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Zha C, Liu K, Wu J, Li P, Hou L, Liu H, Huang R, Wu W. Combining genome-wide association study based on low-coverage whole genome sequencing and transcriptome analysis to reveal the key candidate genes affecting meat color in pigs. Anim Genet 2023; 54:295-306. [PMID: 36727217 DOI: 10.1111/age.13300] [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: 08/07/2022] [Revised: 01/04/2023] [Accepted: 01/16/2023] [Indexed: 02/03/2023]
Abstract
Meat color is an attractive trait that influences consumers' purchase decisions at the point of sale. To decipher the genetic basis of meat color traits, we performed a genome-wide association study based on low-coverage whole-genome sequencing. In total, 669 (Pietrain × Duroc) × (Landrace × Yorkshire) pigs were genotyped using low-coverage whole-genome sequencing. Single nucleotide polymorphism (SNP) calling and genotype imputation were performed using the BaseVar + STITCH channel. Six individuals with an average depth of 12.05× whole-genome resequencing were randomly selected to assess the accuracy of imputation. Heritability evaluation and genome-wide association study for meat color traits were conducted. Functional enrichment analysis of the candidate genes from genome-wide association study and integration analysis with our previous transcriptome data were conducted. The imputation accuracy parameters, allele frequency R2 , concordance rate, and dosage R2 were 0.959, 0.952, and 0.933, respectively. The heritability values of a*45 min , b*45 min , L*45 min , C*, and H0 were 0.19, 0.11, 0.06, 0.16, and 0.26, respectively. In total, 3884 significant SNPs and 15 QTL, corresponding to 382 genes, were associated with meat color traits. Functional enrichment analysis revealed that 10 genes were the potential candidates for regulating meat color. Moreover, integration analysis revealed that DMRT2, EFNA5, FGF10, and COL11A2 were the most promising candidates affecting meat color. In summary, this study provides new insights into the molecular basis of meat color traits, and provides a new theoretical basis for the molecular breeding of meat color traits in pigs.
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Affiliation(s)
- Chengwan Zha
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, China
| | - Kaiyue Liu
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, China
| | - Jian Wu
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, China
| | - Pinghua Li
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, China
| | - Liming Hou
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, China
| | - Honglin Liu
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, China
| | - Ruihua Huang
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, China
| | - Wangjun Wu
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, China
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Weighted Single-Step Genomic Best Linear Unbiased Prediction Method Application for Assessing Pigs on Meat Productivity and Reproduction Traits. Animals (Basel) 2022; 12:ani12131693. [PMID: 35804591 PMCID: PMC9264777 DOI: 10.3390/ani12131693] [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: 03/19/2022] [Revised: 06/10/2022] [Accepted: 06/28/2022] [Indexed: 11/16/2022] Open
Abstract
Changes in the accuracy of the genomic estimates obtained by the ssGBLUP and wssGBLUP methods were evaluated using different reference groups. The weighting procedure’s reasonableness of application Pwas considered to improve the accuracy of genomic predictions for meat, fattening and reproduction traits in pigs. Six reference groups were formed to assess the genomic data quantity impact on the accuracy of predicted values (groups of genotyped animals). The datasets included 62,927 records of meat and fattening productivity (fat thickness over 6–7 ribs (BF1, mm)), muscle depth (MD, mm) and precocity up to 100 kg (age, days) and 16,070 observations of reproductive qualities (the number of all born piglets (TNB) and the number of live-born piglets (NBA), according to the results of the first farrowing). The wssGBLUP method has an advantage over ssGBLUP in terms of estimation reliability. When using a small reference group, the difference in the accuracy of ssGBLUP over BLUP AM is from −1.9 to +7.3 percent points, while for wssGBLUP, the change in accuracy varies from +18.2 to +87.3 percent points. Furthermore, the superiority of the wssGBLUP is also maintained for the largest group of genotyped animals: from +4.7 to +15.9 percent points for ssGBLUP and from +21.1 to +90.5 percent points for wssGBLUP. However, for all analyzed traits, the number of markers explaining 5% of genetic variability varied from 71 to 108, and the number of such SNPs varied depending on the size of the reference group (79–88 for BF1, 72–81 for MD, 71–108 for age). The results of the genetic variation distribution have the greatest similarity between groups of about 1000 and about 1500 individuals. Thus, the size of the reference group of more than 1000 individuals gives more stable results for the estimation based on the wssGBLUP method, while using the reference group of 500 individuals can lead to distorted results of GEBV.
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Ding R, Zhuang Z, Qiu Y, Ruan D, Wu J, Ye J, Cao L, Zhou S, Zheng E, Huang W, Wu Z, Yang J. Identify known and novel candidate genes associated with backfat thickness in Duroc pigs by large-scale genome-wide association analysis. J Anim Sci 2022; 100:6509022. [PMID: 35034121 PMCID: PMC8867564 DOI: 10.1093/jas/skac012] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Accepted: 01/14/2022] [Indexed: 01/18/2023] Open
Abstract
Backfat thickness (BFT) is complex and economically important traits in the pig industry, since it reflects fat deposition and can be used to measure the carcass lean meat percentage in pigs. In this study, all 6,550 pigs were genotyped using the Geneseek Porcine 50K SNP Chip to identify SNPs related to BFT and to search for candidate genes through genome-wide association analysis in two Duroc populations. In total, 80 SNPs, including 39 significant and 41 suggestive SNPs, and 6 QTLs were identified significantly associated with the BFT. In addition, 9 candidate genes, including a proven major gene MC4R, 3 important candidate genes (RYR1, HMGA1, and NUDT3) which were previously described as related to BFT, and 5 novel candidate genes (SIRT2, NKAIN2, AMH, SORCS1, and SORCS3) were found based on their potential functional roles in BFT. The functions of candidate genes and gene set enrichment analysis indicate that most important pathways are related to energy homeostasis and adipogenesis. Finally, our data suggest that most of the candidate genes can be directly used for genetic improvement through molecular markers, except that the MC4R gene has an antagonistic effect on growth rate and carcass lean meat percentage in breeding. Our results will advance our understanding of the complex genetic architecture of BFT traits and laid the foundation for additional genetic studies to increase carcass lean meat percentage of pig through marker-assisted selection and/or genomic selection.
