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Lorenzetti WR, Ibelli AMG, Peixoto JDO, Savoldi IR, Mores MAZ, de Souza Romano G, do Carmo KB, Ledur MC. The downregulation of genes encoding muscle proteins have a potential role in the development of scrotal hernia in pigs. Mol Biol Rep 2024; 51:822. [PMID: 39023774 DOI: 10.1007/s11033-024-09766-1] [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: 02/15/2024] [Accepted: 06/30/2024] [Indexed: 07/20/2024]
Abstract
BACKGROUND Testicular descent is a physiological process regulated by many factors. Eventually, disturbances in the embryological/fetal development path facilitate the occurrence of scrotal hernia, a congenital malformation characterized by the presence of intestinal portions within the scrotal sac due to the abnormal expansion of the inguinal ring. In pigs, some genes have been related to this anomaly, but the genetic mechanisms involved remain unclear. This study aimed to investigate the expression profile of a set of genes potentially involved with the manifestation of scrotal hernia in the inguinal ring tissue. METHODS AND RESULTS Tissue samples from the inguinal ring/canal of normal and scrotal hernia-affected male pigs with approximately 30 days of age were used. Relative expression analysis was performed using qPCR to confirm the expression profile of 17 candidate genes previously identified in an RNA-Seq study. Among them, the Myosin heavy chain 1 (MYH1), Desmin (DES), and Troponin 1 (TNNI1) genes were differentially expressed between groups and had reduced levels of expression in the affected animals. These genes encode proteins involved in the formation of muscle tissue, which seems to be important for increasing the resistance of the inguinal ring to the abdominal pressure, which is essential to avoid the occurrence of scrotal hernia. CONCLUSIONS The downregulation of muscular candidate genes in the inguinal tissue clarifies the genetic mechanisms involved with this anomaly in its primary site, providing useful information for developing strategies to control this malformation in pigs and other mammals.
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Affiliation(s)
- William Raphael Lorenzetti
- Programa de Pós-graduação em Zootecnia, Centro de Educação Superior do Oeste (CEO), Universidade do Estado de Santa Catarina, UDESC, Rua Beloni Trombeta Zanin 680E, Chapecó, Santa Catarina, 89815-630, Brazil
| | - Adriana Mércia Guaratini Ibelli
- Embrapa Suínos e Aves, Rodovia BR153, km 110, Distrito de Tamanduá, Caixa Postal: 321, Concórdia, Santa Catarina, 89715-899, Brazil
- Programa de Pós-Graduação em Ciências Veterinárias, Universidade Estadual do Centro-Oeste, Alameda Élio Antonio Dalla Vecchia, 838, Guarapuava, Paraná, 85040-167, Brazil
- Embrapa Pecuária Sudeste, Rodovia Washington Luiz, Km 234, São Carlos, São Paulo, 13560-970, Brazil
| | - Jane de Oliveira Peixoto
- Embrapa Suínos e Aves, Rodovia BR153, km 110, Distrito de Tamanduá, Caixa Postal: 321, Concórdia, Santa Catarina, 89715-899, Brazil
- Programa de Pós-Graduação em Ciências Veterinárias, Universidade Estadual do Centro-Oeste, Alameda Élio Antonio Dalla Vecchia, 838, Guarapuava, Paraná, 85040-167, Brazil
| | - Igor Ricardo Savoldi
- Programa de Pós-graduação em Zootecnia, Centro de Educação Superior do Oeste (CEO), Universidade do Estado de Santa Catarina, UDESC, Rua Beloni Trombeta Zanin 680E, Chapecó, Santa Catarina, 89815-630, Brazil
- Laudo laboratório Avícola, Rodovia BR-365, Morumbi, Uberlândia, Minas Gerais, 38407180, Brazil
| | - Marcos Antônio Zanella Mores
- Embrapa Suínos e Aves, Rodovia BR153, km 110, Distrito de Tamanduá, Caixa Postal: 321, Concórdia, Santa Catarina, 89715-899, Brazil
| | | | - Kamilla Bleil do Carmo
- Universidade do Contestado, Concórdia, Santa Catarina, Brazil
- Instituto Federal Catarinense, Rodovia SC 283, km 17, Concórdia, Santa Catarina, 89703-720, Brazil
| | - Mônica Corrêa Ledur
- Programa de Pós-graduação em Zootecnia, Centro de Educação Superior do Oeste (CEO), Universidade do Estado de Santa Catarina, UDESC, Rua Beloni Trombeta Zanin 680E, Chapecó, Santa Catarina, 89815-630, Brazil.
- Embrapa Suínos e Aves, Rodovia BR153, km 110, Distrito de Tamanduá, Caixa Postal: 321, Concórdia, Santa Catarina, 89715-899, Brazil.
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2
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Kumar S, Bhushan B, Kumar A, Panigrahi M, Bharati J, Kumari S, Kaiho K, Banik S, Karthikeyan A, Chaudhary R, Gaur GK, Dutt T. Elucidation of novel SNPs affecting immune response to classical swine fever vaccination in pigs using immunogenomics approach. Vet Res Commun 2024; 48:941-953. [PMID: 38017322 DOI: 10.1007/s11259-023-10262-3] [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: 09/11/2023] [Accepted: 11/19/2023] [Indexed: 11/30/2023]
Abstract
The host genetic makeup plays a significant role in causing the within-breed variation among individuals after vaccination. The present study was undertaken to elucidate the genetic basis of differential immune response between high and low responder Landlly (Landrace X Ghurrah) piglets vis-à-vis CSF vaccination. For the purpose, E2 antibody response against CSF vaccination was estimated in sampled animals on the day of vaccination and 21-day post-vaccination as a measure of humoral immune response. Double-digestion restriction associated DNA (ddRAD) sequencing was undertaken on 96 randomly chosen Landlly piglets using Illumina HiSeq platform. SNP markers were called using standard methodology. Genome-wide association study (GWAS) was undertaken in PLINK program to identify the informative SNP markers significantly associated with differential immune response. The results revealed significant SNPs associated with E2 antibody response against CSF vaccination. The genome-wide informative SNPs for the humoral immune response against CSF vaccination were located on SSC10, SSC17, SSC9, SSC2, SSC3 and SSC6. The overlapping and flanking genes (500Kb upstream and downstream) of significant SNPs were CYB5R1, PCMTD2, WT1, IL9R, CD101, TMEM64, TLR6, PIGG, ADIPOR1, PRSS37, EIF3M, and DNAJC24. Functional enrichment and annotation analysis were undertaken for these genes in order to gain maximum insights into the association of these genes with immune system functionality in pigs. The genetic makeup was associated with differential immune response against CSF vaccination in Landlly piglets while the identified informative SNPs may be used as suitable markers for determining variation in host immune response against CSF vaccination in pigs.
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Affiliation(s)
- Satish Kumar
- Division of Animal Genetics, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, U.P, 243122, India.
- ICAR-National Research Centre on Pig, Rani, Guwahati, Assam, 781131, India.
| | - Bharat Bhushan
- Division of Animal Genetics, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, U.P, 243122, India.
| | - Amit Kumar
- Division of Animal Genetics, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, U.P, 243122, India.
| | - Manjit Panigrahi
- Division of Animal Genetics, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, U.P, 243122, India
| | - Jaya Bharati
- ICAR-National Research Centre on Pig, Rani, Guwahati, Assam, 781131, India
| | - Soni Kumari
- Division of Animal Genetics, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, U.P, 243122, India
| | - Kaisa Kaiho
- Division of Animal Genetics, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, U.P, 243122, India
| | - Santanu Banik
- ICAR-National Research Centre on Pig, Rani, Guwahati, Assam, 781131, India
| | - A Karthikeyan
- Division of Animal Genetics, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, U.P, 243122, India
| | - Rajni Chaudhary
- Division of Animal Genetics, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, U.P, 243122, India
| | - G K Gaur
- Livestock Production and Management Section, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, U.P, 243122, India
| | - Triveni Dutt
- Livestock Production and Management Section, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, U.P, 243122, India
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3
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Liang Y, Xie J, Zhang Q, Wang X, Gou S, Lin L, Chen T, Ge W, Zhuang Z, Lian M, Chen F, Li N, Ouyang Z, Lai C, Liu X, Li L, Ye Y, Wu H, Wang K, Lai L. AGBE: a dual deaminase-mediated base editor by fusing CGBE with ABE for creating a saturated mutant population with multiple editing patterns. Nucleic Acids Res 2022; 50:5384-5399. [PMID: 35544322 PMCID: PMC9122597 DOI: 10.1093/nar/gkac353] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 04/13/2022] [Accepted: 04/26/2022] [Indexed: 12/26/2022] Open
Abstract
Establishing saturated mutagenesis in a specific gene through gene editing is an efficient approach for identifying the relationships between mutations and the corresponding phenotypes. CRISPR/Cas9-based sgRNA library screening often creates indel mutations with multiple nucleotides. Single base editors and dual deaminase-mediated base editors can achieve only one and two types of base substitutions, respectively. A new glycosylase base editor (CGBE) system, in which the uracil glycosylase inhibitor (UGI) is replaced with uracil-DNA glycosylase (UNG), was recently reported to efficiently induce multiple base conversions, including C-to-G, C-to-T and C-to-A. In this study, we fused a CGBE with ABE to develop a new type of dual deaminase-mediated base editing system, the AGBE system, that can simultaneously introduce 4 types of base conversions (C-to-G, C-to-T, C-to-A and A-to-G) as well as indels with a single sgRNA in mammalian cells. AGBEs can be used to establish saturated mutant populations for verification of the functions and consequences of multiple gene mutation patterns, including single-nucleotide variants (SNVs) and indels, through high-throughput screening.
