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Zhao X, Qiu Y, Meng F, Zhuang Z, Ruan D, Wu J, Ma F, Zheng E, Cai G, Yang J, Yang M, Wu Z. Genome-wide association studies for loin muscle area, loin muscle depth and backfat thickness in DLY pigs. Anim Genet 2024; 55:134-139. [PMID: 38098441 DOI: 10.1111/age.13386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 10/17/2023] [Accepted: 11/28/2023] [Indexed: 01/04/2024]
Abstract
This study aimed at identifying genes associated with loin muscle area (LMA), loin muscle depth (LMD) and backfat thickness (BFT). We performed single-trait and multi-trait genome-wide association studies (GWASs) after genotyping 685 Duroc × (Landrace × Yorkshire) (DLY) pigs using the Geneseek Porcine 50K SNP chip. In the single-trait GWASs, we identified two, eight and two significant SNPs associated with LMA, LMD and BFT, respectively, and searched genes within the 1 Mb region near the significant SNPs with relevant functions as candidate genes. Consequently, we identified one (DOCK5), three (PID1, PITX2, ELOVL6) and three (CCR1, PARP14, CASR) promising candidate genes for LMA, LMD and BFT, respectively. Moreover, the multi-trait GWAS identified four significant SNPs associated with the three traits. In conclusion, the GWAS analysis of LMA, LMD and BFT in a DLY pig population identified several associated SNPs and candidate genes, further deepening our understanding of the genetic basis of these traits, and they may be useful for marker-assisted selection to improve the three traits in DLY pigs.
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Affiliation(s)
- Xiang Zhao
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, Guangdong, China
| | - Yibin Qiu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, Guangdong, China
| | - Fanming Meng
- State Key Laboratory of Livestock and Poultry Breeding/Guangdong Key Laboratory of Animal Breeding and Nutrition, Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, Guangdong, China
| | - Zhanwei Zhuang
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, Guangdong, China
| | - Donglin Ruan
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, Guangdong, China
| | - Jie Wu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, Guangdong, China
| | - Fucai Ma
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, Guangdong, China
| | - Enqin Zheng
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou, Guangdong, China
| | - Gengyuan Cai
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou, Guangdong, China
- Guangdong Zhongxin Breeding Technology Co., Ltd, Guangzhou, Guangdong, China
| | - Jie Yang
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou, Guangdong, China
| | - Ming Yang
- Guangdong Zhongxin Breeding Technology Co., Ltd, Guangzhou, Guangdong, China
- College of Animal Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou, Guangdong, China
| | - Zhenfang Wu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou, Guangdong, China
- Yunfu Subcenter of Guangdong Laboratory for Lingnan Modern Agriculture, Yunfu, Guangdong, China
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2
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Li Y, Yang H, Guo J, Yang Y, Yu Q, Guo Y, Zhang C, Wang Z, Zuo P. Uncovering the candidate genes related to sheep body weight using multi-trait genome-wide association analysis. Front Vet Sci 2023; 10:1206383. [PMID: 37662987 PMCID: PMC10469697 DOI: 10.3389/fvets.2023.1206383] [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: 04/15/2023] [Accepted: 08/04/2023] [Indexed: 09/05/2023] Open
Abstract
In sheep, body weight is an economically important trait. This study sought to map genetic loci related to weaning weight and yearling weight. To this end, a single-trait and multi-trait genome-wide association study (GWAS) was performed using a high-density 600 K single nucleotide polymorphism (SNP) chip. The results showed that 43 and 56 SNPs were significantly associated with weaning weight and yearling weight, respectively. A region associated with both weaning and yearling traits (OARX: 6.74-7.04 Mb) was identified, suggesting that the same genes could play a role in regulating both these traits. This region was found to contain three genes (TBL1X, SHROOM2 and GPR143). The most significant SNP was Affx-281066395, located at 6.94 Mb (p = 1.70 × 10-17), corresponding to the SHROOM2 gene. We also identified 93 novel SNPs elated to sheep weight using multi-trait GWAS analysis. A new genomic region (OAR10: 76.04-77.23 Mb) with 22 significant SNPs were discovered. Combining transcriptomic data from multiple tissues and genomic data in sheep, we found the HINT1, ASB11 and GPR143 genes may involve in sheep body weight. So, multi-omic anlaysis is a valuable strategy identifying candidate genes related to body weight.
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Affiliation(s)
- Yunna Li
- College of Animal Science and Technology, Northeast Agricultural University,, Harbin, China
- State Key Laboratory of Sheep Genetic Improvement and Healthy Production, Xinjiang Academy of Agricultural and Reclamation Science,, Shihezi, China
| | - Hua Yang
- State Key Laboratory of Sheep Genetic Improvement and Healthy Production, Xinjiang Academy of Agricultural and Reclamation Science,, Shihezi, China
| | - Jing Guo
- College of Animal Science and Technology, Northeast Agricultural University,, Harbin, China
- State Key Laboratory of Sheep Genetic Improvement and Healthy Production, Xinjiang Academy of Agricultural and Reclamation Science,, Shihezi, China
| | - Yonglin Yang
- State Key Laboratory of Sheep Genetic Improvement and Healthy Production, Xinjiang Academy of Agricultural and Reclamation Science,, Shihezi, China
| | - Qian Yu
- State Key Laboratory of Sheep Genetic Improvement and Healthy Production, Xinjiang Academy of Agricultural and Reclamation Science,, Shihezi, China
| | - Yuanyuan Guo
- College of Animal Science and Technology, Northeast Agricultural University,, Harbin, China
- State Key Laboratory of Sheep Genetic Improvement and Healthy Production, Xinjiang Academy of Agricultural and Reclamation Science,, Shihezi, China
| | - Chaoxin Zhang
- College of Animal Science and Technology, Northeast Agricultural University,, Harbin, China
- State Key Laboratory of Sheep Genetic Improvement and Healthy Production, Xinjiang Academy of Agricultural and Reclamation Science,, Shihezi, China
| | - Zhipeng Wang
- College of Animal Science and Technology, Northeast Agricultural University,, Harbin, China
- State Key Laboratory of Sheep Genetic Improvement and Healthy Production, Xinjiang Academy of Agricultural and Reclamation Science,, Shihezi, China
| | - Peng Zuo
- State Key Laboratory of Sheep Genetic Improvement and Healthy Production, Xinjiang Academy of Agricultural and Reclamation Science,, Shihezi, China
- College of Science, Northeast Agricultural University, Harbin, China
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3
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Nonneman DJ, Lents CA. Functional genomics of reproduction in pigs: Are we there yet? Mol Reprod Dev 2023; 90:436-444. [PMID: 35704517 DOI: 10.1002/mrd.23625] [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: 03/01/2022] [Revised: 05/26/2022] [Accepted: 06/06/2022] [Indexed: 11/09/2022]
Abstract
Reproductive failure is the main reason for culling females in swine herds and is both a financial and sustainability issue. Because reproductive traits are complex and lowly to moderately heritable, genomic selection within populations can achieve substantial genetic gain in reproductive efficiency. A better understanding of the physiological components affecting the expression of these traits will facilitate greater understanding of the genes affecting reproductive traits and is necessary to improve and optimize management strategies to maximize reproductive success of gilts and sows. Large-scale genotyping with single-nucleotide polymorphism (SNP) arrays are used for genome-wide association studies (GWAS) and have facilitated identification of positional candidate genes. Transcriptomic data can be used to weight SNP for GWAS and could lead to previously unidentified candidate genes. Resequencing and fine mapping of candidate genes are necessary to identify putative functional variants and some of these have been incorporated into new genotyping arrays. Sequence imputation and genotype by sequence are newer strategies that could reveal novel functional mutations. In this study, these approaches are discussed. Advantages and limitations are highlighted where additional research is needed.