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Affiliation(s)
- Rongrong Ding
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, 510642, P. R. China,Guangdong Wens Breeding Swine Technology Co., Ltd., Guangdong, 527400, P. R. China
| | - Zhanwei Zhuang
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, 510642, P. R. China
| | - Yibin Qiu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, 510642, P. R. China
| | - Donglin Ruan
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, 510642, P. R. China
| | - Jie Wu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, 510642, P. R. China
| | - Jian Ye
- Guangdong Wens Breeding Swine Technology Co., Ltd., Guangdong, 527400, P. R. China
| | - Lu Cao
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, 510642, P. R. China
| | - Shenping Zhou
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, 510642, P. R. China
| | - Enqin Zheng
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, 510642, P. R. China,Lingnan Guangdong Laboratory of Modern Agriculture, Guangzhou, 510642, P. R. China
| | - Wen Huang
- Department of Animal Science, Michigan State University, East Lansing, MI, USA
| | - Zhenfang Wu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, 510642, P. R. China,Guangdong Wens Breeding Swine Technology Co., Ltd., Guangdong, 527400, P. R. China,Lingnan Guangdong Laboratory of Modern Agriculture, Guangzhou, 510642, P. R. China
| | - Jie Yang
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, 510642, P. R. China,Lingnan Guangdong Laboratory of Modern Agriculture, Guangzhou, 510642, P. R. China,Corresponding authors:
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Shen Y, Wang H, Xie J, Wang Z, Ma Y. Trait-specific Selection Signature Detection Reveals Novel Loci of Meat Quality in Large White Pigs. Front Genet 2021; 12:761252. [PMID: 34868241 PMCID: PMC8635012 DOI: 10.3389/fgene.2021.761252] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 10/18/2021] [Indexed: 11/24/2022] Open
Abstract
In past decades, meat quality traits have been shaped by human-driven selection in the process of genetic improvement programs. Exploring the potential genetic basis of artificial selection and mapping functional candidate genes for economic traits are of great significance in genetic improvement of pigs. In this study, we focus on investigating the genetic basis of five meat quality traits, including intramuscular fat content (IMF), drip loss, water binding capacity, pH at 45 min (pH45min), and ultimate pH (pH24h). Through making phenotypic gradient differential population pairs, Wright’s fixation index (FST) and the cross-population extended haplotype homozogysity (XPEHH) were applied to detect selection signatures for these five traits. Finally, a total of 427 and 307 trait-specific selection signatures were revealed by FST and XPEHH, respectively. Further bioinformatics analysis indicates that some genes, such as USF1, NDUFS2, PIGM, IGSF8, CASQ1, and ACBD6, overlapping with the trait-specific selection signatures are responsible for the phenotypes including fat metabolism and muscle development. Among them, a series of promising trait-specific selection signatures that were detected in the high IMF subpopulation are located in the region of 93544042-95179724bp on SSC4, and the genes harboring in this region are all related to lipids and muscle development. Overall, these candidate genes of meat quality traits identified in this analysis may provide some fundamental information for further exploring the genetic basis of this complex trait.
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Affiliation(s)
- Yu Shen
- Key Laboratory of Agricultural Animal Genetics, Breeding, and Reproduction of the Ministry of Education and Key Laboratory of Swine Genetics and Breeding of the Ministry of Agriculture, Huazhong Agricultural University, Wuhan, China
| | - Haiyan Wang
- Key Laboratory of Agricultural Animal Genetics, Breeding, and Reproduction of the Ministry of Education and Key Laboratory of Swine Genetics and Breeding of the Ministry of Agriculture, Huazhong Agricultural University, Wuhan, China
| | - Jiahao Xie
- Key Laboratory of Agricultural Animal Genetics, Breeding, and Reproduction of the Ministry of Education and Key Laboratory of Swine Genetics and Breeding of the Ministry of Agriculture, Huazhong Agricultural University, Wuhan, China
| | - Zixuan Wang
- Key Laboratory of Agricultural Animal Genetics, Breeding, and Reproduction of the Ministry of Education and Key Laboratory of Swine Genetics and Breeding of the Ministry of Agriculture, Huazhong Agricultural University, Wuhan, China
| | - Yunlong Ma
- Key Laboratory of Agricultural Animal Genetics, Breeding, and Reproduction of the Ministry of Education and Key Laboratory of Swine Genetics and Breeding of the Ministry of Agriculture, Huazhong Agricultural University, Wuhan, China
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Zhuang Z, Li S, Ding R, Yang M, Zheng E, Yang H, Gu T, Xu Z, Cai G, Wu Z, Yang J. Meta-analysis of genome-wide association studies for loin muscle area and loin muscle depth in two Duroc pig populations. PLoS One 2019; 14:e0218263. [PMID: 31188900 PMCID: PMC6561594 DOI: 10.1371/journal.pone.0218263] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Accepted: 05/29/2019] [Indexed: 01/07/2023] Open
Abstract
Loin muscle area (LMA) and loin muscle depth (LMD) are important traits influencing the production performance of breeding pigs. However, the genetic architecture of these two traits is still poorly understood. To discern the genetic architecture of LMA and LMD, a material consisting of 6043 Duroc pigs belonging to two populations with different genetic backgrounds was collected and applied in genome-wide association studies (GWAS) with a genome-wide distributed panel of 50K single nucleotide polymorphisms (SNPs). To improve the power of detection for common SNPs, we conducted a meta-analysis in these two pig populations and uncovered additional significant SNPs. As a result, we identified 75 significant SNPs for LMA and LMD on SSC6, 7, 12, 16, and 18. Among them, 25 common SNPs were associated with LMA and LMD. One pleiotropic quantitative trait locus (QTL), which was located on SSC7 with a 283 kb interval, was identified to affect LMA and LMD. Marker ALGA0040260 is a key SNP for this QTL, explained 1.77% and 2.48% of the phenotypic variance for LMA and LMD, respectively. Another genetic region on SSC16 (709 kb) was detected and displayed prominent association with LMA and the peak SNP, WU_10.2_16_35829257, contributed 1.83% of the phenotypic variance for LMA. Further bioinformatics analysis determined eight promising candidate genes (GCLC, GPX8, DAXX, FGF21, TAF11, SPDEF, NUDT3, and PACSIN1) with functions in glutathione metabolism, adipose and muscle tissues development and lipid metabolism. This study provides the first GWAS for the LMA and LMD of Duroc breed to analyze the underlying genetic variants through a large sample size. The findings further advance our understanding and help elucidate the genetic architecture of LMA, LMD and growth-related traits in pigs.
