1
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Igoshin AV, Deniskova TE, Yurchenko AA, Yudin NS, Dotsev AV, Selionova MI, Zinovieva NA, Larkin DM. Copy number variants in genomes of local sheep breeds from Russia. Anim Genet 2021; 53:119-132. [PMID: 34904242 DOI: 10.1111/age.13163] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/28/2021] [Indexed: 01/21/2023]
Abstract
Copy number variants (CNVs) are genomic structural variations that contribute to many adaptive and economically important traits in livestock. In this study, we detected CNVs in 354 animals from 16 Russian indigenous sheep breeds and analysed their possible functional roles. Our analysis of the entire sample set resulted in 4527 CNVs forming 1450 CNV regions (CNVRs). When constructing CNVRs for individual breeds, a total of 2715 regions ranging from 88 in Groznensk to 337 in Osetin breeds were identified. To make interbreed CNVR frequency comparison possible, we also identified core CNVRs using CNVs with overlapping chromosomal locations found in different breeds. This resulted in 137 interbreed CNVRs with frequency >15% in at least one breed. Functional enrichment analysis of genes affected by CNVRs in individual breeds revealed 12 breeds with significant enrichments in olfactory perception, PRAME family proteins, and immune response. Function of genes affected by interbreed and breed-specific CNVRs revealed candidates related to domestication, adaptation to high altitudes and cold climates, reproduction, parasite resistance, milk and meat qualities, wool traits, fat storage, and fat metabolism. Our work is the first attempt to uncover and characterise the CNV makeup of Russian indigenous sheep breeds. Further experimental and functional validation of CNVRs would help in developing new and improving existing sheep breeds.
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Affiliation(s)
- A V Igoshin
- The Federal Research Center Institute of Cytology and Genetics SB RAS, Novosibirsk, 630090, Russia
| | - T E Deniskova
- L.K. Ernst Federal Research Center for Animal Husbandry, Podolsk, 142132, Russia
| | - A A Yurchenko
- The Federal Research Center Institute of Cytology and Genetics SB RAS, Novosibirsk, 630090, Russia
| | - N S Yudin
- The Federal Research Center Institute of Cytology and Genetics SB RAS, Novosibirsk, 630090, Russia.,Novosibirsk State University, Novosibirsk, 630090, Russia
| | - A V Dotsev
- L.K. Ernst Federal Research Center for Animal Husbandry, Podolsk, 142132, Russia
| | - M I Selionova
- Russian State Agrarian University, Moscow Timiryazev Agricultural Academy, Moscow, 127550, Russia
| | - N A Zinovieva
- L.K. Ernst Federal Research Center for Animal Husbandry, Podolsk, 142132, Russia
| | - D M Larkin
- The Federal Research Center Institute of Cytology and Genetics SB RAS, Novosibirsk, 630090, Russia.,Royal Veterinary College, University of London, London, NW1 0TU, UK
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2
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Zhang M, Liu Y, Li Z, Lü P, Gardner JD, Ye M, Wang J, Yang M, Shao J, Wang W, Dai Q, Cao P, Yang R, Liu F, Feng X, Zhang L, Li E, Shi Y, Chen Z, Zhu S, Zhai W, Deng T, Duan Z, Bennett EA, Hu S, Fu Q. Ancient DNA reveals the maternal genetic history of East Asian domestic pigs. J Genet Genomics 2021; 49:537-546. [PMID: 34902603 DOI: 10.1016/j.jgg.2021.11.014] [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: 07/28/2021] [Revised: 11/30/2021] [Accepted: 11/30/2021] [Indexed: 10/19/2022]
Abstract
Zoo-archaeological and genetic evidence suggest that pigs were domesticated independently in Central China and Eastern Anatolia along with the development of agricultural communities and civilizations. However, the genetic history of domestic pigs, especially in China, has not been fully explored. In this study, we generated 42 complete mitochondrial DNA sequences from ∼7500- to 2750-year-old individuals from the Yellow River basin. Our results show that the maternal genetic continuity of East Asian domestic pigs dates back to at least the Early to Middle Neolithic. In contrast, the Near Eastern ancestry in European domestic pigs saw a near-complete genomic replacement by the European wild boar. The majority of East Asian domestic pigs share close haplotypes, and the most recent common ancestor of most branches dates back to less than 20,000 years before present, inferred using new substitution rates of whole mitogenomes or combined protein-coding regions. Two major population expansion events of East Asian domestic pigs coincided with changes in climate, widespread adoption of introduced crops, and the development of agrarian societies. These findings add to our understanding of the maternal genetic composition and help to complete the picture of domestic pig evolutionary history in East Asia.