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Affiliation(s)
- Dan J Nonneman
- United States Department of Agriculture, Agriculture Research Service, U.S. Meat Animal Research Center, Clay Center, Nebraska, USA
| | - Clay A Lents
- United States Department of Agriculture, Agriculture Research Service, U.S. Meat Animal Research Center, Clay Center, Nebraska, USA
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Integrated Single-Trait and Multi-Trait GWASs Reveal the Genetic Architecture of Internal Organ Weight in Pigs. Animals (Basel) 2023; 13:ani13050808. [PMID: 36899665 PMCID: PMC10000129 DOI: 10.3390/ani13050808] [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: 01/07/2023] [Revised: 02/18/2023] [Accepted: 02/20/2023] [Indexed: 03/12/2023] Open
Abstract
Internal organ weight is an essential indicator of growth status as it reflects the level of growth and development in pigs. However, the associated genetic architecture has not been well explored because phenotypes are difficult to obtain. Herein, we performed single-trait and multi-trait genome-wide association studies (GWASs) to map the genetic markers and genes associated with six internal organ weight traits (including heart weight, liver weight, spleen weight, lung weight, kidney weight, and stomach weight) in 1518 three-way crossbred commercial pigs. In summation, single-trait GWASs identified a total of 24 significant single- nucleotide polymorphisms (SNPs) and 5 promising candidate genes, namely, TPK1, POU6F2, PBX3, UNC5C, and BMPR1B, as being associated with the six internal organ weight traits analyzed. Multi-trait GWAS identified four SNPs with polymorphisms localized on the APK1, ANO6, and UNC5C genes and improved the statistical efficacy of single-trait GWASs. Furthermore, our study was the first to use GWASs to identify SNPs associated with stomach weight in pigs. In conclusion, our exploration of the genetic architecture of internal organ weights helps us better understand growth traits, and the key SNPs identified could play a potential role in animal breeding programs.
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Peng Y, Derks MFL, Groenen MAM, Zhao Y, Bosse M. Distinct traces of mixed ancestry in western commercial pig genomes following gene flow from Chinese indigenous breeds. Front Genet 2023; 13:1070783. [PMID: 36712875 PMCID: PMC9880450 DOI: 10.3389/fgene.2022.1070783] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Accepted: 12/19/2022] [Indexed: 01/15/2023] Open
Abstract
Studying gene flow between different livestock breeds will benefit the discovery of genes related to production traits and provide insight into human historical breeding. Chinese pigs have played an indispensable role in the breeding of Western commercial pigs. However, the differences in the timing and volume of the contribution of pigs from different Chinese regions to Western pigs are not yet apparent. In this paper, we combine the whole-genome sequencing data of 592 pigs from different studies and illustrate patterns of gene flow from Chinese pigs into Western commercial pigs. We describe introgression patterns from four distinct Chinese indigenous groups into five Western commercial groups. There were considerable differences in the number and length of the putative introgressed segments from Chinese pig groups that contributed to Western commercial pig breeds. The contribution of pigs from different Chinese geographical locations to a given western commercial breed varied more than that from a specific Chinese pig group to different Western commercial breeds, implying admixture within Europe after introgression. Within different Western commercial lines from the same breed, the introgression patterns from a given Chinese pig group seemed highly conserved, suggesting that introgression of Chinese pigs into Western commercial pig breeds mainly occurred at an early stage of breed formation. Finally, based on analyses of introgression signals, allele frequencies, and selection footprints, we identified a ∼2.65 Mb Chinese-derived haplotype under selection in Duroc pigs (CHR14: 95.68-98.33 Mb). Functional and phenotypic studies demonstrate that this PRKG1 haplotype is related to backfat and loin depth in Duroc pigs. Overall, we demonstrate that the introgression history of domestic pigs is complex and that Western commercial pigs contain distinct traces of mixed ancestry, likely derived from various Chinese pig breeds.
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Affiliation(s)
- Yebo Peng
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China
| | - Martijn FL Derks
- Animal Breeding and Genomics, Wageningen University & Research, Wageningen, Netherlands
- Topigs Norsvin Research Center, Beuningen, Netherlands
| | - Martien AM Groenen
- Animal Breeding and Genomics, Wageningen University & Research, Wageningen, Netherlands
| | - Yiqiang Zhao
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China
| | - Mirte Bosse
- Animal Breeding and Genomics, Wageningen University & Research, Wageningen, Netherlands
- Amsterdam Insitute of Life and Environment (A-Life), VU University Amsterdam, Amsterdam, Netherlands
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6
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Heidaritabar M, Huisman A, Krivushin K, Stothard P, Dervishi E, Charagu P, Bink MCAM, Plastow GS. Imputation to whole-genome sequence and its use in genome-wide association studies for pork colour traits in crossbred and purebred pigs. Front Genet 2022; 13:1022681. [PMID: 36303553 PMCID: PMC9593086 DOI: 10.3389/fgene.2022.1022681] [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: 08/18/2022] [Accepted: 09/22/2022] [Indexed: 11/16/2022] Open
Abstract
Imputed whole-genome sequence (WGS) has been proposed to improve genome-wide association studies (GWAS), since all causative mutations responsible for phenotypic variation are expected to be present in the data. This approach was applied on a large number of purebred (PB) and crossbred (CB) pigs for 18 pork color traits to evaluate the impact of using imputed WGS relative to medium-density marker panels. The traits included Minolta A*, B*, and L* for fat (FCOL), quadriceps femoris muscle (QFCOL), thawed loin muscle (TMCOL), fresh ham gluteus medius (GMCOL), ham iliopsoas muscle (ICOL), and longissimus dorsi muscle on the fresh loin (FMCOL). Sequence variants were imputed from a medium-density marker panel (61K for CBs and 50K for PBs) in all genotyped pigs using BeagleV5.0. We obtained high imputation accuracy (average of 0.97 for PBs and 0.91 for CBs). GWAS were conducted for three datasets: 954 CBs and 891 PBs, and the combined CBs and PBs. For most traits, no significant associations were detected, regardless of panel density or population type. However, quantitative trait loci (QTL) regions were only found for a few traits including TMCOL Minolta A* and GMCOL Minolta B* (CBs), FMCOL Minolta B*, FMCOL Minolta L*, and ICOL Minolta B* (PBs) and FMCOL Minolta A*, FMCOL Minolta B*, GMCOL Minolta B*, and ICOL Minolta B* (Combined dataset). More QTL regions were identified with WGS (n = 58) relative to medium-density marker panels (n = 22). Most of the QTL were linked to previously reported QTLs or candidate genes that have been previously reported to be associated with meat quality, pH and pork color; e.g., VIL1, PRKAG3, TTLL4, and SLC11A1, USP37. CTDSP1 gene on SSC15 has not been previously associated with meat color traits in pigs. The findings suggest any added value of WGS was only for detecting novel QTL regions when the sample size is sufficiently large as with the Combined dataset in this study. The percentage of phenotypic variance explained by the most significant SNPs also increased with WGS compared with medium-density panels. The results provide additional insights into identification of a number of candidate regions and genes for pork color traits in different pig populations.
