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Kafi Z, Masoudi AA, Torshizi RV, Ehsani A. Copy number variations affecting growth curve parameters in a crossbred chicken population. Gene 2024; 927:148710. [PMID: 38901536 DOI: 10.1016/j.gene.2024.148710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Revised: 06/01/2024] [Accepted: 06/17/2024] [Indexed: 06/22/2024]
Abstract
Copy number variations (CNVs) are key structural variations in the genome and may contribute to phenotypic differences. In this study, we used a F2 chicken population created from reciprocal crossing between fast-growing Arian broiler line and Urmia native chickens. The chickens were genotyped by 60 K SNP BeadChip, and PennCNV algorithm was used to detect genome-wide CNVs. The growth curve parameters of W0, k, L, Wf, Wi, ti and average GR were used as phenotypic data. The association between CNV and growth curve parameters was carried out using the CNVRanger R/Bioconductor package. Five CNV regions (CNVRs) were chosen for the validation experiment using qPCR. Gene enrichment analysis was done using WebGestalt. The STRING database was used to search for significant pathways. The results identified 966 CNVs and 600 CNVRs including 468 gains, 67 losses, and 65 both events on autosomal chromosomes. Validation of the CNVRs obtained from the qPCR assay were 79 % consistent with the prediction by PennCNV. A total of 43 significant CNVs were obtained for the seven growth curve parameters. The 416 genes annotated for significant CNVs. Six genes out of 416 genes were most related to growth curve parameters. These genes were LCP2, Dock2, CD80, CYFIP1, NIPA1 and NIPA2. Some of these genes in their biological process were associated with the growth, reproduction and development of cells or organs that ultimately lead to the growth of the body. The results of the study could pave the way for better understanding the molecular process of CNVs and growth curve parameters in birds.
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Affiliation(s)
- Zeinab Kafi
- Department of Animal Science, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran.
| | - Ali Akbar Masoudi
- Department of Animal Science, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran.
| | - Rasoul Vaez Torshizi
- Department of Animal Science, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran.
| | - Alireza Ehsani
- Department of Animal Science, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran.
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Choudhury MP, Wang Z, Zhu M, Teng S, Yan J, Cao S, Yi G, Liu Y, Liao Y, Tang Z. Genome-Wide Detection of Copy Number Variations Associated with Miniature Features in Horses. Genes (Basel) 2023; 14:1934. [PMID: 37895283 PMCID: PMC10606273 DOI: 10.3390/genes14101934] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 10/10/2023] [Accepted: 10/10/2023] [Indexed: 10/29/2023] Open
Abstract
Copy number variations (CNVs) are crucial structural genomic variants affecting complex traits in humans and livestock animals. The current study was designed to conduct a comprehensive comparative copy number variation analysis among three breeds, Debao (DB), Baise (BS), and Warmblood (WB), with a specific focus on identifying genomic regions associated with miniature features in horses. Using whole-genome next-generation resequencing data, we identified 18,974 CNVs across 31 autosomes. Among the breeds, we found 4279 breed-specific CNV regions (CNVRs). Baise, Debao, and Warmblood displayed 2978, 986, and 895 distinct CNVRs, respectively, with 202 CNVRs shared across all three breeds. After removing duplicates, we obtained 1545 CNVRs from 26 horse genomes. Functional annotation reveals enrichment in biological functions, including antigen processing, cell metabolism, olfactory conduction, and nervous system development. Debao horses have 970 genes overlapping with CNVRs, possibly causing their small size and mountainous adaptations. We also found that the genes GHR, SOX9, and SOX11 may be responsible for the miniature features of the Debao horse by analyzing their overlapping CNVRs. Overall, this study offers valuable insights into the widespread presence of CNVs in the horse genome. The findings contribute to mapping horse CNVs and advance research on unique miniature traits observed in the Debao horse.
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Affiliation(s)
- Md. Panir Choudhury
- Kunpeng Institute of Modern Agriculture at Foshan, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Foshan 518124, China; (M.P.C.); (G.Y.); (Y.L.)
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Kunpeng Institute of Modern Agriculture at Foshan, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China
- Bangladesh Livestock Research Institute, Ministry of Fisheries and Livestock, Savar, Dhaka 1341, Bangladesh
| | - Zihao Wang
- Animal Husbandry Research Institute, Guangxi Vocational University of Agriculture, Nanning 530002,China; (Z.W.); (M.Z.); (S.T.); (J.Y.); (S.C.)
| | - Min Zhu
- Animal Husbandry Research Institute, Guangxi Vocational University of Agriculture, Nanning 530002,China; (Z.W.); (M.Z.); (S.T.); (J.Y.); (S.C.)
| | - Shaohua Teng
- Animal Husbandry Research Institute, Guangxi Vocational University of Agriculture, Nanning 530002,China; (Z.W.); (M.Z.); (S.T.); (J.Y.); (S.C.)
| | - Jing Yan
- Animal Husbandry Research Institute, Guangxi Vocational University of Agriculture, Nanning 530002,China; (Z.W.); (M.Z.); (S.T.); (J.Y.); (S.C.)
| | - Shuwei Cao
- Animal Husbandry Research Institute, Guangxi Vocational University of Agriculture, Nanning 530002,China; (Z.W.); (M.Z.); (S.T.); (J.Y.); (S.C.)
| | - Guoqiang Yi
- Kunpeng Institute of Modern Agriculture at Foshan, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Foshan 518124, China; (M.P.C.); (G.Y.); (Y.L.)
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Kunpeng Institute of Modern Agriculture at Foshan, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China
| | - Yuwen Liu
- Kunpeng Institute of Modern Agriculture at Foshan, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Foshan 518124, China; (M.P.C.); (G.Y.); (Y.L.)
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Kunpeng Institute of Modern Agriculture at Foshan, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China
| | - Yuying Liao
- Guangxi Veterinary Research Institute, Nanning 530001, China
| | - Zhonglin Tang
- Kunpeng Institute of Modern Agriculture at Foshan, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Foshan 518124, China; (M.P.C.); (G.Y.); (Y.L.)