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Affiliation(s)
- Ming Zhang
- Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China; Key Laboratory of Vertebrate Evolution and Human Origins, Institute of Vertebrate Paleontology and Paleoanthropology, Center for Excellence in Life and Paleoenvironment, Chinese Academy of Sciences, Beijing 100044, China; Shanghai Qi Zhi Institute, Shanghai 200232, China
| | - Yichen Liu
- Key Laboratory of Vertebrate Evolution and Human Origins, Institute of Vertebrate Paleontology and Paleoanthropology, Center for Excellence in Life and Paleoenvironment, Chinese Academy of Sciences, Beijing 100044, China
| | - Zhipeng Li
- Institute of Archaeology, Chinese Academy of Social Sciences, Beijing 100710, China
| | - Peng Lü
- Institute of Archaeology, Chinese Academy of Social Sciences, Beijing 100710, China
| | - Jacob D Gardner
- Key Laboratory of Vertebrate Evolution and Human Origins, Institute of Vertebrate Paleontology and Paleoanthropology, Center for Excellence in Life and Paleoenvironment, Chinese Academy of Sciences, Beijing 100044, China
| | - Maolin Ye
- Institute of Archaeology, Chinese Academy of Social Sciences, Beijing 100710, China; School of History and Culture, Lanzhou University, Lanzhou 730000, China
| | - Jihuai Wang
- Institute of Archaeology, Chinese Academy of Social Sciences, Beijing 100710, China
| | | | - Jing Shao
- Shaanxi Academy of Archaeology, Xi'an 710054, China
| | - Weilin Wang
- School of History and Culture, Shanxi University, Taiyuan 030006, China
| | - Qingyan Dai
- Key Laboratory of Vertebrate Evolution and Human Origins, Institute of Vertebrate Paleontology and Paleoanthropology, Center for Excellence in Life and Paleoenvironment, Chinese Academy of Sciences, Beijing 100044, China
| | - Peng Cao
- Key Laboratory of Vertebrate Evolution and Human Origins, Institute of Vertebrate Paleontology and Paleoanthropology, Center for Excellence in Life and Paleoenvironment, Chinese Academy of Sciences, Beijing 100044, China
| | - Ruowei Yang
- Key Laboratory of Vertebrate Evolution and Human Origins, Institute of Vertebrate Paleontology and Paleoanthropology, Center for Excellence in Life and Paleoenvironment, Chinese Academy of Sciences, Beijing 100044, China
| | - Feng Liu
- Key Laboratory of Vertebrate Evolution and Human Origins, Institute of Vertebrate Paleontology and Paleoanthropology, Center for Excellence in Life and Paleoenvironment, Chinese Academy of Sciences, Beijing 100044, China
| | - Xiaotian Feng
- Key Laboratory of Vertebrate Evolution and Human Origins, Institute of Vertebrate Paleontology and Paleoanthropology, Center for Excellence in Life and Paleoenvironment, Chinese Academy of Sciences, Beijing 100044, China
| | - Lizhao Zhang
- Key Laboratory of Vertebrate Evolution and Human Origins, Institute of Vertebrate Paleontology and Paleoanthropology, Center for Excellence in Life and Paleoenvironment, Chinese Academy of Sciences, Beijing 100044, China
| | - Enwei Li
- Xingtai Cultural Relics Administration, Xingtai 054000, China
| | - Yunzheng Shi
- Xingtai Cultural Relics Administration, Xingtai 054000, China
| | - Zehui Chen
- Key Laboratory of Vertebrate Evolution and Human Origins, Institute of Vertebrate Paleontology and Paleoanthropology, Center for Excellence in Life and Paleoenvironment, Chinese Academy of Sciences, Beijing 100044, China
| | - Shilun Zhu
- Key Laboratory of Vertebrate Evolution and Human Origins, Institute of Vertebrate Paleontology and Paleoanthropology, Center for Excellence in Life and Paleoenvironment, Chinese Academy of Sciences, Beijing 100044, China
| | - Weiwei Zhai
- Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China
| | - Tao Deng
- Key Laboratory of Vertebrate Evolution and Human Origins, Institute of Vertebrate Paleontology and Paleoanthropology, Center for Excellence in Life and Paleoenvironment, Chinese Academy of Sciences, Beijing 100044, China
| | - Ziyuan Duan
- Institute of Genetics and Developmental Biology Chinese Academy of Sciences, Beijing 100101, China
| | - E Andrew Bennett
- Key Laboratory of Vertebrate Evolution and Human Origins, Institute of Vertebrate Paleontology and Paleoanthropology, Center for Excellence in Life and Paleoenvironment, Chinese Academy of Sciences, Beijing 100044, China.