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Affiliation(s)
- Yanhui Liang
- China-New Zealand Joint Laboratory on Biomedicine and Health, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Centre for Regenerative Medicine and Health, Hong Kong Institute of Science and Innovation, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jingke Xie
- China-New Zealand Joint Laboratory on Biomedicine and Health, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Centre for Regenerative Medicine and Health, Hong Kong Institute of Science and Innovation, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China.,Sanya institute of Swine resource, Hainan Provincial Research Centre of Laboratory Animals, Sanya 572000, China.,Guangdong Provincial Key Laboratory of Large Animal models for Biomedicine, Wuyi University, Jiangmen 529020, China
| | - Quanjun Zhang
- China-New Zealand Joint Laboratory on Biomedicine and Health, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Centre for Regenerative Medicine and Health, Hong Kong Institute of Science and Innovation, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China.,Research Unit of Generation of Large Animal Disease Models, Chinese Academy of Medical Sciences (2019RU015), Guangzhou 510530, China.,Sanya institute of Swine resource, Hainan Provincial Research Centre of Laboratory Animals, Sanya 572000, China.,Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou 510005, China
| | - Xiaomin Wang
- China-New Zealand Joint Laboratory on Biomedicine and Health, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Centre for Regenerative Medicine and Health, Hong Kong Institute of Science and Innovation, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China
| | - Shixue Gou
- China-New Zealand Joint Laboratory on Biomedicine and Health, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Centre for Regenerative Medicine and Health, Hong Kong Institute of Science and Innovation, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China.,Sanya institute of Swine resource, Hainan Provincial Research Centre of Laboratory Animals, Sanya 572000, China
| | - Lihui Lin
- China-New Zealand Joint Laboratory on Biomedicine and Health, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Centre for Regenerative Medicine and Health, Hong Kong Institute of Science and Innovation, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China
| | - Tao Chen
- Guangdong Provincial Key Laboratory of Large Animal models for Biomedicine, Wuyi University, Jiangmen 529020, China
| | - Weikai Ge
- China-New Zealand Joint Laboratory on Biomedicine and Health, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Centre for Regenerative Medicine and Health, Hong Kong Institute of Science and Innovation, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China.,Sanya institute of Swine resource, Hainan Provincial Research Centre of Laboratory Animals, Sanya 572000, China.,Guangdong Provincial Key Laboratory of Large Animal models for Biomedicine, Wuyi University, Jiangmen 529020, China
| | - Zhenpeng Zhuang
- China-New Zealand Joint Laboratory on Biomedicine and Health, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Centre for Regenerative Medicine and Health, Hong Kong Institute of Science and Innovation, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Meng Lian
- China-New Zealand Joint Laboratory on Biomedicine and Health, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Centre for Regenerative Medicine and Health, Hong Kong Institute of Science and Innovation, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China.,Sanya institute of Swine resource, Hainan Provincial Research Centre of Laboratory Animals, Sanya 572000, China
| | - Fangbing Chen
- China-New Zealand Joint Laboratory on Biomedicine and Health, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Centre for Regenerative Medicine and Health, Hong Kong Institute of Science and Innovation, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China.,Sanya institute of Swine resource, Hainan Provincial Research Centre of Laboratory Animals, Sanya 572000, China.,Guangdong Provincial Key Laboratory of Large Animal models for Biomedicine, Wuyi University, Jiangmen 529020, China
| | - Nan Li
- China-New Zealand Joint Laboratory on Biomedicine and Health, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Centre for Regenerative Medicine and Health, Hong Kong Institute of Science and Innovation, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China.,Sanya institute of Swine resource, Hainan Provincial Research Centre of Laboratory Animals, Sanya 572000, China.,Guangdong Provincial Key Laboratory of Large Animal models for Biomedicine, Wuyi University, Jiangmen 529020, China
| | - Zhen Ouyang
- China-New Zealand Joint Laboratory on Biomedicine and Health, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Centre for Regenerative Medicine and Health, Hong Kong Institute of Science and Innovation, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China.,Research Unit of Generation of Large Animal Disease Models, Chinese Academy of Medical Sciences (2019RU015), Guangzhou 510530, China.,Sanya institute of Swine resource, Hainan Provincial Research Centre of Laboratory Animals, Sanya 572000, China.,Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou 510005, China.,Guangdong Provincial Key Laboratory of Large Animal models for Biomedicine, Wuyi University, Jiangmen 529020, China
| | - Chengdan Lai
- China-New Zealand Joint Laboratory on Biomedicine and Health, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Centre for Regenerative Medicine and Health, Hong Kong Institute of Science and Innovation, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China.,Research Unit of Generation of Large Animal Disease Models, Chinese Academy of Medical Sciences (2019RU015), Guangzhou 510530, China.,Sanya institute of Swine resource, Hainan Provincial Research Centre of Laboratory Animals, Sanya 572000, China.,Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou 510005, China.,Guangdong Provincial Key Laboratory of Large Animal models for Biomedicine, Wuyi University, Jiangmen 529020, China
| | - Xiaoyi Liu
- China-New Zealand Joint Laboratory on Biomedicine and Health, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Centre for Regenerative Medicine and Health, Hong Kong Institute of Science and Innovation, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Lei Li
- China-New Zealand Joint Laboratory on Biomedicine and Health, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Centre for Regenerative Medicine and Health, Hong Kong Institute of Science and Innovation, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yinghua Ye
- China-New Zealand Joint Laboratory on Biomedicine and Health, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Centre for Regenerative Medicine and Health, Hong Kong Institute of Science and Innovation, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China.,Research Unit of Generation of Large Animal Disease Models, Chinese Academy of Medical Sciences (2019RU015), Guangzhou 510530, China.,Sanya institute of Swine resource, Hainan Provincial Research Centre of Laboratory Animals, Sanya 572000, China.,Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou 510005, China
| | - Han Wu
- China-New Zealand Joint Laboratory on Biomedicine and Health, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Centre for Regenerative Medicine and Health, Hong Kong Institute of Science and Innovation, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China.,Research Unit of Generation of Large Animal Disease Models, Chinese Academy of Medical Sciences (2019RU015), Guangzhou 510530, China.,Sanya institute of Swine resource, Hainan Provincial Research Centre of Laboratory Animals, Sanya 572000, China.,Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou 510005, China
| | - Kepin Wang
- China-New Zealand Joint Laboratory on Biomedicine and Health, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Centre for Regenerative Medicine and Health, Hong Kong Institute of Science and Innovation, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China.,Research Unit of Generation of Large Animal Disease Models, Chinese Academy of Medical Sciences (2019RU015), Guangzhou 510530, China.,Sanya institute of Swine resource, Hainan Provincial Research Centre of Laboratory Animals, Sanya 572000, China.,Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou 510005, China.,Guangdong Provincial Key Laboratory of Large Animal models for Biomedicine, Wuyi University, Jiangmen 529020, China
| | - Liangxue Lai
- China-New Zealand Joint Laboratory on Biomedicine and Health, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Centre for Regenerative Medicine and Health, Hong Kong Institute of Science and Innovation, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China.,Research Unit of Generation of Large Animal Disease Models, Chinese Academy of Medical Sciences (2019RU015), Guangzhou 510530, China.,Sanya institute of Swine resource, Hainan Provincial Research Centre of Laboratory Animals, Sanya 572000, China.,Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou 510005, China.,Guangdong Provincial Key Laboratory of Large Animal models for Biomedicine, Wuyi University, Jiangmen 529020, China
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4
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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: 5] [Impact Index Per Article: 1.7] [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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5
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Xie J, Huang X, Wang X, Gou S, Liang Y, Chen F, Li N, Ouyang Z, Zhang Q, Ge W, Jin Q, Shi H, Zhuang Z, Zhao X, Lian M, Wang J, Ye Y, Quan L, Wu H, Wang K, Lai L. ACBE, a new base editor for simultaneous C-to-T and A-to-G substitutions in mammalian systems. BMC Biol 2020; 18:131. [PMID: 32967664 PMCID: PMC7510086 DOI: 10.1186/s12915-020-00866-5] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Accepted: 09/09/2020] [Indexed: 11/21/2022] Open
Abstract
BACKGROUND Many favorable traits of crops and livestock and human genetic diseases arise from multiple single nucleotide polymorphisms or multiple point mutations with heterogeneous base substitutions at the same locus. Current cytosine or adenine base editors can only accomplish C-to-T (G-to-A) or A-to-G (T-to-C) substitutions in the windows of target genomic sites of organisms; therefore, there is a need to develop base editors that can simultaneously achieve C-to-T and A-to-G substitutions at the targeting site. RESULTS In this study, a novel fusion adenine and cytosine base editor (ACBE) was generated by fusing a heterodimer of TadA (ecTadAWT/*) and an activation-induced cytidine deaminase (AID) to the N- and C-terminals of Cas9 nickase (nCas9), respectively. ACBE could simultaneously induce C-to-T and A-to-G base editing at the same target site, which were verified in HEK293-EGFP reporter cell line and 45 endogenous gene loci of HEK293 cells. Moreover, the ACBE could accomplish simultaneous point mutations of C-to-T and A-to-G in primary somatic cells (mouse embryonic fibroblasts and porcine fetal fibroblasts) in an applicable efficiency. Furthermore, the spacer length of sgRNA and the length of linker could influence the dual base editing activity, which provided a direction to optimize the ACBE system. CONCLUSION The newly developed ACBE would expand base editor toolkits and should promote the generation of animals and the gene therapy of genetic diseases with heterogeneous point mutations.
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Affiliation(s)
- Jingke Xie
- CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
- Research Unit of Generation of Large Animal Disease Models, Chinese Academy of Medical Sciences (2019RU015), Guangzhou, 510530, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Xingyun Huang
- CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Xia Wang
- CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory, GRMH-GDL), Guangzhou, 510005, China
| | - Shixue Gou
- CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yanhui Liang
- CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Fangbing Chen
- CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Nan Li
- CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
- Research Unit of Generation of Large Animal Disease Models, Chinese Academy of Medical Sciences (2019RU015), Guangzhou, 510530, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Zhen Ouyang
- CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
- Research Unit of Generation of Large Animal Disease Models, Chinese Academy of Medical Sciences (2019RU015), Guangzhou, 510530, China
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory, GRMH-GDL), Guangzhou, 510005, China
| | - Quanjun Zhang
- CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
- Research Unit of Generation of Large Animal Disease Models, Chinese Academy of Medical Sciences (2019RU015), Guangzhou, 510530, China
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory, GRMH-GDL), Guangzhou, 510005, China
| | - Weikai Ge
- CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
- Research Unit of Generation of Large Animal Disease Models, Chinese Academy of Medical Sciences (2019RU015), Guangzhou, 510530, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Qin Jin
- CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
- Research Unit of Generation of Large Animal Disease Models, Chinese Academy of Medical Sciences (2019RU015), Guangzhou, 510530, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Hui Shi
- CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Zhenpeng Zhuang
- CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Xiaozhu Zhao
- CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Meng Lian
- CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
- Institute of Physical Science and Information Technology, Anhui University, Hefei, 230601, China
| | - Jiaowei Wang
- CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yinghua Ye
- CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
- Research Unit of Generation of Large Animal Disease Models, Chinese Academy of Medical Sciences (2019RU015), Guangzhou, 510530, China
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory, GRMH-GDL), Guangzhou, 510005, China
| | - Longquan Quan
- CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
- Research Unit of Generation of Large Animal Disease Models, Chinese Academy of Medical Sciences (2019RU015), Guangzhou, 510530, China
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory, GRMH-GDL), Guangzhou, 510005, China
| | - Han Wu
- CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
- Research Unit of Generation of Large Animal Disease Models, Chinese Academy of Medical Sciences (2019RU015), Guangzhou, 510530, China
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory, GRMH-GDL), Guangzhou, 510005, China
| | - Kepin Wang
- CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China.
- Research Unit of Generation of Large Animal Disease Models, Chinese Academy of Medical Sciences (2019RU015), Guangzhou, 510530, China.
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory, GRMH-GDL), Guangzhou, 510005, China.
| | - Liangxue Lai
- CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China.
- Research Unit of Generation of Large Animal Disease Models, Chinese Academy of Medical Sciences (2019RU015), Guangzhou, 510530, China.
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory, GRMH-GDL), Guangzhou, 510005, China.
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Bakoev S, Getmantseva L, Bakoev F, Kolosova M, Gabova V, Kolosov A, Kostyunina O. Survey of SNPs Associated with Total Number Born and Total Number Born Alive in Pig. Genes (Basel) 2020; 11:E491. [PMID: 32365801 PMCID: PMC7291110 DOI: 10.3390/genes11050491] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2020] [Revised: 04/24/2020] [Accepted: 04/29/2020] [Indexed: 12/26/2022] Open
Abstract
Reproductive productivity depend on a complex set of characteristics. The number of piglets at birth (Total number born, Litter size, TNB) and the number of alive piglets at birth (Total number born alive, NBA) are the main indicators of the reproductive productivity of sows in pig breeding. Great hopes are pinned on GWAS (Genome-Wide Association Studies) to solve the problems associated with studying the genetic architecture of reproductive traits of pigs. This paper provides an overview of international studies on SNP (Single nucleotide polymorphism) associated with TNB and NBA in pigs presented in PigQTLdb as "Genome map association". Currently on the base of Genome map association results 306 SNPs associated with TNB (218 SNPs) and NBA (88 SNPs) have been identified and presented in the Pig QTLdb database. The results are based on research of pigs such as Large White, Yorkshire, Landrace, Berkshire, Duroc and Erhualian. The presented review shows that most SNPs found in chromosome areas where candidate genes or QTLs (Quantitative trait locus) have been identified. Further research in the given direction will allow to obtain new data that will become an impulse for creating breakthrough breeding technologies and increase the production efficiency in pig farming.
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Affiliation(s)
- Siroj Bakoev
- Federal Science Center for Animal Husbandry named after Academy Member L.K. Ernst, Dubrovitsy 142132, Russian; (S.B.); (F.B.); (M.K.); (A.K.); (O.K.)
| | - Lyubov Getmantseva
- Federal Science Center for Animal Husbandry named after Academy Member L.K. Ernst, Dubrovitsy 142132, Russian; (S.B.); (F.B.); (M.K.); (A.K.); (O.K.)
| | - Faridun Bakoev
- Federal Science Center for Animal Husbandry named after Academy Member L.K. Ernst, Dubrovitsy 142132, Russian; (S.B.); (F.B.); (M.K.); (A.K.); (O.K.)
- Department of Biology and Biotechnology, Southern Federal University, Rostov-on-Don 344006, Russia;
| | - Maria Kolosova
- Federal Science Center for Animal Husbandry named after Academy Member L.K. Ernst, Dubrovitsy 142132, Russian; (S.B.); (F.B.); (M.K.); (A.K.); (O.K.)
- Department of Biotechnology, Don State Agrarian University, Persianovski 346493, Russia
| | - Valeria Gabova
- Department of Biology and Biotechnology, Southern Federal University, Rostov-on-Don 344006, Russia;
| | - Anatoly Kolosov
- Federal Science Center for Animal Husbandry named after Academy Member L.K. Ernst, Dubrovitsy 142132, Russian; (S.B.); (F.B.); (M.K.); (A.K.); (O.K.)
- Department of Biotechnology, Don State Agrarian University, Persianovski 346493, Russia
| | - Olga Kostyunina
- Federal Science Center for Animal Husbandry named after Academy Member L.K. Ernst, Dubrovitsy 142132, Russian; (S.B.); (F.B.); (M.K.); (A.K.); (O.K.)
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7
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Lowe JWE, Bruce A. Genetics without genes? The centrality of genetic markers in livestock genetics and genomics. HISTORY AND PHILOSOPHY OF THE LIFE SCIENCES 2019; 41:50. [PMID: 31659490 DOI: 10.1007/s40656-019-0290-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2019] [Accepted: 10/18/2019] [Indexed: 05/23/2023]
Abstract
In this paper, rather than focusing on genes as an organising concept around which historical considerations of theory and practice in genetics are elucidated, we place genetic markers at the heart of our analysis. This reflects their central role in the subject of our account, livestock genetics concerning the domesticated pig, Sus scrofa. We define a genetic marker as a (usually material) element existing in different forms in the genome, that can be identified and mapped using a variety (and often combination) of quantitative, classical and molecular genetic techniques. The conjugation of pig genome researchers around the common object of the marker from the early-1990s allowed the distinctive theories and approaches of quantitative and molecular genetics concerning the size and distribution of gene effects to align (but never fully integrate) in projects to populate genome maps. Critical to this was the nature of markers as ontologically inert, internally heterogeneous and relational. Though genes as an organising and categorising principle remained important, the particular concatenation of limitations, opportunities, and intended research goals of the pig genetics community, meant that a progressively stronger focus on the identification and mapping of markers rather than genes per se became a hallmark of the community. We therefore detail a different way of doing genetics to more gene-centred accounts. By doing so, we reveal the presence of practices, concepts and communities that would otherwise be hidden.