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Kunpeng Institute of Modern Agriculture at Foshan, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China
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Genome-Wide Detection and Analysis of Copy Number Variation in Anhui Indigenous and Western Commercial Pig Breeds Using Porcine 80K SNP BeadChip. Genes (Basel) 2023; 14:genes14030654. [PMID: 36980927 PMCID: PMC10047991 DOI: 10.3390/genes14030654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 02/27/2023] [Accepted: 03/01/2023] [Indexed: 03/08/2023] Open
Abstract
Copy number variation (CNV) is an important class of genetic variations widely associated with the porcine genome, but little is known about the characteristics of CNVs in foreign and indigenous pig breeds. We performed a genome-wide comparison of CNVs between Anhui indigenous pig (AHIP) and Western commercial pig (WECP) breeds based on data from the Porcine 80K SNP BeadChip. After analysis using the PennCNV software, we detected 3863 and 7546 CNVs in the AHIP and WECP populations, respectively. We obtained 225 (loss: 178, gain: 47) and 379 (loss: 293, gain: 86) copy number variation regions (CNVRs) randomly distributed across the autosomes of the AHIP and WECP populations, accounting for 10.90% and 22.57% of the porcine autosomal genome, respectively. Functional enrichment analysis of genes in the CNVRs identified genes related to immunity (FOXJ1, FOXK2, MBL2, TNFRSF4, SIRT1, NCF1) and meat quality (DGAT1, NT5E) in the WECP population; these genes were a loss event in the WECP population. This study provides important information on CNV differences between foreign and indigenous pig breeds, making it possible to provide a reference for future improvement of these breeds and their production performance.
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Zhang C, Zhao J, Guo Y, Xu Q, Liu M, Cheng M, Chao X, Schinckel AP, Zhou B. Genome-Wide Detection of Copy Number Variations and Evaluation of Candidate Copy Number Polymorphism Genes Associated With Complex Traits of Pigs. Front Vet Sci 2022; 9:909039. [PMID: 35847642 PMCID: PMC9280686 DOI: 10.3389/fvets.2022.909039] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 06/09/2022] [Indexed: 12/12/2022] Open
Abstract
Copy number variation (CNV) has been considered to be an important source of genetic variation for important phenotypic traits of livestock. In this study, we performed whole-genome CNV detection on Suhuai (SH) (n = 23), Chinese Min Zhu (MZ) (n = 11), and Large White (LW) (n = 12) pigs based on next-generation sequencing data. The copy number variation regions (CNVRs) were annotated and analyzed, and 10,885, 10,836, and 10,917 CNVRs were detected in LW, MZ, and SH pigs, respectively. Some CNVRs have been randomly selected for verification of the variation type by real-time PCR. We found that SH and LW pigs are closely related, while MZ pigs are distantly related to the SH and LW pigs by CNVR-based genetic structure, PCA, VST, and QTL analyses. A total of 14 known genes annotated in CNVRs were unique for LW pigs. Among them, the cyclin T2 (CCNT2) is involved in cell proliferation and the cell cycle. The FA Complementation Group M (FANCM) is involved in defective DNA repair and reproductive cell development. Ten known genes annotated in 47 CNVRs were unique for MZ pigs. The genes included glycerol-3-phosphate acyltransferase 3 (GPAT3) is involved in fat synthesis and is essential to forming the glycerol triphosphate. Glutathione S-transferase mu 4 (GSTM4) gene plays an important role in detoxification. Eleven known genes annotated in 23 CNVRs were unique for SH pigs. Neuroligin 4 X-linked (NLGN4X) and Neuroligin 4 Y-linked (NLGN4Y) are involved with nerve disorders and nerve signal transmission. IgLON family member 5 (IGLON5) is related to autoimmunity and neural activities. The unique characteristics of LW, MZ, and SH pigs are related to these genes with CNV polymorphisms. These findings provide important information for the identification of candidate genes in the molecular breeding of pigs.
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Affiliation(s)
- Chunlei Zhang
- College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, China
| | - Jing Zhao
- College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, China
| | - Yanli Guo
- College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, China
| | - Qinglei Xu
- College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, China
| | - Mingzheng Liu
- College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, China
| | - Meng Cheng
- College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, China
| | - Xiaohuan Chao
- College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, China
| | - Allan P. Schinckel
- Department of Animal Sciences, Purdue University, West Lafayette, IN, United States
| | - Bo Zhou
- College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, China
- *Correspondence: Bo Zhou
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Wang X, D’Alessandro E, Chi C, Moawad AS, Zong W, Chen C, Song C. Genetic Evaluation and Population Structure of Jiangsu Native Pigs in China Revealed by SINE Insertion Polymorphisms. Animals (Basel) 2022; 12:ani12111345. [PMID: 35681812 PMCID: PMC9179424 DOI: 10.3390/ani12111345] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 05/04/2022] [Accepted: 05/18/2022] [Indexed: 12/13/2022] Open
Abstract
Simple Summary In a previous study, 30 SINE-RIPs were applied for population genetic analysis in 7 Chinese miniature pig populations and approved effectively in the genetic distances and breed-relationships between these populations. There are abundant indigenous pigs famous across the world for their prolificacy in the Jiangsu Province of eastern China, such as Meishan, Erhualian. Since pork production relies on limited commercial breeds such as Landrace, Large White, and Duroc pigs, characterized by maximizing productivity in intensive production systems, these indigenous pigs are nowadays decrease sharply. The genetic characterizations of these resources are essential requirements for the development of conservation, selection, and sustainable utilizations. Therefore, SINE-RIPs were selected to evaluate the genetic variation and population structure of Jiangsu pig populations and the results may assist with the conservation and utilization of these native pig populations. Abstract Short interspersed nuclear elements (SINEs), one type of retrotransposon, are considered to be ideal molecular markers due to their wide distribution in the genome, high copy number, and high polymorphism. Preliminary studies have identified more than 35,000 SINE-retrotransposon insertion polymorphisms (RIPs) in the pig genome. In this study, 18 SINE-RIPs were used to evaluate the genetic variation and population structure of seven native pig populations and two crossbreeds in the Jiangsu Province of China. Two commercial pig breeds (Duroc and Large White) and one Italian native breed (Sicilian Black pig) were selected as the control. The results showed that all 18 SINE-RIPs were polymorphic among these pigs. The Jiangsu native pig populations (Erhualian, Fengjing, Middle Meishan, Mi, Shawutou, Small Meishan, and Huai) were shown to be more polymorphic than the crossbreeds (Sushan and Sujiang) and external breeds (Sicilian Black pig, Large White, and Duroc) based on the expected heterozygosity and polymorphic information content values. Some native pigs, including Small Meishan, Mi, Middle Meishan, and Erhualian, had a higher degree of inbreeding according to the FIS values. Based on the neighbor-joining tree, all of the Jiangsu native pig populations formed one branch, while the three external pig breeds formed the other branches, with the two crossbreeds containing more than 50% external pig ancestry. The Huai pigs were independent of the other Jiangsu native pigs but shared a common ancestor with Sujiang and Mi. The results provide a new perspective on the population structure of these native pig breeds and will assist with the conservation and utilization of Chinese native pigs.