| | - Songmei Hu
- Shaanxi Academy of Archaeology, Xi'an 710054, China.
| | - Qiaomei Fu
- Key Laboratory of Vertebrate Evolution and Human Origins, Institute of Vertebrate Paleontology and Paleoanthropology, Center for Excellence in Life and Paleoenvironment, Chinese Academy of Sciences, Beijing 100044, China; Shanghai Qi Zhi Institute, Shanghai 200232, China.
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3
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Nonneman D, Lents CA, Rempel LA, Rohrer GA. Potential functional variants in AHR signaling pathways are associated with age at puberty in swine. Anim Genet 2021; 52:284-291. [PMID: 33667011 DOI: 10.1111/age.13051] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/05/2021] [Indexed: 12/31/2022]
Abstract
Puberty in female pigs is defined as age at first estrus and gilts that have an earlier age at puberty are more likely to have greater lifetime productivity. Because age at puberty is predictive for sow longevity and lifetime productivity, but not routinely measured in commercial herds, it would be beneficial to use genomic or marker-assisted selection to improve these traits. A GWAS at the US Meat Animal Research Center (USMARC) identified several loci associated with age at puberty in pigs. Candidate genes in these regions were scanned for potential functional variants using sequence information from the USMARC swine population founder animals and public databases. In total, 135 variants (SNP and insertion/deletions) in 39 genes were genotyped in 1284 phenotyped animals from a validation population sired by Landrace and Yorkshire industry semen using the Agena MassArray system. Twelve variants in eight genes were associated with age at puberty (P < 0.005) with estimated additive SNP effects ranging from 1.6 to 5.3 days. Nine of these variants were non-synonymous coding changes in AHR, CYP1A2, OR2M4, SDCCAG8, TBC1D1 and ZNF608, two variants were deletions of one and four codons in aryl hydrocarbon receptor, AHR, and the most significant SNP was near an acceptor splice site in the acetyl-CoA carboxylase alpha, ACACA. Several of the loci identified have a physiological and a genetic role in sexual maturation in humans and other animals and are involved in AHR-mediated pathways. Further functional validation of these variants could identify causative mutations that influence age at puberty in gilts and possibly sow lifetime productivity.
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Affiliation(s)
- Dan Nonneman
- USDA, ARS, U.S. Meat Animal Research Center, Clay Center, NE, 68933, USA
| | - Clay A Lents
- USDA, ARS, U.S. Meat Animal Research Center, Clay Center, NE, 68933, USA
| | - Lea A Rempel
- USDA, ARS, U.S. Meat Animal Research Center, Clay Center, NE, 68933, USA
| | - Gary A Rohrer
- USDA, ARS, U.S. Meat Animal Research Center, Clay Center, NE, 68933, USA
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4
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Garud NR, Messer PW, Petrov DA. Detection of hard and soft selective sweeps from Drosophila melanogaster population genomic data. PLoS Genet 2021; 17:e1009373. [PMID: 33635910 PMCID: PMC7946363 DOI: 10.1371/journal.pgen.1009373] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Revised: 03/10/2021] [Accepted: 01/17/2021] [Indexed: 12/12/2022] Open
Abstract
Whether hard sweeps or soft sweeps dominate adaptation has been a matter of much debate. Recently, we developed haplotype homozygosity statistics that (i) can detect both hard and soft sweeps with similar power and (ii) can classify the detected sweeps as hard or soft. The application of our method to population genomic data from a natural population of Drosophila melanogaster (DGRP) allowed us to rediscover three known cases of adaptation at the loci Ace, Cyp6g1, and CHKov1 known to be driven by soft sweeps, and detected additional candidate loci for recent and strong sweeps. Surprisingly, all of the top 50 candidates showed patterns much more consistent with soft rather than hard sweeps. Recently, Harris et al. 2018 criticized this work, suggesting that all the candidate loci detected by our haplotype statistics, including the positive controls, are unlikely to be sweeps at all and that instead these haplotype patterns can be more easily explained by complex neutral demographic models. They also claim that these neutral non-sweeps are likely to be hard instead of soft sweeps. Here, we reanalyze the DGRP data using a range of complex admixture demographic models and reconfirm our original published results suggesting that the majority of recent and strong sweeps in D. melanogaster are first likely to be true sweeps, and second, that they do appear to be soft. Furthermore, we discuss ways to take this work forward given that most demographic models employed in such analyses are necessarily too simple to capture the full demographic complexity, while more realistic models are unlikely to be inferred correctly because they require a large number of free parameters.