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Affiliation(s)
- James W E Lowe
- Science, Technology and Innovation Studies, University of Edinburgh, Old Surgeons' Hall, High School Yards, Edinburgh, EH1 1LZ, UK.
| | - Ann Bruce
- Science, Technology and Innovation Studies, University of Edinburgh, Old Surgeons' Hall, High School Yards, Edinburgh, EH1 1LZ, UK
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8
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GWAS for Meat and Carcass Traits Using Imputed Sequence Level Genotypes in Pooled F2-Designs in Pigs. G3-GENES GENOMES GENETICS 2019; 9:2823-2834. [PMID: 31296617 PMCID: PMC6723123 DOI: 10.1534/g3.119.400452] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
In order to gain insight into the genetic architecture of economically important traits in pigs and to derive suitable genetic markers to improve these traits in breeding programs, many studies have been conducted to map quantitative trait loci. Shortcomings of these studies were low mapping resolution, large confidence intervals for quantitative trait loci-positions and large linkage disequilibrium blocks. Here, we overcome these shortcomings by pooling four large F2 designs to produce smaller linkage disequilibrium blocks and by resequencing the founder generation at high coverage and the F1 generation at low coverage for subsequent imputation of the F2 generation to whole genome sequencing marker density. This lead to the discovery of more than 32 million variants, 8 million of which have not been previously reported. The pooling of the four F2 designs enabled us to perform a joint genome-wide association study, which lead to the identification of numerous significantly associated variant clusters on chromosomes 1, 2, 4, 7, 17 and 18 for the growth and carcass traits average daily gain, back fat thickness, meat fat ratio, and carcass length. We could not only confirm previously reported, but also discovered new quantitative trait loci. As a result, several new candidate genes are discussed, among them BMP2 (bone morphogenetic protein 2), which we recently discovered in a related study. Variant effect prediction revealed that 15 high impact variants for the traits back fat thickness, meat fat ratio and carcass length were among the statistically significantly associated variants.
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9
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Palomar G, Vasemägi A, Ahmad F, Nicieza AG, Cano JM. Mapping of quantitative trait loci for life history traits segregating within common frog populations. Heredity (Edinb) 2019; 122:800-808. [PMID: 30631147 DOI: 10.1038/s41437-018-0175-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2018] [Revised: 12/08/2018] [Accepted: 12/12/2018] [Indexed: 02/01/2023] Open
Abstract
The evolution of complex traits is often shaped by adaptive divergence. However, very little is known about the number, effect size, and location of the genomic regions influencing the variation of these traits in natural populations. Based on a dense linkage map of the common frog, Rana temporaria, we have localized, for the first time in amphibians, three significant and nine suggestive quantitative trait loci (QTLs) for metabolic rate, growth rate, development time, and weight at metamorphosis, explaining 5.6-18.9% of the overall phenotypic variation in each trait. We also found a potential pleiotropic QTL between development time and size at metamorphosis that, if confirmed, might underlie the previously reported genetic correlation between these traits. Furthermore, we demonstrate that the genetic variation linked to fitness-related larval traits segregates within Rana temporaria populations. This study provides the first insight into the genomic regions that affect larval life history traits in anurans, providing a valuable resource to delve further into the genomic basis of evolutionary change in amphibians.
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Affiliation(s)
- Gemma Palomar
- Research Unit of Biodiversity (UO-CSIC-PA), 33600, Mieres, Asturias, Spain. .,Department of Biology of Organisms and Systems, University of Oviedo, 33006, Oviedo, Asturias, Spain. .,Molecular and Behavioral Ecology Group, Institute of Environmental Sciences, Jagiellonian University, 30-387, Krakow, Poland.
| | - Anti Vasemägi
- Department of Biology, University of Turku, 20014, Turku, Finland.,Department of Aquaculture, Institute of Veterinary Medicine and Animal Science, Estonian University of Life Sciences, 51006, Tartu, Estonia.,Department of Aquatic Resources, Institute of Freshwater Research, Swedish University of Agricultural Sciences, 17893, Drottningholm, Sweden
| | - Freed Ahmad
- Department of Biology, University of Turku, 20014, Turku, Finland
| | - Alfredo G Nicieza
- Research Unit of Biodiversity (UO-CSIC-PA), 33600, Mieres, Asturias, Spain.,Department of Biology of Organisms and Systems, University of Oviedo, 33006, Oviedo, Asturias, Spain
| | - José Manuel Cano
- Research Unit of Biodiversity (UO-CSIC-PA), 33600, Mieres, Asturias, Spain.,Department of Biology of Organisms and Systems, University of Oviedo, 33006, Oviedo, Asturias, Spain
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10
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Louro B, Martins RS, Pinto PI, Reinhardt R, de Koning DJ, Canario AV, Power DM. SuperSAGE digital expression analysis of differential growth rate in a European sea bass population. AQUACULTURE AND FISHERIES 2019. [DOI: 10.1016/j.aaf.2018.03.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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11
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Shared Genomic Regions Underlie Natural Variation in Diverse Toxin Responses. Genetics 2018; 210:1509-1525. [PMID: 30341085 PMCID: PMC6283156 DOI: 10.1534/genetics.118.301311] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2018] [Accepted: 10/16/2018] [Indexed: 01/25/2023] Open
Abstract
Phenotypic complexity is caused by the contributions of environmental factors and multiple genetic loci, interacting or acting independently. Studies of yeast and Arabidopsis often find that the majority of natural variation across phenotypes is attributable to independent additive quantitative trait loci (QTL). Detected loci in these organisms explain most of the estimated heritable variation. By contrast, many heritable components underlying phenotypic variation in metazoan models remain undetected. Before the relative impacts of additive and interactive variance components on metazoan phenotypic variation can be dissected, high replication and precise phenotypic measurements are required to obtain sufficient statistical power to detect loci contributing to this missing heritability. Here, we used a panel of 296 recombinant inbred advanced intercross lines of Caenorhabditis elegans and a high-throughput fitness assay to detect loci underlying responses to 16 different toxins, including heavy metals, chemotherapeutic drugs, pesticides, and neuropharmaceuticals. Using linkage mapping, we identified 82 QTL that underlie variation in responses to these toxins, and predicted the relative contributions of additive loci and genetic interactions across various growth parameters. Additionally, we identified three genomic regions that impact responses to multiple classes of toxins. These QTL hotspots could represent common factors impacting toxin responses. We went further to generate near-isogenic lines and chromosome substitution strains, and then experimentally validated these QTL hotspots, implicating additive and interactive loci that underlie toxin-response variation.
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12
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Genome-wide association studies and meta-analysis uncovers new candidate genes for growth and carcass traits in pigs. PLoS One 2018; 13:e0205576. [PMID: 30308042 PMCID: PMC6181390 DOI: 10.1371/journal.pone.0205576] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2018] [Accepted: 09/27/2018] [Indexed: 11/19/2022] Open
Abstract
Genome-wide association studies (GWAS) have been widely used in the genetic dissection of complex traits. As more genomic data is being generated within different commercial or resource pig populations, the challenge which arises is how to collectively investigate the data with the purpose to increase sample size and implicitly the statistical power. This study performs an individual population GWAS, a joint population GWAS and a meta-analysis in three pig F2 populations. D1 is derived from European type breeds (Piétrain, Large White and Landrace), D2 is obtained from an Asian breed (Meishan) and Piétrain, and D3 stems from a European Wild Boar and Piétrain, which is the common founder breed. The traits investigated are average daily gain, backfat thickness, meat to fat ratio and carcass length. The joint and the meta-analysis did not identify additional genomic clusters besides the ones discovered via the individual population GWAS. However, the benefit was an increased mapping resolution which pinpointed to narrower clusters harboring causative variants. The joint analysis identified a higher number of clusters as compared to the meta-analysis; nevertheless, the significance levels and the number of significant variants in the meta-analysis were generally higher. Both types of analysis had similar outputs suggesting that the two strategies can complement each other and that the meta-analysis approach can be a valuable tool whenever access to raw datasets is limited. Overall, a total of 20 genomic clusters that predominantly overlapped at various extents, were identified on chromosomes 2, 7 and 17, many confirming previously identified quantitative trait loci. Several new candidate genes are being proposed and, among them, a strong candidate gene to be taken into account for subsequent analysis is BMP2 (bone morphogenetic protein 2).
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Kojima M, Nakajima I, Arakawa A, Mikawa S, Matsumoto T, Uenishi H, Nakamura Y, Taniguchi M. Differences in gene expression profiles for subcutaneous adipose, liver, and skeletal muscle tissues between Meishan and Landrace pigs with different backfat thicknesses. PLoS One 2018; 13:e0204135. [PMID: 30240433 PMCID: PMC6150482 DOI: 10.1371/journal.pone.0204135] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2018] [Accepted: 09/03/2018] [Indexed: 01/10/2023] Open
Abstract
Backfat thickness is one of the most important traits of commercially raised pigs. Meishan pigs are renowned for having thicker backfat than Landrace pigs. To examine the genetic factors responsible for the differences, we first produced female crossbred pig lines by mating Landrace (L) × Large White (W) × Duroc (D) females (LWD) with Landrace (L) or Meishan (M) boars (i.e., LWD × L = LWDL for Landrace offspring and LWD × M = LWDM for the Meishan offspring). We confirmed that LWDM pigs indeed had a thicker backfat than LWDL pigs. Next, we performed gene expression microarray analysis in both genetic lines to examine differentially expressed genes (DEGs) in energy metabolism-related tissues, subcutaneous adipose (fat), liver, and longissimus dorsi muscle tissues. We analyzed the annotation of DEGs (2-fold cutoff) to functionally categorize them by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways. The number of DEGs in muscle tissues of both lines was much less than that in fat and liver tissues, indicating that DEGs in muscle tissues may not contribute much to differences in backfat thickness. In contrast, several genes related to muscle (in fat tissue) and lipid metabolism (in liver tissue) were more upregulated in LWDM pigs than LWDL pigs, indicating that those DEGs might be responsible for differences in backfat thickness. The different genome-wide gene expression profiles in the fat, liver, and muscle tissues between genetic lines can provide useful information for pig breeders.
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Affiliation(s)
- Misaki Kojima
- Animal Genome Unit, Institute of Livestock and Grassland Science, National Agriculture and Food Research Organization (NARO), Tsukuba, Ibaraki, Japan
| | - Ikuyo Nakajima
- Meat Quality Research Unit, Institute of Livestock and Grassland Science, National Agriculture and Food Research Organization (NARO), Tsukuba, Ibaraki, Japan
| | - Aisaku Arakawa
- Animal Genome Unit, Institute of Livestock and Grassland Science, National Agriculture and Food Research Organization (NARO), Tsukuba, Ibaraki, Japan
| | - Satoshi Mikawa
- Animal Genome Unit, Institute of Livestock and Grassland Science, National Agriculture and Food Research Organization (NARO), Tsukuba, Ibaraki, Japan
| | - Toshimi Matsumoto
- Animal Bioregulation Unit, Institute of Agrobiological Sciences, National Agriculture and Food Research Organization (NARO), Tsukuba, Ibaraki, Japan
| | - Hirohide Uenishi
- Animal Bioregulation Unit, Institute of Agrobiological Sciences, National Agriculture and Food Research Organization (NARO), Tsukuba, Ibaraki, Japan
| | - Yuki Nakamura
- Insect Genome Research Unit, Institute of Agrobiological Sciences, National Agriculture and Food Research Organization (NARO), Tsukuba, Ibaraki, Japan
| | - Masaaki Taniguchi
- Animal Genome Unit, Institute of Livestock and Grassland Science, National Agriculture and Food Research Organization (NARO), Tsukuba, Ibaraki, Japan
- * E-mail:
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14
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Stratz P, Schmid M, Wellmann R, Preuß S, Blaj I, Tetens J, Thaller G, Bennewitz J. Linkage disequilibrium pattern and genome-wide association mapping for meat traits in multiple porcine F 2 crosses. Anim Genet 2018; 49:403-412. [PMID: 29978910 DOI: 10.1111/age.12684] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/28/2018] [Indexed: 12/13/2022]
Abstract
In the present study, data from four F2 crosses were analysed and used to study the linkage disequilibrium (LD) structure within and across the crosses. Genome-wide association analyses (GWASes) for conductivity and dressing out meat traits were conducted using single-marker and Bayesian multi-marker models using the pooled data from all F2 crosses. Porcine F2 crosses generated from the distantly related founder breeds Wild Boar, Piétrain and Meishan, as well as from a porcine F2 cross from the closely related founder breed Piétrain and an F1 Large White × Landrace cross were pooled. A total of 2572 F2 animals were genotyped using a 62K SNP chip. The positions of the SNPs were based on genome assembly Sscrofa11.1. After post-alignment and genotype filtering, approximately 50K SNPs were usable for LD studies and GWASes. The main findings of the present study are that the breakdown of LD was faster in crosses from closely related founder breeds compared to crosses from distantly related founders. The fastest breakdown of LD was observed by pooling the data. Based on the single-marker results and LD structure, clusters and windows were built for 1-Mb intervals. For conductivity and dressing out, 183 and 191 nominal significant associations respectively and six and five clusters respectively were found. Dominance was important for conductivity, and considering dominance in GWASes improved the mapping signals. Most clear signals were found for conductivity on SSC6, 8 and 15 and for dressing out on SSC2 and 7. Considering dominance might contribute to the accuracy of genomic selection and serve as a guide for choosing mating pairs with good combining abilities. However, further research is needed to investigate if dominance is also important in crossbreed pig breeding schemes.