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Affiliation(s)
- Zhanwei Zhuang
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, P.R. China
| | - Shaoyun Li
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, P.R. China
| | - Rongrong Ding
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, P.R. China
| | - Ming Yang
- National Engineering Research Center for Breeding Swine Industry, Guangdong Wens Foodstuffs Group Co., Ltd, Guangdong, P.R. China
| | - Enqin Zheng
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, P.R. China
| | - Huaqiang Yang
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, P.R. China
| | - Ting Gu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, P.R. China
| | - Zheng Xu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, P.R. China
| | - Gengyuan Cai
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, P.R. China
| | - Zhenfang Wu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, P.R. China
- National Engineering Research Center for Breeding Swine Industry, Guangdong Wens Foodstuffs Group Co., Ltd, Guangdong, P.R. China
- * E-mail: (JY); (ZW)
| | - Jie Yang
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, P.R. China
- * E-mail: (JY); (ZW)
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Martínez-Montes ÁM, Fernández A, Muñoz M, Noguera JL, Folch JM, Fernández AI. Using genome wide association studies to identify common QTL regions in three different genetic backgrounds based on Iberian pig breed. PLoS One 2018. [PMID: 29522525 PMCID: PMC5844516 DOI: 10.1371/journal.pone.0190184] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
One of the major limitation for the application of QTL results in pig breeding and QTN identification has been the limited number of QTL effects validated in different animal material. The aim of the current work was to validate QTL regions through joint and specific genome wide association and haplotype analyses for growth, fatness and premier cut weights in three different genetic backgrounds, backcrosses based on Iberian pigs, which has a major role in the analysis due to its high productive relevance. The results revealed nine common QTL regions, three segregating in all three backcrosses on SSC1, 0–3 Mb, for body weight, on SSC2, 3–9 Mb, for loin bone-in weight, and on SSC7, 3 Mb, for shoulder weight, and six segregating in two of the three backcrosses, on SSC2, SSC4, SSC6 and SSC10 for backfat thickness, shoulder and ham weights. Besides, 18 QTL regions were specifically identified in one of the three backcrosses, five identified only in BC_LD, seven in BC_DU and six in BC_PI. Beyond identifying and validating QTL, candidate genes and gene variants within the most interesting regions have been explored using functional annotation, gene expression data and SNP identification from RNA-Seq data. The results allowed us to propose a promising list of candidate mutations, those identified in PDE10A, DHCR7, MFN2 and CCNY genes located within the common QTL regions and those identified near ssc-mir-103-1 considered PANK3 regulators to be further analysed.
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Affiliation(s)
- Ángel M. Martínez-Montes
- Departamento de Genética Animal, Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Madrid, Spain
| | - Almudena Fernández
- Departamento de Genética Animal, Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Madrid, Spain
| | - María Muñoz
- Departamento de Genética Animal, Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Madrid, Spain
- Centro de I+D en Cerdo Ibérico, Zafra, Badajoz, Spain
| | - Jose Luis Noguera
- Departament de Genètica i Millora Animal, Institut de Recerca i Tecnologia Agroalimentàries (IRTA), Lleida, Spain
| | - Josep M. Folch
- Departament de Ciència Animal i dels Aliments, Facultat de Veterinària, Universitat Autònoma de Barcelona (UAB), Bellaterra, Spain
- Plant and Animal Genomics, Centre de Recerca en Agrigenòmica (CRAG), Consorci CSIC-IRTA-UAB-UB, Campus UAB, Bellaterra, Spain
| | - Ana I. Fernández
- Departamento de Genética Animal, Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Madrid, Spain
- * E-mail:
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Hérault F, Damon M, Cherel P, Le Roy P. Combined GWAS and LDLA approaches to improve genome-wide quantitative trait loci detection affecting carcass and meat quality traits in pig. Meat Sci 2017; 135:148-158. [PMID: 29035812 DOI: 10.1016/j.meatsci.2017.09.015] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2017] [Revised: 09/08/2017] [Accepted: 09/27/2017] [Indexed: 01/15/2023]
Abstract
Many QTL affecting meat quality and carcass traits have been reported. However, in most of the cases these QTL have been detected in non-commercial populations. Therefore, a family structured population of 457 F2 pigs issued from an inter-cross between 2 commercial sire lines was used to detect QTL affecting meat quality and carcass traits. All animals were genotyped using the Illumina PorcineSNP60 BeadChip platform. Genome-wide association studies were used in combination with linkage disequilibrium-linkage analysis to identify QTL. A total of 32 QTL were detected. Nine of these QTL exceeded the genome-wide 5% significance threshold. We detected 18 QTL affecting carcass composition traits and 16 QTL affecting meat quality traits. Using post-QTL bioinformatics analysis we highlighted 26 functional candidate genes related to fatness, muscle development, meat color and meat pH. Finally, our results shed light on the advantage of using different QTL detection methodologies to get a global overview of the QTL present in the studied population.