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Affiliation(s)
- Nandita R. Garud
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, California, United States of America
- Department of Human Genetics, University of California, Los Angeles, California, United States of America
| | - Philipp W. Messer
- Department of Computational Biology, Cornell University, Ithaca, New York, United States of America
| | - Dmitri A. Petrov
- Department of Biology, Stanford University, Stanford, California, United States of America
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5
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Bastian FB, Roux J, Niknejad A, Comte A, Fonseca Costa SS, de Farias TM, Moretti S, Parmentier G, de Laval VR, Rosikiewicz M, Wollbrett J, Echchiki A, Escoriza A, Gharib WH, Gonzales-Porta M, Jarosz Y, Laurenczy B, Moret P, Person E, Roelli P, Sanjeev K, Seppey M, Robinson-Rechavi M. The Bgee suite: integrated curated expression atlas and comparative transcriptomics in animals. Nucleic Acids Res 2021; 49:D831-D847. [PMID: 33037820 PMCID: PMC7778977 DOI: 10.1093/nar/gkaa793] [Citation(s) in RCA: 76] [Impact Index Per Article: 25.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 08/24/2020] [Accepted: 09/15/2020] [Indexed: 01/24/2023] Open
Abstract
Bgee is a database to retrieve and compare gene expression patterns in multiple animal species, produced by integrating multiple data types (RNA-Seq, Affymetrix, in situ hybridization, and EST data). It is based exclusively on curated healthy wild-type expression data (e.g., no gene knock-out, no treatment, no disease), to provide a comparable reference of normal gene expression. Curation includes very large datasets such as GTEx (re-annotation of samples as ‘healthy’ or not) as well as many small ones. Data are integrated and made comparable between species thanks to consistent data annotation and processing, and to calls of presence/absence of expression, along with expression scores. As a result, Bgee is capable of detecting the conditions of expression of any single gene, accommodating any data type and species. Bgee provides several tools for analyses, allowing, e.g., automated comparisons of gene expression patterns within and between species, retrieval of the prefered conditions of expression of any gene, or enrichment analyses of conditions with expression of sets of genes. Bgee release 14.1 includes 29 animal species, and is available at https://bgee.org/ and through its Bioconductor R package BgeeDB.