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Affiliation(s)
- P Stratz
- Institute of Animal Science, University of Hohenheim, Garbenstraße 17, 70599, Stuttgart, Germany
| | - M Schmid
- Institute of Animal Science, University of Hohenheim, Garbenstraße 17, 70599, Stuttgart, Germany
| | - R Wellmann
- Institute of Animal Science, University of Hohenheim, Garbenstraße 17, 70599, Stuttgart, Germany
| | - S Preuß
- Institute of Animal Science, University of Hohenheim, Garbenstraße 17, 70599, Stuttgart, Germany
| | - I Blaj
- Institute of Animal Breeding and Husbandry, Christian-Albrechts-University Kiel, Hermann-Rodewald-Straße 6, 24118, Kiel, Germany
| | - J Tetens
- Functional Breeding Group, Department of Animal Science, Georg-August-University Göttingen, Burckhardtweg 2, 37077, Göttingen, Germany
| | - G Thaller
- Institute of Animal Breeding and Husbandry, Christian-Albrechts-University Kiel, Hermann-Rodewald-Straße 6, 24118, Kiel, Germany
| | - J Bennewitz
- Institute of Animal Science, University of Hohenheim, Garbenstraße 17, 70599, Stuttgart, Germany
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15
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Schmid M, Wellmann R, Bennewitz J. Power and precision of QTL mapping in simulated multiple porcine F2 crosses using whole-genome sequence information. BMC Genet 2018; 19:22. [PMID: 29614956 PMCID: PMC5883302 DOI: 10.1186/s12863-018-0604-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2017] [Accepted: 03/14/2018] [Indexed: 11/10/2022] Open
Abstract
Background During the last two decades, many QTL (quantitative trait locus) mapping experiments in pigs have been conducted using F2 crosses established from two outbred founder breeds. The founder breeds were frequently chosen from the Asian and European type breeds. A combination of next-generation sequencing, SNP (single nucleotide polymorphism) genotyping technology using SNP-chips, and genotype imputation techniques, can be used to infer the sequence information of all F2 individuals in a cost-effective way. The aim of the present simulation study was to analyze the power and precision of genome-wide association studies (GWASs) with whole-genome sequence data in several types of F2 crosses, including pooled crosses. Methods Based on a common historical population, three breeds representing two European type breeds (EU1 and EU2) and one Asian type breed (AS) were simulated. Two F2 designs of 500 individuals each were simulated. The cross EU1xEU2 (ASxEU2) was simulated using the phylogenetically closely related breeds EU1 and EU2 (or distantly related breeds AS and EU2) as the founder breeds. The simulated genomes comprised ten chromosomes, each with a length of 1 Morgan and whole-genome sequence information. A polygenic trait with a heritability of 0.5, which was affected by approximately 20 QTL per Morgan, was simulated. GWASs were conducted using single marker mixed linear models, either within the crosses or in their pooled datasets. Additionally, the studies were conducted in the breed EU2, which was a founder breed in both simulated crosses. Results The power to map QTL was high (low) in the ASxEU2 (EU1xEU2) cross and was highest when the data of both crosses were analyzed jointly. By contrast, the mapping precision was the highest in the EU1xEU2 cross. Pooling data led to a precision that was in between the precision of the EU1xEU2 cross and the ASxEU2 cross. A higher mapping precision was observed for QTL segregating within a founder breed. Conclusions These results suggest that the existing F2 crosses are promising databases for QTL mapping when the founder breeds are closely related or several crosses can be pooled. This conclusion is particularly applicable for QTL that segregate in a founder breed.
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Affiliation(s)
- Markus Schmid
- Institute of Animal Science, University of Hohenheim, 70599, Stuttgart, Germany.
| | - Robin Wellmann
- Institute of Animal Science, University of Hohenheim, 70599, Stuttgart, Germany
| | - Jörn Bennewitz
- Institute of Animal Science, University of Hohenheim, 70599, Stuttgart, Germany
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Schmid M, Bennewitz J. Invited review: Genome-wide association analysis for quantitative traits in livestock – a selective review of statistical models and experimental designs. Arch Anim Breed 2017. [DOI: 10.5194/aab-60-335-2017] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Abstract
Abstract. Quantitative or complex traits are controlled by many genes and environmental factors. Most traits in livestock breeding are quantitative traits. Mapping genes and causative mutations generating the genetic variance of these traits is still a very active area of research in livestock genetics. Since genome-wide and dense SNP panels are available for most livestock species, genome-wide association studies (GWASs) have become the method of choice in mapping experiments. Different statistical models are used for GWASs. We will review the frequently used single-marker models and additionally describe Bayesian multi-marker models. The importance of nonadditive genetic and genotype-by-environment effects along with GWAS methods to detect them will be briefly discussed. Different mapping populations are used and will also be reviewed. Whenever possible, our own real-data examples are included to illustrate the reviewed methods and designs. Future research directions including post-GWAS strategies are outlined.
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Wang L, Liu P, Huang S, Ye B, Chua E, Wan ZY, Yue GH. Genome-Wide Association Study Identifies Loci Associated with Resistance to Viral Nervous Necrosis Disease in Asian Seabass. MARINE BIOTECHNOLOGY (NEW YORK, N.Y.) 2017; 19:255-265. [PMID: 28484864 DOI: 10.1007/s10126-017-9747-7] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2016] [Accepted: 04/19/2017] [Indexed: 05/22/2023]
Abstract
Viral nervous necrosis disease (VNN), caused by nervous necrosis virus (NNV), is one major threat to mariculture. Identifying loci and understanding the mechanisms associated with resistance to VNN are important in selective breeding programs. We performed a genome-wide association study (GWAS) using genotyping-by-sequencing (GBS) to study the genomic architecture of resistance to NNV infection in Asian seabass. We genotyped 986 individuals from 43 families produced by 15 founders with 44498 bi-allelic genetic variants using GBS. The GWAS identified three genome-wide significant loci on chromosomes 16, 19, and 20, respectively, and six suggestive loci on chromosomes 1, 8, 14, 15, 21, and 24, respectively, associated with resistance to NNV infection measured as binary and quantitative traits. Using the 500 most significant markers in combination with a training population of 800 samples could reach a genomic prediction accuracy of 0.7. Candidate genes significantly associated with resistance to NNV, including lysine-specific demethylase 2A, beta-defensin 1, and cystatin-B, which play important roles in immune responses against virus infection, were identified. Almost all the candidate genes were differentially expressed in different tissues against NNV infection. The significant genetic variants can be used in genomic selection and help understand the mechanism of resistance to VNN. Future studies should use populations of large effective size and whole genome resequencing to identify more useful genetic variants.
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Affiliation(s)
- Le Wang
- Molecular Population Genetics and Breeding Group, Temasek Life Sciences Laboratory, 1 Research Link, National University of Singapore, Singapore, 117604, Singapore
| | - Peng Liu
- Molecular Population Genetics and Breeding Group, Temasek Life Sciences Laboratory, 1 Research Link, National University of Singapore, Singapore, 117604, Singapore
- Department of Biological Sciences, National University of Singapore, 14 Science Drive 4, Singapore, 117543, Singapore
| | - Shuqing Huang
- Molecular Population Genetics and Breeding Group, Temasek Life Sciences Laboratory, 1 Research Link, National University of Singapore, Singapore, 117604, Singapore
| | - Baoqing Ye
- Molecular Population Genetics and Breeding Group, Temasek Life Sciences Laboratory, 1 Research Link, National University of Singapore, Singapore, 117604, Singapore
| | - Elaine Chua
- Molecular Population Genetics and Breeding Group, Temasek Life Sciences Laboratory, 1 Research Link, National University of Singapore, Singapore, 117604, Singapore
| | - Zi Yi Wan
- Molecular Population Genetics and Breeding Group, Temasek Life Sciences Laboratory, 1 Research Link, National University of Singapore, Singapore, 117604, Singapore
- School of Biological Sciences, Nanyang Technological University, 60 Nanyang Drive, Singapore, 637551, Singapore
| | - Gen Hua Yue
- Molecular Population Genetics and Breeding Group, Temasek Life Sciences Laboratory, 1 Research Link, National University of Singapore, Singapore, 117604, Singapore.
- Department of Biological Sciences, National University of Singapore, 14 Science Drive 4, Singapore, 117543, Singapore.
- School of Biological Sciences, Nanyang Technological University, 60 Nanyang Drive, Singapore, 637551, Singapore.
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Bunter KL, Hermesch S. What does the ‘closed herd’ really mean for Australian breeding companies and their customers? ANIMAL PRODUCTION SCIENCE 2017. [DOI: 10.1071/an17321] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
The perception that the genetic background of the Australian pig population is limiting for genetic improvement of commercial pigs in Australia is considered in the context of well established theory combined with practical evidence. The diversity of pig breeds used in modern commercial pig-breeding programs is diminished worldwide relative to all the pig breeds available. Australia is no different in this respect. The use of predominantly three main breeds (Large White, Landrace, Duroc) and synthetic lines, with contributions from other minor breeds to form the basis of a cross-breeding system for commercial pig production is well established internationally. The Australian concern of relatively small founder populations is potentially of relevance, from a theoretical perspective, for (1) the prevalence of defects or the presence of desirable alleles, and (2) the loss of genetic variation or increase in inbreeding depression resulting from increased inbreeding in closed nucleus lines, potentially reducing response to selection. However, rates of response achieved in Australian herds are generally commensurate with the performance recording and selection emphasis applied, and do not appear to be unduly restricted. Moreover, favourable alleles present in unrepresented breeds are frequently present in the three major breeds elsewhere, and therefore would be expected to be present within the Australian populations. Wider testing would provide confirmation of this. Comparison of estimates of effective population size of Australian populations with experimental selection lines overseas (e.g. INRA) or other intensely selected species (e.g. Holstein cattle) suggest adequate genetic diversity to achieve ongoing genetic improvement in the Australian pig industry. However, fitness traits should be included in breeding goals. What remains to be seen is whether novel phenotypes or genotypes are required to meet future challenges, which might be imposed by changes in the environment (e.g. climate change, disease) or market needs. Given probable overlap in genetic merit across Australian and foreign populations for unselected attributes, we suggest that sufficient genetic resources are already present in Australian herds to continue commercial progress within existing Australian populations that have adapted to Australian conditions.
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Fontanesi L, Scotti E, Speroni C, Buttazzoni L, Russo V. A selective genotyping approach identifies single nucleotide polymorphisms in porcine chromosome 2 genes associated with production and carcass traits in Italian heavy pigs. ITALIAN JOURNAL OF ANIMAL SCIENCE 2016. [DOI: 10.4081/ijas.2011.e15] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Sarup P, Jensen J, Ostersen T, Henryon M, Sørensen P. Increased prediction accuracy using a genomic feature model including prior information on quantitative trait locus regions in purebred Danish Duroc pigs. BMC Genet 2016; 17:11. [PMID: 26728402 PMCID: PMC4700613 DOI: 10.1186/s12863-015-0322-9] [Citation(s) in RCA: 50] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2015] [Accepted: 12/20/2015] [Indexed: 12/31/2022] Open
Abstract
Background In animal breeding, genetic variance for complex traits is often estimated using linear mixed models that incorporate information from single nucleotide polymorphism (SNP) markers using a realized genomic relationship matrix. In such models, individual genetic markers are weighted equally and genomic variation is treated as a “black box.” This approach is useful for selecting animals with high genetic potential, but it does not generate or utilise knowledge of the biological mechanisms underlying trait variation. Here we propose a linear mixed-model approach that can evaluate the collective effects of sets of SNPs and thereby open the “black box.” The described genomic feature best linear unbiased prediction (GFBLUP) model has two components that are defined by genomic features. Results We analysed data on average daily gain, feed efficiency, and lean meat percentage from 3,085 Duroc boars, along with genotypes from a 60 K SNP chip. In addition information on known quantitative trait loci (QTL) from the animal QTL database was integrated in the GFBLUP as a genomic feature. Our results showed that the most significant QTL categories were indeed biologically meaningful. Additionally, for high heritability traits, prediction accuracy was improved by the incorporation of biological knowledge in prediction models. A simulation study using the real genotypes and simulated phenotypes demonstrated challenges regarding detection of causal variants in low to medium heritability traits. Conclusions The GFBLUP model showed increased predictive ability when enough causal variants were included in the genomic feature to explain over 10 % of the genomic variance, and when dilution by non-causal markers was minimal. In the observed data set, predictive ability was increased by the inclusion of prior QTL information obtained outside the training data set, but only for the trait with highest heritability. Electronic supplementary material The online version of this article (doi:10.1186/s12863-015-0322-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Pernille Sarup
- Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, Blichers Allé 20, 8830, Tjele, Denmark.
| | - Just Jensen
- Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, Blichers Allé 20, 8830, Tjele, Denmark.
| | - Tage Ostersen
- SEGES Danish Pig Research Centre, Axeltorv 3, 1609, Copenhagen V, Denmark.
| | - Mark Henryon
- SEGES Danish Pig Research Centre, Axeltorv 3, 1609, Copenhagen V, Denmark.
| | - Peter Sørensen
- Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, Blichers Allé 20, 8830, Tjele, Denmark.
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Identification of pleiotropic genes and gene sets underlying growth and immunity traits: a case study on Meishan pigs. Animal 2015; 10:550-7. [PMID: 26689779 DOI: 10.1017/s1751731115002761] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
Both growth and immune capacity are important traits in animal breeding. The animal quantitative trait loci (QTL) database is a valuable resource and can be used for interpreting the genetic mechanisms that underlie growth and immune traits. However, QTL intervals often involve too many candidate genes to find the true causal genes. Therefore, the aim of this study was to provide an effective annotation pipeline that can make full use of the information of Gene Ontology terms annotation, linkage gene blocks and pathways to further identify pleiotropic genes and gene sets in the overlapping intervals of growth-related and immunity-related QTLs. In total, 55 non-redundant QTL overlapping intervals were identified, 1893 growth-related genes and 713 immunity-related genes were further classified into overlapping intervals and 405 pleiotropic genes shared by the two gene sets were determined. In addition, 19 pleiotropic gene linkage blocks and 67 pathways related to immunity and growth traits were discovered. A total of 343 growth-related genes and 144 immunity-related genes involved in pleiotropic pathways were also identified, respectively. We also sequenced and genotyped 284 individuals from Chinese Meishan pigs and European pigs and mapped the single nucleotide polymorphisms (SNPs) to the pleiotropic genes and gene sets that we identified. A total of 971 high-confidence SNPs were mapped to the pleiotropic genes and gene sets that we identified, and among them 743 SNPs were statistically significant in allele frequency between Meishan and European pigs. This study explores the relationship between growth and immunity traits from the view of QTL overlapping intervals and can be generalized to explore the relationships between other traits.
