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Zhang Y, Tang J, Zheng Y, Guo W, Guo Y, Chang M, Wang H, Li Y, Chang Z, Xu Y, Wang Z. Evolutionary and Expression Analysis of the Pig MAGE Gene Family. Animals (Basel) 2024; 14:2095. [PMID: 39061557 PMCID: PMC11274276 DOI: 10.3390/ani14142095] [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: 05/25/2024] [Revised: 07/13/2024] [Accepted: 07/16/2024] [Indexed: 07/28/2024] Open
Abstract
The melanoma-associated antigen (MAGE) family found in eukaryotes plays a crucial role in cell proliferation and differentiation, spermatogenesis, neural development, etc. This study explored the validation and evolution of MAGE genes in eukaryotic genomes and their distribution and expression patterns in pigs. In total, 249 MAGE genes were found on 13 eukaryotic species. In total, 33, 25, and 18 genes were located on human, mouse, and pig genomes, respectively. We found eight, four, and three tandemly duplicated gene clusters on the human, mouse, and pig genomes, respectively. The majority of MAGE genes in mammals are located on the X chromosome. According to the phylogenetic analysis, the MAGE family genes were classified into 11 subfamilies. The NDN gene in zebrafish (DreNDN) was the root of this evolutionary tree. In total, 10 and 11 MAGE genes on human and mouse genomes, respectively, exhibited a collinearity relationship with the MAGE genes on pig genomes. Taking the MAGE family genes in pigs, the MAGE subfamilies had similar gene structures, protein motifs, and biochemical attributes. Using the RNA-seq data of Duroc pigs and Rongchang pigs, we detected that the expression of type I MAGE genes was higher in reproductive tissues, but type II MAGE genes were predominantly expressed in the brain tissue. These findings are a valuable resource for gaining insight into the evolution and expression of the MAGE family genes.
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Affiliation(s)
- Yu Zhang
- College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, China; (Y.Z.); (J.T.); (Y.Z.); (W.G.); (Y.G.); (M.C.); (H.W.); (Y.L.); (Z.C.)
- Center for Bioinformatics, Northeast Agricultural University, Harbin 150030, China
| | - Jian Tang
- College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, China; (Y.Z.); (J.T.); (Y.Z.); (W.G.); (Y.G.); (M.C.); (H.W.); (Y.L.); (Z.C.)
- Center for Bioinformatics, Northeast Agricultural University, Harbin 150030, China
| | - Yiwen Zheng
- College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, China; (Y.Z.); (J.T.); (Y.Z.); (W.G.); (Y.G.); (M.C.); (H.W.); (Y.L.); (Z.C.)
- Center for Bioinformatics, Northeast Agricultural University, Harbin 150030, China
| | - Wanshu Guo
- College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, China; (Y.Z.); (J.T.); (Y.Z.); (W.G.); (Y.G.); (M.C.); (H.W.); (Y.L.); (Z.C.)
- Center for Bioinformatics, Northeast Agricultural University, Harbin 150030, China
| | - Yuanyuan Guo
- College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, China; (Y.Z.); (J.T.); (Y.Z.); (W.G.); (Y.G.); (M.C.); (H.W.); (Y.L.); (Z.C.)
- Center for Bioinformatics, Northeast Agricultural University, Harbin 150030, China
| | - Minghang Chang
- College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, China; (Y.Z.); (J.T.); (Y.Z.); (W.G.); (Y.G.); (M.C.); (H.W.); (Y.L.); (Z.C.)
- Center for Bioinformatics, Northeast Agricultural University, Harbin 150030, China
| | - Hui Wang
- College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, China; (Y.Z.); (J.T.); (Y.Z.); (W.G.); (Y.G.); (M.C.); (H.W.); (Y.L.); (Z.C.)
- Center for Bioinformatics, Northeast Agricultural University, Harbin 150030, China
| | - Yanyan Li
- College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, China; (Y.Z.); (J.T.); (Y.Z.); (W.G.); (Y.G.); (M.C.); (H.W.); (Y.L.); (Z.C.)
- Center for Bioinformatics, Northeast Agricultural University, Harbin 150030, China
| | - Zhaoyue Chang
- College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, China; (Y.Z.); (J.T.); (Y.Z.); (W.G.); (Y.G.); (M.C.); (H.W.); (Y.L.); (Z.C.)