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Affiliation(s)
- Frederic B Bastian
- Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Julien Roux
- Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Anne Niknejad
- Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Aurélie Comte
- Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Sara S Fonseca Costa
- Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Tarcisio Mendes de Farias
- Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Sébastien Moretti
- Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Gilles Parmentier
- Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Valentine Rech de Laval
- Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Marta Rosikiewicz
- Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Julien Wollbrett
- Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Amina Echchiki
- Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Angélique Escoriza
- Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Walid H Gharib
- Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Mar Gonzales-Porta
- Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Yohan Jarosz
- Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Balazs Laurenczy
- Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Philippe Moret
- Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Emilie Person
- Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Patrick Roelli
- Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Komal Sanjeev
- Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Mathieu Seppey
- Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Marc Robinson-Rechavi
- Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
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6
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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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7
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Zou H, Yu D, Du X, Wang J, Chen L, Wang Y, Xu H, Zhao Y, Zhao S, Pang Y, Liu Y, Hao H, Zhao X, Du W, Dai Y, Li N, Wu S, Zhu H. No imprinted XIST expression in pigs: biallelic XIST expression in early embryos and random X inactivation in placentas. Cell Mol Life Sci 2019; 76:4525-4538. [PMID: 31139846 PMCID: PMC11105601 DOI: 10.1007/s00018-019-03123-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2018] [Revised: 04/12/2019] [Accepted: 04/29/2019] [Indexed: 11/29/2022]
Abstract
Dosage compensation, which is achieved by X-chromosome inactivation (XCI) in female mammals, ensures balanced X-linked gene expression levels between the sexes. Although eutherian mammals commonly display random XCI in embryonic and adult tissues, imprinted XCI has also been identified in extraembryonic tissues of mouse, rat, and cow. Little is known about XCI in pigs. Here, we sequenced the porcine XIST gene and identified an insertion/deletion mutation between Asian- and Western-origin pig breeds. Allele-specific analysis revealed biallelic XIST expression in porcine ICSI blastocysts. To investigate the XCI pattern in porcine placentas, we performed allele-specific RNA sequencing analysis on individuals from reciprocal crosses between Duroc and Rongchang pigs. Our results were the first to reveal that random XCI occurs in the placentas of pigs. Next, we investigated the H3K27me3 histone pattern in porcine blastocysts, showing that only 17-31.8% cells have attained XCI. The hypomethylation status of an important XIST DMR (differentially methylated region) in gametes and early embryos demonstrated that no methylation is pre-deposited on XIST in pigs. Our findings reveal that the XCI regulation mechanism in pigs is different from that in mice and highlight the importance of further study of the mechanisms regulating XCI during early porcine embryo development.
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Affiliation(s)
- Huiying Zou
- Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, 100193, China
| | - Dawei Yu
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Xuguang Du
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, 100193, China
| | - Jing Wang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, 100193, China
| | - Lei Chen
- Chongqing Academy of Animal Science, Chongqing, 402460, China
| | - Yangyang Wang
- Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Huitao Xu
- Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Yunxuan Zhao
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Shanjiang Zhao
- Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Yunwei Pang
- Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Yan Liu
- Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Haisheng Hao
- Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Xueming Zhao
- Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Weihua Du
- Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Yunping Dai
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, 100193, China
| | - Ning Li
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, 100193, China
| | - Sen Wu
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, 100193, China.
- Beijing Advanced Innovation Center for Food Nutrition and Human Health, China Agricultural University, Beijing, 100193, China.
| | - Huabin Zhu
- Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China.
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8
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Genetic Diversity of Indigenous Pigs from South China Area Revealed by SNP Array. Animals (Basel) 2019; 9:ani9060361. [PMID: 31208134 PMCID: PMC6616596 DOI: 10.3390/ani9060361] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2019] [Revised: 06/11/2019] [Accepted: 06/11/2019] [Indexed: 02/07/2023] Open
Abstract
Simple Summary The pig is one of the most important livestock animals, providing the majority of protein for humans. The population genetics analysis of pigs not only helps humans understand the domestication of the pig but also helps breeders in the genetic improvement of pigs. In this study, the population genetics of 11 pig breeds of South China were analyzed with the help of single nucleotide polymorphism (SNP) chips. The results showed that the genetic diversity of South China indigenous pigs is declining rapidly, and gene introgression from commercial pigs to indigenous pigs was detected. Selection signature analysis showed differences among South China indigenous pig breeds, commercial pig breeds, and wild pig breeds were present for meat quality and growth. Our study deepened understanding of the conservation status and selection mechanisms of Chinese indigenous pigs. Abstract To investigate the genetic diversity, population structure, extent of linkage disequilibrium (LD), effective population size (Ne), and selection signatures in indigenous pigs from Guangdong and Guangxi in China, 226 pigs belonging to ten diverse populations were genotyped using single nucleotide polymorphism (SNP) chips. The genetic divergence between Chinese and Western pigs was determined based on the SNP chip data. Low genetic diversity of Dahuabai (DHB), Luchuan (LC), Lantang (LT), and Meihua (MH) pigs, and introgression of Western pigs into Longlin (LL), MH, and Yuedonghei (YDH) pigs were detected. Analysis of the extent of LD showed that indigenous pigs had low LD when pairwise SNP distance was short and high LD when pairwise SNP distance was long. Effective population size analysis showed a rapid decrease for Chinese indigenous pigs, and some pig populations had a relatively small Ne. This result indicated the loss of genetic diversity in indigenous pigs, and introgression from Western commercial pigs. Selection signatures detected in this study overlapped with meat quality traits, such as drip loss, intramuscular fat content, meat color b*, and average backfat thickness. Our study deepened understanding of the conservation status and domestication of Chinese indigenous pigs.