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Affiliation(s)
- Marzieh Heidaritabar
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, Canada
- *Correspondence: Marzieh Heidaritabar,
| | - Abe Huisman
- Hendrix Genetics Research, Boxmeer, Netherlands
| | - Kirill Krivushin
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, Canada
| | - Paul Stothard
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, Canada
| | - Elda Dervishi
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, Canada
| | | | | | - Graham S. Plastow
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, Canada
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7
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Yin Y, Hou L, Liu C, Li K, Guo H, Niu P, Li Q, Huang R, Li P. Genome-Wide Association Study Identified a Quantitative Trait Locus and Two Candidate Genes on Sus scrofa Chromosome 2 Affecting Vulvar Traits of Suhuai Pigs. Genes (Basel) 2022; 13:genes13081294. [PMID: 35893031 PMCID: PMC9330916 DOI: 10.3390/genes13081294] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 07/18/2022] [Accepted: 07/19/2022] [Indexed: 12/10/2022] Open
Abstract
Vulvar size and angle are meaningful traits in pig production. Sows with abnormal vulva generally show reproductive disorders. In order to excavate candidate loci and genes associated with pig’s vulvar traits, 270 Suhuai pigs with vulvar phenotype were genotyped by a porcine single nucleotide polymorphisms (SNP) Chip. Then, Chip data were imputed using resequenced data of 30 Suhuai pigs as a reference panel. Next, we estimated the heritability and performed a genome-wide association study (GWAS) for vulvar traits. The heritabilities for the traits vulvar length (VL), vulvar width (VW) and vulvar angle (VA) in this pig population were 0.23, 0.32 and 0.22, respectively. GWAS based on Chip data identified nine significant SNPs on the Sus scrofa chromosomes (SSC) 2, 7, 9 and 13 for VL or VW. GWAS based on imputed data identified 11 new quantitative trait loci (QTL) on SSC1, 2, 7, 8, 9, 11, 13, 16 and 17 for VL or VW. The most significant QTL for VL on SSC2 were refined to a 3.48–3.97 Mb region using linkage disequilibrium and linkage analysis (LDLA). In this refined region, FGF19 and CCND1, involved in the development of the reproductive tract, cell growth and vulvar cancer, could be new candidate genes affecting VL. Our results provided potential genetic markers for the breeding of vulvar traits in pigs and deepened the understanding of the genetic mechanism of vulvar traits.
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Affiliation(s)
- Yanzhen Yin
- Institute of Swine Science, Nanjing Agricultural University, Nanjing 210095, China; (Y.Y.); (L.H.); (C.L.); (K.L.); (H.G.); (R.H.)
- Key Laboratory in Nanjing for Evaluation and Utilization of Livestock and Poultry (Pigs) Resources, Ministry of Agriculture and Rural Areas, Nanjing Agricultural University, Nanjing 210095, China
| | - Liming Hou
- Institute of Swine Science, Nanjing Agricultural University, Nanjing 210095, China; (Y.Y.); (L.H.); (C.L.); (K.L.); (H.G.); (R.H.)
- Key Laboratory in Nanjing for Evaluation and Utilization of Livestock and Poultry (Pigs) Resources, Ministry of Agriculture and Rural Areas, Nanjing Agricultural University, Nanjing 210095, China
- Huaian Academy, Nanjing Agricultural University, Huaian 223005, China;
| | - Chenxi Liu
- Institute of Swine Science, Nanjing Agricultural University, Nanjing 210095, China; (Y.Y.); (L.H.); (C.L.); (K.L.); (H.G.); (R.H.)
- Key Laboratory in Nanjing for Evaluation and Utilization of Livestock and Poultry (Pigs) Resources, Ministry of Agriculture and Rural Areas, Nanjing Agricultural University, Nanjing 210095, China
| | - Kaijun Li
- Institute of Swine Science, Nanjing Agricultural University, Nanjing 210095, China; (Y.Y.); (L.H.); (C.L.); (K.L.); (H.G.); (R.H.)
- Key Laboratory in Nanjing for Evaluation and Utilization of Livestock and Poultry (Pigs) Resources, Ministry of Agriculture and Rural Areas, Nanjing Agricultural University, Nanjing 210095, China
| | - Hao Guo
- Institute of Swine Science, Nanjing Agricultural University, Nanjing 210095, China; (Y.Y.); (L.H.); (C.L.); (K.L.); (H.G.); (R.H.)
- Key Laboratory in Nanjing for Evaluation and Utilization of Livestock and Poultry (Pigs) Resources, Ministry of Agriculture and Rural Areas, Nanjing Agricultural University, Nanjing 210095, China
| | - Peipei Niu
- Huaian Academy, Nanjing Agricultural University, Huaian 223005, China;
| | - Qiang Li
- Huaiyin Pig Breeding Farm of Huaian City, Huaian 223322, China;
| | - Ruihua Huang
- Institute of Swine Science, Nanjing Agricultural University, Nanjing 210095, China; (Y.Y.); (L.H.); (C.L.); (K.L.); (H.G.); (R.H.)
- Key Laboratory in Nanjing for Evaluation and Utilization of Livestock and Poultry (Pigs) Resources, Ministry of Agriculture and Rural Areas, Nanjing Agricultural University, Nanjing 210095, China
- Huaian Academy, Nanjing Agricultural University, Huaian 223005, China;
| | - Pinghua Li
- Institute of Swine Science, Nanjing Agricultural University, Nanjing 210095, China; (Y.Y.); (L.H.); (C.L.); (K.L.); (H.G.); (R.H.)
- Key Laboratory in Nanjing for Evaluation and Utilization of Livestock and Poultry (Pigs) Resources, Ministry of Agriculture and Rural Areas, Nanjing Agricultural University, Nanjing 210095, China
- Huaian Academy, Nanjing Agricultural University, Huaian 223005, China;
- Correspondence:
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8
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Chen JQ, Zhang MP, Tong XK, Li JQ, Zhang Z, Huang F, Du HP, Zhou M, Ai HS, Huang LS. Scan of the endogenous retrovirus sequences across the swine genome and survey of their copy number variation and sequence diversity among various Chinese and Western pig breeds. Zool Res 2022; 43:423-441. [PMID: 35437972 PMCID: PMC9113972 DOI: 10.24272/j.issn.2095-8137.2021.379] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Accepted: 04/12/2022] [Indexed: 11/21/2022] Open
Abstract
In pig-to-human xenotransplantation, the transmission risk of porcine endogenous retroviruses (PERVs) is of great concern. However, the distribution of PERVs in pig genomes, their genetic variation among Eurasian pigs, and their evolutionary history remain unclear. We scanned PERVs in the current pig reference genome (assembly Build 11.1), and identified 36 long complete or near-complete PERVs (lcPERVs) and 23 short incomplete PERVs (siPERVs). Besides three known PERVs (PERV-A, -B, and -C), four novel types (PERV-JX1, -JX2, -JX3, and -JX4) were detected in this study. According to evolutionary analyses, the newly discovered PERVs were more ancient, and PERV-Bs probably experienced a bottleneck ~0.5 million years ago (Ma). By analyzing 63 high-quality porcine whole-genome resequencing data, we found that the PERV copy numbers in Chinese pigs were lower (32.0±4.0) than in Western pigs (49.1±6.5). Additionally, the PERV sequence diversity was lower in Chinese pigs than in Western pigs. Regarding the lcPERV copy numbers, PERV-A and -JX2 in Western pigs were higher than in Chinese pigs. Notably, Bama Xiang (BMX) pigs had the lowest PERV copy number (27.8±5.1), and a BMX individual had no PERV-C and the lowest PERV copy number (23), suggesting that BMX pigs were more suitable for screening and/or modification as xenograft donors. Furthermore, we identified 451 PERV transposon insertion polymorphisms (TIPs), of which 86 were shared by all 10 Chinese and Western pig breeds. Our findings provide systematic insights into the genomic distribution, variation, evolution, and possible biological function of PERVs.