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Wright D. The Genetic Architecture of Domestication in Animals. Bioinform Biol Insights 2015; 9:11-20. [PMID: 26512200 PMCID: PMC4603525 DOI: 10.4137/bbi.s28902] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2015] [Revised: 08/24/2015] [Accepted: 08/26/2015] [Indexed: 12/12/2022] Open
Abstract
Domestication has been essential to the progress of human civilization, and the process itself has fascinated biologists for hundreds of years. Domestication has led to a series of remarkable changes in a variety of plants and animals, in what is termed the “domestication phenotype.” In domesticated animals, this general phenotype typically consists of similar changes in tameness, behavior, size/morphology, color, brain composition, and adrenal gland size. This domestication phenotype is seen in a range of different animals. However, the genetic basis of these associated changes is still puzzling. The genes for these different traits tend to be grouped together in clusters in the genome, though it is still not clear whether these clusters represent pleiotropic effects, or are in fact linked clusters. This review focuses on what is currently known about the genetic architecture of domesticated animal species, if genes of large effect (often referred to as major genes) are prevalent in driving the domestication phenotype, and whether pleiotropy can explain the loci underpinning these diverse traits being colocated.
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Affiliation(s)
- Dominic Wright
- IFM Biology, AVIAN Behavioural Genomics and Physiology Group, Linköping University, Linköping, Sweden
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23
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Endometrial gene expression profile of pregnant sows with extreme phenotypes for reproductive efficiency. Sci Rep 2015; 5:14416. [PMID: 26435523 PMCID: PMC5155628 DOI: 10.1038/srep14416] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2015] [Accepted: 08/19/2015] [Indexed: 12/22/2022] Open
Abstract
Prolificacy can directly impact porcine profitability, but large genetic variation and low heritability have been found regarding litter size among porcine breeds. To identify key differences in gene expression associated to swine reproductive efficiency, we performed a transcriptome analysis of sows' endometrium from an Iberian x Meishan F2 population at day 30-32 of gestation, classified according to their estimated breeding value (EBV) as high (H, EBV > 0) and low (L, EBV < 0) prolificacy phenotypes. For each sample, mRNA and small RNA libraries were RNA-sequenced, identifying 141 genes and 10 miRNAs differentially expressed between H and L groups. We selected four miRNAs based on their role in reproduction, and five genes displaying the highest differences and a positive mapping into known reproductive QTLs for RT-qPCR validation on the whole extreme population. Significant differences were validated for genes: PTGS2 (p = 0.03; H/L ratio = 3.50), PTHLH (p = 0.03; H/L ratio = 3.69), MMP8 (p = 0.01; H/L ratio =4.41) and SCNN1G (p = 0.04; H/L ratio = 3.42). Although selected miRNAs showed similar expression levels between H and L groups, significant correlation was found between the expression level of ssc-miR-133a (p < 0.01) and ssc-miR-92a (p < 0.01) and validated genes. These results provide a better understanding of the genetic architecture of prolificacy-related traits and embryo implantation failure in pigs.
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Zhang X, Huang L, Wu T, Feng Y, Ding Y, Ye P, Yin Z. Transcriptomic Analysis of Ovaries from Pigs with High And Low Litter Size. PLoS One 2015; 10:e0139514. [PMID: 26426260 PMCID: PMC4591126 DOI: 10.1371/journal.pone.0139514] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2015] [Accepted: 09/13/2015] [Indexed: 01/03/2023] Open
Abstract
Litter size is one of the most important economic traits for pig production as it is directly related to the production efficiency. Litter size is affected by interactions between multiple genes and the environment. While recent studies have identified some genes associated with prolificacy in pigs, transcriptomic studies of specific genes affecting litter size in porcine ovaries are rare. In order to identify candidate genes associated with litter size in swine, we assessed gene expression differences between the ovaries of Yorkshire pigs with extremely high and low litter sizes using the RNA-Seq method. A total of 1 243 differentially expressed genes were identified: 897 genes were upregulated and 346 genes were downregulated in high litter size ovary samples compared with low litter size ovary samples. A large number of these genes related to steroid hormone regulation in animal ovaries, including 59 Gene Ontology terms and 27 Kyoto Encyclopedia of Genes and Genomes pathways involved in steroid biosynthesis and ovarian steroidogenesis. From these differentially expressed genes, we identified a total of 11 genes using a bioinformatics screen that may be associated with high litter size in Yorkshire pigs. These results provide a list of new candidate genes for porcine litter size and prolificacy to be further investigated.
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Affiliation(s)
- Xiaodong Zhang
- Key Laboratory of Local Animal Genetic Resources Conservation and Bio-breeding of Anhui province, College of Animal Science and Technology, Anhui Agricultural University, Hefei, Anhui Province, People’s Republic of China
| | - Long Huang
- Key Laboratory of Local Animal Genetic Resources Conservation and Bio-breeding of Anhui province, College of Animal Science and Technology, Anhui Agricultural University, Hefei, Anhui Province, People’s Republic of China
| | - Tao Wu
- Key Laboratory of Local Animal Genetic Resources Conservation and Bio-breeding of Anhui province, College of Animal Science and Technology, Anhui Agricultural University, Hefei, Anhui Province, People’s Republic of China
| | - Yifang Feng
- Key Laboratory of Local Animal Genetic Resources Conservation and Bio-breeding of Anhui province, College of Animal Science and Technology, Anhui Agricultural University, Hefei, Anhui Province, People’s Republic of China
| | - Yueyun Ding
- Key Laboratory of Local Animal Genetic Resources Conservation and Bio-breeding of Anhui province, College of Animal Science and Technology, Anhui Agricultural University, Hefei, Anhui Province, People’s Republic of China
| | - Pengfei Ye
- Key Laboratory of Local Animal Genetic Resources Conservation and Bio-breeding of Anhui province, College of Animal Science and Technology, Anhui Agricultural University, Hefei, Anhui Province, People’s Republic of China
| | - Zongjun Yin
- Key Laboratory of Local Animal Genetic Resources Conservation and Bio-breeding of Anhui province, College of Animal Science and Technology, Anhui Agricultural University, Hefei, Anhui Province, People’s Republic of China
- * E-mail:
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25
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Wang K, Liu D, Hernandez-Sanchez J, Chen J, Liu C, Wu Z, Fang M, Li N. Genome Wide Association Analysis Reveals New Production Trait Genes in a Male Duroc Population. PLoS One 2015; 10:e0139207. [PMID: 26418247 PMCID: PMC4587933 DOI: 10.1371/journal.pone.0139207] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2015] [Accepted: 09/10/2015] [Indexed: 01/07/2023] Open
Abstract
In this study, 796 male Duroc pigs were used to identify genomic regions controlling growth traits. Three production traits were studied: food conversion ratio, days to 100 KG, and average daily gain, using a panel of 39,436 single nucleotide polymorphisms. In total, we detected 11 genome-wide and 162 chromosome-wide single nucleotide polymorphism trait associations. The Gene ontology analysis identified 14 candidate genes close to significant single nucleotide polymorphisms, with growth-related functions: six for days to 100 KG (WT1, FBXO3, DOCK7, PPP3CA, AGPAT9, and NKX6-1), seven for food conversion ratio (MAP2, TBX15, IVL, ARL15, CPS1, VWC2L, and VAV3), and one for average daily gain (COL27A1). Gene ontology analysis indicated that most of the candidate genes are involved in muscle, fat, bone or nervous system development, nutrient absorption, and metabolism, which are all either directly or indirectly related to growth traits in pigs. Additionally, we found four haplotype blocks composed of suggestive single nucleotide polymorphisms located in the growth trait-related quantitative trait loci and further narrowed down the ranges, the largest of which decreased by ~60 Mb. Hence, our results could be used to improve pig production traits by increasing the frequency of favorable alleles via artificial selection.
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Affiliation(s)
- Kejun Wang
- Department of Animal Genetics and Breeding, National Engineering Laboratory for Animal Breeding, MOA Laboratory of Animal Genetics and Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, People’s Republic of China
| | - Dewu Liu
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, Guangdong, 510642, People’s Republic of China
| | - Jules Hernandez-Sanchez
- Research Methods Group| Institute of Health and Biomedical Innovation (IHBI), Queensland University of Technology (QUT), 60 Musk Ave/cnr. Blamey St, Kelvin Grove, QLD 4059, Australia
| | - Jie Chen
- Department of Animal Genetics and Breeding, National Engineering Laboratory for Animal Breeding, MOA Laboratory of Animal Genetics and Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, People’s Republic of China
| | - Chengkun Liu
- Department of Animal Genetics and Breeding, National Engineering Laboratory for Animal Breeding, MOA Laboratory of Animal Genetics and Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, People’s Republic of China
| | - Zhenfang Wu
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, Guangdong, 510642, People’s Republic of China
| | - Meiying Fang
- Department of Animal Genetics and Breeding, National Engineering Laboratory for Animal Breeding, MOA Laboratory of Animal Genetics and Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, People’s Republic of China
- * E-mail:
| | - Ning Li
- State Key Laboratory for Agrobiotechnology, China Agricultural University, Beijing, 100094, People’s Republic of China
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Hausman GJ, Basu U, Wei S, Hausman DB, Dodson MV. Preadipocyte and adipose tissue differentiation in meat animals: influence of species and anatomical location. Annu Rev Anim Biosci 2015; 2:323-51. [PMID: 25384146 DOI: 10.1146/annurev-animal-022513-114211] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Early in porcine adipose tissue development, the stromal-vascular (SV) elements control and dictate the extent of adipogenesis in a depot-dependent manner. The vasculature and collagen matrix differentiate before overt adipocyte differentiation. In the fetal pig, subcutaneous (SQ) layer development is predictive of adipocyte development, as the outer, middle, and inner layers of dorsal SQ adipose tissue develop and maintain layered morphology throughout postnatal growth of SQ adipose tissue. Bovine and ovine fetuses contain brown adipose tissue but SQ white adipose tissue is poorly developed structurally. Fetal adipose tissue differentiation is associated with the precocious expression of several genes encoding secreted factors and key transcription factors like peroxisome proliferator activated receptor (PPAR)γ and CCAAT/-enhancer-binding protein. Identification of adipocyte-associated genes differentially expressed by age, depot, and species in vivo and in vitro has been achieved using single-gene analysis, microarrays, suppressive subtraction hybridization, and next-generation sequencing applications. Gene polymorphisms in PPARγ, cathepsins, and uncoupling protein 3 have been associated with back fat accumulation. Genome scans have mapped several quantitative trait loci (QTL) predictive of adipose tissue-deposition phenotypes in cattle and pigs.
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27
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Transcriptomic analysis to elucidate the molecular mechanisms that underlie feed efficiency in meat-type chickens. Mol Genet Genomics 2015; 290:1673-82. [PMID: 25782841 DOI: 10.1007/s00438-015-1025-7] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2014] [Accepted: 03/02/2015] [Indexed: 01/20/2023]
Abstract
Feed efficiency phenotypes defined by genotypes or gene markers are unknown. To date, there are only limited studies on global gene expression profiling on feed efficiency. The objective of this study was to identify genes and pathways associated with residual feed intake (RFI) through transcriptional profiling of duodenum at two different ages in a chicken population divergently selected for low (LRFI) or high (HRFI) RFI. The global gene expression differences in LRFI and HRFI were assessed by the Affymetrix GeneChip(®) Chicken Genome Array and RT-PCR using duodenal tissue on days 35 and 42. The Ingenuity Pathway Analysis program was used to identify canonical and gene network pathways associated with RFI. A global view of gene expression differences between LRFI and HRFI suggest that RFI can be explained by differences in cell division, growth, proliferation and apoptosis, protein synthesis, lipid metabolism, and molecular transport of cellular molecules. Chickens selected for improved RFI achieve efficiency by reducing feed intake with a nominal or no change in weight gain by either up-regulating CD36, PPARα, HMGCS2, GCG or down-regulating PCSK2, CALB1, SAT1, and SGK1 genes within the lipid metabolism, small molecule biochemistry, molecular transport, cell death, and protein synthesis molecular and cellular functions. Chickens selected for reduced RFI via reduced feed intake with no change in weight gain achieve feed efficiency for growth by the up-regulation of genes that reduce appetite with increased cellular oxidative stress, prolonged cell cycle, DNA damage, and apoptosis in addition to increased oxidation of dietary fat and efficient fatty acids transported from the intestines.