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9
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Diao S, Huang S, Chen Z, Teng J, Ma Y, Yuan X, Chen Z, Zhang H, Li J, Zhang Z. Genome-Wide Signatures of Selection Detection in Three South China Indigenous Pigs. Genes (Basel) 2019; 10:genes10050346. [PMID: 31067806 PMCID: PMC6563113 DOI: 10.3390/genes10050346] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Revised: 04/28/2019] [Accepted: 05/02/2019] [Indexed: 01/11/2023] Open
Abstract
South China indigenous pigs are famous for their superior meat quality and crude feed tolerance. Saba and Baoshan pigs without saddleback were located in the high-altitude area of Yunnan Province, while Tunchang and Ding’an pigs with saddleback were located in the low-altitude area of Hainan Province. Although these pigs are different in appearance, the underlying genetic differences have not been investigated. In this study, based on the single-nucleotide polymorphism (SNP) genotypes of 124 samples, both the cross-population extended haplotype homozygosity (XP-EHH) and the fixation index (FST) statistic were used to identify potential signatures of selection in these pig breeds. We found nine potential signatures of selection detected simultaneously by two methods, annotated 22 genes in Hainan pigs, when Baoshan pigs were used as the reference group. In addition, eleven potential signatures of selection detected simultaneously by two methods, annotated 24 genes in Hainan pigs compared with Saba pigs. These candidate genes were most enriched in GO: 0048015~phosphatidylinositol-mediated signaling and ssc00604: Glycosphingolipid biosynthesis—ganglio series. These selection signatures were likely to overlap with quantitative trait loci associated with meat quality traits. Furthermore, one potential selection signature, which was associated with different coat color, was detected in Hainan pigs. These results contribute to a better understanding of the underlying genetic architecture of South China indigenous pigs.
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Affiliation(s)
- Shuqi Diao
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding/National Engineering Research Centre for Breeding Swine Industry/College of Animal Science, South China Agricultural University, Guangzhou 510642, China.
| | - Shuwen Huang
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding/National Engineering Research Centre for Breeding Swine Industry/College of Animal Science, South China Agricultural University, Guangzhou 510642, China.
| | - Zitao Chen
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding/National Engineering Research Centre for Breeding Swine Industry/College of Animal Science, South China Agricultural University, Guangzhou 510642, China.
| | - Jinyan Teng
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding/National Engineering Research Centre for Breeding Swine Industry/College of Animal Science, South China Agricultural University, Guangzhou 510642, China.
| | - Yunlong Ma
- Key Laboratory of Agricultural Animal Genetics, Breeding, and Reproduction of Ministry of Education, College of Animal Sciences and Technology, Huazhong Agricultural University, Wuhan 430070, China.
| | - Xiaolong Yuan
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding/National Engineering Research Centre for Breeding Swine Industry/College of Animal Science, South China Agricultural University, Guangzhou 510642, China.
| | - Zanmou Chen
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding/National Engineering Research Centre for Breeding Swine Industry/College of Animal Science, South China Agricultural University, Guangzhou 510642, China.
| | - Hao Zhang
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding/National Engineering Research Centre for Breeding Swine Industry/College of Animal Science, South China Agricultural University, Guangzhou 510642, China.
| | - Jiaqi Li
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding/National Engineering Research Centre for Breeding Swine Industry/College of Animal Science, South China Agricultural University, Guangzhou 510642, China.
| | - Zhe Zhang
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding/National Engineering Research Centre for Breeding Swine Industry/College of Animal Science, South China Agricultural University, Guangzhou 510642, China.