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Affiliation(s)
- Jia-Qi Chen
- State Key Laboratory for Swine Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, Jiangxi 330045, China
| | - Ming-Peng Zhang
- State Key Laboratory for Swine Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, Jiangxi 330045, China
| | - Xin-Kai Tong
- State Key Laboratory for Swine Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, Jiangxi 330045, China
| | - Jing-Quan Li
- State Key Laboratory for Swine Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, Jiangxi 330045, China
| | - Zhou Zhang
- State Key Laboratory for Swine Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, Jiangxi 330045, China
| | - Fei Huang
- State Key Laboratory for Swine Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, Jiangxi 330045, China
| | - Hui-Peng Du
- State Key Laboratory for Swine Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, Jiangxi 330045, China
| | - Meng Zhou
- State Key Laboratory for Swine Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, Jiangxi 330045, China
| | - Hua-Shui Ai
- State Key Laboratory for Swine Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, Jiangxi 330045, China. E-mail:
| | - Lu-Sheng Huang
- State Key Laboratory for Swine Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, Jiangxi 330045, China. E-mail:
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9
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Zhou S, Ding R, Zhuang Z, Zeng H, Wen S, Ruan D, Wu J, Qiu Y, Zheng E, Cai G, Yang J, Wu Z, Yang M. Genome-Wide Association Analysis Reveals Genetic Loci and Candidate Genes for Chest, Abdominal, and Waist Circumferences in Two Duroc Pig Populations. Front Vet Sci 2022; 8:807003. [PMID: 35224076 PMCID: PMC8865076 DOI: 10.3389/fvets.2021.807003] [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: 11/01/2021] [Accepted: 12/15/2021] [Indexed: 12/16/2022] Open
Abstract
Chest circumference (CC), abdominal circumference (AC), and waist circumference (WC) are regarded as important indicators for improving economic traits because they can reflect the growth and physiological status in pigs. However, the genetic architecture of CC, AC, and WC is still elusive. Here, we performed single-trait and multi-trait genome-wide association studies (GWASs) for CC, AC, and WC in 2,206 American origin Duroc (AOD) and 2,082 Canadian origin Duroc (COD) pigs. As a result, one novel quantitative trait locus (QTL) on Sus scrofa chromosome (SSC) one was associated with CC and AC in COD pigs, which spans 6.92 Mb (from 170.06 to 176.98 Mb). Moreover, multi-trait GWAS identified 21 significant SNPs associated with the three conformation traits, indicating the multi-trait GWAS is a powerful statistical approach that uncovers pleiotropic locus. Finally, the three candidate genes (ITGA11, TLE3, and GALC) were selected that may play a role in the conformation traits. Further bioinformatics analysis indicated that the candidate genes for the three conformation traits mainly participated in sphingolipid metabolism and lysosome pathways. For all we know, this study was the first GWAS for WC in pigs. In general, our findings further reveal the genetic architecture of CC, AC, and WC, which may offer a useful reference for improving the conformation traits in pigs.
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Affiliation(s)
- Shenping Zhou
- College of Animal Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou, China
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, China
| | - Rongrong Ding
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, China
- Guangdong Wens Breeding Swine Technology Co., Ltd., Yunfu, China
| | - Zhanwei Zhuang
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, China
| | - Haiyu Zeng
- Guangdong Wens Breeding Swine Technology Co., Ltd., Yunfu, China
| | - Shuxian Wen
- Guangdong Wens Breeding Swine Technology Co., Ltd., Yunfu, China
| | - Donglin Ruan
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, China
| | - Jie Wu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, China
| | - Yibin Qiu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, China
| | - Enqin Zheng
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, China
| | - Gengyuan Cai
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, China
- Guangdong Wens Breeding Swine Technology Co., Ltd., Yunfu, China
| | - Jie Yang
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, China
| | - Zhenfang Wu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, China
- Guangdong Wens Breeding Swine Technology Co., Ltd., Yunfu, China
- *Correspondence: Zhenfang Wu
| | - Ming Yang
- College of Animal Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou, China
- Ming Yang
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10
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Ramayo-Caldas Y, Zingaretti LM, Pérez-Pascual D, Alexandre PA, Reverter A, Dalmau A, Quintanilla R, Ballester M. Leveraging host-genetics and gut microbiota to determine immunocompetence in pigs. Anim Microbiome 2021; 3:74. [PMID: 34689834 PMCID: PMC8543910 DOI: 10.1186/s42523-021-00138-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 10/12/2021] [Indexed: 01/13/2023] Open
Abstract
BACKGROUND The gut microbiota influences host performance playing a relevant role in homeostasis and function of the immune system. The aim of the present work was to identify microbial signatures linked to immunity traits and to characterize the contribution of host-genome and gut microbiota to the immunocompetence in healthy pigs. RESULTS To achieve this goal, we undertook a combination of network, mixed model and microbial-wide association studies (MWAS) for 21 immunity traits and the relative abundance of gut bacterial communities in 389 pigs genotyped for 70K SNPs. The heritability (h2; proportion of phenotypic variance explained by the host genetics) and microbiability (m2; proportion of variance explained by the microbial composition) showed similar values for most of the analyzed immunity traits, except for both IgM and IgG in plasma that was dominated by the host genetics, and the haptoglobin in serum which was the trait with larger m2 (0.275) compared to h2 (0.138). Results from the MWAS suggested a polymicrobial nature of the immunocompetence in pigs and revealed associations between pigs gut microbiota composition and 15 of the analyzed traits. The lymphocytes phagocytic capacity (quantified as mean fluorescence) and the total number of monocytes in blood were the traits associated with the largest number of taxa (6 taxa). Among the associations identified by MWAS, 30% were confirmed by an information theory network approach. The strongest confirmed associations were between Fibrobacter and phagocytic capacity of lymphocytes (r = 0.37), followed by correlations between Streptococcus and the percentage of phagocytic lymphocytes (r = -0.34) and between Megasphaera and serum concentration of haptoglobin (r = 0.26). In the interaction network, Streptococcus and percentage of phagocytic lymphocytes were the keystone bacterial and immune-trait, respectively. CONCLUSIONS Overall, our findings reveal an important connection between gut microbiota composition and immunity traits in pigs, and highlight the need to consider both sources of information, host genome and microbial levels, to accurately characterize immunocompetence in pigs.
