tel.: +86-451-55191486
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Identifying the key genes and functional enrichment pathways associated with feed efficiency in cattle. Gene 2022; 807:145934. [PMID: 34478820 DOI: 10.1016/j.gene.2021.145934] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 08/21/2021] [Accepted: 08/27/2021] [Indexed: 12/22/2022]
Abstract
Residual feed intake (RFI) is a measurement of feed efficiency, and is inversely correlated with feed efficiency. The differentially expressed genes (DEGs) associated with RFI vary substantially among studies, posing great challenges in finding the RFI-related marker genes. This study attempted to resolve this issue by integrating and comparing the multiple transcriptome sequencing data associated with RFI in the cattle liver, using differential, functional enrichment, protein-protein interaction (PPI) network, weighted co-expression network (WGCNA), and gene set enrichment analyses (GSEA) to identify the candidate genes and functional enrichment pathways that are closely associated with RFI. Four candidate genes namely SHC1, GPX4, ACADL, and IGF1 were identified and validated as the marker genes for RFI. Four functional enrichment pathways, namely the fatty acid metabolism, sugar metabolism, energy metabolism, and protein ubiquitination were also found to be closely related to RFI. This study identified several genes and signaling pathways with shared characteristics, which will provide new insights into the molecular mechanisms related to the regulation of feed efficiency, and provide basis for molecular markers related to feed efficiency in beef cattle.
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Zhang X, Luan P, Cao D, Hu G. A High-Density Genetic Linkage Map and Fine Mapping of QTL For Feed Conversion Efficiency in Common Carp ( Cyprinus carpio). Front Genet 2021; 12:778487. [PMID: 34868267 PMCID: PMC8633483 DOI: 10.3389/fgene.2021.778487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 10/22/2021] [Indexed: 12/02/2022] Open
Abstract
Feed conversion efficiency (FCE) is an economically crucial trait in fish, however, little progress has been made in genetics and genomics for this trait because phenotypes of the trait are difficult to measure. In this study, we constructed a high-density and high-resolution genetic linkage map with 28,416 SNP markers for common carp (Cyprinus carpio) based on high throughput genotyping with the carp 250K single nucleotide polymorphism (SNP) array in a full-sib F1 family of mirror carp (Cyprinus carpio) consisting of 141 progenies. The linkage map contained 11,983 distinct loci and spanned 3,590.09 cM with an average locus interval of 0.33 cM. A total of 17 QTL for the FCE trait were detected on four LGs (LG9, LG20, LG28, and LG32), explaining 8.9-15.9% of the phenotypic variations. One major cluster containing eight QTL (qFCE1-28, qFCE2-28, qFCE3-28, qFCE4-28, qFCE5-28, qFCE6-28, qFCE7-28, and qFCE8-28) was detected on LG28. Two clusters consisting of four QTL (qFCE1-32, qFCE2-32, qFCE3-32, and qFCE4-32) and three QTL (qFCE1-20, qFCE2-20, and qFCE3-20) were detected on LG32 and LG20, respectively. Nine candidate genes (ACACA, SCAF4, SLC2A5, TNMD, PCDH1, FOXO, AGO1, FFAR3, and ARID1A) underlying the feed efficiency trait were also identified, the biological functions of which may be involved in lipid metabolism, carbohydrate metabolism, energy deposition, fat accumulation, digestion, growth regulation, and cell proliferation and differentiation according to GO (Gene Ontology). As an important tool, high-density and high-resolution genetic linkage maps play a crucial role in the QTL fine mapping of economically important traits. Our novel findings provided new insights that elucidate the genetic basis and molecular mechanism of feed efficiency and the subsequent marker-assisted selection breeding in common carp.
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Affiliation(s)
- Xiaofeng Zhang
- National and Local United Engineering Laboratory for Freshwater Fish Breeding, Heilongjiang River Fisheries Research Institute, Chinese Academy of Fishery Sciences, Harbin, China
| | | | | | - Guo Hu
- National and Local United Engineering Laboratory for Freshwater Fish Breeding, Heilongjiang River Fisheries Research Institute, Chinese Academy of Fishery Sciences, Harbin, China
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