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10
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Yu D, Wang J, Zou H, Feng T, Chen L, Li J, Qi X, Li Z, Duan X, Xu C, Zhang L, Long X, Lan J, Chen C, Wang C, Xu X, Ren J, Zhao Y, Hu X, Lian Z, Men H, Pan D, Li N, Capecchi MR, Du X, Zhao Y, Wu S. Silencing of retrotransposon-derived imprinted gene RTL1 is the main cause for postimplantational failures in mammalian cloning. Proc Natl Acad Sci U S A 2018; 115:E11071-E11080. [PMID: 30381455 PMCID: PMC6255163 DOI: 10.1073/pnas.1814514115] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Substantial rates of fetal loss plague all in vitro procedures involving embryo manipulations, including human-assisted reproduction, and are especially problematic for mammalian cloning where over 90% of reconstructed nuclear transfer embryos are typically lost during pregnancy. However, the epigenetic mechanism of these pregnancy failures has not been well described. Here we performed methylome and transcriptome analyses of pig induced pluripotent stem cells and associated cloned embryos, and revealed that aberrant silencing of imprinted genes, in particular the retrotransposon-derived RTL1 gene, is the principal epigenetic cause of pregnancy failure. Remarkably, restoration of RTL1 expression in pig induced pluripotent stem cells rescued fetal loss. Furthermore, in other mammals, including humans, low RTL1 levels appear to be the main epigenetic cause of pregnancy failure.
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Affiliation(s)
- Dawei Yu
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, 100193 Beijing, China
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, 100101 Beijing, China
| | - Jing Wang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, 100193 Beijing, China
| | - Huiying Zou
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, 100193 Beijing, China
- Embryo Biotechnology and Reproduction Laboratory, Institute of Animal Science, Chinese Academy of Agricultural Sciences, 100193 Beijing, China
| | - Tao Feng
- College of Veterinary Medicine, China Agricultural University, 100193 Beijing, China
- Beijing Advanced Innovation Center for Food Nutrition and Human Health, China Agricultural University, 100193 Beijing, China
| | - Lei Chen
- Chongqing Academy of Animal Science, 402460 Chongqing, China
| | - Jia Li
- Center for Epigenetics & Disease Prevention, Institute of Biosciences and Technology, College of Medicine, Texas A&M University, Houston, TX 77030
| | - Xiaolan Qi
- College of Veterinary Medicine, China Agricultural University, 100193 Beijing, China
- Beijing Advanced Innovation Center for Food Nutrition and Human Health, China Agricultural University, 100193 Beijing, China
| | - Zhifang Li
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, 100193 Beijing, China
| | - Xiaoyue Duan
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, 100193 Beijing, China
| | - Chunlong Xu
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, 100193 Beijing, China
| | - Liang Zhang
- Chongqing Academy of Animal Science, 402460 Chongqing, China
| | - Xi Long
- Chongqing Academy of Animal Science, 402460 Chongqing, China
| | - Jing Lan
- Chongqing Academy of Animal Science, 402460 Chongqing, China
| | - Chao Chen
- Tang Tang Biomedical Technology (Beijing) Co., 100101 Beijing, China
| | - Chao Wang
- Department of Computer and Technology, Tsinghua University, 100101 Beijing, China
| | - Xinyu Xu
- School of Life Sciences, Tsinghua University, 100101 Beijing, China
| | - Jilong Ren
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, 100101 Beijing, China
| | - Yiqiang Zhao
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, 100193 Beijing, China
| | - Xiaoxiang Hu
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, 100193 Beijing, China
| | - Zhengxing Lian
- College of Animal Science and Technology, China Agriculture University, 100193 Beijing, China
| | - Hongsheng Men
- Rat Resource and Research Center, University of Missouri, Columbia, MO 65201
- Department of Veterinary Pathobiology, College of Veterinary Medicine, University of Missouri, Columbia, MO 65201
| | - Dengke Pan
- Embryo Biotechnology and Reproduction Laboratory, Institute of Animal Science, Chinese Academy of Agricultural Sciences, 100193 Beijing, China
| | - Ning Li
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, 100193 Beijing, China
| | - Mario R Capecchi
- Department of Human Genetics, University of Utah School of Medicine, Salt Lake City, UT 84112
| | - Xuguang Du
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, 100193 Beijing, China;
- College of Animal Science and Technology, China Agriculture University, 100193 Beijing, China
| | - Yaofeng Zhao
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, 100193 Beijing, China;
| | - Sen Wu
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, 100193 Beijing, China;
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11
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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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