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Affiliation(s)
- Yuliaxis Ramayo-Caldas
- Animal Breeding and Genetics Program, IRTA, Torre Marimón, 08140 Caldes de Montbui, Barcelona Spain
| | - Laura M. Zingaretti
- Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain
| | - David Pérez-Pascual
- Unité de Génétique des Biofilms, Institut Pasteur, UMR CNRS2001, Paris, France
| | | | - Antonio Reverter
- CSIRO Agriculture and Food, St. Lucia, Brisbane, QLD 4067 Australia
| | - Antoni Dalmau
- Animal Welfare Subprogram, IRTA, 17121 Monells, Girona Spain
| | - Raquel Quintanilla
- Animal Breeding and Genetics Program, IRTA, Torre Marimón, 08140 Caldes de Montbui, Barcelona Spain
| | - Maria Ballester
- Animal Breeding and Genetics Program, IRTA, Torre Marimón, 08140 Caldes de Montbui, Barcelona Spain
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11
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Dauben CM, Pröll-Cornelissen MJ, Heuß EM, Appel AK, Henne H, Roth K, Schellander K, Tholen E, Große-Brinkhaus C. Genome-wide associations for immune traits in two maternal pig lines. BMC Genomics 2021; 22:717. [PMID: 34610786 PMCID: PMC8491387 DOI: 10.1186/s12864-021-07997-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Accepted: 09/09/2021] [Indexed: 11/13/2022] Open
Abstract
Background In recent years, animal welfare and health has become more and more important in pig breeding. So far, numerous parameters have been considered as important biomarkers, especially in the immune reaction and inflammation. Previous studies have shown moderate to high heritabilities in most of these traits. However, the genetic background of health and robustness of pigs needs to be extensively clarified. The objective of this study was to identify genomic regions with a biological relevance for the immunocompetence of piglets. Genome-wide Association Studies (GWAS) in 535 Landrace (LR) and 461 Large White (LW) piglets were performed, investigating 20 immune relevant traits. Besides the health indicators of the complete and differential blood count, eight different cytokines and haptoglobin were recorded in all piglets and their biological dams to capture mediating processes and acute phase reactions. Additionally, all animals were genotyped using the Illumina PorcineSNP60v2 BeadChip. Results In summary, GWAS detected 25 genome-wide and 452 chromosome-wide significant SNPs associated with 17 immune relevant traits in the two maternal pig lines LR and LW. Only small differences were observed considering the maternal immune records as covariate within the statistical model. Furthermore, the study identified across- and within-breed differences as well as relevant candidate genes. In LR more significant associations and related candidate genes were detected, compared with LW. The results detected in LR and LW are partly in accordance with previously identified quantitative trait loci (QTL) regions. In addition, promising novel genomic regions were identified which might be of interest for further detailed analysis. Especially putative pleiotropic regions on SSC5, SSC12, SSC15, SSC16 and SSC17 are of major interest with regard to the interacting structure of the immune system. The comparison with already identified QTL gives indications on interactions with traits affecting piglet survival and also production traits. Conclusion In conclusion, results suggest a polygenic and breed-specific background of immune relevant traits. The current study provides knowledge about regions with biological relevance for health and immune traits. Identified markers and putative pleiotropic regions provide first indications in the context of balancing a breeding-based modification of the porcine immune system. Supplementary Information The online version contains supplementary material available at (10.1186/s12864-021-07997-1).
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Affiliation(s)
- Christina M Dauben
- Institute of Animal Sciences, University of Bonn, Endenicher Allee 15, Bonn, 53115, Germany
| | | | - Esther M Heuß
- Institute of Animal Sciences, University of Bonn, Endenicher Allee 15, Bonn, 53115, Germany
| | - Anne K Appel
- BHZP GmbH, An der Wassermühle 8, Dahlenburg-Ellringen, 21368, Germany
| | - Hubert Henne
- BHZP GmbH, An der Wassermühle 8, Dahlenburg-Ellringen, 21368, Germany
| | - Katharina Roth
- Institute of Animal Sciences, University of Bonn, Endenicher Allee 15, Bonn, 53115, Germany
| | - Karl Schellander
- Institute of Animal Sciences, University of Bonn, Endenicher Allee 15, Bonn, 53115, Germany
| | - Ernst Tholen
- Institute of Animal Sciences, University of Bonn, Endenicher Allee 15, Bonn, 53115, Germany
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12
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Yan G, Liu X, Xiao S, Xin W, Xu W, Li Y, Huang T, Qin J, Xie L, Ma J, Zhang Z, Huang L. An imputed whole-genome sequence-based GWAS approach pinpoints causal mutations for complex traits in a specific swine population. SCIENCE CHINA-LIFE SCIENCES 2021; 65:781-794. [PMID: 34387836 DOI: 10.1007/s11427-020-1960-9] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/17/2021] [Accepted: 05/19/2021] [Indexed: 01/08/2023]
Abstract
Sequencing-based genome-wide association studies (GWAS) have facilitated the identification of causal associations between genetic variants and traits in diverse species. However, it is cost-prohibitive for the majority of research groups to sequence a large number of samples. Here, we carried out genotype imputation to increase the density of single nucleotide polymorphisms in a large-scale Swine F2 population using a reference panel including 117 individuals, followed by a series of GWAS analyses. The imputation accuracies reached 0.89 and 0.86 for allelic concordance and correlation, respectively. A quantitative trait nucleotide (QTN) affecting the chest vertebrate was detected directly, while the investigation of another QTN affecting the residual glucose failed due to the presence of similar haplotypes carrying wild-type and mutant allelesin the reference panel used in this study. A high imputation accuracy was confirmed by Sanger sequencing technology for the most significant loci. Two candidate genes, CPNE5 and MYH3, affecting meat-related traits were proposed. Collectively, we illustrated four scenarios in imputation-based GWAS that may be encountered by researchers, and our results will provide an extensive reference for future genotype imputation-based GWAS analyses in the future.
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Affiliation(s)
- Guorong Yan
- State Key Laboratory for Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, 330045, China
- Institute of Photomedicine, Shanghai Skin Disease Hospital, School of Medicine, Tongji University, Shanghai, 200092, China
| | - Xianxian Liu
- State Key Laboratory for Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, 330045, China
| | - Shijun Xiao
- State Key Laboratory for Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, 330045, China
| | - Wenshui Xin
- State Key Laboratory for Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, 330045, China
| | - Wenwu Xu
- State Key Laboratory for Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, 330045, China
| | - Yiping Li
- State Key Laboratory for Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, 330045, China
| | - Tao Huang
- State Key Laboratory for Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, 330045, China
| | - Jiangtao Qin
- State Key Laboratory for Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, 330045, China
| | - Lei Xie
- State Key Laboratory for Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, 330045, China
| | - Junwu Ma
- State Key Laboratory for Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, 330045, China.
| | - Zhiyan Zhang
- State Key Laboratory for Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, 330045, China.