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Affiliation(s)
- Frédéric Hérault
- INRA, UMR1348 PEGASE, 16 le Clos, 35590 Saint-Gilles, France; Agrocampus Ouest, UMR1348 PEGASE, 65 rue de Saint Brieuc, 35042 Rennes, France.
| | - Marie Damon
- INRA, UMR1348 PEGASE, 16 le Clos, 35590 Saint-Gilles, France; Agrocampus Ouest, UMR1348 PEGASE, 65 rue de Saint Brieuc, 35042 Rennes, France
| | - Pierre Cherel
- iBV-institut de Biologie Valrose, Université Nice-Sophia Antipolis, UMR CNRS 7277, Inserm U1091, Parc Valrose, F-06108 Nice, France
| | - Pascale Le Roy
- INRA, UMR1348 PEGASE, 16 le Clos, 35590 Saint-Gilles, France; Agrocampus Ouest, UMR1348 PEGASE, 65 rue de Saint Brieuc, 35042 Rennes, France.
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Verardo LL, Sevón-Aimonen ML, Serenius T, Hietakangas V, Uimari P. Whole-genome association analysis of pork meat pH revealed three significant regions and several potential genes in Finnish Yorkshire pigs. BMC Genet 2017; 18:13. [PMID: 28193157 PMCID: PMC5307873 DOI: 10.1186/s12863-017-0482-x] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2016] [Accepted: 02/07/2017] [Indexed: 12/11/2022] Open
Abstract
Background One of the most commonly used quality measurements of pork is pH measured 24 h after slaughter. The most probable mode of inheritance for this trait is oligogenic with several known major genes, such as PRKAG3. In this study, we used whole-genome SNP genotypes of over 700 AI boars; after a quality check, 42,385 SNPs remained for association analysis. All the boars were purebred Finnish Yorkshire. To account for relatedness of the animals, a pedigree-based relationship matrix was used in a mixed linear model to test the effect of SNPs on pH measured from loin. A bioinformatics analysis was performed to identify the most promising genes in the significant regions related to meat quality. Results Genome-wide association study (GWAS) revealed three significant chromosomal regions: one on chromosome 3 (39.9 Mb–40.1 Mb) and two on chromosome 15 (58.5 Mb–60.5 Mb and 132 Mb–135 Mb including PRKAG3). A conditional analysis with a significant SNP in the PRKAG3 region, MARC0083357, as a covariate in the model retained the significant SNPs on chromosome 3. Even though linkage disequilibrium was relatively high over a long distance between MARC0083357 and other significant SNPs on chromosome 15, some SNPs retained their significance in the conditional analysis, even in the vicinity of PRKAG3. The significant regions harbored several genes, including two genes involved in cyclic AMP (cAMP) signaling: ADCY9 and CREBBP. Based on functional and transcription factor-gene networks, the most promising candidate genes for meat pH are ADCY9, CREBBP, TRAP1, NRG1, PRKAG3, VIL1, TNS1, and IGFBP5, and the key transcription factors related to these genes are HNF4A, PPARG, and Nkx2-5. Conclusions Based on SNP association, pathway, and transcription factor analysis, we were able to identify several genes with potential to control muscle cell homeostasis and meat quality. The associated SNPs can be used in selection for better pork. We also showed that post-GWAS analysis reveals important information about the genes’ potential role on meat quality. The gained information can be used in later functional studies.
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Affiliation(s)
- Lucas L Verardo
- Department of Animal Science/Animal Breeding, Federal University of Viçosa, Viçosa, Brazil
| | | | | | - Ville Hietakangas
- Department of Biosciences, University of Helsinki, Helsinki, Finland.,Institute of Biotechnology, University of Helsinki, Helsinki, Finland
| | - Pekka Uimari
- Department of Agricultural Sciences, University of Helsinki, Helsinki, Finland.