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Affiliation(s)
- Xiaoyan Wang
- College of Animal Science & Technology, Yangzhou University, Yangzhou 225009, China; (X.W.); (C.C.); (A.S.M.); (W.Z.); (C.C.)
| | - Enrico D’Alessandro
- Unit of Animal Production, Department of Veterinary Science, University of Messina, 98168 Messina, Italy;
| | - Chenglin Chi
- College of Animal Science & Technology, Yangzhou University, Yangzhou 225009, China; (X.W.); (C.C.); (A.S.M.); (W.Z.); (C.C.)
| | - Ali Shoaib Moawad
- College of Animal Science & Technology, Yangzhou University, Yangzhou 225009, China; (X.W.); (C.C.); (A.S.M.); (W.Z.); (C.C.)
- Department of Animal Production, Faculty of Agriculture, Kafrelsheikh University, Kafrelsheikh 33516, Egypt
| | - Wencheng Zong
- College of Animal Science & Technology, Yangzhou University, Yangzhou 225009, China; (X.W.); (C.C.); (A.S.M.); (W.Z.); (C.C.)
| | - Cai Chen
- College of Animal Science & Technology, Yangzhou University, Yangzhou 225009, China; (X.W.); (C.C.); (A.S.M.); (W.Z.); (C.C.)
| | - Chengyi Song
- College of Animal Science & Technology, Yangzhou University, Yangzhou 225009, China; (X.W.); (C.C.); (A.S.M.); (W.Z.); (C.C.)
- Correspondence: ; Tel./Fax: +86-514-87979034
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Improving read alignment through the generation of alternative reference via iterative strategy. Sci Rep 2020; 10:18712. [PMID: 33127969 PMCID: PMC7599232 DOI: 10.1038/s41598-020-74526-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Accepted: 09/30/2020] [Indexed: 11/08/2022] Open
Abstract
There is generally one standard reference sequence for each species. When extensive variations exist in other breeds of the species, it can lead to ambiguous alignment and inaccurate variant calling and, in turn, compromise the accuracy of downstream analysis. Here, with the help of the FPGA hardware platform, we present a method that generates an alternative reference via an iterative strategy to improve the read alignment for breeds that are genetically distant to the reference breed. Compared to the published reference genomes, by using the alternative reference sequences we built, the mapping rates of Chinese indigenous pigs and chickens were improved by 0.61-1.68% and 0.09-0.45%, respectively. These sequences also enable researchers to recover highly variable regions that could be missed using public reference sequences. We also determined that the optimal number of iterations needed to generate alternative reference sequences were seven and five for pigs and chickens, respectively. Our results show that, for genetically distant breeds, generating an alternative reference sequence can facilitate read alignment and variant calling and improve the accuracy of downstream analyses.
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Stolpovsky YA, Piskunov AK, Svishcheva GR. Genomic Selection. I: Latest Trends and Possible Ways of Development. RUSS J GENET+ 2020. [DOI: 10.1134/s1022795420090148] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Liu C, Li P, Zhou W, Ma X, Wang X, Xu Y, Jiang N, Zhao M, Zhou T, Yin Y, Ren J, Huang R. Genome Data Uncover Conservation Status, Historical Relatedness and Candidate Genes Under Selection in Chinese Indigenous Pigs in the Taihu Lake Region. Front Genet 2020; 11:591. [PMID: 32582299 PMCID: PMC7296076 DOI: 10.3389/fgene.2020.00591] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Accepted: 05/15/2020] [Indexed: 12/17/2022] Open
Abstract
Chinese indigenous pig breeds in the Taihu Lake (TL) region of Eastern China are well documented by their exceptional prolificacy. There are seven breeds in this region including Meishan (MS), Erhualian (EHL), Jiaxing Black (JXB), Fengjing (FJ), Shawutou (SWT), Mi (MI), and Hongdenglong (HDL). At present, these breeds are facing a great threat of population decline, inbreeding depression and lineage admixture since Western commercial pigs have dominated in Chinese pig industry. To provide better conservation strategies and identify candidate genes under selection for these breeds, we explored genome-wide single nucleotide polymorphism (SNP) markers to uncover genetic variability and relatedness, population structure, historical admixture and genomic signature of selection of 440 pigs representing the most comprehensive lineages of these breeds in TL region in a context of 1228 pigs from 45 Eurasian breeds. We showed that these breeds were more closely related to each other as compared to other Eurasian breeds, defining one of the main ancestral lineages of Chinese indigenous pigs. These breeds can be divided into two subgroups, one including JXB and FJ, and the other comprising of EHL, MI, HDL, MS, and SWT. In addition, HDL was highly inbred whereas EHL and MS had more abundant genetic diversity owing to their multiple conservation populations. Moreover, we identified a list of candidate genes under selection for body size and prolificacy. Our results would benefit the conservation of these valuable breeds and improve our understanding of the genetic mechanisms of body size and fecundity in pigs.