- Center for Bioinformatics, Northeast Agricultural University, Harbin 150030, China
| | - Yuan Xu
- College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, China; (Y.Z.); (J.T.); (Y.Z.); (W.G.); (Y.G.); (M.C.); (H.W.); (Y.L.); (Z.C.)
| | - Zhipeng Wang
- College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, China; (Y.Z.); (J.T.); (Y.Z.); (W.G.); (Y.G.); (M.C.); (H.W.); (Y.L.); (Z.C.)
- Center for Bioinformatics, Northeast Agricultural University, Harbin 150030, China
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Yang Y, Gan M, Yang X, Zhu P, Luo Y, Liu B, Zhu K, Cheng W, Chen L, Zhao Y, Niu L, Wang Y, Zhang H, Wang J, Shen L, Zhu L. Estimation of genetic parameters of pig reproductive traits. Front Vet Sci 2023; 10:1172287. [PMID: 37415962 PMCID: PMC10321596 DOI: 10.3389/fvets.2023.1172287] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 06/01/2023] [Indexed: 07/08/2023] Open
Abstract
Introduction In this study, we aimed to estimate the genetic parameters of the reproductive traits in three popular commercial pig breeds: Duroc, Landrace, and Yorkshire. Additionally, we evaluated the factors that influence these traits. Method We collected data from a large number of litters, including 1,887 Duroc, 21,787 Landrace, and 74,796 Yorkshire litters. Using the ASReml-R software to analyze 11 traits, which included: total number of pigs born (TNB); number of piglets born alive (NBA); number of piglets born healthy (NBH); number of piglets born weak (NBW); number of new stillborn piglets (NS); number of old stillborn piglets (OS); number of piglets born with malformation (NBM); number of mummified piglets (NM); total litter birthweight (LBW); litter average weight (LAW); duration of gestational period (GP). We investigated the effects of 4 fixed factors on the genetic parameters of these traits. Results Among the 11 reproductive-related traits, the gestational period belonged to the medium heritability traits (0.251-0.430), while remaining traits showed low heritability, ranging from 0.005 to 0.159. TNB, NBA, NBH, LBW had positive genetic correlation (0.737 ~ 0.981) and phenotype correlation (0.711 ~ 0.951). There was a negative genetic correlation between NBW and LAW (-0.452 ~ -0.978) and phenotypic correlation (-0.380 ~ -0.873). LBW was considered one of the most reasonable reproductive traits that could be used for breeding improvement. Repeatability of the three varieties was within the range of 0.000-0.097. In addition, the fixed effect selected in this study had a significant effect on Landrace and Yorkshire (p < 0.05). Discussion We found a positive correlation between LBW and TNB, NBA, and NBH, suggesting the potential for multi-trait association breeding. Factors such as farm, farrowing year, breeding season, and parity should be taken into consideration in practical production, as they may impact the reproductive performance of breeding pigs.
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Affiliation(s)
- Yiting Yang
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, China
- Key Laboratory of Livestock and Poultry Multi-Omics, Ministry of Agriculture and Rural Affairs, College of Animal and Technology, Sichuan Agricultural University, Chengdu, China
| | - Mailin Gan
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, China
- Key Laboratory of Livestock and Poultry Multi-Omics, Ministry of Agriculture and Rural Affairs, College of Animal and Technology, Sichuan Agricultural University, Chengdu, China
| | - Xidi Yang
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, China
- Key Laboratory of Livestock and Poultry Multi-Omics, Ministry of Agriculture and Rural Affairs, College of Animal and Technology, Sichuan Agricultural University, Chengdu, China
| | - Peng Zhu
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, China
- Key Laboratory of Livestock and Poultry Multi-Omics, Ministry of Agriculture and Rural Affairs, College of Animal and Technology, Sichuan Agricultural University, Chengdu, China
| | - Yi Luo
- Sichuan Dekon Livestock Foodstuff Group, Chengdu, China
| | - Bin Liu
- Sichuan Dekon Livestock Foodstuff Group, Chengdu, China
| | - Kangping Zhu
- Sichuan Dekon Livestock Foodstuff Group, Chengdu, China
| | | | - Lei Chen
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, China
- Key Laboratory of Livestock and Poultry Multi-Omics, Ministry of Agriculture and Rural Affairs, College of Animal and Technology, Sichuan Agricultural University, Chengdu, China
| | - Ye Zhao
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, China
- Key Laboratory of Livestock and Poultry Multi-Omics, Ministry of Agriculture and Rural Affairs, College of Animal and Technology, Sichuan Agricultural University, Chengdu, China
| | - Lili Niu
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, China
- Key Laboratory of Livestock and Poultry Multi-Omics, Ministry of Agriculture and Rural Affairs, College of Animal and Technology, Sichuan Agricultural University, Chengdu, China
| | - Yan Wang
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, China