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Fontanesi L, Schiavo G, Scotti E, Galimberti G, Calò DG, Samorè AB, Gallo M, Russo V, Buttazzoni L. A retrospective analysis of allele frequency changes of major genes during 20 years of selection in the Italian Large White pig breed. J Anim Breed Genet 2015; 132:239-46. [PMID: 25727360 DOI: 10.1111/jbg.12127] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2014] [Accepted: 10/01/2014] [Indexed: 12/01/2022]
Abstract
In this study, we investigated whether a selection programme based on boar genetic evaluation obtained with a classical BLUP animal model can change allele frequencies in a pig population. All Italian Large White boars born from 1992 to 2012 with estimated breeding value reliability >0.85 (n = 200) were selected among all boars of this breed. Boars were genotyped with markers in major genes (IGF2 intron3-g.3072G>A, MC4R p.D298N, VRTN PRE1 insertion, PRKAG3 p.I199V and FTO g.276T>G). Genotyping data were analysed grouping boars in eight classes according to their year of birth. To evaluate the influence of time on allele frequencies of the genotyped markers, multinomial logistic regression models were computed. Four of five polymorphic sites (IGF2, MC4R, VRTN and FTO) showed significant (p < 0.01) changes in allele frequencies over time due to a progressive and continuous increase of one allele (associated with higher lean meat content, higher average daily gain and favourable feed: gain ratio) and, consequently, decrease of the other one, following the directional selection of the selection programme of this pig breed. The retrospective analysis that was carried out in Italian Large White boars suggests that selection based on methodologies assuming the infinitesimal model is able to modify in a quite short period of time allele frequencies in major genes, increasing the frequency of alleles explaining a relevant (non-infinitesimal) fraction of the overall genetic variability for production traits.
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Affiliation(s)
- L Fontanesi
- Department of Agricultural and Food Sciences, Division of Animal Sciences, University of Bologna, Bologna, Italy
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29
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Fu W, Dekkers JCM, Lee WR, Abasht B. Linkage disequilibrium in crossbred and pure line chickens. Genet Sel Evol 2015; 47:11. [PMID: 25887184 PMCID: PMC4341223 DOI: 10.1186/s12711-015-0098-4] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2014] [Accepted: 01/26/2015] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Both genome-wide association (GWA) studies and genomic selection depend on the level of non-random association of alleles at different loci, i.e. linkage disequilibrium (LD), across the genome. Therefore, characterizing LD is of fundamental importance to implement both approaches. In this study, using a 60K single nucleotide polymorphism (SNP) panel, we estimated LD and haplotype structure in crossbred broiler chickens and their component pure lines (one male and two female lines) and calculated the consistency of LD between these populations. RESULTS The average level of LD (measured by r(2)) between adjacent SNPs across the chicken autosomes studied here ranged from 0.34 to 0.40 in the pure lines but was only 0.24 in the crossbred populations, with 28.4% of adjacent SNP pairs having an r(2) higher than 0.3. Compared with the pure lines, the crossbred populations consistently showed a lower level of LD, smaller haploblock sizes and lower haplotype homozygosity on macro-, intermediate and micro-chromosomes. Furthermore, correlations of LD between markers at short distances (0 to 10 kb) were high between crossbred and pure lines (0.83 to 0.94). CONCLUSIONS Our results suggest that using crossbred populations instead of pure lines can be advantageous for high-resolution QTL (quantitative trait loci) mapping in GWA studies and to achieve good persistence of accuracy of genomic breeding values over generations in genomic selection. These results also provide useful information for the design and implementation of GWA studies and genomic selection using crossbred populations.
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Affiliation(s)
- Weixuan Fu
- Department of Animal and Food Sciences, University of Delaware, Newark, DE, 19716, USA.
| | - Jack C M Dekkers
- Department of Animal Science, Iowa State University, Ames, IA, 50011, USA.
| | | | - Behnam Abasht
- Department of Animal and Food Sciences, University of Delaware, Newark, DE, 19716, USA.
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Rothammer S, Kremer PV, Bernau M, Fernandez-Figares I, Pfister-Schär J, Medugorac I, Scholz AM. Genome-wide QTL mapping of nine body composition and bone mineral density traits in pigs. Genet Sel Evol 2014; 46:68. [PMID: 25359100 PMCID: PMC4210560 DOI: 10.1186/s12711-014-0068-2] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2014] [Accepted: 09/19/2014] [Indexed: 12/20/2022] Open
Abstract
Background Since the pig is one of the most important livestock animals worldwide, mapping loci that are associated with economically important traits and/or traits that influence animal welfare is extremely relevant for efficient future pig breeding. Therefore, the purpose of this study was a genome-wide mapping of quantitative trait loci (QTL) associated with nine body composition and bone mineral traits: absolute (Fat, Lean) and percentage (FatPC, LeanPC) fat and lean mass, live weight (Weight), soft tissue X-ray attenuation coefficient (R), absolute (BMC) and percentage (BMCPC) bone mineral content and bone mineral density (BMD). Methods Data on the nine traits investigated were obtained by Dual-energy X-ray absorptiometry for 551 pigs that were between 160 and 200 days old. In addition, all pigs were genotyped using Illumina’s PorcineSNP60 Genotyping BeadChip. Based on these data, a genome-wide combined linkage and linkage disequilibrium analysis was conducted. Thus, we used 44 611 sliding windows that each consisted of 20 adjacent single nucleotide polymorphisms (SNPs). For the middle of each sliding window a variance component analysis was carried out using ASReml. The underlying mixed linear model included random QTL and polygenic effects, with fixed effects of sex, housing, season and age. Results Using a Bonferroni-corrected genome-wide significance threshold of P < 0.001, significant peaks were identified for all traits except BMCPC. Overall, we identified 72 QTL on 16 chromosomes, of which 24 were significantly associated with one trait only and the remaining with more than one trait. For example, a QTL on chromosome 2 included the highest peak across the genome for four traits (Fat, FatPC, LeanPC and R). The nearby gene, ZNF608, is known to be associated with body mass index in humans and involved in starvation in Drosophila, which makes it an extremely good candidate gene for this QTL. Conclusions Our QTL mapping approach identified 72 QTL, some of which confirmed results of previous studies in pigs. However, we also detected significant associations that have not been published before and were able to identify a number of new and promising candidate genes, such as ZNF608. Electronic supplementary material The online version of this article (doi:10.1186/s12711-014-0068-2) contains supplementary material, which is available to authorized users.
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Affiliation(s)
| | | | | | | | | | - Ivica Medugorac
- Chair of Animal Genetics and Husbandry, Ludwig-Maximilians-University Munich, Veterinärstrasse 13, Munich, 80539, Germany.
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Diniz DB, Lopes MS, Broekhuijse MLWJ, Lopes PS, Harlizius B, Guimarães SEF, Duijvesteijn N, Knol EF, Silva FF. A genome-wide association study reveals a novel candidate gene for sperm motility in pigs. Anim Reprod Sci 2014; 151:201-7. [PMID: 25459079 DOI: 10.1016/j.anireprosci.2014.10.014] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2014] [Revised: 09/25/2014] [Accepted: 10/15/2014] [Indexed: 01/16/2023]
Abstract
Sperm motility is one of the most widely used parameters in order to evaluate boar semen quality. However, this trait can only be measured after puberty. Thus, the use of genomic information appears as an appealing alternative to evaluate and improve selection for boar fertility traits earlier in life. With this study we aimed to identify SNPs with significant association with sperm motility in two different commercial pig populations and to identify possible candidate genes within the identified QTL regions. We performed a single-SNP genome-wide association study using genotyped animals from a Landrace-based (L1) and a Large White-based (L2) pig populations. For L1, a total of 602 animals genotyped for 42,551 SNPs were used in the association analysis. For L2, a total of 525 animals genotyped for 40,890 SNPs were available. After the association analysis, a false discovery rate q-value ≤0.05 was used as the threshold for significant association. No SNPs were significantly associated with sperm motility in L1, while six SNPs on Sus scrofa chromosome 1 (position 117.26-119.56Mb) were significant in L2. The mitochondrial methionyl-tRNA formyltransferase (MTFMT) gene, which affects translation efficiency of proteins in sperm cells, was identified as a putative candidate gene. The significant markers identified in this study may be useful to enhance the genetic improvement of sperm motility by selection of boars at an earlier age under a marker assisted selection strategy.
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Affiliation(s)
- D B Diniz
- Universidade Federal de Viçosa, Animal Science Department, 36570-000 Viçosa, Minas Gerais, Brazil.
| | - M S Lopes
- TOPIGS Research Center IPG B.V., P.O. Box 43, 6640 AA Beuningen, The Netherlands; Wageningen University, Animal Breeding and Genomics Centre, P.O. Box 338, 6700 AH Wageningen, The Netherlands.
| | - M L W J Broekhuijse
- TOPIGS Research Center IPG B.V., P.O. Box 43, 6640 AA Beuningen, The Netherlands.
| | - P S Lopes
- Universidade Federal de Viçosa, Animal Science Department, 36570-000 Viçosa, Minas Gerais, Brazil.
| | - B Harlizius
- TOPIGS Research Center IPG B.V., P.O. Box 43, 6640 AA Beuningen, The Netherlands.
| | - S E F Guimarães
- Universidade Federal de Viçosa, Animal Science Department, 36570-000 Viçosa, Minas Gerais, Brazil.
| | - N Duijvesteijn
- TOPIGS Research Center IPG B.V., P.O. Box 43, 6640 AA Beuningen, The Netherlands; Wageningen University, Animal Breeding and Genomics Centre, P.O. Box 338, 6700 AH Wageningen, The Netherlands.
| | - E F Knol
- TOPIGS Research Center IPG B.V., P.O. Box 43, 6640 AA Beuningen, The Netherlands.
| | - F F Silva
- Universidade Federal de Viçosa, Animal Science Department, 36570-000 Viçosa, Minas Gerais, Brazil.
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Tvrdoň Z, Humpolíček P. Impact of hyperprolific line of litter size in multiplication herd. ACTA UNIVERSITATIS AGRICULTURAE ET SILVICULTURAE MENDELIANAE BRUNENSIS 2014. [DOI: 10.11118/actaun200957020151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
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Lee KT, Lee YM, Alam M, Choi BH, Park MR, Kim KS, Kim TH, Kim JJ. A Whole Genome Association Study on Meat Quality Traits Using High Density SNP Chips in a Cross between Korean Native Pig and Landrace. ASIAN-AUSTRALASIAN JOURNAL OF ANIMAL SCIENCES 2014; 25:1529-39. [PMID: 25049513 PMCID: PMC4093033 DOI: 10.5713/ajas.2012.12474] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/04/2012] [Revised: 09/28/2012] [Accepted: 09/27/2012] [Indexed: 11/27/2022]
Abstract
A whole genome association (WGA) study was performed to detect significant polymorphisms for meat quality traits in an F2 cross population (N = 478) that were generated with Korean native pig sires and Landrace dams in National Livestock Research Institute, Songwhan, Korea. The animals were genotyped using Illumina porcine 60k SNP beadchips, in which a set of 46,865 SNPs were available for the WGA analyses on ten carcass quality traits; live weight, crude protein, crude lipids, crude ash, water holding capacity, drip loss, shear force, CIE L, CIE a and CIE b. Phenotypes were regressed on additive and dominance effects for each SNP using a simple linear regression model, after adjusting for sex, sire and slaughter stage as fixed effects. With the significant SNPs for each trait (p<0.001), a stepwise regression procedure was applied to determine the best set of SNPs with the additive and/or dominance effects. A total of 106 SNPs, or quantitative trait loci (QTL) were detected, and about 32 to 66% of the total phenotypic variation was explained by the significant SNPs for each trait. The QTL were identified in most porcine chromosomes (SSCs), in which majority of the QTL were detected in SSCs 1, 2, 12, 13, 14 and 16. Several QTL clusters were identified on SSCs 12, 16 and 17, and a cluster of QTL influencing crude protein, crude lipid, drip loss, shear force, CIE a and CIE b were located between 20 and 29 Mb of SSC12. A pleiotropic QTL for drip loss, CIE L and CIE b was also detected on SSC16. These QTL need to be validated in commercial pig populations for genetic improvement in meat quality via marker-assisted selection.
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Affiliation(s)
- K-T Lee
- Animal Genomics and Bioinformatics Division, National Institute of Animal Science, Suwon, Korea
| | - Y-M Lee
- Animal Genomics and Bioinformatics Division, National Institute of Animal Science, Suwon, Korea
| | - M Alam
- Animal Genomics and Bioinformatics Division, National Institute of Animal Science, Suwon, Korea
| | - B H Choi
- Animal Genomics and Bioinformatics Division, National Institute of Animal Science, Suwon, Korea
| | - M R Park
- Animal Genomics and Bioinformatics Division, National Institute of Animal Science, Suwon, Korea
| | - K-S Kim
- Department of Animal Science, Chungbuk National University, Cheongju, Korea
| | - T-H Kim
- Animal Genomics and Bioinformatics Division, National Institute of Animal Science, Suwon, Korea
| | - J-J Kim
- Animal Genomics and Bioinformatics Division, National Institute of Animal Science, Suwon, Korea
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Stratz P, Wimmers K, Meuwissen THE, Bennewitz J. Investigations on the pattern of linkage disequilibrium and selection signatures in the genomes of German Piétrain pigs. J Anim Breed Genet 2014; 131:473-82. [PMID: 25047461 DOI: 10.1111/jbg.12107] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2014] [Accepted: 06/24/2014] [Indexed: 12/29/2022]
Abstract
The aim of this study was to study the population structure, to characterize the LD structure and to define core regions based on low recombination rates among SNP pairs in the genome of Piétrain pigs using data from the PorcineSNP60 BeadChip. This breed is a European sire line and was strongly selected for lean meat content during the last decades. The data were used to map signatures of selection using the REHH test. In the first step, selection signatures were searched genome-wide using only core haplotypes having a frequency above 0.25. In the second step, the results from the selection signature analysis were matched with the results from the recently conducted genome-wide association study for economical relevant traits to investigate putative overlaps of chromosomal regions. A small subdivision of the population with regard to the geographical origin of the individuals was observed. The extent of LD was determined genome-wide using r(2) values for SNP pairs with a distance ≤5 Mb and was on average 0.34. This comparable low r(2) value indicates a high genetic diversity in the Piétrain population. Six REHH values having a p-value < 0.001 were genome-wide detected. These were located on SSC1, 2, 6 and 17. Three positional candidate genes with potential biological roles were suggested, called LOC100626459, LOC100626014 and MIR1. The results imply that for genome-wide analysis especially in this population, a higher marker density and higher sample sizes are required. For a number of nine SNPs, which were successfully annotated to core regions, the REHH test was applied. However, no selection signatures were found for those regions (p-value < 0.1).