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Affiliation(s)
- Chenxi Liu
- Institute of Swine Science, Nanjing Agricultural University, Nanjing, China
| | - Pinghua Li
- Institute of Swine Science, Nanjing Agricultural University, Nanjing, China.,Huaian Academy, Nanjing Agricultural University, Huaian, China
| | - Wuduo Zhou
- Institute of Swine Science, Nanjing Agricultural University, Nanjing, China
| | - Xiang Ma
- Institute of Swine Science, Nanjing Agricultural University, Nanjing, China
| | - Xiaopeng Wang
- College of Animal Science, South China Agricultural University, Guangzhou, China
| | - Yan Xu
- Jiangsu Provincial Station of Animal Husbandry, Nanjing, China
| | - Nengjing Jiang
- Institute of Swine Science, Nanjing Agricultural University, Nanjing, China
| | - Moran Zhao
- Institute of Swine Science, Nanjing Agricultural University, Nanjing, China
| | - Tianwei Zhou
- Institute of Swine Science, Nanjing Agricultural University, Nanjing, China
| | - Yanzhen Yin
- Institute of Swine Science, Nanjing Agricultural University, Nanjing, China
| | - Jun Ren
- College of Animal Science, South China Agricultural University, Guangzhou, China
| | - Ruihua Huang
- Institute of Swine Science, Nanjing Agricultural University, Nanjing, China.,Huaian Academy, Nanjing Agricultural University, Huaian, China
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Wang Z, Guo J, Guo Y, Yang Y, Teng T, Yu Q, Wang T, Zhou M, Zhu Q, Wang W, Zhang Q, Yang H. Genome-Wide Detection of CNVs and Association With Body Weight in Sheep Based on 600K SNP Arrays. Front Genet 2020; 11:558. [PMID: 32582291 PMCID: PMC7297042 DOI: 10.3389/fgene.2020.00558] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Accepted: 05/07/2020] [Indexed: 01/30/2023] Open
Abstract
Copy number variations (CNVs) are important genomic structural variations and can give rise to significant phenotypic diversity. Herein, we used high-density 600K SNP arrays to detect CNVs in two synthetic lines of sheep (DS and SHH) and in Hu sheep (a local Chinese breed). A total of 919 CNV regions (CNVRs) were detected with a total length of 48.17 Mb, accounting for 1.96% of the sheep genome. These CNVRs consisted of 730 gains, 102 losses, and 87 complex CNVRs. These CNVRs were significantly enriched in the segmental duplication (SD) region. A CNVR-based cluster analysis of the three breeds revealed that the DS and SHH breeds share a close genetic relationship. Functional analysis revealed that some genes in these CNVRs were also significantly enriched in the olfactory transduction pathway (oas04740), including members of the OR gene family such as OR6C76, OR4Q2, and OR4K14. Using association analyses and previous gene annotations, we determined that a subset of identified genes was likely to be associated with body weight, including FOXF2, MAPK12, MAP3K11, STRBP, and C14orf132. Together, these results offer valuable information that will guide future efforts to explore the genetic basis for body weight in sheep.
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Affiliation(s)
- Zhipeng Wang
- College of Animal Science and Technology, Northeast Agricultural University, Harbin, China.,Key Laboratory of Animal Genetics, Breeding and Reproduction, Education Department of Heilongjiang Province, Harbin, China
| | - Jing Guo
- College of Animal Science and Technology, Northeast Agricultural University, Harbin, China.,Key Laboratory of Animal Genetics, Breeding and Reproduction, Education Department of Heilongjiang Province, Harbin, China
| | - Yuanyuan Guo
- College of Animal Science and Technology, Northeast Agricultural University, Harbin, China.,Key Laboratory of Animal Genetics, Breeding and Reproduction, Education Department of Heilongjiang Province, Harbin, China
| | - Yonglin Yang
- State Key Laboratory of Sheep Genetic Improvement and Healthy Production, Xinjiang Academy of Agricultural and Reclamation Science, Shihezi, China
| | - Teng Teng
- Institute of Animal Nutrition, Northeast Agricultural University, Harbin, China
| | - Qian Yu
- State Key Laboratory of Sheep Genetic Improvement and Healthy Production, Xinjiang Academy of Agricultural and Reclamation Science, Shihezi, China
| | - Tao Wang
- College of Animal Science and Technology, Northeast Agricultural University, Harbin, China.,Key Laboratory of Animal Genetics, Breeding and Reproduction, Education Department of Heilongjiang Province, Harbin, China
| | - Meng Zhou
- College of Animal Science and Technology, Northeast Agricultural University, Harbin, China.,Key Laboratory of Animal Genetics, Breeding and Reproduction, Education Department of Heilongjiang Province, Harbin, China
| | - Qiusi Zhu
- College of Animal Science and Technology, Northeast Agricultural University, Harbin, China.,Key Laboratory of Animal Genetics, Breeding and Reproduction, Education Department of Heilongjiang Province, Harbin, China
| | - Wenwen Wang
- Department of Animal Genetics and Breeding, College of Animal Science and Technology, Shandong Agricultural University, Tai'an, China
| | - Qin Zhang
- Department of Animal Genetics and Breeding, College of Animal Science and Technology, Shandong Agricultural University, Tai'an, China
| | - Hua Yang
- State Key Laboratory of Sheep Genetic Improvement and Healthy Production, Xinjiang Academy of Agricultural and Reclamation Science, Shihezi, China
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Jiang Y, Jiang Y, Wang S, Zhang Q, Ding X. Optimal sequencing depth design for whole genome re-sequencing in pigs. BMC Bioinformatics 2019; 20:556. [PMID: 31703550 PMCID: PMC6839175 DOI: 10.1186/s12859-019-3164-z] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Accepted: 10/16/2019] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND As whole-genome sequencing is becoming a routine technique, it is important to identify a cost-effective depth of sequencing for such studies. However, the relationship between sequencing depth and biological results from the aspects of whole-genome coverage, variant discovery power and the quality of variants is unclear, especially in pigs. We sequenced the genomes of three Yorkshire boars at an approximately 20X depth on the Illumina HiSeq X Ten platform and downloaded whole-genome sequencing data for three Duroc and three Landrace pigs with an approximately 20X depth for each individual. Then, we downsampled the deep genome data by extracting twelve different proportions of 0.05, 0.1, 0.15, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8 and 0.9 paired reads from the original bam files to mimic the sequence data of the same individuals at sequencing depths of 