| | - Lusheng Huang
- State Key Laboratory for Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, 330045, China
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13
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Yang R, Guo X, Zhu D, Tan C, Bian C, Ren J, Huang Z, Zhao Y, Cai G, Liu D, Wu Z, Wang Y, Li N, Hu X. Accelerated deciphering of the genetic architecture of agricultural economic traits in pigs using a low-coverage whole-genome sequencing strategy. Gigascience 2021; 10:giab048. [PMID: 34282453 PMCID: PMC8290195 DOI: 10.1093/gigascience/giab048] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 03/21/2021] [Accepted: 06/15/2021] [Indexed: 11/20/2022] Open
Abstract
BACKGROUND Uncovering the genetic architecture of economic traits in pigs is important for agricultural breeding. However, high-density haplotype reference panels are unavailable in most agricultural species, limiting accurate genotype imputation in large populations. Moreover, the infinitesimal model of quantitative traits implies that weak association signals tend to be spread across most of the genome, further complicating the genetic analysis. Hence, there is a need to develop new methods for sequencing large cohorts without large reference panels. RESULTS We describe a Tn5-based highly accurate, cost- and time-efficient, low-coverage sequencing method to obtain 11.3 million whole-genome single-nucleotide polymorphisms in 2,869 Duroc boars at a mean depth of 0.73×. On the basis of these single-nucleotide polymorphisms, a genome-wide association study was performed, resulting in 14 quantitative trait loci (QTLs) for 7 of 21 important agricultural traits in pigs. These QTLs harbour genes, such as ABCD4 for total teat number and HMGA1 for back fat thickness, and provided a starting point for further investigation. The inheritance models of the different traits varied greatly. Most follow the minor-polygene model, but this can be attributed to different reasons, such as the shaping of genetic architecture by artificial selection for this population and sufficiently interconnected minor gene regulatory networks. CONCLUSIONS Genome-wide association study results for 21 important agricultural traits identified 14 QTLs/genes and showed their genetic architectures, providing guidance for genetic improvement harnessing genomic features. The Tn5-based low-coverage sequencing method can be applied to large-scale genome studies for any species without a good reference panel and can be used for agricultural breeding.
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Affiliation(s)
- Ruifei Yang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, No. 2 Yuanmingyuan west road, Haidian district, Beijing 100193, China
| | - Xiaoli Guo
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, No. 2 Yuanmingyuan west road, Haidian district, Beijing 100193, China
| | - Di Zhu
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, No. 2 Yuanmingyuan west road, Haidian district, Beijing 100193, China
| | - Cheng Tan
- National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, No. 483 Wushan road, Tianhe district, Guangdong 510640, China
| | - Cheng Bian
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, No. 2 Yuanmingyuan west road, Haidian district, Beijing 100193, China
| | - Jiangli Ren
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, No. 2 Yuanmingyuan west road, Haidian district, Beijing 100193, China
| | - Zhuolin Huang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, No. 2 Yuanmingyuan west road, Haidian district, Beijing 100193, China
| | - Yiqiang Zhao
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, No. 2 Yuanmingyuan west road, Haidian district, Beijing 100193, China
| | - Gengyuan Cai
- National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, No. 483 Wushan road, Tianhe district, Guangdong 510640, China
| | - Dewu Liu
- National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, No. 483 Wushan road, Tianhe district, Guangdong 510640, China
| | - Zhenfang Wu
- National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, No. 483 Wushan road, Tianhe district, Guangdong 510640, China
| | - Yuzhe Wang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, No. 2 Yuanmingyuan west road, Haidian district, Beijing 100193, China
- National Research Facility for Phenotypic and Genotypic Analysis of Model Animals (Beijing), China Agricultural University, No. 2 Yuanmingyuan west road, Haidian district, Beijing 100193, China
| | - Ning Li
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, No. 2 Yuanmingyuan west road, Haidian district, Beijing 100193, China
| | - Xiaoxiang Hu
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, No. 2 Yuanmingyuan west road, Haidian district, Beijing 100193, China
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14
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Deng Z, Huang T, Yan G, Yang B, Zhang Z, Xiao S, Ai H, Huang L. A further look at quantitative trait loci for growth and fatness traits in a White Duroc × Erhualian F 3 intercross population. Anim Biotechnol 2021; 33:1205-1216. [PMID: 34010090 DOI: 10.1080/10495398.2021.1884087] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Genetic analysis of porcine growth and fatness traits is beneficial to the swine industry and provides a reference to understand human obesity. Here, we obtained 29 growth and fatness traits for 473 individuals from a White Duroc × Erhualian F3 intercross population. Basic statistical analyses showed that: (1) Positive correlations between different-stage body weights were detected, the shorter the time interval the stronger the correlation. (2) Strong correlations existed in the paired fatness traits. (3) With the growth of age, the correlation between fatness and body weight was increasing. All pigs were genotyped by Illumina 50 K SNP chips and their whole-genome genotypes were imputed referred to 109 re-sequencing data. We performed common and imputation-based GWASs for these traits. Two genome-wide significant loci on swine chromosome (SSC) 4 and 7 were repeatedly detected. The strongest association (P = 3.24 × 10-19) was detected at 31.96 Mb on SSC7 for leaf fat weight. On this locus, seven major haplotypes were identified, of which two were novel and had an increasing-fatness effect. In the imputation-based GWAS, three new loci were identified. Our findings provide further insights into and enhance our understanding of genetic mechanism of porcine growth and fat deposition.
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Affiliation(s)
- Zheng Deng
- State Key Laboratory for Swine Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, China
| | - Tao Huang
- State Key Laboratory for Swine Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, China
| | - Guorong Yan
- State Key Laboratory for Swine Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, China
| | - Bin Yang
- State Key Laboratory for Swine Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, China
| | - Zhiyan Zhang
- State Key Laboratory for Swine Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, China
| | - Shijun Xiao
- State Key Laboratory for Swine Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, China
| | - Huashui Ai
- State Key Laboratory for Swine Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, China
| | - Lusheng Huang
- State Key Laboratory for Swine Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, China
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15
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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: 8] [Impact Index Per Article: 2.0] [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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16
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Chen H, Huang M, Yang B, Wu Z, Deng Z, Hou Y, Ren J, Huang L. Introgression of Eastern Chinese and Southern Chinese haplotypes contributes to the improvement of fertility and immunity in European modern pigs. Gigascience 2021; 9:5788434. [PMID: 32141510 PMCID: PMC7059266 DOI: 10.1093/gigascience/giaa014] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Revised: 10/17/2019] [Accepted: 02/07/2020] [Indexed: 12/04/2022] Open
Abstract
Background Pigs were domesticated independently from European and Asian wild boars nearly 10,000 years ago. Chinese indigenous pigs have been historically introduced to improve Europe local pigs. However, the geographic origin and biological functions of introgressed Chinese genes in modern European pig breeds remain largely unknown. Results Here we explored whole-genome sequencing data from 266 Eurasian wild boars and domestic pigs to produce a fine-scale map of introgression between French Large White (FLW) and Chinese pigs. We show that FLW pigs had historical admixture with both Southern Chinese (SCN) and Eastern Chinese (ECN) pigs ∼200–300 years ago. Moreover, a set of SCN haplotypes was shown to be beneficial for improving disease resistance and ECN haplotypes are favorable for improved reproductive performance in FLW pigs. In addition, we confirm human-mediated introgression events at the AHR locus, at which the haplotype of most likely ECN origin contributes to increased fertility of FLW pigs. Conclusions This study advances our understanding of the breeding history of global domestic pigs and highlights the importance of artificial introgression in the formation of phenotypic characteristics in domestic animals.