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Affiliation(s)
- P Stratz
- Institute of Animal Husbandry and Breeding, University of Hohenheim, Stuttgart, Germany
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Stratz P, Wellmann R, Preuss S, Wimmers K, Bennewitz J. Genome-wide association analysis for growth, muscularity and meat quality in Piétrain pigs. Anim Genet 2014; 45:350-6. [DOI: 10.1111/age.12133] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/27/2013] [Indexed: 12/21/2022]
Affiliation(s)
- P. Stratz
- Institute of Animal Husbandry and Breeding; University of Hohenheim; D-70599 Stuttgart Germany
| | - R. Wellmann
- Institute of Animal Husbandry and Breeding; University of Hohenheim; D-70599 Stuttgart Germany
| | - S. Preuss
- Institute of Animal Husbandry and Breeding; University of Hohenheim; D-70599 Stuttgart Germany
| | - K. Wimmers
- Research Unit Molecular Biology; Institute for Genome Biology; Leibniz Institute for Farm Animal Biology (FBN); Wilhelm-Stahl-Allee 2 D-18196 Dummerstorf Germany
| | - J. Bennewitz
- Institute of Animal Husbandry and Breeding; University of Hohenheim; D-70599 Stuttgart Germany
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Du ZQ, Eisley CJ, Onteru SK, Madsen O, Groenen MAM, Ross JW, Rothschild MF. Identification of species-specific novel transcripts in pig reproductive tissues using RNA-seq. Anim Genet 2014; 45:198-204. [PMID: 24450499 DOI: 10.1111/age.12124] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/14/2013] [Indexed: 02/04/2023]
Abstract
Although structural properties of the porcine reproductive system are shared by many placental mammals, some combination of these properties is unique to pigs. To explore whether genomic elements specific to pigs could potentially underlie this uniqueness, we made the first step to identify novel transcripts in two representative pig reproductive tissues by the technique of massively parallel sequencing. To automate the whole process, we built a computational pipeline, which can also be easily extended for similar studies in other species. In total, 5516 and 9061 novel transcripts were found, and 159 and 252 novel transcripts appear to be specific to pigs for the placenta and testis respectively. Furthermore, these novel transcripts were found to be enriched in quantitative trait loci (QTL) regions for reproduction traits in pigs. We validated eight of these novel transcripts by quantitative real-time PCR. With respect to their genomic organization and their functional relationship to reproduction, these transcripts need to be further validated and explored in various pig breeds to better comprehend the relevant aspects of pig physiology that contribute to reproductive performance.
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Affiliation(s)
- Z-Q Du
- Department of Animal Science and Center for Integrated Animal Genomics, Iowa State University, 2255 Kildee Hall, Ames, IA, 50011, USA
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Nikitin SV, Knyazev SP, Yermolaev VI. Genetic components and the uncertainty of the phenotypic realization of the mass of newborns in domestic pigs Sus scrofa L. RUSS J GENET+ 2014. [DOI: 10.1134/s1022795414010086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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Kunej T, Jevsinek Skok D, Zorc M, Ogrinc A, Michal JJ, Kovac M, Jiang Z. Obesity gene atlas in mammals. J Genomics 2013; 1:45-55. [PMID: 25031655 PMCID: PMC4091431 DOI: 10.7150/jgen.3996] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Obesity in humans has increased at an alarming rate over the past two decades and has become one of the leading public health problems worldwide. Studies have revealed a large number of genes/markers that are associated with obesity and/or obesity-related phenotypes, indicating an urgent need to develop a central database for helping the community understand the genetic complexity of obesity. In the present study, we collected a total of 1,736 obesity associated loci and created a freely available obesity database, including 1,515 protein-coding genes and 221 microRNAs (miRNAs) collected from four mammalian species: human, cattle, rat, and mouse. These loci were integrated as orthologs on comparative genomic views in human, cattle, and mouse. The database and genomic views are freely available online at: http://www.integratomics-time.com/fat_deposition. Bioinformatics analyses of the collected data revealed some potential novel obesity related molecular markers which represent focal points for testing more targeted hypotheses and designing experiments for further studies. We believe that this centralized database on obesity and adipogenesis will facilitate development of comparative systems biology approaches to address this important health issue in human and their potential applications in animals.
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Affiliation(s)
- Tanja Kunej
- 1. Department of Animal Science, Biotechnical Faculty, University of Ljubljana, Domzale, Slovenia
| | - Dasa Jevsinek Skok
- 1. Department of Animal Science, Biotechnical Faculty, University of Ljubljana, Domzale, Slovenia
| | - Minja Zorc
- 1. Department of Animal Science, Biotechnical Faculty, University of Ljubljana, Domzale, Slovenia; ; 2. Centre for Mathematical and Computational Biology, Rothamsted Research, Harpenden, Herts, AL5, 2JQ, UK
| | - Ana Ogrinc
- 1. Department of Animal Science, Biotechnical Faculty, University of Ljubljana, Domzale, Slovenia
| | - Jennifer J Michal
- 3. Department of Animal Sciences, Washington State University, Pullman, Washington 99164-6351, USA
| | - Milena Kovac
- 1. Department of Animal Science, Biotechnical Faculty, University of Ljubljana, Domzale, Slovenia
| | - Zhihua Jiang
- 3. Department of Animal Sciences, Washington State University, Pullman, Washington 99164-6351, USA
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A genetical genomics approach reveals new candidates and confirms known candidate genes for drip loss in a porcine resource population. Mamm Genome 2013; 24:416-26. [PMID: 24026665 DOI: 10.1007/s00335-013-9473-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2013] [Accepted: 08/05/2013] [Indexed: 10/26/2022]
Abstract
In this study lean meat water-holding capacity (WHC) of a Duroc × Pietrain (DuPi) resource population with corresponding genotypes and transcriptomes was investigated using genetical genomics. WHC was characterized by drip loss measured in M. longissimus dorsi. The 60K Illumina SNP chips identified genotypes of 169 F2 DuPi animals. Whole-genome transcriptomes of muscle samples were available for 132 F2 animals using the Affymetrix 24K GeneChip® Porcine Genome Array. Performing genome-wide association studies of transcriptional profiles, which are correlated with phenotypes, allows elucidation of cis- and trans-regulation. Expression levels of 1,228 genes were significantly correlated with drip loss and were further analyzed for enrichment of functional annotation groups as defined by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways. A hypergeometric gene set enrichment test was performed and revealed glycolysis/glyconeogenesis, pentose phosphate pathway, and pyruvate metabolism as the most promising pathways. For 267 selected transcripts, expression quantitative trait loci (eQTL) analysis was performed and revealed a total of 1,541 significant associations. Because of positional accordance of the gene underlying transcript and the eQTL location, it was possible to identify eight eQTL that can be assumed to be cis-regulated. Comparing the results of gene set enrichment and the eQTL detection tests, molecular networks and potential candidate genes, which seemed to play key roles in the expression of WHC, were detected. The α-1-microglobulin/bikunin precursor (AMBP) gene was assumed to be cis-regulated and was part of the glycolysis pathway. This approach supports the identification of trait-associated SNPs and the further biological understanding of complex traits.
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Veroneze R, Lopes PS, Guimarães SEF, Silva FF, Lopes MS, Harlizius B, Knol EF. Linkage disequilibrium and haplotype block structure in six commercial pig lines. J Anim Sci 2013; 91:3493-501. [DOI: 10.2527/jas.2012-6052] [Citation(s) in RCA: 45] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- R. Veroneze
- Departamento de Zootecnia, Universidade Federal de Viçosa, Av. P.H. Holfs, 36570-000, Viçosa, MG, Brazil
| | - P. S. Lopes
- Departamento de Zootecnia, Universidade Federal de Viçosa, Av. P.H. Holfs, 36570-000, Viçosa, MG, Brazil
| | - S. E. F. Guimarães
- Departamento de Zootecnia, Universidade Federal de Viçosa, Av. P.H. Holfs, 36570-000, Viçosa, MG, Brazil
| | - F. F. Silva
- Departamento de Estatística, Universidade Federal de Viçosa, Av. P.H. Holfs, 36570-000, Viçosa, MG, Brazil
| | - M. S. Lopes
- TOPIGS Research Center IPG, PO Box 43, 6640 AA, Beuningen, The Netherlands
| | - B. Harlizius
- TOPIGS Research Center IPG, PO Box 43, 6640 AA, Beuningen, The Netherlands
| | - E. F. Knol
- TOPIGS Research Center IPG, PO Box 43, 6640 AA, Beuningen, The Netherlands
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Chalupová P, Urban T, Knoll A. Association analysis of interleukin-18 gene with performance traits in Czech Large White pigs. ACTA UNIVERSITATIS AGRICULTURAE ET SILVICULTURAE MENDELIANAE BRUNENSIS 2013. [DOI: 10.11118/actaun201260050097] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
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Pena RN, Noguera JL, Casellas J, Díaz I, Fernández AI, Folch JM, Ibáñez-Escriche N. Transcriptional analysis of intramuscular fatty acid composition in the longissimus thoracis muscle of Iberian × Landrace back-crossed pigs. Anim Genet 2013; 44:648-60. [PMID: 23826865 DOI: 10.1111/age.12066] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/29/2013] [Indexed: 11/29/2022]
Abstract
This study aimed at identifying differential gene expression conditional on the fatty acid profile of the longissimus thoracis (Lt) muscle, a prime cut of economic relevance for fresh and cured pork production. A population of 110 Iberian (25%) × Landrace (75%) back-crossed pigs was used, because these two breeds exhibit extreme profiles of intramuscular saturated fatty acid, monounsaturated fatty acid (MUFA) and polyunsaturated fatty acid (PUFA) contents. Total RNA from Lt muscle was individually hybridized to GeneChip Porcine Genome arrays (Affymetrix). A principal component analysis was performed with data from the 110 animals to select 40 extreme animals based on the total fatty acid profile and the MUFA composition (MAP). Comparison of global transcription levels between extreme fatty acid profile pigs (n = 40) resulted in 219 differentially expressed probes (false discovery rate <0.10). Gene ontology, pathway and network analysis indicated that animals with higher percentages of PUFA exhibit a shift toward a more oxidative muscular metabolism state, with a raise in mitochondria function (PPARGC1A, ATF2), fatty acid uptake and oxidation (FABP5, MGLL). On the other hand, 87 probes were differentially expressed between MUFA composition groups (n = 40; false discovery rate <0.10). In particular, muscles rich in n-7 MUFA expressed higher levels of genes involved in lipid metabolism (GLUL, CRAT, PLA2G15) and lower levels of fatty acid elongation genes (ELOVL5). Moreover, the chromosomal position of FABP5, PAQR3, MGLL, PPARGC1A, GLUL and ELOVL5 co-localized with very relevant QTL for fat deposition and composition described in the same resource population. This study represents a complementary approach to identifying genes underlying these QTL effects.
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Affiliation(s)
- R N Pena
- Genètica i Millora Animal, IRTA, 191 Av. Rovira Roure, 25198, Lleida, Spain; Departament de Producció Animal, ETSEA, Universitat de Lleida, 191 Av. Rovira Roure, 25198, Lleida, Spain
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van den Berg I, Fritz S, Boichard D. QTL fine mapping with Bayes C(π): a simulation study. Genet Sel Evol 2013; 45:19. [PMID: 23782975 PMCID: PMC3700753 DOI: 10.1186/1297-9686-45-19] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2012] [Accepted: 06/07/2013] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Accurate QTL mapping is a prerequisite in the search for causative mutations. Bayesian genomic selection models that analyse many markers simultaneously should provide more accurate QTL detection results than single-marker models. Our objectives were to (a) evaluate by simulation the influence of heritability, number of QTL and number of records on the accuracy of QTL mapping with Bayes Cπ and Bayes C; (b) estimate the QTL status (homozygous vs. heterozygous) of the individuals analysed. This study focussed on the ten largest detected QTL, assuming they are candidates for further characterization. METHODS Our simulations were based on a true dairy cattle population genotyped for 38,277 phased markers. Some of these markers were considered biallelic QTL and used to generate corresponding phenotypes. Different numbers of records (4387 and 1500), heritability values (0.1, 0.4 and 0.7) and numbers of QTL (10, 100 and 1000) were studied. QTL detection was based on the posterior inclusion probability for individual markers, or on the sum of the posterior inclusion probabilities for consecutive markers, estimated using Bayes C or Bayes Cπ. The QTL status of the individuals was derived from the contrast between the sums of the SNP allelic effects of their chromosomal segments. RESULTS The proportion of markers with null effect (π) frequently did not reach convergence, leading to poor results for Bayes Cπ in QTL detection. Fixing π led to better results. Detection of the largest QTL was most accurate for medium to high heritability, for low to moderate numbers of QTL, and with a large number of records. The QTL status was accurately inferred when the distribution of the contrast between chromosomal segment effects was bimodal. CONCLUSIONS QTL detection is feasible with Bayes C. For QTL detection, it is recommended to use a large dataset and to focus on highly heritable traits and on the largest QTL. QTL statuses were inferred based on the distribution of the contrast between chromosomal segment effects.
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Affiliation(s)
- Irene van den Berg
- INRA, UMR1313 Génétique animale et biologie intégrative, Domaine de Vilvert, 78350 Jouy-en-Josas, France.