1.09X, 2.18X, 3.26X, 4.35X, 6.53X, 8.70X, 10.88X, 13.05X, 15.22X, 17.40X, 19.57X and 21.75X to evaluate the influence of genome coverage, the variant discovery rate and genotyping accuracy as a function of sequencing depth. In addition, SNP chip data for Yorkshire pigs were used as a validation for the comparison of single-sample calling and multisample calling algorithms. RESULTS Our results indicated that 10X is an ideal practical depth for achieving plateau coverage and discovering accurate variants, which achieved greater than 99% genome coverage. The number of false-positive variants was increased dramatically at a depth of less than 4X, which covered 95% of the whole genome. In addition, the comparison of multi- and single-sample calling showed that multisample calling was more sensitive than single-sample calling, especially at lower depths. The number of variants discovered under multisample calling was 13-fold and 2-fold higher than that under single-sample calling at 1X and 22X, respectively. A large difference was observed when the depth was less than 4.38X. However, more false-positive variants were detected under multisample calling. CONCLUSIONS Our research will inform important study design decisions regarding whole-genome sequencing depth. Our results will be helpful for choosing the appropriate depth to achieve the same power for studies performed under limited budgets.
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Affiliation(s)
- Yifan Jiang
- National Engineering Laboratory for Animal Breeding, Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, 100193 China
| | - Yao Jiang
- National Engineering Laboratory for Animal Breeding, Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, 100193 China
| | - Sheng Wang
- National Engineering Laboratory for Animal Breeding, Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, 100193 China
| | - Qin Zhang
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Technology, Shandong Agricultural University, Taian, 271001 China
| | - Xiangdong Ding
- National Engineering Laboratory for Animal Breeding, Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, 100193 China
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Keel BN, Nonneman DJ, Lindholm-Perry AK, Oliver WT, Rohrer GA. A Survey of Copy Number Variation in the Porcine Genome Detected From Whole-Genome Sequence. Front Genet 2019; 10:737. [PMID: 31475038 PMCID: PMC6707380 DOI: 10.3389/fgene.2019.00737] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Accepted: 07/12/2019] [Indexed: 12/11/2022] Open
Abstract
Copy number variations (CNVs) are gains and losses of large regions of genomic sequence between individuals of a species. Although CNVs have been associated with various phenotypic traits in humans and other species, the extent to which CNVs impact phenotypic variation remains unclear. In swine, as well as many other species, relatively little is understood about the frequency of CNV in the genome, sizes, locations, and other chromosomal properties. In this work, we identified and characterized CNV by utilizing whole-genome sequence from 240 members of an intensely phenotyped experimental swine herd at the U.S. Meat Animal Research Center (USMARC). These animals included all 24 of the purebred founding boars (12 Duroc and 12 Landrace), 48 of the founding Yorkshire-Landrace composite sows, 109 composite animals from generations 4 through 9, 29 composite animals from generation 15, and 30 purebred industry boars (15 Landrace and 15 Yorkshire) used as sires in generations 10 through 15. Using a combination of split reads, paired-end mapping, and read depth approaches, we identified a total of 3,538 copy number variable regions (CNVRs), including 1,820 novel CNVRs not reported in previous studies. The CNVRs covered 0.94% of the porcine genome and overlapped 1,401 genes. Gene ontology analysis identified that CNV-overlapped genes were enriched for functions related to organism development. Additionally, CNVRs overlapped with many known quantitative trait loci (QTL). In particular, analysis of QTL previously identified in the USMARC herd showed that CNVRs were most overlapped with reproductive traits, such as age of puberty and ovulation rate, and CNVRs were significantly enriched for reproductive QTL.
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Affiliation(s)
- Brittney N Keel
- USDA, ARS, U.S. Meat Animal Research Center, Clay Center, NE, United States
| | - Dan J Nonneman
- USDA, ARS, U.S. Meat Animal Research Center, Clay Center, NE, United States
| | | | - William T Oliver
- USDA, ARS, U.S. Meat Animal Research Center, Clay Center, NE, United States
| | - Gary A Rohrer
- USDA, ARS, U.S. Meat Animal Research Center, Clay Center, NE, United States
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12
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Crespo-Piazuelo D, Criado-Mesas L, Revilla M, Castelló A, Fernández AI, Folch JM, Ballester M. Indel detection from Whole Genome Sequencing data and association with lipid metabolism in pigs. PLoS One 2019; 14:e0218862. [PMID: 31246983 PMCID: PMC6597088 DOI: 10.1371/journal.pone.0218862] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2018] [Accepted: 06/11/2019] [Indexed: 12/15/2022] Open
Abstract
The selection in commercial swine breeds for meat-production efficiency has been increasing among the past decades, reducing the intramuscular fat content, which has changed the sensorial and technological properties of pork. Through processes of natural adaptation and selective breeding, the accumulation of mutations has driven the genetic divergence between pig breeds. The most common and well-studied mutations are single-nucleotide polymorphisms (SNPs). However, insertions and deletions (indels) usually represents a fifth part of the detected mutations and should also be considered for animal breeding. In the present study, three different programs (Dindel, SAMtools mpileup, and GATK) were used to detect indels from Whole Genome Sequencing data of Iberian boars and Landrace sows. A total of 1,928,746 indels were found in common with the three programs. The VEP tool predicted that 1,289 indels may have a high impact on protein sequence and function. Ten indels inside genes related with lipid metabolism were genotyped in pigs from three different backcrosses with Iberian origin, obtaining different allelic frequencies on each backcross. Genome-Wide Association Studies performed in the Longissimus dorsi muscle found an association between an indel located in the C1q and TNF related 12 (C1QTNF12) gene and the amount of eicosadienoic acid (C20:2(n-6)).