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Bernal Rubio YL, Gualdrón Duarte JL, Bates RO, Ernst CW, Nonneman D, Rohrer GA, King DA, Shackelford SD, Wheeler TL, Cantet RJC, Steibel JP. Implementing meta-analysis from genome-wide association studies for pork quality traits. J Anim Sci 2016; 93:5607-17. [PMID: 26641170 DOI: 10.2527/jas.2015-9502] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Pork quality plays an important role in the meat processing industry. Thus, different methodologies have been implemented to elucidate the genetic architecture of traits affecting meat quality. One of the most common and widely used approaches is to perform genome-wide association (GWA) studies. However, a limitation of many GWA in animal breeding is the limited power due to small sample sizes in animal populations. One alternative is to implement a meta-analysis of GWA (MA-GWA) combining results from independent association studies. The objective of this study was to identify significant genomic regions associated with meat quality traits by performing MA-GWA for 8 different traits in 3 independent pig populations. Results from MA-GWA were used to search for genes possibly associated with the set of evaluated traits. Data from 3 pig data sets (U.S. Meat Animal Research Center, commercial, and Michigan State University Pig Resource Population) were used. A MA was implemented by combining -scores derived for each SNP in every population and then weighting them using the inverse of estimated variance of SNP effects. A search for annotated genes retrieved genes previously reported as candidates for shear force (calpain-1 catalytic subunit [] and calpastatin []), as well as for ultimate pH, purge loss, and cook loss (protein kinase, AMP-activated, γ 3 noncatalytic subunit []). In addition, novel candidate genes were identified for intramuscular fat and cook loss (acyl-CoA synthetase family member 3 mitochondrial []) and for the objective measure of muscle redness, CIE a* (glycogen synthase 1, muscle [] and ferritin, light polypeptide []). Thus, implementation of MA-GWA allowed integration of results for economically relevant traits and identified novel genes to be tested as candidates for meat quality traits in pig populations.
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Qiao R, Gao J, Zhang Z, Li L, Xie X, Fan Y, Cui L, Ma J, Ai H, Ren J, Huang L. Genome-wide association analyses reveal significant loci and strong candidate genes for growth and fatness traits in two pig populations. Genet Sel Evol 2015; 47:17. [PMID: 25885760 PMCID: PMC4358731 DOI: 10.1186/s12711-015-0089-5] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2014] [Accepted: 01/08/2015] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Recently, genome-wide association studies (GWAS) have been reported on various pig traits. We performed a GWAS to analyze 22 traits related to growth and fatness on two pig populations: a White Duroc × Erhualian F2 intercross population and a Chinese Sutai half-sib population. RESULTS We identified 14 and 39 loci that displayed significant associations with growth and fatness traits at the genome-wide level and chromosome-wide level, respectively. The strongest association was between a 750 kb region on SSC7 (SSC for Sus scrofa) and backfat thickness at the first rib. This region had pleiotropic effects on both fatness and growth traits in F2 animals and contained a promising candidate gene HMGA1 (high mobility group AT-hook 1). Unexpectedly, population genetic analysis revealed that the allele at this locus that reduces fatness and increases growth is derived from Chinese indigenous pigs and segregates in multiple Chinese breeds. The second strongest association was between the region around 82.85 Mb on SSC4 and average backfat thickness. PLAG1 (pleiomorphic adenoma gene 1), a gene under strong selection in European domestic pigs, is proximal to the top SNP and stands out as a strong candidate gene. On SSC2, a locus that significantly affects fatness traits mapped to the region around the IGF2 (insulin-like growth factor 2) gene but its non-imprinting inheritance excluded IGF2 as a candidate gene. A significant locus was also detected within a recombination cold spot that spans more than 30 Mb on SSCX, which hampered the identification of plausible candidate genes. Notably, no genome-wide significant locus was shared by the two experimental populations; different loci were observed that had both constant and time-specific effects on growth traits at different stages, which illustrates the complex genetic architecture of these traits. CONCLUSIONS We confirm several previously reported QTL and provide a list of novel loci for porcine growth and fatness traits in two experimental populations with Chinese Taihu and Western pigs as common founders. We showed that distinct loci exist for these traits in the two populations and identified HMGA1 and PLAG1 as strong candidate genes on SSC7 and SSC4, respectively.
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Affiliation(s)
- Ruimin Qiao
- Key Laboratory for Animal Biotechnology of Jiangxi Province and the Ministry of Agriculture of China, Jiangxi Agricultural University, Nanchang, China.
| | - Jun Gao
- Key Laboratory for Animal Biotechnology of Jiangxi Province and the Ministry of Agriculture of China, Jiangxi Agricultural University, Nanchang, China.
| | - Zhiyan Zhang
- Key Laboratory for Animal Biotechnology of Jiangxi Province and the Ministry of Agriculture of China, Jiangxi Agricultural University, Nanchang, China.
| | - Lin Li
- Key Laboratory for Animal Biotechnology of Jiangxi Province and the Ministry of Agriculture of China, Jiangxi Agricultural University, Nanchang, China.
| | - Xianhua Xie
- Key Laboratory for Animal Biotechnology of Jiangxi Province and the Ministry of Agriculture of China, Jiangxi Agricultural University, Nanchang, China.
| | - Yin Fan
- Key Laboratory for Animal Biotechnology of Jiangxi Province and the Ministry of Agriculture of China, Jiangxi Agricultural University, Nanchang, China.
| | - Leilei Cui
- Key Laboratory for Animal Biotechnology of Jiangxi Province and the Ministry of Agriculture of China, Jiangxi Agricultural University, Nanchang, China.
| | - Junwu Ma
- Key Laboratory for Animal Biotechnology of Jiangxi Province and the Ministry of Agriculture of China, Jiangxi Agricultural University, Nanchang, China.
| | - Huashui Ai
- Key Laboratory for Animal Biotechnology of Jiangxi Province and the Ministry of Agriculture of China, Jiangxi Agricultural University, Nanchang, China.
| | - Jun Ren
- Key Laboratory for Animal Biotechnology of Jiangxi Province and the Ministry of Agriculture of China, Jiangxi Agricultural University, Nanchang, China.
| | - Lusheng Huang
- Key Laboratory for Animal Biotechnology of Jiangxi Province and the Ministry of Agriculture of China, Jiangxi Agricultural University, Nanchang, China.