- Key Laboratory of Livestock and Poultry Multi-Omics, Ministry of Agriculture and Rural Affairs, College of Animal and Technology, Sichuan Agricultural University, Chengdu, China
| | - Hui Zhang
- Sichuan Center for Animal Disease Control, Chengdu, China
| | - Jingyong Wang
- Chongqing Academy of Animal Science, Chongqing, China
| | - Linyuan Shen
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, China
- Key Laboratory of Livestock and Poultry Multi-Omics, Ministry of Agriculture and Rural Affairs, College of Animal and Technology, Sichuan Agricultural University, Chengdu, China
| | - Li Zhu
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, China
- Key Laboratory of Livestock and Poultry Multi-Omics, Ministry of Agriculture and Rural Affairs, College of Animal and Technology, Sichuan Agricultural University, Chengdu, China
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Yu G, Wang C, Wang Y. Genetic parameter analysis of reproductive traits in Large White pigs. Anim Biosci 2022; 35:1649-1655. [PMID: 36108704 PMCID: PMC9659455 DOI: 10.5713/ab.22.0119] [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: 03/24/2022] [Revised: 05/10/2022] [Accepted: 07/04/2022] [Indexed: 06/15/2023] Open
Abstract
OBJECTIVE The primary objective of this study was to determine the genetic parameters for reproductive traits among Large White pigs, including the following traits: total number born (TNB), number born alive (NBA), litter birth weight (LBW), average birth weight (ABW), gestation length (GL), age at first service (AFS) and age at first farrowing (AFF). METHODS The dataset consisted of 19,036 reproductive records from 4,986 sows, and a multi-trait animal model was used to estimate genetic variance components of seven reproductive traits. RESULTS The heritability estimates for these reproductive traits ranged from 0.09 to 0.26, with the highest heritability for GL and AFF, and the lowest heritability for NBA. The repeatabilities for TNB, NBA, LWB, ABW, and GL were ranged from 0.16 to 0.34. Genetic and phenotypic correlations ranged from -0.41 to 0.99, and -0.34 to 0.98, respectively. In particular, the correlations between TNB, NBA and LBW, between AFS and AFF, exhibited a strong positive correlation. Furthermore, for TNB, NBA, LBW, ABW, and GL, genetic correlations of the same trait between different parities were moderately to strongly correlated (0.32 to 0.97), and the correlations of adjacent parities were higher than those of nonadjacent parities. CONCLUSION All the results in the present study can be used as a basis for the genetic assessment of the target population. In the formulation of dam line selection index, AFS or AFF can be considered to combine with TNB in a multiple trait swine breeding value estimation system. Moreover, breeders are encouraged to increase the proportion of sows at parity 3-5 and reinforce the management of sows at parity 1 and parity ≥8.
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Affiliation(s)
- Guanghui Yu
- College of Animal Science and Technology, Qingdao Agricultural University, Qingdao 266109,
China
| | - Chuduan Wang
- College of Animal Science and Technology, China Agricultural University, Beijing 100193,
China
| | - Yuan Wang
- College of Animal Science and Technology, Qingdao Agricultural University, Qingdao 266109,
China
- College of Animal Science and Technology, China Agricultural University, Beijing 100193,
China
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Ogawa S, Ohnishi C, Ishii K, Uemoto Y, Satoh M. Genetic relationship between litter size traits at birth and body measurement and production traits in purebred Duroc pigs. Anim Sci J 2021; 91:e13497. [PMID: 33368835 DOI: 10.1111/asj.13497] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Revised: 11/14/2020] [Accepted: 12/03/2020] [Indexed: 11/28/2022]
Abstract
Heritabilities of litter size traits at birth (total number born (TNB), number born alive (NBA), and number still born (NSB)) and their genetic correlations with body measurement (body height, body length, front width (FW), chest width (CW), hind width, chest depth, chest girth, front cannon circumference, and rear cannon circumference) and production traits (ages at the start and end of performance testing (D30 and D105), average daily gain (ADG), backfat thickness, and loin muscle area) in purebred Duroc pigs were estimated. Records of performance testing for 2,835 animals and farrowing records of 1,168 litters from 437 dams were used. Genetic parameters were estimated using single-trait and two-trait animal models. Permanent environment effect was considered for litter size traits and common litter environmental effect was considered for body measurement and production traits. The estimated heritability was 0.10 ± 0.06 for TNB, 0.16 ± 0.06 for NBA, and 0.08 ± 0.05 for NSB. Positive genetic correlation of NBA was estimated with D30, D105, and ADG (0.51, 0.11, and 0.39). The estimated genetic correlation of NBA was 0.47 ± 0.17 with FW and 0.55 ± 0.18 with CW, implying that FW and CW could be promising indicator traits for efficiently improving NBA.