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Affiliation(s)
- Daniel Crespo-Piazuelo
- Plant and Animal Genomics, Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB Consortium, Bellaterra, Spain
- Departament de Ciència Animal i dels Aliments, Facultat de Veterinària, Universitat Autònoma de Barcelona (UAB), Bellaterra, Spain
- * E-mail:
| | - Lourdes Criado-Mesas
- Plant and Animal Genomics, Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB Consortium, Bellaterra, Spain
| | - Manuel Revilla
- Plant and Animal Genomics, Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB Consortium, Bellaterra, Spain
- Departament de Ciència Animal i dels Aliments, Facultat de Veterinària, Universitat Autònoma de Barcelona (UAB), Bellaterra, Spain
| | - Anna Castelló
- Plant and Animal Genomics, Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB Consortium, Bellaterra, Spain
- Departament de Ciència Animal i dels Aliments, Facultat de Veterinària, Universitat Autònoma de Barcelona (UAB), Bellaterra, Spain
| | - Ana I. Fernández
- Departamento de Mejora Genética Animal, Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Madrid, Spain
| | - Josep M. Folch
- Plant and Animal Genomics, Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB Consortium, Bellaterra, Spain
- Departament de Ciència Animal i dels Aliments, Facultat de Veterinària, Universitat Autònoma de Barcelona (UAB), Bellaterra, Spain
| | - Maria Ballester
- Departament de Genètica i Millora Animal, Institut de Recerca i Tecnologia Agroalimentàries (IRTA), Caldes de Montbui, Spain
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13
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Cloning and Expression Levels of TFAM and TFB2M Genes and their Correlation with Meat and Carcass Quality Traits in Jiaxing Black Pig. ANNALS OF ANIMAL SCIENCE 2019. [DOI: 10.2478/aoas-2018-0056] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Abstract
The coding sequences (CDS) of TFAM and TFB2M genes from Jiaxing Black Pig (JBP) were first obtained by RT-PCR and DNA-seq in the present study. Sequence analyses showed that the TFAM gene contains a 741-bp CDS region encoding 246 amino acids sharing a 100% homology with the sequence on NCBI, while TFB2M gene contains a CDS region of 1176 bp encoding 391 amino acids with two missense mutations. The results of quantitative Real-Time PCR for TFAM and TFB2M revealed that transcripts of the genes were both presented at the highest levels in spleen tissue followed by liver tissue, while the least levels in longissimus dorsi muscle (LDM), and obviously the higher levels in two adipose tissues than those in LDM tissue (P<0.01). Meanwhile, a total of forty-two JBPs were employed in this experiment to investigate the effect of these two genes on the carcass, meat quality traits and flavor substances such as fatty acids, intramuscular fat (IMF) in LDM. As expected, some strong correlations of gene expression abundance of TFAM and TFB2M mRNA in particular tissues such as liver and LDM with carcass and meat quality traits including marbling score, as well as the content of saturated fatty acid (SFA), in JBP were found.
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14
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Wang C, Chen H, Wang X, Wu Z, Liu W, Guo Y, Ren J, Ding N. Identification of copy number variations using high density whole-genome SNP markers in Chinese Dongxiang spotted pigs. ASIAN-AUSTRALASIAN JOURNAL OF ANIMAL SCIENCES 2019; 32:1809-1815. [PMID: 30744341 PMCID: PMC6819687 DOI: 10.5713/ajas.18.0696] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/12/2018] [Accepted: 01/08/2019] [Indexed: 01/13/2023]
Abstract
Objective Copy number variations (CNVs) are a major source of genetic diversity complementary to single nucleotide polymorphism (SNP) in animals. The aim of the study was to perform a comprehensive genomic analysis of CNVs based on high density whole-genome SNP markers in Chinese Dongxiang spotted pigs. Methods We used customized Affymetrix Axiom Pig1.4M array plates containing 1.4 million SNPs and the PennCNV algorithm to identify porcine CNVs on autosomes in Chinese Dongxiang spotted pigs. Then, the next generation sequence data was used to confirm the detected CNVs. Next, functional analysis was performed for gene contents in copy number variation regions (CNVRs). In addition, we compared the identified CNVRs with those reported ones and quantitative trait loci (QTL) in the pig QTL database. Results We identified 871 putative CNVs belonging to 2,221 CNVRs on 17 autosomes. We further discarded CNVRs that were detected only in one individual, leaving us 166 CNVRs in total. The 166 CNVRs ranged from 2.89 kb to 617.53 kb with a mean value of 93.65 kb and a genome coverage of 15.55 Mb, corresponding to 0.58% of the pig genome. A total of 119 (71.69%) of the identified CNVRs were confirmed by next generation sequence data. Moreover, functional annotation showed that these CNVRs are involved in a variety of molecular functions. More than half (56.63%) of the CNVRs (n = 94) have been reported in previous studies, while 72 CNVRs are reported for the first time. In addition, 162 (97.59%) CNVRs were found to overlap with 2,765 previously reported QTLs affecting 378 phenotypic traits. Conclusion The findings improve the catalog of pig CNVs and provide insights and novel molecular markers for further genetic analyses of Chinese indigenous pigs.