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Ropka-Molik K, Zukowski K, Eckert R, Gurgul A, Piórkowska K, Oczkowicz M. Comprehensive analysis of the whole transcriptomes from two different pig breeds using RNA-Seq method. Anim Genet 2014; 45:674-84. [PMID: 24961663 DOI: 10.1111/age.12184] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/05/2014] [Indexed: 02/03/2023]
Abstract
Next-generation sequencing RNA-Seq technology is a powerful tool that creates new possibilities for whole-transcriptome analysis. In our study, the RNA-Seq method was applied to analyze global changes in transcriptome from muscle tissue (m. semimembranosus) in two pig breeds (Pietrain and Polish Landrace, PL). The breeds differ in terms of muscularity, growth rate and reproduction traits. Using three different approaches (deseq, cufflinks and edger) and taking into account the most restrictive criteria, 35 genes differentially expressed between Pietrain and PL pigs were identified. In both breeds, the most abundant were transcripts encoding ribosomal and cytoskeletal proteins (TPM3, TCAP, TMOD4, TPM2, TNNC1) and calcium-binding proteins involved in muscle contraction, calcium-mediated signaling or cation transport (CASQ1, MLC2V, SLC25A4, MYL3). In PL pigs, we identified up-regulation of several genes that play crucial roles in reproduction: female gamete generation (BDP1, PTPN21, USP9X), fertilization (EGFR) and embryonic development (CPEB4). In the Pietrain breed, only seven genes were over-expressed (CISH, SPP1, TUBA8, ATP6V1C2, IGKC, predicted LOC100510960 and LOC100626400), and they play important roles in, for example, negative regulation of apoptosis, immune response, cell-cell signaling, cell growth and migration as well as the metabolic process. The functions of the majority of selected genes were consistent with phenotypic variation in investigated breeds; thus, we proposed a new panel of candidate genes that can be associated with economically important pig traits.
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Affiliation(s)
- Katarzyna Ropka-Molik
- Laboratory of Genomics, National Research Institute of Animal Production, Krakowska 1, 32-083, Balice, Poland
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Uimari P, Sironen A. A combination of two variants in PRKAG3 is needed for a positive effect on meat quality in pigs. BMC Genet 2014; 15:29. [PMID: 24580963 PMCID: PMC3943410 DOI: 10.1186/1471-2156-15-29] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2013] [Accepted: 02/07/2014] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Color and pH of meat measured 24 h post mortem are common selection objectives in pig breeding programs. Several amino acid substitutions in PRKAG3 have been associated with various meat quality traits. In our previous study ASGA0070625, a SNP next to PRKAG3, had the most significant association with meat quality traits in the Finnish Yorkshire. However, the known amino acid substitutions, including I199V, did not show any association. The aims of this study were to characterize further variation in PRKAG3 and its promoter region, and to test the association between these variants and the pH and color of pork meat. RESULTS The data comprised of 220 Finnish Landrace and 230 Finnish Yorkshire artificial insemination boars with progeny information. We sequenced the coding and promoter region of PRKAG3 in these and in three additional wild boars. Genotypes from our previous genome-wide scans were also included in the data. Association between SNPs or haplotypes and meat quality traits (deregressed estimates of breeding values from Finnish national breeding value estimation for pH, color lightness and redness measured from loin or ham) was tested using a linear regression model. Sequencing revealed several novel amino acid substitutions in PRKAG3, including K24E, I41V, K131R, and P134L. Linkage disequilibrium was strong among the novel variants, SNPs in the promoter region and ASGA0070625, especially for the Yorkshire. The strongest associations were observed between ASGA0070625 and the SNPs in the promoter region and pH measured from loin in the Yorkshire and between I199V and pH measured from ham in the Landrace. In contrast, ASGA0070625 was not significantly associated with meat quality traits in the Landrace and I199V not in the Yorkshire. Haplotype analysis showed a significant association between a haplotype consisting of 199I and 24E alleles (or g.-157C or g.-58A alleles in the promoter region) and pH measured from loin and ham in both breeds (P-values varied from 1.72 × 10⁻⁴ to 1.80 × 10⁻⁸). CONCLUSIONS We conclude that haplotype g.-157C - g.-58A - 24E - 199I in PRKAG3 has a positive effect on meat quality in pigs. Our results are readily applicable for marker-assisted selection in pigs.
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Affiliation(s)
- Pekka Uimari
- MTT Agrifood Research Finland, Biotechnology and Food Research, FI-31600 Jokioinen, Finland.
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13
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Scheffler TL, Scheffler JM, Park S, Kasten SC, Wu Y, McMillan RP, Hulver MW, Frisard MI, Gerrard DE. Fiber hypertrophy and increased oxidative capacity can occur simultaneously in pig glycolytic skeletal muscle. Am J Physiol Cell Physiol 2013; 306:C354-63. [PMID: 24304835 DOI: 10.1152/ajpcell.00002.2013] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
An inverse relationship between skeletal muscle fiber cross-sectional area (CSA) and oxidative capacity suggests that muscle fibers hypertrophy at the expense of oxidative capacity. Therefore, our objective was to utilize pigs possessing mutations associated with increased oxidative capacity [AMP-activated protein kinase (AMPKγ3(R200Q))] or fiber hypertrophy [ryanodine receptor 1 (RyR1(R615C))] to determine if these events occur in parallel. Longissimus muscle was collected from wild-type (control), AMPKγ3(R200Q), RyR1(R615C), and AMPKγ3(R200Q)-RyR1(R615C) pigs. Regardless of AMPK genotype, RyR(R615C) increased fiber CSA by 35%. In contrast, AMPKγ3(R200Q) pig muscle exhibited greater citrate synthase and β-hydroxyacyl CoA dehydrogenase activity. Isolated mitochondria from AMPKγ3(R200Q) muscle had greater maximal, ADP-stimulated oxygen consumption rate. Additionally, AMPKγ3(R200Q) muscle contained more (∼50%) of the mitochondrial proteins succinate dehydrogenase and cytochrome c oxidase and more mitochondrial DNA. Surprisingly, RyR1(R615C) increased mitochondrial proteins and DNA, but this was not associated with improved oxidative capacity, suggesting that altered energy metabolism in RyR1(R615C) muscle influences mitochondrial proliferation and protein turnover. Thus pigs that possess both AMPKγ3(R200Q) and RyR(R615C) exhibit increased muscle fiber CSA as well as greater oxidative capacity. Together, our findings support the notion that hypertrophy and enhanced oxidative capacity can occur simultaneously in skeletal muscle and suggest that the signaling mechanisms controlling these events are independently regulated.