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Affiliation(s)
- Shinichiro Ogawa
- Graduate School of Agricultural Science, Tohoku University, Sendai, Japan
| | - Chika Ohnishi
- National Livestock Breeding Center, Miyazaki Station, Kobayashi, Japan
| | - Kazuo Ishii
- Division of Animal Breeding and Reproduction, Institute of Livestock and Grassland Science, NARO, Tsukuba, Japan
| | - Yoshinobu Uemoto
- Graduate School of Agricultural Science, Tohoku University, Sendai, Japan
| | - Masahiro Satoh
- Graduate School of Agricultural Science, Tohoku University, Sendai, Japan
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Willson HE, Rojas de Oliveira H, Schinckel AP, Grossi D, Brito LF. Estimation of Genetic Parameters for Pork Quality, Novel Carcass, Primal-Cut and Growth Traits in Duroc Pigs. Animals (Basel) 2020; 10:ani10050779. [PMID: 32365996 PMCID: PMC7278482 DOI: 10.3390/ani10050779] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Revised: 04/26/2020] [Accepted: 04/27/2020] [Indexed: 01/21/2023] Open
Abstract
Simple Summary There is a growing interest in worldwide swine breeding programs to genetically select for pork quality and primal cuts in addition to the traditional growth and carcass leanness traits. Accurate population genetic parameters are needed to estimate correlated responses to selection and incorporate novel traits in the selection objective. Therefore, we estimated heritabilities and genetic correlations for 39 pork quality, growth and carcass traits in Duroc pigs. In general, moderate and favorable genetic correlations were observed between pork quality (e.g., loin color and marbling scores) and carcass traits. Additionally, moderate to low correlations were found among pork quality, growth and carcass traits. Our findings suggest that pig breeders can successfully incorporate pork quality and novel carcass traits in the selection objectives without undesirable impacts upon growth rate and carcass leanness. Abstract More recently, swine breeding programs have aimed to include pork quality and novel carcass (e.g., specific primal cuts such as the Boston butt or belly that are not commonly used in selection indexes) and belly traits together with growth, feed efficiency and carcass leanness in the selection indexes of terminal-sire lines, in order to efficiently produce pork with improved quality at a low cost to consumers. In this context, the success of genetic selection for such traits relies on accurate estimates of heritabilities and genetic correlations between traits. The objective of this study was to estimate genetic parameters for 39 traits in Duroc pigs (three growth, eight conventional carcass (commonly measured production traits; e.g., backfat depth), 10 pork quality and 18 novel carcass traits). Phenotypic measurements were collected on 2583 purebred Duroc gilts, and the variance components were estimated using both univariate and bivariate models and REML procedures. Moderate to high heritability estimates were found for most traits, while genetic correlations tended to be low to moderate overall. Moderate to high genetic correlations were found between growth, primal-cuts and novel carcass traits, while low to moderate correlations were found between pork quality and growth and carcass traits. Some genetic antagonisms were observed, but they are of low to moderate magnitude. This indicates that genetic progress can be achieved for all traits when using an adequate selection index.
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Affiliation(s)
- Hannah E. Willson
- Department of Animal Sciences, Purdue University, West Lafayette, IN 47907, USA; (H.E.W.); (H.R.d.O.); (A.P.S.)
| | - Hinayah Rojas de Oliveira
- Department of Animal Sciences, Purdue University, West Lafayette, IN 47907, USA; (H.E.W.); (H.R.d.O.); (A.P.S.)
| | - Allan P. Schinckel
- Department of Animal Sciences, Purdue University, West Lafayette, IN 47907, USA; (H.E.W.); (H.R.d.O.); (A.P.S.)
| | | | - Luiz F. Brito
- Department of Animal Sciences, Purdue University, West Lafayette, IN 47907, USA; (H.E.W.); (H.R.d.O.); (A.P.S.)
- Correspondence:
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