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Affiliation(s)
- Hao Chen
- State Key Laboratory of Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang 330045, P. R. China
| | - Min Huang
- State Key Laboratory of Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang 330045, P. R. China
| | - Bin Yang
- State Key Laboratory of Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang 330045, P. R. China
| | - Zhongping Wu
- State Key Laboratory of Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang 330045, P. R. China
| | - Zheng Deng
- State Key Laboratory of Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang 330045, P. R. China
| | - Yong Hou
- State Key Laboratory of Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang 330045, P. R. China
| | - Jun Ren
- State Key Laboratory of Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang 330045, P. R. China
| | - Lusheng Huang
- State Key Laboratory of Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang 330045, P. R. China
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17
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Cai Z, Sarup P, Ostersen T, Nielsen B, Fredholm M, Karlskov-Mortensen P, Sørensen P, Jensen J, Guldbrandtsen B, Lund MS, Christensen OF, Sahana G. Genomic diversity revealed by whole-genome sequencing in three Danish commercial pig breeds. J Anim Sci 2020; 98:5873883. [PMID: 32687196 DOI: 10.1093/jas/skaa229] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Accepted: 07/14/2020] [Indexed: 01/04/2023] Open
Abstract
Whole-genome sequencing of 217 animals from three Danish commercial pig breeds (Duroc, Landrace [LL], and Yorkshire [YY]) was performed. Twenty-six million single-nucleotide polymorphisms (SNPs) and 8 million insertions or deletions (indels) were uncovered. Among the SNPs, 493,099 variants were located in coding sequences, and 29,430 were predicted to have a high functional impact such as gain or loss of stop codon. Using the whole-genome sequence dataset as the reference, the imputation accuracy for pigs genotyped with high-density SNP chips was examined. The overall average imputation accuracy for all biallelic variants (SNP and indel) was 0.69, while it was 0.83 for variants with minor allele frequency > 0.1. This study provides whole-genome reference data to impute SNP chip-genotyped animals for further studies to fine map quantitative trait loci as well as improving the prediction accuracy in genomic selection. Signatures of selection were identified both through analyses of fixation and differentiation to reveal selective sweeps that may have had prominent roles during breed development or subsequent divergent selection. However, the fixation indices did not indicate a strong divergence among these three breeds. In LL and YY, the integrated haplotype score identified genomic regions under recent selection. These regions contained genes for olfactory receptors and oxidoreductases. Olfactory receptor genes that might have played a major role in the domestication were previously reported to have been under selection in several species including cattle and swine.
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Affiliation(s)
- Zexi Cai
- Center for Quantitative Genetics and Genomics, Faculty of Technical Sciences, Aarhus University, Tjele, Denmark
| | - Pernille Sarup
- Center for Quantitative Genetics and Genomics, Faculty of Technical Sciences, Aarhus University, Tjele, Denmark
| | - Tage Ostersen
- SEGES Danish Pig Research Centre, Copenhagen, Denmark
| | | | - Merete Fredholm
- Department of Veterinary and Animal Sciences, University of Copenhagen, Copenhagen, Denmark
| | | | - Peter Sørensen
- Center for Quantitative Genetics and Genomics, Faculty of Technical Sciences, Aarhus University, Tjele, Denmark
| | - Just Jensen
- Center for Quantitative Genetics and Genomics, Faculty of Technical Sciences, Aarhus University, Tjele, Denmark
| | - Bernt Guldbrandtsen
- Center for Quantitative Genetics and Genomics, Faculty of Technical Sciences, Aarhus University, Tjele, Denmark
| | - Mogens Sandø Lund
- Center for Quantitative Genetics and Genomics, Faculty of Technical Sciences, Aarhus University, Tjele, Denmark
| | - Ole Fredslund Christensen
- Center for Quantitative Genetics and Genomics, Faculty of Technical Sciences, Aarhus University, Tjele, Denmark
| | - Goutam Sahana
- Center for Quantitative Genetics and Genomics, Faculty of Technical Sciences, Aarhus University, Tjele, Denmark
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Genetic parameters and associated genomic regions for global immunocompetence and other health-related traits in pigs. Sci Rep 2020; 10:18462. [PMID: 33116177 PMCID: PMC7595139 DOI: 10.1038/s41598-020-75417-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Accepted: 10/15/2020] [Indexed: 12/16/2022] Open
Abstract
The inclusion of health-related traits, or functionally associated genetic markers, in pig breeding programs could contribute to produce more robust and disease resistant animals. The aim of the present work was to study the genetic determinism and genomic regions associated to global immunocompetence and health in a Duroc pig population. For this purpose, a set of 30 health-related traits covering immune (mainly innate), haematological, and stress parameters were measured in 432 healthy Duroc piglets aged 8 weeks. Moderate to high heritabilities were obtained for most traits and significant genetic correlations among them were observed. A genome wide association study pointed out 31 significantly associated SNPs at whole-genome level, located in six chromosomal regions on pig chromosomes SSC4, SSC6, SSC17 and SSCX, for IgG, γδ T-cells, C-reactive protein, lymphocytes phagocytic capacity, total number of lymphocytes, mean corpuscular volume and mean corpuscular haemoglobin. A total of 16 promising functionally-related candidate genes, including CRP, NFATC2, PRDX1, SLA, ST3GAL1, and VPS4A, have been proposed to explain the variation of immune and haematological traits. Our results enhance the knowledge of the genetic control of traits related with immunity and support the possibility of applying effective selection programs to improve immunocompetence in pigs.
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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: 25] [Impact Index Per Article: 5.0] [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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Bovo S, Ballan M, Schiavo G, Gallo M, Dall'Olio S, Fontanesi L. Haplotype-based genome-wide association studies reveal new loci for haematological and clinical-biochemical parameters in Large White pigs. Anim Genet 2020; 51:601-606. [PMID: 32511786 DOI: 10.1111/age.12959] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2019] [Revised: 02/24/2020] [Accepted: 05/02/2020] [Indexed: 01/21/2023]
Abstract
We report haplotype-based GWASs for 33 blood parameters measured in 843 Italian Large White pigs. In the single-trait analysis, a total of 30 QTL for number of basophils, six erythrocyte traits (haemoglobin, haematocrit, mean corpuscular haemoglobin, mean corpuscular haemoglobin concentration, mean corpuscular volume and red blood cell count) and two clinical-biochemical traits (alkaline phosphatase and Ca2+ contents) were identified. In the multiple-trait analysis, a total of five QTL affected three different clusters of traits. Only four of these QTL were already reported in the single-marker and multi-marker GWASs we previously carried out on the same pig population. QTL on SSC11 and SSC17 showed effects on multiple traits. These results further dissected the genetic architecture of parameters that could be used as proxies in breeding programmes for more complex traits. In addition, these results might help to better define the pig as an animal model for several blood-related biological functions.