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Affiliation(s)
- T L Scheffler
- Department of Animal and Poultry Sciences, Virginia Tech, Blacksburg, Virginia; and
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Ma J, Yang J, Zhou L, Zhang Z, Ma H, Xie X, Zhang F, Xiong X, Cui L, Yang H, Liu X, Duan Y, Xiao S, Ai H, Ren J, Huang L. Genome-wide association study of meat quality traits in a White Duroc×Erhualian F2 intercross and Chinese Sutai pigs. PLoS One 2013; 8:e64047. [PMID: 23724019 PMCID: PMC3665833 DOI: 10.1371/journal.pone.0064047] [Citation(s) in RCA: 50] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2012] [Accepted: 04/07/2013] [Indexed: 12/31/2022] Open
Abstract
Thousands of QTLs for meat quality traits have been identified by linkage mapping studies, but most of them lack precise position or replication between populations, which hinder their application in pig breeding programs. To localize QTLs for meat quality traits to precise genomic regions, we performed a genome-wide association (GWA) study using the Illumina PorcineSNP60K Beadchip in two swine populations: 434 Sutai pigs and 933 F2 pigs from a White Duroc×Erhualian intercross. Meat quality traits, including pH, color, drip loss, moisture content, protein content and intramuscular fat content (IMF), marbling and firmness scores in the M. longissimus (LM) and M. semimembranosus (SM) muscles, were recorded on the two populations. In total, 127 chromosome-wide significant SNPs for these traits were identified. Among them, 11 SNPs reached genome-wise significance level, including 1 on SSC3 for pH, 1 on SSC3 and 3 on SSC15 for drip loss, 3 (unmapped) for color a*, and 2 for IMF each on SSC9 and SSCX. Except for 11 unmapped SNPs, 116 significant SNPs fell into 28 genomic regions of approximately 10 Mb or less. Most of these regions corresponded to previously reported QTL regions and spanned smaller intervals than before. The loci on SSC3 and SSC7 appeared to have pleiotropic effects on several related traits. Besides them, a few QTL signals were replicated between the two populations. Further, we identified thirteen new candidate genes for IMF, marbling and firmness, on the basis of their positions, functional annotations and reported expression patterns. The findings will contribute to further identification of the causal mutation underlying these QTLs and future marker-assisted selection in pigs.
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Affiliation(s)
- Junwu Ma
- Key Laboratory for Animal Biotechnology of Jiangxi Province and the Ministry of Agriculture of China, Jiangxi Agricultural University, Nanchang, China
| | - Jie Yang
- Key Laboratory for Animal Biotechnology of Jiangxi Province and the Ministry of Agriculture of China, Jiangxi Agricultural University, Nanchang, China
| | - Lisheng Zhou
- Key Laboratory for Animal Biotechnology of Jiangxi Province and the Ministry of Agriculture of China, Jiangxi Agricultural University, Nanchang, China
| | - Zhiyan Zhang
- Key Laboratory for Animal Biotechnology of Jiangxi Province and the Ministry of Agriculture of China, Jiangxi Agricultural University, Nanchang, China
| | - Huanban Ma
- Key Laboratory for Animal Biotechnology of Jiangxi Province and the Ministry of Agriculture of China, Jiangxi Agricultural University, Nanchang, China
| | - Xianhua Xie
- Key Laboratory for Animal Biotechnology of Jiangxi Province and the Ministry of Agriculture of China, Jiangxi Agricultural University, Nanchang, China
| | - Feng Zhang
- Key Laboratory for Animal Biotechnology of Jiangxi Province and the Ministry of Agriculture of China, Jiangxi Agricultural University, Nanchang, China
| | - Xinwei Xiong
- Key Laboratory for Animal Biotechnology of Jiangxi Province and the Ministry of Agriculture of China, Jiangxi Agricultural University, Nanchang, China
| | - Leilei Cui
- Key Laboratory for Animal Biotechnology of Jiangxi Province and the Ministry of Agriculture of China, Jiangxi Agricultural University, Nanchang, China
| | - Hui Yang
- Key Laboratory for Animal Biotechnology of Jiangxi Province and the Ministry of Agriculture of China, Jiangxi Agricultural University, Nanchang, China
| | - Xianxian Liu
- Key Laboratory for Animal Biotechnology of Jiangxi Province and the Ministry of Agriculture of China, Jiangxi Agricultural University, Nanchang, China
| | - Yanyu Duan
- Key Laboratory for Animal Biotechnology of Jiangxi Province and the Ministry of Agriculture of China, Jiangxi Agricultural University, Nanchang, China
| | - Shijun Xiao
- Key Laboratory for Animal Biotechnology of Jiangxi Province and the Ministry of Agriculture of China, Jiangxi Agricultural University, Nanchang, China
| | - Huashui Ai
- Key Laboratory for Animal Biotechnology of Jiangxi Province and the Ministry of Agriculture of China, Jiangxi Agricultural University, Nanchang, China
| | - Jun Ren
- Key Laboratory for Animal Biotechnology of Jiangxi Province and the Ministry of Agriculture of China, Jiangxi Agricultural University, Nanchang, China
| | - Lusheng Huang
- Key Laboratory for Animal Biotechnology of Jiangxi Province and the Ministry of Agriculture of China, Jiangxi Agricultural University, Nanchang, China
- * E-mail:
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15
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Molecular advances in QTL discovery and application in pig breeding. Trends Genet 2013; 29:215-24. [DOI: 10.1016/j.tig.2013.02.002] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2012] [Revised: 02/12/2013] [Accepted: 02/13/2013] [Indexed: 11/21/2022]