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Bischoff SR, Tsai SQ, Hardison NE, Motsinger-Reif AA, Freking BA, Nonneman DJ, Rohrer GA, Piedrahita JA. Differences in X-chromosome transcriptional activity and cholesterol metabolism between placentae from swine breeds from Asian and Western origins. PLoS One 2013; 8:e55345. [PMID: 23383161 PMCID: PMC3561265 DOI: 10.1371/journal.pone.0055345] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2012] [Accepted: 12/21/2012] [Indexed: 12/19/2022] Open
Abstract
To gain insight into differences in placental physiology between two swine breeds noted for their dissimilar reproductive performance, that is, the Chinese Meishan and white composite (WC), we examined gene expression profiles of placental tissues collected at 25, 45, 65, 85, and 105 days of gestation by microarrays. Using a linear mixed model, a total of 1,595 differentially expressed genes were identified between the two pig breeds using a false-discovery rate q-value ≤0.05. Among these genes, we identified breed-specific isoforms of XIST, a long non-coding RNA responsible X-chromosome dosage compensation in females. Additionally, we explored the interaction of placental gene expression and chromosomal location by DIGMAP and identified three Sus scrofa X chromosomal bands (Xq13, Xq21, Xp11) that represent transcriptionally active clusters that differ between Meishan and WC during placental development. Also, pathway analysis identified fundamental breed differences in placental cholesterol trafficking and its synthesis. Direct measurement of cholesterol confirmed that the cholesterol content was significantly higher in the Meishan versus WC placentae. Taken together, this work identifies key metabolic pathways that differ in the placentae of two swine breeds noted for differences in reproductive prolificacy.
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Affiliation(s)
- Steve R. Bischoff
- Department of Molecular Biomedical Sciences, College of Veterinary Medicine, North Carolina State University, Raleigh, North Carolina, United States of America
- USDA, ARS, U.S. Meat Animal Research Center, Clay Center, Nebraska, United States of America
- Center for Comparative Medicine and Translational Research, North Carolina State University, Raleigh, North Carolina, United States of America
| | - Shengdar Q. Tsai
- Department of Molecular Biomedical Sciences, College of Veterinary Medicine, North Carolina State University, Raleigh, North Carolina, United States of America
- Center for Comparative Medicine and Translational Research, North Carolina State University, Raleigh, North Carolina, United States of America
| | - Nicholas E. Hardison
- Program in Statistical Genetics, Department of Statistics, North Carolina State University, Raleigh, North Carolina, United States of America
| | - Alison A. Motsinger-Reif
- Program in Statistical Genetics, Department of Statistics, North Carolina State University, Raleigh, North Carolina, United States of America
| | - Bradley A. Freking
- USDA, ARS, U.S. Meat Animal Research Center, Clay Center, Nebraska, United States of America
| | - Dan J. Nonneman
- USDA, ARS, U.S. Meat Animal Research Center, Clay Center, Nebraska, United States of America
| | - Gary A. Rohrer
- USDA, ARS, U.S. Meat Animal Research Center, Clay Center, Nebraska, United States of America
| | - Jorge A. Piedrahita
- Department of Molecular Biomedical Sciences, College of Veterinary Medicine, North Carolina State University, Raleigh, North Carolina, United States of America
- Center for Comparative Medicine and Translational Research, North Carolina State University, Raleigh, North Carolina, United States of America
- * E-mail:
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Bartz M, Szydlowski M, Kociucka B, Salamon S, Jeleń HH, Switonski M. Transcript abundance of the pig stearoyl-CoA desaturase gene has no effect on fatty acid composition in muscle and fat tissues, but its polymorphism within the putative microRNA target site is associated with daily body weight gain and feed conversion ratio1. J Anim Sci 2013; 91:10-9. [DOI: 10.2527/jas.2012-5380] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023] Open
Affiliation(s)
- M. Bartz
- Department of Genetics and Animal Breeding, Poznan University of Life Sciences, Poland
| | - M. Szydlowski
- Department of Genetics and Animal Breeding, Poznan University of Life Sciences, Poland
| | - B. Kociucka
- Department of Genetics and Animal Breeding, Poznan University of Life Sciences, Poland
| | - S. Salamon
- Department of Genetics and Animal Breeding, Poznan University of Life Sciences, Poland
| | - H. H. Jeleń
- Faculty of Food Science and Nutrition, Poznan University of Life Sciences, Poland
| | - M. Switonski
- Department of Genetics and Animal Breeding, Poznan University of Life Sciences, Poland
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46
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Hernández-Sánchez J, Amills M, Pena RN, Mercadé A, Manunza A, Quintanilla R. Genomic architecture of heritability and genetic correlations for intramuscular and back fat contents in Duroc pigs. J Anim Sci 2012; 91:623-32. [PMID: 23230112 DOI: 10.2527/jas.2012-5270] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Genetic parameters such as heritability and correlations of fat traits in a Duroc population were dissected using molecular markers. The heritabilities of intramuscular fat in 2 muscles, the gluteus medius and LM, and back fat were 0.54, 0.48, and 0.23, respectively. The genetic correlations were well estimated with standardized SNP effects, being 0.65 between intramuscular fat traits and ∼0.37 between any intramuscular fat trait and back fat. Genetic correlations were overestimated when ignoring molecular information. Twelve chromosomes showed additive genetic variance for intramuscular fat compared with 8 for back fat. Population structure was accommodated using 4 different models. The number of significant, P < 5 × 10(-5) (suggestive, P < 2 × 10(-3)), SNP varied across models and ranged from 0 to 4 (2 to 261) for intramuscular fat in the gluteus medius, from 0 to 57 (9 to 564) for intramuscular fat in the LM, and from 3 to 4 (22 to 168) for back fat. Several SNP showed significant deviations from an additive mode of action. Only 2 SNP significantly affected 2 traits simultaneously.
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Affiliation(s)
- J Hernández-Sánchez
- IRTA, Genètica i Millora Animal, 191 Av. Alcalde Rovira Roure, 25198 Lleida, Spain.
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47
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Nikitin SV, Knyazev SP, Ermolaev VI. Model of genetic control of the number and location of nipples in domestic pig. RUSS J GENET+ 2012. [DOI: 10.1134/s1022795412110075] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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48
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Uenishi H, Morozumi T, Toki D, Eguchi-Ogawa T, Rund LA, Schook LB. Large-scale sequencing based on full-length-enriched cDNA libraries in pigs: contribution to annotation of the pig genome draft sequence. BMC Genomics 2012; 13:581. [PMID: 23150988 PMCID: PMC3499286 DOI: 10.1186/1471-2164-13-581] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2011] [Accepted: 08/09/2012] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND Along with the draft sequencing of the pig genome, which has been completed by an international consortium, collection of the nucleotide sequences of genes expressed in various tissues and determination of entire cDNA sequences are necessary for investigations of gene function. The sequences of expressed genes are also useful for genome annotation, which is important for isolating the genes responsible for particular traits. RESULTS We performed a large-scale expressed sequence tag (EST) analysis in pigs by using 32 full-length-enriched cDNA libraries derived from 28 kinds of tissues and cells, including seven tissues (brain, cerebellum, colon, hypothalamus, inguinal lymph node, ovary, and spleen) derived from pigs that were cloned from a sow subjected to genome sequencing. We obtained more than 330,000 EST reads from the 5'-ends of the cDNA clones. Comparison with human and bovine gene catalogs revealed that the ESTs corresponded to at least 15,000 genes. cDNA clones representing contigs and singlets generated by assembly of the EST reads were subjected to full-length determination of inserts. We have finished sequencing 31,079 cDNA clones corresponding to more than 12,000 genes. Mapping of the sequences of these cDNA clones on the draft sequence of the pig genome has indicated that the clones are derived from about 15,000 independent loci on the pig genome. CONCLUSIONS ESTs and cDNA sequences derived from full-length-enriched libraries are valuable for annotation of the draft sequence of the pig genome. This information will also contribute to the exploration of promoter sequences on the genome and to molecular biology-based analyses in pigs.
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Affiliation(s)
- Hirohide Uenishi
- Agrogenomics Research Center, National Institute of Agrobiological Sciences, 2 Ikenodai, Tsukuba, Ibaraki, 305-8602, Japan
- Division of Animal Sciences, National Institute of Agrobiological Sciences, 2 Ikenodai, Tsukuba, Ibaraki, 305-8602, Japan
- Animal Genome Research Program, 2 Ikenodai, Tsukuba, Ibaraki, 305-8602, Japan
| | - Takeya Morozumi
- Animal Genome Research Program, 2 Ikenodai, Tsukuba, Ibaraki, 305-8602, Japan
- Animal Research Division, Japan Institute of Association for Techno-innovation in Agriculture, Forestry and Fisheries, 446-1 Ippaizuka, Kamiyokoba, Tsukuba, Ibaraki, 305-0854, Japan
| | - Daisuke Toki
- Animal Genome Research Program, 2 Ikenodai, Tsukuba, Ibaraki, 305-8602, Japan
- Animal Research Division, Japan Institute of Association for Techno-innovation in Agriculture, Forestry and Fisheries, 446-1 Ippaizuka, Kamiyokoba, Tsukuba, Ibaraki, 305-0854, Japan
| | - Tomoko Eguchi-Ogawa
- Agrogenomics Research Center, National Institute of Agrobiological Sciences, 2 Ikenodai, Tsukuba, Ibaraki, 305-8602, Japan
- Animal Genome Research Program, 2 Ikenodai, Tsukuba, Ibaraki, 305-8602, Japan
| | - Lauretta A Rund
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, 1206 West Gregory Drive, Urbana, IL, 61801, USA
| | - Lawrence B Schook
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, 1206 West Gregory Drive, Urbana, IL, 61801, USA
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49
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Fontanesi L, Schiavo G, Galimberti G, Calò DG, Scotti E, Martelli PL, Buttazzoni L, Casadio R, Russo V. A genome wide association study for backfat thickness in Italian Large White pigs highlights new regions affecting fat deposition including neuronal genes. BMC Genomics 2012; 13:583. [PMID: 23153328 PMCID: PMC3499287 DOI: 10.1186/1471-2164-13-583] [Citation(s) in RCA: 76] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2012] [Accepted: 10/25/2012] [Indexed: 11/17/2022] Open
Abstract
Background Carcass fatness is an important trait in most pig breeding programs. Following market requests, breeding plans for fresh pork consumption are usually designed to reduce carcass fat content and increase lean meat deposition. However, the Italian pig industry is mainly devoted to the production of Protected Designation of Origin dry cured hams: pigs are slaughtered at around 160 kg of live weight and the breeding goal aims at maintaining fat coverage, measured as backfat thickness to avoid excessive desiccation of the hams. This objective has shaped the genetic pool of Italian heavy pig breeds for a few decades. In this study we applied a selective genotyping approach within a population of ~ 12,000 performance tested Italian Large White pigs. Within this population, we selectively genotyped 304 pigs with extreme and divergent backfat thickness estimated breeding value by the Illumina PorcineSNP60 BeadChip and performed a genome wide association study to identify loci associated to this trait. Results We identified 4 single nucleotide polymorphisms with P≤5.0E-07 and additional 119 ones with 5.0E-07<P≤5.0E-05. These markers were located throughout all chromosomes. The largest numbers were found on porcine chromosomes 6 and 9 (n=15), 4 (n=13), and 7 (n=12) while the most significant marker was located on chromosome 18. Twenty-two single nucleotide polymorphisms were in intronic regions of genes already recognized by the Pre-Ensembl Sscrofa10.2 assembly. Gene Ontology analysis indicated an enrichment of Gene Ontology terms associated with nervous system development and regulation in concordance with results of large genome wide association studies for human obesity. Conclusions Further investigations are needed to evaluate the effects of the identified single nucleotide polymorphisms associated with backfat thickness on other traits as a pre-requisite for practical applications in breeding programs. Reported results could improve our understanding of the biology of fat metabolism and deposition that could also be relevant for other mammalian species including humans, confirming the role of neuronal genes on obesity.
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Affiliation(s)
- Luca Fontanesi
- Department of Agricultural and Food Science and Technology, University of Bologna, Viale Fanin 46, Bologna 40127, Italy.
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Immunogenomics for identification of disease resistance genes in pigs: a review focusing on Gram-negative bacilli. J Anim Sci Biotechnol 2012; 3:34. [PMID: 23137309 PMCID: PMC3554502 DOI: 10.1186/2049-1891-3-34] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2012] [Accepted: 10/10/2012] [Indexed: 01/11/2023] Open
Abstract
Over the past years, infectious disease has caused enormous economic loss in pig industry. Among the pathogens, gram negative bacteria not only cause inflammation, but also cause different diseases and make the pigs more susceptible to virus infection. Vaccination, medication and elimination of sick pigs are major strategies of controlling disease. Genetic methods, such as selection of disease resistance in the pig, have not been widely used. Recently, the completion of the porcine whole genome sequencing has provided powerful tools to identify the genome regions that harboring genes controlling disease or immunity. Immunogenomics, which combines DNA variations, transcriptome, immune response, and QTL mapping data to illustrate the interactions between pathogen and host immune system, will be an effective genomics tool for identification of disease resistance genes in pigs. These genes will be potential targets for disease resistance in breeding programs. This paper reviewed the progress of disease resistance study in the pig focusing on Gram-negative bacilli. Major porcine Gram-negative bacilli and diseases, suggested candidate genes/pathways against porcine Gram-negative bacilli, and distributions of QTLs for immune capacity on pig chromosomes were summarized. Some tools for immunogenomics research were described. We conclude that integration of sequencing, whole genome associations, functional genomics studies, and immune response information is necessary to illustrate molecular mechanisms and key genes in disease resistance.
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