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Affiliation(s)
- Chengbin Wang
- State Key Laboratory of Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang 330045, China
| | - Hao Chen
- State Key Laboratory of Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang 330045, China
| | - Xiaopeng Wang
- State Key Laboratory of Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang 330045, China
| | - Zhongping Wu
- State Key Laboratory of Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang 330045, China
| | - Weiwei Liu
- State Key Laboratory of Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang 330045, China
| | - Yuanmei Guo
- State Key Laboratory of Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang 330045, China
| | - Jun Ren
- State Key Laboratory of Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang 330045, China
| | - Nengshui Ding
- State Key Laboratory of Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang 330045, China
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15
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Zhao P, Yu Y, Feng W, Du H, Yu J, Kang H, Zheng X, Wang Z, Liu GE, Ernst CW, Ran X, Wang J, Liu JF. Evidence of evolutionary history and selective sweeps in the genome of Meishan pig reveals its genetic and phenotypic characterization. Gigascience 2018; 7:5001425. [PMID: 29790964 PMCID: PMC6007440 DOI: 10.1093/gigascience/giy058] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2018] [Accepted: 05/11/2018] [Indexed: 12/18/2022] Open
Abstract
Background Meishan is a pig breed indigenous to China and famous for its high fecundity. The traits of Meishan are strongly associated with its distinct evolutionary history and domestication. However, the genomic evidence linking the domestication of Meishan pigs with its unique features is still poorly understood. The goal of this study is to investigate the genomic signatures and evolutionary evidence related to the phenotypic traits of Meishan via large-scale sequencing. Results We found that the unique domestication of Meishan pigs occurred in the Taihu Basin area between the Majiabang and Liangzhu Cultures, during which 300 protein-coding genes have underwent positive selection. Notably, enrichment of the FoxO signaling pathway with significant enrichment signal and the harbored gene IGF1R were likely associated with the high fertility of Meishan pigs. Moreover, NFKB1 exhibited strong selective sweep signals and positively participated in hyaluronan biosynthesis as the key gene of NF-kB signaling, which may have resulted in the wrinkled skin and face of Meishan pigs. Particularly, three population-specific synonymous single-nucleotide variants occurred in PYROXD1, MC1R, and FAM83G genes; the T305C substitution in the MCIR gene explained the black coat of the Meishan pigs well. In addition, the shared haplotypes between Meishan and Duroc breeds confirmed the previous Asian-derived introgression and demonstrated the specific contribution of Meishan pigs. Conclusions These findings will help us explain the unique genetic and phenotypic characteristics of Meishan pigs and offer a plausible method for their utilization of Meishan pigs as valuable genetic resources in pig breeding and as an animal model for human wrinkled skin disease research.
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Affiliation(s)
- Pengju Zhao
- National Engineering Laboratory for Animal Breeding; Key Laboratory of Animal Genetics, Breeding, and Reproduction, Ministry of Agriculture; College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Ying Yu
- National Engineering Laboratory for Animal Breeding; Key Laboratory of Animal Genetics, Breeding, and Reproduction, Ministry of Agriculture; College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Wen Feng
- National Engineering Laboratory for Animal Breeding; Key Laboratory of Animal Genetics, Breeding, and Reproduction, Ministry of Agriculture; College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Heng Du
- National Engineering Laboratory for Animal Breeding; Key Laboratory of Animal Genetics, Breeding, and Reproduction, Ministry of Agriculture; College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Jian Yu
- National Engineering Laboratory for Animal Breeding; Key Laboratory of Animal Genetics, Breeding, and Reproduction, Ministry of Agriculture; College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Huimin Kang
- National Engineering Laboratory for Animal Breeding; Key Laboratory of Animal Genetics, Breeding, and Reproduction, Ministry of Agriculture; College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Xianrui Zheng
- National Engineering Laboratory for Animal Breeding; Key Laboratory of Animal Genetics, Breeding, and Reproduction, Ministry of Agriculture; College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Zhiquan Wang
- Department of Agricultural, Food & Nutritional Science, University of Alberta, Edmonton, T6G 2C8, Canada
| | - George E Liu
- Animal Genomics and Improvement Laboratory, BARC, USDA-ARS, Beltsville, MD 20705-2350, USA
| | | | - Xueqin Ran
- School of Animal Science, Guizhou University, Guiyang, 550025, China
| | - Jiafu Wang
- School of Animal Science, Guizhou University, Guiyang, 550025, China
| | - Jian-Feng Liu
- National Engineering Laboratory for Animal Breeding; Key Laboratory of Animal Genetics, Breeding, and Reproduction, Ministry of Agriculture; College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
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16
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Liu D, Chen Z, Zhang Z, Sun H, Ma P, Zhu K, Liu G, Wang Q, Pan Y. Detection of genome-wide structural variations in the Shanghai Holstein cattle population using next-generation sequencing. ASIAN-AUSTRALASIAN JOURNAL OF ANIMAL SCIENCES 2018; 32:320-333. [PMID: 30056674 PMCID: PMC6409473 DOI: 10.5713/ajas.18.0204] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/14/2018] [Accepted: 06/22/2018] [Indexed: 12/30/2022]
Abstract
Objective The Shanghai Holstein cattle breed is susceptible to severe mastitis and other diseases due to the hot weather and long-term humidity in Shanghai, which is the main distribution centre for providing Holstein semen to various farms throughout China. Our objective was to determine the genetic mechanisms influencing economically important traits, especially diseases that have huge impact on the yield and quality of milk as well as reproduction. Methods In our study, we detected the structural variations of 1,092 Shanghai Holstein cows by using next-generation sequencing. We used the DELLY software to identify deletions and insertions, cn.MOPS to identify copy-number variants (CNVs). Furthermore, we annotated these structural variations using different bioinformatics tools, such as gene ontology, cattle quantitative trait locus (QTL) database and ingenuity pathway analysis (IPA). Results The average number of high-quality reads was 3,046,279. After filtering, a total of 16,831 deletions, 12,735 insertions and 490 CNVs were identified. The annotation results showed that these mapped genes were significantly enriched for specific biological functions, such as disease and reproduction. In addition, the enrichment results based on the cattle QTL database showed that the number of variants related to milk and reproduction was higher than the number of variants related to other traits. IPA core analysis found that the structural variations were related to reproduction, lipid metabolism, and inflammation. According to the functional analysis, structural variations were important factors affecting the variation of different traits in Shanghai Holstein cattle. Our results provide meaningful information about structural variations, which may be useful in future assessments of the associations between variations and important phenotypes in Shanghai Holstein cattle. Conclusion Structural variations identified in this study were extremely different from those of previous studies. Many structural variations were found to be associated with mastitis and reproductive system diseases; these results are in accordance with the characteristics of the environment that Shanghai Holstein cattle experience.