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Affiliation(s)
- S Bovo
- Department of Agricultural and Food Sciences, Division of Animal Sciences, University of Bologna, Viale G. Fanin 46, Bologna, 40127, Italy
| | - M Ballan
- Department of Agricultural and Food Sciences, Division of Animal Sciences, University of Bologna, Viale G. Fanin 46, Bologna, 40127, Italy
| | - G Schiavo
- Department of Agricultural and Food Sciences, Division of Animal Sciences, University of Bologna, Viale G. Fanin 46, Bologna, 40127, Italy
| | - M Gallo
- Associazione Nazionale Allevatori Suini, Via Nizza 53, Roma, 00198, Italy
| | - S Dall'Olio
- Department of Agricultural and Food Sciences, Division of Animal Sciences, University of Bologna, Viale G. Fanin 46, Bologna, 40127, Italy
| | - L Fontanesi
- Department of Agricultural and Food Sciences, Division of Animal Sciences, University of Bologna, Viale G. Fanin 46, Bologna, 40127, Italy
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Zhang J, Zhang Y, Gong H, Cui L, Ma J, Chen C, Ai H, Xiao S, Huang L, Yang B. Landscape of Loci and Candidate Genes for Muscle Fatty Acid Composition in Pigs Revealed by Multiple Population Association Analysis. Front Genet 2019; 10:1067. [PMID: 31708975 PMCID: PMC6824322 DOI: 10.3389/fgene.2019.01067] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2019] [Accepted: 10/04/2019] [Indexed: 01/19/2023] Open
Abstract
Genome wide association analyses in diverse populations can identify complex trait loci that are specifically present in one population or shared across multiple populations, which help to better understand the genetic architecture of complex traits in a broader genetic context. In this study, we conducted genome-wide association studies and meta-analysis for 38 fatty acid composition traits with 12–19 million imputed genome sequence SNPs in 2446 pigs from six populations, encompassing White Duroc × Erhualian F2, Sutai, Duroc-Landrace-Yorkshire (DLY) three-way cross, Laiwu, Erhualian, and Bamaxiang pigs that were originally genotyped with 60 K or 1.4 million single nucleotide polymorphism (SNP) chips. The analyses uncovered 285 lead SNPs (P < 5 × 10-8), among which 78 locate more than 1 Mb to the lead chip SNPs were considered as novel, largely augmented the landscape of loci for porcine muscle fatty acid composition. Meta-analysis enhanced the association significance at loci near FADS2, ABCD2, ELOVL5, ELOVL6, ELOVL7, SCD, and THRSP genes, suggesting possible existence of population shared mutations underlying these loci. Further haplotype analysis at SCD loci identified a shared 3.7 kb haplotype in F2, Sutai and DLY pigs showing consistent effects of decreasing C18:0 contents in the three populations. In contrast, at FASN loci, we found an Erhualian specific haplotype explaining the population specific association signals in Erhualian pigs. This study refines our understanding on landscape of loci and candidate genes for fatty acid composition traits of pigs.
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Affiliation(s)
- Junjie Zhang
- State Key Laboratory for Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, China
| | - Yifeng Zhang
- State Key Laboratory for Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, China
| | - Huanfa Gong
- State Key Laboratory for Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, China
| | - Leilei Cui
- State Key Laboratory for Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, China
| | - Junwu Ma
- State Key Laboratory for Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, China
| | - Congying Chen
- State Key Laboratory for Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, China
| | - Huashui Ai
- State Key Laboratory for Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, China
| | - Shijun Xiao
- State Key Laboratory for Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, China
| | - Lusheng Huang
- State Key Laboratory for Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, China
| | - Bin Yang
- State Key Laboratory for Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, China
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Xu W, Chen D, Yan G, Xiao S, Huang T, Zhang Z, Huang L. Rediscover and Refine QTLs for Pig Scrotal Hernia by Increasing a Specially Designed F 3 Population and Using Whole-Genome Sequence Imputation Technology. Front Genet 2019; 10:890. [PMID: 31608119 PMCID: PMC6768097 DOI: 10.3389/fgene.2019.00890] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2019] [Accepted: 08/23/2019] [Indexed: 11/14/2022] Open
Abstract
Pig scrotal hernia is one of the most common congenital defects triggered by both genetic and environmental factors, leading to severe economic loss as well as poor animal welfare in the pig industry. Identification and implementation of genomic regions controlling scrotal hernia in breeding is of great appeal to reduce incidences of hernia in pig production. The aim of this study was to identify such regions or molecular markers affecting scrotal hernia in pigs. First of all, we summarized and analyzed the results of some international teams on scrotal hernia and designed a specially population which contains 246 male individuals. We then performed genome-wide association study (GWAS) in this specially designed population using two scenarios, i.e., the target panel data before and after imputation, which contain 42,365 SNPs and 18,756,672 SNPs, respectively. In addition, a series of methods including genetic differentiation analysis, linkage disequilibrium and linkage analysis (LDLA), and haplotype sharing analysis were appropriate to provide for further analysis to identify the potential gene underlying the QTL. The GWAS in this report detected a highly significant region affecting scrotal hernia within a 24.8Mb region (114.1-138.9Mb) on SSC8. And the result of genetic differentiation analysis also showed a strong genetic differentiation signal between 116.1 and 132.7Mb on SSC8. In addition, the QTL interval was refined to 2.99Mb by combining LDLA and genetic differentiation analysis. Finally, two susceptibility haplotypes were identified through haplotype sharing analysis, with one potential causal gene in it. Our study provided deeper insights into the genetic architecture of pig scrotal hernia and contributed to further fine-mapping and characterize haplotype and gene that influence scrotal hernia in pigs.
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Affiliation(s)
| | | | | | | | | | - Zhiyan Zhang
- State Key Laboratory for Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, China
| | - Lusheng Huang
- State Key Laboratory for Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, China
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Huang T, Zhang M, Yan G, Huang X, Chen H, Zhou L, Deng W, Zhang Z, Qiu H, Ai H, Huang L. Genome-wide association and evolutionary analyses reveal the formation of swine facial wrinkles in Chinese Erhualian pigs. Aging (Albany NY) 2019; 11:4672-4687. [PMID: 31306098 PMCID: PMC6660038 DOI: 10.18632/aging.102078] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2019] [Accepted: 07/01/2019] [Indexed: 04/12/2023]
Abstract
Wrinkles are uneven concave-convex folds, ridges or creases in skin. Facial wrinkles appear in head, typically increasing along with aging. However in several Chinese indigenous pigs, such as Erhualian pigs, rich facial wrinkles have been generated during the growth stages as one of their breed characteristics. To investigate the genetic basis underlying the development of swine facial wrinkles, we estimated the folding extent of facial wrinkles in a herd of Erhualian pigs (n=332), and then conducted genome-wide association studies and multi-trait meta-analysis for facial wrinkles using 60K porcine chips. We found that facial wrinkles had high heritability estimates of ~0.7 in Erhualian pigs. Notably, only one genome-wide significant QTL was detected at 34.8 Mb on porcine chromosome 7. The most significant SNP rs80983858 located at the 3255-bp downstream of candidate gene GRM4, and the G allele was of benefit to increase facial wrinkles. Evolutionary and selection analyses suggested that the haplotypes containing G allele were under artificial selection, which was consistent with local animal sacrificial custom praying for longevity. Our findings made important clues for further deciphering the molecular mechanism of swine facial wrinkles formation, and shed light on the research of skin wrinkle development in human or other mammals.
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Affiliation(s)
- Tao Huang
- State Key Laboratory of Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, P.R. China
| | - Mingpeng Zhang
- State Key Laboratory of Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, P.R. China
| | - Guorong Yan
- State Key Laboratory of Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, P.R. China
| | - Xiaochang Huang
- State Key Laboratory of Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, P.R. China
| | - Hao Chen
- State Key Laboratory of Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, P.R. China
| | - Liyu Zhou
- State Key Laboratory of Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, P.R. China
| | - Wenjiang Deng
- State Key Laboratory of Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, P.R. China
| | - Zhen Zhang
- State Key Laboratory of Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, P.R. China
| | - Hengqing Qiu
- State Key Laboratory of Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, P.R. China
| | - Huashui Ai
- State Key Laboratory of Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, P.R. China
| | - Lusheng Huang
- State Key Laboratory of Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, P.R. China
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