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Stratz P, Baes C, Rückert C, Preuss S, Bennewitz J. A two-step approach to map quantitative trait loci for meat quality in connected porcine F(2) crosses considering main and epistatic effects. Anim Genet 2012; 44:14-23. [PMID: 22509991 DOI: 10.1111/j.1365-2052.2012.02360.x] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/08/2012] [Indexed: 11/30/2022]
Abstract
The aim of this study was to map QTL for meat quality traits in three connected porcine F(2) crosses comprising around 1000 individuals. The three crosses were derived from the founder breeds Chinese Meishan, European Wild Boar and Pietrain. The animals were genotyped genomewide for approximately 250 genetic markers, mostly microsatellites. They were phenotyped for seven meat quality traits (pH at 45 min and 24 h after slaughter, conductivity at 45 min and 24 h after slaughter, meat colour, drip loss and rigour). QTL mapping was conducted using a two-step procedure. In the first step, the QTL were mapped using a multi-QTL multi-allele model that was tailored to analyse multiple connected F(2) crosses. It considered additive, dominance and imprinting effects. The major gene RYR1:g.1843C>T affecting the meat quality on SSC6 was included as a cofactor in the model. The mapped QTL were tested for pairwise epistatic effects in the second step. All possible epistatic effects between additive, dominant and imprinting effects were considered, leading to nine orthogonal forms of epistasis. Numerous QTL were found. The most interesting chromosome was SSC6. Not all genetic variance of meat quality was explained by RYR1:g.1843C>T. A small confidence interval was obtained, which facilitated the identification of candidate genes underlying the QTL. Epistasis was significant for the pairwise QTL on SSC12 and SSC14 for pH24 and for the QTL on SSC2 and SSC5 for rigour. Some evidence for additional pairwise epistatic effects was found, although not significant. Imprinting was involved in epistasis.
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Affiliation(s)
- P Stratz
- Institute of Animal Husbandry and Breeding, University of Hohenheim, D-70599, Stuttgart, Germany.
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17
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Liaubet L, Lobjois V, Faraut T, Tircazes A, Benne F, Iannuccelli N, Pires J, Glénisson J, Robic A, Le Roy P, Sancristobal M, Cherel P. Genetic variability of transcript abundance in pig peri-mortem skeletal muscle: eQTL localized genes involved in stress response, cell death, muscle disorders and metabolism. BMC Genomics 2011; 12:548. [PMID: 22053791 PMCID: PMC3239847 DOI: 10.1186/1471-2164-12-548] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2011] [Accepted: 11/04/2011] [Indexed: 01/03/2023] Open
Abstract
Background The genetics of transcript-level variation is an exciting field that has recently given rise to many studies. Genetical genomics studies have mainly focused on cell lines, blood cells or adipose tissues, from human clinical samples or mice inbred lines. Few eQTL studies have focused on animal tissues sampled from outbred populations to reflect natural genetic variation of gene expression levels in animals. In this work, we analyzed gene expression in a whole tissue, pig skeletal muscle sampled from individuals from a half sib F2 family shortly after slaughtering. Results QTL detection on transcriptome measurements was performed on a family structured population. The analysis identified 335 eQTLs affecting the expression of 272 transcripts. The ontologic annotation of these eQTLs revealed an over-representation of genes encoding proteins involved in processes that are expected to be induced during muscle development and metabolism, cell morphology, assembly and organization and also in stress response and apoptosis. A gene functional network approach was used to evidence existing biological relationships between all the genes whose expression levels are influenced by eQTLs. eQTLs localization revealed a significant clustered organization of about half the genes located on segments of chromosome 1, 2, 10, 13, 16, and 18. Finally, the combined expression and genetic approaches pointed to putative cis-drivers of gene expression programs in skeletal muscle as COQ4 (SSC1), LOC100513192 (SSC18) where both the gene transcription unit and the eQTL affecting its expression level were shown to be localized in the same genomic region. This suggests cis-causing genetic polymorphims affecting gene expression levels, with (e.g. COQ4) or without (e.g. LOC100513192) potential pleiotropic effects that affect the expression of other genes (cluster of trans-eQTLs). Conclusion Genetic analysis of transcription levels revealed dependence among molecular phenotypes as being affected by variation at the same loci. We observed the genetic variation of molecular phenotypes in a specific situation of cellular stress thus contributing to a better description of muscle physiologic response. In turn, this suggests that large amounts of genetic variation, mediated through transcriptional networks, can drive transient cell response phenotypes and contribute to organismal adaptative potential.
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Affiliation(s)
- Laurence Liaubet
- Laboratoire de Génétique Cellulaire, INRA UMR444, Chemin de Borde Rouge, F-31326 Castanet-Tolosan, France.
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