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Affiliation(s)
- Dengying Liu
- Department of Animal Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China.,Shanghai Key Laboratory of Veterinary Biotechnology, Shanghai 200240, China
| | - Zhenliang Chen
- Department of Animal Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China.,Shanghai Key Laboratory of Veterinary Biotechnology, Shanghai 200240, China
| | - Zhe Zhang
- Department of Animal Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China.,Shanghai Key Laboratory of Veterinary Biotechnology, Shanghai 200240, China
| | - Hao Sun
- Department of Animal Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China.,Shanghai Key Laboratory of Veterinary Biotechnology, Shanghai 200240, China
| | - Peipei Ma
- Department of Animal Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China.,Shanghai Key Laboratory of Veterinary Biotechnology, Shanghai 200240, China
| | - Kai Zhu
- Shanghai Dairy Cattle Breeding Centre Co., Ltd, Shanghai 201901, China
| | - Guanglei Liu
- Shanghai Dairy Cattle Breeding Centre Co., Ltd, Shanghai 201901, China
| | - Qishan Wang
- Department of Animal Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China.,Shanghai Key Laboratory of Veterinary Biotechnology, Shanghai 200240, China
| | - Yuchun Pan
- Department of Animal Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China.,Shanghai Key Laboratory of Veterinary Biotechnology, Shanghai 200240, China
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17
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A genome scan for selection signatures in Taihu pig breeds using next-generation sequencing. Animal 2018; 13:683-693. [PMID: 29987993 DOI: 10.1017/s1751731118001714] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
Taihu pig breeds are the most prolific breeds of swine in the world, and they also have superior economic traits, including high resistance to disease, superior meat quality, high resistance to crude feed and a docile temperament. The formation of these phenotypic characteristics is largely a result of long-term artificial or natural selection. Therefore, exploring selection signatures in the genomes of the Taihu pigs will help us to identify porcine genes related to productivity traits, disease and behaviour. In this study, we used both intra-population (Relative Extend Haplotype Homozygosity Test (REHH)) and inter-population (the Cross-Population Extend Haplotype Homozygosity Test (XPEHH); F-STATISTICS, F ST ) methods to detect genomic regions that might be under selection process in Taihu pig breeds. As a result, we found 282 (REHH) and 112 (XPEHH) selection signature candidate regions corresponding to 159.78 Mb (6.15%) and 62.29 Mb (2.40%) genomic regions, respectively. Further investigations of the selection candidate regions revealed that many genes under these genomic regions were related to reproductive traits (such as the TLR9 gene), coat colour (such as the KIT gene) and fat metabolism (such as the CPT1A and MAML3 genes). Furthermore, gene enrichment analyses showed that genes under the selection candidate regions were significantly over-represented in pathways related to diseases, such as autoimmune thyroid and asthma diseases. In conclusion, several candidate genes potentially under positive selection were involved in characteristics of Taihu pig. These results will further allow us to better understand the mechanisms of selection in pig breeding.
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18
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Yang R, Fang S, Wang J, Zhang C, Zhang R, Liu D, Zhao Y, Hu X, Li N. Genome-wide analysis of structural variants reveals genetic differences in Chinese pigs. PLoS One 2017; 12:e0186721. [PMID: 29065176 PMCID: PMC5655481 DOI: 10.1371/journal.pone.0186721] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2017] [Accepted: 10/08/2017] [Indexed: 11/19/2022] Open
Abstract
Pigs have experienced long-term selections, resulting in dramatic phenotypic changes. Structural variants (SVs) are reported to exert extensive impacts on phenotypic changes. We built a high resolution and informative SV map based on high-depth sequencing data from 66 Chinese domestic and wild pigs. We inferred the SV formation mechanisms in the pig genome and used SVs as materials to perform a population-level analysis. We detected the selection signals on chromosome X for northern Chinese domestic pigs, as well as the differentiated loci across the whole genome. Analysis showed that these loci differ between southern and northern Chinese domestic pigs. Our results based on SVs provide new insights into genetic differences in Chinese pigs.
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Affiliation(s)
- Ruifei Yang
- Beijing Advanced Innovation Center for Food Nutrition and Human Health, China Agricultural University, Beijing, P. R. China
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, P. R. China
| | - Suyun Fang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, P. R. China
| | - Jing Wang
- Beijing Advanced Innovation Center for Food Nutrition and Human Health, China Agricultural University, Beijing, P. R. China
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, P. R. China
| | - Chunyuan Zhang
- Beijing Advanced Innovation Center for Food Nutrition and Human Health, China Agricultural University, Beijing, P. R. China
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, P. R. China
| | - Ran Zhang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, P. R. China
| | - Di Liu
- Institute of Animal Industry, Heilongjiang Academy of Agricultural Sciences, Harbin, P. R. China
| | - Yiqiang Zhao
- Beijing Advanced Innovation Center for Food Nutrition and Human Health, China Agricultural University, Beijing, P. R. China
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, P. R. China
- * E-mail: (XH); (YZ)
| | - Xiaoxiang Hu
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, P. R. China
- National Engineering Laboratory for Animal Breeding, China Agricultural University, Beijing, P. R. China
- * E-mail: (XH); (YZ)
| | - Ning Li
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, P. R. China
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